Smart Energy for Smart Transport: Proceedings of the 6th Conference on Sustainable Urban Mobility, CSUM2022, August 31–September 2, 2022, Skiathos Island, Greece 303123720X, 9783031237201

Citation preview

Lecture Notes in Intelligent Transportation and Infrastructure Series Editor: Janusz Kacprzyk

Eftihia G. Nathanail Nikolaos Gavanas Giannis Adamos   Editors

Smart Energy for Smart Transport Proceedings of the 6th Conference on Sustainable Urban Mobility, CSUM2022, August 31–September 2,  2022, Skiathos Island, Greece

Lecture Notes in Intelligent Transportation and Infrastructure Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

The series “Lecture Notes in Intelligent Transportation and Infrastructure” (LNITI) publishes new developments and advances in the various areas of intelligent transportation and infrastructure. The intent is to cover the theory, applications, and perspectives on the state-of-the-art and future developments relevant to topics such as intelligent transportation systems, smart mobility, urban logistics, smart grids, critical infrastructure, smart architecture, smart citizens, intelligent governance, smart architecture and construction design, as well as green and sustainable urban structures. The series contains monographs, conference proceedings, edited volumes, lecture notes and textbooks. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable wide and rapid dissemination of high-quality research output.

Eftihia G. Nathanail · Nikolaos Gavanas · Giannis Adamos Editors

Smart Energy for Smart Transport Proceedings of the 6th Conference on Sustainable Urban Mobility, CSUM2022, August 31–September 2, 2022, Skiathos Island, Greece

Editors Eftihia G. Nathanail Department of Civil Engineering University of Thessaly Volos, Greece

Nikolaos Gavanas Department of Planning and Regional Development University of Thessaly Volos, Greece

Giannis Adamos Department of Civil Engineering University of Thessaly Volos, Greece

ISSN 2523-3440 ISSN 2523-3459 (electronic) Lecture Notes in Intelligent Transportation and Infrastructure ISBN 978-3-031-23720-1 ISBN 978-3-031-23721-8 (eBook) https://doi.org/10.1007/978-3-031-23721-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

This publication contains the proceedings of CSUM2022, the 6th Conference on Sustainable Urban Mobility, which was held in Skiathos Island, on 31 August– 2 September 2022. Main theme was “Smart Energy for Smart Transport”, which covers the triplet of sustainability; electromobility, renewable sources in transport and energy preservation through new and shared mobility and transport services. The book is organized into 22 parts, each covering aspects of the following thematic tracks: • • • • • •

Electric and clean energy in transportation Emerging and innovative technologies in transport Active and non-motorized travel Equitable, just and inclusive transportation Sustainable and resilient supply chain Urban planning and transport infrastructure.

Addressing the needs of academia, researchers and professionals in the domain, this volume offers a unique collection of up-to-date models, techniques and applications, which foster achieving and delivering operable, efficient and sustainable systems. Acknowledging the invaluable contribution of authors and reviewers, which resulted in the high quality of the featured papers, we wish you a fruitful and constructive reading. November 2022

Eftihia G. Nathanail Nikolaos Gavanas Giannis Adamos

v

Organization

Conference Chairs Eftihia Nathanail University of Thessaly, Greece Nikolaos Gavanas University of Thessaly, Greece Giannis Adamos University of Thessaly, Greece

Thematic Track Coordinators Francesco Viti Eftihia Nathanail Rosaldo Rossetti Guohui Zhang Ioannis Kaparias Soora Rasouli Giannis Adamos Pnina Plaut Nikolaos Gavanas

University of Luxembourg, Luxembourg University of Thessaly, Greece University of Porto, Portugal University of Hawaii at Manoa, USA University of Southampton, UK Eindhoven University of Technology, The Netherlands University of Thessaly, Greece Technion Israel Institute of Technology, Israel University of Thessaly, Greece

Scientific Committee Members Giannis Adamos El-Houssaine Aghezzaf Ryosuke Ando Socrates Basbas Angel Batalla

University of Thessaly, Greece Ghent University, Belgium Toyota Transportation Research Institute, Japan Aristotle University of Thessaloniki, Greece LastMile Team, Spain

vii

viii

Sönke Beckmann Ciprian Bejenar Evangelos Bekiaris Maria Boile Luca Braidotti Kathryn Bulanowski Valeria Caiati Alessandro Calvi Tiziana Campisi Charis Chalkiadakis Dimitra Chondrogianni Konstantinos Christidis Calin Ciufudean Maria Vittoria Corazza Sofoklis Dais Teresa de la Cruz Giovanni De Nunzio Vladimir Djoric Schahram Dustdar Nikolaos Eliou Domokos Esztergár-Kiss Sonja Forward Angelo Furno Athanasios Galanis Michal Gath Morad Damianos Gavalas Nikolaos Gavanas Matthieu Guillot Na’Amah Hagiladi Tünde Hajnal Kai Philip Hempel Junichi Hirose Bin Jiang Georgios Kalogerakos Ioannis Kaparias Ioannis Karakikes Astrid Kemperman Seheon Kim

Organization

Otto-von-Guericke-University Magdeburg, Germany University “S, tefan cel Mare” of Suceava, Romania Centre for Research and Technology Hellas, Greece Centre for Research and Technology Hellas, Greece Venice International University, Italy European Passengers’ Federation, Belgium Eindhoven University of Technology, The Netherlands Roma Tre University, Italy Kore University of Enna, Italy National Technical University of Athens, Greece University of Patras, Greece Democritus University of Thrace, Greece Stefan cel Mare University, Romania DICEA—Sapienza University of Rome, Italy Centre for Research and Technology Hellas, Greece Zaragoza Logistics Centre, Spain IFP Energies Nouvelles University of Belgrade, Serbia Vienna University of Technology, Austria University of Thessaly, Greece Budapest University of Technology and Economics, Hungary VTI, Sweden University of Lyon, ENTPE, Université Gustave Eiffel, LICIT, France International Hellenic University, Greece ETH Zurich, Switzerland University of Aegean, Greece University of Thessaly, Greece Gustave Eiffel University, France Technion Israel Institute of Technology, Israel BKK Centre for Budapest Transport, Hungary Otto-von-Guericke-Universität Magdeburg, Germany HIDO, Japan University of Gävle, Sweden University of Thessaly, Greece University of Southampton, UK University of the Aegean, Greece Eindhoven University of Technology, The Netherlands Eindhoven University of Technology, The Netherlands

Organization

Konstantinos Kokkinos Spyros Kontogiannis Aristomenis Kopsacheilis Fumitaka Kurauchi Gianna Kurtz Claudio Lantieri Shangqi Li Tai-Yu Ma Ila Maltese Claudio Mantero Karel Martens Ioannis Matsas Sorin Mih˘ailescu Urvashi Mishra Evangelos Mitsakis Tal Modai-Snir Talbi Mourad Rim Moussa Glykeria Myrovali Eftihia Nathanail Marialisa Nigro Alexandros Nikitas Anastasia Nikolaidou Apostolos Papagiannakis Anestis Papanikolaou Yannis Paraskevopoulos Margherita Pazzini Pnina Plaut Dimitra Politaki Ioannis Politis Amalia Polydoropoulou Gina Porter Christos Pyrgidis Soora Rasouli Xueting Ren Francisco Rodero Benjamin Rolf

ix

University of Thessaly, Greece University of Ioannina, Greece Aristotle University of Thessaloniki, Greece Gifu University, Japan Otto-von-Guericke-University, Germany University of Bologna, Italy Eindhoven University of Technology, The Netherlands Luxembourg Institute of Socio-Economic Research, Luxembourg Roma Tre University, Italy Horarios do Funchal, Transportes Publicos SA, Potrugal Technion Israel Institute of Technology, Israel University of Cyprus, Cyprus University of Petro¸sani, Romani Mata Gujri Mahila Mahavidyalaya Jabalpur, Madhya Pradesh, India Centre for Research and Technology Hellas, Greece Technion Israel Institute of Technology, Israel Faculty of Sciences of Tunis, Tunisia ENICarthage, Tunisia Centre for Research and Technology Hellas, Greece University of Thessaly, Greece Università Roma Tre, Italy University of Huddersfield, Huddersfield Business School, UK Aristotle University of Thessaloniki, Greece Aristotle University of Thessaloniki, Greece Volkswagen AG, Germany National Technical University of Athens, Greece University of Bologna, Italy Technion Israel Institute of Technology, Israel Inlecom Innovation, Greece Aristotle University of Thessaloniki, Greece University of the Aegean, Greece Durham University, UK Aristotle University of Thessaloniki, Greece Eindhoven University of Technology, The Netherlands Eindhoven University of Technology, The Netherlands CENIT, Spain Otto-von-Guericke-University Magdeburg, Germany

x

Rosaldo Rossetti Antonio Russo Nicholas Samaras Veronica Saud Jens Schade Johannes Scholz Alexandros Sdoukopoulos Marcin Seredynski Ioanna Spyropoulou Nikiforos Stamatiadis Nikolas Thomopoulos Stefanos Tsigdinos Dimitris Tzanis Nikolaos Valantasis Kanellos Thierry Vanelslander Francesco Viti George Yannis Guohui Zhang Xu Zhang

Organization

University of Porto, Portugal Università degli Studi di Enna “Kore”, Italy University of Thessaly, Greece University College London, UK TU Dresden, Germany Graz University of Technology, Austria Aristotle University of Thessaloniki, Greece E-Bus Competence Center, France National Technical University of Athens, Greece University of Kentucky, USA University of Surrey, UK National Technical University of Athens, Greece Centre for Research and Technology Hellas, Greece Technological University Dublin, Ireland University of Antwerp, Belgium University of Luxembourg, Luxembourg National Technical University of Athens, Greece University of Hawaii at Manoa, USA Technological University Dublin, Ireland

Contents

Electric and Clean Energy in Transportation: Shifting to Electric and Cleaner Solutions for Fighting Climate Change Is the Shift to Electrification and Powertrain Improvement Sufficient to Change Urban Mobility’s Impact on Climate Change? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vasiliki V. Georgatzi and Yeoryios Stamboulis The Dynamic Relation of Climate Change and Energy Transition with Transport and Mobility Policies in the EU Through Social Media Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anastasia Nikolaidou, Aristomenis Kopsacheilis, Nikolaos Gavanas, and Ioannis Politis Park-and-Ride: The Case for Coupling EV Charging Stations with Micro-mobility Hubs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aikaterini Moschopoulou, Ioannis Frantzeskakis, Konstandinos Grizos, and Theocharis Vlachopanagiotis Metro Braking Energy for Station Electric Loads: The Business Case of a Smart Hybrid Storage System . . . . . . . . . . . . . . . . . . . . . . . . . . . . George Leoutsakos, Alexandros Deloukas, Kanellina Giannakopoulou, Maria Zarkadoula, Dimitris Kyriazidis, and Astrid Bensmann The Impact of the Transport Sector on the Environment in the Context of Globalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cristiana Tudor and Robert Sova

3

17

29

50

63

xi

xii

Contents

Electric and Clean Energy in Transportation: Modelling and Optimizing Electric and Cleaner Vehicles and Services Forecasting the Passenger Car Demand Split from Public Perceptions of Electric, Hybrid, and Hydrogen-Fueled Cars in Greece . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Konstantinos Christidis, Vassilios Profillidis, George Botzoris, and Lazaros Iliadis

77

Demand Responsive Feeder Bus Service Using Electric Vehicles with Timetabled Transit Coordination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yumeng Fang and Tai-Yu Ma

91

Investigation of User’s Preferences on Electric Passenger Cars . . . . . . . . Panagiotis Papantoniou, Christos Mylonas, Panagiota Spanou, and Dimosthenis Pavlou

104

A Large Scale Simulation of the Electrification Effects of SAVs . . . . . . . Riccardo Iacobucci, Marco Pruckner, and Jan-Dirk Schmöcker

115

Design, Development and Real-Time Demonstration of Supercapacitor Powered Electric Bicycle . . . . . . . . . . . . . . . . . . . . . . . . . A. Bharathi Sankar Ammaiyappan and Seyezhai Ramalingam Driver-in-the-Loop Simulator of Electric Vehicles . . . . . . . . . . . . . . . . . . . Csaba Antonya, C˘alin Husar, Silviu Butnariu, Claudiu Pozna, and Alexandra B˘aicoianu Observations on the Driving of Plug-In Hybrid Cars in Real-World Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jaime Suarez, Andres Laverde, Alessandro Tansini, Markos A. Ktistakis, Dimitrios Komnos, and Georgios Fontaras

125 135

143

Electric and Clean Energy in Transportation: Integrating Smart Transport and Smart Grids Research Trends and Opportunities Related to Charging and Supply Systems for Vehicles with Electric/Hybrid Propulsion . . . . . Ciprian Bejenar, Mihai Rat, a˘ , Gabriela Rat, a˘ , and Laurent, iu-Dan Milici

157

Dynamic Charging Management for Electric Vehicle Demand Responsive Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tai-Yu Ma

171

A Regional Civilian Airport Model at Remote Island for Smart Grid Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Georgios Vontzos and Dimitrios Bargiotas

183

Contents

An Innovative Smart Charging Framework for Efficient Integration of Plug-In Electric Vehicles into the Grid . . . . . . . . . . . . . . . . Stylianos I. Vagropoulos, Stratos D. Keranidis, Zafeirios N. Bampos, and Konstantinos D. Afentoulis A Blockchain-Based Smart Contractual Framework for the Electric Vehicle Charging Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . Konstantinos D. Afentoulis, Zafeirios N. Bampos, Stylianos I. Vagropoulos, and Stratos D. Keranidis Investigating the Option of Developing a Power Supply Network Using Electricity in Greek Islands: The Case of Skiathos Island . . . . . . . Ioannis Gagtzas and Giannis Adamos

xiii

193

206

219

Emerging and Innovative Technologies in Transport: Technological Innovations in Transport and Mobility Innovative Non-polluting Traffic Light Crossroads . . . . . . . . . . . . . . . . . . . Calin Ciufudean and Corneliu Buzduga

223

System for Avoiding Traffic Jams of Intervention Vehicles . . . . . . . . . . . . Calin Ciufudean and Corneliu Buzduga

232

A Mobile Computing Based Tool for Low-Emission Driving . . . . . . . . . . Nikos Dimokas, Dimitris Margaritis, Sébastien Faye, Ramiro Camino, Orhan Alanku¸s, and Engin Ozatay

238

Discrepancy Between Hyperpath and Actual Route Choices Based on Smart-Card Data in Shizuoka, Japan . . . . . . . . . . . . . . . . . . . . . . Rattanaporn Kaewkluengklom, Fumitaka Kurauchi, and Takenori Iwamoto

251

Developing a Heuristic Route Planning Method to Support Seamless Mobility Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Domokos Esztergár-Kiss, Alireza Ansariyar, and Géza Katona

266

A Large-Scale Traffic Scenario of Berlin for Evaluating Smart Mobility Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Karl Schrab, Robert Protzmann, and Ilja Radusch

276

Emerging and Innovative Technologies in Transport: On-Demand Transport Services Smart Parking System (SPS): An Intelligent Image-Processing Based Parking Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Keerthi Lavanyeswari Pasala, Charitha Sree Jayaramireddy, Sree Veera Venkata Sai Saran Naraharisetti, Steven Atilho, Benjamin Greenfield, Benjamin Placzek, Mohamed Nassar, and Mehdi Mekni

291

xiv

Contents

The Impact of Total Cost of Ownership on MaaS System Appeal Using an Agent-Based Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carolina Cisterna, Federico Bigi, and Francesco Viti A Recommendation Engine for a Smart Parking Ecosystem . . . . . . . . . . Spyros Kontogiannis, Nikos Zacharatos, and Christos Zaroliagis A Crowdsourcing Framework for Reporting Available Parking Spots in Urban Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Grigorios Christainas, Dionysios Kehagias, Athanasios Salamanis, Pavlos Spanidis, Menelaos Kyrkoy, and Dimitrios Tzovaras A Review of Use Cases of Gamification in Mobility Systems and Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Luís Barreto, António Amaral, Teresa Pereira, and Sara Paiva An Innovative Mobile Application for Booking Parking Spots . . . . . . . . Pavlos Spanidis, Nikos Dimokas, Mary Panou, George Christainas, Athanasios Salamanis, and Dionysios Kehagias

300 311

323

335 348

AI Driven Adaptive Scheduling for On-Demand Transportation in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Veneta Markovska, Margarita Ruseva, and Stanimir Kabaivanov

360

Decision Intelligence Based on Big Data for User-Oriented Trip Planner Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alise Dinko, Irina Yatskiv Jackiva, and Evelina Budilovich Budiloviˇca

372

A Framework for Urban C-ITS GLOSA Evaluation . . . . . . . . . . . . . . . . . Thomas Otto, Michael Klöppel-Gersdorf, and Ina Partzsch

382

Emerging and Innovative Technologies in Transport: Co-creating Innovative Technologies in Transport Pursuing Technological Solutions for Tourists’ Urban Travel Behavior Change in the Post COVID-19 Era; The SUSTOURISMO App . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kornilia Maria Kotoula, Glykeria Myrovali, and Maria Morfoulaki Stakeholders’ Survey on the Introduction of Connected and Automated Vehicles in Greece . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evangelia Gaitanidou, Evangelos Bekiaris, and Panagiotis Papaioannou Autonomous Mobility as a Means of Innovation Diffusion: The Case of Trikala, Greece . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Georgios Kalogerakos and Nikolaos Gavanas

395

406

420

Contents

xv

Autonomous Vehicles: Impact on Human Life- A Statistic and Descriptive Overview of Research Results, Using the Delphi Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ioannis C. Matsas, George Mintsis, Socrates Basbas, and Christos Taxiltaris

435

Big Data Analytics for Modelling Consumer Preferences and Satisfaction in Public Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . Yulia Dzhabarova, Aygun Erturk, and Stanimir Kabaivanov

451

Citizen and Stakeholder Engagement in the Development and Deployment of Automated Mobility Services, as Exemplified in the SHOW Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Delphine Grandsart, Henriette Cornet, Matina Loukea, Stéphanie Coeugnet-Chevrier, Natacha Metayer, Anna Anund, and Anna Sjörs Dahlman Transportation Systems of Asia: Investigating the Preferences for Their Implementation in Greece with the Use of the Maximum Difference (MaxDiff) Scaling Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Melpomeni Mokka, Georgios Palantzas, Ioannis Politis, and Dimitrios Nalmpantis

468

482

Emerging and Innovative Technologies in Transport: Cooperative Intelligent Transport Systems Digital Infrastructure Service Role and Functional Model for Urban ITS Service Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junichi Hirose

497

Innovative Technologies and Systems for Urban Mobility: The Case of Padua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marco Mazzarino, Luca Braidotti, Beatriz Royo, and Teresa de la Cruz

504

Using C-ITS for Shockwave Damping and Preventing on Highways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marina Kouta, Konstantina Marousi, and Athanasios Koukounaris

520

Relationship and Differences Between Entrepreneurship and Research in the CrowdMapping Project for Crowdsourced Urban Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mátyás Szántó and László Vajta Repurposing Open Traffic Data for Estimating the Mobility Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Špela Verovsek, Tadeja Zupanˇciˇc, Matevž Juvanˇciˇc, Lucija Ažman Momirski, Miha Janež, and Miha Moškon

531

542

xvi

Contents

Evaluating the Quality of Public Spaces Using Crowdsourcing Data: The Case of the Metropolitan Area of Thessaloniki . . . . . . . . . . . . . Alexandros Sdoukopoulos, Nikolaos Gavanas, and Magda Pitsiava-Latinopoulou

556

Emerging and Innovative Technologies in Transport: Vehicle Automation and Smart Equipment Application of Smart Windows Equipped with Radiant Internal Curtains to Improve Thermal Comfort in Urban Transport Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eusébio Conceição, Mª Inês Conceição, Mª Manuela Lúcio, João Gomes, and Hazim Awbi Urban Transport Vehicles Equipped with HVAC Based on Ceiling-Mounted Air Distribution Systems . . . . . . . . . . . . . . . . . . . . . . . Eusébio Conceição, João Gomes, Mª Inês Conceição, Mª Manuela Lúcio, and Hazim Awbi Automated Vehicles’ Effects on Urban Traffic Flow Parameters . . . . . . . Andrea Gemma, Ernesto Cipriani, Umberto Crisalli, and Livia Mannini

571

582

593

The Impact of CNG on Buses Fleet Decarbonization: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . João Paulo Fontoura Oliveira, Tânia Fontes, and Teresa Galvão

606

A Study on the Use of Autonomous Vehicles for the Interconnection of Urban Transport Interchanges . . . . . . . . . . . . Anastasia Georganti, Nikolaos Soumpasis, and Giannis Adamos

621

Fostering the Autonomous Driving in Urban Mobility Operation (Passengers and Goods) – INTEGRA Network . . . . . . . . . . . . . . . . . . . . . . Sergio Güerri Ferraz and Mireia Calvo Monteagudo

622

Emerging and Innovative Technologies in Transport: New Energy and Mobility Outlook for the Netherlands Optimization-Based Comparison of Rebalanced Docked and Dockless Micromobility Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fabio Paparella, Banchon Sripanha, Theo Hofman, and Mauro Salazar

633

Quantifying the Charging Flexibility of Electric Vehicles; An Improved Agent-Based Approach with Realistic Travel Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peter Hogeveen, Vincent A. Mosmuller, Maarten Steinbuch, and Geert P. J. Verbong

645

Contents

xvii

How Shared Autonomous Electric Vehicles Could Slash Resource Use and Make Cities More Enjoyable . . . . . . . . . . . . . . . . . . . . . Auke Hoekstra, Peter Hogeveen, and Pim Labee

663

A Stated Adaptation Approach to Assess Mode Change Behavior of Car Drivers in Presence of Park and Ride Facilities; Evidence from 2 European Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Valeria Caiati and Soora Rasouli Estimating Availability Effects in Travel Mode Choice Among E-bikes and Other Sustainable Mobility Services: Results of a Stated Portfolio Choice Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xueting Ren, Soora Rasouli, Harry J. P. Timmermans, and Astrid Kemperman Role of Service Uncertainty in Decision to Use Demand Responsive Transport Services, a Stated Adaptation Choice Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shangqi Li, Soora Rasouli, and Harry J. P. Timmermans

677

696

697

Active and Non-motorized Travel: Walking and Cycling Infrastructure How is the Redesign of Public Space for Active Mobility and Healthy Neighborhoods Perceived and Accepted? Experiences from a Temporary Real-World Experiment in Berlin . . . . . Katharina Goetting and Julia Jarass

701

Emergent Bicycle Infrastructure During the COVID-19 Pandemic: The Karamanli Avenue Pop-Up Cycle Lane in Thessaloniki, Greece . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Garyfallia Katsavounidou, Apostolos Papagiannakis, Iordanis Christakidis, and Odysseas Mavros

714

Pedestrian Movement and the Built Environment – A Big Data-Based Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Avital Angel and Pnina Plaut

728

Walkability Evaluation: The Case Studies of Veroia and Igoumenitsa, Greece . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ilianna Boulmou, Konstantina Tsakelidou, Georgios Palantzas, Evangelos Genitsaris, and Dimitrios Nalmpantis Evaluation of Temporary Mobility Measures Due to the COVID-19 Pandemic in the City of Thessaloniki, Greece . . . . . . . Anastasia Totokotsi, Vagia Topouzli, Georgios Palantzas, and Dimitrios Nalmpantis

729

741

xviii

Contents

Anthropocentric Design of Sidewalks with the Use of Conjoint Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anna Anyfanti, Ioannis Frantzeskakis, Georgios Palantzas, and Dimitrios Nalmpantis

751

Active and Non-motorized Travel: Promoting Active Mobility Context-Aware Bicycle Route Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Damianos Gavalas, Theofanis Gerodimos, and Christos Zaroliagis Promoting Public and Active Transport Based Intermodal Mobility; The Adriatic – Ionian Experience . . . . . . . . . . . . . . . . . . . . . . . . . Glykeria Myrovali and Maria Morfoulaki Social Media and Urban Mobility Choices: How a Transport-Related Content Could Be Influential in Social Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maria Karatsoli and Eftihia Nathanail

765

777

791

Smart and Resilient Urban Mobility During the COVID-19 Pandemic: The Case of a Southern European Medium-Sized City . . . . . Zoi Olympisiou and Apostolos Papagiannakis

820

Supporting a Behavioural Change Towards Cycling Through Safe Cycling Training for Children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alexandros Skeparianos and Eleni Anoyrkati

833

Policies to Promote and Uptake Soft Mobility in the Mediterranean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christos Gioldasis, Zoi Christoforou, Kosmas Anagnostopoulos, Claudia Ribeiro, Alessia Giorgutti, and Laia Vinyes Marce Active Mobility Versus Motorized Transport of High School Students in Orestiada Municipality of Greece . . . . . . . . . . . . . . . . . . . . . . . Zoi Tampaki, Thomas Panagopoulos, Paraskevi Karanikola, Stilianos Tampakis, and Sotiria Ralousi Modal Shift Towards Active Transport During the Covid-19 Restrictions: Can We Maintain This Trend? . . . . . . . . . . . . . . . . . . . . . . . . Panagiotis-Nikolaos Kezios and Ioanna Spyropoulou

846

847

858

Active and Non-motorized Travel: Understanding Active Travel Behaviour Evaluating Route Choice Characteristics of E-Scooters . . . . . . . . . . . . . . Panagiotis Papantoniou, Sofia-Ioanna Machaira, and Ioanna Pagoni

873

Contents

What Is Leading the Choice Between Motorized and Non-motorized Transport Modes? The Case of Porto Metropolitan Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hudyeron Rocha, António Lobo, José Pedro Tavares, and Sara Ferreira A Joint Methodological Approach for Interpreting School Mobility Patterns and Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kornilia Maria Kotoula, Stylianos Kolidakis, George Botzoris, and Georgia Aifadopoulou Mobility Patterns in the Campus of the University of Patras . . . . . . . . . . Christos Gioldasis, Anna Mariam Psarrou Kalakoni, Zoi Christoforou, Garyfallia Liappi, and Maria Giannoulaki The Impact of Covid-19 Pandemic on Active Mobility – Belgrade Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ana Trpkovi´c, Eleni Anoyrkati, Vladislav Maraš, Predrag Živanovi´c, and Sreten Jevremovi´c Understanding Group Social Ties and Their Impact on Travel Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Na’amah Hagiladi and Pnina Plaut An Evaluation of Agent-Based Models for Simulating E-Scooter Sharing Services in Urban Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eirini Stavropoulou, Lambros Mitropoulos, Panagiotis G. Tzouras, Christos Karolemeas, and Konstantinos Kepaptsoglou Investigating the Factors that Reinforce Competitiveness of Transport SMEs, the Case of West Midlands SMEs, UK . . . . . . . . . . . Eleni Anoyrkati, Alba Avarello, and Giuliana Famiglietti-Pipola

xix

882

902

916

931

942

959

977

Equitable, Just and Inclusive Transportation: Inclusive and Equitable Mobility Feelings of Insecurity, Obstacles and Conflicts: Issues Blind People Have with e-scooters in Public Space and Potential Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Michael Hardinghaus and Rebekka Oostendorp

991

Involving Passengers in Creating Inclusive Digital Mobility Solutions—Findings from the INDIMO Project . . . . . . . . . . . . . . . . . . . . . 1001 Kathryn Bulanowski, Floridea Di Ciommo, and Sandra Lima Multimodal Route Planning for Blind and Visually Impaired People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1017 Catarina Costa, Sara Paiva, and Damianos Gavalas

xx

Contents

Insularity, Accessibility, and Affordability of Transport Services in Greek Islands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1027 Theodore Tsekeris Is Shared Mobility Equally Accessible to All? An Income Analysis of Service Adoption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1045 Ignacio Martín, Oliva G. Cantú-Ros, and Javier Burrieza-Galán Transit Fare Equity: Understanding the Factors Affecting Different Groups of Users’ Payment Method . . . . . . . . . . . . . . . . . . . . . . . . 1058 Michael Lu and Ehab Diab Co-designing Transport Solutions Towards an Inclusive Public Transport in the City of Bologna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1059 Chiara Lepori, Giuseppe Liguori, Elvia Vasconcelos de Gouveia, and Matteo Brusa Conceptual Architecture for an Inclusive and Real-Time Solution for Parking Assistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1083 Sara Paiva, António Amaral, Teresa Pereira, and Luis Barreto Equitable, Just and Inclusive Transportation: User-Centric Transport User Acceptance of Automated Shuttle Buses—Results of a Passenger Survey in Stolberg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1095 Sönke Beckmann and Hartmut Zadek Public Transport Versus Demand Responsive Transport Services in (Extremely) Low Demand Areas: The Case of the Sicilian Hinterland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1108 Tiziana Campisi, Socrates Basbas, Anestis Papanikolaou, Antonino Canale, and Giovanni Tesoriere A Network Analysis Model to Measure the Walkability of Public Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1121 Asterios Binopoulos, Eleni Evangelidou, Theocharis Vlachopanagiotis, and Konstandinos Grizos An Investigation of Distraction Factors on Road Safety . . . . . . . . . . . . . . 1135 George Botzoris, Vassilios Profillidis, Athanasios Galanis, Panagiotis Lemonakis, and Gerasimos Argyropoulos Optimization of a Prospective Carpooling Service in the Regional Unit of Thessaloniki with the Use of Conjoint Analysis and Market Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1149 Ioannis Ouranos, Vasileios Chatzizisis, Georgios Palantzas, Evangelos Genitsaris, and Dimitrios Nalmpantis

Contents

xxi

Micro-mobility and Micro-mobility’s Status Quo in Greece . . . . . . . . . . . 1161 Panagiota Mavrogenidou, Amalia Polydoropoulou, and Athena Tsirimpa Sustainable and Resilient Supply Chain: New Trends and Emerging Modes in Last Mile Deliveries An Emerging and Innovation Transport Solution: Towards Transforming Parking Lot to Urban Consolidation Centre: Madrid Living Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1179 Beatriz Royo, Dimitra Politaki, Juan Nicolas Gonzalez, and Angel Batalla How Digital Services Implementation Along International Supply Chains Influences the Performance of Logistics Operations? A Bottom-Up Approach for Impact Validation and Categorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1192 Sofoklis Dais, Leonidas Parodos, Georgia Aifadopoulou, and Elpida Xenou Efficient Management of Operations in Ro-Ro/Ro-Pax Terminals Using a Cloud-Based Yard Management Platform . . . . . . . . . 1205 Georgios Tsoukos, Athanasios Giannopoulos, and Apostolos Bizakis The Use of Drones in City Logistics—A Case Study Application . . . . . . 1218 Theonymphi Xydianou and Eftihia Nathanail Towards an Integrated Framework for Smart Goods Distribution in a Middle-Sized City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1233 Eleftherios Tsolkas and Giannis Adamos Crowd Shipping for Urban Logistics: Investigating the Factors Affecting Consumer Adoption in Hanoi, Vietnam . . . . . . . . . . . . . . . . . . . . 1234 Thi My Thanh Truong Sustainable and Resilient Supply Chain: Advances in Operation and Management of Freight Transport and Logistics Assessing the Evolution of Urban Planning and Last Mile Delivery in the Era of E-commerce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1253 Tiziana Campisi, Antonio Russo, Socrates Basbas, Ioannis Politis, Efstathios Bouhouras, and Giovanni Tesoriere Is It Necessary to Calculate Passenger Car Equivalent Value for Commercial Vehicles in Urban Areas? . . . . . . . . . . . . . . . . . . . . . . . . . . 1266 Efstathios Bouhouras, Konstantina Kikeni, Athanasios Siginos, Apostolos Vouitsis, Konstantina Voulgari, and Socrates Basbas

xxii

Contents

Appropriate Key Performance Indicators for Evaluating Integrated Passenger-Freight Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1278 Francesco Bruzzone, Federico Cavallaro, and Silvio Nocera The Main Problems of the Road Freight Transport Sector in Greece . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1291 Dimitrios Zekos, Georgios Palantzas, and Dimitrios Nalmpantis EN.I.R.I.S.S.T Road Freight Transport Service: An Impact Oriented Policy Advice Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1305 Ioannis Karakikes, Amalia Polydoropoulou, Athena Tsirimpa, and Ioanna Pagoni Anticipation of New and Emerging Trends for Sustainable Last-Mile Urban Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1316 Vasco Silva, António Amaral, and Tânia Fontes The Importance of Logistics Performance for Mitigating Transportation-Caused Pollution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1330 Robert Sova and Cristiana Tudor Sustainable and Resilient Supply Chain: Cargo Bikes for Sustainable Mobility and Logistics Optimizing the Route and Location Planning for Cargo Bikes and Mobile Parcel Lockers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1343 Benjamin Rolf, Gianna Kurtz, Kai Hempel, and Hartmut Zadek Evaluation of Station Distribution Strategies for Next-Generation Bike-Sharing System . . . . . . . . . . . . . . . . . . . . . . . . . . 1358 Vasu Dev Mukku, Imen Haj Salah, Abhirup Roy, and Tom Assmann Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo-Bike Fleets . . . . . . . . . . . . . . . . . . . . . . . . . . 1374 Malte Kania and Tom Assmann Determining the Demand for Loading/Unloading Zones in Urban Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1406 Patrick Mayregger Diversification of the Bicycle Market and Consequences for Urban Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1418 Dennis Knese and Lukas Fassnacht Collaborative Distribution Solutions in Last Mile Logistics . . . . . . . . . . . 1433 Anna Buerklen, Nicolas Schuete, and Christian Rudolph

Contents

xxiii

Urban Planning and Transport Infrastructure: Challenges and Solutions for Sustainable Public Transport Investigating Taxi Driver Behavioral Aspects: Evidence from Athens, Greece . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1449 Dimitrios Argyriou, Athanasios Kopsidas, and Konstantinos Kepaptsoglou Water Management Solutions to Decrease Water Consumption and Mitigate CO2 Emissions in Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1463 Maria Vittoria Corazza, Anita Toni, and Daniela Vasari Sustainable Public Transport in Petro¸sani Basin—Current Status and Development Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1476 Sorin Mih˘ailescu and Gabriel Praporgescu The Impact of Weather on Daily Ridership of the Urban Rail Transportation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1490 Oruc Altintasi, Dilan Oruczade, and Mehmet Can Guven Estimating the Environmental Footprint of the O.A.S.A Group . . . . . . . 1501 Marlen Michali, Konstantinos Karampourniotis, Panagiotis Zafeiriou, and Laoura Vavaliou A Hybrid MCDA Methodology to Evaluate Ferry Fleet Assignment to Routes in the Greek Islands . . . . . . . . . . . . . . . . . . . . . . . . . . 1517 Georgios Papaioannou, Eftihia Nathanail, and Amalia Polydoropoulou Urban Planning and Transport Infrastructure: Safe and Sustainable Transport Infrastructure and Services Sustainable Mobility at the Core of Sustainable Tourism in 6 European Islands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1543 Claudio Mantero Evaluating Road Network Hierarchy Planning Suggestions in SUMPs. Evidence from 7 Greek Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . 1555 Stefanos Tsigdinos, Aglaia Sfakaki, Anastasia Zachou, Yannis Paraskevopoulos, Panagiotis Tzouras, and Efthimios Bakogiannis AURORA—Creating Space for Urban Air Mobility in Our Cities . . . . . 1568 Kathryn Bulanowski, Dominique Gillis, Elham Fakhraian, Sandra Lima, and Ivana Semanjski

xxiv

Contents

Moving Towards Safe and Sustainable Mobility: The Development of a RoaD AccidEnts InformAtion CenteR for Greece (DEAR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1586 Amalia Polydoropoulou, Ioannis Politis, Georgios Georgiadis, Ioanna Pagoni, Alexandros Sdoukopoulos, Danai Kouniadi, Efthymis Papadopoulos, Nikoleta Krousouloudi, Ioannis Fyrogenis, and Aristomenis Kopsacheilis A Portable Device for Supporting Autonomous and Healthy Urban Ageing: The PROLONG System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1598 Despoina Petsani, Efstathios Sidiropoulos, Dimitris Bamidis, Nikolaos Kyriakidis, Giuseppe Conti, Leonardo Lizzi, and Evdokimos Konstantinidis Asset Management: Rules for Enhancing Resilience . . . . . . . . . . . . . . . . . 1611 Afroditi Anagnostopoulou, Aggelos Aggelakakis, Maria Boile, and Arjan Hijdra Urban Planning and Transport Infrastructure: Integrated Planning and Policies for Sustainable Urban Development Impact of Covid-19 on Urban Traffic Patterns . . . . . . . . . . . . . . . . . . . . . . . 1627 Nikolaos Mexis and Ioanna Spyropoulou Definition of a Variable Speed Limit System for the Northern Access to the City of Barcelona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1643 Francisco Rodero and Pere Arrom Regenerating Athens City Center to a Low Pollution and Restricted Vehicle Traffic Zone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1654 Dimitra Chondrogianni, Yorgos J. Stephanedes, and Panagiota-Gerogia Saranti Analysis and Evaluation of Mobility Solutions and Targeted Interventions to Support the Urban Regeneration of the Canal Port of Rimini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1663 Rachele Corticelli, Margherita Pazzini, Lorna Dragonetti, Cecilia Mazzoli, Claudio Lantieri, Annarita Ferrante, and Andrea Simone Urban Air Mobility (UAM) Integration to Urban Planning . . . . . . . . . . . 1676 Dionysia G. Perperidou and Dimitrios Kirgiafinis

Contents

xxv

Urban Planning and Transport Infrastructure: Assessment and Evaluation of Sustainable Urban Mobility How to Monitor and Assess Sustainable Urban Mobility? An Application of Sustainable Urban Mobility Indicators in Four Greek Municipalities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1689 Ioannis Chatziioannou, Konstantinos Nakis, Panagiotis G. Tzouras, and Efthimios Bakogiannis Revision of the Budapest Mobility Plan, and the Alignment of Urban and Transport Strategies in Budapest . . . . . . . . . . . . . . . . . . . . . 1711 Diána Kimmer and Tünde Hajnal An FCM Approach to Achieve Near Zero-CO2 Urban Mobility: The Case of Larissa, Greece . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1724 Konstantinos Kokkinos and Eftihia Nathanail Analyzing Pollutant Concentrations in Two Main Greek Urban Centers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1736 Cristiana Tudor Urban Planning and Transport Infrastructure: Urban Sustainability INVESTL2 Ontology: Semantic Modeling of Sustainable Living Labs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1753 Omiros Iatrellis, Areti Bania, Rik Eweg, Liisa Timonen, and Ekaterina Arabska Evaluation of the Current State and Trends for the Sustainable Development of the Agri-Food Sector of the South-Central Region of Bulgaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1769 Nazan Arifoglu Sen, Mariana Ivanova, and Svetla Dimitrova Information Communication Technologies (ICTs) and Disaster Risk Management (DRM): Systematic Literature Review . . . . . . . . . . . . 1779 Areti Bania, Omiros Iatrellis, and Nicholas Samaras Innovative Bio-based Circular Economy Schemes: The Case of Biomass and Food Waste Utilization as an Enabler of Regional Resilience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1795 Maria Batsioula, Apostolos Malamakis, Sotiris I. Patsios, Dimitrios Geroliolios, Stamatia Skoutida, Lefteris Melas, and Georgios F. Banias Relevant Research and Development Competences – Case INVEST4EXCELLENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1807 Liisa Timonen, Helena Puhakka-Tarvainen, and Tiina Muhonen

xxvi

Contents

Optimizing Urban Resilience via FCM and Participatory Modeling: The Case of Joensuu Finland . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1828 Konstantinos Kokkinos, Omiros Iatrellis, Liisa Timonen, and Nicholas Samaras Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1841

Electric and Clean Energy in Transportation: Shifting to Electric and Cleaner Solutions for Fighting Climate Change

Is the Shift to Electrification and Powertrain Improvement Sufficient to Change Urban Mobility’s Impact on Climate Change? Vasiliki V. Georgatzi(B)

and Yeoryios Stamboulis

Department of Economics, University of Thessaly, Volos, Greece {vageorgatzi,ystambou}@uth.gr

Abstract. Transportation is one of the most polluting activities, with urban mobility owning the higher percentage. Currently, 99.8% of the vehicles worldwide have internal combustion engines (ICE), and 95% of these use liquid fuels made from petroleum. The private car is the most common mode of urban mobility (UM) in Western countries, as it accounts for about two-thirds of daily commuting. Moreover, the car lies at the core of the socio-technical transportation system, characterised lock-ins. Policies are set to foster powertrains’ technological improvement for emissions reduction to reach emission targets, while there is also interest in shifting from ICE improvement to electric vehicles (EVs). But are the technological improvement and swift to different vehicle powertrains sufficient conditions to reach the emission targets? Are other policies, regulations, or actions necessary? Several researchers have analysed the transition of UM, focusing primarily on technological change and less on changes in mobility modes, with digitalisation and business model disruption. We investigate UM as a socio-technical system in transition consisting of four subsystems. We present a system dynamics (SD) model for the transition from the current state of the dominant regime based on ICE technology and private car to a new one. In the new system, new modes of mobility (ride-hailing, car-sharing) challenge incumbent ones (private car, taxi, public transport), and new technologies arise as niche innovations (EVs, ICTs). Finally, the developed SD model helps us explore different scenarios and policy mixes. Keywords: System dynamics · Transition · Urban mobility · Helices · Sustainability · Modes of mobility · Technological improvement · Technology shift · Alternative modes of urban mobility

1 Introduction The need for urban mobility is constantly rising, resulting in an emissions increase, noise, congestion, and infrastructure overload (Karlsson et al. 2019). In this paper, we examine if technological improvement and the entrance of EVs are sufficient for UM to become sustainable. We consider UM as a socio-technical system that fulfils citizens’ needs for mobility and “consist[s] of artefacts, knowledge, capital, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_1

4

V. V. Georgatzi and Y. Stamboulis

labour, cultural meaning, etc.” (Geels 2004, p. 900). This system is influenced by external and internal-external pressures that behaviorze its structure, changing the way mobility needs been fulfilled until now. This change is due to the emergence of new modes of UM and new technologies provoking transition in the system. New modes compete against old ones and amongst themselves in an evolutionary process towards a new regime ensemble, involving the development of different assets – resources, technologies, infrastructures, etc. – and institutional arrangements. To examine the system of UM, we apply a systemic approach, wherein different helices (society, government, industry, and academia) coexist and interact. We examine the different factors necessary to lead to a new, more sustainable mix of mobility modes (e.g., financial, user behavior), both from the viewpoint of business models under which the different mobility modes operate and from the technological viewpoint. Finally, we study the way different elements of each helix influence the trajectory of each UM mode and the system in general. In the rest of the paper, we present a concise literature review in Sect. 2, the methodological approach in Sect. 3, the causal loop and the stock-and-flow diagrams (SFDs) of the SD model in Sect. 4, and the results of the scenarios examined in Sect. 5. In Sect. 6, we discuss our findings and present our conclusions.

2 UM Transition The UM transition is based on three pillars: environmental concern and “obligation” to become more sustainable; technology improvement; and business model innovation (Lüdeke-Freund 2019). Various factors operate as barriers and drivers to these pillars, including existing socio-technical systems, regimes, and infrastructures that impede change due to lock-ins and path dependencies (Klitkou et al. 2015). Fuel and transport taxes positively impact climate change mitigations (Giblin and McNabola 2009; Timilsina and Dulal 2011). Contrary, an established regime for which substantial investments have been made to provide a favourable environment and complementary infrastructures creates lock-ins that impedes change (Klitkou et al. 2015; Kotilainen et al. 2019). Behavioural factors may operate as drivers of climate change mitigation, e.g., environmental awareness (Egbue and Long 2012), or as barriers, e.g., lack of knowledge of alternative modes of urban mobility (AMUMs) or new technologies (Balint et al. 2017; Benvenutti et al. 2017; Cruz and Sarmento 2020; Georgatzi and Stamboulis 2021; Langbroek et al., 2016). Income level has a negative influence as more affluent individuals tend to commute more (Lei et al. 2012; Ivanova et al. 2018), while population density appears to hurt the private car choice (Timilsina and Dulal 2011; Lei et al. 2012; Ivanova et al. 2018). Finally, if access to public transport (PT) is high and its network density is sufficient, its attractiveness increases (Timilsina and Dulal 2011; Lei et al. 2012). The choice of powertrain technology depends on fuel and energy prices, availability of complementary assets, and cost of purchasing. ICE vehicles are produced at a much higher volume than EVs, making them cheaper and more famous (Köhler et al. 2018b, 2009; Struben and Sterman 2008). Availability of complementary assets, infrastructures (e.g., charging stations), and maintenance facilities accelerate EV diffusion (Köhler et al. 2018a; Kwon 2012; Struben and Sterman 2008).

Is the Shift to Electrification and Powertrain Improvement

5

Improvement of EVs’ technology in terms of mileage range and battery life (Struben and Sterman 2008; Walther et al. 2010) and the increasing variety of EVs type render EVs an attractive choice (Janssen et al. 2006; Struben and Sterman 2008). Subsidies and credits for purchasing EVs are also essential for their diffusion (Kwon 2012; Santos et al. 2010; Struben and Sterman 2008; Walther et al. 2010) while increasing environmental awareness positively impacts EVs diffusion (Köhler et al. 2009; Struben and Sterman 2008). AMUMs (vis-à-vis the private car) can lead commuters away from the private car under the appropriate motivation. Taxes on the use of the private car (e.g., fuel taxes, transport taxes) discourage commuters from using and buying them (Bernardino et al. 2015; Smith et al. 2018), while economic incentives, like fare discounts, encourage commuting with other modes of transport (Smith et al. 2018; Karlsson et al. 2019). The fleet size of AMUM increases the quality of their service (Kim 2015), and greater population density favours their choice (Timilsina and Dulal 2011; Bernardino et al. 2015; Kim 2015). On the other hand, a high-income level discourages AMUM selection (Bernardino et al. 2015; Kim 2015). Finally, as the level of ICT usage by citizens increases, it is more likely for citizens to select a more digitalised mode (Köhler et al. 2009). Commuters’ opinion of different modes of mobility affects their preferences (Bernardino et al. 2015), e.g., a commuter’s environmental awareness level may render a mode unattractive because of its emission level (Bernardino et al. 2015; Pangbourne et al. 2020; Shen et al. 2008).

3 Methodological Approach The most common approaches used to study UM transition are multi-level perspective (MLP)–which focuses more on the development and diffusion of new artefactual technologies (Sarasini and Linder 2018), transition management (TM) – which is usually biased toward incumbent regimes and gives insufficient attention to niches (Lachman 2013), innovation systems (IS) - focuses on how systems affect innovation development, diffusion, and use, strategic niche management (SNM) - used chiefly for ex-post analyses (Lachman 2013), and agent-based modelling (ABM) - lacks a broader system view, missing the feedback and the synergies of the system (McDowall 2014). SNM, TM, and IS also focus on technology, but they overlook organisational and business model innovation issues (Sarasini and Linder 2018). Few studies have dealt with mobility using SD. Most of them stay at the level of causal loop diagram (CLD) and focus on the effects of specific transport segments such as urban transport (Jifeng et al. 2008; Pangbourne et al. 2020), transport modes (Kim 2015; Karlsson et al. 2019), or road transport, individual policy measures (Karlsson et al. 2019), and specific technologies (Walther et al. 2010). Existing research does not provide a high level of detail and is not explanatory enough on the interactions between UM’s technological and modal change. In this research, we combine the N-tuple Innovation Helix Model (Leydesdorff 2012) (government, academia, industry, and society) with MLP, and we use SD to examine the interactions within and amongst helices and their impact on the transition from an old mix of technologies and UM modes to a new one. The level of analysis in this

6

V. V. Georgatzi and Y. Stamboulis

study is the city, where new and incumbent UM modes and business models compete against each other. Technical change and knowledge development take place at a global level, i.e. their impact is not confined to the effort made by local industry or academia. Thus, we consider the academia and technical change as external to the city system and assess the impact of the latter as an external scenario parameter (“Rate of technology improvement”). Along the lines of the MLP approach, we perceive modes of mobility as different regimes (Geels 2018), while niche innovations are the available alternative technologies, and landscape pressures are the environmental crisis, economic growth, the pandemic, etc. Three different types of interactions occur within the UM system amongst the different regimes/modes of mobility: competition, symbiosis, or integration. Competition interactions will drive a “modal shift” from mobility with private cars to AMUM (e.g. carsharing, ride-hailing). Symbiosis amongst the regimes means that the co-existing regimes interact, but they are relatively independent, while integration means that the regimes closely interact to form a new future system. Various UM modes can be integrated into inter-modal transport systems (e.g. MaaS) (Geels 2018).

4 The SD Model Here, we use the SD methodology to capture the dynamics of change and the interactions at different levels (landscape, regime, and niche) and spheres (helices/subsystems). We present a CLD, a flexible and valuable tool for diagramming the feedback structure of systems and a SFD which emphasises their underlying physical structure. Stocks and flows track material, money, and information accumulations as they move through a system (Sterman 2000). Four reinforcing and two balancing loops are salient in the CLD. The first two reinforcing loops (R1 & R2) show that as a mode’s quality of service (QoS) increases, it affects its attractiveness positively, as adoption from word of mouth and advertising increase as well, and more users are convinced to use a particular mode, increasing the cumulative experience leading to better QoS. The third reinforcing loop (R3) shows the effect of environmental awareness on the demand for urban mobility modes (UMMs). The less pollution a mode produces, the higher the attractiveness of the mode, which consequently increases the adoption rate, leading to higher demand. The fourth reinforcing loop presents the impact of policy gap results (policy targets-total emissions). As the policy gap increases, policymakers are obliged to take more measures and tighten the policies to reach targets, so policy goals are set higher - increasing the policy gap. The first balancing loop (B1) concerns the impact of the change in mode saturation (vehicles necessary to cover demand/mode fleet). As Mode saturation increases, fewer commuters are serviced, and QoS falls, causing the mode’s attractiveness to decrease and lessen the adoption rate. The total amount of users decreases, causing a decrease in the mode saturation. B2 shows that an increase in the purchase of new, less polluting vehicles leads to a decrease in the average mode emissions and, in the long-term, leads to a decrease in total emissions produced by the system’s activity. The decreased total emissions lead to a decrease in policy gap, causing the policies to become less strict (taxes) and less supportive (subsidies) in purchasing new vehicles (Fig. 1).

Is the Shift to Electrification and Powertrain Improvement + Policy gap -

Planning goals -

7

Total emissions

R4

-

Government

+

Policy Stringency

Technology Improvement

+

+

Β2

Industry

Avg Mode emissions -

Society

+

Taxes +

New less polluting Vehicles Purchase + to cover demand +

Subsidies

R3 Adoption from adertising +

+ UMM attractiveness +

+

R2

UMM demand +

+ + Users of UMM

Adoption from WoM

R1

+ QoS

B1 + UMM cummulative experience

+ Mode saturation

+ -

Fig. 1. UM Causal loop diagram

We model four different road modes of passenger mobility, three incumbent ones, private car, taxi, and PT dominated by the ICE vehicles, and two AMUMs, car-sharing and ride-hailing. We take into consideration the shift from ICE to electric vehicles. The change measures transition is stock values (fleets of various mode-powertrain mixes, mode users, emission levels). The impact is measured as total emissions produced by the system activity and the gap between them and the policy goals. The combined effect of factors from various helices (government: taxation, subsidies; society: digital literacy, environmental awareness, population density; and industry: technical change, investment in complementary assets, etc.) is modelled along with their interaction. The current landscape has some of the required conditions for new UM modes and new powertrains (e.g., roads, parking spaces, charging stations). However, there is still a need for new complementary assets (e.g., maintenance networks, charging infrastructures, etc.). The SFD is constructed using building blocks (variables) categorised as stocks, flows, delays, converters, and constants. Stocks (rectangles) are the state variables, and flows (valves) are the rates of change in stock variables. They represent those activities that fill in or drain the stock variables. Converters (circles) are intermediate variables used for auxiliary calculations. Constants (rhombuses) are the model parameters. Finally, the connectors, represented by simple arrows, are the information links representing the cause and effects within the model structure. Double lines across the arrows indicate delayed information.

8

V. V. Georgatzi and Y. Stamboulis

The SFD is a graphical representation of the mathematical model. The embedded mathematical equations are divided into two main categories: the stock equations, defining the accumulations within the system through the time integrals of the net flow rates, and the rate equations, defining the flows among the stocks as time functions. The equations of our model have been regulated by using data from the road passenger urban transport of the city of Athens. We use the Bass diffusion model (Sterman 2000), which describes how users adopt a specific UM mode. In the Bass diffusion model, we add one more parameter, the mode attractiveness as a complementary factor affecting adoption; attractiveness is affected by various industry, social and governance factors, as shown in Fig. 2. Potential adopters are the total population minus the already users of each mode. This number is reducing by the adoption rate and increasing by the discard rate of users each year. The adoption rate is affected by advertising and word-of-mouth. The mode attractiveness is influenced by (1) the time that travel requires to accomplish; PT takes more time, while AMUM and taxi seem to take less, (2) the access to PT, (3) the COVID19 impact, with the PT losing the higher part of its market share, (4) quality of service (QoS), with the mode attractiveness being higher as the QoS rises, (5) environmental awareness and the emissions produced by the mode, (6) ICT usage growth; influences more the modes that are digitalised and “oblige” users to be digitally literal to use them, and finally, (7) the existence of an integrated system of mobility, like mobility as a service (MaaS). The average distance travelled per trip is affected by transport taxes, price change, income changes and urban density change, teleworking-immobility rate, and average mode emissions produced by each vehicle. The efficiency of the vehicles is a factor that can provoke more travelling as the cost of travelling reduces. Furthermore, we assume that the main factors influencing the choice between the two examining powertrains are sensitivity to the vehicle price change, environmental issues (vehicle emissions), and complementary services (e.g. maintenance network, charging infrastructure, etc.). Cumulative experience is a factor that aids in calculating the QoS for each mode of mobility; cumulative experience is based on the volume of use. The number of commuters during peak hours change, determining the number of vehicles to be purchased to cover demand. Each powertrain’s emissions per km of use are calculated with an average rate of technology improvement, causing annual emissions improvement. Finally, regarding the government helix, we aim to compare the total emissions produced by passenger road mobility through time with the planning goals for emissions to examine if the policies set are effective and if new, more strict policies are necessary to reach the environmental targets and render UM sustainable. SD practitioners have developed various tests to improve models (Sterman 2000). We first tested the model’s structural validity, precisely, its dimensional consistency. Each equation was checked to confirm that the left part’s dimensions were the same as the right part. Then we conducted extreme-condition tests to check whether the model behaves realistically no matter how extreme the policies imposed may be. We used the Euler numeric method. We use various tests to test our model’s validity (Sterman 2000). First, we tested the model’s structural validity, precisely, its dimensional consistency. Each equation was

Fig. 2. UM stock and flow diagram

Is the Shift to Electrification and Powertrain Improvement

9

10

V. V. Georgatzi and Y. Stamboulis

checked to confirm that the left part’s dimensions were the same as the right part. Then we conducted extreme-condition tests to check whether the model behaves realistically no matter how extreme the policies imposed may be. We used the Euler numeric method. The simulation horizon is set equal to thirty years (2021–2050), a strategic horizon to study the improvement of a mobility system performance and facilitate the tuning of model parameters, following the policies horizon. We initially set the integrating time step (DT) at 0.33 years and ran the model. Then we set the DT at 0.2 and reran the model. The results did not significantly change, so we chose the DT 0.33. The calibration of the model was based on data from the period 2000–2011 for the parameters that describe the system’s behaviour. These parameters on which the calibration of the model was based, their meanings, and data sources are shown in Table 1. The behaviour of the Athens urban transport system from 2000 to 2011 is given in Table 2. We checked the relevant and mean absolute percentage error (MAPE) between the fundamental values and the equivalents obtained by simulation results for six parameters, to check the model’s behavior reproduction (Sterman 2000, p. 874). Since the MAPE values are less than 10%, according to Lewis’s critical values (Lewis 1982, p. 40), the model is characterised by high accuracy. Table 1. Data for model calibration Factor – Indicator

Meaning

Unit of measurement

Average CO2 emissions per km from new passenger cars

Avg CO2 emissions per gCO2 /km km

EEA

Mode j fleet [Private car]

Number of passenger cars

Eurostat

Mode j fleet [Public transport]

Number of buses and Vehicles coaches in UM services

OASA

Mode j fleet [Taxi]

Number of taxi vehicles Vehicles

Eurostat

Users share [Private car]

% of passenger movements done with private cars

%

OECD

Users share [Public transport]

% of passenger movements done with PT

%

OECD

Vehicles

Source of data

5 Results We run the SD simulation model under three different scenarios (base, optimistic, behavioural change) for the period 2021–2050. The parameters that were different in these scenarios were the Existence of a Booking Platform – MaaS, Marketing Effectiveness, Subsidy rate, Time to restrict ICE powertrain, Vehicle occupancy, and Contact

Is the Shift to Electrification and Powertrain Improvement

11

Table 2. Historical data and simulation results for the examined system Year

Average CO2 emissions per km from new passenger cars

Mode j fleet [Private car]

Historical data

Simulation results

Relevant error (%)

Historical data

Simulation results

Relevant error (%)

2000

168,000

168,000

0.000

1669,693

1669,693,000

0.000

2001

166,500

169,021

1.514

1786,226

1878,177,536

4.596

2002

167,800

165,313

1.482

1898,147

2016,876,761

4.097

2003

168,900

162,922

3.539

2000,941

2146,345,157

2.194

2004

168,800

161,042

4.596

2122,931

2286,999,296

1.205

2005

167,400

159,352

4.808

2243,921

2422,181,650

4.483

2006

166,500

157,715

5.277

2373,947

2554,067,273

7.859

2007

165,300

156,059

5.590

2510,151

2673,330,240

11.074

2008

160,800

154,337

4.019

2629,158

2782,378,398

13.290

2009

157,400

152,495

3.116

2695,909

2893,292,608

13.481

2010

143,700

150,427

4.682

2739,129

2990,873,549

12.557

2011

132,700

131,266

1.081

2745,727

3110,169,966

9.634

MAPE

57,598

Year

Mode j fleet [Taxi]

77,208 Mode j fleet [Public transport]

Historical data

Simulation results

Relevant error (%)

Historical data

Simulation results

Relevant error (%)

2000

16,923

16923,000

0.000

1830,000

1830,000

0.000

2001

16,923

16589,392

1.773

1852,000

1853,101

0.052

2002

16,923

16382,829

2.679

1841,000

1878,362

2.000

2003

16,923

16297,492

2.749

1839,000

1904,459

3.506

2004

16,923

16330,739

1.998

2001,000

1932,381

3.502

2005

16,923

16477,477

0.450

2093,000

1961,079

6.398

2006

16,923

16734,179

1.886

2093,000

1991,534

4.970

2007

16,923

16817,195

3.047

2091,000

2022,695

3.418

2008

16,923

16909,643

4.375

2091,000

2055,052

1.902 (continued)

12

V. V. Georgatzi and Y. Stamboulis Table 2. (continued)

Year

Average CO2 emissions per km from new passenger cars

Mode j fleet [Private car]

Historical data

Simulation results

Relevant error (%)

Historical data

Simulation results

Relevant error (%)

2009

16,923

17016,098

5.961

2150,000

2050,193

4.852

2010

16,923

17139,967

7.962

2152,000

2048,260

5.016

2011

16,923

17292,900

10.683

2145,000

2049,682

4.625

MAPE

66,914

Year

Users share [Private car]

3,2571 Users share [Public transport]

Historical data

Simulation results

Relevant error (%)

Historical data

Simulation results

Relevant error (%)

2000

85,200

82,418

3.266

22,590

15,797

4.945

2001

85,400

82,785

3.446

21,420

15,331

3.061

2002

85,700

83,163

3.748

20,700

14,860

1.979

2003

85,600

83,840

3.614

19,890

14,135

0.571

2004

85,500

84,340

3.486

18,810

13,575

1.686

2005

85,300

84,757

3.247

18,000

13,096

3.417

2006

85,700

85,108

3.682

17,280

12,682

5.009

2007

85,300

85,302

3.201

16,650

12,408

6.390

2008

85,100

85,573

2.926

16,110

12,075

7.479

2009

15,210

11,823

9.832

2010

15,570

11,682

7.124

2011

15,840

10,880

3.545

MAPE

34,017

53,585

rate – Exposure rate. In all scenarios, technological improvement of 1.5% is assumed for ICE vehicles and 2% for EVs. Also, urban density and ICT usage from citizens is increasing by 1%, while teleworking rate is increasing by 0.5% per year. 5.1 Scenario 1 – Base Case In the base scenario, we assume that AMUMs do not exist, and the contact rate with them and their marketing effectiveness is 0. The marketing effectiveness of the other three modes of UM is positive. EVs are present, and sales of ICE vehicles are not restricted. There are no subsidies, and vehicle occupancy is set to 1.2 persons for private cars, 1,5 persons for taxis, and 70 persons for PT. Also, there is no integrated booking platform.

Is the Shift to Electrification and Powertrain Improvement

13

5.2 Scenario 2 – Optimistic In the optimistic scenario, the AMUMs are present, and there is an integrated booking platform. The marketing effectiveness for private cars is negative, while it is positive for the other modes. The vehicle occupancy is the same as in the base scenario, while subsidies regarding purchasing are set at 30% for Evs and 0% for ICE vehicles. The sales of the latter will be restricted after 2030. Vehicle occupancy is the same as in Scenario 1. 5.3 Scenario 3 – Behavioural Change In this scenario, AMUMs are present, and there is an integrated booking platform. Marketing effectiveness is negative for private cars and positive for the other three modes of mobility. Subsidies regarding the new vehicle purchase are set at 30% for EVs and 0% for ICE vehicles, and the latter’s sales will be restricted after 2030. Here, the vehicle occupancy changes to 2 persons for the three modes of mobility while for public transport remains the same at 70 persons. 5.4 Simulation’s Findings The base scenario is not sufficient for reaching policy goals. Apart from the technological improvement and the increase of EVs purchased, further policies and regulations are necessary to reach the 60% reduction of 1990 CO2 emissions by the EU target of 2050. The optimistic scenario achieves policy goals in 2042, eight years before 2050. The entrance of AMUMs, the existence of a booking platform, negative marketing effectiveness for private cars, and restriction of ICE vehicle sales seem to be sufficient to “decarbonise” UM in the city of Athens. Scenario 3 has even better results as it achieves planning goals seven years earlier than the optimistic scenario - 2035. In all scenarios, there is a decrease in the share of users that choose private cars. This decrease leads to a reduction in the number of private cars and a rise in the taxi, PT and AMUM fleets (Scenarios 2 & 3). The reduction in the number of private cars is higher in Scenarios 2 & 3, where the available mobility options increase as AMUMs enter the system. The entrance of EVs is more aggressive in scenarios 2 & 3 as there is policy support (purchase subsidies). The total number of vehicles (excluding buses) is higher in Scenario 2 than in Scenario 1, but with improved technology, thus achieving lower emissions in total (Fig. 3).

6 Conclusions and Policy Implications Our literature review has revealed a gap in the study of the interaction between technological change and business model innovation, which takes the form of new modes of UM. We explore the drivers of system change under technological and modal change. Combining MLP and N-tuple innovation helix model with SD, we investigate the behaviour and interactions at the level of UM modes (regimes) and helices (as quasi-actors) involved in the emerging transition to a new UM system.

14

V. V. Georgatzi and Y. Stamboulis

Fig. 3. Simulation results

From the Scenarios analysed for the case of Athens, we conclude that the shift to EVs and the annual improvement of the powertrains are not sufficient to achieve the policy goals set. New modes of UM are necessary to improve the system’s efficiency. Citizens need to change their habits and adopt new modes of mobility. For faster results, we need to organize our commutes better and increase vehicle occupancy to reduce the total number of commutes, and the distances travelled per person. We can understand the system’s behaviour and map the dominant feedback loops through the multi methodology approach. Providing an SD simulation as a participatory policy-making tool facilitates policy design addressing citizens’ and cities’ needs. We need policies that encourage introducing and using alternative mobility services, such as integrated booking platforms. Finally, there is a need for information campaigns to increase citizens’ environmental awareness and inform them of the damage that individual commute provokes to the environment, encouraging them to share their travels with other people. Acknowledgements. This research work is co-financed by Greece and the European Union (European Social Fund—ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning” in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by the State Scholarships Foundation (IKY).

Is the Shift to Electrification and Powertrain Improvement

15

References 1. Balint, T., Lamperti, F., Mandel, A., Napoletano, M., Roventini, A., Sapio, A.: Complexity and the economics of climate change: a survey and a look forward. Ecol. Econ. 138, 252–265 (2017) 2. Benvenutti, L.M.M., Ribeiro, A.B., Uriona, M.: Long term diffusion dynamics of alternative fuel vehicles in Brazil. J. Clean. Prod. 164, 1571–1585 (2017) 3. Bernardino, J. et al.: Transport demand evolution in Europe-factors of change, scenarios and challenges. Eur. J. Futures Res. 3(13) (2015) 4. Cruz, C.O., Sarmento, J.M.: “Mobility as a service” platforms: a critical path towards increasing the sustainability of transportation systems. Sustainability 12(16), 6368 (2020) 5. Egbue, O., Long, S.: Barriers to widespread adoption of electric vehicles: an analysis of consumer attitudes and perceptions. Energy Policy 48(2012), 717–729 (2012) 6. Geels, F.W.: From sectoral innovation systems to socio-technical systems, insights about dynamics and change from sociology and institutional theory. Res. Policy 33, 897–920 (2004) 7. Geels, F.W.: Low-carbon transition via system reconfiguration? A socio-technical whole system analysis of passenger mobility in Great Britain (1990–2016). Energy Res. Soc. Sci. 46, 86–102 (2018) 8. Georgatzi, V.V., Stamboulis, Y.: Urban mobility transition to sustainability: a system dynamics approach. In: Nathanail, E.G., Adamos, G., Karakikes, I. (eds.) Advances in Mobility-as-aService Systems. CSUM 2020. Advances in Intelligent Systems and Computing, vol. 1278. Springer, Cham (2021) 9. Giblin, S., McNabola, A.: Modelling the impacts of a carbon emission-differentiated vehicle tax system on CO2 emissions intensity from new vehicle purchases in Ireland. Energy Policy 37(4), 1404–1411 (2009) 10. Ivanova, D., et al.: Carbon mitigation in domains of high consumer lock-in. Glob. Environ. Chang. 52, 117–130 (2018) 11. Janssen, A., et al.: Model aided policy development for the market penetration of natural gas vehicles in Switzerland. Transp. Res. Part A: Policy Pract. 40(4), 316–333 (2006) 12. Jifeng, W., Huapu, L., Hu, P.: System dynamics model of urban transportation system and its application. J. Transp. Syst. Eng. Inf. Technol. 8(3), 83–89 (2008) 13. Karlsson, I.C.M., et al.: Development and implementation of Mobility-as-a-Service—a qualitative study of barriers and enabling factors. Transp. Res. Part A: Policy Pract. (2019) 14. Kim, K.: Can carsharing meet the mobility needs for the low-income neighborhoods? Lessons from carsharing usage patterns in New York City. Transp. Res. Part A: Policy Pract. 77, 249–260 (2015) 15. Klitkou, A. et al.: The role of lock-in mechanisms in transition processes: the case of energy for road transport. Environ. Innov. Soc. Transitions 16, 22–37 (2015) 16. Köhler, J., et al.: A transitions model for sustainable mobility. Ecol. Econ. 68(12), 2985–2995 (2009) 17. Köhler, J. et al.: Modelling sustainability transitions: An assessment of approaches and challenges. JASSS 21(1) (2018a) 18. Köhler, J., Turnheim, B., Hodson, M.: Low carbon transitions pathways in mobility: applying the MLP in a combined case study and simulation bridging analysis of passenger transport in the Netherlands. Technol. Forecast. Soc. Chang. (2018b) 19. Kotilainen, K., Aalto, P., Valta, J., Rautiainen, A., Kojo, M., Sovacool, B.K.: From path dependence to policy mixes for Nordic electric mobility: Lessons for accelerating future transport transitions. Policy Sci. 52(4), 573–600 (2019). https://doi.org/10.1007/s11077-01909361-3

16

V. V. Georgatzi and Y. Stamboulis

20. Kwon, T.: Hyeong: Strategic niche management of alternative fuel vehicles: a system dynamics model of the policy effect. Technol. Forecast. Soc. Chang. 79(9), 1672–1680 (2012) 21. Lachman, D.A.: A survey and review of approaches to study transitions. Energy Policy 58, 269–276 (2013) 22. Langbroek, J.H.M., Franklin, J.P., Susilo, Y.O.: The effect of policy incentives on electric vehicle adoption. Energy Policy 94, 94–103 (2016) 23. Lei, X., Zhang, J., Li, J.: A system dynamics model for urban low-carbon transport and simulation in the city of Shanghai, China. Adv. Inf. Sci. Serv. Sci. 4(1.31), 239–246 (2012) 24. Lewis, C.D.: Industrial and Business Forecasting Methods: A Radical Guide to Exponential Smoothing and Curve Fitting. Butterworth Scientific, London, Boston (1982) 25. Leydesdorff, L.: The triple helix, quadruple helix, …, and an n-tuple of helices: explanatory models for analyzing the knowledge-based economy? J. Knowl. Econ. 3, 25–35 (2012) 26. Lüdeke-Freund, F.: Sustainable entrepreneurship, innovation, and business models: integrative framework and propositions for future research. Bus. Strat. Environ. 1–17 (2019) 27. McDowall, W.: Exploring possible transition pathways for hydrogen energy: a hybrid approach using socio-technical scenarios and energy system modelling. Futures 63, 1–14 (2014) 28. Pangbourne, K. et al.: Questioning mobility as a service: unanticipated implications for society and governance. Transp. Res. Part A: Policy Pract. 131, 35–49 (2020) 29. Santos, G., et al.: Part I: Externalities and economic policies in road transport. Res. Transp. Econ. 28(1), 2–45 (2010) 30. Sarasini, S., Linder, M.: Integrating a business model perspective into transition theory: the example of new mobility services. Environ. Innov. Soc. Trans. 27, 16–31 (2018) 31. Shen, J., Sakata, Y., Hashimoto, Y.: Is individual environmental consciousness one of the determinants in transport mode choice? Appl. Econ. 40(10), 1229–1239 (2008) 32. Smith, G., Sochor, J., Karlsson, I.C.M.: Public-private innovation: barriers in the case of mobility as a service in West Sweden. Public Manage. Rev. 21(1), 116–137 (2018) 33. Sterman, J.D.: Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill (2000) 34. Struben, J., Sterman, J.D.: Transition challenges for alternative fuel vehicle and transportation systems. Environ. Plann. B. Plann. Des. 35(6), 1070–1097 (2008) 35. Timilsina, G.R., Dulal, H.B.: Urban road transportation externalities: costs and choice of policy instruments. World Bank Res. Observer 26(1), 162–191 (2011) 36. Walther, G., et al.: Impact assessment in the automotive industry: mandatory market introduction of alternative powertrain technologies. Syst. Dyn. Rev. 26(3), 239–261 (2010)

The Dynamic Relation of Climate Change and Energy Transition with Transport and Mobility Policies in the EU Through Social Media Data Mining Anastasia Nikolaidou1(B)

, Aristomenis Kopsacheilis1 and Ioannis Politis1

, Nikolaos Gavanas2

,

1 Department of Civil Engineering, Faculty of Engineering, Transport Engineering Laboratory,

Aristotle University of Thessaloniki, 54124 Thessaloniki, GR, Greece [email protected] 2 Department of Planning and Regional Development, School of Engineering, University of Thessaly, 38334 Volos, Greece [email protected]

Abstract. The 2019 Green Deal Communication aims to renew the growth strategy of the European Union (EU) and its commitment to tackle climate and environmental challenges. In 2020, the EU Sustainable and Smart Mobility Strategy Communication was published in response to the Green Deal’s priority for accelerating the shift towards sustainable and smart transport. The Strategy focuses, among others, on innovative mobility solutions, such as Mobility as a Service (MaaS), electromobility and hydrogen fuel cells. However, the promotion of smart mobility solutions has been at the core of EU transport policy for over a decade. The purpose of the current paper is to analyse the evolution of the relation between the climate change and energy transition priorities and the transport and mobility strategies in EU policy. The research is based on data mining from “Twitter”, a platform widely used by EU policy makers for the communication of policy initiatives and priorities. In this context, the research focuses on tracking and analyzing the evolution of user-generated content related to climate change, energy transition and (smart) mobility, published in the period 2011 (publication of the EU 2011 Transport White Paper) until today. The results derive from the application of text-mining techniques, and comprise of a series of metrics, analyses, and conclusions on the dynamics of relevant EU policies and their influence. Research outcomes could assist policy-makers and researchers to better understand the role of social media in the promotion of sustainable transport strategies and assess the acceptance of such policies. Keywords: Social media · Climate change · Energy transition · Transport · Mobility · Data mining

1 Introduction The European Union’s (EU) transport sector is responsible for about a quarter of total Greenhouse Gas (GHG) emissions [1]. Road transport, which is the focus of the current © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_2

18

A. Nikolaidou et al.

research, corresponds to approximately 70% of GHG emissions from transport [2, 3]. In the period 1990–2017, the energy consumption of EU road transport increased by 28.5%, surpassed only by the rise in the energy consumption of international aviation [4]. The EU policy makers conduct a continuous effort to address the transport sector’s impact on climate change and energy consumption. Since 1992, the EU transport policy, in order to tackle environmental challenges, promotes research and development for cleaner vehicles, fuels and engines and shifting to collective transport [5]. At the turn of the century, the 2001 Transport White Paper aims at the modernization and sustainability of transport. The document emphasizes on the need to develop innovative solutions for alternative fuels and engines, identifying at that time biofuels, natural gas and hydrogen as promising alternative fuels, and hybrid and battery electric engines, as promising alternative car engine technologies. Moreover, the White Paper mentions the potential of “car-sharing” practices [6]. In 2007, the Green Paper on urban mobility, addresses the need to cut GHG emissions in cities through improvements of Internal Combustion Engines (ICE) and new technologies for biofuels, hydrogen and fuel cells. The paper also mentions the role of driver assistance technologies towards “green” driving behavior [7]. The 2011 Transport White Paper highlights that the EU transport sector depends on oil for the 96% of its energy needs and aims at the development and deployment of alternative fuels and propulsion systems. The document depicts the importance of refueling/recharging stations for clean vehicles, the potential of cities as test beds for the early deployment of electric, hydrogen and hybrid technologies and the role of smart mobility to reduce the carbon intensity of transport [8]. In the last decade, the EU is intensifying efforts to reduce the impact of transport on climate change and energy intensity, committed to achieve the related targets against global challenges, i.e. the United Nation’s (UN) Sustainable Development Goals (SDGs), and to contribute to limit global warming to “well below 2, preferably to 1.5 °C, compared to pre-industrial levels” [9, 10]. At the same time, the decoupling from non-renewable energy sources is a main goal of the European economy due to its high dependency on imported energy, which is approximately 60%, with Russia being the main supplier of crude oil, natural gas and solid fossil fuels [11, 12]. In addition, the fourth industrial revolution (Industry 4.0) brings forward new opportunities to improve decarbonization and energy efficiency through digitalization and, namely, through connectivity, artificial intelligence and flexible automation [13]. These trends are affecting the EU transport sector, with main examples being the promotion of Connected, Cooperative and Automated Mobility, but also the digitalization of many activities (teleworking, e-commerce, remote learning etc.), which were unprecedently increased due to COVID-19 and have the potential to change mobility patterns and, thus, affect emissions and energy consumption [14, 15]. In this context, the EU strategies for the new Programming Period (2021–2027) are outlined in the European Green Deal, aiming at transformative changes to achieve the just and sustainable transition to a carbon neutral and resource efficient EU economy [16]. The pathway for the implementation of the Green Deal in the transport sector is outlined in the EU Sustainable and Smart Mobility Strategy for the 90% reduction of transport emissions by 2050 [17]. The strategy sets specific targets by 2030, such as the circulation

The Dynamic Relation of Climate Change and Energy Transition

19

of at least 30 million zero-emission vehicles (with the ambition to reach 100% of the total vehicle fleet by 2050) and the wide-scale implementation of automated mobility, while supporting the European Green Deal mission: “100 Climate Neutral Cities”. The document focuses on the market take-up of battery and hydrogen fuel-cell electric and other zero-emission vehicles, the provision of adequate refueling/charging infrastructure and their integration to the energy grid. In addition, it aims at shifting towards shared and active transport modes and automated mobility to reduce pollution and congestion. Based on the above EU policy milestones, the purpose of the current research is to analyse the relation between the climate change and energy transition priorities and the transport and mobility strategies in Europe. The research is based on data mining from the microblogging platform “Twitter”, which is widely used by EU policy makers for the communication of policy initiatives and priorities, but also by the media, the industry and citizens. In this framework, the present study focuses on tracking and analysing the evolution of user-generated content related to climate change, energy transition and transport/mobility. The research outcomes can enhance the understanding of both policymakers and researchers on the role of social media in the promotion of sustainable transport strategies and assess the acceptance of such policies by stakeholders. Following the introductory section, the paper comprises the description of the methodological approach, the discussion of results and the presentation of conclusive remarks.

2 Methodology 2.1 Data Collection The current research focuses on tracking and analyzing the evolution of user-generated content related to climate change, energy transition and smart and sustainable mobility and is based on data mining from Twitter. The microblogging platform of Twitter was selected due to its suitability for the following reasons: • Based on recent studies, Twitter is one of the leading social networks worldwide based on the number of active users [18] • Twitter is also used to a large extent by EU policy makers as a communication tool for promoting EU initiatives and priorities [19] • Unlike other social media sites, like Facebook, Twitter data is open and accessible to all through the Twitter API. Additionally, the use of Academic Research product track allows the collection of historical “tweets” [20] • Twitter data can contain information related both to posts (“tweet” text, time stamp, number of re-“tweets”, etc.) and users (author id, number of followers and following, etc.), allowing for a more thorough analysis of the sample. The current research covers the period 01/01/2011 - 31/12/2021, so as to ensure compatibility with the above-presented timeline of key EU policy documents. A set of keywords related to the subject of the research were used during the data collection stage of the methodology. The selection of the suitable keywords was based on the concept of monitoring the evolution of specific terms presented in users’ posts that link transport policies for sustainable mobility with climate change and energy transition priorities.

20

A. Nikolaidou et al.

More specifically, keywords related to transport were used alongside keywords related to climate change and energy transition. The groups of selected keywords were organized against four (4) different categories. The general terms of transport and mobility are related to the overall issue of sustainable development (in the first category), to the specific challenge of climate change (in the second category) and to the specific terms that describe the policy priorities to address climate change (in the third category). The fourth category refers to the relation of these priorities with transport policy priorities with focus on electromobility, connectivity, automation and shared mobility. Table 1 presents the categories that were formulated as well as the selected keywords for each category. Table 1. Data collection keywords and categories Category

Keyword 1

Keyword 2

Description

1

Mobility; Transport; Smart Sustainable development; mobility; Smart transport Sustainability

Transport and sustainable development in general

2

Mobility; Transport

Climate change; Climate urgency; Global warming; Carbon footprint

Transport and climate change

3

Mobility; Transport

Decarbonisation; Climate Transport and policy neutral; Zero carbon; Low priorities for tackling carbon; Energy transition; climate change Clean energy; Green energy; Climate mitigation; Climate adaptation; Climate resilience; Climate resilient

4

Decarbonisation; Climate neutral; Zero carbon; Low carbon; Energy transition; Clean energy; Green energy; Climate mitigation; Climate adaptation; Climate resilience; Climate resilient

Autonomous transport; Autonomous mobility; Automated transport; Automated mobility; Transport automation; Connected transport; Connected mobility; Transport connectivity; Electrification; Electromobility; Electric vehicle; Hydrogen; Fuel cell; Mobility as a service; Shared mobility; Shared transport

Policy priorities for tackling climate change and transport policy priorities

The Dynamic Relation of Climate Change and Energy Transition

21

2.2 Data Preprocessing The accuracy of every analysis process and the validity of the derived results are based on the quality of the available data. In order to ascertain the best possible dataset, we took certain steps towards the exclusion of redundant “tweets” and terms in our dataset. All necessary steps were taken with the use of the Python programming language (version 3.9). The first step of the process is related to the filtering of non-English “tweets”. Our initial dataset included 34,812 non-English “tweets”, which were excluded from the analysis. Succeeding this, the database was searched for duplicate “tweets”. From this step, 44,043 “tweets” were further excluded. Finally, since our research framework is focused entirely on the mobility policies of the EU, we took into consideration only “tweets” that were originated by users based in the EU. In order to ensure this, we processed our dataset manually and removed “tweets” that were originated in countries outside of the EU. Since our analysis period spans from 2011 to 2021, prior to the Brexit, we also included “tweets” from the United Kingdom. From this process 89,162 “tweets” were filtered out, thus resulting in our final dataset, which includes 63,373 “tweets”. The next step of the data preprocessing included the removal of unnecessary words/terms from each “tweet”. In this step, webpage links, re-“tweets”, numbers, hashtags, punctuation marks, special characters and whitespaces were filtered out from each “tweet”, in order to end up with a more concise dataset. Additionally, by utilizing the Natural Language Toolkit (NLTK) of Python, we also removed stop words (e.g., to, by, so, it, etc.), since these terms do not provide to the overall semantics of each “tweet”. In cases where two-character words were still present in certain “tweets”, a final step for the removal of these terms was taken. 2.3 Latent Dirichlet Allocation (LDA) The Latent Dirichlet allocation (LDA) is a Bayesian model which is used for topic detection, proposed by Blei et al. in 2003 [21]. In LDA it is assumed that each topic is comprised of similar words, and thus, latent topics can be identified by detecting words that appear frequently together in sentences or “tweets”. Additionally, another principle of LDA is that topics are based on the probability distributions of words, which differentiates the algorithm from other topic modeling methods that strictly base their results on term occurrence frequencies. For the application of the LDA model in our paper, we exploited the LDAvis method through the gensim Python library [22]. LDAvis links terms with a unique id, while the collection of all words forms the corpus. 2.4 Bigrams (n-grams) Prevalent word pairs in a corpus of “tweet” tokens can assist in the determination of the context of the “tweet”. Bigrams are probabilistic language models that have the ability to compute the occurrence frequency of word pairs. Although, a higher frequency associated with a word pair can be a good determinant of the semantics of a collection of “tweets” it should be noted that in certain cases popular expressions may rank highly in frequency without contributing significantly to the determination of the context [23].

22

A. Nikolaidou et al.

3 Results and Discussion 3.1 Descriptive Statistics of Twitter Data Sample In total, 231,390 posts were collected from the Twitter network and were processed in the context of the present research for the total data collection period. After removing “tweets” based on specific criteria, as presented in the data preprocessing stage, the final sample consisted of 63,373 “tweets” for the period 2011–2021. Figure 1 presents both the total number of “tweets” that were collected per year, as well as the percentage of “tweets” that belong to each data content category. The thickness of each chart column represents the absolute size of the data collected each year, while the different colors represent the different categories based on which data collection was performed.

Fig. 1. Total number of “tweets” per year and per category

Based on the results presented in Fig. 1, the total number of collected “tweets” is constantly increasing over time. This increase that is observed from year to year, may be on the one hand due to the increase in the popularity of social media sites in general but, on the other hand, it is also most probably related to the increase in the popularity of the climate change topic. The largest increase is recorded in the years 2012, 2015, 2018 and 2019 that is associated with the afore-mentioned EU policy milestones, i.e. the implementation of the 2011 Transport White Paper, the commitment to the UN SDGs and the Paris Agreement and the transition to the current EU Programming Period with the publication of the European Green Deal. A small decrease of about 5% is recorded in the social activity of users for the specific subject in 2020. This decrease is probably related to the focus of EU policy on the COVID-19 pandemic, an issue which also dominated in the posts of various users. Regarding the grouping of publications based on the four content categories that were formed during the data collection stage of the methodology, it can be observed that the largest percentage of publications belongs to Category 1, a category that includes

The Dynamic Relation of Climate Change and Energy Transition

23

mostly publications related to the general issue of transport and sustainable development. The lowest number of users’ posts in total belongs to Category 4, which contains mostly posts related to transport policy priorities as means for tackling climate change. Until 2015, the number of posts that belongs to Category 1 increases steadily in contrast to the number of posts that belong to the other 3 categories. However, since 2015, which is the year of the EU commitment to achieve specific targets for climate change by 2030 and 2050, there has been a significant reduction in posts related to Category 1, while respectively there is an increase in the other categories with the largest increase being noted in Category 4. This is due to the fact that Category 4 refers to specific policies for transport and mobility against specific priorities to achieve decarbonization and energy transition. Essentially, a transition is observed in the period 2011–2021 from stressing the importance of the relation of transport with sustainable development and climate change (Categories 1 and 2 respectively) to understanding the role of transport in the targets for decarbonization and energy transition and the promotion of specific transport and mobility policies to achieve these targets (Categories 3 and 4 respectively). 3.2 Topic Modeling The scope of this part of the methodology was the identification of major topics that users refer to in their “tweets”. In order to achieve this, an LDA model was developed, which was used to determine the most prevalent topics. The effective detection of topics by an LDA can be estimated by assessing the ‘Coherence’ and ‘Perplexity’ metrics. Although a higher value for ‘Coherence’ and a lower value for ‘Perplexity’ indicate a good fit for the model, a trade-off between the value of the two metrics and the number of topics is usually sought after, since a high number of topics could possibly lead to fragmentation of wider topics and subsequently to misinterpretation of the results. For our analysis, the two metrics’ values for various numbers of topics were assessed and concluded that the choice of 10 topics had the biggest positive impact on the coherence score (0.2588), while at the same time the perplexity score was kept at a low value (−9.684). The coherence of the output was further enhanced manually through setting the criterion to exclude any topics that concentrate less than 10% of the total tokens in the dataset (corpus). This process led to the identification of six (6) major topics. Table 2 presents the 15 most salient terms in these six (6) topics, which according to the literature, can provide a clear picture of the context of each topic [24]. Each of the topics presented is accompanied with a short description that highlights its context, while the keywords that are mostly influencing to the topic description are marked with bold font. As can be surmised from the results of Table 2, the main focus areas of the six (6) topics are ‘Energy Transition’, ‘Alternative Fuels’ and ‘Sustainable Mobility’. More to the point, Topic 1 refers to the production, storage and use of renewable fuels and hydrogen. The topic mirrors the EU’s “hydrogen strategy for a climate-neutral Europe” issued in 2020, which aims at investing in the production and use of clean hydrogen (as opposed to natural gas currently produced by hydrogen today), while at the same time bringing together the industry and key stakeholders in the coordination of necessary investments [25]. Topic 2 refers to the role of technological innovation and electrification for the future of urban mobility. Similarly, the third topic focuses on the potential of

24

A. Nikolaidou et al. Table 2. Six (6) most prevalent topics’ description (LDA)

Topic

Keywords (15 most frequent)

Description

Percent of total tokens

Topic 1

Hydrogen; Renewable; Need; Gas; Fuel; Production; Potential; Future; Storage; Technology; Use; Role; Industry; New; Public

Renewable fuel technologies: Production, storage and use

14.2

Topic 2

Electric vehicles; Register; Future; Via; Tech; Electric; World; Cities; New; Today; Pandemic; Webinar; Event; Find; Innovation

Electrification and technological innovation for urban mobility

12.2

Topic 3

Net; Electrification; Help; Netzero; Achieve; Decarbonise; Heat; Evs; Looking; See; Forward; New; Sector; Decarbonization

Electromobility and decarbonization

10.9

Topic 4

Cop; Conference; Safety; Climateaction; Week; Event; Achieving; Delighted; Key; Future; Air; Registration; Economy; Road; Part

COP Conference 10.3 & Climate Action

Topic 5

Public; Would; Hydrogen; Electric; Trains; Cars; Towards; Need; Cycling; Piece; Time; Could; People; Greendeal; Walking

Green Deal and active mobility

10.1

Topic 6

Recovery; Vehicles; Future; Sustainablemobility; Cities; Electric; Modal; Car; Autonomous; Plan; Companies; Carbonneutral; Zeroemission; Big; Stay

Autonomous electric vehicles for future sustainable urban mobility

10

electromobility to achieve the decarbonization targets of the EU transport policy. The fourth topic refers to the Conference of Parties (COP) 21 Sustainable Innovation Forum organised by the United Nations [26]. The COP 21 is placed at the forefront of the overall policymaking regarding actions related to the mitigation and adaptation of climate change to various economic sectors, by verifying the global need to implement the 2030 Agenda and the SDGs and by adopting the Paris Agreement. The fifth topic is relevant to the European Green Deal agenda and its main focal point, which is carbon neutrality, in our case through the further promotion of active transport modes such as cycling and walking. Finally, topic 6 concentrates on autonomous and electric vehicles to shape the future of sustainable urban mobility. 3.3 User Category Analysis In the current research, the total sample of “tweets” was categorized according to the type of user. In specific, the initial sample was divided into two categories, i.e., regular “Users” and “Policy Influencers”. “Policy Influencers” correspond to users with verified

The Dynamic Relation of Climate Change and Energy Transition

25

accounts (blue verified badge on Twitter), as this type of user was considered to represent official organizations or constitute of high-profile accounts (accounts with high number of followers) whose opinion can reach out to a large part of the society [27]. All other users, without blue verified badge, were considered as “Users”. The “Policy Influencers” were further divided into three categories: (a) Media, representing official news websites, (b) Industry, representing industrial organizations and firms and (c) Policy makers, representing official political bodies and organizations like the European Commission, and the official representatives of these organizations, like members of the European Parliament. Sample statistics revealed that almost 90% of the “tweets” were published by users, 7% by policy makers, 2% by the industry field and 1% by media sites. The categorization of posts based on user category was used to further study the type of information that is promoted by various types of policy influencers and how this information is adopted by users. In this context, the analysis focuses on the identification of the main areas of interest per user category, in an effort to detect similarities or differences in publications that may arise due to the different scope and perception of each user category. Although the detection of the main areas of interest that are addressed by each user category can be implemented by developing an LDA model, as implemented for the whole sample and presented in the previous section, the limited number of “tweets” in certain categories may affect the performance of the model in the analysis by user category. Alternatively, the use of bigrams can give an abstract view of the different areas of interest that are discussed in a corpus, by assessing the most frequent word pairs encountered. For each user category, the 50 most common word pairs were analyzed. The initial sample of word pairs were further processed in order to remove words/phrases that do not contribute to the scope of the current research. Then the remaining word pairs were organized around common areas of interest. In total 10 areas of interest were identified through bigrams and are presented in Fig. 2 against different user categories. The thickness of each chart column represents the absolute value of the number of “tweets” per topic.

Fig. 2. The ten (10) most frequently discussed areas of interest per user’ category (bigrams)

26

A. Nikolaidou et al.

Based on the results presented in Fig. 2, the following key conclusions are drawn. The policy makers are posting “tweets” regarding various areas of interest such as gas emissions, air quality, electrification, and public transport while focusing on the issue of active travel mainly as a mean towards environmental sustainability and climate change control. The industry focuses particularly on the transition from fossil fuels to hydrogen and electrification as posts are mainly related to the specific topics. Publications made by official media sites mainly concern issues such as electrification and the use of fossil fuels. The posts from the general public, i.e., users, concern all areas of interest, with a specific focus on the electrification and the use of hydrogen. The more general concept of transport policies concerns mainly users but also the industry but often in different context, as consumer-oriented and manufacturer/service provider-oriented policies respectively. The concept of electrification is a matter of special interest for all user categories highlighting in this way how the specific concept is strongly connected with the general issue of tackling climate change. On the other hand, the issue of active travel although it is significantly promoted as a measure for tackling climate change by official policy organizations and their representatives, it does not appear to be promoted by the industry or by the media and does not seem to be adopted by the users.

4 Conclusions For over three decades, the EU transport policy aims to promote environmental sustainability and energy efficiency. Since 2015, European transport policy makers are gradually shifting their focus on specific aspects of sustainable development, i.e., the decarbonization of the transport system and the transition towards zero-carbon technologies and alternative fuels from renewable sources. This is due to the EU commitment to achieve ambitious targets in the context of the global effort to control climate change. Moreover, it is becoming increasingly important for Europe to improve energy efficiency and the resilience against potential disruptions. Another main driver refers to the unprecedent technological innovation, which can contribute towards the improvement of energy efficiency and the reduction of the carbon footprint from the transport system. In order to communicate their policy priorities and receive feedback from the various stakeholders, the EU policy makers are making extensive use of social media and, more specifically, the “Twitter” platform. This creates an active discussion between policy makers, industrial stakeholders, the media and the general public regarding the role of transport policy in tackling the challenges of climate change and energy transition. In this context, the data mining of the current research tracked the evolution of the corresponding user-generated content on the “Twitter” platform, which has fueled this discussion in Europe over the period 2011–2021. Furthermore, both the content and the platform users have been categorized in order to analyse and identify the emerging topics over the whole sample and the areas of interest for each group of stakeholders, using state-of-the-art techniques. The analysis clearly demonstrates the significant increase over time in the volume of “tweets” that refer to the relation between transport and mobility and the climate crisis and the process of energy transition. This fact is linked to both the increase of the use of the specific social medium and the increase of the significance of the specific aspect

The Dynamic Relation of Climate Change and Energy Transition

27

in the overall transport policy framework. Moreover, the discussion evolves from the general interaction between transport and sustainability, in the first half of the period of the analysis, towards specific priorities regarding the role of vehicle, engine and fuel technologies to address the decarbonization and energy transition targets, in the second half. This evolution follows the increase of the global interest on climate related policies and the rapid development of relevant technologies. According to the research findings, the overall discussion on the “Twitter” platform revolves around the development, deployment and management of low-carbon fuel technologies, especially battery electric vehicles and hydrogen fuel cells. The main concerns are the production and storage of energy and the deployment of electrification for sustainable urban mobility, together with active transport. Apart from fuel technologies, automation is another technological trend that is analysed in relation to their potential contribution to the decarbonization and the improvement of energy efficiency in the transport sector. Milestones in the recent climate policy framework, i.e., the COP21 Sustainable Innovation Forum and the European Green Deal, are also frequently discussed in the context of transport and mobility. The analysis by user category verifies the strong interest of policy makers to shift the transport sector from fossil to electrification and the use of hydrogen fuels, which is adopted by the media, the industry and the users. However, the interest of policy makers on active and public transport as means to tackle climate change is not taken up by the industry. The above findings provide evidence as regards the evolution of the communication of EU transport policy that address the challenges of climate change and energy transition, based on the timeline of key policy documents and milestones. The research may be used as the starting point for the development of a monitoring and evaluation framework at the EU and/or national level, in order to assess the progress in communicating strategic priorities in the transport or other sectors towards the mitigation and adaptation to climate change. Moreover, the research demonstrates a methodology for data mining, processing and analysis in order to enhance the scientific knowledge and increase the understanding of how social media can affect the exchange between stakeholders and users regarding contemporary societal challenges.

References 1. European Commission: Together towards competitive and resource-efficient urban mobility. COM 913 final (2013). Brussels, 17.12.2013, Brussels (2013) 2. European Environment Agency: Greenhouse Gas Emissions from Transport in Europe. https://www.eea.europa.eu/ims/greenhouse-gas-emissions-from-transport. Last accessed 07 Apr 2022 3. European Commission: A European Strategy for Low-Emission Mobility. COM (2016) 501 final. Brussels, 20.7.2016, Brussels (2016) 4. Eurostat: Energy, Transport and Environment Statistics (2019) 5. European Commission: The future development of the common transport policy. Global approach to the construction of a Community framework for sustainable mobility. COM (92) 494 final. Brussels, 02.12.1992, Brussels (1992) 6. European Commission: White Paper. European transport policy for 2010: time to decide. COM (2001) 370 final. Brussels, 12.9.2001, Brussels (2001)

28

A. Nikolaidou et al.

7. European Commission: Green Paper. Towards a new culture for urban mobility. COM (2007) 551 final. Brussels, 25.9.2007, Brussels (2007) 8. European Commission: White Paper. Roadmap to a Single European Transport Area – Towards a competitive and resource efficient transport system. COM (2011) 144 final. Brussels, 28.3.2011, Brussels (2011) 9. United Nations: Transforming our world: the 2030 Agenda for Sustainable Development. A/RES/70/1 (2015) 10. United Nations: Paris Agreement (2015) 11. Energy Statistics - An Overview - Statistics Explained. https://ec.europa.eu/eurostat/statis tics-explained/index.php?title=Energy_statistics_-_an_overview. Last accessed 07 Apr 2022 12. Eurostat: Shedding light on energy in the EU - A guided tour of energy statistics. (2020) 13. World Economic Forum: White Paper. The next economic growth engine. Scaling Fourth Industrial Revolution technologies in production. REF 110118 - case 00039546 (2018) 14. European Commission: Europe on the move. Sustainable Mobility for Europe: safe, connected, and clean. COM (2018) 293 final. Brussels, 17.5.2018, Brussels (2018) 15. European Commission: Staff Working Document accompanying COM (2020) 789 final, SWD (2020) 331 final, Brussels, 9.12.2020, Brussels (2020) 16. European Commission: The European Green Deal. COM (2019) 640 final. Brussels, 11.12.2019, Brussels (2019) 17. European Commission: Sustainable and Smart Mobility Strategy – putting European transport on track for the future. COM (2020) 789 final. Brussels, 9.12.2020, Brussels (2020) 18. Twitter - Statistics & Facts | Statista. https://www.statista.com/topics/737/twitter/#dossierKe yfigures. Last accessed 07 Apr 2022 19. Drachenberg, R., Phillips, E.: The Twitter activity of members of the European Council. A content analysis of EU leaders’ use of Twitter in 2019–2020 (2021) 20. Twitter API for Academic Research | Products | Twitter Developer Platform, https://develo per.twitter.com/en/products/twitter-api/academic-research. Last accessed 07 Apr 2022 21. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3 (2003). https://doi.org/10.1016/b978-0-12-411519-4.00006-9 22. Sievert, C., Shirley, K.: LDAvis: A method for visualizing and interpreting topics. In: Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces. pp. 63–70 (2015). https://doi.org/10.3115/v1/w14-3110 23. Wang, X., McCallum, A., Wei, X.: Topical N-grams: phrase and topic discovery, with an application to information retrieval. In: Proceedings - IEEE International Conference on Data Mining, ICDM (2007). https://doi.org/10.1109/ICDM.2007.86 24. Medford, R.J., Saleh, S.N., Sumarsono, A., Perl, T.M., Lehmann, C.U.: An “Infodemic”: Leveraging high-volume twitter data to understand early public sentiment for the Coronavirus disease 2019 outbreak. Open Forum Infect. Dis. 7 (2020). https://doi.org/10.1093/ofid/ofa a258 25. European Commission: A Hydrogen Strategy for a Climate-Neutral Europe. COM/2020/301 final. Brussels (2020) 26. Conference of the Parties (COP) | UNFCCC, https://unfccc.int/process/bodies/supreme-bod ies/conference-of-the-parties-cop. Last accessed 24 Mar 2022 27. Twitter Verification Requirements - How to get the Blue Check. https://help.twitter.com/en/ managing-your-account/about-twitter-verified-accounts. Last accessed 07 Apr 2022

Park-and-Ride: The Case for Coupling EV Charging Stations with Micro-mobility Hubs Aikaterini Moschopoulou(B) , Ioannis Frantzeskakis , Konstandinos Grizos , and Theocharis Vlachopanagiotis Rhoé Politechneiou 21, 54626 Thessaloniki, Greece {k.moschopoulou,i.frantzeskakis,k.grizos, t.vlachopanagiotis}@rhoe.gr

Abstract. Electric vehicle (EV) ownership is skyrocketing while authorities are scrambling to expand their charging infrastructure networks. In urban areas, they all face the ever-pertinent question of maximizing the marginal benefits of each new EV charger. This paper aims to make the case of coupling EV charging stations with micro mobility-hubs as a way of increasing each station’s area of influence, thus amplifying its potential service population. The case study of the Municipality of Kifissia is used to demonstrate the benefits of an EV charging station coupling with micro-mobility. The model will take into account the potential EV users of the selected area, available and planned EV charging stations and the local road network characteristics in order to formulate an alternative way of combined micro-mobility and EV infrastructure planning. The model is then tested in existing urban environments and with emerging results indicating that the theory can be a valuable tool in charging infrastructure planning. The developed methodology led to the delineation of the influence area of EV chargers with or without micromobility and the determination of the most suitable ones for coupling. Thus, it paves the way for a novel less-is-more approach to incentivizing EV usage and planning the necessary infrastructure – one of particular importance to national and local authorities that are just now embracing e-mobility. Keywords: Micro-mobility · Electric vehicles · First and last mile · Connection · Infrastructure

1 Introduction Urban mobility is on the verge of breaking down, making urban mobility one of the most difficult challenges cities confront today [1]. Increased urbanization and a boom in the automobile industry, are worsening the air pollution, energy problems, and, especially the depleting natural gas and rising petrol cost, in the world today. [2]. The implications of this expansion can be seen in terms of traffic congestion, air pollution, and the

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_3

30

A. Moschopoulou et al.

contribution of automobile emissions to greenhouse gas (GHGs) emissions and global warming [1]. Aside from the rising demand for urban mobility, there has been a shift in mobility requirements. Consequently, changes in travel habits, as well as an everincreasing demand for services that improve convenience, speed, and predictability [1], necessitate the invention of new mobility modes to fill gaps in mobility and accessibility [3]. Nowadays, sustainability is one of the most important goals to achieve for both humans and the environment, and methods to reduce traffic congestion and net greenhouse gas (GHG) emissions are required [1]. Shared mobility and electrification are two main aspects of transportation systems because they have the potential to improve many different elements [4]. Specifically, the utilization of micro-mobility and Electric Vehicles (EVs) may be beneficial for the environment and reduce the greenhouse gases released [5]. Evs, being a cleaner energy alternative mode of transportation, are more efficient than identical conventional internal combustion engine (ICE) vehicles [6] and emit fewer GHGs [7]. Evs have their origins in the past, but they are now the next automotive technology to be broadly disseminated in society [8]. Evs, are defined as any passenger vehicle that draws energy from the electric grid and stores it on board for propulsion [9, 10]. The charging station is a vital infrastructure that ensures that Evs operate correctly, and its location is critical [2]. There are two types of EV chargers: fast-charging stations (DC), which are frequently used in central municipality areas where parking alternation is high, and slow-charging stations (AC), which are mainly used when the vehicle is stationary for longer time. Obviously, the first type can take the place of private or workplace charging stations in circumstances where the EV owner does not possess one [11]. However, it is realistic to anticipate that slow-charging would become the dominant charging option due to its affordability and ease in terms of electricity costs [11]. Given the critical nature of developing a suitable EVCS network, site selection for each station becomes a critical component in determining the success of an EV idea [11]. According to the United States Department of Transportation (USDOT), the average U.S. household produces about 9.5 trips a day. Approximately half of these journeys are within three miles (4.82 km), while micro-mobility accounts for less than 2% of those trips. Micro-mobility (defined as docked or dockless shared bikes, e-bikes, scooters, escooters, skateboards) represents a substantial possibility to replace short Private Owned Vehicle (POV) trips while reducing transportation sector GHG emissions. Recent years have seen an explosion in micro-mobility around the world. While shared micro-mobility is not carbon neutral, it consumes less energy and emits less greenhouse gases per person-kilometer over the course of its life than private autos. Given that micro mobility consumes significantly less road space than privately-owned cars and can serve similar functions for short trips in modern cities, it is viewed as a feasible and low-cost alternative to many short car excursions in urban areas. [12]. Park and Ride (P&R) is a term that refers to the practice of privately parking cars at a parking lot linked to a train station, bus stop or a micro mobility hub and then transferring to public transportation to reach other locations [13]. Park and Ride (P&R) is an effective method of reducing traffic congestion; it combines a public transit system with private vehicles [13]. In suburban regions, combining the use of EV and transit

Park-and-Ride: The Case for Coupling EV Charging

31

systems through an EV Park-Charge-Ride (PCR) approach has the potential to improve transit accessibility, facilitate EV charging and adoption, and minimize the need for long-distance driving and its associated consequences [14]. This research conducts a review of the scholarly literature for coupling electric vehicles with micro-mobility options and to validate it through a case study in the densely populated municipality located of Kifissia, Athens. It takes into consideration, the current and the future potential EV charging station infrastructure, how micro mobility contributes on last mile problem and examines the combination of electric vehicles with micro-mobility, aiming to make the choice of charging station placement more sustainable. The findings and insights will aid in identifying opportunities and impediments, paving the way for a different approach that will be crucial for national and municipal authorities that are only now embracing e-mobility as a viable future transportation system.

2 Methodology 2.1 General Information The spatial distribution of EV chargers raises a more fundamental question: how far may an EV charger be placed from its driver’s destination? In other words, how far is an EV user willing to walk to and from their car? The study’s starting premise is that the spatial distribution of EV chargers has a major impact on the total cost of implementing new charging infrastructures. As a result, the study’s assumption is as follows: if people could travel a longer distance to their charging stations, the total cost of infrastructure would be dramatically lowered because these stations could be shared by a higher number of EV owners [15]. Micro-mobility hubs can be an effective answer to this. The method used in this paper derives from the Multicriteria analysis approach, which was proposed in the journal “Case Studies on Transport Policy” [16]. According to this method, it is of major importance for the Analytic Hierarchy Process (AHP) to be initially applied. The AHP method, decomposes decision issue into a hierarchical structure, originating from a goal to criteria and subcriteria [17]. Also, it provides a process for assisting decision-making defining priorities through pairwise comparisons [16]. After the application of the AHP method, the conception of an evaluation index about the suitability of micro-mobility at EV chargers’ stations, is following. 2.2 Criteria Categories As mentioned above the assessment elements (indicators) used in the analysis were grouped into 11 categories, regarding the field of study they belong to (criteria). These criteria are: (i) population characteristics, (ii) travel behavior characteristics, (iii) landuse features, (iv) weather conditions, (v) aesthetic attractiveness, (vi) road network and comfort, (vii) road safety, (viii) security, (ix) connectivity and intermodality, (x) attractiveness compared to alternative modes and (xi) health and environmental benefit. It is worthy of note that most of the criteria, except (ix), (x), (xi) and part of (i) were rated according to data from the municipal unit they are located and not their exact point, due to lack of available data. The most influential criteria are analysed below. [16].

32

i.

ii.

iii.

iv.

v.

vi.

A. Moschopoulou et al.

Population characteristics This criterion aims to evaluate whether the social, economic, and demographic characteristics of the study area’s population, are in favour or against the installation of micro-mobility station along with the EV charging station. Higher population density leads to higher trip generation. [3] Also, lower median age, higher median income, higher percentage or young to middle aged population and higher percentage of educated population, they all lead to higher demand for micro-mobility use in the selected area. Travel ehavior characteristics The criterion of travel ehavior characteristics refers to the suitability of the population close to the station regarding their mobility profile and their travel ehavior. Higher motorization and vehicle ownership rate, higher cycling and car modal share as well as higher use of shared mobility services, encourage the use of micro-mobility coupling with EV charging spots, resulting in higher rates. Land use features Land use feature criterion affects trip generation, and according to several studies mixed-used areas are associated with higher trip generation capacity for all modes of micro-mobility [4]. Indicators such as land use mix, type of residential land use, car parking availability, commercial or office land use and other uses, the more they exist in an area the higher the land use diverse and so the higher trip generation, thus the rate of each indicator is also higher. Road network and comfort This criterion assesses the comfort of the road network for micro-mobility users, considering infrastructure geometrics (slope, pavement-bike lanes width, pavement quality) and traffic characteristics (flow, speed limits, heavy vehicles, road hierarchy). It is obvious that better pedestrian and bike infrastructure means better suitability for micro-mobility. Also, the high-speed limits are deterrent factor for micro-mobility. Connectivity and intermodality The criterion of “connectivity and intermodality” includes the rate of demand for Evs, the frequency of local public transport services, the certainty of parking facilities for micro-mobility at EV charging spots (meaning the space availability for placing micro-mobility station). Additionally, the criterion includes the indicator about coherence of the micro-mobility friendly network (the proximity to bike lanes, pedestrian routes etc.) and the average trip distance of the station’s area. It is worthy of note that the demand for Evs was defined as “1” in the rating, because of the low Evs demand in the study year (2025), while the average trip distance was set as “2” (“average”) for the municipal units of Kifissia and Nea Erithrea, and “3” (“long”) for the municipal unit of Ekali, because of the buildings’ scarcity and the overall areas’ characteristics. Attractiveness compared to alternative modes This criterion evaluates the attractiveness of using micro-mobility and electric vehicles, instead of other transport modes in a particular area [1]. This criterion calculates the delay experienced in local trips by car, the car parking availability, the delay experienced during charging time, the crowding in EV stations, the certainty of finding free charging spot at the charging stations and the average time spent on

Park-and-Ride: The Case for Coupling EV Charging

33

a walking trip. The delay during charging time was set according to the charger’s type (AC or DC), that defines the total charging time. Additionally, the crowding n EV stations along with the reliability of EV charging spot, were generally set as “2” or “3”, because of the low demand for Evs in the study year.[16] 2.3 Weights of the Criteria and Indicators Calculation via the AHP Method To determine the weights of the primary criteria and their associated indicators, the classical AHP approach was used. At this point, we have developed comparison matrices based on the opinions of experts consulted by Psarrou Kalakoni et al. [16] for their “A novel methodology for micro mobility system assessment using multi-criteria analysis” research. The research of Psarrou Kalakoni et al. [16] was conducted with the assistance of a group of 12 experts: 5 researchers in transportation and micro mobility, 4 business executives in shared mobility services, and 3 public institution staff members. According to their research, the experts were selected based on their expertise in the topic of micro mobility, whether in academia or the business environment, as well as their knowledge of the areas chosen as the case studies of the work. Each responder completed a questionnaire designed to allow for pairwise comparisons between any two criteria. In this questionnaire they were asked to evaluate the relative relevance of one criterion to another. For a complete pairwise comparison of the 11 criteria, 55 comparisons were required. The same procedure of pairwise comparisons was used for each set of indicators, resulting in the construction of 11 comparison matrices for each of the criteria’s 11 groups of indicators (one matrix for each group). The results were then combined using the average values of all expert appraisals for each element of a matrix to produce the final matrices from which the weights of the criterion and indicators were derived [16]. The sum of the weights assigned to the primary criteria equals 100% of the overall assessment, with the following criteria receiving higher weights: “Travel Behavior Characteristics”, “Population Characteristics”, and “Land Use Features”. The subsequent most critical are: “Connectivity & Intermobility”, and “Road Network & Comfort”. This underlines the fact that transportation specialists in micro-mobility placed higher importance on these parameters in order to accomplish the analysis’ overall objective, which is to evaluate an area for prioritizing micro-mobility system investment [16]. Tables 1, 2 (Appendix) contain the estimated weights for all criteria and indicators along with the rating given to them. The matrices examples shown are for M.U. of Kifissia and for a station located in Nea Erithrea. 2.4 The Rating Scale for Indicators and the Index Calculation The primary goal of the research was to develop an easy-to-use index, based on a weighted multi-criteria approach, for identifying electric vehicle charging stations that are more or less conducive to micro-mobility system integration. The AHP approach was solely utilized to determine the weights of the selected criteria and indicators, which were used then to calculate the index (Eq. (1)). After defining the weights for the criteria and indicators, the next step was to evaluate a study area according to the criteria of analysis. [16].

34

A. Moschopoulou et al.

To evaluate the research area, it is necessary to allocate a score to each of the indicators. The score is computed using a three-point rating scale, with a score of “3” indicating that the specific characteristic in the research area is highly suitable of the integration of micro mobility modes, and a score of “1” indicating that it is less suitable. The implementation of a simple three-point rating scale for the index’s evaluation questions was intended to provide a method that could be used both when detailed data for a charging station were available and when precise data are not available. In the cases with imprecise estimates for some indicators, one should be able to provide a rate ranging from 1 (low) to 3 (high) for each evaluation item, allowing the assessment of an EV charging station via the index computation. [16]. Given that some indicators have a positive impact on the station suitability index while others have a negative impact, the development of a distinct rating scale for each indicator, with a score of “3” representing a strong motivator and a score of “1” representing a low motivator in all scenarios. Table 4 presents, as an example, the case of the indicators formulated under the criterion “Connectivity & intermodality”. After we have assigned a score from “1” (low motivator) to “3” (strong motivator) for each indicator of each criterion, we can obtain the overall index assessing the station suitability through a score between 1 and 3. The overall index can be determined using a simple formula (Eq. (1)) utilizing the given weights and ratings assigned to each indicator for the researched charging station [16]. n  m c   wc wi si (1) SSI = c=1

i=1

2.5 Coupling Stations’ Selection After the calculation of all EV charging stations locations’ indexes, a process must be followed to determine the spots where the micro-mobility-EV charging hubs could be located. The authors of this paper decided to follow a plain method for the process, which follows. Firstly, the stations with the highest index were chosen for the placing of the hubs. The new influence area of the EV charging stations that were coupled with micro-mobility is tripled [18]. Afterwards, more stations are selected for the installation of the micromobility hubs, until the highest possible percentage of municipality’s area is serviced. Consequently, the spots that are located within others’ influence area with highest S.S.I. are removed. The exact placement of the micro mobility-EV charging stations’ hubs was determined considering the criteria and indicators mentioned above (pavement width, road segment width, signage, local parking restrictions), aiming the unimpeded passage of road users (vehicles and pedestrians). Finally, the EV stations with micro-mobility hubs are decided, thus the overall influence area of all the stations is determined.

Park-and-Ride: The Case for Coupling EV Charging

35

2.6 Presentation of the Results After the indices for each station are calculated with the use of matrixes either for each municipal unit or for each station (based on the available data of each indicator), a wide variety of presentation methods exists. The presentation methods used in this paper considered as the most suitable and are also used in the source paper [1]. These are the radar diagram and the maps. Regarding radar diagram, it comprises an easy method to depict the results of the indices. In this paper, an average of each municipal unit’s stations criteria was extracted and inserted in the diagram, in order to simplify the results and enable the authors to conclude them within the limited paper’s size [1]. The tool used, is the Microsoft Excel, where the data were inserted, and the diagrams were created. As it regards the maps, they were used to express the geospatial data of the stations, with the use of G.I.S., and especially the open-source programme QGIS. The authors of this paper chose to combine each station’s index (S.S.I.) with the average area of influence, based on multiple studies [18, 19]. Thus, 2 multimaps were created: the first one (Fig. 1) shows the process followed for the selection of the ideal EV charging stations and the second one (Fig. 3) depicts the influence areas of the EV charging stations. Regarding the first map (Fig. 1) the map at the bottom of this multimap (Fig. 1) includes all the existing and planned (for 2025) EV charging stations. The map at the centre of Fig. 1 includes the spots’ S.S.I. score, which aids the selection of hubs’ locations. Finally, the map on the top of Fig. 1 demonstrates the selected spots for the hubs location. The second multi-map (Fig. 3) demonstrates the total influence area of EV charging stations located in the Municipality of Kifissia, analysed at layer’s level. The bottom map includes EV chargers not coupled with micro-mobility while the above shows their influence area. Correspondingly, the third map shows the EV chargers’ spots that coupled with micro-mobility and the above map shows their influence area. Finally, the map on the top is the combination of all the previous maps.

3 Results The purpose of this study was to develop a context for evaluating the suitability of EV charging stations for the introduction of micro-mobility modes, as well as to identify the optimal stations for coupling micro-mobility with EV chargers, based on the area’s features. The research is based on a project for EV charging stations (S.F.I.O.) that is currently proceeding in Greece. The locations of proposed EV charging stations are derived from that project, while the existing charger infrastructure is derived from the website PlugShare.com. The total performance of the three study regions is depicted in Fig. 2 based on the scores they earn for each of the 11 key criteria. We observe that all the districts have similar performance, while minor distinctions exist in the categories “Land Use Features”, “Road Network & Comfort”, “Road Safety”, “Mode attractiveness compared to alternatives” and “Aesthetic Attractiveness”. This is a reasonable outcome, given the plethora and increasing accuracy of data for the specific categories. For each of the study districts, Tables 1, 2 (Appendix) present the score for each of the primary criteria and the total index obtained using the Eq. (4).

36

A. Moschopoulou et al.

Fig. 1. The process of spots’ selection for the hubs. The maps show the total number of existing or planned EV charging spots (left), the S.S.I. for each spot (center) and the selected spots for the coupling of EV chargers with micro-mobility.

Based on the results, some of the Kifissia’s Municipal Unit (M.U.) EV charging stations have the highest performance in comparison to stations located at the other two M.U.s. The conclusion is reasonable, given that these stations come up with the highest S.S.I. The high S.S.I. can be explained initially by the area’s spatial distribution, including origin–destination pairings. Kifissia is an urban region defined by a high mix of land use diversity, a significant commercial activity, and many points of trip destination. According to experts, “Land Use” criterion has a significant weight and hence has a significant impact on the final S.S.I result. Additionally, high residential density of

Park-and-Ride: The Case for Coupling EV Charging

37

Kifissia favors micro-mobility over privately owned vehicles, as users must find parking for their autos. The results for Nea Erithrea indicate that some stations, though not as many as those in Kifissia, favor micro-mobility. This outcome can be explained again based on the criteria with the highest rate. Specifically, Nea Erithrea has diverse land uses, particularly in the M.U’s south, where the stations have high S.S.I. The M.U’s south side, which borders Kifissia’s M.U., has a high population density. As in the case of Kifissia, Nea Erithrea has a high population density which translates into a high rate of private vehicle ownership, resulting in a lack of available space, which is a favorable factor for micro-mobility. Another factor favoring the installation of micro-mobility at the EVs’ charging stations in the M.U. of Nea Erithrea is the bicycle lane’s length in comparison to the other M.U’s. Ekali, on the other hand, has the lowest S.S.I. rates. This M.U. is a solely residential area with low population density but high rate of private vehicle ownership and absence of commercial zones, this can explain the low S.S.I. There is still a deficiency in sidewalk infrastructure, while the cycleway has the shortest length compared to the other units, making installation of micro-mobility at the stations challenging.

S.S.I. in each M.U. M.U. of Kifisia

AestheƟc AƩracƟveness Mode aƩracƟveness compared to…

ConnecƟvity & Intermodality

M.U. of Nea Erithrea M.U. of Ekali Travel Behavior CharacterisƟcs 3 Land Use Features 2.5 2 1.5 Weather CondiƟons 1 0.5 0 Road Network & Comfort

PopulaƟon CharacterisƟcs Health & Environmental…

Road Safety Security

Fig. 2. The overall S.S.I. score of each indicator at Municipal Units’ level.

The overall findings indicate that the research’s EV charging stations have a similar level for integrating micro-mobility modes which is average to low. EV charging stations suitability of Nea Erithrea ranks higher with a score of 2.00, followed by Kifissia with 1.97 and Ekali with 1.94. Between the three M.U.s, Nea Erithrea is more conducive to micro-mobility, owing to the area’s land use features, the population’s sociodemographics and travel characteristics, and the area’s existing bicycle infrastructure. Kifissia, which has almost the same characteristics as Nea Erithrea, considered also favorable in the case of the installation of a micro-mobility system, considerably benefiting

38

A. Moschopoulou et al.

from better connectivity. Ekali seems to be barely favorable, owing to a lack of bicycle infrastructure and the appeal of other modes of mobility, such as public transportation (connectivity) and high rate of private vehicles.

4 Discussion The adoption of electric vehicles (EVs) could be a fundamental pillar in the direction of clean energy and decarbonization [20]. Although, some issues come up with the wide implementation of the electric mobility. One of the major problems is the charging infrastructure for Evs and how they could be distributed to maximize their benefit for the users. Increasing the influence area of the stations, to serve a larger proportion of the population is a way of solving the issue above. This paper examines whether the EV charging stations, located in the municipality of Kifissia, in Attica region of Greece at the framework of project for EV charging stations placement (S.F.I.O.), are suitable for the coupling with micro-mobility stations, thus creating a transport hub, to serve park and ride. For this purpose an index (S.S.I.) was formulated, based upon the index described at the paper about “Micro-mobility System Assessment” [16] and appropriately adapted to the needs of this paper. Applying the index’s formula, as described at the “methodology” section, with the use of the Analytic Hierarchy Process (AHP), a value for each EV charging spot’s suitability for micro-mobility is extracted. This value is input in a GIS programme and maps about the suitability for Kifissia Municipality are extracted. Using the AHP, means that the weighting and the rating of the criteria and their indicators reflect the subjective views of the individual experts and the conductors of this study [16]. After that, a short process is followed, were the spots of the EV chargers-micromobility hubs are decided, thus the final map that depicts the influence areas of all the stations (with or without micro-mobility coupling) is extracted. This map, along with the results of the S.S.I. matrices could become a useful tool mainly for Kifissia municipality, but also for other administrative areas, to decide the optimal location for micro-mobility hubs, increasing the influence area of their EV chargers, thus increasing their financial benefits. Financial benefit increase could be achieved either with the increased demand that such a hub could have, or with the adjustment of the next phases of so-called “S.F.I.O.” project, to reduce the different locations of EV chargers and create clusters with larger number of spots via micro-mobility.

5 Conclusion and Further Reading This paper investigated a wide range of non-monetary criteria affecting the suitability of placing a micro-mobility station at a planned or existing EV charging station, thus creating a hub. Although financial factors affect the ideal locations’ choice, they are not considered in this analysis. [16]. Due to the limited number of the individual experts and their subjective views, other or more respondents could provide different valuations, thus changing the results. Research in the subject area of suitability for micro-mobility, usually focuses on bicycles, but this paper studies all modes of micro-mobility [16].

Park-and-Ride: The Case for Coupling EV Charging

39

Fig. 3. The influence areas of EV charging spots not selected for the coupling (yellow spots/blue heatmap) and the influence areas of EV charging spots with micro-mobility hub (blue spots/orange heatmap).

40

A. Moschopoulou et al.

The advantage of this paper’s method is the use of an EV charging spot as unit of analysis. Furthermore, the simplicity of the method allows it to be used as well by nonexperts, assisting the decision making for micro-mobility location at detail level. Also, the three-point rating scale used for the evaluation of the indicators, can be used both in the cases where detailed data are provided and the cases with little or no available precise data [16]. It is worthy of note that the limitations set for this paper, made some procedures less accurate, to fit within the extend of the present project. For example, the average population density around the stations, the land use features, the road network characteristics as well as the aesthetics of each examined area could also be extracted with the use of satellite images and maps provided by the municipal authority via AI. Also, the method used for the selection of the spots that will finally be chosen for the hubs could also be differentiated if more data would be available.

Appendix See Tables 1, 2 and 3. Table 1. The criteria indicators with their weights and their rating, at the M.U. of Kifissia. Municipality of Kifissia Municipal Unit of Kifissia Criterion

Weight (Wc) Indicator

Rate (1–3) Weight (Wi)

Travel Behavior Characteristics

0.222

Motorization rate

3

0.078

Household vehicle ownership rate

3

0.078

Car modal share

3

0.135

Public transport modal share

2

0.135

Walking modal share

1

0.135

Cycling modal share

2

0.135

Use of shared mobility services

1

0.061

Percentage of short distance trips

1

0.244

Land use mix

3

0.141

Land Use Features

0.163

(continued)

Park-and-Ride: The Case for Coupling EV Charging

41

Table 1. (continued) Municipality of Kifissia Municipal Unit of Kifissia Criterion

Weather Conditions

Road Network & Comfort

Weight (Wc) Indicator

0.029

0.099

Rate (1–3) Weight (Wi)

Type of residential land use

2

0.084

Car parking availability

1

0.091

Educational land use

2

0.133

Neighborhood commercial land use

2

0.141

Large-scale commercial land use

1

0.061

Office commercial land use

3

0.131

Recreational services and 3 facilities

0.156

Administrative services and institutions

3

0.061

Average high temperature 2

0.107

Average low temperature

2

0.121

Average annual precipitation

2

0.205

Average annual precipitation days

2

0.196

Average annual sunshine hours

3

0.099

Average annual snowfall days

3

0.065

Average wind speed

2

0.205

Average slope

3

0.115

Pavement quality

2

0.051

Share of local roads

2

0.135

Zones 30

2

0.076

High-speed zones

2

0.055 (continued)

42

A. Moschopoulou et al. Table 1. (continued)

Municipality of Kifissia Municipal Unit of Kifissia Criterion

Road Safety

Security

Weight (Wc) Indicator

0.074

0.022

Health & Environmental 0.020 Benefit

Rate (1–3) Weight (Wi)

Level of car traffic flow

2

0.106

Share of heavy vehicles

2

0.051

Obstacles (on street parking)

3

0.040

Micromobility-friendly network

2

0.179

Width of bike lanes

2

0.116

Width of sidewalks

3

0.076

Road accident rate

2

0.169

Soft mode accident rate

2

0.215

Micromobility-friendly network

2

0.145

Separation from other motorized traffic

2

0.150

Street lighting

2

0.043

Level of car traffic flow

2

0.086

Zones 30

2

0.163

Unsignalized intersections

3

0.029

Personal theft rate

2

0.478

Vehicle theft rate

2

0.199

Video protection

2

0.111

Street lighting

2

0.107

Secure parking areas (for 2 bicycles & other personal mobility devices)

0.058

Presence of police officers

2

0.048

Air pollution level

2

0.385

Noise pollution level

1

0.370

Physical activity level

2

0.185

Social awareness on environmental issues

2

0.059

Park-and-Ride: The Case for Coupling EV Charging

43

Table 2. All the existing and planned EV charging stations used for the analysis, in Municipality of Kifissia. Station ID

Address

Coordinates

Plugs

X

Y

Plug Type (kW)

Source

Municipal Unit of Kifissia KI-001

Nikis 7–15, Kifissia 145 61

483130.47

4212873.48

2

DC 50

S.F.I.O

Kl-005

Lebidou 5, Kifissia 145 62

483528.54

4213610.41

4

DC 50

S.F.I.O

KI-006

Irodou 483064.72 Attikou 11–15 (opposite), Kifissia 145 61

4213614.06

4

AC 22

S.F.I.O

Kl-007

Mirsinis 2–4, Kifissia 145 62

483607.57

4214135.612

4

AC 22

S.F.I.O

KI-010

Kolokotroni 11, Kifissia 145 62

483792.40

4213598.70

2

DC 50

S.F.I.O

Kl-012

Filadelfeos 8 (opposite), Kifissia 145 61

484116.50

4213782.25

2

DC 50

S.F.I.O

Kl-015

Solonos 14, Kifissia 145 63

484957.68

4214884.32

2

AC 22

S.F.I.O

Kl-018

Plateon 1–5, Kifissia 145 61

482690.91

4212860.74

2

AC 22

S.F.I.O

Kl-107

Elaion 40–42, Kifissia 145 64

481953.64

4215819.89

4

DC 50

S.F.I.O

Kl-109

Proteos 25, 483013.85 Athens 145 64

4215362.82

2

AC 22

S.F.I.O (continued)

44

A. Moschopoulou et al. Table 2. (continued)

Station ID

Address

Kl-111

Coordinates

Plugs

Plug Type (kW)

Source

X

Y

Kifissou 52, Kifissia 145 64

482763.43

4216365.41

2

AC 22

S.F.I.O

Kl-113

Pelo onisou 10–12, Kifissia 145 64

482147.84

4216540.02

2

AC 22

S.F.I.O

KI-114

Adrianou 4–14, Alsos Parking Car Park, Kifissia 145 61

483307.90

4213684.91

2

AC 22

plugshare.com

Kl-115

S/M Thanopoulos Dragoumi 9, Kifissia 145 61

483067.77

421378.57

2

AC 22

plugshare.com

Kl-116

AB Erythrai, Kifissias Avenue 326, Kifissia 145 63

483775.83

421508.57

4

AC 22

plugshare.com

Kl-117

Elaion 38, 482035.65 S/M Thanopoulos Eleon, Kifissia145 64

421573.67

2

AC 22

plugshare.com

Kl-118

ElpeFuture EKO Fisikon Kifissias (Kalipso 35), Kifissias Avenue, Kifissia 145 62

421413.36

3

AC 22 AC 50

plugs hare.com

483446.05

(continued)

Park-and-Ride: The Case for Coupling EV Charging

45

Table 2. (continued) Station ID

Address

Kl-119

Hotel Semiramis, Xarilaou Trikoupi 48, Kifissia 145 62

Coordinates

Plugs

Plug Type (kW)

Source

X

Y

484183.97

421381.60

1

AC 22

plugshare.com

Municipal Unit of Ekali EK-001

Agoras, Ekali 145 78

485717.82

4217523.11

2

AC 22

S.F.I.O

EK-002

Vasileous 485984.52 Pavlou S uare, Ekali 145 78

4217811.30

2

AC 22

S.F.I.O

EK-003

Iatrou Square, 486368.53 Ekali 145 78

4217694.46

2

AC 22

S.F.I.O

EK-004

Leoforos Thiseos 103, Ekali

421823.19

2

AC 11

plugshare.com

486534.41

Municipal Unit of Nea Erithrea NE-001

Xarilaou Trikoupi 152, Nea Erithraia 146 71

483612.87

4215372.35

2

DC 50

S.F.I.O

NE-003

Georgiou Papandreou 17 (opposite), Nea Erithrea 146 71

484101.68

4215487.14

2

AC 22

S.F.I.O

NE-004

Nikolaou Plastira 20, Nea Eritrea 146 71

483954.31

4215554.35

2

DC 50

S.F.I.O

(continued)

46

A. Moschopoulou et al. Table 2. (continued)

Station ID

Address

Coordinates

NE-005

Ioannas 484279.13 Dribeti 2, Nea Erithrea 146 71

4215975.84

2

AC 22

S.F.I.O

NE-010

Nikou Kazantzaki 22, Nea Erithtrea 145 64

483454.52

4216593.97

2

AC 22

S.F.I.O

NE-011

Samothrakis, Nea Erithrea 146 71

484078.92

4217123.60

2

AC 22

S.F.I.O

NE-012

S/M Thanopoulos Eantos, Xarilaou Trikoupi 2, NeaEritrea 146 71

483468.67

421536.53

2

AC 22

plugshare.com

X

Plugs

Plug Type (kW)

Y

Source

Table 3. The criteria and indicators with their weights and their rating, at an EV charging station in Nea Erithrea. Criterion

Weight

Indicator

Rate (1–3)

Weight

Population Characteristics

0.169

Population density

2

0.084

Median age

2

0.134

Median monthly income

3

0.063

Percentage of population aged 15–34 years

3

0.134

Percentage of students

3

0.125

Percentage of population of higher intellectual profession and executives

3

0.125

(continued)

Park-and-Ride: The Case for Coupling EV Charging

47

Table 3. (continued) Criterion

Weight

Connectivity and intermodality 0.114

Mode attractiveness compared to alternatives

Aesthetic Attractiveness

0.039

0.048

Indicator

Rate (1–3)

Weight

Percentage of population with higher educational diploma

3

0.055

Percentage of one-person households

1

0.072

Employment density

2

0.090

Demand for EVs

1

0.381

Frequency of local bus service

2

0.133

Secure parking facilities 1 for micromobility modes at EV charging spots

0.068

Coherence of the micromobility- friendly network (bike lanes, cycle paths, pedestrian routes)

2

0.064

Average trip distance

2

0.354

Delay experienced in local 2 trips by car

0.199

Car parking availability

2

0.263

Delay experienced during charging time

3

0.178

Crowding in EV stations

2

0.097

Reliability of EV charging spot (certainity of finding free spot)

2

0.055

Average time spent on a walking trip

2

0.208

Presence of trees

3

0.189

Green spaces & waterbodies

2

0.351

Aesthetics of buildings

2

0.109

Elements of historical and cultural interest

3

0.351

S.S.I

1.9983

48

A. Moschopoulou et al.

References 1. Brenna, M., Foiadelli, F., Longo, M., Grillo, S.: Charging optimization for electric vehicles in large Park & Ride areas. In: 2016 IEEE Power and Energy Society General Meeting (PESGM), pp. 1–5. IEEE, Boston, MA, USA (2016) 2. Pan, Z., Zhang, Y.: A novel centralized charging station planning strategy considering urban power network structure strength. Electr. Power Syst. Res. 136, 100–109 (2016). https://doi. org/10.1016/j.epsr.2016.01.019 3. Dehdari Ebrahimi, Z., Bridgelall, R., Momenitabar, M.: Extending micromobility deployments: a concept and local case study. In: Stahlbock, R., Weiss, G.M., Abou-Nasr, M., Yang, C.-Y., Arabnia, H.R., Deligiannidis, L. (eds.) Advances in Data Science and Information Engineering, pp. 299–314. Springer International Publishing, Cham (2021). https://doi.org/ 10.1007/978-3-030-71704-9_19 4. Ioakimidis, C.S., Rycerski, P., Koutra, S., Genikomsakis, K.N.: A university e-bike sharing system used as a real-time monitoring emissions tool under a smart city concept. WEVJ 8, 963–973 (2016). https://doi.org/10.3390/wevj8040963 5. Ehsani, M., Gao, Y., Emadi, A.: Modern Electric, Hybrid Electric, and Fuel Cell Vehicles: Fundamentals, Theory, and Design, 2nd edn. CRC, Boca Raton (2010) 6. Helmers, E., Marx, P.: Electric cars: technical characteristics and environmental impacts. Environ. Sci. Eur. 24, 14 (2012). https://doi.org/10.1186/2190-4715-24-14 7. Wu, H., Niu, D.: Study on influence factors of electric vehicles charging station location based on ISM and FMICMAC. Sustainability 9, 484 (2017). https://doi.org/10.3390/su9040484 8. Zarazua de Rubens, G., Noel, L., Kester, J., Sovacool, B.K.: The market case for electric mobility: Investigating electric vehicle business models for mass adoption. Energy 194, 116841 (2020). https://doi.org/10.1016/j.energy.2019.116841 9. Seddig, K., Jochem, P., Fichtner, W.: Two-stage stochastic optimization for cost-minimal charging of electric vehicles at public charging stations with photovoltaics. Appl. Energy 242, 769–781 (2019). https://doi.org/10.1016/j.apenergy.2019.03.036 10. Chen, C., Zarazua de Rubens, G., Noel, L., Kester, J., Sovacool, B.K.: Assessing the sociodemographic, technical, economic and behavioral factors of Nordic electric vehicle adoption and the influence of vehicle-to-grid preferences. Renew. Sustain. Energy Rev. 121, 109692 (2020). https://doi.org/10.1016/j.rser.2019.109692 11. Janji´c, A., Velimirovi´c, L., Velimirovi´c, J., Vrani´c, P.: Estimating the optimal number and locations of electric vehicle charging stations: the application of multi-criteria p-median methodology. Transp. Plan. Technol. 44, 827–842 (2021). https://doi.org/10.1080/03081060.2021. 1992177 12. Fan, Z., Harper, C.D.: Congestion and environmental impacts of short car trip replacement with micromobility modes. Transp. Res. Part D: Transp. Environ. 103, 103173 (2022). https:// doi.org/10.1016/j.trd.2022.103173 13. Takahashi, K., Masrur, H., Nakadomari, A., Narayanan, K., Takahashi, H., Senjyu, T.: Optimal sizing of a microgrid system with EV charging station in Park & Ride facility. In: 2020 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), pp. 1–4. IEEE, Nanjing, China (2020) 14. Ai, N., Zheng, J., Chen, X.: Electric vehicle park-charge-ride programs: a planning framework and case study in Chicago. Transp. Res. Part D: Transp. Environ. 59, 433–450 (2018). https:// doi.org/10.1016/j.trd.2018.01.021 15. Mashhoodi, B., van Timmeren, A., van der Blij, N.: The two and half minute walk: fast charging of electric vehicles and the economic value of walkability. Environ. Plann. B: Urban Anal. City Sci. 48, 638–654 (2021). https://doi.org/10.1177/2399808319885383

Park-and-Ride: The Case for Coupling EV Charging

49

16. Psarrou Kalakoni, A.M., Christoforou, Z., Farhi, N.: A novel methodology for micromobility system assessment using multi-criteria analysis. Case Stud. Transp. Policy S2213624X22000621 (2022). https://doi.org/10.1016/j.cstp.2022.03.010 17. Saaty, T.L.: How to make a decision: the analytic hierarchy process. Eur. J. Oper. Res. 48, 9–26 (1990). https://doi.org/10.1016/0377-2217(90)90057-I 18. Zuo, T., Wei, H., Chen, N., Zhang, C.: First-and-last mile solution via bicycling to improving transit accessibility and advancing transportation equity. Cities 99, 102614 (2020). https:// doi.org/10.1016/j.cities.2020.102614 19. Nikiforiadis, A., Aifadopoulou, G., Salanova Grau, J.M., Boufidis, N.: Determining the optimal locations for bike-sharing stations: methodological approach and application in the city of Thessaloniki, Greece. Transp. Res. Procedia 52, 557–564 (2021). https://doi.org/10.1016/ j.trpro.2021.01.066 20. Karolemeas, C., Tsigdinos, S., Tzouras, P.G., Nikitas, A., Bakogiannis, E.: Determining electric vehicle charging station location suitability: a qualitative study of Greek stakeholders employing thematic analysis and analytical hierarchy process. Sustainability 13, 2298 (2021). https://doi.org/10.3390/su13042298

Metro Braking Energy for Station Electric Loads: The Business Case of a Smart Hybrid Storage System George Leoutsakos1 , Alexandros Deloukas1(B) , Kanellina Giannakopoulou2 , Maria Zarkadoula2 , Dimitris Kyriazidis3 , and Astrid Bensmann4 1 Attiko Metro S.A, Athens, Greece

{gleoutsakos,adeloukas}@ametro.gr 2 Centre for Renewable Energy Sources and Saving, Pikermi Attiki, Greece 3 Urban Rail Transport S.A, Athens, Greece 4 Leibniz Universität Hannover, Hannover, Germany

Abstract. The utilization of excess energy produced through vehicle movements stands in the center of efficiency measures in the transport sector. In case of electric trains, the excess energy of vehicle regenerative braking is mostly wasted as heat. Instead of an instantaneous waste, a later re-use of this energy requests the adoption of an electric storage system. The paper describes real data obtained through onsite and train on-board measurement schemes and a methodology to achieve metro system energy savings redirecting unused energy produced from braking metro trains to the metro station grid consumption. An emphasis is on cost/returns analysis and environmental benefits of the storage system. The Hybrid Energy Storage System (HESS) design developed for the Athens Metro combines efficiently the higher power density and (dis)charging cycles of supercapacitors (coping the high frequency of train stops producing energy) with the superior energy density of batteries (matching a slower release and a longer energy consumption time of stations’ current drain). A smart energy management and control strategy allows upon demand for an internal energy transfer between both storage technologies. So far, single-technology, onboard or wayside storage systems servicing mainly the traction of accelerating trains were available. The novelty here is the dualtechnology HESS, located at stations servicing the energy demand of the latter. Preliminary results confirm the feasibility of the energy saving concept indicating a large potential for the MetroHESS reuse of 5000–6000 kWh/day per rectifier substation of otherwise unused braking energy of a metro line and a subsequent s sizing of the stationary HESS is performed. About 30% of the braking energy accrued can be reused through the MetroHESS to cover about 90% of the station energy demand while the residual braking energy will be dissipated in the train braking resistors. An implementation of the stationary storage system to Line 2&3 rectifier substations would cost 17 mi.e, saving on an annual base about 4 mi.e electricity expenses for the operator as well as 8.600 tons CO2 for the sake of the community. Keywords: Electric vehicle regenerative braking · Smart hybrid energy storage system · Metro energy savings · Station energy loads

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_4

Metro Braking Energy for Station Electric Loads

51

1 Introduction In an era of economic and environmental crisis there is an intense preoccupation with the improvement of the energy efficiency of urban rail systems. An adequate reuse of the metro braking energy is pivotal in this respect. This paper focuses on the configuration of a stationary hybrid energy storage system, located in metro traction substations in turn located inside Metro stations. The recuperation energy of the metro braking phase is then reused to feed stationary electrical loads of metro stations. The aim is to achieve energy savings with subsequent cost reductions for the operator and environmental benefits for the society at large. The proposed system configuration and sizing is based on real electrical measurements of traction substations and on-board moving trains in a synchronized manner as well as of selected stationary electrical loads in metro stations, showcasing Athens Metro Line 2. The system is evaluated through a small-scale demonstrator and related simulations. A business case of energy and cost savings as well as the environmental benefits in terms of CO2 savings are elaborated for the proposed dual-technology system. As similar work, a business case for a stationary single-technology storage system has been developed by Teymourfar et al. (2012). The principle is that the train motors of a DC metro system during the braking phase act as generators, converting the kinetic energy of the train into electricity (Ciccarelli et al. 2012). A part of the converted energy is regenerative and the rest is a rheostatic one. The latter is wasted as heat in braking resistors mounted on the train, heating up to 400 °C, this constituting a high % (> 30%) of the input traction energy. The very frequent alteration of traction and braking phase of moving trains provides a potential to improve the energy efficiency of metro operations through a proper reuse of the regenerative energy. In the base case, a 15–25% share of the trains ‘input traction energy is regenerated and instantaneously reused in case of nearby accelerating trains, in analogy to the train headways. The share of energy that can be fed back to the system through the 3rd rail is positively correlated with the traffic density with the latter being higher during the peak period. The simplest way of increasing regenerative energy reuse in large metro systems with heavy traffic, would be timetable synchronization at the same or neighboring traction electrical sections, so that decelerating trains would feed with power the accelerating trains. However, supplementary strategies are requested due to the upper limits of this ‘natural’ reuse. Different strategies have been proposed to further increase the share of the regenerative braking. An already implemented strategy is the storage of the braking energy of each train in on-board supercapacitors and its almost instantaneous subsequent use during the acceleration phase with the strength of this strategy aiming to reduce energy transmission losses. On the other hand, the weaknesses of this method are the surplus weight of the trains leading to higher traction energy consumption, the higher maintenance loads on multitudes of supercapacitors, the additional complex on-board power electronics, the possible additional clearance gauge limitations (Ciccarelli et al. 2012), while violent supercapacitors’ failures although infrequent, are also still posing a passengers’ safety issue.

52

G. Leoutsakos et al.

Another strategy for the reuse of the regenerative energy is a major retrofitting of the unidirectional traction substations (based on rectifiers for AC/DC conversion) to bidirectional ones (adding DC/AC inverters) and returning the generated energy back to the city Medium Voltage AC main grid, at 20 kV. Analysis has shown (Leoutsakos et al. 2021) that up to an additional 12% of the traction energy may be returned back to the city grid. The advantage of this strategy is that no storage devices are needed while the disadvantage is that the inverters return harmonics or reactive power to the main grid and mitigation measures are complex to design and costly in this respect (Tian et al. 2018). However, for new rail lines, this is also a feasible solution in economic terms. The local regulatory environment is rather a constraining than an enabling parameter for this strategy. Another potential energy saving strategy is to install stationary storage devices along the rail network, absorbing energy from multiple moving trains. Locating such equipment in interstation track sections has the disadvantages of increased electrical transmission losses and unstable temperature conditions while locations close/within traction substations avoid the said disadvantages, if the recovered energy is re-directed and reused for station electrical loads. When reused for traction, inevitably losses on the line will still prevail. A critical issue in all aforementioned strategies is how to optimize the reuse of the regenerative braking energy. In an almost instantaneous reuse of the said energy, when storage devices are installed, a single-technology solution with supercapacitors is preferred. Supercaps possess high efficiency, power density, high number of (dis)charging cycles, output power, temperature insensitivity as well as very fast charging time. The less costly batteries on the other side, exhibit much higher energy density but much lower number of (dis)charging cycles, temperature insensitivity as well as slow (dis)charging time. The business case of this paper pertains to the reuse of the braking energy to cover electrical loads of the Athens metro stations. Shabanova and Biryukov (2018) conclude that feeding auxiliary station loads is the most appropriate option to reuse braking energy. The novelty in this regard is the usage of a dual-technology (i.e. hybrid) storage solutions for the specific energy production – consumption scheme. The frequent stops of metro trains generating high power peaks (say, in the order of 2000 kW) during each braking phase make supercapacitors a suitable and fast storage component. On the other side, the energy consumers at a station (lighting, ventilation, electro-mechanical systems and other actuators) have lower power needs (say, in the order of 200 kW) and longer consumption times. In all, there are two different time scales. One scale results from the station-specific energy production (say, 5’ headway or 24 stops/hour of almost 20” train braking duration each, in both directions). The other scale follows from the station-specific energy consumption (daily cycle of fully using the stored energy). Therefore, a storage system is required that can handle high peaks and has sufficient capacity for the short-term (daily) energy storage. In this context, a hybrid energy storage system (HESS) is suitable, that can make a cooperative use of the advantages from the individual technologies, i.e. the high specific power and cycle stability of supercapacitors as well as the high energy density of batteries. The discharge time of batteries will be much longer than the charge time of the supercaps.

Metro Braking Energy for Station Electric Loads

53

Günther et al. (2018) optimize in a generic, transport-independent, case a hybrid storage system, thus avoiding an over- or under-dimensioning of the single technologies in terms of power or energy capacity. This is important due to the high prices of storage technologies. Strong points of locating HESS close or within traction substations are i.a. low energy transmission losses, better serviceability and stability of the temperature conditions for the batteries. A schematic of such a HESS is shown in principle in Fig. 1 below, where the energy flows from the decelerating trains to the Rectifier Substation 750 V DC circuit breakers, from there to the energy storage devices and from there to the Lighting and Auxiliaries Substation and is connected to the various power distribution and lighting switchboards.

Fig. 1. MetroHESS concept layout at station/system level

The paper is structured as follows: Section 2 describes the electrical measurements conducted in the Athens metro system in the context of the MetroHESS project which generated this research effort. Section 3 develops the MetroHESS configuration and sizing, based on the real data collected through the electrical measurements. Section 4 unfolds the business case of MetroHESS and the next section estimates the environmental benefits of the latter. The paper concludes with the potential of the developed prototype for further commercial exploitation.

54

G. Leoutsakos et al.

2 Electrical Measurements 2.1 Synchronized Traction Substation and Train Measurements The Athens metro 6-car trains measured in the context of the present work have 4 motorcars and 16 motors, each with a nominal traction power of 140 kW (series I, in operation since 2000) or 170 kW (series III, in operation since 2013). The typical auxiliary power of a train (e.g. lighting, HVAC) amounts to 240 kW. The rated train power amounts to 2000 kW (series I) and 2480 kW (series III) respectively. A fully loaded train has a mass of approximately 250 tons. The maximum operating speed is 80 km/h and the mean commercial speed 30 km/h. The train draws power in the traction phase and releases power in the braking phase. One train from series I (no. 11) and one from series III (no. 64) were selected for electrical measurements. A 750 V DC traction substation of the Athens metro has a rated power of 3 MW (rectifier) and 3.3 MVA (transformer) respectively. Three consecutive traction substations on Line 2 have been selected for electrical measurements (Sepolia, Agios Antonios, Peristeri). Measuring devices were installed in the three traction substations and on board the two selected trains. Three weekly measurement campaigns with increasing sampling rates of 10 Hz, 1 kHz and 10 kHz were performed. Impedances of braking resistors and other system components were given, so that power figures may be derived from the measured voltages and currents. For measurement details see Leoutsakos et al. (2020). The very large quantity of measurement data (voltage and current) requests their filtering to smooth aliasing (envelope filter) and noise (low pass filter). An adapted Ramer-Douglas-Peucker algorithm has been applied. The original data size of 33 GB per set of measurements was reduced to 200 MB with a negligible loss of information quality. The rheostatic braking energy was then computed from measured currents and given system resistants’ ratings. Automatic Train Supervision (ATS) log-files of time-stamped train positions enabled a sufficiently accurate synchronization of train and traction substation measurements within the range of a signaling section (200 m). Peak power measurements due to the resistive braking of train no.11 (of Series I) and no. 64 (of Series III) exhibited different time distributions, possibly due to differing driver behavior during braking or random energy recuperation and reuse from other accelerating trains in the vicinity. However, a mean released braking energy of 10 kWh per train braking until stop was measured and remained consistently the same for both types of trains. The resulting overall available braking energy was measured between 4000 and 6000 kWh/day per rectifier substation, depending on the day of the week and the train frequency (traffic density during workdays vs. weekend). 2.2 Station Measurements The Athens Metro Lines 2 & 3 currently consists of 39 stations, 39.4 km underground railway and 66 6-car trains. The commercial service starts at 05:30 and ends at 00:30,

Metro Braking Energy for Station Electric Loads

55

apart from Friday and Saturday when the service ends at 02:30. The rest of the night constitutes engineering hours dedicated to network maintenance. The electricity consumption as measured from Jan/2013 to Aug/2019 of Lines 2 & 3 amount to 809.100 MWh, of which 53% refer to traction, 38% to stations and 9% to depot & train stabling areas. During the said period, 37 stations were in operation and effectively consumed 43.140 MWh annually; another 3.297 MWh refer to standby installed power for emergencies. Note that SYNTAGMA, the multi-level central interchange station of Lines 2 & 3, is doubly counted. The source of the consumption figures is the Urban Rail S.A. (STASY), the Athens metro operator. Again, two stations on Line 2 have been selected, housing 2 out of the 3 measured traction substations (Sepolia, Peristeri). Their main characteristics, also related to energy consumption are shown in Table 1. Table 1. Line 2 Sepolia (1st generation) and Peristeri (3rd generation) stations’ characteristics.

Year of operation

Sepolia station

Peristeri station

2000

2013

Area (m2 )

6000

7500

Daily passengers – 2020 (reduced by Covid-19)

5100

3360

Lighting

LED

Fluorescent

Ventilation

Forced ventilation

Piston effect-based

Escalators

2

10

Energy audits and weekly on-site measurements have been conducted in both stations (Zarkadoula et al. 2022). The instrumentation recorded voltage and current providing power and energy figures for the measured consumers. Each station houses a power distribution network (Lighting and Auxiliaries Substation, LAS) fed by the 20kV medium voltage main grid. Transformers with a rated power 1200–2000 kVA distribute power to all station systems. Auxiliary AC loads refer to 24/7 lighting, ventilation, electromechanical systems as escalators and elevators, HVAC and small power of staff rooms, transformer & secondary windings, ad panels, pumps, tunnel fans, signaling, farebox ticketing or safety and security systems (fire-fighting, CCTV, intrusion detection, telecom, Public Address). The measured electrical loads are non-safety critical, significant station loads with rather steady base consumption during the year. Such consumers are suitable for the reuse of regenerative braking energy. Tunnel fans and pumps are only occasionally used. Weekly energy profiles per measured consumer have been mapped. Lighting, ventilation and electromechanical systems are the most significant loads in both stations. The range of the measured station loads stretches from 1916 kWh/day (Sepolia) to 2018 kWh/day (Peristeri). The annual specific consumption spans from 99 kWh/sq.m. for the energy efficient and more recent Peristeri to 117 kWh/m2 for the older Sepolia station. Next-generation stations are more energy efficient than the first-generation ones,

56

G. Leoutsakos et al.

so that Peristeri figure better represents the current and near future energy consumption of the Athens metro station system.

3 MetroHESS Configuration and Sizing The traction substations of the Athens metro convert medium voltage 20kV AC city grid power to the distribution network to 750 V DC power and supply the trains’ motorcars through a bi-metal 3rd rail system. The fluctuation of the 3rd rail input voltage depends on the traction or braking phase of the nearby moving trains and ranges from 600 to 900 V. The 3rd rail provides to the trains traction power and auxiliary power (e.g. lighting, HVAC) or receives power during the braking phase. In the latter phase, when the overvoltage reaches 900 V, a considerable part of the braking energy is currently dissipated in the train resistors. The MetroHESS devices by design detect the overvoltage and start to actively shave this peak voltage (Günther and Bensmann 2021). The conceptual design of MetroHESS foresees a base storage (batteries) and a peak storage (supercaps) with respective power P and energy E characteristics. The peak storage shall be only charged when necessary, i.e. when the input power exceeds the power capacity of the base storage, and shall be discharged whenever possible. Günther and Bensmann (2018) use for configuration and sizing purposes a P(E) hybridization diagram, neglecting in the first instance losses and other non-ideal modifiers. Different combinations of sized storage technologies (e.g. batteries and supercaps) may be then graphically tested and optimized. The dimensioning of the MetroHESS combined capacity in the said optimization is based on the electrical measurements of the produced braking energy of trains moving close to substations as well as of the energy consumed at the metro stations. The smart MetroHESS management depends on the current state-of-charge (SoC) of the base and peak storages. A rule-based control strategy is selected. The basic idea of the energy management is that as much as possible of the available braking energy of the 3rd rail should be stored or directly used. The priorities set are: first, deliver station energy demand instantaneously or from storage, second, (dis)charge of batteries, third, (dis)charge of supercaps. This way, the charging capacity is always as high as possible to harvest as must braking energy as possible. When no braking power is available, energy is transferred from the supercaps (peak) storage to the batteries (base) storage. A MetroHESS efficiency of 90% in terms of discharged vs. charged energy is estimated, along with a number of 4 charging cycles/day. The deployable MetroHESS for the Athens metro consists of 5 parts (see Fig. 2). • A passive conditioner i.e. with a filter, a unidirectional diode and a voltage limiter, fed by the 3rd rail and offering overvoltage and peaks protection to MetroHESS • A supercapacitor storage system consisting of a scalable number (here: 7) of parallel strings of 220kW each, to reach the desired power of 1540kW. Each string consists of 6 serial modules @ 96 V adding up to the desired voltage, and a high-power booster with 220 kW per string. • A battery storage system consisting of a scalable number (here: 6) of parallel strings. Each string consists of 12 serial modules, with an energy content of 54 kWh per string

Metro Braking Energy for Station Electric Loads

57

providing a total of 324 kWh. The battery is actively connected to a DC link by means of string power booster with 60 kW. The total peak power will be about 360 kW. • An energy management system (EMS) controlling the power flows into the storage units by monitoring the available infeed from the 3rd rail, the available power based on the DC link voltage, the output into the AC station load and the actual SoC of the battery as well as the supercaps. The smart EMS is implemented on a microcontroller. • An AC grid inverter (100 kVA), feeding energy to the station grid, combined with a LCL and EMI filter as well as an output transformer. The inverter works in sync with the mains and is controlled by the EMS to make sure that as much “self-consumption” as possible is used from HESS and the grid is only contributing in case of lack of energy to the HESS system.

Fig. 2. MetroHESS layout at the substation level

If the available braking power or the power demand of the station are within certain limits, an energy transfer between the supercaps and the batteries takes place, such that the supercaps become available again for charging as fast as possible (peak power shaving). It is important to note that the station-specific electricity demand in the Athens metro is always smaller than the available regenerative braking energy, even close to peripheral, lower-frequency stations. MetroHESS sizing refers primarily to the station demand.

58

G. Leoutsakos et al.

The MetroHESS design has been tested by means of small-scale prototype (demonstrator) to determine its technical feasibility. The demonstrator parameters have been set based on the measurement data and the extracted load profiles. In the context of the project, a demonstrator unit was assembled and tested. The scale of the demonstrator compared to the full scale deployment is about 1:14 referring to the number of battery and supercap modules as well as to the battery storage capacity in kWh. Dynamic tests of the demonstrator included the EMS distributing the available power and energy between the supercap and battery system. The most challenging task was the test response to the simulated power load profiles of the Athens metro trains. Overall, the tests have proven the technical feasibility of MetroHESS.

4 Business Case The World Bank approach for the mass transit technology valuation has been selected (Armstrong-Wright 1986) for analyzing the present business case of the MetroHESS concept, where the operators’ costs are also relevant and critical. A lifetime of 6 years is assumed for the supercaps and the batteries. The valuation stretches out over a period of 12 years, considering the much lower cost of a MetroHESS refurbishment after the 6th year. It is noted that the Athens metro Lines 2&3 contain 32 traction substations located within metro passenger stations which is where metro HESS may be implemented. The MetroHESS initial investment costs for the dual-technology solution amount to 523.000 e per substation (stand 2022). A mass purchase discount of 15% has been already considered. Energy storage modules, active power electronics (boosters), input conditioner, smart energy management controller and interconnected AC grid inverter for the station loads are included. Cabling and cooling system of the storage room amounts to 8.000 e per substation. Mass refurbishment cost for batteries and supercaps after the 6th year amounts to 200.000 e per substation. The capital costs are annualized and represent depreciation and interest charges. The MetroHESS investment is assumed to be financed by two loans for the length of 6 years each. The first loan is taken in the beginning of the whole investment and the second in the 7th year of the 12-year period. An interest rate of 3% is applied to each year of the loan (3% is assumed to be the prevailing average interest rate, i.e. rate adjusted for inflation). Constant annuities are made on each loan. It is assumed that supercaps and batteries will be refurbished at the end of their useful life and the refurbishment will be financed by the new loan. Discounted annual payments are calculated using capital recovery (present worth) factors (Annex VIII, Armstrong-Wright 1986). The annual payments cover both the principal and the interest of the loan. Sources of the unit costs are the project partner who designs the supercaps and batteries as well as the Athens Metro project owner. Annual operating & maintenance costs (labour, spares, and overheads, e.g. insurance) are assumed to amount 3% of the initial investment costs. The effective annual power consumption of 32 stations containing traction substations amounts to 37.310 MWh, according to the Metro operator’s energy bills. The measured lighting, ventilation and electromechanical systems cover about 60–67% of

Metro Braking Energy for Station Electric Loads

59

this consumption. The MetroHESS efficiency is estimated to be 90% hence a very high % of these energy needs could easily be covered by the regenerated, stored and reused braking energy. The current, relatively high electricity price level of 0.18 e/kWh is assumed to be held in the future (2022 price). Fixed parts of energy bills are potentially lower because less power needs to be installed when MetroHESS is deployed. However, adopting a more conservative approach, the cost of the installed power is assumed to remain constant. The regenerative energy replaces resistive energy, thus dissipation of heat in tunnels and stations is saved. About 30% of the energy consumed in ventilation is then also saved due to MetroHESS heat avoidance impact. Based on the real energy consumed (i.e. under platform and over track exhaust ventilation) in Peristeri and Sepolia stations, the aggregate ventilation energy saved over 32 stations would reach 1132 MWh p.a. The net present value of MetroHESS over 12 years reaches 15.8 mi. e and the saving over cost ratio B/C is 1.49, thus the investment is financially viable for the operator. Table 2 shows a sensitivity analysis which reveals favorable Benefit/Cost (B/C) ratios, which in turn imply that the financial viability of MetroHESS is a robust one. Table 2. MetroHESS implementation Benefit/Cost (B/C) ratios for various financial conditions cases Case evaluated over 12 years Financial conditions

Benefit/Cost (B/C) ratio

(a)

Base case with unit energy cost of 0,18 e/kWh

1.49

(b)

5% interest rate

1.42

(c)

10% lower electricity price

1.34

(d)

10% higher investment/refurbishment 1.36 cost

(e)

Worst case scenario – combination of 1.22 (c) and (d)

The European Investment Bank (EIB) as well as ERDF is considered as potential funding sources for this exemplary sustainable investment.

5 Environmental Benefits The large scale of metro operations requires a substantial amount of electrical energy. The electricity produced by the Public Power Company for the mainland of Greece is provided from several sources, as shown in Table 3. The Athens Metro Lines 2 & 3 for which the energy saving mechanism is being designed for has 32 traction substations located inside passenger stations, where the regenerated energy could be consumed on the Lighting and Auxiliaries Substations (LAS) of those stations.

60

G. Leoutsakos et al.

Table 3. Public Power Company energy generation sources breakdown and subsequent Metro energy saving and reuse and subsequent CO2 reduction through MetroHESS

Power source

TWh/year (typical)

% dictribution per source

Lignite 5,5 0,120 Natural Gas 21 0,457 Hydroelectric 5 0,109 Energy Import 4,5 0,098 Renewable Sources 10 0,217 Petrol (only islands - not considered in Metro consumption Total 46 100,00%

Emitted Kg CO2 / KWh per source 1,5 0,4 0,0 0,5 0,0 0,6 Average

Emitted % Kg CO2 per Kg CO2 / KWh source when user consumed by consumes user 0,179 0,183 0,000 0,049 0,000

0,437 0,444 0,000 0,119 0,000

0,411

100,00%

MetroHESS - Metro Braking Energy Saving and Re-use Concept Number of Metro Conversion CO2 not emitted stations with Rectifier CO2 not KWh/day saved KWh/day Substations (RS) and factor of per rectifier saved (tons/day) emitted due to Lighting & Auxiliaries emitted substation due to on Lines 2 & 3 Lines 2 &3 per rectifier Substations (LAS), HESS due to HESS Kg CO2 / KWh substation (tons/day) where HESS may be installed 1800 0,411 0,74 32 57600 23,67

Hence, as shown in Table 3, for an average energy reuse on the LAS Substations of the Lines 2 & 3 stations of 1800 kWh/day/station, this results in a net overall energy saving of 57.600 kWh/day on Metro Lines 2 & 3, which translates into a direct CO2 emission reduction of 23.7 tons daily or 8.600 tons p.a. to the atmosphere. An additional intangible benefit is reduced heat dissipation in tunnels and stations (requesting less ventilation energy), while improving the thermal comfort of the passengers, especially during the summer period.

6 Conclusions A hybrid Energy Storage System termed MetroHESS foresees the storage and reuse of regenerative train braking energy through an active combination of batteries covering base power electrical consumer loads in Metro stations and supercapacitors able to receive the energy power peaks from train braking. The testing of a small-scale prototype - demonstrator has proven the technical feasibility of MetroHESS. MetroHESS will drastically reduce energy consumption on Metro Lines 2 & 3 by 57.6 MWh daily and will proportionally cut the electricity bills for Metro operators, while the CO2 emissions will be correspondingly reduced by 8600 tons p.a., implying important environmental upgrading for the community. Increasing electricity prices and the urgent issue of the climate change make the investment more attractive. Based on the business case, indicating favorable B/C ratios of 1.2–1.5 it is assured that the full-scale, scalable MetroHESS applications are fully viable and may become available for commercial exploitation when appropriately designed and parameterized.

Metro Braking Energy for Station Electric Loads

61

The MetroHESS modular design and its related business case has already been presented to relevant stakeholders (i.e. international metro providers, power system developers), who expressed their keen interest in this respect. Acknowledgements. The present study was conducted in the framework of the MetroHESS research project of the “Bilateral and Multilateral Cooperation between Greece and Germany”, co-financed and funded by the German Federal Ministry of Education and Research (BMBF) with funding code 03SF0560A, by the European Regional Development Fund (ERDF) and by the Greek National Resources through OP: Competitiveness, Entrepreneurship & Innovation (EPANEK) with funding code T2DGE-0327. The authors would like to thank the funding authorities, the Athens Metro project Owner Attiko Metro SA, the Athens Metro Operations company (STASY S.A.), the project coordinator CRES in Athens, the Leibniz University of Hannover (LUH) and the Stercom Power Solutions GmbH company (Stercom) who are the MetroHESS project partners. The last partner (Stercom) is the one co-configuring and assembling the Hybrid Energy Saving System demonstrator.

References 1. Armstrong-Wright, A.: Urban transit systems: guidelines for examining options. World Bank Technical Paper No. 52 (1986) 2. Barrero, R., Tackoen, X., Van Mierlo, J.: Improving energy efficiency in public transport: stationary supercapacitor based energy storage systems for a metro network. In: IEEE Vehicle Power and Propulsion Conference (VPPC), September 3–5, 2008, Harbin, China. ISBN: 978-1-4244-1849-7/08/. IEEE (2008) 3. Ciccarelli, F., Iannuzzi, D., Tricoli, P.: Control of metro-trains equipped with onboard supercapacitors for energy saving and reduction of power peak demand. Transp. Res. Part C: Emerg. Technol. 24, 36–49 (2012). https://doi.org/10.1016/j.trc.2012.02.001 4. Günther, S., Bensmann, A., Hanke-Rauschenbach, R.: Theoretical dimensioning and sizing limits of hybrid energy storage systems. Appl. Energy 210, 127–137 (2018). https://doi.org/ 10.1016/j.apenergy.2017.10.116 5. Günther S., Bensmann, A.: Hybrid energy storage system for the utilization of regenerative braking energy in metro stations–Technical Description. Leibnitz Universität Hannover-Institute of Electric Power Systems (LUH-IfES), MetroHESS Deliverable Report WP3/WP4/WP6 (2021) 6. Leoutsakos, G., Papadogiannis, K., Dimeas, A., Kleftakis, V., Palaiogiannis, F.: Energy benefits from bidirectional electrical substations in metro railway systems. In: CIRED 26th International Conference & Exhibition on Electricity Distribution, Geneva (2021) 7. Leoutsakos G., Sarris, K., Kyriazidis, D.: Hybrid energy storage system for the utilization of regenerative braking energy in metro stations—energy measurements on board two trains and in three rectifier substations. Attiko Metro - MetroHESS Deliverable Report 2.2 – rev2, WP2 (2020) 8. Shabanova, E., Biryokov, V.: Increase efficiency of braking energy in metro, AIME 2018. Adv. Eng. Res. 157, 553–557 (2018) 9. Teymourfar, R., Behzad, A., Hossein, I.-E.E., Razieh, N.: Stationary super-capacitor energy storage system to save regenerative braking energy in a metro line. Energy Convers. Manage. 56, 206–214 (2012) 10. Tian, Zh., Zhang, G., Zhao, N., Hillmansen, S., Tricoli, P., Roberts, C.: Energy evaluation for DC railway systems with inverting substations. In: IEEE International Conference on ESARSITEC, Nottingham, UK (2018). https://doi.org/10.1109/ESARS-ITEC.2018.8607710

62

G. Leoutsakos et al.

11. Zarkadoula, M., Giannakopoulou, K., Goumas, G., Tsarmpopoulou, M., Leoutsakos, G., Deloukas, A., Apostolopoulos, I., Kiriazidis, D.: Energy audit in Athens metro stations for identifying energy consumption profiles of stationary loads. Int. J. Sustain. Energy (GSOL) 2028787 (2022). https://doi.org/10.1080/14786451.2022.2028787

The Impact of the Transport Sector on the Environment in the Context of Globalization Cristiana Tudor1(B) and Robert Sova2 1 International Business and Economics Department, The Bucharest University of Economic

Studies, 010374 Bucharest, Romania [email protected] 2 Management Information Systems Department, The Bucharest University of Economic Studies, 010374 Bucharest, Romania [email protected]

Abstract. The role of transport for economic development has long been recognized, but so has its environmental impact. Transportation investments are able to generate or complement structural change and can significantly contribute to mitigating urban pollution. However, for developing countries, there is a long road ahead toward decoupling transportation investments and pollution. This paper aims to contribute to the existing body of knowledge by bringing new and updated evidence on the relationship between transport investments, economic growth, globalization, and pollution for a wide panel of 94 low- and middle-income countries over the 1990–2018 period. The main findings that emerge from robust System GMM estimations indicate that transport investments contribute to increased pollution in low and middle-income countries, and the effect is stronger for lowincome countries. Moreover, globalization is found to negatively affect environmental quality in both low and middle-income economies, revealing the lack of adequate environmental regulations in these countries. Keywords: Transportation investments · GHG emissions · Globalization · System-GMM

1 Introduction Transport is critical to economic growth, job creation, and linking people to crucial services such as healthcare and education (World Bank 2022). However, it is imperative to lessen transportation’s environmental impact, as domestic and international transportation account for 27% of global GHG emissions as of 2020 (EPA 2022), with estimations further indicating that transport emissions could increase by up to 60% by 2050 if population, economy, and the need for mobility continue to grow (World Bank 2022). Moreover, transport has emerged as the fastest-growing source of energy-related polluting emissions in the world and is responsible for anywhere between 12 and 70% of urban air pollution and more than 184,000 deaths as of 2010 (World Bank 2020). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_5

64

C. Tudor and R. Sova

Transportation investments can be substantial and transformative in nature, generating or complementing structural change (Berg et al. 2017). However, our data confirms that for developing countries, transport investments can act as drivers of pollution, indicating that for this group of economies, investments are still directed toward unsustainable transportation projects and reflecting inadequate transport regulations. Figure 1 reflects the relationship between transport investments (i.e. public-private partnership investments) and total greenhouse gas emissions (GHG) for a wide panel of 94 middle and low-income countries in the most recent year of available data (i.e. 2018). It is especially worth noticing the high association between transport investments and pollution in South Asian countries and India, whereas the data shows that Turkey has managed to decouple transport investments from environmental degradation.

Fig. 1. Transport investments (public private partnerships investments in transport, current USD) and pollution (GHG emissions), most recent year of available data per country; Source World Bank’s Development Indicators (WDI) database.

In this context, low-carbon transport investments are seen as crucial for a successful shift toward a sustainable transport sector. Furthermore, projections indicate that low-carbon investments could not only mitigate urban emissions by 90% by 2050 but also have significantly higher returns compared to other sectors (Coalition for Urban Transitions 2019). Our paper aims to contribute to the existing body of knowledge by bringing new and updated evidence on the relationship between transport investments, economic growth, globalization, and pollution and by uncovering potential asymmetric impacts on two income-based sub-panels of countries.

The Impact of the Transport Sector on the Environment

65

The remainder of the paper is organized as follows. Section 2 describes the data and the method employed in the empirical investigations. Section 3 presents and discusses the empirical results. Section 4 concludes the research.

2 Materials and Methods 2.1 Data All data for all variables employed in estimations are sourced from the World Bank’s World Development Indicators (WDI) database and cover the 1990–2018 period. Based on country/year data availability, we developed an unbalanced panel covering a period of 29 years for 94 countries. Of note, the sample of countries that emerged on the basis of data availability includes exclusively low and middle-income countries (i.e., only one country with a GDP per capita above 12,500 USD and equal to 14,200 USD). Hence, the results of the empirical investigation should be interpreted with care, as they pertain to the sample of developing countries over the analysis period. Table 1 offers an overview of all the variables employed in the study, including the corresponding WDI codes and descriptions. Table 1. The variables employed in estimations Variable

Variable code (World Bank WDI database)

Variable description

Pollution

EN.ATM.GHGT.KT.CE

Total greenhouse gas emissions in kt of CO2 equivalent

Transport

IE.PPI.TRAN.CD

Public private partnerships investment in transport (current US$)

Income

NY.GDP.PCAP.KD

GDP per capita (gross domestic product divided by midyear population) in constant 2015 US$

Globalization

NE.TRD.GNFS.ZS

Trade openness is a common proxy for globalization and represents the sum of exports and imports of goods and services measured as a share of gross domestic product (% of GDP)

The dependent variable, i.e., pollution, is represented in this investigation by total GHG emissions measured in kt of CO2 equivalent. Similar to Tudor and Sova (2021, 2022), we argue that GHG emissions are a more relevant proxy for pollution than CO2 emissions, which account for approximately 76% of total greenhouse gas emissions. Further, the sample of countries is divided into two sub-panels based on income levels, allowing us to detect the potential asymmetric impact of transport investments on pollution. Consequently: (1) the middle-income (ML) panel includes 69 countries with a GDP per capita below 12,500 USD per capita, but above 1500 USD; whereas

66

C. Tudor and R. Sova

(2) the low-income panel includes 39 countries with a GDP per capita below 1500 USD. Some countries can be included in distinct categories in different years, so the total num of countries included in the analysis is smaller than the sum of the two subpanel components. As previously mentioned, because of data unavailability, a distinct high-income panel could not be constructed. R software is employed to implement the method and perform estimations. 2.2 Exploratory Data Analysis Firstly, Fig. 2 presents some relevant exploratory tools, i.e. the histograms of the variables included in the model, which show that all variables present right-skewed distributions, as most values cluster on the left.

Fig. 2. Histograms for the variables of interest.

Next, Fig. 3 informs that there is a high heterogeneity across countries when it comes to pollution levels, indicating that country effects must be considered for robust estimations. The time evolution reflected in Fig. 4 highlights the worrying increase in GHG emissions over the sample period.

The Impact of the Transport Sector on the Environment

67

Fig. 3. The mean level of GHG emissions by country over 1990–2018, including the confidence intervals (Panel A).

2.3 Method The main relationship of interest that is subsequently assessed through a dynamic systemGMM panel model, is as follows: Pollution ∼ Income + Transport + Globalization

(1)

where, as per Table 1, pollution is proxied by total GHG emissions and it is a function of three independent variables including transport (Transport Investments), economic income (GDP per capita) and globalization (trade openness). As in Bui et al. (2021), to smooth the data and produce more consistent results, we proceed to convert all variables to their natural logarithm. Thus, Eq. (1) is rewritten as: LnPollutionit = β0 + β1 LnGDPit + β2 LnTransportit + β3 LnGlobalizationit + εit (2) where β0 designates the constant term; β1 to β3 are elasticities that represent the impacts of independent factors on pollution; ε stands for the error terms; the subscript i (i = 1, …, N) denotes the country i in the data sample, N = 94, and t (t = 1, …, T) is the time period, with T = 29. The generalized method of moments (GMM) that permits the inclusion of the lagged level of pollution is employed to estimate the dynamic data model depicted in Eq. (2).

68

C. Tudor and R. Sova

Fig. 4. The evolution of GHG emissions (average level among 94 low and middle-income) over 1990–2018, with confidence intervals. Source World Bank’s Development Indicators (WDI) database.

Moreover, we implement the System GMM estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998), which introduces a set of instrumental variables and is found to be robust in the presence of potential endogeneity of regressors (Lee 2007; Canh et al. 2019) and/or heteroscedasticity and autocorrelation (Roodman 2009). Thus, the System GMM emerges as an optimum choice for a data sample with a structure as that employed in this study (Roodman 2009) and carries the advantage of having been validated as a strong estimator in numerous previous researches (Baltagi 2008; Canh et al. 2019). GMM estimators are of two different types, i.e. difference and system, and both have a one-step or a two-step version. The set of instruments introduced in estimations differs as follows: the difference-GMM estimator includes all the available lags in difference of the endogenous variables and the strictly exogenous regressors, whereas the systemGMM estimator also includes the lagged values of the dependent variable. Hence, the system-GMM enables the handling of omitted dynamics in static panel models, which is a non-trivial issue (Bond 2002; Omri and Nguyen 2014). Finally, the final form of the estimated models is:

The Impact of the Transport Sector on the Environment

69

LnPollutionit = β0 + β1 Ln(Pollution)it−1 + β2 LnIncomeit + β3 LnTransportit + β4 LnGlobalizationit + μi + φt + εit i = 1, . . . , 94 and t = 1990, . . . , 2018.

(3)

where the dependent variable that stands for total pollution is explained by its own lagged value and the three other explanatory variables from in Eq. (3). Additionally, μi are fixed country specific effects, φt are time-effects and εit is the zero-mean error term. To assure the consistency of the System GMM estimations, empirical results are reported together with model diagnostics: the J-test of over-identifying restrictions (Sargan 1958; Hansen 1982) and the Arellano and Bond (1991) tests for the second-order serial correlations in the residuals.

3 Results and Discussion Table 2 shows the System GMM regression results from estimating Eq. (3) for the global panel and the two sub-panels. Because two-step GMM estimators can be severely biased downwards in finite samples (Blundell and Bond 1998), we opt to use the one-step version, as in Berk et al. (2020). Furthermore, as previously stated, the robustness of the System GMM estimators is dependent on both the assumption that the error term does not have a serial correlation problem and the validity of the instruments. As a result, first, the Arellano-Bond test for no second-order serial correlation in the error terms (AR2) confirms the validity of the model specifications. Moreover, the Hansen/Sargan test does not reject the null hypothesis of instrument validity in all specifications. Table 2. The effect of explanatory variables on pollution (One-step system-GMM estimates) Whole sample (94 countries)

Low-income (HI)

Middle-income (LMI)

Dependent variable: Pollution Independent variables

Estimate

Pollution(-1)

0.9923 ***

1.0020 ***

0.9923 ***

GDP

0.0045

0.0285

0.0073

Transport

0.0044 **

0.0051 *

0.0024

Globalization

0.0240

0.0267*

0.0407 *

Hansen/Sargan J-test (p-value)

0.3739

0.9959

0.8299

AR2 test (p-value)

0.2677

0.8308

0.1128

* Indicates significance at 10% level, respectively. ** Indicates significance at 5% level,

respectively. *** Indicates significance at 1% level, respectively.

First, the findings for all three panels confirm that pollution is persistent, and thus higher pollution in the previous period contributes to higher pollution in the current

70

C. Tudor and R. Sova

period. Furthermore, another significant impact, which is present in two of the panels, is encountered for the transport factor, such that an increase in transport investments spurs pollution (i.e. GHG emissions), with the highest effect for low-income countries (i.e. slope coefficient of 0.0051). These findings support the assertions of Ozkan et al. (2019) that higher transportation capacity is accompanied by increased energy demand, and of Liu et al. (2022) that energy sources are widely used in the transportation industry around the world, resulting in massive polluting emissions. Unsurprisingly, low and middleincome economies that are the subject of the current analysis are not able to afford more expensive sustainable energy sources, which explains the current findings and is in line with results from Xu et al. (2016). This further supports the assertion from Investment Monitor (2022) that the investment gap in transforming transport services in a sustainable and equitable manner is considerable, particularly in developing nations. As such, we argue that countries, especially low-income economies, should prioritize sustainable transport investments in their quest to decrease their contribution to world pollution. We thus fully agree with Mirzaei and Bekri (2017) that the responsibility and obligation to limit carbon emissions from the transportation sector lies with the governments. Moreover, it should be noted that the positive effect of transport investments on pollution could also be transmitted through an indirect channel. As such, whereas Jiang et al. (2017) confirm a positive link between transportation investment and economic growth, Mazzarino (2000) points out that GDP growth is the main driver of carbon dioxide emissions. Consequently, transport investments can influence pollution both directly and indirectly through the economic growth channel, further highlighting the necessity of policy interventions toward environmentally-friendly transportation investments and, ultimately, a sustainable transportation system. For example, to back up this claim, the World Bank (2020) asserts that transitioning to a more efficient and electric car fleet in cities throughout the world will necessitate an $8.6 trillion total incremental investment, including the increased expenses of owning, maintaining, and fueling electric vehicles and vehicles with improved fuel efficiency. Estimations from the World Bank (2020) indicate that such an investment would be recovered in eight years, with yearly returns of US$320 billion by 2030 exceeding US$1 trillion by 2050, and a net present value of US$3.7 trillion. These returns are mostly direct savings from reduced gasoline consumption and saved fuel costs, without taking into account the economic benefits of lower pollutants and cleaner air, which would offer even larger economic returns. Further estimates indicate that this investment could potentially support 3.6 million jobs by 2030 and prevent 0.71 GtCO2 -e emissions by 2050 (World Bank 2020). Finally, globalization is found to have an increasing effect on pollution in low and middle-income countries, in line with Solarin et al. (2017). Trade openness has been previously criticized for harming environmental quality by encouraging more manufacturing with poor production processes in developing countries that generally impose laxer environmental constraints than high-income economies (Ling et al. 2015). Our findings thus support these assertions and indicate the need for more stringent environmental regulations in low and middle-income countries, which could in turn decrease the environmental cost of globalization for these nations.

The Impact of the Transport Sector on the Environment

71

4 Conclusions The transportation sector continues to be a major contributor to world pollution, with recent estimates indicating that it contributes nearly 25% of total global greenhouse gas emissions. This issue is especially relevant to developing nations, which still have a significant investment gap in transforming transport services in a sustainable and equitable manner. Hence, understanding the transmission channels between the transport sector and pollution is a timely research topic, and carries important policy implications. Consequently, more research is needed to uncover the dynamics between transport and pollution and to assist policymakers in issuing effective policies aimed at a low-carbon transportation system. This paper addresses the impact of economic growth, transport investments, and globalization on total GHG emissions for a wide sample of low and middle-income economies. We develop a dynamic panel data model and use the System GMM estimator to assess the relationships of interest. We assure the results’ robustness by extracting the empirical evidence from a wide panel consisting of 94 countries, as well as from two income-based subpanels (low-income and middle-income, respectively). Moreover, two diagnostic tests are estimated, respectively, the Arellano-Bond test for no second-order serial correlation in the error terms and the Hansen/Sargan test for the validity of the instruments, both of which confirm the validity of the model assumptions. The main results over a 29-year analysis period (1990–2018) indicate that: (i) transportation investments are conducive to pollution in low and middle-income countries; (ii) globalization is also a driver of pollution in the two income-based sub-panels of countries, confirming previous findings that international trade contributes to increased pollution in developing nations; (iii) economic growth is not conducive to pollution in none of the panels; and (iv) pollution is persistent, thus highlighting the need and urgency for more efficient climate policies. These results have important policy implications. As such, policymakers must consider that transportation investment can influence pollution both directly and indirectly, through the economic growth channel. Consequently, these effects must be taken into account to design effective and efficient policies aimed at encouraging low-carbon transport investments.

References 1. Arellano, M., Bond, S.: Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econ. Stud. 58, 277–297 (1991) 2. Arellano, M., Bover, O.: Another look at the instrumental variable estimation of errorcomponents models. J. Econ. 68(1), 29–51 (1995) 3. Baltagi, B.H.: Econometric Analysis of Panel Data, vol. 4. Wiley, Chichester (2008) 4. Berg, C.N., Deichmann, U., Liu, Y., Selod, H.: Transport policies and development. J. Dev. Stud. 53(4), 465–480 (2017) 5. Blundell, R., Bond, S.: Initial conditions and moment restrictions in dynamic panel data models. J. Econ. 87(1), 115–143 (1998)

72

C. Tudor and R. Sova

6. Bond, S.R.: Dynamic panel data models: a guide to micro data methods and practice. Port. Econ. J. 1(2), 141–162 (2002) 7. Berk, I., Kasman, A., Kılınç, D.: Towards a common renewable future: The System-GMM approach to assess the convergence in renewable energy consumption of EU countries. Energy Econ. 87, 103922 (2020) 8. Bui, Q., Wang, Z., Zhang, B., Le, H.P., Vu, K.D.: Revisiting the biomass energy-economic growth linkage of BRICS countries: a panel quantile regression with fixed effects approach. J. Clean. Prod. 316, 128382 (2021) 9. Canh, N.P., Schinckus, C., Thanh, S.D.: Do economic openness and institutional quality influence patents? Evidence from GMM systems estimates. Int. Econ. 157, 134–169 (2019) 10. Coalition for Urban Transitions: Climate Emergency, Urban Opportunity; Available at: https:// urbantransitions.global/en/publication/climate-emergency-urban-opportunity/ (2019) 11. Hansen, L.P.: Large sample properties of generalized method of moments estimators. Econometrica: J. Econ. Soc. 1029–1054 (1982) 12. Investment Monitor: How can transport contribute to a more inclusive and sustainable world?, Available at: https://www.investmentmonitor.ai/analysis/transport-contribute-inclus ive-sustainable-world (2022) 13. Jiang, X., Heb, X., Zhang, L., Qina, H., Shao, F.: Multimodal transportation infrastructure investment and regional economic development: a structural equation modeling empirical analysis in China from 1986 to 2011. Transp. Policy 23, 43–52 (2017) 14. Lee, L.F.: GMM and 2SLS estimation of mixed regressive, spatial autoregressive models. J. Econ. 137(2), 489–514 (2007) 15. Ling, C.H., Ahmed, K., Binti Muhamad, R., Shahbaz, M.: Decomposing the tradeenvironment nexus for Malaysia: what do the technique, scale, composition, and comparative advantage effect indicate? Environ. Sci. Pollut. Res. 22(24), 20131–20142 (2015). https://doi. org/10.1007/s11356-015-5217-9 16. Liu, J., Quddoos, M.U., Akhtar, M.H., Amin, M.S., Yu, Z., Janjua, L.R.: Investigating the impact of transport services and renewable energy on macro-economic and environmental indicators. Front. Environ. Sci. 589 (2022) 17. Mazzarino, M.: The economics of the greenhouse effect: evaluating the climate change impact due to the transport sector in Italy. Energy Policy 28(13), 957–966 (2000) 18. Mirzaei, M., Bekri, M.: Energy consumption and CO2 emissions in Iran 2025. Environ. Res. 154, 345–351 (2017) 19. Omri, A., Nguyen, D.K.: On the determinants of renewable energy consumption: international evidence. Energy 72, 554–560 (2014) 20. Ozkan, T., Yanginlar, G., Kalaycı, S.: Testing the transportation-induced environmental Kuznets curve hypothesis: evidence from eight developed and developing countries. Int. J. Energy Econ. Policy (2019) 21. Roodman, D.: How to do xtabond2: an introduction to difference and system GMM in Stata. Stata J. 9(1), 86–136 (2009) 22. Sargan, J.D.: The estimation of economic relationships using instrumental variables. Econometrica: J. Econ. Soc. 393–415 (1958) 23. Solarin, S.A., Al-mulali, U., Sahu, P.K.: Globalisation and its effect on pollution in Malaysia: the role of trans-pacific partnership (TPP) agreement. Environ. Sci. Pollut. Res. 24(29), 23096–23113 (2017). https://doi.org/10.1007/s11356-017-9950-0 24. Tudor, C., Sova, R.: Benchmarking GHG emissions forecasting models for global climate policy. Electronics 10(24), 3149 (2021) 25. Tudor, C., Sova, R.: EU net-zero policy achievement assessment in selected members through automated forecasting algorithms. ISPRS Int. J. Geo Inf. 11(4), 232 (2022) 26. U.S. Environmental Protection Agency (EPA): Sources of Greenhouse Gas Emissions. Available at: https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions (2022)

The Impact of the Transport Sector on the Environment

73

27. World Bank: Why is Sustainable Urban Transport a Great Investment? Available at: https:// blogs.worldbank.org/transport/why-sustainable-urban-transport-great-investment (2020) 28. World Bank: Transport. Available at: https://www.worldbank.org/en/topic/transport/overview (2022) 29. Xu, B., Luo, L., Lin, B.: A dynamic analysis of air pollution emissions in China: evidence from nonparametric additive regression models. Ecol. Ind. 63, 346–358 (2016)

Electric and Clean Energy in Transportation: Modelling and Optimizing Electric and Cleaner Vehicles and Services

Forecasting the Passenger Car Demand Split from Public Perceptions of Electric, Hybrid, and Hydrogen-Fueled Cars in Greece Konstantinos Christidis(B) , Vassilios Profillidis , George Botzoris , and Lazaros Iliadis Department of Civil Engineering, Democritus Thrace University, Kimmeria Campus, 67100 Xanthi, Greece [email protected], {vprofill,gbotzori, liliadis}@civil.duth.gr

Abstract. Efforts to reduce greenhouse gas emissions from the land transport sector revolve around replacing the Internal Combustion Engine with alternative power units. Indeed, governments within the European Union and beyond move to ban the sale of new internal combustion engine vehicles in the near future. A number of technologies are proposed as alternatives, such as electric motors powered by batteries or hydrogen fuel cells, and hybrid power units. These new technologies rely on new infrastructure (charging stations, electrical grid upgrades, hydrogen production, storage and fueling facilities), which will need to be put in place to meet the needs of a transforming vehicle fleet. As such, forecasting the demand for the different technologies will be crucial in planning investments. We use machine learning techniques, specifically a Multilayer Perceptron and an Adaptive Neural Fuzzy Inference System, to forecast the demand split from public perceptions as captured through an online survey. Keywords: Demand split forecasting · Machine learning · Neuro-fuzzy · Electric car · Hydrogen fuel cell car · Hybrid car

1 Introduction Governments around the world place importance in reducing greenhouse gas emissions in the transport sector; and as such forecasting individuals’ choices when buying a passenger car is useful in planning policy. 1.1 Research Aim The aim of the present paper is to explore the feasibility of machine learning techniques to forecast the demand for passenger cars with motor units other than Internal Combustion Engines (ICE) and it builds upon previous research presented in [1], which focused on electric cars and found that the Multilayer Perceptron (MLP) performs favorably © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_6

78

K. Christidis et al.

compared to the Logistic Regression (LOGIT). The MLP and Adaptive Neural Fuzzy Inference System (ANFIS) are examined as means to predict consumers’ future choices, assess the influence of different factors on such decisions, and take into account the fuzzy nature of questionnaire responses. Developed models can be used to predict the split in demand if changes to public perception can be achieved or identify targets for changes in public perceptions to realize desired demand splits. 1.2 Literature Review There have been many efforts to identify factors that affect alternative fuel car demand and develop corresponding models. A transactions choice model calibrated on a stated preference survey was used to model the demand for alternative fuel vehicles in [2]. Future alternative fuel passenger vehicle demand through an innovation diffusion approach was examined in [3]. The emergence of hybrid-electric cars was examined in [4]. An examination of battery electric, hydrogen fuel cell, and biofuel powered vehicles was conducted in [5]. An approach based on the willingness to pay as revealed through a stated preference survey was presented in [6]. A stated preference survey with dynamic attributes and a multiyear time frame was presented in [7]. The demand for hybrid cars was examined using Technology Forecasting using Data Envelopment Analysis (TFDEA) in [8]. Attitudes and perceptions are used for forecasting electric vehicle demand in [9]. A system dynamics model is presented for Istanbul in [10]. The penetration of plug-in electric vehicles was examined for seven different markets in [11]. The market for Alternative Fuel Vehicles in Texas was examined in [12]. A forecast for vehicle sales and fuel consumption for the North American Transportation Market was presented in [13]. Ex-ante market simulation was used in [14] to forecast the performance of next-generation vehicles. Factors affecting the demand for hybrid and electric vehicles were examined in [15]. A Socio-Technical analysis of the electric vehicle market diffusion in Turkey is presented in [16]. A logistic growth model is used to predict electric vehicle sales in [17]. An investigation of electric vehicles market diffusion with agent-based modeling simulation was presented in [18].

2 Materials and Methods 2.1 Data Data was collected on SurveyMonkey in March, 2022, over a randomized audience of 453 individuals. The survey was designed so that individuals revealed their socioeconomic profile first, then their intent to buy a passenger car over the coming 12 months, the type of car they wish to buy, and lastly their perceptions regarding the different types of cars using the Likert Scale [19]. Responses to “do you intend to buy a car within the next twelve months?” and “were you to buy a car tomorrow, what type of car would you choose, within your financial means?” are presented in Figs. 1 and 2. 2.2 Software MatLAB R2021b with the Deep Learning and Fuzzy Logic extensions, [20], was used.

Forecasting the Passenger Car Demand Split

79

Fig. 1. Stated intent to buy a car within the next twelve months.

Fig. 2. Stated intent to buy a car of a specific type.

3 Theory 3.1 Explanatory Variables The selection of explanatory variables for the choice between passenger cars (Petrol and Diesel ICE, Hybrid, Hydrogen, and Battery Electric) is based on previous research [1] and textbooks addressing discrete choice modeling in transport, [21] and [22]. The following were identified as potential candidates: Age EL PRF

The respondent’s age in years, The highest attained educational level, The professional profile (1 = unemployed or otherwise not having an income, 2 = salaried individual, 3 = businessperson or freelance professional, 4 = pensioner), MDHI The Monthly Disposable Household Income, NoC Number of cars already owned by the household, NoPTW Number of powered two-wheelers already owned by the household,

80

K. Christidis et al.

HPS WSP ResAS

WSTM LPCi

LOCi LMCi CSAi TCMi STAi EFi SIB SUN

Number of parking spots in the household, Availability of parking space at the workplace or school, The size, as population, of the conurbation that the household resides in. This serves as a proxy for trip characteristics in terms of distance and urban or rural environment. Respondents chose between the Athens and Thessaloniki Metropolitan Areas, Large Cities, with a population over 100,000, Medium Cities, between 50,000 and 100,000, small cities, between 10,000 and 50,000, and town under 10,000, The transport mode the respondent uses for his trip to work or school, The respondent’s agreement to the statement that choice i has a low purchase cost (1 = Absolutely Disagree, 2 = Probably Disagree, 3 = Neither Agree, Not Disagree, 4 = Probably Agree, 5 = Absolutely Agree), Agreement to the statement that i has a low operating cost, Agreement to the statement that i has a low maintenance cost, Agreement to the statement that i has enough refueling or charging stations available, Agreement to the statement that choice i is technologically mature, Agreement to the statement that choice i conveys status to its owner, Agreement to the statement that choice i is environmentally friendly, The respondent’s stated intent to buy a passenger car within the next twelve months, and The respondents stated choice between buying a used or new vehicle.

Different types of vehicles, subscript i above, are defined as: PICE DICE HYBR HEV BEV

Petrol Internal Combustion Engine, Diesel Internal Combustion Engine, Hybrid, Hydrogen Electric Vehicle, and Battery Electric Vehicle.

The responses to “were you to buy a passenger car tomorrow, within your financial means, what type of car would you buy” is the output, denoted by SPCT. Two different types of choice are modelled: first, the direct response as above, where the five different types of motor units with the used or new vehicle choice are forecasted as a single output of ten different categories, while the other approach is to first forecast the choice between used or new vehicles, and then the choice between motor unit types. 3.2 Multilayer Perceptron The MLP was introduced by Werbos, who presented the backpropagation algorithm in 1975 [23]. This approach allowed for the effective training of multi-layer networks by distributing the error term back up through the layers and adjusting weights. An MLP is a perceptron with two or more trainable weight layers, and it has been proven to be a universal function approximator in [24]. For a comprehensive discussion of the technique for Transport Modelling, see [22]. MatLAB’s implementation of the technique

Forecasting the Passenger Car Demand Split

81

uses cross-entropy as the error term for the training algorithm. The basic structure of such a network is shown in Fig. 3.

Fig. 3. Multilayer perceptron structure.

3.3 Adaptive Neuro-fuzzy Inference System ANFIS are an extension of Sugeno Fuzzy Inference Systems, see [25–28], which were pioneered in [29–32]. They use a network-type structure that adapts input and output membership function parameters to map inputs to outputs. An ANFIS structure for two inputs is shown in Fig. 4. Adaptable model parameters include membership function parameters (for inputs and outputs) and weights between different nodes, which determine the firing strength of different fuzzy rules. 3.4 Data Pre-processing ANFIS model training becomes slow as the number of trainable parameters increases. Input data is pre-processed to minimize the number of input membership functions and fuzzy rules. Group variables for inputs of similar background, e.g., perceptions of cost, are defined as linear combinations of the explanatory variables discussed in Sect. 3.1 above and divided such that they correspond to the Likert Scale.

82

K. Christidis et al.

Fig. 4. Adaptive neuro-fuzzy inference system structure. Adapted from [25].

For the stated intent to buy a passenger car, the following group variables are used: Parking =

HPS + WPS 2

NoC + NoPTW 2    PRF + MDHI 5000 ∗5 Occupation = 2 Vehicles =

(1) (2)

(3)

For Passenger Car Type Choice models: LPCi + LOCi + LMCi 3  CSAi + ResAS 1000000 Rangei = 2 Costi =

Statei =

TCMi + STAi + EFi 3

(4) (5) (6)

4 Calculation 4.1 Stated Intent to Buy a Passenger Car Model training results depend on initial conditions, the size of the hidden layer, and the training sample split. There were multiple attempts to train a model for various such combinations. The best-performing models for each explanatory variable combination are presented in Table 1.

Forecasting the Passenger Car Demand Split

83

Table 1. MLP and ANFIS models for stated intent to buy a passenger car within the next 12 months. Model identifier

Explanatory variables

Hidden layer size

Success rate (%)

SIB_MLP_M1

MDHI

5

21.6

SIB_MLP_M2

Age, EL, MDHI

10

32.9

SIB_MLP_M3

Age, EL, MDHI, NoC, HPS, WSP, 10 WSTM

34.7

SIB_MLP_M4

EL, MDHI, NoC, HPS, WSP, WSTM

10

34.7

SIB_MLP_M5

MDHI, NoC, HPS, WSP, WSTM

10

37.5

SIB_MLP_M6

MDHI, NoC, NoPTW, HPS, WSP 10

34.0

SIB_MLP_M7

MDHI, NoC, NoPTW, HPS, WSP, 10 WSTM

38.2

SIB_MLP_M8

PRF, MDHI

10

34.2

SIB_MLP_M9

PRF, MDHI, NoC, HPS, WSP, WSTM

10

33.8

SIB_MLP_MA

PRF, MDHI, NoC, NoPTW, HPS, WSP, WSTM

10

36.9

SIB_ANFIS_M1

MDHI

N/A

24.1

SIB_ANFIS_M2

Age, EL, MDHI

N/A

41.5

SIB_ANFIS_M3

Age, EL, MDHI, NoC, HPS, WSP, N/A WSTM

70.9

SIB_ANFIS_M4

EL, MDHI, NoC, HPS, WSP, WSTM

N/A

38.9

SIB_ANFIS_M5

MDHI, NoC, HPS, WSP, WSTM

N/A

36.4

SIB_ANFIS_M6

MDHI, NoC, NoPTW, HPS, WSP N/A

40.2

SIB_ANFIS_M7

MDHI, NoC, NoPTW, HPS, WSP, N/A WSTM

46.4

SIB_ANFIS_M8

PRF, MDHI

N/A

26.7

SIB_ANFIS_M9

PRF, MDHI, NoC, HPS, WSP, WSTM

N/A

45.9

SIB_ANFIS_MA

PRF, MDHI, NoC, NoPTW, HPS, WPS, WSTM

N/A

60.7

SIB_ANFIS_MB

MDHI, NoC, Parking, WSTM

N/A

32.7

SIB_ANFIS_MC

MDHI, Vehicles, Parking

N/A

31.5

SIB_ANFIS_MD

MDHI, Vehicles, Parking, WSTM

N/A

33.0

SIB_ANFIS_ME

Occupation

N/A

24.2

84

K. Christidis et al.

4.2 Stated Choice to Buy a Used or New Car The choice to buy a used or new car may affect the choice of motive unit, and as such corresponding models are developed. The various examined models are presented in Table 2. Table 2. MLP and ANFIS models for the choice to purchase a used or new car. Model identifier

Explanatory variables

Hidden layer size Success rate (%)

SUN_MLP_M1

SIB, MDHI

5

SUN_MLP_M2

SIB, EL, MDHI

5

64.9

SUN_MLP_M3

SIB, EL, PRF, MDHI

5

65.6

SUN_MLP_M4

SIB, Occupation

5

60.5

SUN_MLP_M5

SIB, EL, Occupation

5

64.9

SUN_MLP_M6

SIB, WSTM

5

59.4

SUN_MLP_M7

SIB, MDHI, WSTM

5

63.4

SUN_MLP_M8

SIB, EL, PRF, MDHI, WSTM

10

67.3

SUN_MLP_M9

SIB, WSTM, Occupation

5

60.3

SUN_MLP_MA

SIB, WSTM, Occupation, Ownership

5

61.6

SUN_MLP_MB

SIB, WSTM, Occupation, Vehicles, 5 Parking

62.7

SUN_ANFIS_M1

SIB, MDHI

N/A

63.1

SUN_ANFIS_M2

SIB, EL, MDHI

N/A

67.6

SUN_ANFIS_M3

SIB, EL, PRF, MDHI

N/A

71.3

SUN_ANFIS_M4

SIB, Occupation

N/A

61.4

SUN_ANFIS_M5

SIB, EL, Occupation

N/A

65.1

SUN_ANFIS_M6

SIB, WSTM

N/A

59.8

SUN_ANFIS_M7

SIB, MDHI, WSTM

N/A

62.7

SUN_ANFIS_M8

SIB, EL, PRF, MDHI, WSTM

N/A

70.4

SUN_ANFIS_M9

SIB, WSTM, Occupation

62.9

N/A

61.6

N/A

61.8

SUN_ANFIS_MB SIB, WSTM, Occupation, Vehicles, N/A Parking

68.4

SUN_ANFIS_MA SIB, WSTM, Occupation, Vehicles

4.3 Stated Passenger Car Type Choice Models for different explanatory variable combinations and topologies were tested multiple times, so as to identify the best-performing ones and proceed with their evaluation (Table 3).

Forecasting the Passenger Car Demand Split

85

Table 3. Multilayer perceptron and ANFIS models type choice of passenger car type. Model identifier

Explanatory variables

Hidden layer size

Success rate (%)

SPCT_MLP_M1

LPCi , LOCi , LMCi

10

31.3

SPCT_MLP_M2

LPCi , LOCi , LMCi , CSAi, TCMi

25

38.9

SPCT_MLP_M3

LPCi , LOCi , LMCi , CSAi , TCMi , STAi , EFi

35

40.4

SPCT_MLP_M4

EL, MDHI, LPCi , LOCi , LMCi , CSAi , TCMi , STAi , EFi

25

48.8

SPCT_MLP_M5

EL, MDHI, ResAS, LPCi , LOCi , LMCi , CSAi , TCMi , STAi , EFi

45

47.9

SPCT_MLP_M6

EL, MDHI, ResAS, WSTM, LPCi , LOCi , 50 LMCi , CSAi , TCMi , STAi , EFi

49.7

SPCT_MLP_M7

EL, PRF, MDHI, ResAS, WSTM, LPCi , LOCi , LMCi , CSAi , TCMi , STAi , EFi

45

49.0

SPCT_MLP_M8

SUN, Costi

10

46.4

SPCT_MLP_M9

SUN, Rangei

10

46.6

SPCT_MLP_MA

SUN, Statei

10

47.0

SPCT_MLP_MB

SUN, Costi , Rangei

15

53.0

SPCT_MLP_MC

SUN, Rangei , Statei

15

50.3

SPCT_MLP_MD

SUN, Costi , Statei

15

51.4

SPCT_MLP_ME

SUN, Costi , Rangei , Statei

20

51.9

SPCT_ANFIS_M1

Costi

N/A

8.5

SPCT_ANFIS_M2

Costi , Rangei

N/A

17.5

SPCT_ANFIS_M3

Costi , Rangei , Statei

N/A

71.4

SPCT_ANFIS_M4

MDHI, Costi , Rangei , Statei

N/A

70.4

SPCT_ANFIS_M5

Occupation, Costi , Rangei , Statei

N/A

73.7

SPCT_ANFIS_M6

WSTM, Occupation, Costi , Rangei , Statei N/A

72.6

SPCT_ANFIS_M7

WSTM, Occupation, Costi , Rangei , Vehicles, Statei

N/A

73.9

SPCT_ANFIS_M8

SUN, Costi

N/A

36.2

SPCT_ANFIS_M9

SUN, Rangei

N/A

38.6

SPCT_ANFIS_MA

SUN, Statei

N/A

31.6

SPCT_MLP_MB

SUN, Costi , Rangei

N/A

60.9

SPCT_ANFIS_MC

SUN, Rangei , Statei

N/A

50.6

SPCT_ANFIS_MD

SUN, Costi , Statei

N/A

41.5

SPCT_ANFIS_ME

SUN, Costi , Rangei , Statei

N/A

76.4

4.4 Check for Overfitting MLP and ANFIS models may suffer from overfitting, especially when a large number of explanatory variables are used. Datasets are split in three groups; training, validation, which is used to identify the algorithm’s iteration where the procedure is stopped, and testing, which is used to check the generality of the trained model. The further a model’s

86

K. Christidis et al.

performance over the testing dataset is to the training and validation sets, the more likely it is that it has been overfitted and generalizes poorly. An overfitting score is used: OS =

SROverall − SRTE SROverall

(7)

where: OS Overfitting Score, SROverall The model’s Success Rate over all three datasets, and SRTE The model’s Success Rate for the testing dataset. Figure 5 compares the models with the highest success rates.

Fig. 5. Selected model overfitting score.

ANFIS models perform well for the choice between used or new cars, but not for the type of car or the intent to buy a car. MLP models which incorporate the choice for used or new car as an explanatory variable perform better than ones that do not.

5 Results Output surfaces for selected models are generated and compared to evaluate their behavior (Fig. 6).

Forecasting the Passenger Car Demand Split

87

Fig. 6. Output surface for model SUN_MLP_M3 for individuals with a university degree (0 = No car, 1 = Used car, 2 = New car).

It can be seen from Fig. 7 that the likelihood of someone opting for a new car increases with their disposable household income and as their profession’s income becomes more stable.

Fig. 7. Output surface for model SUN_MLP_MB for individuals whose work or school trips mode is taxi with 0 (left) and 2 (right) parking spots available (0 = No Car, 1 = Used car, 2 = New car).

As individuals become more affluent and have more parking spots available to them, the more likely they are to buy a new rather than used car. For model SPCT_MLP_MD, given the large number of explanatory variables, the number of output surfaces that need to be checked is rather large. Selected examples are presented in Fig. 8. The model’s outputs reveal that individuals who choose to buy a used car are more likely to choose an ICE car, whereas they are not likely to buy a hydrogen electric vehicle.

88

K. Christidis et al.

Fig. 8. Output surface for model SPCT_MLP_MD with regards to cost and technology state perceptions for battery electric and petrol internal combustion vehicles. Perceptions for other types of vehicles are assumed to be neutral. (1 = PICE, 2 = DICE, 3 = HYBR, 4 – HYEV, 5 = BEV).

6 Discussion MLP models appear to be suitable for discrete choice modeling. ANFIS models perform well for a limited number of output categories only and have a tendency for overfitting. It was found that with regards to the selection between used and new cars, the chosen work or school trip mode choice and the compound variables Occupation, Vehicles, and Parking had a significant effect on respondent’s choice. Conversely, for the type of vehicle motor unit, the compound variables Cost and State, as well as the choice of used or new vehicle, had a significant impact on respondent’s choice. The following procedure is proposed to forecast the split between different types of passenger cars: • Conduct or obtain the results of a survey regarding individuals’ intent to buy a car. Such a survey is conducted yearly to determine Consumer Confidence [26]. • Forecast the split between used and new cars with model SUN_MLP_MB, taking into account socioeconomic and work or school trip mode distributions for the studied area (i.e., develop forecasts for different metropolitan areas). • Further to a forecast of perceptions of different kinds of vehicles or an appropriate survey, use model SPCT_MLP_MD to find the demand splits.

Forecasting the Passenger Car Demand Split

89

Based on the presented models, if the government wishes to promote the adoption of alternative motor unit passenger cars over ICE ones, then it will need to alleviate the perception of high purchase cost, promote the perception of technological maturity, and advance higher paid stable occupation, as this greatly affects the choice for a new car, the most significant determinant of choosing an alternative motor unit car. Future research should explore models to forecast the evolution of individuals’ perceptions of different types of vehicles further to changes in their cost of ownership, operation and maintenance, availability of charging or refueling stations, and availability of different vehicle models (particularly for hydrogen electric cars). Efforts to include individuals’ perceptions as fuzzy explanatory variables will need to explore neuro-fuzzy options beyond the ANFIS model.

References 1. Christidis, K., Profillidis, V., Botzoris, G., Iliadis, L.: Electric vehicle demand forecasting with the use of artificial neural networks. In: Proceedings of the 10th International Congress on Transportation Research. Hellenic Institute of Transportation Engineers, Rhodes (2021) 2. Brownstone, D., Bunch, D.S., Golob, T.F., Ren, W.: A transactions choice model for forecasting demand for alternative-fuel vehicles. Res. Transp. Econ. 4(1), 87–129 (1996) 3. Cao, X., Mokhtarian, P.L.: The Future Demand for Alternative Fuel Passenger Vehicles: A Diffusion of Innovation Approach. University of California at Davis, Davis (2004) 4. Dijk, M., Yarime, M.: The emergence of hybrid-electric cars: innovation path creation through co-evolution of supply and demand. Technol. Forecast. Soc. Chang. 77(8), 1371–1390 (2010) 5. Contestabile, M., Offer, G., Slade, R., Jaeger, F.: Battery electric vehicles, hydrogen fuel cells, and biofuels. Which will be the winner? Imperial College Centre for Energy Policy and Technology, London (2011) 6. Massiani, J.: Using stated preferences to forecast the market diffusion of alternative fuel vehicles. Italian J. Reg. Sci. 11(3), 93–122 (2012) 7. Maness, M., Cirillo, C.: Measuring future vehicle preferences: stated preference survey approach with dynamic attributes and multiyear time frame. Transp. Res. Rec. 2285(1), 100–109 (2012) 8. Jahromi, S.R., Tudori, A.A., Anderson, T.R.: Forecasting hybrid electric vehicles using TFDEA. In: Proceedings of PICMET’13: Technology Management in the IT-Driven Services, pp. 2098–2107. IEEE, San Jose (2013) 9. Glerum, A., Stankovikj, L., Thémans, M., Bierlaire, M.: Forecasting the demand for electric vehicles: accounting for attitudes and perceptions. Transp. Sci. 48(4), 483–499 (2013) 10. Turan, Ö., Yücel, G.: Analyzing electric vehicle diffusion scenarios for Istanbul. In: Proceedings of the 32nd International Conference of the System Dynamics Society. Delft (2014) 11. Vergis, S., Turrentine, T.S., Fulton, L., Fulton, E.: Plug-in Electric Vehicles: A Case Study of Seven Markets. University of California at Davis, Davis (2014) 12. Baker, R., Cochran, L., Norboge, N., Moran, M., Wagner, J., Storey, B.: Alternative fuel vehicle forecasts. Texas A&M Transport Institute, Bryan/College Station (2016) 13. Navigant Research: Tomorrow’s Vehicles: An Overview of Vehicle Sales and Fuel Consumption through 2025. The Fuels Institute, Alexandria (2017) 14. Shin, J., Lim, T., Kim, M.Y., Choi, J.Y.: Can next-generation vehicles sustainably survive in the automobile market? Evidence from ex-ante market simulation and segmentation. Sustainability 10(3), 607 (2018)

90

K. Christidis et al.

15. Christidis, P., Focas, C.: Factors affecting the uptake of hybrid and electric vehicles in the European Union. Energies 12(18), 3414 (2019) 16. Akyol, H.B.: Socio-technical Analysis of the Electric Vehicle Market Diffusion in Turkey. Warsaw University of Technology, Warsaw (2020) 17. Rietmann, N., Hügler, B., Lieven, T.: Forecasting the trajectory of electric vehicle sales and the consequences for worldwide CO2 emissions. J. Clean. Prod. 261, 121038 (2020) 18. Ebrie, A.S., Kim, Y.J.: Investigating market diffusion of electric vehicles with experimental design of agent-based modeling simulation. Systems 10(2), 28 (2022) 19. Preedy, V.R., Watson, R.R.: 5-point Likert scale. In: Preedy, V.R., Watson, R.R. (eds.) Handbook of Disease Burdens and Quality of Life Measures. Springer, New York (2010) 20. MatLAB R2021b, The MathWorks, Inc. Natick, Massachusetts (2021) 21. Ben-Akiva, M., Lerman, S.R.: Discrete Choice Analysis: Theory and Application to Travel Demand. The MIT Press, Cambridge, Massachusetts (1985) 22. Profillidis, V.A., Botzoris, G.N.: Modeling of Transport Demand: Analyzing, Calculating, and Forecasting Transport Demand. Elsevier, Amsterdam (2018) 23. Werbos, P.J.: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Harvard University, Seattle (1975) 24. Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst. 2(4), 303–314 (1989) 25. Takagi, T., Sugeno, M.: Derivation of fuzzy control rules from human operator’s control action. In: Proceedings of the IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis, pp. 55–60. Marseille (1983) 26. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 1, 116–132 (1985) 27. Sugeno, M., Kang, G.T.: Structure identification of fuzzy model. Fuzzy Sets Syst. 28(1), 15–33 (1988) 28. Takagi, H., Hayashi, I.: NN-driven fuzzy reasoning. Int. J. Approximate Reasoning 5(3), 191–212 (1991) 29. Jang, J.S.R.: Fuzzy modeling using generalized neural networks and Kalman filter algorithm. In: Proceedings of the 9th National Conference on Artificial Intelligence, pp. 762–767. AAAI, Anaheim (1991) 30. Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993) 31. Jang, J.S., Sun, C.T.: Neuro-fuzzy modeling and control. Proc. IEEE 83(3), 378–406 (1995) 32. Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, New Jersey (1997)

Demand Responsive Feeder Bus Service Using Electric Vehicles with Timetabled Transit Coordination Yumeng Fang(B) and Tai-Yu Ma Luxembourg Institute of Socio-Economic Research (LISER), L-4366 Esch-Sur-Alzette, Luxembourg {yumeng.fang,tai-yu.ma}@liser.lu

Abstract. Traditional bus service in low-demand areas is usually designed with a low frequency planning strategy, where buses have to visit all fixed bus stops even though some do not have any passenger requests. To improve its efficiency and reduce the negative impacts on the environment, a user-centered service can be conceived by integrating the bus service as a feeder to transit. We study this problem considering also the use of electric vehicles, which are currently being widely introduced for such services. However, most studies neglect the synchronization issues of the feeder service and timetabled transit to minimize customers’ waiting time at transit stations. Moreover, existing studies on electric vehicle routing problems assume charging stations to be uncapacitated. To address these issues, this study proposes an on-demand first-mile feeder service to coordinate its service with timetabled transit using electric buses/shuttles. The problem is modeled on a departure-expanded (layered) graph and formulated as a mixed-integer linear programming problem. Several new contributions are proposed in this study: considering flexible bus stops based on meeting points (within a walking distance) of customers’ origins, coordinating bus arrival times at transit stations to minimize customers’ waiting time, and coordinating electric bus charging scheduling to ensure charging station capacity constraints. We conduct numerical studies on a set of instances to validate the proposed methodology. Keywords: Feeder service · Demand responsive transport · Electric vehicle routing · Coordination

1 Introduction In low-density areas, mass transit stations are designed with long distance and low service frequency because of low customer demand. Residents in this area usually need to take buses to the nearest transit station if they want to use the mass transit service. However, traditional bus service in the low-demand area is also low frequent, and buses have to visit all fixed bus stops even though some do not have any passenger requests. Although fixed-route bus service has proved efficient in high-demand areas [1], this kind of design in dispersed areas leads to unnecessary travel time and operational cost. On-demand © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_7

92

Y. Fang and T.-Y. Ma

feeder service could provide user-centered mobility solutions to increase connectivity and accessibility in the low-demand area. Different methods of on-demand feeder service have been proposed to minimize vehicles’ routing costs while considering customers’ convenience [2–5]. However, most studies neglect the synchronization issue of feeder service and timetabled mass transit to minimize customers’ waiting time at transit stations. On the other hand, with the climate change crisis, transport network companies start deploying electric vehicles (EVs) to reduce their CO2 emission. This emerging trend brings new challenges to managing the charging scheduling of EVs. While existing studies on electric vehicle routing problems have developed optimization models for partial recharges, most assume charging stations are uncapacitated [6, 7]. To address the above issues, we propose an on-demand first-mile feeder service to coordinate its service with timetabled transit using electric buses/shuttles. The problem is modeled on a departure-expanded (layered) graph and formulated as a mixed-integer linear programming (MILP) problem. Our new contributions with respect to the state-ofthe-art methodologies are three-fold. First, we consider flexible bus stops based on the meeting points (within a walking distance) of customers’ origins. Second, a departureexpanded (layered) network is developed to coordinate bus arrival times at transit stations to minimize customers’ waiting time. Third, we schedule electric bus charging to ensure charging station capacity constraints. Numerical studies are also conducted on a set of instances to validate the proposed methodology.

2 Related Studies In recent years, studies combing demand-responsive transport and mass transit service have grown rapidly. As dispatching and routing problems of on-demand service are related to the general dial-a-ride problem (DARP) [8], Häll et al. [9] proposed a flexible public transport system integrating demand-responsive transport with a fixed-route service, called integrated dial-a-ride problem (IDARP). Since the door-to-door demandresponsive transport is costly, the problem allows a certain leg of the service to be performed by an existing fixed-route line to reduce the overall operation cost. However, this study does not consider customers’ waiting time at transfer stations. To cooperate with the timetable of fixed-route service and ensure a smooth transfer, Marcus et al. [10] provide a richer version of timetabled IDARP. They extend IDARP by considering collaboration with timetables for fixed-route lines, heterogeneous vehicles with a different number of wheelchairs, and different speeds of demand-responsive vehicles and fixedroute lines. The problem is formulated as a MILP using a set of dummy nodes associated with pickup locations and transit stops, resulting in a rapidly expanded network as customer requests increase. To reduce the problem size, the authors also present a method in which dummy nodes for a physical transit stop are distinguished by visiting numbers. Each visiting number denotes the sequence of customers’ arrivals at each transit station. The results show that the proposed method can reduce substantial solving time with more customer requests. Montenegro et al. [5] develop a novel demand-responsive feeder service integrating the features of traditional and on-demand transport. They define mandatory bus stops and

Demand Responsive Feeder Bus Service

93

optional bus stops within a service line to serve customers. Buses must visit mandatory stops, whereas optional bus stops can only be visited if there are customer requests nearby, which means the timetable and route of this service will change according to customer demand. Customers need to book the service if they want to board at optional bus stops, but they can still board at mandatory stops without reservation. A MILP model is developed with the objective function to minimize a weighted sum of buses’ total travel time, walking time to bus stops and deviation to customer’s desired arrival times at the destination. A large neighborhood search is developed, which can solve large instances up to 158 customers, 12 mandatory bus stops, and 11 optional bus stops efficiently. Several related studies for the first-mile or last-mile problem have been proposed recently. For instance, Chen et al. [4] consider a first-mile ridesharing problem using autonomous vehicles, where customer request groups are generated to construct the ridesharing network. Each customer request group is a combination of customer requests that can be served by one vehicle from their origins to a metro station. Hence, a feasible customer request group satisfies the conditions of maximum travel time and vehicle capacity. To solve large-scale problems, a cluster-based solution is developed to determine the number and location of clusters based on pickup points. However, this paper does not collaborate with fixed-route service, and customers’ waiting time at transit stations is not considered. Wang [11] designed a last-mile service system, where customers could select a stop closest to their final destination and inform the system of their arrival time at transit stations. Unlike most DARP studies, a set of feasible routes within maximum travel time and the number of bus stops are preselected as input of their model. The study also discretizes time into intervals with one minute, and decisions will be made every time interval to minimize customers’ waiting time and travel time. Considering vehicle routing problems or DARP using EVs brings additional challenges. Due to the limited driving range and long charging time of EVs, many studies have been conducted related to optimal charging policy planning, charge/discharging modelling, and waiting time modelling at charging stations (see reviews in [12, 13]). For recharging methods, the partial recharge policy has been proved to save charging time and cost while meeting customer demand [14]. Most studies assume uncapacitated charging stations, where EVs can start charging immediately [6, 7, 14, 15]. However, even though fleet operators privately own chargers, waiting at chargers still exists among vehicles within the fleet, especially when the fleet size becomes large. Therefore, we add additional constraints in our optimization model to avoid conflict at charging stations.

3 Methodology We consider an electric feeder service routing problem with timetabled transit coordination. A fleet of homogenous electric buses/shuttles is utilized in a service area to provide on-demand feeder service to connect to a predefined transit station for a given planning period. Different from door-to-door feeder service, the considered problem is based on the concept of meeting points where customers need to walk a certain distance to catch up with buses [5, 16, 17]. Customers book their service in advance by informing their origin locations, desired drop-off transit stations, and desired departures of trains via a dedicated service platform. Based on the booking information and the optimized routing

94

Y. Fang and T.-Y. Ma

policy (described later), the operator assigns customers to a bus stop within a maximum walking distance, which is regarded as a meeting point for customers. We assume that all requests are served for which at least one optional bus stop within each customer’s walking distance will be generated. Figure 1 presents an illustrative example where a set of optional bus stops are generated around a transit stop to meet customer demand. Customer 5 in Fig. 1 can walk to bus stop 2 and 3, but the customer will be assigned to one of them in order to minimize an objective function.

Fig. 1. An illustrative example

Electric buses start their service at depot(s), visit bus stops with assigned customers only, and drop off customers at transit stops within a maximum waiting time (e.g., 15 min) ahead of their desired train’s departures to limit customers’ waiting time. The pickup locations are a set of optional bus stops (i.e., meeting points with a maximum walking distance from customers’ origins). Buses’ initial states of charge (SOCs) are heterogeneous at the beginning of the planning period. Buses can only visit charging station(s) after dropping off all customers. Chargers are assumed private-owned and heterogeneous in terms of charging speed. We consider partial recharge with linear charging behavior, and the energy consumption is also proportional to the distance traveled. Note that we assume customers are informed in real-time about the arrival times of their buses, and the pickup time at their assigned bus stops will be provided before the starting of the service. The objective is to find vehicle routes and a charging plan that minimizes overall operation cost and user inconvenience for the planning horizon.

Demand Responsive Feeder Bus Service

95

Notations Sets and variables 0, N + 1

Instances of the depot

L

Set of L departures of trains (layers) with positive ride requests for the planning period,

G

Set of dummy (artificial) bus nodes g for m optional bus stops,    G = ∪ G , where G = g1 , g2 , . . . , gm

D

Set of dummy (artificial) transit station (transfer) nodes d for t transit stations,   D = ∪ D , where D = d1 , d2 , . . . , d  t

S

Set of physical chargers

S

Set of dummy (artificial) charger nodes, S  = ∪ So , where So is the set of dummy charger nodes

L = 1, 2, 3, . . . , L

∈L

∈L





o∈S

for a physical charger o, o ∈ S R

Set of customers

K

Set of electric buses

V

Set of vertices expect depots, i.e. V = G ∪ D ∪ S 

V0 , VN +1 , V0,N +1

AR

V0 = V ∪ {0}, VN +1 = V ∪ {N + 1}, V0,N +1 = V ∪ {0, N + 1}   Set of walking arcs, i.e. AR = (r, j)|r ∈ R, j ∈ G(d r ) .

A

Set of all arcs, i.e. , A = {(i, j)|i ∈ 0, j ∈ G ∪ D ∪ N + 1} ∪ {(i, j)|i ∈ 0 ∪ S  ∪ D, j ∈ N + 1} ∪ {(i, j)|i ∈ S  , j ∈ G ∪ N + 1} ∪ {(i, j)|i ∈ D , j ∈ S  ∪ G ,  ∈ L, (i) < (j)} ∪ {(i, j)|i ∈ G , j ∈ G ∪ D ,  ∈ L, (i) = (j)} ∪ AR

Hr

Ride time of customer r

Qik

Load of bus k at vertex i

Eik

SOC of bus k at vertex i

 hss  kk

1 if bus k  arrives at charging dummy s later than k arrives at the dummy s, and 0 otherwise. 

s, s ∈ So , o ∈ S

Parameters wrj

Walking distance from customer r ’s location to optional bus stop j

cij

Distance from vertex i to vertex j

tij

Bus travel time from vertex i to vertex j

wmax

Maximum walking distance for customers

u

Service duration at optional bus stop

qi

Change of load at transit stop i

Qmax   ei , l i

Capacity of bus

dr

Customers’ desired (transit) departure at his/her drop-off transit station, ∀d r ∈ L

k Emin , Emax , Einit

Minimum, maximum, initial states of charge (SOC) of buses

Desired time windows at node i

αs

Charging rate of charger s ∈ S 

β

Energy consumption rate per kilometer traveled

T

Planning horizon

M

Big positive number

Decision variable yri

1 if customer r is assigned to the optional bus stop i, and 0 otherwise

xijk

1 if arc (i, j) is traversed by bus k , and 0 otherwise (continued)

96

Y. Fang and T.-Y. Ma

(continued) Aki

Arrival time of bus k at vertex i

τsk

Charging duration for bus k at charger s, s ∈ S 

3.1 Departure-Expanded (Layered) Network Here we explain the departure-expanded (layered) network. Each layer  is associated with a train’s departure at transit stations. Hence, the number of layers L equals the number of train departures. At each layer, dummy nodes are generated for bus and transit stops. For instance, a bus stop dummy node gi denotes a physical bus stop i at the layer , and di is a dummy for a physical transit station i at the layer . All bus and transit stop dummies in the same layer  are then represented by G and D . Buses are only allowed to visit stops within the same layer before they drop off customers at the transit station. After dropping off customers at the transit station before their preferred train departing time, buses can go to the next layers with customer requests. Buses can also go to chargers after dropping off customers since no customer is onboard while charging. Noted that nodes of chargers and depot(s) are not part of the layers because visits to these nodes are not linked with trains’ departure. However, electric buses can enter layers from the origin depot or chargers, and leave layers to the destination depot or chargers.

Fig. 2. Layered network of the illustrative example

Figure 2 describes creating the layered network for the illustrative example in Fig. 1. In this example, a train departs every 30 min at the transit station from 6 A.M. to 9:30 A.M. The number of departures during this operation period is eight, which is the number of layers as shown in Fig. 2. Each customer is assigned to a bus stop, the pickup node. Bus 1 and 2 leave the origin depot to serve customers at bus stops and end their service at the destination depot. It also shows buses only visit bus stops within the same layer before dropping off customers and visit chargers without customers on board.

Demand Responsive Feeder Bus Service

97

3.2 Mathematic Model We model the meeting-point-based electric feeder service problem on a layered directed graph G = (V, A), where V denotes the set of vertices, and A is the set of arcs. Due to the time window constraints at drop-off stations (train stations), the graph G can be trimmed down to a transit timetabled departure-expanded network in which the number of layers L is the same as the number of served transit line’s departures with positive customers’ ride requests. The vertices V contain all vertices including the depots (origin and destination depots), customers’ origin locations, a set of nodes for chargers, and layered dummy nodes for optional bus stops and transit stations. A defines the legal arcs within the layers and arcs from/to depots and chargers as described in Sect. 3.1. The objective is to minimize the overall operational time including vehicle routing time, charging time, customers’ riding times, and the deviation from customers’ transit departure time.      ( tij xijk + τsk ) + λ2 H r + λ3 (l j − Akj )xijk (1) Minλ1 s∈S 

k∈K (i,j)∈A\AR

r∈R



i∈V j∈D k∈K

k x0j = 1, ∀k ∈ K

(2)

k xi,N +1 = 1, ∀k ∈ K

(3)

xsjk ≤ 1, ∀k ∈ K, s ∈ S 

(4)

xijk ≤ 1, ∀k ∈ K, j ∈ G

(5)

j∈G∪{N +1}



i∈{0}∪S  ∪D



j∈G∪{N +1}





k∈K i∈V0



xijk −

i∈V0



j∈V0

yri = 1, ∀r ∈ R

(7)

wri yri ≤ wmax , ∀r ∈ R

(8)

i∈G

i∈G





(6)

i∈VN +1



r∈R

xjik = 0, ∀k ∈ K, j ∈ V

yrj ≤ M 



xijk , ∀k ∈ K, j ∈ G

(9)

i∈V0 k xid r = 1, ∀k ∈ K, r ∈ R

(10)

i∈G

xjik ≥



k xjd r − M (1 − yri ), ∀k ∈ K, i ∈ G, r ∈ R

j∈G

Qjk ≥ Qik +

 r∈R

  yrj − M 1 − xijk , ∀k ∈ K, i ∈ V0 , j ∈ G

(11) (12)

98

Y. Fang and T.-Y. Ma

Qjk ≤ Qik +



  yrj + M 1 − xijk , ∀k ∈ K, i ∈ V0 , j ∈ G

(13)

r∈R

  Qjk ≥ Qik − qj − M 1 − xijk , ∀k ∈ K, i ∈ G, j ∈ D

(14)

  Qjk ≤ Qik − qj + M 1 − xijk , ∀k ∈ K, i ∈ G, j ∈ D

(15)

0 ≤ Qik ≤ Qmax , ∀k ∈ K, i ∈ V0,N +1

(16)

Akj ≥ Aki + tij + u − M (1 − xijk ), ∀k ∈ K, i ∈ V \S  , j ∈ VN +1

(17)

H r ≥ Akd r − Aki − u − M (1 − yri ), ∀k ∈ K, r ∈ R, i ∈ G

(18)

H r ≤ Akd r − Aki − u + M (1 − yri ), ∀k ∈ K, r ∈ R, i ∈ G

(19)

ei ≤ Aki ≤ l i , ∀k ∈ K, i ∈ D

(20)

k E0k = Einit , ∀k ∈ K

(21)

Emin ≤ Eik ≤ Emax , ∀k ∈ K, i ∈ V

(22)

  Ejk ≥ Eik − βcij − M 1 − xijk , ∀k ∈ K, i ∈ V0 \S  , j ∈ VN +1

(23)

  Ejk ≤ Eik − βcij + M 1 − xijk , ∀k ∈ K, i ∈ V0 \S  , j ∈ VN +1

(24)

  Ejk ≥ Esk + αs τsk − βcsj − M 1 − xsjk , ∀k ∈ K, s ∈ S  , j ∈ G

(25)

Akj ≥ Aks + τsk + tsj − M (1 − xsjk ), ∀k ∈ K, s ∈ S  , j ∈ G

(26)







   Aks − Aks ≤ Mhss kk  , ∀s, s ∈ So , o ∈ S, k  = k , k, k ∈ K

(27)

     Aks − Aks ≥ M hss − 1 , ∀s, s ∈ So , o ∈ S, k = k  , k, k  ∈ K  kk

(28)

        Aks ≥ Aks + τsk − M 1 − hss kk  , ∀s, s ∈ So , o ∈ S, k  = k , k, k ∈ K

(29)

xijk ∈ {0, 1}, ∀k ∈ K, i, j ∈ V0,N +1

(30)

yri ∈ {0, 1}, ∀k ∈ K, r ∈ R, i ∈ G

(31)

Demand Responsive Feeder Bus Service 



99

   hss kk  ∈ {0, 1}, ∀s, s ∈ So , o ∈ S, k  = k , k, k ∈ K

(32)

τsk ≥ 0, H r ≥ 0, Aki ≥ 0, ∀k ∈ K, s ∈ S  , r ∈ R, i ∈ V0,N +1

(33)

The problem is formulated as a MILP problem. The objective function (1) minimizes the weighted sum of total bus travel time and charging time, customers’ in-vehicle ride time, and customers’ waiting time at transit stations. Constraints (2) and (3) state that buses start and end their services at the origin and destination depot. Constraints (4) and (5) limits that each charger dummy node and each bus stop node can be visited by a bus at most once. Constraint (6) is the flow conservation at nodes. Constraints (7) and (8) ensure each customer is connected by one bus stop with a walking distance of less than wmax . Constraint (9) states that a bus stop must be visited when there are assigned customers. Constraints (10) and (11) ensure buses drop off customers at their desired departure transit nodes, and customers are picked up and dropped off by the same buses. Constraints (12)–(16) state the changes of (customer) load at bus stops and transit stops, and load capacity constraints. Constraint (18)–(19) calculates the customer’s ride time. Constraint (20) states buses’ time windows constraint at transit stations. Constraints (21)–(25) are buses’ SOC capacity constraints, and SOC changes after discharging and charging. Bus arrival time consistency is ensured by constraints (17) and (26). Constraints (30)–(33) define the range of all variables. Note that all arcs belong to the arc set A. Equations (27)–(29) are constraints to avoid conflict of different buses at the same  charger. Multiple visits are allowed by creating dummy nodes So for each charger o, where o denotes a physical charger. To model capacitated charging stations, we add a  binary variable hss kk  to indicate the arrival sequence of buses at dummy nodes of each charger. If two buses k and k  visit the charger o’s dummy node s and s separately (s = s    is allowed), hss kk  indicates whether k arrives at dummy s later than k arrives at s, which  ss is given by Eqs. (27) and (28). If hkk  is 1, constraint (29) is activated to ensure bus k  will not arrive at the charger before bus k finishes its charging. 3.3 Preprocessing We solve the proposed MILP model by commercial solvers. A preprocessing procedure is applied by eliminating infeasible arcs to tighten the problem size and reduce the computational time. The unused vertices/arcs elimination rules are as follows. • Bus stop elimination: delete optional bus stops if no customer around them within the maximum walking distance. • Layer elimination: remove the corresponding layer if a departure layer is not associated with customer demand (unused layers in Fig. 2). • Arc elimination: remove arcs from/to the deleted layers. • Time-window tightening: all customers are outbound users with time-window constraint at their drop-off transit stations. Let T denote the end of the planning period. The late arrival l i at a bus stop dummy i is min l j − tij − u, T , where i ∈ D , j ∈ G for a layer .

100

Y. Fang and T.-Y. Ma

4 Numerical Studies We validate the proposed MILP model on a set of numerical test instances. We use the Gurobi solver to find the optimal solution using a laptop with Intel i5-7200U CPU, 2 Cores, and 8GB memory. The parameter setting is presented in Table 1. Two types of chargers are considered with different charging speed α1 and α2 , corresponding to Level 2 and DC fast charger types respectively. Maximum battery capacity and energy consumption rate are set as Volkswagen’s Electric Wheelchair Minibuses.1 Table 1. Parameter setting. Parameter

Value

wmax

1 km

u

1 min

Qmax

10 passengers/bus

α 1 , α2

22kW/h, 50kW/h

β

0.2387 kW/km

dr

Randomly generated from 1 to 8

k Einit

Randomly distributed between 50%-80% of maximum battery capacity (35.8 kWh)

Emin

3.58 kWh (10% of maximum battery capacity)

Emax

28.64 kWh (80% of maximum battery capacity)

Bus speed

50 km/hour

Maximum battery capacity

35.8 kWh

λ1 ,λ2 ,λ3

1

M

1000

Ten numerical test instances are generated as presented in Table 2. For these ten instances, there is one transit station with eight train departures every 30 min from 6 A.M. to 9:30 A.M. Bus stops scatters randomly around the transit station within 6km. The depot (both origin and destination) is 500 m away from the transit station. Level 2 charger and DC fast charger are in the same location as the depot. We also randomly create customer locations with at least one bus stop near customers within the maximum walking distance (1 km). The number of bus stops shown in Table 2 excludes bus stops without customer requests nearby. Customers’ desired departures at the train station are also randomly generated from 1 to 8, which is the number of departures. Table 2 gives the result of customers’ average waiting time at the transit station and average in-vehicle time, the optimal solution and corresponding computational time. As minimizing waiting time at the transit station is part of the objective function, the result shows almost no waiting time for customers. However, we can add a buffer time before 1 https://www.tribus-group.com/wheelchair-accessible-minibuses/.

Demand Responsive Feeder Bus Service

101

Table 2. Numerical results. Number of customers

Number of Number optional of bus stops vehicles

Wating time (minute)

In-vehicle time (minute)

Optimal solution (minute)

Computational time (sec.)

4

1

1

0.0

2.0

21.2

0.0

8

2

1

0.0

6.0

97.1

0.1

12

3

2

0.0

6.9

186.0

14.2

14

3

2

0.0

7.3

215.4

8.1

16

3

2

0.0

5.1

165.6

15.7

16

3

3

0.0

6.5

203.4

36.6

20

4

2

0.0

11.0

392.7

936.5

20

4

3

0.0

8.4

290.9

2381.4

20

5

3

0.0

7.8

305.7

10321.6

30

6

3

N.A

N.A

N.A

>3h

each train departure in practice, so that customers have time to access their platforms. In addition, computational time increases dramatically with the size of the test instances. The commercial solver can solve the problem up to 20 customers, three vehicles, and three bus stops with a computational time of 172 min. All instances are available on https://github.com/YMF2022.

5 Conclusion and Discussions This paper proposes a new electric on-demand feeder bus service using meeting points to reduce operation costs and consider bus arrival time synchronization with timetabled mass transit. The problem is modeled on a (transit timetabled) departure-expanded network to minimize the weighted sum of bus operational time (travel time and charging time), customer riding time, and customers’ waiting time concerning customers’ desired departure at transit stations. The proposed departure-expanded (layered) network enables coordination with train departures at the transit stop, which is valuable for the first-mile problem to reduce customers’ waiting time at transit stops and further improve customer convenience. A MILP model is developed by considering partial recharge with the individual charger’s capacity and occupation constraint allowing multiple visits at the charger without charging conflicts. The developed model is tested by a couple of instances. The computational results show that customers can arrive at the transit station before their preferred departure time, and buses visit chargers without overlapping. The future extension includes developing heuristics (e.g., guided local research or adaptive large neighborhood search) to efficiently solve large problems for its realistic applications. The proposed methodology can also be adapted to a system with both first mile and last-mile service or as a part of an integrated multimodal service to minimize customers overall journey time. It would be beneficial that the system serves customers

102

Y. Fang and T.-Y. Ma

with both directions from/to transit stations [3, 10]. Also, it is helpful to compare the proposed service with other similar studies to show its performance. In addition, the problem size of this model increases dramatically with more transit stations with different timetables. Future studies can consider group timetables with close departures to reduce the number of layers. Acknowledgements. This work was supported by the Luxembourg National Research Fund (C20/SC/14703944).

References 1. Li, X., Quadrifoglio, L.: Feeder transit services: choosing between fixed and demand responsive policy. Transp. Res. Part C Emerg. Technol. 18, 770–780 (2010) 2. Wang, Y., Bi, J., Guan, W., Zhao, X.: Optimising route choices for the travelling and charging of battery electric vehicles by considering multiple objectives. Transp. Res. Part D Transp. Environ. 64, 246–261 (2018) 3. Ma, T.Y., Rasulkhani, S., Chow, J.Y.J., Klein, S.: A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers. Transp. Res. Part E Logist. Transp. Rev. 128, 417–442 (2019) 4. Chen, S., Wang, H., Meng, Q.: Solving the first-mile ridesharing problem using autonomous vehicles. Comput. Civ. Infrastruct. Eng. 35, 45–60 (2020) 5. Galarza Montenegro, B.D., Sörensen, K., Vansteenwegen, P.: A large neighborhood search algorithm to optimize a demand-responsive feeder service. Transp. Res. Part C Emerg. Technol. 127, 103102 (2021) 6. Keskin, M., Çatay, B.: Partial recharge strategies for the electric vehicle routing problem with time windows. Transp. Res. Part C Emerg. Technol. 65, 111–127 (2016) 7. Bongiovanni, C., Kaspi, M., Geroliminis, N.: The electric autonomous dial-a-ride problem. Transp. Res. Part B Methodol. 122, 436–456 (2019) 8. Cordeau, J.F., Laporte, G.: The dial-a-ride problem: models and algorithms. Ann. Oper. Res. 153, 29–46 (2007) 9. Häll, C.H., Andersson, H., Lundgren, J.T., Värbrand, P.: The integrated dial-a-ride problem. Public Transp. 11(1), 39–54 (2008) 10. Posada, M., Andersson, H., Häll, C.H.: The integrated dial-a-ride problem with timetabled fixed route service. Public Transp. 91(9), 217–241 (2016) 11. Wang, H.: Routing and scheduling for a last-mile transportation system. Transp. Sci. 53, 131–147 (2017). https://doi.org/10.1287/trsc20170753 12. Shen, Z.J.M., Feng, B., Mao, C., Ran, L.: Optimization models for electric vehicle service operations: a literature review. Transp. Res. Part B Methodol. 128, 462–477 (2019) 13. Erdelic, T., Cari´c, T., Lalla-Ruiz, E.: A survey on the electric vehicle routing problem: variants and solution approaches. J. Adv. Transp. 2019 (2019) 14. Felipe, Á., Ortuño, M.T., Righini, G., Tirado, G.: A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges. Transp. Res. Part E Logist. Transp. Rev. 71, 111–128 (2014) 15. Schneider, M., Stenger, A., Goeke, D.: The electric vehicle-routing problem with time windows and recharging stations. Transp. Sci. 48, 500–520 (2014)

Demand Responsive Feeder Bus Service

103

16. Czioska, P., Kutadinata, R., Trifunovi´c, A., Winter, S., Sester, M., Friedrich, B.: Real-world meeting points for shared demand-responsive transportation systems. Public Transp. 11(2), 341–377 (2019). https://doi.org/10.1007/s12469-019-00207-y 17. Ma, T.Y., Chow, J.Y.J., Klein, S., Ma, Z.: A user-operator assignment game with heterogeneous user groups for empirical evaluation of a microtransit service in Luxembourg. Transp. A Transp. Sci. 17, 946–973 (2021)

Investigation of User’s Preferences on Electric Passenger Cars Panagiotis Papantoniou(B) , Christos Mylonas, Panagiota Spanou, and Dimosthenis Pavlou University of West Attica, Aigaleo, Greece {ppapant,tg15039,dpavlou}@uniwa.gr, [email protected]

Abstract. The objective of this research is to investigate user’s preferences on the selection and use of electric passenger vehicles. In order to achieve this scope a questionnaire has been developed, for a sample of 150 users, consisting of difference sections (mobility characteristics, demographic, knowledge on electric vehicles). Moreover, the key part of the questionnaire referred to a stated preferences survey through hypothetical scenarios with attributes including purchase cost, fuel cost per 400km and driving autonomy. Subsequently, multinomial logistic regression models were developed from which the selection coefficients were obtained that mathematically describe the selection parameters of electric and hybrid passenger vehicles. Results indicate that, although purchase cost is an important factor, as expected, both driving autonomy and fuel cost have a significant effect on the model with high elasticity rates indicating that the next step on energy transition relies on several key parameters, that should be equally improved in the following years. Keywords: Electric vehicles · Questionnaire · Stated preference analysis

1 Background and Objectives While fossil fuel is depleting gradually due to excessive use to propel in the conventional vehicular system, electric vehicles are a growing market for new vehicle purchases, as more and more people make the transition from the gas station to an electric outlet for their vehicles (Ida et al. 2014; Bhaskar et al. 2019). One highly important aspect of this transformation is the reduction of CO2 emissions to mitigate climate change, which requires major changes in many areas. One of the most important of these areas is the transport sector, which is responsible for 24% of global CO2 emissions (International Energy Agency 2018). Electric Vehicles use electrical energy to drive the vehicle and for the electrical appliances in the vehicle to function. According to the International Electro-Technical Commission’s Technical Committee, if the vehicle uses two or more energy sources, storage device, and converter to drive the vehicle, then it’s called a Hybrid Electric Vehicle (HEV) as long as at least one source is providing electrical energy (Awasthia et al. 2017). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_8

Investigation of User’s Preferences on Electric Passenger Cars

105

Although the diffusion of electric cars can contribute to more sustainability in the transport sector, but diffusion rates in most countries are still low. The public debate focuses on rational aspects such as the purchase price or new technological demands (e.g., limited range and a new charging system) (Bobeth and Kastner 2020). Danielis et al. (2020) investigated the motivations for the limited but growing EV uptake through a stated preference survey on Italian drivers. The econometric analysis of the stated choices confirmed that the vehicle attributes such as purchase price, fuel economy, and driving range play a very relevant role. The time spent to charge the vehicle affects negatively the respondents’ utility, while the fast charging network density is not yet perceived as significant or carries a counter-intuitive sign. On the contrary, the possibility to park EVs for free, even for a limited time, in the city central areas is positively valued by the respondents. Focusing on the acceptance of shared e-mobility linked to the user’s profile, Campisi et al. (2022) developed specific correlations between the socio-demographic data, the choice of the vehicle power supply and the influence of the characteristics of the service. Based on authors, these factors, along with the mobility patterns, must be taken into consideration for the improvement and promotion of the service. Moreover, a questionnaire study was conducted in the island of Rhodes, Greece aiming to investigate the willingness of tourists to use shared electric vehicles for intradestination trips. The results of the analysis showed that electric car-sharing can expect a greater demand comparing to electric moto-sharing and electric bike-sharing. Also, the results indicated that electric car-sharing will mainly attract a market share from the traditional car rental schemes. Finally, Thurrner et al. (2022) investigated the willingness among Russia’s population to try out three new transport technologies: electric cars, car-sharing, and autonomous driving. For this purpose three news variables were developed, values of self-expression, attitudes towards science and technology and attitudes towards novelties in general to explain the likelihood to try out these transport innovations. Based on the above several researchers are being implemented in the last decade focusing on electromobility, however the fuel cost is becoming in the last months even more important, due to the energy crisis, in the mobility characteristics of participants. As a results the investigation and the correlation of the effect of fuels cost, purchase cost of the vehicles and autonomy is a very important issue that is missing in the literature. In order to achieve this gap, the objective of the present study is to investigate user’s preferences on the selection and use of electric passenger vehicles. The paper is structured as follows. In the next chapter the methodological approach is presented including the description of the questionnaire, sample characteristics and the theoretical background of the analysis. Then, the development of the statistical model is presented, while the results are discussed.

2 Methodology For the purposes of the present research a questionnaire has been developed based on a stated preference approach in order to infer critical information regarding the attributes that have a significant impact on electric passenger cars selection and use. In the present chapter the questionnaire is analysed and the theoretical background is presented.

106

P. Papantoniou et al.

2.1 Questionnaire The questionnaire consisted of four parts as presented below: • • • •

Mobility characteristics Electric cars preferences Stated preference scenarios Demographic characteristics.

In the first section, questions related to mobility characteristics of participants were included in order to investigate the characteristics of the participants in their daily routine. Indicative questions are presented below: • If you use private car, what fuel does your main vehicle have? • What is the main purpose of your daily journeys? • What is the average time of your daily journey in minutes? The second section was devoted to people’s views on electric vehicles. Specifically, it included four questions in a scale from 1 to 5: • How important do you consider the following parameters for choosing to purchase a passenger vehicle • How much do you agree with the following views about electric cars? • In how many years do you think electric cars will become the majority? • Are you thinking of buying a new electric car in the near future? The third section contained the core of the questionnaire, more specifically the stated preference scenarios. In particular, six scenarios concerning the preference between a Conventional vehicle, a Hybrid vehicle and an Electric vehicle were developed. Their choice depended directly on three attributes, Purchase cost (in e), Fuel cost per 400 km (in e) and Autonomy (in km) in which in each scenario the values were different. The attributes and levels of the experiment are presented in the Table 1. Table 1. Attributes and levels of the experiment. Attribute

Levels Conventional vehicle

Hybrid vehicle

Electric vehicle

Purchase cost (e)

10.000|20.000|30.000

20.000|30.000|40.000

30.000|40.000|50.000

Fuel cost per 400 km (e)

60|80|100

40|60|80

20|40|60

Autonomy (in km)

400|600|800

400|600|800

300|500|700

In order to complete the scenarios, the following text was provided to the respondents:

Investigation of User’s Preferences on Electric Passenger Cars

107

Below there are 6 hypothetical scenarios in which you are asked to choose the type of passenger car that you would prefer to buy and use among three types of passenger vehicles: Conventional, Hybrid and Electric

Table 2. Indicative scenario Conventional vehicle

Hybrid vehicle

Electric vehicle

Purchase cost (e)

10.000

20.000

30.000

Fuel cost per 400 km (e)

100

80

60

Autonomy (in km)

400

400

300

The fourth and final section of the questionnaire included questions related to the demographic information of the respondents. Participants were asked to answer questions about gender, age, educational level, profession, family status and income (Table 2). 2.2 Sample Characteristics The survey was carried out using an online questionnaire that has been placed on the Internet, using the Google Forms service with an average duration of completion of eight minutes. The questionnaire language was Greek thus it focused to Greek participants. The data collection process lasted approximately two months, January - February 2022 while it was clearly stated in the questionnaire that the information gathered will be used only for scientific reasons and all DGPR protocols have been taken into account. Finally, a total of 143 people completed this questionnaire. The basic sample characteristics are presented below indicating that there is a total counterbalance in the gender of the sample while regarding the age group more participants are young aged under 30 years old (Fig. 1).

Fig. 1. Gender and age group characteristics

108

P. Papantoniou et al.

2.3 Theoretical Background Multinomial logistic regression is used to predict categorical placement in or the probability of category membership on a dependent variable based on multiple independent variables. The independent variables can be either dichotomous (i.e., binary) or continuous (i.e., interval or ratio in scale). Multinomial logistic regression is a simple extension of binary logistic regression that allows for more than two categories of the dependent or outcome variable. Like binary logistic regression, multinomial logistic regression uses maximum likelihood estimation to evaluate the probability of categorical membership. Multinomial logistic regression does necessitate careful consideration of the sample size and examination for outlying cases. Like other data analysis procedures, initial data analysis should be thorough and include careful univariate, bivariate, and multivariate assessment. Specifically, multicollinearity should be evaluated with simple correlations among the independent variables. Also, multivariate diagnostics (i.e. standard multiple regression) can be used to assess for multivariate outliers and for the exclusion of outliers or influential cases (Schwab 2002). Analysis results lead to the development of a mathematical model that gives the odds of this event occurring, depending on factors that affect it. The odds are expressed by the logit link function as follows: log it(πi ) = log it

πi = β0 + βι xι . 1 − πi

(1)

The related outcome is specified as: yi= β0 + βι xι + e0i .

(2)

The formulation of the linear mixed effects model, assuming a random intercept reflecting the repeated measurements (i) over drivers (j), is as follows: yij = β0j + βi xij + e0ij

(3)

β0j = β0 + u0i

(4)

It is noted that the intercept in the outcome Eq. (2) consists of two terms: a fixed component β0 and a driver-specific component, i.e. the random effect u0j which is assumed to be normally distributed. The trip specific error term e0ij in Eq. (2) is assumed to follow a logistic distribution.

3 Results 3.1 Descriptive Statistics Before proceeding to the core of the analysis, several graphs are extracted within the framework of the descriptive analysis in order to extract interesting information about some key issues around the electromobility. The following graph indicates the effect of several parameters on the purchase of an electric passenger car (Fig. 2).

Investigation of User’s Preferences on Electric Passenger Cars

109

How much would the following parameters affect the purchase of a passenger car 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0

Fig. 2. Parameters affecting the purchase of a passenger car

Results indicate, as expected, that the highest effect refers to the purchase cost of the vehicles while on the other hand it is interesting that vehicle brand does not seem to play a critical role. Fuel cost, maintenance cost, taxes and driving convenience remain key factors affecting the purchase of a passenger vehicle. It is also interesting that the environmental impact of the vehicle also has a minor influence on the users. In the next figure the assessment of participants in several statements regarding electric vehicles is presented (Fig. 3).

How much do you agree with the following opinions 160 140 120 100 80 60 40 20 0 Electric vehiclesElectric vehicles Electric cars Electric vehicles Incen ves are The can save car can completely can help are too not sufficient infrastructure buyers a lot of replace reduce global expensive for to buy an is not sufficient money conven onal warming most buyers electric vehicle to purchase a vehicles vehicle I strongly disagree

I disagree

Neutral

Agree

Fig. 3. Opinions on electric vehicles

Strongly agree

110

P. Papantoniou et al.

Results indicate that most respondents strongly agree that electric vehicles are still too expensive as well as that the incentives are not yet sufficient. It is also interesting that users are not convinced that they will save money through the use of an electric vehicle. Finally, when asked how many years from now they believe that electric cars will be the majority of all cars, opinions were not optimistic. Most suggested that they will be the majority of cars in six to ten years while only 2% believe they will be the majority in less than 5 years (Fig. 4).

In how many years do you think that electric cars will make up the majority of all cars? 2% In less than 5 years

31% 36%

In 6 to 10 years In 11 to 15 years More than 15 years

31%

Fig. 4. In how many years do you think that electric cars will make up the majority of all cars?

3.2 Multinomial Logistic Regression Proceeding to the core of the analysis, in order to model the selection between a Conventional vehicle, a Hybrid vehicle and an Electric passenger vehicle (as presented in the stated preference part of the questionnaire, Multinomial Logistic Regression models were developed in R-studio. Finally, a MLR model was developed resulting in 2 utility functions, one for hybrid and one for electric vehicles. The utility functions include the fixed terms as well as the coefficients of the variables included in the model. The process of selecting the variables included in-depth tests, in order for the significance of the variables in the model to depend on the value Pr (> | t |). That is, if this value for each variable resulted in an absolute value of less than 0.05, then it was considered statistically significant for the model. The parameters that are used in the model are described in Table 3. Based on the above, the final utility functions U2 and U3 for hybrid and electric vehicles respectively, with reference level for conventional vehicles are presented in the Table 4. The model selected and developed in R-Studio has coefficient R2 = 0.16619 which is acceptable.

Investigation of User’s Preferences on Electric Passenger Cars

111

Table 3. Description of the parameters used in the models. Independent variable

Description

Purchase cost

Purchase cost in e

Cost of operation

Fuel cost per 400 km in e

Autonomy

Autonomy in km

Gender

Gender (0 = Male, 1 = Female)

Thinking to buy a new electric car Thinking to buy a new electric car (1 = “Yes, in the near future” and “Yes, within the next 10 years”, 2 = “No I do not intend to buy (generally) a car in the near future” and “No I do not intend to buy an electric car in the near future”) Use of public transport (1 = “Never” and “Rare”, 2 = “Often” and “Daily”)

Use of public transport

Table 4. Utility function U2 for hybrid vehicles and Utility function U3 for electric vehicles U2 hybrid vehicles

U3 Electric vehicles

Estimate

z-value

Pr(>|z|)

Estimate

z-value

Pr(>|z|)

Intercept

8.0068e−01

3.1423

0.0016763

2.7660e−01

0.7378

0.4606373

Purchase cost

−9.5156e−05 −11.2741 qmin 3: if |V| > 0 then 4: for each v ∈ V do 5: δv = yv + τav ,i + (1 − qv )/2 + sgv /2 6: end for 7: select vehicle v  with lowest δv 8: wk = δv  9: let xk be a random variable with P r(xk = 1) = pk calculated in (1) 10: if xk = 1 then 11: hv = hv + τav ,dk (t) 12: av  = d k 13: end if 14: end if 15: end for Table 1. Parameters used Parameter

Value Unit

Technical parameters ωd

Consumption

0.15

kWh/km

B

Battery capacity

50

kWh

Pc

Power connection 20

kW

Operational parameters Max. idle time

5

min

Min. SoC

0.2

–

Max. SoC

1

–

Mode choice parameters z

SAV tariff

1

e/km

zmin

Min. tariff

3

e

VOT

Value of time

15

e/h

lowest δv to offer a trip to passenger k with an estimated waiting time wk . If passenger k accepts the trip with the estimated waiting time, then the vehicle v is assigned to trip k. Otherwise, we consider passenger k to have chosen the original mode. 3.2

Case Study

We use the same case study for the city of Munich described in [8]. It is based on the largest transport survey in Germany, the Mobilit¨ at in Deutschland (MiD), or Mobility in Germany study [1]. Our service area comprises 1089 square cells

120

R. Iacobucci et al. Table 2. Results for different fleet sizes for ICEVs and BEVs Modal shift (%) Case

Fleet Total Bicycle Car Foot

ICEV 2500

VKT RV eVKT

7.29 80.7 630

437 15.78

4.97 26.38 17.63 83.6 526

411 11.96

BEV

2.11 11.92

313 32.58

4.57 0.77

1.68 15.46

VT

ICEV 5000 11.19 1.11 2500

5.37 0.39

Fleet results PT

5.80 71.9 537

BEV 5000 9.19 1.48 4.79 21.25 13.14 71.4 474 305 28.31 VT = vehicle-trips; VKT = vehicle-km traveled; RV = revenue per vehicle (e); eVKT = empty VKT share (%). All quantities are per day averages.

which represent areas with sides of 1 km, thus covering an area of 33 by 33 km centered in the city of Munich. We use the trip dataset generated from a statistical model trained on the survey. The dataset includes over 30 million trips during a week. Each trip is associated with a origin and destination node, trip starting time, and original transport mode. We also found travel time during the day considering average traffic conditions from Google Maps data. Based on the survey, we assign to each passenger a perceived utility uk dependent on their current mode. This allows us to estimate the modal shift to SAEV with Eq. 1. For more details on the scenario and the detailed assumptions refer to [8].

4 4.1

Results Base Scenario

We run the simulations both with conventional internal combustion engine vehicles (ICEV) and with battery electric vehicles (BEV) with limited range. In all the simulations in this work we use 100 relocation clusters. For the base scenario, we position a charging station in each of the 100 relocation clusters (thus about 10% of nodes have charging stations). This means that relocating vehicles can connect to charge when arriving to their destination node. We test the case with a 50 kWh battery, which gives an effective range of 267 km considering the constraint on the minimum state of charge. The full list of parameters used is in Table 1. The results with fleet sizes of 2500 and 5000 vehicles are in Table 2. As expected, the introduction of electrification reduces the modal shift due to the lower availability of vehicles. Interestingly, while the shift is significantly reduced from public transport users and from walking trips, the shift from car trips is similar (with 5,000 vehicles) or higher (for the case with 2,500 vehicles). This may be due to the different availability of electric vehicles during the day. Availability tends to drop significantly in the evening as batteries get more and more depleted, especially for smaller batteries (see Fig. 1b). The very narrow band of SoC seen when average SoC levels are low is the result of our assignment algorithm: we prioritize vehicles with the highest charge to respond to requests. This effectively ensures that vehicles cannot have much higher SoC

A Large Scale Simulation of the Electrification Effects of SAVs

121

than the minimum when the system is charge-constrained. The battery depletion also creates extreme inefficiencies, as vehicles spend most of their time moving between serving passengers and charging. This can be avoided by specifying a minimum amount of charging time before vehicles disconnect (as in [5]) or a minimum SoC for the vehicle to become available again when connected. This inefficiency is the main reason for the much higher empty vehicle-km traveled for electric vehicles. 5000 connected idle moving relocating reloc. to CS

vehicles

4000 3000 2000 1000 0 0

4

8

12

16

20

24

hours

(a)

(b)

Fig. 1. a) State of vehicles during one day of simulation for the case with 50 kWh battery; b) State of charge of vehicles during one day: comparison between 50 and 100 kWh batteries. The lightly shaded area represents the total range within the fleet; the darker shaded area are SoC between percentiles 2.5 and 97.5; the solid line is the median SoC of the fleet

4.2

Sensitivity Analysis

To study the effect of charging stations number, in addition to the results shown above with 100 charging stations we also ran simulations with 20 and 50 charging stations. For these and the following simulations we only use the first day of the week (Monday) which has average trip numbers, to reduce the computational time. We select the k cluster centers with charging stations using the k-medoids clustering method on the nodes, using the travel time as metric (see Fig. 2). Results for these scenarios are in Fig. 3a. The number of charging stations does not significantly affect the results in terms of modal shift, with the shift only decreasing slightly with lower number of CS. The results may be significantly different in the case where charging stations are positioned in a less efficient way. We also test the case for these additional battery sizes: 40 kWh (effective range: 213 km), 70 kWh (373 km), and 100 kWh (533 km). The results are in Fig. 3b. This has a much more significant effect on modal shift. This is because below about 100 kWh, the vehicle range is not enough to cover the average daily vehicle distance, thus a charge during the day is needed. Larger batteries also reduce inefficiency inherent in the premature disconnection of vehicles and associated extra trips to charging stations.

122

R. Iacobucci et al.

km

30 relocation center 100 stations 50 stations 20 stations

20 10 0 0

10

20

30

km Fig. 2. Map of nodes. Relocation cluster centers highlighted with a filled circle. These also correspond to charging station positions for the case with 100 stations. Triangles and squares represent the position of the stations in the case with 20 and 50 stations, respectively 25

25 foot foot

15

20

mode shift (%)

mode shift (%)

20 public transport

10

total car

5

public transport

15 10 5

total car

bicycle

0 20

bicycle

50

100

Charging stations

(a)

0 40

50

70

100

Battery

(b)

Fig. 3. Modal shift sensitivity to: a) charging station numbers; b) battery capacity

5

Conclusions

In this work we aimed to investigate the effect of electrification on the demand for shared autonomous vehicles, and in particular on how this would change the modal shift from current modes. We expanded a SAEV simulation model with the addition of mode choice, as well as other improvements. For our case study, we use a trip generation model based on a large person trip survey from Germany, resulting in a dataset with over 30 million trips during a week. Our results show that electrification somewhat reduces the modal shift, but its effects are relatively minor overall. However, the effects are not the same across the different modes, with significant decreases in shift from walking and PT trips, and negligible decreases or even increases for car and bicycle trips. This

A Large Scale Simulation of the Electrification Effects of SAVs

123

is likely caused by the different trip patterns of passengers using these modes. We also show that charging station numbers does not have a significant effect on the results, while battery size (and therefore range) is a more significant factor. There are several aspects of this work that can be improved. A better management of charging vehicles is needed to avoid inefficiencies related to repeated connections and disconnections. Charging speed also has an influence on the performance, and its effects could be investigated in future work. Moreover, limitations in charging station sizes could be included for more realistic results, as this is likely a significant factor in the availability of vehicles. Our charging station positioning method minimizes the average travel time to each charging station from each node. However, as noted before, in reality charging station positioning needs to take into account several constraints related to land availability and cost. For example, large charging stations are more likely to be positioned in the outskirts of the city rather than in the city center, thus increasing the travel time to charge for vehicles. We plan to study this aspect in future work. In the future we also plan to investigate the case where the charging of the vehicles is optimized according to real time prices or carbon emissions from the power grid. This could be a promising approach to better integrate electrified transport as well as intermittent renewable energy sources.

References 1. Follmer, R., Gruschwitz, D.: Mobility in Germany - short report, 2019. www. mobilitaet-in-deutschland.de/pdf/MiD2017 ShortReport.pdf 2. Gao, K., Shao, M., Axhausen, K.W., Sun, L., Tu, H., Wang, Y.: Inertia effects of past behavior in commuting modal shift behavior: interactions, variations and implications for demand estimation. Transportation (2021). ISSN 0049-4488, 15729435. https://doi.org/10.1007/s11116-021-10203-6. https://link.springer.com/10. 1007/s11116-021-10203-6 3. Golbabaei, F., Yigitcanlar, T., Bunker, J.: The role of shared autonomous vehicle systems in delivering smart urban mobility: A systematic review of the literature. Int. J. Sustain. Transp. 15(10), 731–748 (2021). ISSN 1556–8318, 1556– 8334. https://doi.org/10.1080/15568318.2020.1798571. www.tandfonline.com/doi/ full/10.1080/15568318.2020.1798571 4. Heilig, M., Hilgert, T., Mallig, N., Kagerbauer, M., Vortisch, P.: Potentials of autonomous vehicles in a changing private transportation system - a case study in the Stuttgart region. Transp. Res. Procedia 26, 13–21 (2017). ISSN 23521465. https://doi.org/10.1016/j.trpro.2017.07.004. https://linkinghub. elsevier.com/retrieve/pii/S2352146517308633 5. Iacobucci, R., McLellan, B., Tezuka, T.: Modeling shared autonomous electric vehicles: potential for transport and power grid integration. Energy (2018). ISSN 03605442. https://doi.org/10.1016/j.energy.2018.06.024 6. Iacobucci, R., Bruno, R., Schm¨ ocker, J.-D.: An integrated optimisation-simulation framework for scalable smart charging and relocation of shared autonomous electric vehicles. Energies 14(12), 3633 (2021). ISSN 1996-1073. https://doi.org/10.3390/ en14123633. www.mdpi.com/1996-1073/14/12/3633

124

R. Iacobucci et al.

7. Iacobucci, R., Donhauser, J., Schm¨ ocker, J., Pruckner, M.: Frequency control reserve provision from a fleet of shared autonomous electric vehicles. In: 7th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2021) (2021) 8. Iacobucci, R., Donhauser, J., Schm¨ ocker, J.-D., Pruckner, M.: Modal shift induced by shared autonomous vehicles: a large-scale simulation using mobility survey data. In review (2022). http://trans.kuciv.kyoto-u.ac.jp/its/pdf/Iacobucci%20et%20al SAV Munich.pdf Modal shift induced by shared autonomous vehicles: a large-scale simulation using mobility survey data. In review (2022). http://trans.kuciv.kyotou.ac.jp/its/pdf/Iacobucci%20et%20al SAV Munich.pdf 9. LaMondia, J.J., Fagnant, D.J., Qu, H., Barrett, J., Kockelman, K.: Long-distance travel mode shifts due to automated vehicles: a statewide mode-shift simulation experiment and travel survey analysis. In: Presented at the 95th Annual Meeting of the Transportation Research Board, vol. 1, p. 3, Issue: 2 (2016) 10. Mo, B., Cao, Z., Zhang, H., Shen, Y., Zhao, J.: Competition between shared autonomous vehicles and public transit: a case study in Singapore. Transp. Res. Part C: Emerg. Technol. 127, 103058 (2021). ISSN 0968090X. https://doi.org/10.1016/j.trc.2021.103058. https://linkinghub.elsevier. com/retrieve/pii/S0968090X21000863 11. Mobileye Inc.: Launching our Self-Driving Robotaxi in Germany, Sept. 2021. www. mobileye.com/blog/self-driving-robotaxi-sixt-germany-iaa/ 12. Narayanan, S., Chaniotakis, E., Antoniou, C.: Shared autonomous vehicle services: a comprehensive review. Transp. Res. Part C: Emerg. Technol. 111, 255–293 (2020). ISSN 0968090X. https://doi.org/10.1016/j.trc.2019.12.008 13. The Associated Press: Cruise offers public first driverless rides in San Francisco (2022). https://techxplore.com/pdf562954181.pdf 14. Sprei, F.: Disrupting mobility. Energy Res. Soc. Sci. 37, 238–242 (2018). ISSN 2214–6296. https://doi.org/10.1016/j.erss.2017.10.029. www.sciencedirect. com/science/article/pii/S2214629617303596 15. White, J.: Waymo opens driverless robo-taxi service to the public in Phoenix. Reuters (2020). www.reuters.com/article/us-waymo-autonomous-phoenixidUKKBN26T2Y3 16. Zhang, Q, Ma, S., Tian, J., Rose, J.M., Jia, N.: Mode choice between autonomous vehicles and manually-driven vehicles: an experimental study of information and reward. Transp. Res. A: Policy Practi. 157, 24–39 (2022). ISSN 0965– 8564. https://doi.org/10.1016/j.tra.2022.01.011. www.sciencedirect.com/science/ article/pii/S0965856422000118

Design, Development and Real-Time Demonstration of Supercapacitor Powered Electric Bicycle A. Bharathi Sankar Ammaiyappan1(B) and Seyezhai Ramalingam2 1 Centre for Advanced Data Science, School of Electronics Engineering, Vellore Institute of

Technology Chennai Campus, Chennai, India [email protected] 2 Department of Electrical and Electronics Engineering, Renewable Energy Conversion Laboratory, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

Abstract. Supercapacitor powered electric vehicles have attracted worldwide interest in transportation sector owing to their promising characteristics such as rapid charging cycle (2 min), long durability (up to 1 million cycles), delivering high power during cranking and acceleration of electric vehicles using burst mode power delivery and free maintenance. Currently, the focusing on design, development, and demonstration of E-bicycle by using commercial Maxwell Supercapacitor as an indigenous module for the prototype demonstration. Further, self-discharge of supercapacitor module and data generation of supercapacitor Ebicycle parameters such as motor current, driving range and energy consumption during various load and speed have been collected. The charge and discharge cycles with voltage booster have been monitored on a regular basis. Keywords: Supercapacitor · DC-DC converter · Brushless DC motor

1 Introduction Electric bicycles have been a transportation mainstay in developed countries due to the ease of the vehicle maintenance, well-developed infrastructure, systematic driving conditions and most importantly its eco-friendly nature. The electric bicycle, a selfexplanatory term meaning the power, either partially or fully comes from an electric motor. The electric bicycles are currently used for short distances. Advanced research on both battery and drive technology benefits the market regarding the practicality of electric bicycles [1–4]. Environmental issues related to increased emanations and conventional fuel demand have resuscitated the manufacture of electric vehicle industrial and research sector and research community to work on new vehicular systems to improve the efficiency of city driving. Electrically assisted bicycles have a more advantage of additional power, say during acceleration and cranking yet retaining characteristics of conventional geared bicycles [5–12]. Initially, Maxwell SC cells with the module specifications of 2.85 V, 3400 F and 3.85 Wh (energy stored), and the individual supercapacitor cell was welded with aluminum © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_10

126

A. Bharathi Sankar Ammaiyappan and S. Ramalingam

bus bar using laser welding. Later 18 supercapacitor cells were connected in series pack to obtain an SC module with 51.4 V and 69 Wh which is require to run the brushless DC motor (48 V, 250 W, 10 A) in the Bicycle. Further, the developed supercapacitor management system (CMS) to regulate and monitor the supercapacitor voltage and current in each cell during charge and discharge cycles. The developed SC powered Bicycle has been successfully tested in real conditions with the driving range of 2 km as shown in Fig. 1. The self-discharge of SC module has been monitored periodically and the data generation of E-bicycle with parameters such as motor current, driving range, energy consumption under different load conditions and speed was systematically carried out. In order to extend the driving range of E-bicycle, boost converter in addition to CMS, has been designed to boost up the voltage by recovering the remaining stored energy in SC module. Finally, Developed super capacitor powered E-bicycle with voltage booster and has successfully demonstrated on-road with an extended driving range of 2.5 km.

Fig. 1. Block diagram of Super capacitor powered electric bicycle.

2 Methodology 2.1 Design of Power Electronic Voltage Booster This research work is based on three approaches to the development of voltage booster simulations; Circuit construction, Physical construction of PCB, Virtual booster program development. MATLAB software simulations are a valuable asset for the design of electronic circuits, and we apply the same for charging circuit and booster circuit as shown in Fig. 2 [13–17]. The converter is used for operating the booster to develop MATLAB software simulation model and further test external program coding component for the FPGA program [18–22]. In order to set the size of the DC to DC boost converter appropriately, the electric bicycle was powered with the 51 V, 3400 F & 71 Wh Supercapacitor module pack. This is mentioned in the specification of Power DC-DC Converter Voltage 51.0 V, Current 11.2 A, Power 250 W.

Design, Development and Real-Time Demonstration of Supercapacitor

127

Fig. 2. Circuit diagram of power electronic voltage booster with Supercapacitor Pack.

Inputs from the voltage and current sensor are needed to turn on the MOSFET of the power converter which is used further to estimate the switching duty cycle of the Pulse Width Modulation. This supercapacitor module is on cut off voltage and ready for voltage booster operation and recover stored energy of the supercapacitor module. The input (Supercapacitor module) and output (Power electronic booster) sides contain Voltage sensor circuits to feed signals to the controller [23–27]. The duty cycle can be favorably adjusted based on the comparative studies of the above-mentioned signals.

3 Results and Discussion 3.1 Simulation Results The MATLAB Simulink model of the supercapacitor powered electric bicycle with power electronic booster is shown in Fig. 3. The smart power electronic booster to extending the range of supercapacitor powered electric bicycle. The proposed work is to design and development of a pulse width modulation based power converter with the specifications mentioned below: Fig. 4(a–e), • • • •

Input Voltage (Ultra Capacitor): 43.2 V Output Voltage (Booster): 48.5 V Output Power: 250 W Switching Frequency: 50 kHz.

Figure 4 (a) & (c) shows that initial and final voltage of super capacitor module which are about 42.5 V & 49.5 V respectively. Figure 4 (e) shows that the driving current of BLDC is about 7 to 9.5A. Figure 4 (b) & (d) depicts the values of SC charging booster current (16 A) and on the road, driving boosting current (23.5 A). State of charge (SOC) level of super capacitor is about 95%, and the present energy of the SC is 69 Wh. 44 Wh is the remaining amount of energy after discharge from a topof-charge condition. Another vital parameter to be considered is Depth of charge (DOD)

128

A. Bharathi Sankar Ammaiyappan and S. Ramalingam

Fig. 3. Matlab Simulink model of super capacitor powered electric bicycle with Power electronic booster

which is defined as the percentage of energy to which a super capacitor is discharged. Maximum temperature the super cap can withstand is around 36 °C. The simulated results of BLDC motor speed, BLDC motor electromagnetic torque, BLDC motor stator current and back EMF are shown in Fig. 5 (a–d) respectively. Figure 5a and b shows that the BLDC motor speed is settled at 300 rpm and starting torque is about 15 Nm. Figure 5c and d shows that the BLDC motor stator current and back EMF voltage. 3.2 Hardware Implementation of Supercapacitor Powered E-bicycle Design, Development, and Demonstration of super capacitor powered electric Bicycle using commercial Maxwell SC cells is done. The Supercapacitor cell specifications, C = 2.85 V, 3400 F, Stored Energy each cell, 3.85 WH, Capacitor Module nominal voltage, V = 51.4 V, Total Stored Energy in capacitor module, E total: 69 Wh (18 S cells) and Drive range Super Cap module voltage: 2 km. As the number of capacitor units increases the requirement of capacitor management system along with voltage balancing is necessary to prevent individual cells from going into over- voltage. Development of capacitor management system to regulate each cell Voltage management, Current Management, Thermal management and Equalization using Passive balancing system. Maximum charging voltage (V): 2.85 V (single cell), Maximum charging current (A): 4.5 A, Maximum discharge current (A): 13 A, Over-load protection: 20 A (Fuse) and Voltage Equalization Startup Voltage (V): 2.50 V. Super capacitor Cells are tightly encapsulated into plastic fixtures at three layers (top, middle, and bottom) to arrest any vibration. Inter supercapacitor cell welding is done under the encapsulated condition to provide more sturdiness. Any gap between plastic fixtures and Cells on top and bottom is sealed with Silicone adhesive. The whole module is protected again from vibration from the outer cover, using rubber beadings. The minimal amount of heat is generated in the super capacitor during high-duty which tends to extend the service life of all electronic components associated with the module. Heat sinks and exhaust is accommodated internally to provide proper cooling at the rated current. A simple forced

Design, Development and Real-Time Demonstration of Supercapacitor

129

Fig. 4. (a) Initial voltage characteristics of supercapacitor module. (b) Charging booster voltage characteristics of supercapacitor module. (c) Final voltage characteristics of supercapacitor module. (d) Discharging booster current characteristics of supercapacitor module. (e) Driving current characteristics of supercapacitor module.

130

A. Bharathi Sankar Ammaiyappan and S. Ramalingam

Fig. 4. (continued)

air cooling arrangement is also provided. Besides, a fan is also provided for effecting cooling of the Capacitor Management System. The Super Cap Bike should have a minimum of 2 km Range at 120kg gross loading capacity (40kg vehicle weight + 80 kg rider weight) and maximum speed is 25 km/hr. BLDC Motor - 48V-250W (Motor minimum working voltage: 41V), Sine Wave Motor Controller - 48V - 12A, DC Charger 54V – 4.5 A. Designing of electronic power booster with a capacitive management technique being the proposed work is to optimize the flow of power from the super capacitor module to BLDC motor. Further, E-bicycle with the driving range of 2 km at 120 kg gross loading with the maximum speed of 25 km/h has been successfully demonstrated super capacitor module (51.4 V, 3400 F, 71.1Wh). To further extend the driving range of super capacitor E-bike from 2 to 2.5 km, an effort has been made to design a power electronic converter to boost up the voltage of the super capacitor module. After consumption of 29 WH out of 69 WH energy for 2 km riding, the remaining stored energy (42 WH) in Super capacitor module can be partly (11 WH) recovered using active capacitor management system (CMS) with a boost converter. Before assembling of boost converter for super capacitor module, simulation and verification of voltage boost up concept have been checked in a single super capacitor cell using PCB boards. Development of field programmable

Design, Development and Real-Time Demonstration of Supercapacitor

131

Fig. 5. (a) Speed characteristics of BLDC motor. (b) Torque characteristics of BLDC motor. (c) Stator current characteristics of BLDC motor. (d) Back EMF characteristics of BLDC motor.

132

A. Bharathi Sankar Ammaiyappan and S. Ramalingam

gate array and integration of digital voltage booster into the super capacitor module has been done. Finally, the driving range of super capacitor E-bike has been successfully extended from 2.0 to 2.5 km using a developed voltage booster. During the discharging time supercapacitor volt- age dropout, the energy from the super capacitor is used to boost converter that can supply 15A of output current at 43.2V to 49 V. This output can be used to hold up the DC bus voltage for extending the E-bicycle range 2 to 2.5 km range as shown in Figs. 6 and 7.

Fig. 6. Hardware prototype verification of power electronics booster with SC powered E-Bicycle

Fig. 7. Supercapacitor powered electric bicycle.

4 Conclusion A 48 V, 250W rear BLDC hub motor based electric bicycle powered by a super capacitor pack. Serial connections of supercapacitor cell were made between super capacitor

Design, Development and Real-Time Demonstration of Supercapacitor

133

module (48 V, 71 WH) and power electronic booster with controlled Field programmable gate array controller-based DC-DC power converter which will interface power between the supercapacitor and BLDC motor. The proposed work method for the power DCDC converter was developed using various inputs of BLDC motor and supercapacitor pack (current and voltage sensor). Supercapacitor powered electric bicycle has been successfully demonstrated. Further, self-discharge of super capacitor module and data generation of super capacitor E-bicycle parameters such as motor current, driving range and energy consumption during various load and speed have been collected. The charge and discharge cycles with voltage booster have been monitored on a regular basis.

References 1. Ates, Y., Erdinc, O., Uzunoglu, M., Vural, B.: Energy management of an FC/UC hybrid vehicular power system using a combined neural network- Wavelet transform based strategy. Int. J. Hydrogen Energy 35(2), 774–783 (2010) 2. Marzougui, H., Amari, M., Kadri, A., Bacha, F., Ghouili, J.: Energy management of fuel cell/battery/ultra capacitor in electrical hybrid vehicle. Int. J. Hydrogen Energy 42(13), 8857– 8869 (2016) 3. Amari, M., Bacha, F., Ghouili, J.: Average model of dual active bridge inter-facing ultracapacitor used in electrical vehicle. Int. J. Energy Optim. Eng. 4, 36–54 (2015) 4. Payman, A., Pierfederici, S., Meibody-Tabar, F.: Energy control of supercapacitor/fuel cell hybrid power source. Energy Convers. Manage. 49(6), 1637–1644 (2008) 5. Zhang, L., Wang, Z., Hu, X., Sun, F., Dorrell, D.G.: A comparative study of equivalent circuit models of ultracapacitors for electric vehicles. J. Power Sources 274, 899–906 (2015) 6. Stefan, S.: Power Sources for Hybrid Electric Vehicles. M. Thesis, Lund University, Faculty of Engineering, USA (2009) 7. Cultura, A.B., Salameh, Z.M.: Modeling, evaluation and simulation of a supercapacitor module for energy storage application. In: Proceedings of the International Conference on Computer Information Systems and Industrial Application, pp. 876–882 (2015) 8. Zhang, L., Zhenpo, W., Sun, F., Dorrell, D.: Ultra capacitor modelling and parameter identification using the extended Kalman filter. ITEC Asia Pacific, vol. 7, pp. 3204–3217 (2014) 9. Shi, L., Crow, M.L.: Comparison of ultra capacitor electric circuit models. In: Proceedings of the IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century. PES, Pittsburgh, PA, USA (2008) 10. Ban, S., Zhang, J., Zhang, L., Tsay, K., Song, D., Zou, X.: Charging and discharging electrochemical supercapacitors in the presence of both parallel leakage process and electro chemical decomposition of solvent. Electrochim. Acta 90, 542–549 (2013) 11. Jin, L.-Q., Zheng, Y., Li, J.-H., Liu, Y.-L.: A study of novel regenerative braking system based on supercapacitor for electric vehicle driven by in-wheel motors. Adv. Mech. Eng. 7(3), 1–12 (2015) 12. Hala, S., Khalil Faris, N., Khaled Faris, N.: Ultra capacitor: modelling and characterization for electric vehicle application. Int. J. Eng. Res. 5, 681–686 (2015) 13. Fletcher, S., Kirkpatrick, L., Drink, R., Puttock, R., Thring, R., Howroyd, S.: The modelling of carbon-based supercapacitors: distributions of time constants and Pascal equivalent circuits. J. Power Syst. 345, 247–253 (2017) 14. Fuyuan, Y., Languang, L., Yuping, Y., He, Y.: Characterization, analysis and modelling of an ultracapacitor. World Electr. Veh. J. 4, 358–369 (2010)

134

A. Bharathi Sankar Ammaiyappan and S. Ramalingam

15. Zhang, L., Wang, Z., Sun, F., Dorrell, D.G.: Online parameter identification of ultra capacitor models using the extended Kalman filter. Energies 7(5), 3204–3217 (2014) 16. Plett, G.L.: Extended Kalman filtering for battery management systems of Li PB-based HEV battery packs—part 1. Background. J. Power Sources 134(2), 252–261 (2004) 17. Michalczuk, M., Grzesiak Lech, M., Ufnalski, B.: A lithium battery and ultra capacitor hybrid energy source for an urban electric vehicle. Politechnika Warszawa 4, 158–162 (2012) 18. Ashar, M.: Integration of ultra capacitor with battery using DC-DC bidirectional buck boost converter in an electric vehicle. Int. Res. J. Eng. Technol. 3, 1513–1517 (2016) 19. Bottu, M., Crow, M.L., Elmore, A.C., Atcitty, S.: A Power electronic conditioner using ultracapacitors to improve wind turbine power quality. Smart Grid Renew Energy 4, 69–75 (2013) 20. Parker, A.A.: Electric power-assisted bicycles reduce oil dependence and enhance the mobility of the elderly electric power assisted bicycle. Presented at 29th Australian Transport Research Forum 21. Tuite, D.: Get the Lowdown on Ultra Capacitors. Technology Report Electronic Design Online (2007) 22. Schneuwly, A., Maher, B., Auer, J.: Ultracapacitors, The New Thinking in the Automotive World. Maxwell Technologies Inc 23. Pay, S., Baghzouz, Y.: Effectiveness of battery-supercapacitor combination in electric vehicles. In: Power Tech Conference Proceedings, vol. 3, p. 6, 23-26 June 2003. IEEE, Bologna (2003) 24. Dixon, J.W., Ortuzar, M.E.: Ultracapacitors + DC-DC converters in regenerative braking system. Aerosp. Electron. Syst. Mag. 17(8), 16–21 (2002) 25. Paymen, A., Oierfederici, S.: Energy control of supercapacitor/fuel cell hybrid power source. Energy Convers. Manage. 49, 1637–1644 (2008) 26. Farzanehfard, H., Beyragh, D.S.: A bidirectional soft switched ultracapacitor interface circuit for hybrid electric vehicles. Energy Convers. Manage. 49, 3578–3584 (2008) 27. George, M., Choi, S.: Contrasting Hybrid Electric Bicycles and Electric Bicycle. InterProfessional Project 315 Spring 2003, pp. 20–25

Driver-in-the-Loop Simulator of Electric Vehicles Csaba Antonya1(B)

, C˘alin Husar2 , Silviu Butnariu1 and Alexandra B˘aicoianu1

, Claudiu Pozna1 ,

1 Transilvania University of Brasov, 29 Eroilor, 500036 Brasov, Romania

[email protected] 2 Siemens Industry Software SRL, 500227 Brasov, Romania

Abstract. The paper presents the hardware and software components of a driving simulator for electric vehicles. The simulator is providing the user with realistic feedback regarding the required visual and kinesthetic information. The motion of the driving simulator is imposed by a 6 degrees of freedom Stewart hexapod platform. The command signal of the platform is obtained through the motion cueing algorithms in which the reference accelerations of the vehicle are transformed into displacement commands of the platform. The driver is interacting with the pedals and steering wheel with the simulator and the dynamic model of the electric vehicle is obtained with state-of-the-art simulation software (Simcenter Amesim). The simulator can be used to evaluate driving scenarios and electric vehicles’ performance, to analyze the driver’s action, and to identify the driver’s decision in safety-critical scenarios in precisely controlled driving conditions. Keywords: Driving simulator · Electric vehicle · Motion cueing · Data transfer · Driver · Virtual model

1 Introduction Electric vehicle development in the last century has increased substantially. All major car manufacturers have launched their own electric vehicles and they continuously work on extending the range, reducing energy consumption, reducing the time to market, and also developing new simulation techniques. Software and hardware-in-the-loop simulations of electric vehicles are often overlooking the human factor [1]. A driver is expected to react based on his experience with different tracks and environmental conditions (variations in weather, tire degradation, fuel consumption). The testing of virtual cars in a multi-modal virtual environment is an important step in the validation process of new concepts and technologies. Driving simulators (DS) provide the user with realistic feedback regarding the required visual, auditory, haptic, and kinesthetic information. The most common way of imposing the motion of the driving simulator is by the six degrees of freedom Stewart hexapod platform. The movement of the motion platform is obtained through the motion cueing algorithms (MCA) in which the reference accelerations of the vehicle are transformed into displacement commands of the platform. A driving © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_11

136

C. Antonya et al.

simulator with realistic interaction, operating environment and feedback eliminates the difficulties of the road test but allows the understanding of driving behavior, testing driver assistant systems, and traffic research. A static driving simulator is lacking the required displacement and acceleration feedback, but a hexapod motion system can reproduce some of these with high fidelity. Driving and especially safe driving is a collection of competencies that are acquired, refined, automated, and maintained. Driving behavior can also be influenced by the driver’s desire for smooth driving [2]. The main parameters proposed in the literature to assess the driver behavior are the longitudinal and the lateral accelerations [3], which can be reproduced accurately with the DS. Driver behavior can be modeled as a dynamic system in a phase transition framework as changes in the physiological system [4] and is also correlated with age, gender, and sensation seeking [5]. The evaluation of driving scenarios is complex because it is subject to many closely interconnected variables depending not only on the different types of drivers, but also on the road environment, the traffic characteristics, and the categories of road infrastructure. Driving simulators are used in a wide range of applications and they represent a tool for driver behavior and vehicle evaluation in reproducible and controlled environments. A comprehensive list of driving simulators can be found in [6] and validation studies on driving simulators in [7]. This paper is presenting a driving simulator for electric vehicles mounted on a hexapod parallel robot. The architecture of the simulator is presented in Sect. 2, while the other two sections are dedicated to details about the dynamic simulator and data transfer to the motion platform.

2 The Driving Simulator’s Architecture The architecture of the proposed electric vehicle simulator is presented in Fig. 1. The driver is interacting with the virtual model of the electric vehicle through the steering wheel and pedals. The vehicle dynamic simulation software (Simcenter Amesim) is providing the visual output and the displacement commands for the motion platform. The latter is transferred to the motion platform through a Linux server. The motion platform is in charge of providing the required kinesthetic feedback. 2.1 Hardware The hardware components of the simulator are the Logitech G29 steering wheel with pedals, the Moog Motion System 6DOF 2000E motion platform, the driver’s seat with a seatbelt, three high-definition monitors, and two computers (one for the dynamic model and one for the data transfer). The movement of the motion platform is imposed with the Moog motion platform with six identical electromechanical actuators with high-performance electric motors, a power supply system, and servo controllers. The platform has a base frame and a moving platform. The actuators of the Stewart platform have permanent magnet synchronous motors, belt drive, and a ball-screw mechanism, and are controlling the position of the moving platform.

Driver-in-the-Loop Simulator of Electric Vehicles

137

Fig. 1. The simulator’s architecture

The motion platform is providing six degrees of freedom (roll, pitch, heave, surge, yaw, lateral) with acceleration up to 0.6g and 500 deg/s2 . The dynamic analysis of the motion platform was analyzed in [8] and a co-simulation environment for the analysis of the driving simulator’s actuation was proposed in [9]. 2.2 Software The main loop is simulating the dynamics of the electric vehicle using the input data provided by the driver. Two sets of data deserve special interest, on one hand, the performance parameter of the electric vehicle and on the other hand, the linear and angular accelerations of the vehicle. The first set is important in evaluating the electric vehicle (state of charge, consumption, driving range, and efficiency) and the second feeds the input data into the MCA which computes the required movement of the motion platform to reproduce with high fidelity the sensation of a moving vehicle in the simulated environment. The driving simulator of the electric vehicles with the motion platform and on its top the driver’s seat with steering wheel, pedals, and displays are shown in Fig. 2.

3 The Vehicle Dynamic Model The comprehensive vehicle model (Fig. 3) was developed using the Siemens Simcenter Amesim (Advanced Modeling Environment for Simulation of engineering systems) software [10]. Simcenter Amesim is an integrated and scalable system simulation platform with several ready-to-use multi-physics libraries. In the main simulation loop, the driver’s input together with the road geometry is controlling the real-time dynamic simulation. In the simulation, the driver’s seat accelerations are measured in a fixed reference frame. These are transformed with the MCA into the displacement and rotation commands of the hexapod motion platform. The Simcenter Amesim vehicle model represents the configuration of a city car, where the modeling is done by using the libraries: Vehicle Dynamics, Signal and Control, Electric Motor and Drives, Automotive and Mechanical.

138

C. Antonya et al.

Fig. 2. The motion platform and the driving simulator

The main components of the electric vehicle driver-in-the loop simulator are: • The human driver (the driver in the loop) who, with the help of a Logitech G29 device, controls the steering wheel angle, the acceleration pedal (which controls the power train torque), and the braking system (which controls wheel torques) in order to drive the electric vehicle on the track; • The chassis system, which takes into account the front and rear axle kinematics with their basic components: spring, damper, lower and higher-end stop, and antiroll bar; • The functional electric drive, which is an average model of an electric drive system; • The battery model, representing a functional 400V battery pack with one electric phase, where the equivalent circuit is a variable voltage source, with the open-circuit voltage and the variable resistance calculated as a function of the state of charge (SOC) expressed in percentage;

Driver-in-the-Loop Simulator of Electric Vehicles

139

Fig. 3. The Simcenter Amesim model

• The auxiliary consumer is introduced in the virtual model as a constant electrical load and modeled with a generic automotive electric load; • The powertrain system, where the engine torque generated by the electric motor is applied to the driving gear of the differential; • The sensors which provide information about the electric vehicle (position, velocity, acceleration, oversteering angle at wheel, wheel positions, etc.) for vehicle dynamics post-processing; • The “Ground model” of the terrain on which we can simulate the maneuver of the vehicle, but with an adherence generator model included; • The motion cueing algorithm with the data transfer using share memory procedure.

4 Motion Cueing Algorithm and Data Transfer The MCA transforms the computed acceleration and angular velocities into displacement commands of the motion platform. The efficiency of a simulator is defined by the MCA’s characteristics [11], which consist of several filters that take into account the physical limits of the simulator as well as the threshold of the driver’s motion perception [12]. The most common implementations of the MCA are the classical implementation, adaptive implementation, optimal control, and Model Predictive Control [13, 14]. Usually, the

140

C. Antonya et al.

MCA is tuned by different methods like genetic optimization algorithms [15] or fuzzy logic [16]. The selected MCA is based on the classical algorithm. This has been proven to have similar performance for small and medium-sized simulators in terms of perception compared with other methods like adaptive or optimal control methods [17]. The block diagram of the selected MCA is presented in Fig. 4. The reference accelerations (linear and angular) are obtained from the dynamic analysis. These values are transformed into displacement and rotational commands of the motion platform by scaling and filtering. The primary function of filters is to transform the time-driven inertia forces into the frequency domain and the secondary is to guarantee that the movement is restricted within the kinematic and dynamic capabilities of the moving platform. This later requirement is also ensured with a rate limiter, implemented with a hyperbolic tangent function. The simulator has also a predictive component presented in [18] which is pulling back gently the user’s seat in the ideal position in the center of its range of displacement. This is the washout component, which has a speed below the perceptibility thresholds of the user. The data is transferred to the motion platform through UDP packages. The dynamic simulation program is computing the required displacement and is transferring this data to a Linux server. Here, an application developed in C based on the Server-Client model receives the data and constantly sends it at a frequency of at least 60 Hz to the motion platform. The data is sent via the UDP protocol in Little Endian byte order.

Fig. 4. The block diagram of the motion cueing algorithm

5 Conclusions The proposed driving simulator is using state-of-the-art software (Simcenter Amesim) and a motion platform (Moog hexapod system) to evaluate electric vehicles. The required motion trajectory of the driving simulator is generated with a parallel robot, capable of moving with 6 degrees of freedom. It intends to stimulate drivers to behave as close as

Driver-in-the-Loop Simulator of Electric Vehicles

141

possible to real-life situations. The user is interacting with the vehicle with a steering wheel with pedals and is receiving visual and motion feedback. The simulator can be used to evaluate driving scenarios, analyze the driver’s actions in concordance with the movement of other cars and pedestrians, and identify the driver’s actions and decisions in critical and noncritical scenarios. This simulator is providing the prospect to study safety-critical scenarios, the driving conditions can be controlled, and the parameters can be recorded. Future work will be to provide force feedback to the steering wheel, modify the MCA to eliminate false cues introduced by the linear form of the filters, and design the safety-critical driving scenarios. Acknowledgment. This work was supported by a grant of the Romanian Ministry of Research, Innovation and Digitization, CNCS/CCCDI – UEFISCDI, project number PN-III-P2–2.1-PED2019–4366 (431PED) within PNCDI III.

References 1. Riener, A., Jeon, M., Alvarez, I., Frison, A.K.: Driver in the loop: best practices in automotive sensing and feedback mechanisms. In: Meixner, G., Müller, C. (eds.) Automotive User Interfaces. Human–Computer Interaction Series, pp. 295–323, Springer, Cham (2017) 2. Wang, J., Sun, F., Ge, H.: Effect of the driver’s desire for smooth driving on the car-following model. Phys. A 512, 96–108 (2018) 3. Vaiana, R., et al.: Driving behavior and traffic safety: an acceleration-based safety evaluation procedure for smartphones. Mod. Appl. Sci. 8(1), 88 (2014) 4. Mirman, J.H.: A dynamical systems perspective on driver behavior. Transp. Res. F: Traffic Psychol. Behav. 63, 193–203 (2019) 5. Witt, M., Kompaß, K., Wang, L., Kates, R., Mai, M., Prokop, G.: Driver profiling–data-based identification of driver behavior dimensions and affecting driver characteristics for multi-agent traffic simulation. Transp. Res. F: Traffic Psychol. Behav. 64, 361–376 (2019) 6. Slob, J.J.: State-of-the-art driving simulators, a literature survey. DCT Report, 107 (2008) 7. Wynne, R.A., Beanland, V., Salmon, P.M.: Systematic review of driving simulator validation studies. Saf. Sci. 117, 138–151 (2019) 8. Irimia, C., Antonya, C., Grovu, M., Husar, C.: Dynamic analysis of the Stewart platform for the motion system of a driving simulator. In: Uhl, T. (ed.) IFToMM World Congress on Mechanism and Machine Science, pp. 3079–3086. Springer, Cham (2019) 9. Antonya, C., Irimia, C., Grovu, M., Husar, C., Ruba, M.: Co-simulation environment for the analysis of the driving simulator’s actuation. In: 2019 7th International Conference on Control, Mechatronics and Automation (ICCMA), pp. 315–321. IEEE (2019) 10. Simcenter Amesim: Siemens Industry Software NV. https://www.plm.automation.siemens. com/global/en/products/simcenter (2020). Last accessed 20 Jan 2022 11. Olivari, M., Pretto, P., Venrooij, J., Bülthoff, H.H.: Defining the kinematic requirements for a theoretical driving simulator. Transp. Res. F: Traffic Psychol. Behav. 61, 5–15 (2019) 12. Fang, Z., Kemeny, A.: Motion cueing algorithms for a real-time automobile driving simulator. In: Driving Simulation Conference, pp. 159–174 (2012) 13. Casas, S., Olanda, R., Dey, N.: Motion cueing algorithms: a review: algorithms, evaluation and tuning. Int. J. Virtual Augmented Reality (IJVAR) 1(1), 90–106 (2017) 14. Aykent, B., Paillot, D., Me´rienne, F.D.R., Fang, Z., Kemeny, A.: Study of the influence of different washout algorithms on simulator sickness for a driving simulation task. In World Conference on Innovative Virtual Reality, vol. 44328, pp. 331–341 (2011)

142

C. Antonya et al.

15. Asadi, H., Mohamed, S., Rahim Zadeh, D., Nahavandi, S.: Optimisation of nonlinear motion cueing algorithm based on genetic algorithm. Veh. Syst. Dyn. 53(4), 526–545 (2015) 16. Qazani, M.R.C., Asadi, H., Bellmann, T., Perdrammehr, S., Mohamed, S., Nahavandi, S.: A new fuzzy logic based adaptive motion cueing algorithm using parallel simulation-based motion platform. In: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8. IEEE (2020) 17. Arioui, H., Nehaoua, L., Amouri, H.: Classic and adaptive washout comparison for a low cost driving simulator. In: Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control 2005, pp. 586–591. IEEE (2005) 18. Antonya, C., Carabulea, L., Pauna, C.: Predictive actuation of a driving simulator. In: Burnete, N., Varga, B. (eds.) Proceedings of the 4th International Congress of Automotive and Transport Engineering (AMMA 2018), pp. 128–135. Springer, Cham (2018)

Observations on the Driving of Plug-In Hybrid Cars in Real-World Conditions Jaime Suarez1(B) , Andres Laverde2 , Alessandro Tansini1 , Markos A. Ktistakis3 , Dimitrios Komnos3 , and Georgios Fontaras1 1 Joint Research Centre, European Commission, Ispra, Italy

[email protected] 2 VRAIN, Universitat Politècnica de València, 46002 Valencia, Spain 3 Fincons SA, Milano, Italy

Abstract. Sustainable mobility requires a clean and decarbonised road transport sector. Plug-in Hybrid Electric Vehicles (PHEVs) stand as an appealing transitional solution for mitigating transport emissions due to their reduced lifetime Fuel Consumption (FC). However, their contribution to air quality and fuel savings depends on actual Real-World (RW) usage and the share of electric driving. This study analyses the use of PHEVs in RW conditions and the influence of vehicle technology thereon. An ad-hoc experimental driving campaign has been designed and is being conducted, with several volunteers driving a PHEV and a conventional Internal Combustion Engine (ICE) vehicle used as reference. Comparing the RW collected data from drivers, and the two cars, provides insights into understanding the actual use of PHEV vehicles and how the vehicle technology influences the users’ driving behaviour. The preliminary results of this novel campaign indicate that the drivers adopted similar habits when moved to the PHEV model. Additionally, an analysis showed clear correlations between the battery state of charge (SOC) at the beginning of the trip and the corresponding trip FC. It is concluded that the increase in PHEVs sales can contribute to transport decarbonisation, provided that the vehicles are driven as expected by the regulations. In this sense, policy objectives could also address usage patterns such as the battery charging frequency. Keywords: Plug-in hybrid vehicles · Fuel consumption · CO2 emissions · Real world operation

1 Introduction Achieving sustainable urban mobility involves significantly reducing the transportderived Greenhouse Gas emissions and other air pollutants. In line with the Paris agreement [1], the European Green Deal establishes specific targets for the decarbonisation of the road transport sector in the following decades to reach carbon neutrality by 2050 [2]. Vehicle electrification appears to be one of the most promising solutions to effectively reduce the CO2 emissions from road transport, with Battery Electric Vehicles (BEVs) expected to gain large fleet shares because of their zero-tailpipe CO2 emissions. Such © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_12

144

J. Suarez et al.

vehicle technology requires significant adaptation effort from industry, infrastructure, and users. In this context, Plug-in Hybrid Electric Vehicles (PHEVs) stand as an appealing transitional technology. Because of their powertrain design, PHEVs are associated with long-range and low official CO2 emissions. Considering the sharp sales increase in recent years [3], they could significantly contribute to the reduction of CO2 emissions from the fleet. PHEVs are vehicles that combine an electric motor with an Internal Combustion Engine (ICE), reducing the Fuel Consumption (FC) without limiting the driving distance range of the car. The potential of PHEVs to minimise the lifetime CO2 emissions is strongly linked to the actual share of electric driving, when the propulsion is achieved through a zero-tailpipe emissions operation. Regarding the electric battery use, the vehicle operation can be divided into battery Charge Depleting (CD) and Charge Sustaining (CS) modes. In the first one, the vehicle is mainly propelled with the energy from the electric battery, while in the second, the vehicle moves at the cost of FC without additionally depleting the battery. From this interplay between Charge Depleting (CD) and Charge Sustaining (CS) modes, one can expect a wide variability of CO2 emissions under real-world (RW) conditions. Recent evidence on PHEVs shows that real-world (RW) operation conditions are usually less favourable than a priori expected [5, 6]. There is a wide variety of factors responsible for this divergence. The on-road driving conditions (e.g., traffic and weather conditions, road gradient or asphalt conditions) and the trip characteristics, such as the trip length or the vehicle speeds, directly impact the energy efficiency. On the other hand, other factors are related to the user’s behaviour; for instance, the use of electric auxiliaries (heating and air conditioning system), the aggressiveness in the driving style and, most notably, the frequency in the battery recharging events. In particular, the State of Charge (SOC) of the battery at the beginning of the trip significantly impacts the vehicle’s electric autonomy. Combining all these factors results in high variability in the energy demand, remaining a largely case-specific question [7]. Predictions for vehicle driving are generally established based on the usage and habits that users have shown in the past. For example, Type Approval granting procedures set the share between electric and combustion modes in PHEVs from the Utility Factor (UF) parameter, which is derived from the daily mileage of an average European driver. But due to the predominance of internal combustion cars in the EU vehicle fleet, this statistic reflects the use of conventional vehicles. One might then question whether this statistic also stands for PHEVs, and whether the UF would become unrealistic in a shortcoming future when the share of PHEVs increases even more. This study aims to analyse and quantify what are the real-world driving conditions of PHEVs and how they are related to the fuel economy. In particular, two main questions regarding the use of PHEVs are addressed. The first one is whether the vehicle powertrain technology influences the driving behaviour of PHEV real-world users. The second question regards the variability among different PHEV users regarding various trip parameters. The present paper is structured as follows: Sect. 1 introduces PHEVs. Section 2 presents the vehicles, the experimental driving campaign and the data processing. In Sect. 3 the trip characteristics are analysed, comparing the footprints of different drivers

Observations on the Driving of Plug-In Hybrid Cars

145

on the two vehicles. Finally, Sect. 4 draws conclusions regarding what type of factors should be considered in future policy measures.

2 Material and Methods An ad-hoc novel experimental driving campaign involving a representative sample of 6 users was designed and conducted. Based on a real-world daily basis analysis, the aim was to assess how is the user’s driving pattern influenced by driving a PHEV. To unambiguously identify the importance of the vehicle’s powertrain, the same set of people drove a PHEV and an ICE conventional vehicle of similar characteristics. The comparative analysis among drivers serves as a preliminary basis to establish the variability ranges that can be expected in the EU fleet regarding trip characteristics. Finally, the correlation between significant trip characteristics and the FC efficiency and CO2 emissions was explored. From these results, important conclusions to optimise the FC based on the vehicle usage patterns were derived. 2.1 Vehicles and Experimental Setup The data collection activity was carried out on two vehicles considered representative of the European market. The two cars are of the same constructor, model, size, and category to minimise the vehicle-type influence on the observations. One of them, which can be considered the reference vehicle, is an ICE-based passenger car, whereas the other is a PHEV passenger car (see Table 3 in Appendix for the technical specifications). The two vehicles were specifically chosen to have almost the same properties (dimensions, segment, number of doors and seats) that would let the drivers feel at ease when moving from one to the other. Tailored data-logging systems were prepared and installed on each vehicle, relying on the availability of signals from the vehicle On-Board Diagnostics (OBD) for standardised parameters and Unified Diagnostics Services (UDS) for vehicle-specific parameters. Such systems were permanently mounted on the vehicles for the whole duration of the activity. All the vehicle signals gathered from OBD/UDS were collected and stored with a sampling frequency of approximately 1 Hz. 2.2 Real-World Data Collection and Drivers Characterisation The driving campaign was designed for a complete year (2021–2022) in order to capture the variabilities in terms of weather conditions. This period included further tests in the laboratory to ascertain the performance of each vehicle in standard conditions. The two cars were driven by six different drivers voluntarily, with a rental time of 22 days per car for each user. The users were selected among candidates who could access a charging station for the Plug-in Hybrid vehicle at home or at their workplace. They were driving on a mix of urban, rural and motorway sections. However, most of the trips took place in the surroundings of the JRC site in Ispra, Varese (Italy), where rural conditions are prevalent. To confirm the real-world representativeness of the measurements, the

146

J. Suarez et al.

drivers should keep the same driving behaviour as in their normal life. In particular, the refuelling and recharging costs were at the user’s expense. The characterisation of each trip involved the registration of an ensemble of 45– 50 instantaneous parameters on each vehicle. This extensive dataset enables to capture the operating conditions encountered in RW operation, quantify the fuel and energy consumption, and the key affecting parameters: environmental and vehicle conditions, trip characteristics, and driving style. The main parameters used for this study are vehicle speed, distance, engine speed, instantaneous FC, ambient temperature, engine coolant temperature and State of Charge (SOC). The final dataset for the analysis was obtained by identifying the individual trips and removing corrupted and inaccurate records. Between 25 and 27 parameters were calculated based on the original trip recordings from each trip. The driven distance and instantaneous acceleration were calculated based on the OBD vehicle speed. The final cleaned data set consists of 29.133 data points collected during 1079 trips, where 675 trips were done on the ICE vehicle and 404 trips on the PHEV, one by the same six drivers.

3 Results and Discussion 3.1 Comparison Between PHEV and ICE Vehicle In this first part of the analysis, we assess how vehicle technology influences driving behaviour. This analysis involves mainly two variables: the trip’s distance and the average speed. The first question we address is to analyse if the PHEV vehicles are used for the same daily distances as conventional vehicles. With this scope, we compare the mileage driven per day of all users of the PHEV vehicle against the distance they drive on the conventional ICE car. Figure 1 shows histograms of the daily mileage for both vehicles. The corresponding cumulative probabilities, shown in blue (ICE vehicle) and red (PHEV) solid lines, can be interpreted as the probability that the daily mileage of the average driver in our pool is below a certain distance. The comparison between both cumulative probabilities proves that there is no significant distance limitation. On the contrary, the cumulative probability of the PHEV is below the ICE one for the range of moderate distances (less than 100 km), which means that the drivers were driving slightly larger daily distances with the PHEV. A possible explanation is that the lower fuel consumption of the electrified version increases the drivers’ willingness to use the vehicle more, but further analyses are to be carried out on a larger amount of data to exclude biases in the sample affecting the comparison and the conclusions drawn. The daily distance driven by an average user is also a key aspect concerning the PHEVs lifetime fuel consumption. Indeed, the UF determines the share of kilometers driven in CD mode per day with nearly zero FC, based on the assumption that the car is recharged once daily. The official FC values in homologation procedures are obtained from a weighted average between Charge Depleting (CD) and Charge Sustaining (CS) modes, taking the UF as the weighting coefficient. The WLTP Type Approval procedure followed in the European Union (EU) [8, 9] considers a UF that reflects the statistics of

Observations on the Driving of Plug-In Hybrid Cars

147

the average daily mileage in Europe. Given the scarcity of evidence about the practical use of PHEVs back in 2017, such UF curve was built on statistics about the use of conventional vehicles, under the assumption that the driving distances were similar to PHEVs. The present UF is then linked to the probability that an average European driver drives up to a certain distance in a day. The comparison with the cumulative probabilities of Fig. 1 is then straightforward. From the observations, our pool of drivers appears to be more prone to drive longer distances than the average European driver, represented by the UF curve. This effect can be attributed to the fact that rural conditions were prevalent in the driving area.

Fig. 1. Probability distribution of the daily distance (in km) of drivers on ICE (blue) and PHEV (red), and cumulative probabilities (solid lines) representing the probability that a certain user drives less than a certain distance per day. The average daily driven distance assumed for the calculation of the Utility Factor curve as defined in Regulation EU 2017/1151 is also displayed (black dashed line).

The second step of our analysis concerns the speed regime explored by the drivers of both type of vehicles. We present a comparative analysis of the average speed per trip. Figure 2 displays the average speed recorded for each trip on the two vehicles in relation to the trip distance. Both set of data were fitted to a logarithmic expression establishing a relationship between distance and speed. Long-distance trips usually take place on motorways where the average speed is higher. The logarithmic regression obtained on the PHEVs observation lies very slightly below the one obtained on the ICE vehicle. There is no clear difference between the two configurations, and it is worth noting that a previous similar experimental campaign performed on a conventional vehicle showed a logarithmic trend similar to the PHEV [10, 11]. Complementing this figure, we calculated the probability distribution functions of the average positive and negative acceleration

148

J. Suarez et al.

per trip in both vehicles (Fig. 3). The figure shows an apparent reduction in the magnitude of the positive and negative accelerations for the drivers on PHEV with regards to the ICE vehicle. The difference between the mean values of both distributions is about 30% of the absolute value for both the acceleration and the deceleration (0.83 and − 0.93 m/s2 in PHEV against 1.18 m/s2 and −1.25 m/s2 in ICE). Although the variances of both vehicles are quite similar, the ICE distribution is shifted towards more prominent accelerations and decelerations. Such a pattern suggests that the driver shows a more conservative driving acceleration style on PHEV, likely linked to the drivers adopting a more eco-friendly driving style where both the accelerations and the decelerations are smoother. However, this effect could also be caused by the ca. 200 kg mass difference in favour of the ICE car, giving more inertia to the PHEV car.

Fig. 2. Average speed of a trip as a function of the trip distance for both the ICE (blue) and the PHEV (orange) vehicle. The scattered distributions of points have been fitted to logarithmic functions, and compared to previous similar study in an ICE vehicle (green line). Side-panels show the projected density functions. Broken vertical lines specify the mean value of the average speed distribution (29.5 km/h for ICE and 27.2 km/h for the PHEV).

3.2 Drivers Comparison The second part of this section is a comparative study among different drivers on the same PHEV vehicle. The target is to evaluate the expected variability of the trip factors

Observations on the Driving of Plug-In Hybrid Cars

149

Fig. 3. Probability distributions of the negative (a) and positive (b) trip accelerations on both the ICE vehicle and the PHEV one. Dashed vertical lines show the average value of the distributions.

that have a more profound impact on the fuel economy of PHEVs. Table 1 presents a summary of the average values of different parameters and the average FC of each driver. To better capture the day-to-day behaviour of the user, both from a driving and a battery charging perspective, we have collected a high number of kilometres (typically above 1000 km) from each volunteer. The six drivers show high variability in the average FC. Compared to the 0.9 L/100 km officially reported for this vehicle, only Driver 1 shows a lower FC value, while the rest of the drivers are notably above, in a range that is more characteristic of conventional vehicles. Table 1. Trip parameters for drivers on PHEVs Driver

Total distance (km)

Avg. distance (km)

Avg. speed (km/h)

Avg. acceleration (m/s2 )

Initial SOC > 85% (%)

Avg. Batt. depletion (%)

FC (L/100 km)

Driver 1

930.6

10.7

27.0

0.85

33.3

11.0

0.65

Driver 2

1764.4

34.6

37.3

0.88

11.1

7.5

2.95

Driver 3

898.6

14.5

22.9

0.88

19.4

9.8

2.86

Driver 4

2120.0

27.2

30.0

0.84

14.1

5.6

6.10

Driver 5

1664.5

29.2

27.2

0.90

24.6

5.5

2.88

Driver 6

1940.2

27.7

24.9

0.72

21.4

8.1

2.17

150

J. Suarez et al.

We first analyse the influence of the average distance the user drives on the fuel economy. The average distance per trip varies between 10 km for Driver 1 up to 72 km for Driver 2. Considering that the official electric range of this vehicle is established at 70 km, trips above this value are typically driven on Charge Sustaining mode (at the expense of the FC). Figure 4 presents bins of driven distance (< 10 km, 10–30 km, 30–50 km, > 50 km) for each driver. The figure clearly indicates that Drivers 2, 4 and 5 are those with a larger share of trips above 60 km. On the other hand, Driver 1 and Driver 3 travel shorter distances, and thus are more prone to operate on the zero-tailpipe emissions electric mode, with lower FC averages. In fact, these are the two drivers with the lowest FC, although in the case of Driver 3 the FC is quite high and should be explained by other factors.

Fig. 4. Shares of driving distances for different drivers on the PHEV, grouped into segments [0–10 km], [10–30 km], [30–50 km] and > 50 km.

A second aspect concerns the role of the kinetic profile, in particular average speed and positive acceleration. Again, the highest value for the average speed is found for Driver 2, the one with a higher FC. However, the correlation is not as clear as for the trip distance. We also considered the impact of the average positive acceleration. Driver 5 seems to drive very dynamically, with aggressive accelerations, which are reasonably related to higher FC. A particular key asset regarding the FC of a PHEV car is the user’s charging habits. Indeed, a low battery level (semi-depleted battery) will force the vehicle to operate in CS mode, with an even higher FC to a conventional vehicle of similar power and mass characteristics. We have identified the percentage of times that a user initiates the trip with a fully charged battery above 85% SOC). Driver 1 starts one-third of the trips in a

Observations on the Driving of Plug-In Hybrid Cars

151

fully-charged battery state (33%). In contrast, Drivers 2 and 4 show a very poor initial SOC (less than 15%). Finally, concerning the average battery depletion, this magnitude varies typically between 5 and 10% among all drivers. There is a clear inverse trend with the FC, since both magnitudes are complementary factors of the total propulsion energy. This is, the vehicle can either operate at the expense of the battery or the expense of the fuel consumed. The analysis carried out so far was based on the relation of the FC with average values of the different parameters. However, the driver-intrinsic variability cannot be revealed in such a representation. Thus, in Fig. 5, we present probability functions of the initial SOC, where we can observe different trends. In general, all the drivers with the exception of Driver 2 and Driver 4 show a maximum value at maximum charge. These two drivers have a very low rate of full battery charge, and most trips take place while the battery is almost depleted. They show a distinct contribution at low initial SOC (below 50%). Therefore, we expect that the vehicle will operate most of the time in CS mode, and the FC will be higher. This analysis shows the importance of a correct and consistent battery charging habit.

Fig. 5. Probability distributions of the initial State of Charge (SOC) (%) at the beginning of each trip

To conclude, we assess the driver-to-driver expected variability on the PHEV compared to the baseline ICE vehicle for different driving parameters. The relevant parameters are found in Table 2. The average distance inter-driver variability on the PHEV is approximately (10 km, 50 km), considerably higher than the (10 km, 30 km) range of

152

J. Suarez et al.

the ICE case, but this can be attributed to the contribution from Driver 2, who can be considered an outlier in this case. For the average speed, the PHEV drivers present a more uniform behaviour (shorter variability), and the mean value is 3 km/h downshifted with respect to ICE case. ICE drivers show a more aggressive driving style than PHEV drivers from the point of view of accelerations. The same conclusion follows from the average pedal position, which is clearly higher for ICE drivers (from 21 to 24%) than for PHEV drivers (only 7 to 14%). This is also in line with the findings of the previous subsection. Finally, the Fuel Consumption variance in the case of PHEV is much larger than in the case of ICE. In the last one, the FC is quite uniform along the fleet of drivers, varying between 6.0 g/100km and 6.9 g/km. This figure is also in line with the official Type Approval value (6.0 L/100 km). The case of the PHEV is very different, with fuel consumption values ranging from 0.61 L/100 km of Driver 1 to 5.5 L/100 km of Driver 2. Interestingly, while the lowest value is quite close to the PHEV Type Approval value (0.9 g/100 km), the highest one approaches the official reference fuel consumption value of the ICE vehicle. In other words, under certain driving conditions or inappropriate driving/charging habits, the fuel consumption of a plug-in hybrid vehicle can be as high as the baseline conventional car, which shows the importance of a correct driving behavior to achieve the fuel economy target. Table 2. Driver variability (10%, 50%, 90% quantiles) on the ICE and the PHEV cars ICE

PHEV

10%

50%

Avg. distance (km)

9.3

16.2

Avg. speed (km/h)

26.3

31.3

Avg. Pos. Acc. (m/s2 ) Avg. Pedal Position (%) FC (L/100 km)

90%

10%

50%

90%

28.6

12.6

27.4

50.7

39.3

23.9

27.1

32.9

1.1

1.2

1.3

0.7

0.8

0.9

20.9

23.3

24.1

7.4

9.0

14.2

6.0

6.1

6.9

1.4

3.1

6.1

4 Conclusions This paper presented a comparative analysis regarding the role of vehicle technology and user behaviour in using PHEVs. To do so, we designed and carried out an experimental driving campaign with six drivers who were consecutively driving a PHEV vehicle and a conventional one. The comparative analysis performed was two-folded. On one side, we addressed the question from the perspective of vehicle technology, comparing the usage of a PHEV and an ICE vehicle for the same users. In this way, we assessed how different is the driving behaviour of PHEVs compared to conventional vehicles. We analysed the trip distance, the average speed and the positive acceleration as the key aspects to understand the impact of the vehicle technology on the user’s perception. We concluded that there

Observations on the Driving of Plug-In Hybrid Cars

153

is no significant impact on what refers to the trip distance, with PHEV users travelling even longer distances per day. The same conclusions can be applied to the average speed, while the PHEV accelerations seem to be smoother than in the case of conventionals. In any case, further data collection and investigation is needed for deriving concrete conclusions. The second part dealt with the variabilities expected from different users on the same PHEV vehicle and the role these parameters have on the FC. Our conclusions show a clear trend between FC and trip distance, with a severe increase in the FC for users driving long trips. At the same time, we confirmed the key role played by the initial State of Charge of the battery and how it negatively impacts the FC. All these features highlight the importance of the user’ correct driving behaviour and consistent battery charging habits to achieve real reductions in FC and CO2 emissions. The results of this study can be used as a reference point for understanding the RW usage of PHEVs and for supplementing the EU policies oriented to assign type approval FC values aligned with their RW operation. List of Acronyms CD CS EU FC ICE JRC OBD PHEV SOC UDS

Charge Depleting Charge Sustaining European Union Fuel Consumption. Internal Combustion Engine Joint Research Centre On-Board Diagnostics Plug-in Hybrid Electric Vehicle State Of Charge Unified Diagnostics Services

Acknowledgements. The authors acknowledge the JRC VELAs personnel for the support in the experimental activities, and in particular Mr. Fabrizio Forloni (JRC) for the extensive support in retrieving the vehicles’ signals from OBD. The authors would like to thank also Ms. Elena Paffumi for helpful suggestions and discussions on Utily Factors. Disclaimer The views expressed in this paper are purely those of the authors and should not be considered as an official position of the European Commission. Contact For further information, please contact [email protected].

154

J. Suarez et al.

Appendix See Table 3. Table 3. Vehicles technical specifications Vehicle Type Powertrain

ICE vehicle

PHEV

C-Segment, 5 seats

C-Segment, 5 seats

Diesel

Gasoline PHEV

Wltp test mass (kg)

1488

1698

Engine displacement (cm)

1598

1395

Engine power (kw)

85

110

Electric motor power (kw)

–

80

Combined power (kw)

–

150

Equivalent all electric range (km)

–

70

Wltp fuel consumption (l/100km)

5.1

0.9

References 1. United Nations: Paris Agreement (2015) 2. European Commission: European Green Deal (2019) 3. ACEA: Fuel Types of New Cars: Battery Electric 7.5%, Hybrid 19.3%, Petrol 41.8% Market Share in Q2 2021 (2021) 4. Duoba, M.: Developing a utility factor for battery electric vehicles. SAE Int. J. Alt. Power. 2, 362–368 (2013). https://doi.org/10.4271/2013-01-1474 5. Wu, X., Aviquzzaman, M., Lin, Z.: Analysis of plug-in hybrid electric vehicles’ utility factors using GPS-based longitudinal travel data. Transp. Res. Part C Emerg. Technol. 57, 1–12 (2015). https://doi.org/10.1016/j.trc.2015.05.008 6. Ramírez Sanchez, P.P., Ndiaye, A.B., Martín-Cejas, R.R.: Plug-in hybrid electric vehicles (PHEVS): a possible perverse effect generated by environmental policies. Int. J. Transp. Dev. Integr. 3, 259–270 (2019). https://doi.org/10.2495/TDI-V3-N3-259-270 7. Plug-in hybrids: Is Europe heading for a new Dieselgate? Transport & Environment. https://www.transportenvironment.org/discover/plug-hybrids-europe-heading-new-die selgate/ (2020). Accessed 13 Apr 2022 8. COMMISSION REGULATION (EU) 2017/1151 of 1 June 2017 9. https://unece.org/fileadmin/DAM/trans/doc/2015/wp29grpe/GRPE-72-02-Rev.1.pdf 10. Pavlovic, J., et al.: Understanding the origins and variability of the fuel consumption gap: lessons learned from laboratory tests and a real-driving campaign. Environ. Sci. Eur. 32(1), 1–16 (2020). https://doi.org/10.1186/s12302-020-00338-1 11. Komnos, D., Tsiakmakis, S., Pavlovic, J., Laverde Marín lng, A., Chatzipanagi lng, A, Fontaras, G.: An Analysis of Modern Vehicle Road Loads for Fleetwide Energy Consumption Modelling (2021). https://doi.org/10.4271/2021-24-0080

Electric and Clean Energy in Transportation: Integrating Smart Transport and Smart Grids

Research Trends and Opportunities Related to Charging and Supply Systems for Vehicles with Electric/Hybrid Propulsion Ciprian Bejenar(B)

, Mihai Rat, a˘ , Gabriela Rat, a˘ , and Laurent, iu-Dan Milici

Faculty of Electrical Engineering and Computer Science, University “S, tefan cel Mare” of Suceava, Suceava, Romania [email protected], {mihai.rata,gabriela.rata,dam}@usm.ro

Abstract. This scientific work is meant to reveal the current research context and the future opportunities in the case of charging and/or supply systems for vehicles with electric or hybrid propulsion, which are analyzed in the light of their actual evolving dynamics and on the basis of which there are highlighted expectations regarding the main problems that the electric mobility is currently facing in the research and development environment, as well as its focus direction on eventual solutions that are considered in present and on their shortcomings in a future with evolved necessities. Keywords: Electric vehicle · Charging system · Research · Development · Trends · Potential · Data analysis · Bibliometric network

1 Introduction In the 19th century, electrically-propelled vehicles were more used than other means of transport, at least in different parts of the world. However, this technology was later abandoned in the 20th century, in favor of the development of conventionally-propelled vehicles, which are powered by thermal engines for their traction [1]. A century later, in present, conventionally-propelled vehicles are being abandoned in the favor of the development of vehicles with hybrid propulsion besides the main priority of a transition to electrically-propelled vehicles, in that electric mobility is the main contemporary concern in the field of transportation, as with the evolution of society and climate change caused by human activity, humanity is faced with the problem of reducing pollutants and carbon dioxide (CO2 ) in the Earth’s atmosphere, through better strategies and less polluting solutions [2]. The transition to electric mobility and the motivation of using clean and/or renewable sources of electrical energy, conventional and/or unconventional, is underpinned by adopted legislation, technological progress and consumer behavior, hence some of the decisions in the modeling of charging and/or supply systems for vehicles with electric or hybrid propulsion [2]. To address the topic proposed by this paper, VOSviewer (version 1.6.17 released on 22.07.2021) software tool is used, which is appropriate for building and visualizing © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_13

158

C. Bejenar et al.

bibliometric networks regarding any subject, but also for discovering the research trends and the opportunities related to charging and/or supply systems for vehicles with electric or hybrid propulsion, after examining gathered scientific data for topic-specific queries. A bibliometric network may refer to scientific publications, scientific journals or researchers and it may be built based on co-authorship or bibliographic links, as it represents a scientific landscape of important terms and their co-occurring links in a body of scientific literature, being a rich resource that is suitable for data analysis [3].

2 Software Configuration In the purpose of creating bibliometric networks with the help of the VOSviewer software tool, the results are generated, mainly, starting from text-based data, which are saved in the form of an appropriate file (e.g., .txt), after it is generated and downloaded directly from one of the online platforms that host databases of scientific papers (e.g., Web of Science, Scopus, Dimensions, Lens, PubMed, etc.), so that it includes complete data fields (e.g., title, authors, affiliation, abstract, etc.) regarding a collection of scientific papers that are selected for analysis [3]. After they are gathered, a selected collection of data is imported into the VOSviewer software tool and are further interpreted according to software configurations based on default values (unless otherwise specified), which are slightly adjusted to provide improved readability after they are similarly imposed for each generated result, as in this manner is easy to obtain a bibliometric network that offers a raw and clear overview, while it highlights undistorted ideas that address the topic proposed by this paper. Therefore, from the point of view of the realization of bibliometric networks by processing imported text data, the data fields that are extracted consists of title and abstract, for which there are ignored the structures of abstract labeling and copyright statements and the counting method is binary, while from the point of view of the realization of bibliometric networks by processing the imported bibliographic data, the data fields that are extracted consists of authors, there are ignored the scientific works whose number of authors exceeds the value 25 and the counting method is full, for which the delimitation of the position of one element in relation to another, in case of comparable ordering criteria, is done according to an additional delimitation criterion. Regarding the general rules for bibliometric analysis, the process is performed according to configurations that are similarly imposed to all generated results. Therefore, from the point of view of normalizing the strength of the links between the elements, it is chosen the association strength method, the way of locating the elements in the formed links is based on a configuration with predefined values, the grouping technique is based on a resolution that has the value 1, the minimum size of a group has the value 1, the small clusters are merged into large clusters and the elements are not additionally rotated. Regarding the general rules for the graphical representation of bibliometric networks in their processed form, the visualization of the elements and the linking between them is performed according to configurations that are similarly imposed for each generated graphical representation. Therefore, from the point of view of visualizing the results after processing the entered data, the scale of the graphical representation has the value 1, the

Research Trends and Opportunities Related to Charging

159

network of links between the elements is dimensioned in accordance with the number of their occurrences in the entered data, as well the representation of the elements is colored according to the obtained score depending on the average year of a publication whose interval is automatic, not normalized, with colors between blue and yellow, for which the label size of each component has the value 1, the size variation of the link lines between the elements has the value 1, the minimum strength of the link lines has the value 0 and the maximum number of link lines is 1000. In the sense of the topic proposed by this paper, the larger the elements size and/or the stronger their links, the greater the interest of the research and development environment in expanding the scientific knowledge in that field of terms, but at the same time, the brighter their color, the more actual is that interest.

3 Considerations on the Research and Development Dynamics of Charging and Supply Systems Actual general trending regarding the research and development dynamics of charging and/or supplying systems for vehicles with electric or hybrid propulsion is characterized by the permanent evolution process of their constructive variants, which are developed and customized to satisfy more and more varied needs and to solve increasingly difficult problems, with the involvement of modern prototyping solutions and the use of the most advanced electrical and electronic components, but so that the form of experimental prototypes and/or equivalent simulation models proposes an improved configuration and are accompanied, as appropriate, by a superior mathematical model for control and/or command [4–13]. Searching for the group of terms “electric vehicle charging system type” on the online platform Web of Science (Core Collection), on 17.10.2021, offers 898 results with recently published scientific papers (between 2017 and 2021) compared to 1445 results always published (between 1992 and 2021), of which full data are saved {1} for all recently published results. Searching for the group of terms “electric vehicle converter prototype” on the online platform Web of Science (Core Collection), on 07.10.2021, offers 841 results with recently published scientific papers (between 2017 and 2021) compared to 1468 results always published (between 1995 and 2021), of which full data are saved {2} for all recently published results. Searching for the group of terms “electric vehicle silicon carbide” on the online platform Web of Science (Core Collection), on 04.10.2021, offers 257 results with recently published scientific papers (between 2017 and 2021) compared to 421 results always published (between 1996 and 2021), of which full data are saved {3} for all recently published results. Bibliometric network in Fig. 1 is composed on the basis of data {1} and it is a graphical representation for which the threshold value of the minimum number of occurrences of a term is 21, therefore out of 20,056 terms loaded for analysis, only 199 terms meet this condition, from which there are selected the most relevant 119 terms (60%), that are further filtered by removing those terms that would be less relevant (“account”, “battery electric vehicle”, “bms”, “china”, “day”, “degrees c”, “driver”, “electrical vehicle”,

160

C. Bejenar et al.

“evs”, “li ion battery”, “pev”, “phev”, “review”, “road”, “series”, “user”, “v2g”, “wireless power transfer”, “wpt” and “year”), while in its final form, the graphical representation contains 99 elements, 3 clusters, 4362 links and a total link strength of 33,836.

Fig. 1. Bibliometric network composed on the basis of data {1}, which refers to the key syntax “electric vehicle charging system type”.

Regarding the representation of the bibliometric network in Fig. 1, the analyzed subject suggests that among the most important aspects related to the charging and/or supply variants of vehicles with electric or hybrid propulsion are those that the considered scientific works on the development dynamics of the electrical systems involved in the respective processes are drawn up by research subjects on the basis of data {1}. They refer, on the one hand, to the relationship between charging systems and what their management signifies in terms of the infrastructure they propose, the impact they have on the load of the electrical networks to which they are connected and the costs that these aspects generate, but on the other hand, to the relationship between charging and/or supply systems and what their management means from the point of view of electric battery operation, control of the charging or discharging process of the component elements which store and/or dispose of energy in one form or another and of experimental and/or simulation models, all the more so since both directions have in common the efficiency with which those systems transfer electrical energy, a performance characteristic for the power converter modules used in charging and/or power supply modules [{1}], but best underlined in [5–7]. Bibliometric network in Fig. 2 is composed on the basis of data {2} and it is a graphical representation for which the threshold value of the minimum number of occurrences of a term is 20, therefore out of 14,216 terms loaded for analysis, only 190 terms meet this condition, from which there are selected the most relevant 85 terms (45%), that are further filtered by removing those terms that would be less relevant (“bidirectional dc converter”, “dab”, “dc bus”, “dc link voltage”, “ev battery”, “fuel cell vehicle”, “high voltage gain”, “inductive power transfer”, “ipt”, “kw prototype”, “low voltage”, “motor”,

Research Trends and Opportunities Related to Charging

161

“operation principle”, “pev”, “pfc”, “power factor correction”, “power rating”, “step”, “turn”, “unity power factor”, “v2g”, “w prototype” and “wpt”), while in its final form, the graphical representation contains 63 elements, 3 clusters, 1608 links and a total link strength of 6840.

Fig. 2. Bibliometric network composed on the basis of data {2}, which refers to the key syntax “electric vehicle converter prototype”.

Regarding the representation of the bibliometric network in Fig. 2, the analyzed subject suggests that among the most important aspects related to the prototyping of power converter modules for applications dedicated to the charging and/or supply systems for vehicles with electric or hybrid propulsion are those that the considered scientific works on the development dynamics of the electrical systems involved in the respective processes are drawn up by research subjects on the basis of data {2}. They refer, mainly, to the development of charging and/or supply systems that transfers electrical energy through conversion, so that from a constructive point of view they adopt a low-loss, unidirectional or bidirectional conversion topology, that presents components with superior qualities and whose operating performance is determined by a sufficiently modeled strategy, after studying theoretical aspects, but also experimental results, because the prototyping process is generally carried out with the help of the MATLAB & Simulink software tool [{2}], but best underlined in [8–10]. Bibliometric network in Fig. 3 is composed on the basis of data {3} and it is a graphical representation for which the threshold value of the minimum number of occurrences of a term is 9, therefore out of 5910 terms loaded for analysis, only 126 terms meet this condition, from which there are selected the most relevant 76 terms (60%), that are further filtered by removing those terms that would be less relevant (“dc converter”, “degrees

162

C. Bejenar et al.

c”, “emi”, “experiment”, “gan”, “gate bipolar transistor”, “igbts”, “khz”, “metal oxide semiconductor field effect transistor”, “pcb”, “si device”, “silicon”, “traction inverter”, “wbg” and “wide band gap”), while in its final form, the graphical representation contains 61 elements, 4 clusters, 1398 links and a total link strength of 4444.

Fig. 3. Bibliometric network composed on the basis of data {3}, which refers to the key syntax “electric vehicle silicon carbide”.

Regarding the representation of the bibliometric network in Fig. 3, the analyzed subject suggests that among the most important aspects related to switching semiconductor elements made of silicon carbide (SiC) used in the charging and/or supply systems for vehicles with electric or hybrid propulsion are those that the considered scientific works on the development dynamics of the electrical systems involved in the respective processes are drawn up by research subjects on the basis of data {3}. They refer, mainly, to the construction of power converter modules, so they have to favor the adoption and the extended study of a variety of topologies for the conversion of electrical energy, because they can be easy to study due to the availability of strategies that allow for their effective control and/or command, which arouses a strong interest in an additional amount of data, but also for the case of combination with other semiconductor elements for switching, such as those made of gallium nitride (GaN) [{3}], but best underlined in [11–13]. Top 5 countries that have contributions in the publications that make up the data regarding the topic of research and development dynamics of charging and/or supply

Research Trends and Opportunities Related to Charging

163

systems ({1}, {2} and {3}), comprising a total of 1996 scientific works, are China (581) (29%), United States of America (383) (19%), India (180) (9%), South Korea (154) (8%) and Canada (118) (6%).

4 Considerations on the Development of Monitoring and Diagnostic Systems in Relation with Charging and Supply Systems Actual general trending regarding the development of monitoring and diagnostic systems in relation with charging and supply systems for vehicles with electric or hybrid propulsion is characterized by the concern to develop extended possibilities for monitoring the charging and/or power supplying process, along with the identification and differentiation between specific behaviors, as appropriate, of normal or faulty operating regimes, so that the related signatures can be recognized, as appropriate, for diagnosis and compensation, until the moment of maintenance of the eventually affected electrical systems, but a more important aspect is the existence of relevant data, experimental and analytical, collected or replicated using experimental electrical models and/or equivalent simulation models, which are being useful for education, maintenance and research, but perhaps more importantly, for the improvement of remote monitoring and diagnostic systems, centralized and intelligent, characterized by autonomy [14–22]. Searching for the group of terms “electric vehicle charging monitoring” on the online platform Web of Science (Core Collection), on 04.10.2021, offers 543 results with recently published scientific papers (between 2017 and 2021) compared to 937 results always published (between 1992 and 2021), of which full data are saved {4} for all recently published results. Searching for the group of terms “electric vehicle charging fault” on the online platform Web of Science (Core Collection), on 04.10.2021, offers 223 results with recently published scientific papers (between 2017 and 2021) compared to 337 results always published (between 2003 and 2021), of which full data are saved {5} for all recently published results. Searching for the group of terms “electric vehicle charging diagnosis” on the online platform Web of Science (Core Collection), on 04.10.2021, offers 160 results with recently published scientific papers (between 2017 and 2021) compared to 211 results always published (between 2008 and 2021), of which full data are saved {6} for all recently published results. Searching for the group of terms “electric vehicle charging tolerant” on the online platform Web of Science (Core Collection), on 04.10.2021, offers 72 results with recently published scientific papers (between 2017 and 2021) compared to 108 results always published (between 2002 and 2021), of which full data are saved {7} for all recently published results. Searching for the group of terms “electric vehicle charging tolerant” on the online platform Web of Science (Core Collection), on 04.10.2021, offers 231 results with recently published scientific papers (between 2017 and 2021) compared to 354 results always published (between 1993 and 2021), of which full data are saved {8} for all recently published results.

164

C. Bejenar et al.

Bibliometric network in Fig. 4 is composed on the basis of data {4} and it is a graphical representation for which the threshold value of the minimum number of occurrences of a term is 15, therefore out of 12,755 terms loaded for analysis, only 160 terms meet this condition, from which there are selected the most relevant 96 terms (60%), that are further filtered by removing those terms that would be less (“article”, “bms”, “day”, “degrees c”, “electricity”, “electrochemical impedance spectroscopy”, “evs”, “iot”, “lib”, “libs”, “li ion battery”, “lithium ion”, “lithium ion batteries”, “series”, “soc estimation”, “test”, “thing”, “uav”, “unmanned aerial vehicle”, “vehicle” and “year”), while in its final form, the graphical representation contains 75 elements, 2 clusters, 2492 links and a total link strength of 21,649.

Fig. 4. Bibliometric network composed on the basis of data {4}, which refers to the key syntax “electric vehicle charging monitoring”.

Regarding the representation of the bibliometric network in Fig. 4, the analyzed subject suggests that among the most important aspects related to the monitoring of the charging and/or supply systems for vehicles with electric or hybrid propulsion, are those that the considered scientific works on the development dynamics of monitoring and diagnostic systems in relation to those processes are drawn up by research subjects on the basis of data {4}. They refer, mainly, to the monitoring of electrical networks in terms of their degree of load in the context of the possibilities of their interconnection with chargers or charging stations that can absorb, at a given moment, a considerable amount of electrical energy and the main problems concern the energy management of this still insufficiently well-combined ecosystem, by increasing the adaptability and communication capacity through embedded technologies, so that the costs are reduced, but moreover, to the monitoring of electric battery charging process so that the data obtained can be used to increase its lifespan, but also to identify in time potential safety problems in their operation or related to the electrical systems in which they are operated [{4}], but best underlined in [15] and [16].

Research Trends and Opportunities Related to Charging

165

Bibliometric network in Fig. 5 is composed on the basis of data {5} and it is a graphical representation for which the threshold value of the minimum number of occurrences of a term is 8, therefore out of 5507 terms loaded for analysis, only 129 terms meet this condition, from which there are selected the most relevant 85 terms (66%), that are further filtered by removing those terms that would be less relevant (“bms”, “li ion battery”, “lithium ion battery pack”, “motor”, “presence”, “review”, “soc estimation”, “use”, “v2g”, “vehicle” and “way”), while in its final form, the graphical representation contains 74 elements, 3 clusters, 2004 links and a total link strength of 6832.

Fig. 5. Bibliometric network composed on the basis of data {5}, which refers to the key syntax “electric vehicle charging fault”.

Regarding the representation of the bibliometric network in Fig. 5, the analyzed subject suggests that among the most important aspects related to the faults in the charging and/or power supplying process of vehicles with electric or hybrid propulsion, are those that the considered scientific works on the development dynamics of monitoring and diagnostic systems in relation to those processes are drawn up by research subjects on the basis of data {5}. They refer, mainly, to the way in which the charging and/or power supplying process changes its behavior due to the faults that may occur at a given time and it is producing effects, as appropriate, in accordance with the process control method, the transferred electrical power and the period of time they are allowed to evolve and their manifestation affects the electricity grid, chargers, charging stations, charging and/or power supply modules and power converter modules [{5}], but best underlined in [17, 18].

166

C. Bejenar et al.

Bibliometric network in Fig. 6 is composed on the basis of data {6} and {8} and it is a graphical representation for which the threshold value of the minimum number of occurrences of a term is 13, therefore out of 9277 terms loaded for analysis, only 128 terms meet this condition, from which there are selected the most relevant 77 terms (60%), that are further filtered by removing those terms that would be less relevant (“account”, “bms”, “evs”, “hev”, “lib”, “li ion battery”, “lithium ion battery pack”, “maintenance cost”, “pack”, “soc estimation”, “square” and “vehicle”), while in its final form, the graphical representation contains 65 elements, 2 clusters, 1810 links and a total link strength of 12,334.

Fig. 6. Bibliometric network composed on the basis of data {6} and {8}, which refers to the key syntaxes “electric vehicle charging diagnosis” and “electric vehicle charging maintenance”.

Regarding the representation of the bibliometric network in Fig. 6, the analyzed subject suggests that among the most important aspects related to the diagnosis and maintenance of the charging and/or supply systems for vehicles with electric or hybrid propulsion are those that the considered scientific works on the development dynamics of monitoring and diagnostic systems in relation to those processes are drawn up by research subjects on the basis of data {6} and {8}. They refer, on the one hand, to the ability to identify the state of an electrical system according to the signature of the relevant electrical parameters, after which it is possible to estimate their degree of degradation, but on the other hand, to the use of this precious information in the maintenance process, to reduce the costs it implies and to develop technologies that improve its efficiency [{6}] and [{8}], but best underlined in [19, 20]. Bibliometric network in Fig. 7 is composed on the basis of data {7} and it is a graphical representation for which the threshold value of the minimum number of occurrences of a term is 4, therefore out of 2144 terms loaded for analysis, only 96 terms meet this condition, from which there are selected the most relevant 96 terms (100%), that are further filtered by removing those terms that would be less relevant (“approach”, “article”, “coil”, “coupling”, “css”, “driver”, “electric vehicle”, “electric vehicle application”,

Research Trends and Opportunities Related to Charging

167

“evs”, “misalignment”, “pad”, “paper”, “phase”, “review”, “use”, “vehicle”, “wireless power transfer”, “work” and “wtp”), while in its final form, the graphical representation contains 77 elements, 4 clusters, 1909 links and a total link strength of 4449.

Fig. 7. Bibliometric network composed on the basis of data {7}, which refers to the key syntax “electric vehicle charging tolerant”.

Regarding the representation of the bibliometric network in Fig. 7, the analyzed subject suggests that among the most important aspects related to the tolerance of the charging and/or power supplying process of vehicles with electric or hybrid propulsion are those that the considered scientific works on the development dynamics of monitoring and diagnostic systems in relation to those processes are drawn up by research subjects on the basis of data {7}. They refer, mainly, to maintenance services that can be postponed by the development of a fault-tolerant capability in correspondence with the involved electrical systems, case in which the best results are obtained through concomitant development and testing, through experiment and simulation, as one important aspect is implying modelling and simulation software environments disposing of deployment capabilities and that can handle real-time data processing while communicating

168

C. Bejenar et al.

with a dedicated equipment, as these challenges increases the necessity for implementing fault-tolerant systems which eventually demands for an intelligent identification and compensation algorithm that may be developed through a performant prototyping technique considering, both, data from experiments and results from simulations [{7}], but best underlined in [21, 22]. Top 5 countries that have contributions in the publications that make up the data regarding the topic of development of monitoring and diagnostic systems in relation with charging and/or supply systems ({4}, {5}, {6}, {7} and {8}), comprising a total of 1229 scientific works, are China (381) (31%), United States of America (191) (16%), India (91) (7%), Canada (54) (4%) and United Kingdom (52) (4%).

5 Conclusions The potential for research and development of charging and/or supply systems for vehicles with electric or hybrid propulsion is large enough, while the process of developing solutions cannot be motivated solely by legislative changes (strict rules), the intention of adopting only the latest technology (energy efficiency) or the condition of the existence of a prepared society in general (opening for novelty). A potential direction for the research and development of charging and/or supply systems for vehicles with electric or hybrid propulsion is represented by the performance of the power converter modules, both in terms of their design and optimized control capability, in close connection with the dynamic operation of the charging and/or power supply modules that they make up, respectively with the evolution of the dynamic prototyping techniques. Another potential direction for the research and development of charging and/or supply systems for vehicles with electric or hybrid propulsion consists of the extension of monitoring, identification, diagnosis, compensation and maintenance possibilities, both in terms of data availability and interpretation capabilities, as well as of the related tools involved in the same processes, in close connection with the expansion of the communication infrastructure and interconnected storage systems within an internet network, respectively with the improvement of extensible and intelligent systems for data processing, which facilitates their autonomy while operating. It would be wise to think about the future because, ultimately, it will be more and more complicated, but without disregarding the past, as it represents the landmark to which we relate and without neglecting the present because it is, after all, the one that will define us continuously as the wheel of time capsizes, over and over again. Acknowledgement. This paper has been financially supported within the project entitled “DECIDE – Development through entrepreneurial education and innovative doctoral and postdoctoral research”, project code POCU/380/6/13/125031, project co-financed from the European Social Fund through the 2014–2020 Operational Program Human Capital.

Research Trends and Opportunities Related to Charging

169

References 1. Burton, N.: A history of electric cars. Crowood, Ramsbury, England, UK (2013) 2. Zhou, S.W.W.: Carbon management for a sustainable environment. Springer, New York, NY, USA (2020) 3. VOSviewer Homepage. http://www.vosviewer.com/. Accessed on 01 Oct 2021 4. Bejenar, C., Afanasov, C.: Preliminary analysis of full-bridge DC-DC power converters with hard-switching topology and silicon carbide (SiC) semiconductors. In: 11th International Conference and Exposition on Electrical and Power Engineering (EPE). Iasi, Romania (2020) 5. Habib, S., Khan, M.M., Hashmi, K., Ali, M., Tang, H.: A comparative study of electric vehicles concerning charging infrastructure and power levels. In: 15th International Conference on Frontiers of Information Technology (FIT). Islamabad, Pakistan (2017) 6. Hannan, M.A., Hoque, M.M., Hussain, A., Yusof, Y., Ker, P.J.: State-of-the-art and energy management system of lithium-ion batteries in electric vehicle applications: issues and recommendations. IEEE Access 6, 19362–19378 (2018) 7. Aretxabaleta, I., De Alegría, I.M., Andreu, J., Kortabarria, I., Robles, E.: High-voltage stations for electric vehicle fast-charging: trends, standards, charging modes and comparison of unity power-factor rectifiers. IEEE Access 9, 102177–102194 (2021) 8. Lee, I.O., Lee, J.Y.: A high-power DC-DC converter topology for battery charging applications. Energies 10(7), 871 (2017) 9. Chinmaya, K.A., Singh, G.K.: A multifunctional integrated on-board battery charger for plugin electric vehicles (PEVs). In: 18th International Power Electronics and Motion Control Conference (PEMC). Budapest, Hungary (2018) 10. Zou, S., Lu, J., Mallik, A., Khaligh, A.: Bi-directional CLLC converter with synchronous rectification for plug-in electric vehicles. IEEE Trans. Ind. Appl. 54(2), 998–1005 (2018) 11. Abdelrahman, A.S., Erdem, Z., Attia, Y., Youssef, M.Z.: Wide bandgap devices in electric vehicle converters: a performance survey. Can. J. Electr. Comput. Eng. 41(1), 45–54 (2018) 12. Matallana, A., Ibarra, E., Lopez, I., Andreu, J., Garate, J.I., Jorda, X., Rebollo, J.: Power module electronics in HEV/EV applications: new trends in wide-bandgap semiconductor technologies and design aspects. Renew. Sustain. Energy Rev. 113, 109264 (2019) 13. Rajendran, G., Vaithilingam, C.A., Naidu, K., Prakash, K.S., Ahmed, M.R.: Hard switching characteristics of SiC and GaN devices for future electric vehicle charging stations. 14th International Engineering and Computing Research Conference—Shaping the Future through Multidisciplinary Research (EURECA). Subang Jaya, Malaysia (2020) 14. Bejenar, C., Bejenar, M., Toader, V.E., Grosu, O.V., Paval, M., Milici, L.D.: Extended possibilities for studying and diagnosis of electric vehicles AC (Three-Phase) charging systems. In: 9th International Conference on Modern Power Systems (MPS). Cluj-Napoca, Romania (2021) 15. Xiang, Y., Wang, Y., Xia, S., Teng, F.: Charging load pattern extraction for residential electric vehicles: a training-free nonintrusive method. IEEE Trans. Ind. Inf. 17(10), 7028–7039 (2021) 16. Shah, F.A., Shahzad Sheikh, S., Mir, U.I., Owais Athar, S.: Battery health monitoring for commercialized electric vehicle batteries: lithium-ion. In: 5th International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET). Istanbul, Turkey (2019) 17. Gao, D., Hou, J., Liang, K., Yang, Q.: Fault diagnosis system for electric vehicle charging devices based on fault tree analysis. In: 37th Chinese Control Conference (CCC). Wuhan, China (2018) 18. Kumar, R., Saxena, D.: Fault analysis of a distribution system embedded with plug-in electric vehicles. In: 2nd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE). Noida, India (2017)

170

C. Bejenar et al.

19. Gao, H., Meng, X., Qian, K., Zhang, W.: Research on intelligent diagnosis strategy and treatment method of EV charging fault. In: 5th International Conference on Control, Automation and Robotics (ICCAR). Beijing, China (2019) 20. Meng, X., Gao, H., Zhang, W., Liang, H.: Research on fault diagnosis of electric vehicle power battery based on attribute recognition. In: 5th Asia Conference on Power and Electrical Engineering (ACPEE). Chengdu, China (2020) 21. Aravena, I., Chapin, S.J., Ponce, C.: Decentralized failure-tolerant optimization of electric vehicle charging. IEEE Trans. Smart Grid 12(5), 4068–4078 (2021) 22. Ghavami, M., Singh, C.: Reliability evaluation of electric vehicle charging systems including the impact of repair. In: IEEE Industry Applications Society Annual Meeting (IAS). Cincinnati, OH, USA (2017)

Dynamic Charging Management for Electric Vehicle Demand Responsive Transport Tai-Yu Ma(B) Luxembourg Institute of Socio-Economic Research (LISER), 11 Porte Des Sciences, 4366 Esch-Sur-Alzette, Luxembourg [email protected]

Abstract. With the climate change challenges, transport network companies started to electrify their fleet to reduce CO2 emissions. However, such ecological transition brings new research challenges for dynamic electric fleet charging management under uncertainty. In this study, we address the dynamic charging scheduling management of shared ride-hailing services with public charging stations. A two-stage charging scheduling optimization approach under a rolling horizon framework is proposed to minimize the overall charging operational costs of the fleet, including vehicles’ access times, charging times, and waiting times, by anticipating future public charging station availability. The charging station occupancy prediction is based on a hybrid LSTM (Long short-term memory) network approach and integrated into the proposed online vehicle-charger assignment. The proposed methodology is applied to a realistic simulation study in the city of Dundee, UK. The numerical studies show that the proposed approach can reduce the total charging waiting times of the fleet by 48.3% and the total charged amount of energy of the fleet by 35.3% compared to a need-based charging reference policy. Keywords: Demand-responsive transport · Electric vehicle · Charging management · Charging station occupancy · Long short-term memory neural network

1 Introduction User-centered shared mobility services have been widely studied to reduce personal car use and enhance the accessibility of transit services in low-demand areas [3, 12]. With the climate change crisis, many transport network companies (TNC) started deploying electric vehicles for ride-hailing services to reduce CO2 emissions. However, due to limited driving range and long charging time, how to minimize electric vehicle (EV) charging costs and times given stochastic customer demand and uncertain public charging station availability has become an active research issue in recent years [5, 9, 13]. While the charging scheduling problems have been widely studied in the context of logistics, these studies focus mainly on deterministic green vehicle routing problems: partial recharge, non-linear charging function, capacitated charging station consideration © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_14

172

T.-Y. Ma

[2, 6]. However, electrified ride-hailing service operational policy design presents a more challenging issue that needs to jointly optimize the fleet management and charging scheduling under uncertainty. The problem involves the decisions related to determining when and how much energy to charge by considering vehicles’ driving needs and charged energy costs, and where to assign vehicles to charge during the daytime to minimize the waiting time and charging time with uncertain availability of public fast/rapid charging stations. A recent empirical study shows that electric ride-hailing service operations mainly rely on public DC fast-charging stations to save charging time [4]. As the number of DC fast chargers in a city is limited due to its high investment cost, an efficient charging management strategy needs to be developed by considering stochastic waiting time [7]. However, existing studies either assume uncapacitated charging stations or do not consider the stochastic charging demand of other EVs. Furthermore, most studies assume constant energy prices for recharging, while considering varying energy prices in vehicle routing-related problems are still limited [8]. While the location planning of the charging station might impact the operational costs of the operator, it is out of the scope of this study. In this work, a dynamic and predictive charging scheduling and vehicle-charging station assignment strategy with public charging stations are proposed for dynamic shared ride-hailing services using a fleet of EVs. The novelty of this research is considering the varying energy prices and uncertain public charging station availability to minimize the overall charging costs and waiting times at charging stations of the fleet while satisfying customers’ demands. The main contributions are summarized as follows. i.

Incorporate time-of-use energy price for vehicle charging scheduling optimization to minimize the overall charging costs of the fleet. ii. Integrate a predictive model using hybrid long short-term memory (LSTM) neural networks for public charging station occupancy prediction [10] into online vehiclecharging station assignment to minimize the overall charging operational time of the fleet. The results show that this predictive assignment strategy could reduce vehicle waiting times and energy costs significantly. iii. A numerical study is conducted using public charging session data of the city of Dundee, UK, to evaluate the performance of the proposed methodology.

2 Methodology Consider a TNC operating a fleet of EVs for providing shared ride-hailing service. Customers arrive stochastically on short notice with requested pick-up and drop-off locations with desired pickup times. A vehicle dispatching and routing policy (e.g. [1, 11]) is applied to provide customers with door-to-door mobility service. The fleet of vehicles is charged to full at the beginning of the day and then recharge during the daytime based on an optimized day-ahead charging plan (described later). We assume that the maximum allowed recharging level is 80 or 90% of battery capacity and the charging rate is linear. Different from existing studies that assume uncapacitated operator-owned charging infrastructure, we consider the problem of charging scheduling using public rapid charging stations with stochastic charging demand from other EVs. In doing so,

Dynamic Charging Management for Electric Vehicle

173

the waiting time when a vehicle arrives at a charging station is stochastic, depending on the day-to-day public charging station occupancy in the service area. We propose a vehicle battery replenishment model the overall charging  to minimize    costs of the fleet, i.e. a set of EVs, K = 1, . . . , k . Let H = 1, . . . , h, . . . , h denote the charging decision planning horizon, discretized into h charging decision epochs with a homogenous time interval  (e.g. 30 min). The energy price on epoch h is denoted as ϑh , unchanged within the same epoch but might vary over the different epochs. We formulate this charging scheduling problem as a mixed-integer linear programming (MILP) below. The objective function (1) minimizes the total charging costs of the fleet over the planning horizon, where ykh is a binary decision variable being 1 if the vehicle k is charged on epoch h. ukh denotes the decision of the amount of energy to be charged on epoch h for vehicle k. ϑh is the energy price on epoch h. c is a fixed cost for recharge, estimated as the average energy consumption costs to reach the charging stations per recharge operation. Constraint (2) is the energy conservation of vehicles with ekh being the state of charge (SOC) of vehicle k at the beginning of epoch h. dkh is the average energy consumption of vehicle k on epoch h, obtained from vehicles’ historical driving patterns. Constraint (3) assures that the SOC of vehicle k after recharging on epoch h is no less than a reserve energy level emin plus the expected energy consumption on that epoch. Constraint (4) ensures that ukh can be positive when ykh = 1. Constraints (5)–(7) setup the range of vehicles’ SOC and the maximum amount of energy umax that can be charged on one epoch, depending on the type of chargers used for vehicles. (P1) min

k  h    ϑh ukh + cykh

(1)

k=1 h=1

subject to ek,h+1 = ekh + ukh − dkh , ∀k ∈ K, h ∈ H

(2)

ekh + ukh ≥ dkh + emin , ∀k ∈ K, h ∈ H

(3)

ukh ≤ Mykh , ∀k ∈ K, h ∈ H

(4)

k ek0 = einit

(5)

emin ≤ ekh ≤ emax , ∀k ∈ K, h ∈ H

(6)

0 ≤ ukh ≤ umax , ∀k ∈ K, h ∈ H

(7)

ykh ∈ {0, 1}∀k ∈ K, h ∈ H

(8)

The above charging scheduling model provides an approximate charging plan for the fleet based on historical driving patterns of vehicles, disregarding different uncertain

174

T.-Y. Ma

factors related to charging station capacity constraints and vehicle access distance to charging stations, and stochastic availability of public charging stations. These factors affect the vehicles’ waiting time and charging operational costs, which will be optimized based on an online vehicle-charger assignment model below. An online vehicle-charger assignment model extended from [9] is proposed by considering public charging stations for which other EVs might compete with the available charging resource (arrival time and charging time of other EVs are unknown). For each charging decision epoch h, we solve the following online vehicle-charger assignment problem to determine where to recharge for the scheduled-to-charge vehicles obtained from the day-ahead charging scheduling model of P1 (Eqs. (1)–(8)). The predictive dynamic charging scheduling and vehicle-charger assignment framework is shown in Fig. 1. The input data includes historical vehicle driving patterns of the fleet, experienced charging waiting times at the charging stations, locations of the charging stations, and energy prices in the study area. First, problem P1 is solved one day ahead while problem P2 (Eqs. (9)–(18) below) is solved at the beginning of each decision epoch h ∈ H . Different from [9], we integrate the charging station occupancy prediction (on individual charger level) based on the hybrid LSTM neural networks (hybrid LSTM block in Fig. 1) under the rolling horizon framework 1 (i.e. next hour from the beginning of decision epoch h) [10]. At the end of the day, the operator updates vehicle experienced waiting times and driving patterns as input for the P1 problem. This P2 problem is formulated as a MILP as (9)–(18) by extending the model of [9].

Fig. 1. Predictive dynamic charging scheduling and assignment framework for shared ride-hailing service.

Notation I

Set of vehicles

J

Set of chargers

tij

Travel time from the location of vehicle i to that of charger j (continued)

Dynamic Charging Management for Electric Vehicle

175

(continued) dij

Travel distance from the location of vehicle i to that of charger j

ei

Energy level of vehicle i at the beginning of epoch h (index h is dropped)

ei∗

Energy level of vehicle i after recharge at the end of epoch h, determined by the charging schedule obtained from solving the P1 problem (index h is dropped)

aij , bij

Predicted start and end time of a charging session of charger j when vehicle i arrives   at charger j. Let gj1 , gj2 , . . . , gjs be the predicted sequence of start/end charging session time series over a predictive horizon (e.g. one-hour ahead) with gj1 and gjs being the start time and end time of the prediction horizon, respectively. For a time slot r, [gjr , gj,r+1 ] with gjr ≤ tij ≤ gj,r+1 , if charger j is not available, then aij = gjr , bij = gj,r+1 , otherwise aij = bij = gjs

μ

Energy consumption rate of vehicles (kWh/km)

ϕ

Charging rate of chargers (kW/min)

M1 , M2 Large positive numbers θ1 , θ2

Parameters

Decision variable Xij

Vehicle i is assigned to charger j for a recharge if Xij = 1, and 0 otherwise

Yij

Amount of energy recharged at charger j for vehicle i

Sij

An artificial variable representing the waiting time of vehicle i at charger j

Wj

Binary variable

(P2) min Z =



tij Xij + θ1

i∈I j∈J



Yij /ϕj + θ2

i∈I j∈J



Sij

(9)

i∈I j∈J

subject to 

Xij = 1, ∀i ∈ I

(10)

Xij ≤ 1, ∀j ∈ J

(11)

j∈J

 i∈I

  emin ≤ ei − μdij Xij + M1 1 − Xij , ∀i ∈ I , j ∈ J

(12)

  ei∗ ≤ Yij + ei − μdij Xij + M1 1 − Xij , ∀i ∈ I , j ∈ J

(13)

Yij ≤ M1 Xij , ∀i ∈ I , j ∈ J

(14)

    aij − tij − M1 1 − Xij ≤ M2 1 − Wj , ∀i ∈ I , j ∈ J

(15)

    bij − tij Xij − M1 1 − Xij ≤ Sij + M2 1 − Wj , ∀i ∈ I , j ∈ J

(16)

176

T.-Y. Ma

  aij − tij + M1 1 − Xij + M2 Wj > 0, ∀i ∈ I , j ∈ J

(17)

Xij , Wj ∈ {0, 1}, Yij , Sij ≥ 0, ∀i ∈ I , j ∈ J

(18)

The objective function (9) minimizes the total charging operational time as a weighted sum of the vehicle’s access time to charging stations tij , charging times Yij /ϕj and expected waiting time at charging stations Sij . Constraints (10) and (11) state that each vehicle is assigned to exactly one charger and each charger can be assigned at most one vehicle. Constraint (12) states the SOC of vehicles is no less than the reserve level emin when arriving at a charger. Constraint (13) states that the SOC of vehicles after recharge needs to be no less than the planned battery level ei∗ . Constraint (14) ensures that the amount of energy to be recharged is positive when there is a charging event. Constraint (15)–(17) calculates the vehicle’s waiting times when arriving at charging stations. Note that in case that |I | ≥ |J |, constraints (10) and (11) need to be revised accordingly [9]. Figure 2 explains how the expected waiting time is calculated by eqs. (15)–(17). In Fig. 2(b) if a charger j is predicted to be occupied by another vehicle within [gj3, gj4 ], vehicle i’s expected waiting time when arriving at tij will be gj3 − tij . The charging occupancy prediction is based on a hybrid LSTM model by incorporating relevant features including time of day, day of the week, whether the day is weekday/weekend, average charging occupancy rate profiles on weekdays and weekends, and historical k-step backward charging occupancy states. The reader is referred to [10] for a more detailed description. The P1 problem can be easily solved using commercial solvers. The P2 problem is a generalized assignment problem for which the Lagrangian relaxation algorithm developed in [9] is applied to obtain near-optimum solutions for large test instances.

Fig. 2. (a) Concept framework of different decision/prediction horizons; (b) Waiting time estimation based on the predicted charging station occupancy using hybrid LSTM approach [10].

3 Numerical Study 3.1 Study Area and Charging Session Data We consider a simulation case study of shared ride-hailing service in the city of Dundee, UK (Fig. 3). Customers’ ride requests are generated randomly in the study area. A

Dynamic Charging Management for Electric Vehicle

177

TNC operates a fleet of small electric capacitated vehicles to provide users door-to-door microtransit service. Vehicles’ dispatching and routing policy is based on a non-myopic policy by considering future system costs when inserting a new customer on existing routes of vehicles [11]. Customer demand for a full day is assumed 800, randomly generated in the study area following some empirical probability distribution of their arrival time. The fleet size is assumed as 40 4-seater electric vehicles. A depot is located around the center of the study area. We assume that the fleet is fully charged at the beginning of the day and due to the battery capacity constraint, vehicles need to be recharged in the daytime using public rapid chargers to ensure their SOCs are always no less than a reserve level (20% of the battery capacity). We assume that the TNC charges their vehicles using rapid chargers only [4]. The public charging session data (https://data.dundeecity.gov.uk/dataset/ev-charging-data) in the study area is used for the charging station occupancy prediction [10]. This prediction model for a one-hour ahead prediction has an average accuracy of 81.87%. An illustrative example of the observed and predicted occupancies on a rapid charger is shown in Fig. 4. We can observe that the model predicts approximately the charging occupancy pattern of the rapid charger. We generate randomly 15 demand datasets with 800 random ride requests corresponding to 15 weekdays (3 weeks from 14/5/2018 to 1/6/2018). Different from [9], the energy consumption needs dkh in Eq. (2) is obtained using the first two-week datasets with a need-based policy, i.e. a vehicle goes to recharge at a nearest rapid charger whenever its SOC is lower than 25% of the battery capacity. An example of the customer’s arrival time distribution is shown in Fig. 5. Table 1 reports the parameters used for the simulation studies.

Fig. 3. Simulation case study on the city of Dundee, UK. The blue points are randomly generated pickup/drop-off locations of customers. There is one depot and 9 rapid chargers located in 6 different locations.

178

T.-Y. Ma

Fig. 4. Example of observed and predicted occupancies of a rapid charger on a weekday [10]. The y-axis is the binary state (occupied or not by an EV) of a charger. The x-axis is time (in minutes), starting from a reference point. The predicted occupancy is reported for 60 min ahead over time. We can observe that the prediction captures well the charging station occupancy patterns over different vehicle arrival intensities. Table 1. The parameter setting for the simulation case study. Number of customers

800

ϑ

0.252 (£/kWh)

Number of vehicle depots

1

μ

0.204 (kWh/km)

Fleet size

40

c

0.3 £

Capacity of vehicles

4 pers./veh



30 min

Vehicle speed

65 km/hour

T

6:30–22:00

Battery capacity (B)

62 kWh1

ϕDC fast

50/60 (kW/min.)

Number of rapid chargers

9

β

0.025

emin

0.2B

γ

0.5

emax

0.8B

1 Nissan LEAF E+. https://www.tribus-group.com/zero-emission-volkswagen-e-crafter-electric-

wheelchair-minibus/. https://ev-database.org/car/1144/Nissan-Leaf-eplus 2 https://www.dundeecity.gov.uk/news/article?article_ref=4137#:~:text=The%20new%20tari

ffs%20will%20be,when%20Scottish%20Government%20subsidies%20ended.

Fig. 5. Example of customer arrival times in the study area.

Dynamic Charging Management for Electric Vehicle

179

The discrete event simulation technique is applied to include queueing delays at charging stations. The simulation pseudocode is described in Algorithm 1. Algorithm 1. Simulation Pseudocode for Optimal Vehicle-Charging Station Assignment

180

T.-Y. Ma

3.2 Results To validate the proposed methodology, we compare the performance of the proposed non-myopic optimal charging policy (OCP) (eqs. (1)–(18)) with the need-based policy. We evaluate also the benefits of with (OCP1) or without (OCP0) predictive information of future charging occupancy states in terms of charging waiting time savings. The simulation results are the average performance obtained from 5 test datasets (i.e. one represents a randomly generated 800 customers in the study area) during the third week. Table 2 presents the results of the comparison. The performance measures in terms of average waiting time, average charging time, and average operational time for a charge are based on each charging session. The total waiting times for recharge of the fleet, total charging time of the fleet, and the total charged amount of energy of the fleet are presented in the last three columns. We can observe that using the OCP0 policy could reduce the average charging waiting time from 24.2 min to 20 min while applying OCP1 could lead to a significant waiting time reduction to 12.2 min (−49.5% compared to the need-based policy). When comparing the benefits of incorporating the predictive information, applying the OCP1 policy could reduce the average charging waiting time

Dynamic Charging Management for Electric Vehicle

181

by −38.8%. In terms of the total charged amount of energy, the OCP1 policy could lead to significant charging costs savings by 35.3% compared to the need-based policy. Table 2. Comparison of the need-based policy and the non-myopic optimal charging policy with and without predictive information. Charging policy1

ACWT2 (min)

ACT (min)

AOTC (min)

TWT (h)

TCT (h)

TCE (kWh)

NP

24.2

43.4

69.4

35.7

63.3

3165.5

OCP0

20.0

27.3

49.1

28.9

39.5

1976.9

−37.0%

−29.3%

−18.9%

−37.5%

27.2

41.6

18.4

41.0

OCP* vs. −49.5% NP

−37.2%

−40.1%

−48.3%

−35.3%

−35.3%

OCP* vs. −38.8% OCP0

−0.4%

−15.2%%

−36.3%

3.6%

3.6%

OCP0 vs. −17.5% NS OCP*

12.2

−37.5% 2048.9

Remark: 1. NP: need-based policy; OCP0: Optimal charging policy without predictive information; OCP*: Optimal charging policy with predictive information. 2. ACWT: Average charging waiting time, ACT: Average charging time, AOTC: Average operational time for a recharge, TWT: Total waiting time of the fleet on a day, TCT: Total charging time of the fleet on a day, TCE: Total amount of charged energy of the fleet on a day.

4 Conclusions and Discussion Dynamic electric ridesharing fleet charging management under uncertainty is a challenging research issue. While existing studies mainly focus on deterministic electric vehicle routing problems in the logistic context, this study considers dynamic charging scheduling optimization problems with stochastic waiting time at public charging stations. A two-stage optimization approach is proposed to solve this problem and compared to a reference need-based charging policy. Different from our previous study [9], we integrate the predictive information of charging station occupancy in a rolling horizon framework to minimize the expected waiting time using public charging stations. The proposed methodology is tested using the public charging session data of the city of Dundee, UK. Our results show that the proposed charging strategy could lead to significant charging waiting time savings and reduce the amount of charged energy on the day, compared to the reference need-based charging policy. The limitation of this research is that the day-to-day energy consumption of vehicles is stochastic and needs to be updated accordingly. Future extensions could consider a learning process to track vehicles’ day-to-day energy consumption patterns to better estimate their energy needs and derive more adaptive charging plans under uncertain

182

T.-Y. Ma

environments. Using a reinforcement learning approach or a hybrid approach to solve such dynamic decision-making problems under uncertainty are promising solutions to be studied in the future. Acknowledgement. The work was supported by the Luxembourg National Research Fund (C20/SC/14703944).

References 1. Alonso-Mora, J., et al.: On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proc. Natl. Acad. Sci. 114(3), 462–467 (2017). https://doi.org/10.1073/pnas. 1611675114 2. Asghari, M., Mirzapour Al-E-Hashem, S.M.J.: Green vehicle routing problem: a state-ofthe-art review. Int. J. Prod. Econ. 231, 107899 (2021). https://doi.org/10.1016/j.ijpe.2020. 107899 3. Chow, J. et al.: Spectrum of Public Transit Operations : From Fixed Route to Microtransit (2020) 4. Jenn, A.: Electrifying Ride-Sharing: Transitioning to a Cleaner Future. https://3rev.ucdavis. edu/policy-brief/electrifying-ride-sharing-transitioning-cleaner-future. Last accessed 08 Dec 2021 5. Keskin, M., et al.: Electric vehicle routing problem with time-dependent waiting times at recharging stations. Comput. Oper. Res. 107, 77–94 (2019). https://doi.org/10.1016/j.cor. 2019.02.014 6. Keskin, M., Çatay, B.: Partial recharge strategies for the electric vehicle routing problem with time windows. Transp. Res. Part C Emerg. Technol. 65, 111–127 (2016). https://doi.org/10. 1016/j.trc.2016.01.013 7. Kim, J., et al.: Scheduling and performance analysis under a stochastic model for electric vehicle charging stations. Omega 66, 278–289 (2017). https://doi.org/10.1016/j.omega.2015. 11.010 8. Klein, P.S., Schiffer, M.: Electric Vehicle Charge Scheduling with Flexible Service Operations (2022). https://arxiv.org/abs/2201.03972 9. Ma, T.-Y.: Two-stage battery recharge scheduling and vehicle-charger assignment policy for dynamic electric dial-a-ride services. PLoS ONE 16(5), e0251582 (2021). https://doi.org/10. 1371/journal.pone.0251582 10. Ma, T.-Y., Faye, S.: Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks. Energy 244, 123217 (2022). https://doi.org/10.1016/j.energy. 2022.123217 11. Ma, T.Y., et al.: A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers. Transp. Res. Part E Logist. Transp. Rev. 128, 417–442 (2019). https://doi.org/10.1016/j.tre.2019.07.002 12. Ma, T.Y., et al.: A user-operator assignment game with heterogeneous user groups for empirical evaluation of a microtransit service in Luxembourg. Transp. A Transp. Sci. 17(4), 946–973 (2021). https://doi.org/10.1080/23249935.2020.1820625 13. Shi, J., et al.: Operating electric vehicle fleet for ride-hailing services with reinforcement learning. IEEE Trans. Intell. Transp. Syst. 21(11), 4822–4834 (2020). https://doi.org/10.1109/ TITS.2019.2947408

A Regional Civilian Airport Model at Remote Island for Smart Grid Simulation Georgios Vontzos(B) and Dimitrios Bargiotas Department of Electrical and Computer Engineering, University of Thessaly, Thessaly, Greece [email protected]

Abstract. The purpose of this study is to design and implement a scientific tool which will be used to investigate the application of smart grids in the aviation industry and to evaluate the proof of concept. A case study for a regional Greek airport is proceeded with the development of a co-simulation agent-based model which includes building and electrical system simulation, climate data, flights, and passengers’ flow. In terms of methodology, the load types and schedule will be studied, like HVAC, building and runway lighting. It is presented how passengers fluctuation affects each type of load and, as a result, energy consumption throughout different hours of the day, depending on weather conditions. After the collection and validation of the above data, a model of each type of load at a typical regional civilian airport will be created. Finally, the models will be used with software packages and the co-simulation framework. The produced results are evaluated and are presented. Keywords: Airport · Energy · Co-simulation · Smart grid · Microgrid

1 Introduction The rapid growth of flights and the number of passengers which are travelling worldwide, leads to an increased pressure to the aviation infrastructures. The need for parking lots increases, terminal capacities exceed the expected, construction of bigger aircrafts requires larger facilities. Also, check-in, baggage handling and passenger information collection requires innovative technological methods. Additionally, airports in general have high-energy consumption. To operate, airports consume large amounts of electricity, among other natural resources. A key factor to minimize energy consumption at airports is understanding the nature of energy use and consumption behavior, due to the various parameters and singularities involved. The purpose of this study is to design a scientific tool which will be used to investigate the application of smart grids in the aviation industry and to evaluate the proof of concept. In current document, recent literature is reviewed (Sect. 2). The airport categorization by size, usage and region typology is presented. Airport building types and infrastructure are demonstrated. Smart airport concept is analyzed, and airport energy autonomy is reviewed. Section 3 analyses the methodology of the research and the selected software packages. In Sect. 4 a case study for a regional civilian airport at remote island is presented. Finally, on Sect. 5 the main conclusions are summarized, and future work are discussed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_15

184

G. Vontzos and D. Bargiotas

2 Literature Review 2.1 Airport’s Categorization An airport is the defined area on land or water (including any buildings, installations, and equipment) intended to be used either wholly or in part for the arrival, departure and surface movement of aircraft [1]. Airports are classified into three basic categories according to the size, the runway - buildings usage and region typology. In the first category, airports are distinguished according to their size as main or major and regional. Main airports serve several million passengers per year from international and domestic flights. The evolution of main airports is the airport cities (Aerotropolis) and the hubtropolis. On the other hand, regional airports serve only several thousand passengers per year, mostly from domestic flights. In the second category, the majority of airports are used for civilian aviation including cargo flights. Moreover, runway and buildings could exclusively utilize for military purposes, or as a combination of heterogenous usage for military and civilian flights. Furthermore, an airport could host Search and Rescue (SAR) services [2]. In the third category, five typologies of airport regions are distinguished. The urban airports with high concentrations of urban land use and the population at their adjacency. Urban periphery airports consist of the airports in proximity of urban areas and high concentration of industrial and leisure. Agricultural-area airports are solely characterized by adjacency to agricultural land use. Natural-area airports are characterized by closeness to natural areas and distance from leisure, industry, and major road network. Remote airports are characterized by being located at a long distance from all five land uses and population centers [3]. 2.2 Buildings and Infrastructure An airport could be analogized and function as a city with three major types of buildings and infrastructure. These are commercial, industrial, and residential according to the size and usage of the airport and the surrounding area. As commercial buildings could be considered passenger terminals, hotels, shopping malls, parking garages, consolidated rental-car facilities, fire and rescue stations and administrative buildings. As industrial infrastructure could be assumed Air Traffic Control (ATC) towers, airport pavements (runway, taxiway, and apron), cargo buildings, hangars, Maintenance, Repair and Overhaul Facilities (MRO), and storage facilities [4]. As residential buildings could be the residencies of workers and employees around the central hub of an airport. Empirically, a regional civilian airport has a passenger’s terminal as a commercial building and an ATC tower and runway as industrial infrastructure. Moreover, a military airport which hosts a regional civilian airport has commercial buildings only at the civilian airport side, and industrial infrastructure only at the military side. On the other hand, a main or major airport incorporates all the above-mentioned types of buildings and infrastructure at a different grade of complexity and dimensioning. Largely airports or ‘aerotropolis’, like London’s Heathrow or Singapore’s Changi airports, incorporate residential buildings to host the urban community based around the central hub of the

A Regional Civilian Airport Model at Remote Island

185

airport [5]. Additionally hubtropolis, like the Shanghai Hongqiao integrated transportation hub, a brand-new urban form of integrating air, rail, and other transportation modes as well as urban functions [6]. 2.3 Smart Airport The Smart airport model, also known as Airport 4.0, in the concept of a smart city is the future of airport operation [7]. The ISO Standard 37120/2018, titled “Sustainable cities and communities”, defines nineteen indicators that are focused on city services and quality of life as a contribution to the sustainability of the city [8]. A smart airport as a part of a smart city could adopt several indicators and connected externally with the subsystems of a smart city. For example, information systems of energy consumption, environmental properties, human resources, etc. The adaption of smart technologies and the use of the Internet of Things (IoT) could lead to improved passengers’ convenience [9], ensure aviation security [10], improve operational efficiency [11] and optimize limited resource [12, 13]. The Smart airport concept is the best solution for optimum utilization of limited airport resources including terminal, airside, landside, and energy. On top of that, smart technology helps to reduce energy consumption by energizing lighting and Heating, Ventilation and Air Conditioning (HVAC) based on passengers’ flow [9]. 2.4 Airport Energy Autonomy In terms of airports energy autonomy, many attempts have been made in order to minimize the energy consumption from the commercial grid, to mitigate CO2 emissions and to achieve the global decarbonization goal by 2050. Systems which are being installed in airports to produce electricity, are cogeneration plants [14], solar photovoltaic systems [15], and wind turbines [16].

3 Methodology Even though existing literature mostly focuses on Renewable Energy Sources (RES) which can be utilized in airports, few studies have investigated the applicability of smart grid technologies in aviation industry. The electrical distribution network, the renewable sources, and the electrical storage systems of the airport could be considered as a Micro Grid (MG) or as a part of a larger grid, which incorporates Machine Learning (ML) and Artificial Intelligence (AI) technologies to predict consumption, generation, and bidding price of energy, to energize building automation systems and to perform load shedding. This MG could use Energy Storage Systems (ESS) in order to store electrical energy from renewable sources. Furthermore, on the lack of these sources, the ESS are charged from the grid at a low electricity tariff. The stored energy can be used for peak load shaving in peak hours. Types of ESS could be Battery Storage Systems (BSS) and Electrical Vehicles (EV). Additionally, the airport’s MG could sell electrical energy, in excess of energy, to the grid.

186

G. Vontzos and D. Bargiotas

In order to perform a study on smart grid technologies applied on airport infrastructures and electrical distribution networks, a scientific tool should be developed due to various parameters should take into consideration by the researchers during the simulation of the case studies. The simulation platform chosen for this is the Transactive Energy Simulation Platform (TESP) [17] which uses the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS) [18] to interact during execution between the simulators and custom agents. Agents were developed using Python language. The designed simulation framework is illustrated in Fig. 1.

Fig. 1. The designed simulation framework

3.1 Terminal Building Model For the design and simulation of the terminal’s model, software package EnergyPlus [19] is used. The modelled building is a simplified version of the actual terminal station. Several construction parts of the building, like shading, second floor, skylights, offices, basement, and shops are left intentionally, from the researchers, as a future work, which are taken into consideration that will affect slightly the results. The building consists of three thermal zones corridor – check-in area, arrivals, and departures. For each zone various internal gains are set, which influence the energy consumption, for instance people – occupants, internal lights, and electric equipment. The maximum values for each type are illustrated at Table 1. The HVAC system is sized and simulated from the software package with the use of Energy Plus Weather (EPW) files.

A Regional Civilian Airport Model at Remote Island

187

Table 1. Thermal zones’ internal gains maximum values Zone

Lighting (W)

Electric equipment (W)

Corridor

50,000

10,000

Arrivals

40,000

10,000

Departures

40,000

10,000

3.2 Flights and Passengers The passengers flow in the terminal’s thermal zones increases significantly the thermal load and affects the energy consumption. The number of people in the unit of time should be change respectively with flights schedule and not to be a constant number. So as to succeed that an agent was designed to generate daily passengers time series for each thermal zone. Due to lack of flights and passengers’ hourly data the agent was designed accordingly to the airport’s dataset for each month of simulation [20]. Flights are distributed evenly to each day. Percentage capacity of each flight is calculated randomly according to the quotient which results when passengers per day is divided by flights per day. The algorithm used to produce the passengers time series is a slight variation to that proposed from other researchers [21–23], with the following assumptions: (i) international and domestic flights use the same size of aircraft during the summer months simulation, (ii) flights are on schedule, (iii) no passengers for transit flights, (iv) passengers are checked-in online and carry only hand luggage, (v) passengers go on directly from zone to zone, (vi) passengers arrival time at check-in before the scheduled time of departure (STD) is assumed that follows the relationship that Ashford et al. demonstrates [24] and (vii) airport operates only certain predefined hours. 3.3 Electrical Distribution Network Model A model of the electrical distribution network is designed to interconnect the previously described EnergyPlus building model with the other loads and buildings which compose the airport’s complex. ATC tower building is designed as constant power load. On the contrary, runway and taxi lighting is considered as a constant power and scheduled load. For the current simulation period the runway lights are activated for four hours, from 20:00 to 24:00. In addition, a wind turbine is connected at Node 2 in order to validate the feasibility to supply the airport complex exclusively from RES. In Fig. 2, the designed distribution network and its components are presented. 3.4 Climate Data The simulations are performed with weather conditions from the under-investigation airport. The climate data came from a PVGIS [25] in EPW format for the EnergyPlus software and TMY3 format to feed an agent to forecast weather conditions for each hour of the simulation period.

188

G. Vontzos and D. Bargiotas

Fig. 2. The designed distribution model

4 Case Study Analysis: A Regional Civilian Airport at Remote Island In order to test, at the early stages of this research, the proposed simulation framework, a typical regional civilian airport, in Karpathos Island, Greece, was selected as a case study. This island was selected because is not a popular tourist destination and the island’s airport could serve confined number of passengers and flights. The apron capacity is two big jet engine passenger aircrafts and two smaller. In year 2018 served nearly 3.6K flights and 246K passengers. Domestic airline flights flew the whole year and were doubled in amount during the summer holidays months. On the other hand, international airlines flew from May to October. The 61% of the flights are from the mainland and rest 39% are from abroad. In contrast, almost 80% of the passengers came from international airline flights and the rest 20% from domestic flights. The difference between flights and passengers is due to the size of the airplane’s airlines use. Domestic, off season, flights are served with small propeller airplanes, with maximum capacity of 78 passengers, and international airlines operate with bigger jet aircrafts with capacity of 160 passengers. 4.1 Study Design The airport building model is roughly presented at the previously paragraphs. The model is simulated for a thirty-day period from 1 to 30 July and various variables of the building are computed. During simulation, meteorological data from Karpathos Island is used. The passengers time series agent uses the airport’s historical data for the year 2018. Additionally, the airport’s working hours are from 8:00 to 20:00. The generated time

A Regional Civilian Airport Model at Remote Island

189

series from passenger’s agent is illustrated in Fig. 3 which shows passengers’ fluctuation in terminal’s thermal zones for four different days in the simulation period. Furthermore, due to airport’s working hours and the month of simulation, flights arrive after sunrise, depart before the sunset and runway - taxi lighting is not energized.

Fig. 3. Passengers’ fluctuation in airport’s terminal thermal zones, with red dashed vertical lines airport’s working hours is illustrated.

4.2 Results Analysis Terminal’s Electrical Demand. Figure 4 presents the simulated terminal’s load shape (Total) for several days of simulation. It is analyzed in three individual loads equipment, lights, and HVAC. In the second row the passengers’ fluctuation at each thermal zone is presented. Additionally, in the third row outdoor, indoor, and cooling setpoint temperatures are plotted. It can be seen, especially in simulation days 10, 19 and 26, how the peaks of occupants affect indoor temperature, power consumption and are captured as corresponding peaks at indoor temperature plot. Furthermore, Table 2 lists the minimum, mean and maximum power demand for the terminal and three individual loads. Airport’s Complex Power Consumption-Production. The total power that is produced from the installed wind turbine oversubscribes airport’s complex consumed power. Although, as shown in Fig. 5a, the produced power from the wind is not stable and is capable to feed entirely the airport complex approximately for 60% of the simulation period. Moreover, Fig. 5b presents real power measured at the substation during the simulation. The power which is consumed from the airport complex is shown with positive values. The remaining power, which wind turbine produces, is returned to the network and is shown with negative values.

190

G. Vontzos and D. Bargiotas

Fig. 4. Terminal’s power demand, passengers time series, and temperatures for several days of simulation, with red dashed vertical lines airport’s working hours is presented. A timestep of 5-min interval is used for the simulation. Table 2. Terminal’s power demand Building electrical demand (MW) Min

Mean

Max

Total facility power

0.138

0.265

0.357

HVAC power

0.101

0.174

0.210

Lights power

0.002

0.018

0.036

Equipment power

0.010

0.010

0.010

Fig. 5. a) Airport’s power consumption and wind turbine power production, b) Total power and power losses at substation.

5 Conclusions The number of passengers flying between destinations increases power consumption at airports. In this paper a scientific tool has been developed to investigate the nature of

A Regional Civilian Airport Model at Remote Island

191

energy use and consumption behavior, which is a co-simulation agent-based model with the use of several software packages. This model incorporates building and electrical distribution system simulation, climate data, flights, and passengers’ flow. A case study was conducted for a typical regional civilian airport in Greece. The results of this case study indicate that the increased gathering of passengers to a terminal’s thermal zone, increases the indoor temperature and power consumption from the HVAC. Additionally, the installed wind turbine could not feed entirely the airport complex due to wind speed variation. However, the remaining power could be used to charge a BSS in order to feed the airport in case of reduced or zero power production from the wind turbine. Alternatively, the excess power could be sold to the network for reduction of CO2 emissions from fossil fuel power generation. As mentioned above, several key points must be taken into consideration to generate more accurate results. Future work will concentrate on creating a detailed terminal building model and minimizing the assumptions of the passenger’s time-series generation model. In the following step, the distribution network will be modified and other types of RES and ESS will be added. Moreover, airplane hangar and fire station building loads will be added. Furthermore, the ATC tower will be designed as an EnergyPlus building model so weather conditions and occupants affect building power demand. This work is suitable for smart-grid simulations which include passengers’ behavior, climate data and energy pricing policies. For that reason, several smart-grid simulation experiments are in the designing process to confirm the effectiveness of the proposed model.

References 1. International Civil Aviation Organization: Aerodromes - Volume I: Aerodrome Design and Operations. Annex 14 to the Convention on International Civil Aviation (2018) 2. Dziedzic, M., Njoya, E.T., Warnock-Smith, D., Hubbard, N.: Determinants of air traffic volumes and structure at small European airports. Res. Transp. Econ. 79, 100749 (2020). https:// doi.org/10.1016/j.retrec.2019.100749 3. Mashhoodi, B., van Timmeren, A.: Airport location in European airport regions: five typologies based on the regional road network and land use data. Data Br 29, 105317 (2020). https:// doi.org/10.1016/j.dib.2020.105317 4. Wikipedia Airport: https://en.wikipedia.org/wiki/Airport. Accessed 30 Jul 2020 5. Charles, M.B., Barnes, P., Ryan, N., Clayton, J.: Airport futures: towards a critique of the aerotropolis model. Futures 39, 1009–1028 (2007). https://doi.org/10.1016/j.futures.2007. 03.017 6. Chen, X., Lin, L.: The node-place analysis on the “hubtropolis” urban form: the case of Shanghai Hongqiao air-rail hub. Habitat Int. 49, 445–453 (2015). https://doi.org/10.1016/j. habitatint.2015.06.013 7. Rajapaksha, A., Jayasuriya, D.N.: Smart airport: a review on future of the airport operation. Glob. J. Manage. Bus. Res. 25–34 (2020). https://doi.org/10.34257/gjmbravol20is3pg25 8. ISO 37120:2018(en): Sustainable Cities and Communities—Indicators for City Services and Quality of Life. https://www.iso.org/obp/ui/#iso:std:68498:en. Accessed 12 Aug 2020 9. Almashari, R., Aljurbua, G., Alhoshan, L., et al.: IoT-based smart airport solution. In: 2018 International Conference on Smart Applications, Communications and Networking, SmartNets 2018. Institute of Electrical and Electronics Engineers Inc. (2018)

192

G. Vontzos and D. Bargiotas

10. Jalali, R., Zeinali, S.: Smart flight security in airport using IOT (Case study: airport of Birjand). Int. J. Comput. Sci. Softw. Eng. 7 (2018) 11. Al Nuaimi, E., Al Neyadi, H., Mohamed, N., Al-Jaroodi, J.: Applications of big data to smart cities. J. Internet Serv. Appl. 6(1), 1–15 (2015). https://doi.org/10.1186/s13174-015-0041-5 12. Castillo-Manzano, J.I., López-Valpuesta, L.: Check-in services and passenger behaviour: self service technologies in airport systems. Comput. Human Behav. 29, 2431–2437 (2013). https://doi.org/10.1016/j.chb.2013.05.030 13. Chang, Y.C., Lee, W.H., Wu, C.H.: Potential opportunities for Asian airports to develop selfconnecting passenger markets. J. Air Transp. Manage. 77, 7–16 (2019). https://doi.org/10. 1016/j.jairtraman.2019.02.001 14. Alba, S.O., Manana, M.: Energy research in airports: a review. Energies 9, 1–19 (2016). https://doi.org/10.3390/en9050349 15. Sreenath, S., Sudhakar, K., Yusop, A.F.: Airport-based photovoltaic applications. Prog. Photovoltaics Res. Appl. (2020). https://doi.org/10.1002/pip.3265 16. Hariprasad, V., Anand, A.V., Sahana, J., et al.: Energy support to airports by wind systems. In: Proceedings of the International Conference on Electronics, Communication and Aerospace Technology, ICECA 2017, pp. 748–751. Institute of Electrical and Electronics Engineers Inc. (2017) 17. Battelle Memorial Institute: Transactive Energy Simulation Platform (TESP). https://tesp.rea dthedocs.io/en/latest/TESP_Overview.html (2020). Accessed 27 Aug 2021 18. Palmintier, B., Krishnamurthy, D.: Design of the HELICS High-Performance TransmissionDistribution-Communication-Market Co-Simulation Framework (2017). ieeexplore.ieee.org 19. EnergyPlus Energy Simulation Program. https://energyplus.net/. https://www.researchg ate.net/publication/230606369_EnergyPlus_Energy_Simulation_Program. Accessed 31 Jul 2021 20. Database - Air Passenger Transport by Reporting Country - Eurostat. https://ec.europa.eu/eur ostat/data/database?node_code=avia_paoc. Accessed 1 Mar 2022 21. Fonseca, I., Casas, P., Casanovas, J., Ferran, X.: Passenger flow simulation in a hub airport: an application to the Barcelona international airport. Simul. Model. Pract. Theory 44, 78–94 (2014). https://doi.org/10.1016/J.SIMPAT.2014.03.008 22. Alodhaibi, S., Burdett, R.L., Yarlagadda, P.K.D.V.: Framework for airport outbound passenger flow modelling. Procedia Eng. 174, 1100–1109 (2017). https://doi.org/10.1016/J.PROENG. 2017.01.263 23. Takakuwa, S., Oyama, T.: Simulation analysis of international-departure passenger flows in an airport terminal. Winter Simul. Conf. Proc. 2, 1627–1634 (2003). https://doi.org/10.1109/ wsc.2003.1261612 24. Ashford, N., Hawkins, N., O’Leary, M., et al.: Passenger behavior and design of airport terminals. Transp. Res. Rec. 18–26 (1976) 25. JRC Photovoltaic Geographical Information System (PVGIS) - European Commission. https://re.jrc.ec.europa.eu/pvg_tools/en/tools.html. Accessed 1 Mar 2022

An Innovative Smart Charging Framework for Efficient Integration of Plug-In Electric Vehicles into the Grid Stylianos I. Vagropoulos1(B) , Stratos D. Keranidis1 , Zafeirios N. Bampos2 , and Konstantinos D. Afentoulis1 1 Department of Energy Systems, University of Thessaly, Gaiopolis Campus, Ring Road of

Larissa-Trikala, 41500 Larissa, Greece [email protected] 2 School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Abstract. The massive, uncontrolled charging of numerous electric vehicles from the grid will create problems in the proper and reliable operation of the electricity networks. A very promising solution is the application of controlled and coordinated charging of electric vehicles, also known as smart charging. During smart charging, the charging time and rate of an electric vehicle are controlled. The development of an integrated smart charging solution meets significant technical challenges and requires the cooperation of numerous stakeholders. The electric vehicle aggregator is a new entity that can take over the central management of the smart charging of numerous electric vehicles and interact with the various stakeholders in an optimal way. This paper presents a prototype integrated tool for the management of smart charging by an electric vehicle aggregator in order to provide cost-effective charging to electric vehicle users while providing ancillary services to the system and network operators. Keywords: Ancillary services · Balancing market · Batteries · Electric vehicle · Electric vehicle aggregator · OCPP · Smart charging

1 Introduction In the course of making Europe more sustainable, European Member States have started to adopt ambitious climate policies and legislative initiatives, like the UN’s Paris Agreement (United Nations 2015) for greenhouse gas (GHG) emissions reduction. The transition to a carbon-neutral future is based on three pillars, (a) the mass integration of renewable energy resources (RES) (IEA 2021) to replace conventional production, (b) the Electrification of Transportation (EoT), since road transportation is responsible for 20% of the total GHG emissions and finally (c) energy efficiency, mainly from the building sector, which is responsible for 40% of the energy consumption and 36% of GHG emissions in European Union (European Commission 2020). However, the mass RES © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_16

194

S. I. Vagropoulos et al.

and electric vehicle (EV) penetration poses significant challenges on the existing electrical grid, including energy shortages, unacceptable voltage fluctuations, transformer overloading and increased energy losses. To tackle the above challenges, grid reinforcements are necessary, however, they can be extremely costly and time-consuming. Thus, Transmission System Operators (TSOs) and Distribution System Operators (DSOs) seek to exploit the available flexibility from new distributed resources that are able to manage the variability and uncertainty of supply and demand, in a reliable and cost-effective way, across all relevant timescales. An underexploited source of flexibility is the Demand Side Management that can have a considerable influence on the integration and optimal use of RES (Nojavan et al. 2019). Focusing on plug-in EVs that are rapidly entering the market, the combination of their battery units’ ability to charge quite flexibly, along with the long times that the vehicles remain plugged-in to the network, create an opportunity for valuable Demand Side Management through control of the time and the charging rate, while still charging the batteries at the pre-defined level, before the pre-defined departure time. The flexible management of the charging process is defined as “smart charging”. According to CENELEC (2015) “smart charging of an EV is when the charging cycle can be altered by external events, allowing for adaptive charging habits, providing the EV with the ability to integrate into the whole power system in a grid- and user-friendly way. Smart charging must facilitate the security (reliability) of supply while meeting the mobility constraints and requirements of the user. To achieve the aforementioned goals in a safe, secure, reliable, sustainable, and efficient manner information needs to be exchanged between different stakeholders”. Smart charging is very useful for supporting the system power balance since charging flexibility can be offered to the organized wholesale electricity market as an ancillary service product. The Electric Vehicle Aggregator (EVA) Bessa and Matos (2010) has been identified as the entity that can operate the smart charging by, (a) controlling the charging process and (b) participating in the organized wholesale electricity market and especially in the Balancing Market1 and gaining revenues. A portion of the revenues can be shared with the electric vehicle users (EVUs) as an incentive to participate in controllable charging sessions. Since, the role of EVAs is relatively new, the adoption of viable business models will play a significant role in their development, which highly depends on the interactions between the different stakeholders of the EV charging ecosystem, the regulatory framework, the efficiency of market participation strategies, the willingness of EVUs to participate, and the market opportunity for considerable charging cost savings.

1 An entity the provides services to the Balancing Market may offer two product types, (a)

balancing capacity and (b) balancing energy. The balancing capacity is procured in previous markets and upward activation or downward activation of balancing energy offers are submitted in the real time balancing market. Balancing capacity reservation provides the TSO with the confidence that it can handle any abnormal situation in real-time operation by activating the reserved capacity, if needed, i.e., instructing the entities to provide upward/downward balancing energy based on their respective balancing energy offers submitted at the real-time dispatch process (Vagropoulos et al. 2022).

An Innovative Smart Charging Framework for Efficient Integration

195

This paper proposes a smart charging platform framework, for an EVA, for efficient integration of plug-in EVs into the grid. The proposed framework has been developed under the research project ELVIS (Smart ELectric Vehicles Integration in electric energy Systems) (ELVIS Project 2022). Such frameworks demand a series of tools and processes that are necessary for (a) developing highly accurate online Charging Station (CS) load profiling, (b) developing new mathematical models for optimal bidding strategies in wholesale electricity markets, (c) establishing reliable and flexible real-time communication with CSs, (d) emulating large-scale charging behavior of real CSs and (e) delivering the aforementioned functionalities in a user-friendly environment. 1.1 Related Work The benefits of smart charging are widely recognized and discussed in the literature, with detailed studies reviewing the state-of-the-art on charging modulation solutions (Reza et al. 2020; García-Villalobos et al. 2014), that can provide increasing RES utilization (Baronea et al. 2019; Electrific, a European Union’s Horizon 2020 Project 2020) reduced peak loads (Mahmud et al. 2018), balancing of unbalanced three-phase loads (S. and J. 2016) increased profits for charging providers (van der Meer et al. 2018) and grid frequency regulation services (Yao et al. 2017). Furthermore, business applications have been thriving recently (Ampcontrol.io 2022) (Ev.energy 2022), exploring various aspects of smart charging implementations, like optimal fleet management or GHG emissions reduction. However, in order to exploit the full potential of smart charging, integrated solutions need to be developed that can enable the participation in energy markets via energy and ancillary products trading. These integrated solutions should be able to coordinate the charging process of individual EVUs and/or EV fleet Operators, taking into consideration the prevailing market and grid conditions. The main drawbacks of existing approaches are their lack of coordination with realtime market signals and their inability to offer an integrated set of services. Optimal market participation strategies for EVAs have been proposed in the bibliography (e.g. Hoogvliet et al. (2017), Alipour et al. (2017)), which are however limited in considering only a single market and not being able to model the sequential participation in DayAhead Market (DAM), Intra-day Market (IDM) and Balancing Market (BM), which is the real-world case in EU-type electricity markets. One of the most comprehensive works has been proposed by (Vagropoulos et al. 2016), following a US-based market model, and thus requires enhancement and adaptation in order to be applicable to the EU market. Finally, works in CS load profiling, which is crucial for building competitive market bidding strategies, have been recently proposed (Li et al. 2018; Amini et al. 2016; Yanchong et al. 2020) but are limited, as they do not tackle the challenges of on-line updating. 1.2 Paper Structure The paper is organized as follows: In Sect. 2 the EV charging ecosystem is described, defining the main stakeholders and their interconnections with the EVA. After the clear

196

S. I. Vagropoulos et al.

definition of the EVA environment, the essential components of an EVA smart charging platform are listed and analyzed in Sect. 3. Finally, Sect. 4 summarizes the key conclusions.

2 EV Charging Ecosystem In order to better understand and describe the concept of an EVA, it is important to identify the stakeholders of the EV charging ecosystem and their interactions. Many different stakeholders do exist, directly and/or indirectly impacting the EVA’s scope. Table 1 presents the main stakeholders that are directly connected with this ecosystem, along with a basic description of their role and their main interests. Table 1. Stakeholders of the EV charging ecosystem. Stakeholder

Short Description

EV Users (EVUs) EV Owner (EVO)

Human actor, using a private owned EV

EV Fleet User (EFU)

Human actor, using an EV belonging to a commercial EV fleet

EV Fleet Operator (EFO)

A fleet of EVs under common management

Power system and market Distribution System Operator (DSO)

The entity responsible for the operation of the electricity distribution network

Transmission System Operator (TSO) The entity responsible for the operation of the transmission system and the balancing market Energy Supplier (ES)

The entity that purchases energy from the wholesale electricity market and sells it to end-consumers, i.e. CSPs

Charging Service Provider (CSP)

The entity that manages/operates CSs

Electric Vehicle Aggregator (EVA)

The entity that provides services to CSPs, EVOs, EFUs, TSO and DSOs through the smart charging control

E-Mobility Service Provider (EMSP) The entity that offers EV charging services to EV drivers, mainly by enabling access to a variety of charging points around a geographic area and to CSPs by proving tools for the charging network management

2.1 Stakeholders Interactions The EV charging ecosystem is characterized by significant complexity, mainly due to the large number of stakeholders and entities that need to interoperate with each other. CSPs,

An Innovative Smart Charging Framework for Efficient Integration

197

EVs, EVUs, the Marker Operator, the TSO and/or the DSO are the most relevant ones. For the successful coordination of the above parties, the EVA acts as the intermediate player that focuses on optimal charging of large EV fleets and the coordination of the interactions between all the involved stakeholders in order to provide ancillary services to the TSO and/or DSO. After providing an overview of the stakeholders in the last section, in this section the basic interactions between the engaged entities are described, including data and cash flow streams, as well as contractual relationships. Stakeholder Relations In Fig. 1 a high-level stakeholder interaction review is presented. Starting from the top left of the graph, the interactions between stakeholders are summarized as follows: 1. The EVA interacts with the flexibility markets of the TSO and the DSO, in order to provide ancillary services to the grid. It also interacts with the ES to coordinate demand response services and/or to provide services for imbalance reduction of the ES load portfolio. 2. The ES participates in the wholesale energy market to buy energy, which is the sold to the CSP. 3. The CSP interacts with the EVUs (EVOs and EFUs/EFOs) via the EMSP tools to provide charging services over all connected CSs. As stated, the CSP interacts also with the ES for the supply of energy. Finally, the CSP interacts with the EVA to receive services related with the smart charging or to provide the EVA with data for optimal participation in the energy markets. 4. The EMSP interacts with the CSP to provide management platforms to handle invoicing, CSs availability, maintenance and in some cases to provide forecasting services (e.g., anticipated charging sessions). The EMSP also interacts with the EVUs (EVOs and EFUs/EFOs) in order to provide services like indication of available CSs, pricing of charging at various CSs, routing towards a selected charging station, easy payment methods and even more advanced services related with the provision of financial incentives for using a specific CS and/or others.

Contractual Framework The different stakeholders of the EV charging ecosystem must ensure that their interactions and roles will be clearly defined. For that reason, a contractual framework has to be developed among all engaged entities. The main contracts in this framework are described in Table 2. The ELVIS system aims to deliver smart services to the EVAs and EMSPs, by providing sophisticated algorithms that consider data collected from the connected CSs, along with market data. The algorithms provide optimal scheduling of charging, optimized participation in the energy and balancing markets, and are accompanied with a secure infrastructure for clearing the charging payments and invoices. ELVIS technologies are envisioned as a core plugin component to the existing EV charging ecosystem that has vast potential to provide cost-effective charging to end consumers and grid services to core stakeholders.

198

S. I. Vagropoulos et al.

Fig. 1. Overview of stakeholder interactions

Table 2. Contractual Relationship among Stakeholders. Stakeholder

Short description

EVA - DSO

A contractual agreement between the EVA and the DSO will allow the EVA to provide services to the DSO, representing the load flexibility of its portfolio. The EVA must be compliant with the DSO requirements (mainly technical, but also legal, operational, procedural, financial, pre-qualification)

EVA - TSO

A contractual agreement between the EVA and the TSO will allow the EVA to provide balancing services to the TSO. The EVA must be compliant with the TSO requirements (mainly technical, but also legal, operational, procedural, financial, pre-qualification)

EVA - ES

A contractual agreement between the EVA and the ES will commit the EVA to supply the ES with the data related to the DR activations

EVA - CSP

A contractual agreement between the EVA and the CSP should define the aspects for real-time access of the EVA to the CS data and the access to the CS control unit for charging modulation (of course, upon EVUs consent to CSP or EMSP)

EVA - EMSP A contractual connection between the EVA and the EMSP should define the aspects for real-time dynamic pricing provided by the EVA to EVUs via the EMSP services

An Innovative Smart Charging Framework for Efficient Integration

199

3 EVA’s Platform Specifications 3.1 High Level Architecture The market participation strategies of an EVA need to be designed taking into account the targeted wholesale markets. Under the common pan-European electricity market model (Commission 2015, ENTSO-E, 2022) electricity is traded in various short-term markets, namely the DAM, the IDM and the BM. A high-level overview of the platform scope with the core EVA operations and interactions, is presented in Fig. 2. The figure clearly shows the role of an EVA as a middleman between EVs, CSs and EVUs from the one side and the Market Operator and the TSO on the other side.

Fig. 2. Elvis platform overview

3.2 EVA Platform Components The purpose of this section is to describe in detail all the different components of an EVA platform. Each section presents the technical specification and characteristics of each component and describes the main aspects of their implementation. Figure 3 presents an overview of the EVA platform highlighting the relations among its various components. Figure 3 originates from the (ELVIS Project 2022) architecture representation. Data Management Storage of Market Data Real market data coming from the Market and TSOs are essential to be persisted as historical information in the platform. In addition, dedicated software tools shall parse new market data automatically, through publicly available APIs, in real-time. All data must be stored in dedicated databases, with the appropriate relations between data points. These data points, combined with their relations, play a crucial role in the market understanding and forecasting operations of the EVA. Storage of CS Data The tools that an EVA must develop should rely on both time-dependent and static data, related with the charging procedure. Historical time-dependent CS data, provided by real CS networks, should be gathered and stored in a database, in order to facilitate the

200

S. I. Vagropoulos et al.

Fig. 3. Elvis platform technical overview

training of forecasting models, the data driven simulation procedures and the testing of the developed algorithms. Meanwhile, real-time data should be utilized for deriving the online forecasting of the EV fleet load profile and used for close to real-time executions of the smart charging scheduling. Storage of the collected data should be employed in one or a combination of databases that are optimized to handle both static and real-time data. Real-time fetching of CS data should be decided and implemented with direct interaction with the partnered CSPs and EMSPs. Forecasting The purpose of the forecasting operations is to feed the optimization models run by the EVA for its optimal market participation strategy. CS Profiling and Forecasting Advanced forecasting algorithms must feed the EVA’s market participation models with the CSs load and EVU’s behavior profiling information. The impact of exogenous variables like meteorological conditions and seasonal variables like weekends, summer/winter period, public holidays, market operational data and others must be evaluated exhaustively. A big variety of forecasting methodologies should be evaluated using

An Innovative Smart Charging Framework for Efficient Integration

201

state-of-the-art machine learning and deep learning tools. Forecasting tasks are complex, since the load profiles for each EV portfolio vary a lot depending on the location of the charging infrastructure and the purpose of EV usage (e.g. commercial, private), among other factors. The tools used for online CS load predictions must consider as input the latest CSs’ operational state, along with latest weather conditions and predictions. The uncertainty around the provided forecast needs to be evaluated using probability analysis theory and applied techniques. Finally, the performance of the forecasting tools needs to be evaluated with the data collected by real networks of CSs. Market Data Forecasting The market data forecast can be approached with machine learning algorithms or market simulation that would forecast key market-related variables (e.g., prices, balancing activation estimation, etc.). Initially, for time dependent market variables, naïve models could be tested. If the results of the naïve methods are not satisfactory, more complicated ones that include seasonal components and/or weather-related data should be evaluated. If the results still don’t have the expected performance, more complicated machine learning and deep learning models could be evaluated, taking into consideration the available computational resources as well as any limitations in models’ run time. Optimization Bidding Strategies In this component mathematical models are considered by the EVA to address the challenge of optimal bidding strategy in the various markets (DAM, IDM, BM) of the emerging pan-European electricity market design. The models could be formulated as stochastic linear and mixed-integer linear programs and be solved sequentially (i.e., the market schedule of one market will be the model input for the next market). In that sense, the bidding models for DAM should be solved once per day, for intra-day auctions, one per auction (i.e. thrice per day for the current setup of three auctions per day) and every hour for the continuous intra-day market. Extra emphasis needs to be given to the Balancing Market (BM) bidding model, which should be solved intra-hourly to optimize the balancing services provision and maximize revenue streams for the EVA. (Papavasiliou et al. 2015). Charging Setpoint Allocation Finally, models for optimal charging set-point allocation to the CSs, could be enhanced and integrated into the platform for real-time operation. These models are being fed in real-time with the balancing energy activation commands, the EVA latest market schedules as well as CS data and produce new charging set-points at a predefined period (i.e., 4 s) for charging modulation of the plugged-in EVs (Vagropoulos et al. 2016). EVA Management Platform The EVA’s management platform should be the main interaction point between the EVA’s solutions and the CSPs. The envisioned platform could be based on open-source software with proper extensions that serves better the EVA needs.

202

S. I. Vagropoulos et al.

User Interface The EVA’s management platform should provide visual components that present information about all the connected CSs, e.g., the CS name given during the CS registration and the current CS status (e.g., Available, Charging, Reserved or Disconnected) as shown in Fig. 4. In case of an ongoing charging session, real-time information should be displayed per CS, like the real-time charging rate in kW and the State of Charge (SoC) in percentage. In addition, estimations about the charging duration in a charging station and/or the expected arrival time for a reserved charging session could be also optionally displayed.

Fig. 4. Envisioned screen of the functionality for the ELVIS CS management platform

Furthermore, the administration platform should be able to provide historical charging session information, such as the start/stop time and the overall consumed energy. The EVA’s management platform must provide useful information to the CSPs, not only for the state of each CS at any time, but also for their whole CS portfolio performance, their cumulative load requirements, and the share of each EVU (Fig. 5).

Fig. 5. Daily load curves (left) and charging events for EV fleet charging (right).

Application Programming Interface The EVA should develop Application Programming Interfaces (APIs) to allow data access to third party applications. Behind this API a software layer should exist including all the services the EVA provides to its customers. The EVA’s API should provide full flexibility to its data accessibility, with a very easy integration with the OCPP protocol. OCPP Server One of the main components of an EVA management platform should be a dedicated OCPP server, which acts as the core infrastructure, interconnecting all managed CSs and enabling their remote management and control. A communication protocol implementation is a vital part of the EVA’s activities, since it constitutes the way the EVA

An Innovative Smart Charging Framework for Efficient Integration

203

respond to the managed portfolio needs and transmits control signals to the CSs. The most common protocol for communication between the CSs and a management platform is the OCPP, which by the time of writing this work has two released versions, OCPP 1.6 (Open Charge Alliance 2022) and OCPP 2.0 (Open Charge Alliance 2022). CSs Platform Integration Having established an operational OCPP-based platform, the next step is the integration of CSs to the platform. Pre-defined steps include the registration and configuration of physical CSs on the central OCPP server, along with the tuning of monitored parameters and reporting parameters. Simulation Tools OCPP-based CS Simulator The process of building a robust market bidding strategy and configuring the optimal charging scheduling, requires the EVA, to simulate the real world’s uncertainties by incorporating any potential risks. Such processes demand the proper development of simulation tools for CSs’ functionalities. The goal of this component is to enable the EVA platform to register and interact with virtual CSs that follow pre-configured behavior, in order to test and validate different scenarios simulating a very large number of virtually connected CS. The proper configuration of the virtual CSs could be based on the CS Data and CS consumption profiles derived through the CS profiling and forecasting process.

4 Conclusions The current paper introduces a smart charging platform framework, proposed by the ELVIS project, as a dedicated platform for EVAs. Despite the existence of research works related to the individual components of the presented framework, an integrated solution that considers the interoperability of all of them it is not yet introduced. The proposed solution can be utilized by EVAs to design and build their own platforms for direct participation in the energy and ancillary services market as well as to optimally integrate their managed fleet in the grid. By doing this, EVAs not only can relieve the stress that uncontrolled charging of numerous EVs imposes on the grid but they could also benefit from the inherit flexibility of the charging process by providing new flexibility resources to the grid. All the necessary tools and visual components that need to be integrated in an existing or newly constructed EVA’s platform are described, in order to be able to handle the huge complexity of a bidding strategy composition as well as serving these strategies to a user-friendly environment. Funding Source. The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I) under the “2nd Call for H.R.F.I Research Projects to support PostDoctoral Researchers” (Project Number: 649).

204

S. I. Vagropoulos et al.

References 1. Alipour, M., Mohammadi-Ivatloo, B., Moradi-Dalvand, M., Zare, K.: Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets. Energy 1168–1179 (2017) 2. Amini, M.H., Karfarian, A., Karabasoglou, O.: ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation. Electr. Power Syst. Res. 378–290 (2016) 3. Baronea, G., Buonomanoab, A., Calisea, F., Forzanoc, C., Palomboa, A.: Building to vehicle to building concept toward a novel zero energy paradigm: modelling and case studies. Renew. Sustain. Energy Rev. (2019) 4. Bessa, R.J., Matos, M.A.: The role of an aggregator agent for EV in the electricity market. In: 7th Mediterranean Conference and Exhibition on Power Generation, Transmission, Distribution and Energy Conversion (MedPower 2010) (2010) 5. Burger, C., Kuhlmann, A., Richard, P.: Blockchain in the energy transition. A survey among decision-makers in the German energy industry (2016) 6. CENELEC: E-Mobility Smart Charging. Retrieved from https://ec.europa.eu/energy/sites/ ener/files/documents/xpert_group1_sustainable_processes.pdf (2015) 7. Commission, E. (2015, July 24). COMMISSION REGULATION (EU) 2015/1222 of 24 July 2015 establishing a guideline on capacity allocation and congestion management. Retrieved from http://b.link/reg15 (2015) 8. Deloitte: Blockchain: Enigma. Paradox. Opportunity. Retrieved from https://www2.deloitte. com/content/dam/Deloitte/uk/Documents/Innovation/deloitte-uk-blockchain-full-report.pdf (2016) 9. Docile CP Simulato: (2021). Retrieved from github.com/NewMotion/docile-charge-point 10. Electrific, a European Union’s Horizon 2020 Project. Retrieved from European Union’s Horizon 2020 (2020) 11. ELVIS Project: Retrieved from https://elvisproject.eu/ (2022) 12. ENTSO-E (2022): Network Code Development. Retrieved from https://www.entsoe.eu/net work_codes/ 13. Eurelectric: Smart Charging: Steering the Charge, Driving the Change. Retrieved from https://www.eurelectric.org/media/1925/20032015_paper_on_smart_charging_of_ele ctric_vehicles_finalpsf-2015-2301-0001-01-e.pdf#page=16&zoom=100,72,76 (2015) 14. European Commission: In Focus: Energy Efficiency in Buildings. Retrieved from https://ec. europa.eu/info/news/focus-energy-efficiency-buildings-2020-lut-17_en (2020) 15. Ev.energy: Retrieved from https://ev.energy/ (2022) 16. García-Villalobos, J., Zamora, I., San Martín, J., Asensio, F.J., Aperribay, V.: Plug-in electric vehicles in electric distribution networks: a review of smart charging approaches. Renew. Sustain. Energy Rev. 717–731 (2014) 17. Hoogvliet, TW., Litjens, G.B., Van Sark, W.G.: Provision of regulating and reserve power by electric vehicle owners in the Dutch market. Appl. Energy 190, 1008–1019 (2017) 18. Hou, J., Wang, C., Luo, S. (2020). How to improve the competitiveness of distributed energy resources in China with blockchain technology . Technol. Forecast. Soc. Change 151, 119744 (2020) 19. IEA: Global Energy Review 2021. Retrieved from https://www.iea.org/reports/global-energyreview-2021 (2021) 20. IRENA: Innovation outlook–Smart Charging for Electric Vehicles. Retrieved from https:// www.irena.org/publications/2019/May/Innovation-Outlook-Smart-Charging (2019) 21. Jafari Lillebo, M., Zaferanlouei, S., Zecchino, A., Farahmand, H.: Impact of large-scale EV integration and fast chargers in a Norwegian LV grid. J. Eng. 18, 5104–5108 (2019)

An Innovative Smart Charging Framework for Efficient Integration

205

22. Li, Y., Huang, Y., Zhang, M.: Short-term load forecasting for electric vehicle charging station based on Niche immunity lion algorithm and convolutional neural network. Energies (2018) 23. Mahmud, K., Hossain, M.J., Town, G.E.: Peak-load reduction by coordinated response of photovoltaics, battery storage, and electric vehicles. IEEE Access (2018) 24. Nojavan, S., Ghesmati, H., Zare, K.: Robust optimal offering strategy of large consumer using IGDT considering demand response programs. Electr. Power Syst. Res. 130, 46–58 (2019). https://doi.org/10.1016/J.EPSR.2015.08.017 25. OCPP.Core: Retrieved from https://github.com/dallmann-consulting/OCPP.Core (2021) 26. Open Charge Alliance: OCPP 1.6. Retrieved from https://www.openchargealliance.org/pro tocols/ocpp-16/ (2022) 27. Open Charge Alliance: OCPP 2.0. Retrieved from https://www.openchargealliance.org/pro tocols/ocpp-201/ (2022) 28. Papavasiliou, A., Orenand, S.S., Rountree, B.: Applying high performance computing to transmission-constrained stochastic unit commitment for renewable penetration. IEEE Trans. Power Syst. 30, 1690–1701 (2015) 29. Reza, F., Mahmoud, S., Dennis, V.D., Joakim, M.J.: Smart charging of electric vehicles considering photovoltaic power production and electricity consumption: a review. eTransportation (2020) 30. Weckx, S., Driesen, J.: Load balancing with EV chargers and PV inverters in unbalanced distribution grids. IEEE Trans. Sustain. Energy (2016) 31. United Nations:. The Paris Agreement. Retrieved from https://unfccc.int/process-and-mee tings/the-paris-agreement/the-paris-agreement (2015) 32. Vagropoulos, S.I., Kyriazidis, D.K., Bakirtzis, A.G.: Real-time charging management framework for electric vehicle aggregators in a market environment. IEEE Trans. Smart Grid 948–957 (2016) 33. Vagropoulos, S., Biskas, P., Bakirtzis, A.: Market-based TSO-DSO coordination for enhanced flexibility. Electr. Power Syst. Res. (2022) 34. van der Meer, D., Chandra Mouli, G.R., Morales-España Mouli, G., Elizond, L.R., Bauer, P.: Energy management system with PV power forecast to optimally charge EVs at the workplace. IEEE Trans. Ind. Inform. (2018) 35. Yanchong, Z., Ziyun, S., Yumeng, Z., Linni, J.: A systematic methodology for mid-and-long term electric vehicle charging load forecasting: the case study of Shenzhen, China. Sustain. Cities Soc. (2020) 36. Yao, E., Wong, V.W., Schober, R.: Robust frequency regulation capacity scheduling algorithm for electric vehicles. IEEE Trans. Smart Grid (2017) 37. Ampcontrol.io. (2022). Retrieved from https://www.ampcontrol.io/

A Blockchain-Based Smart Contractual Framework for the Electric Vehicle Charging Ecosystem Konstantinos D. Afentoulis1 , Zafeirios N. Bampos2 , Stylianos I. Vagropoulos1(B) , and Stratos D. Keranidis1 1 Department of Energy Systems, University of Thessaly, Gaiopolis Campus, Ring Road of

Larissa-Trikala, 41500 Larissa, Greece {kafentoulis,svagropoulos,efkerani}@uth.gr 2 School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, AUTh Campus, 54124 Thessaloniki, Greece [email protected]

Abstract. The impending adoption of electric vehicles is rapidly increasing the complexity of the charging process management for the various stakeholders of the EV charging ecosystem. These stakeholders need to cooperate in exchanging metering, billing, and other sensitive and private data required for the provision of EV charging services. Thus, secure, transparent, and reliable data communication networks must be established to enable efficient interaction among the different stakeholders regarding identity and charging management, along with the session settlement and billing processes. Blockchain is a distributed, digital transaction technology that allows securely exchanging and storing data, as well as executing smart contracts in peer-topeer networks amongst different entities without established trusted relationships. These smart contracts may holistically describe the relationships between the different entities, as well as the rules of their interaction. Additionally, blockchain technology has the potential to ensure the integrity, reliability, and efficiency of the established ecosystem, strengthen cybersecurity, protect privacy, and minimize transaction costs by applying sophisticated cryptographic techniques. This paper provides an overview of how blockchain technology could transform and improve an operational EV charging management network and proposes a blockchain-based framework that is suitable for the electric vehicle charging ecosystem. Keywords: Blockchain technology · Charging management · Electric vehicles · Electric vehicle aggregator

1 Introduction The Clean Energy for all Europeans Package (CEP) [1], published by the European Union (EU), and the Green Deal strategy document [2], set the 2030 energy and climate targets along with the regulatory framework to achieve them. According to [3], © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_17

A Blockchain-Based Smart Contractual Framework

207

road transportation is responsible for more than 20% of the total greenhouse gas (GHG) emissions, making Electrification of Transportation a cornerstone for achieving the climate and energy targets. To reduce the transportation sector’s carbon emissions, the e-mobility landscape is undergoing a rapid and vast transition triggered by the impending advancement and the high penetration rate of Electric Vehicles (EVs). However, a mass, uncontrolled charging of numerous EVs would challenge the stability and security of the existing electrical networks. Thus, System Operators (SOs) aim to benefit from the flexibility provided by new distributed resources, which can manage supply and demand unpredictability and uncertainty reliably and cost-effectively. Demand Side Management is an underexploited source of flexibility that can significantly impact the integration and optimal use of renewable energy sources [4]. Focusing on plug-in EVs, the ability of their battery units to charge quite flexibly, combined with the long times that the vehicles remain plugged-in to the network, creates an opportunity for valuable Demand Side Management through control of the time and charging rate, while still charging the batteries at the pre-defined level before the pre-defined departure time. “Smart charging” refers to the flexible management of the charging process. According to [5], “smart charging of an EV is when the charging cycle may be influenced by external events, allowing for adaptive charging habits, and giving the EV with the potential to integrate into the entire power system in a grid- and a user-friendly way”. Smart charging must ensure supply security (reliability) while accommodating users’ mobility limits and requirements. The information must be communicated between diverse stakeholders to fulfill the aforementioned targets in a safe, secure, reliable, sustainable, and efficient manner. These changes foster a new Electric Vehicle Charging Ecosystem (henceforth ecosystem) with entities that aspire to benefit from providing services related to EV charging. The main entities of this ecosystem are the Electric Vehicle Users (EVUs), the Charging Service Providers (CSPs), the Energy Suppliers (ESs), the Electric Vehicle Aggregators (EVAs), and the System Operators (SOs). The interaction and cooperation of these stakeholders are necessary for delivering successful charging sessions to the EVUs. These interactions require a secure software and hardware infrastructure that the different involved parties can trust. This trust is necessary since the different actors must share sensitive personal and commercial data. Many researchers have investigated the vulnerabilities met in such ecosystems/networks or their sub-networks. For instance, the work in [6] identified the communication vulnerabilities that the different actors are exposed to, while the authors in [7] conducted a vulnerability analysis and a risk assessment on the interaction between the EVUs with the electric transportation cyber-physical system. Moreover, many research works have proposed methods to overcome the identified vulnerabilities. For instance, the work in [8] deployed a secure EV charging system using OCPP [9] to communicate between the Charging Stations (CSs) and the control center. Furthermore, the authors in [10] proposed a solution for increasing the security and privacy of the communication between the central system and the CS. However, the adoption of centralized system approaches induces several inevitable vulnerabilities. In contrast, in a decentralized and interconnected system, such information and data exchanges between the

208

K. D. Afentoulis et al.

different ecosystem actors can be securely handled by a distributed ledger technology. Blockchain is one such technology, which provides a cost-optimal and scalable solution to sustain the growing number of interactions and transactions between them. This paper is organized as follows: In Sect. 1, the motivation for this work has been described, while in Sect. 2, the background of the blockchain technology is presented along with selected relevant research papers. In Sect. 3, the Electric Vehicle Charging Ecosystem is described, along with the communication vulnerabilities that this system may face. Additionally, the necessary communication requirements are defined. In Sect. 4, the theoretical background is provided, while the technology features that determine the selection of the blockchain technology are explained. In Sect. 5, the proposed network is described, followed by the Conclusions.

2 Related Work Distributed ledger technology refers to technological infrastructure and protocols that allow simultaneous access, validation, and record updating in an immutable manner across a network that is spread across multiple entities or locations. Blockchain is a secure decentralized digital database based on distributed ledger technology and allows direct peer-to-peer information transferring. The digital database is replicated in a network of distributed computers in different locations. Any changes to the data must be updated concurrently so that all ledgers always hold the same information. Blockchain technology could be helpful for the ecosystem, by providing enhanced decentralization, availability, integrity, auditability, and privacy to the key data exchanges among stakeholders [11]. Decentralization allows the information stored in the blockchain to be replicated across multiple locations, preventing a single point of failure in centralized systems. At the same time, availability enables members to access the required information despite the failure of several machines. Integrity is related to reliable information preservation and avoidance of improper changes. Auditability offers the ability to track all information that has been stored in the blockchain. Multiple layers of intricate cryptography protect the data on the blockchain. The data is considered immutable, because all the ledgers in the system need to be edited simultaneously to successfully alter any entry. This requirement consists data tampering almost impossible. Since blockchain data is maintained in an immutable format, it can be confirmed without the involvement of third parties. Elimination of third-party verification makes blockchain transactions faster and more cost-effective. Data security is improved by the decentralized structure and distributed ledger verification method. Blockchain may be permissioned or permissionless. Any entity can participate in the chain’s operations in a permissionless blockchain. After joining the network, the entity becomes a participant and has access to all the network’s features, such as auditing and proposing transactions. On the contrary, in permissioned blockchains, each entity must be authenticated and authorized to join the network and become a participant executing specific activities only, such as viewing the data of the blocks or auditing data in the chain. A private and permissioned blockchain can secure transactions between a group of known peers the belong to the same business or application network and do not trust each other. The consensus is achieved through a process called “selective endorsement”, where only

A Blockchain-Based Smart Contractual Framework

209

known users verify the transactions. This network type requires a more detailed identity and access control [12]. To sum up, permissionless blockchain faces more privacy issues, since permissioned blockchain networks inherently ensure enhanced privacy with access control. Blockchain technology has been utilized to support energy trading, including the support of EV use cases. For instance, the researchers in [13] proposed a framework for energy trading and data sharing in a Vehicle to Grid network. Additionally, in [14], the authors proposed a blockchain-based smart EV charging system that provides incentives to EVUs for providing flexibility. However, the previous works do not investigate the interactions between the different ecosystem actors, the framework for handling sensitive data and the management of the charging process. In addition, although the authors in [15] proposed a blockchain-supported framework for managing the charging process, it was implemented based on the permissionless blockchain platform of Ethereum, which is inferior in transaction speed, scalability, and maintainability compared to a permissioned blockchain-based platform. Furthermore, the adopted permissionless blockchain platform underperforms in terms of protecting sensitive personal and commercial data, in comparison with the proposed ecosystem that implements the vital feature of cross-organization cooperation. This work proposes a private and permissioned blockchain network to create a more secure environment for the ecosystem that protects sensitive commercial and personal data and enables automated interaction among the different actors. The novelty of this paper relies on the fact that the relevant works [13, 14] do not investigate the interactions between the different ecosystem actors, the framework for handling sensitive data and the management of the charging process. Additionally, to the best of our knowledge, there is no prior work in the literature that exploits the advantages that a private and permissioned blockchain network can offer, in creating a more secure environment for the e-mobility ecosystem.

3 Electric Vehicle Charging Ecosystem In the next section, the complex ecosystem is analyzed in detail, presenting an overview of the key stakeholders and their key interactions. 3.1 Entities of the Electric Vehicle Charging Ecosystem A brief description of the different ecosystem entities and their main interests follows below. Electric Vehicle User (EVU). The human actor that uses an EV and requires charging services. The main interests of EVUs’ include the minimization of charging and maintenance costs while maximizing the availability of the vehicle. The EVU is concerned about privacy and security, along with the user-friendliness of the charging platform. Furthermore, pricing transparency is critical for making optimal decisions about their charging time, duration, and location. Finally, EVUs expect real-time billing data, with easy-to-understand, transparent bills that outline prices, taxes, payment definitions, and special offers.

210

K. D. Afentoulis et al.

Charging Station (CS). This entity refers to the required hardware equipment, software, and communication protocols for efficient and safe energy delivery to the EV. Charging Station Provider (CSP). This entity is responsible for the operation, maintenance, and management of a CS network. The CSP must ensure the CSs’ installation investment viability and the maximum profits from EV charging. CSPs’ management platforms must be futureproofed, with the capacity and scalability to manage an exponential growth of EV charging needs. Additionally, CSPs must ensure that CSs are always available to deliver real-time information necessary to optimize operations and analyze business and technical data for efficient decision-making. E-mobility Service Provider (EMSP). EMSPs develop solutions that support EVUs, including services for locating the available CSs, reserving charging sessions, starting charging events, and checking CS’s specifications. Furthermore, they may offer quick and practical e-billing solutions along with EV charging roaming services. Offering roaming services, EMSPs, enables the EVUs to access many CSs, making charging more efficient, user-friendly, and convenient. Finally, EMSPs could provide tools to CSPs for the charging network management. Energy Supplier (ES). This entity purchases energy from the wholesale electricity market and sells it to end-consumers, i.e., CSPs. Electric Vehicle Aggregator (EVA). The EVA is the entity that aggregates and trades the flexibility that EVs may offer at the Energy Markets. The EVA is responsible for maximizing the value of flexibility in the charging process of numerous plug-in Evs, aggregating it into a dispatchable portfolio, creating services that draw on it, and offering these to different markets, serving different market players. The value received by the EVA in return is shared with CSPs and EVUs as a motivation for them to participate in smart charging programs. To provide these services, real-time communication between the EVA and the CSs is necessary. System Operator (SO). The SO is the entity responsible for the safe and reliable operation of the electricity grid. Mass EV penetration will pose new challenges to the grid’s stability since Evs will represent a large share of the total electricity load. Consequently, Sos must monitor the grid’s conditions, and act efficiently regarding the safety and security of the energy grid. 3.2 Stakeholder Interactions The various stakeholders of the ecosystem must share private and commercial data and data related to the charging process (i.e., energy measurements, charging controls signals, acknowledgment messages, charging session information). In the following diagram, we present the various data and information that need to be exchanged to enable the execution of the charging process and the provision of flexibility. The main interactions regarding the charging management are indicated with numbers in Fig. 1 and briefly described below: 1. The EVU communicates with the EMSP to request charging services

A Blockchain-Based Smart Contractual Framework

211

Fig. 1. Stakeholder coordination and main interactions

2. The EMSP interacts with the CSP to request charging services, on behalf of its customers (i.e., EVUs) 3. The EVU, plugs-in the EV and interacts with the CS to complete a charging session 4. The CS interacts with the CSP to proceed with the charging session and to exchange data. Additionally, the main interactions regarding the flexibility exploitation and the development of the smart charging are indicated with letters in Fig. 1 and briefly described below: A. B. C. D. E.

The EVA offers flexibility to the SO The EVA interacts with the CSP to provide its services The EVA interacts with the CSs to control the energy flows The ES could access the CSs energy consumption data ES and EVA exchange data for the smart charging management.

3.3 Communication Vulnerabilities The ecosystem consists of several sub-systems that communicate and interact continuously. In system architectures, where different entities must cooperate and exchange information and data, cybersecurity is an important issue, as the vulnerability of one sub-system to cyber-attacks could jeopardize the entire system. Such attacks could endanger EVUs privacy and security and cause financial and reputation damages to the stakeholders. According to [6], the main cyber-attack types are the following: • Man-in-the-Middle: includes the insertion of fake data and spoofing by interfering with communication between two entities via a forged party. The attacker could forge the message and deliver the inaccurate information to the endpoint during the message exchange. Data leakage from Man-in-the-Middle attacks can have severe consequences on the ecosystem [16]. • Impersonation: occurs when untrusted entities pose as a trusted contact to access sensitive information from a company, leading to various harmful effects on the system.

212

K. D. Afentoulis et al.

• Denial-of-Service: meant to shut down a machine or network, making it inaccessible to its intended users. These attacks accomplish this by flooding the target with traffic or sending information that triggers a crash. This attack deprives authorized EVUs of the service or resource and may delay message deliveries. • Data Tampering: includes the act of deliberately modifying (e.g., destroying, manipulating, or editing) information that belongs to another entity. Potentially it could cause harmful effects on the system. For instance, the fabrication of tariff information or metering data may result in inaccurate and misleading energy indications and incorrect clearing and billing. • Repudiation: occurs when systems, services, or processes stop performing their intended functions, such as message transmission or data storage. For instance, EVUs can claim to have consumed less energy than stated on the billing record. Similarly, the ES may claim to have delivered more energy to the CSP. 3.4 Cyber-Security Requirements for Stakeholder Communications Following the above threat analysis, the cyber-security requirements for establishing secure communications between the stakeholders of the ecosystem are presented below: • Confidentiality: the safeguarding of personal information against unauthorized access. Sensitive information, such as charging requests, charging session data, and billing and transaction data, should be kept private. • Authentication: the verification process to ensure that the communicating entity is the one it claims to be. To face impersonation attacks, robust mechanisms for entities’ identification must be implemented. • Authorization: the process of granting authorized users legitimate access to resources (e.g., system, data, application, etc.), avoiding harmful impersonation attacks. • Integrity: preventing data tampering attempts by guaranteeing that the original message has not been edited or changed during transmission. • Availability: the property of a system or a system’s resource to be inaccessible and utilized by authorized entities upon demand. This property is vulnerable to denial-ofservice attacks.

4 Blockchain Architecture for the EV Charging Ecosystem This section describes the blockchain features that must be considered when designing a blockchain architecture. Furthermore, based on key selection criteria, a blockchain technology is selected and the ways it addresses the ecosystem’s main cybersecurity requirements are described. Blockchain Technology Features Since many different blockchain platforms exist (i.e., Ethereum, Cosmos, Ripple, Hyperledger Fabric, Corda), it is critical to choose the more suitable one for addressing the considered domain and critical issues. As described in [17] and enhanced by [15], several features must be considered when designing a blockchain implementation. These features are briefly described below:

A Blockchain-Based Smart Contractual Framework

213

• Consensus Mechanism ensures that the nodes are synchronized and in agreement on which transactions are legitimate and should be added to the blockchain. Consensus mechanisms affect energy consumption. • Permission Model defines the entities enabled to handle transactions. In the energy sector, it is most likely that permissioned blockchains will apply. • Resilience is the capability to resist attacks and malicious behaviors. • Deployment follows the blockchain life cycle from the beginning and it includes all the necessary steps, processes, and activities required to develop, deploy, maintain, and update the blockchain implementation. • Maintainability refers to the degree of difficulty and effectiveness the blockchain system may be maintained through updates. The modifications can contain corrections and error handling, system improvements, enriched capabilities, and adaptation. • Scalability signifies the capability of blockchain to handle the increasing number of participants and transactions. Particularly, it expresses the speed that blockchain can reach consensus among nodes and add a new transaction into a block. Blockchain Technology Selection In this work, we propose the adoption of the Hyperledger Fabric (HLF) [18], as the most suitable blockchain smart contract framework for the EV ecosystem. HLF is a private, permissioned framework that enables secure interactions among a group of entities. It supports pluggable consensus protocols that allow the platform to be more flexible to fit the identified particular use cases and the accompanying trust models. One of the main advantages of HLF is that it supports the design and implementation of smart contracts (in HLF are defined as chaincodes). Before the different actors are able to interact, they must define a common set of contracts covering standard terms, data, policies, concept definitions, and processes. Smart contracts provide the necessary means for creating and implementing fully automated contractual agreements among the involved actors describing the business agreements that govern every interaction between transacting parties [19]. Smart contracts mediate and monitor transactions, enable machine-to-machine contracting, provide transparency, enforce contractual clauses, regulate energy supply and payments, and reduce the costs of verification and enforcement. Application developers can create smart contracts in each organization to implement a business process shared by the consortium members. Furthermore, HLF scales better than other popular technologies (e.g., AragonOS, EWC, and Ethereum) due to its permissioned nature and its faster and more efficient consensus mechanism. Scalability is vital to the ecosystem since the number of transactions regarding the charging process is expected to increase rapidly following the impending adoption of the EVs, and their need for charging. Consequently, it is critical that the proposed implementation is able to scale appropriately. Additionally, the permissioned nature of HLF adds a layer of security by authorizing access only to a predefined pool of participants and enables HLF to fulfill the main cybersecurity requirements. Since HLF is designated for deploying and operating on permissioned blockchains, it supports the Membership Service Provider (MSP) for providing identity checks to all nodes in the network. These built-in services can

214

K. D. Afentoulis et al.

be combined with existing digital identity providers, e.g., well-established Certification Authorities [20], leading to a truly decentralized solution. This enables HLF to classify the involved entities into distinct roles and apply strict data access control methods, enhancing some of the key cybersecurity requirements of the ecosystem, such as confidentiality, authorization, and authentication. Moreover, the business purpose design requires the system to quickly recover from attacks without compromising sensitive data. The HLF ledger maintains the sequenced, immutable, entire historical record of all state transactions in the network. All performed transactions are added to the ledger in order of reception and accepted while they are cryptographically secured. This enables the system’s recovery from attacks and enhances integrity and availability. Additionally, HLF offers private channels and private data collections that can be used to improve the ecosystem’s privacy and confidentiality. Private channels enable ecosystem members to form separate consortiums inside a network and communicate privately and separately from the others if needed [21], while providing data isolation. A channel is defined by members (organizations), anchor peers per member, the shared ledger, chaincode application(s), and the ordering service node(s). Each channel has an anchor peer or multiple anchor peers to prevent a single point of failure, allowing for peers belonging to different members to discover all existing peers on a channel. Channels are independent enough to assist businesses in separating their work traffic from various counterparties. However, they are sufficiently interconnected to allow them to coordinate autonomous operations when necessary. Data in the channel is inaccessible through nodes that do not belong to the channel. All in all, channels, allow network members who demand private and confidential transactions to coexist on the same blockchain network with business competitors and other restricted members. Thus, creating separate channels to improve security and privacy in the interaction of two entities may create additional administrative overhead (maintaining chaincode versions, policies, MSPs, etc.). Additionally, this strategy is inappropriate for use cases where channel participants must see the transactions while keeping a portion of the data private. To confront these use cases, HLF enables the creation of personal data collections, which allows a subset of organizations on a channel to endorse, commit, or query private data without creating a new channel [22]. One of the most important concerns of the different entities is data integrity. To improve data integrity, HLF supports digital signatures, that represent the fact that the identified counterparties have agreed to the content of the information contained within it. This information can be either measurements or smart contract agreements. Finally, in most cross-organizational collaboration scenarios, peers are responsible for smart contracts and the ledger’s maintenance participants might have access control policies in place which define what data can be accessed. However, it is not clear what specific data has been requested and what data has been delivered. Thus, the absence of the logging mechanism may lead to disputes between organizations [23]. The HLF-based network will also serve as a logging mechanism. The complete history of transactions and any intermediate results that are crucial to be stored and available will be securely maintained on the ledger improving data availability and confidentiality.

A Blockchain-Based Smart Contractual Framework

215

In the context of the proposed system, HLF is maintained and operated by multiple administrative domains (e.g., Ess, EVAs, SOS, EMSPs, and CSPs). These entities must form a consortium and participate in a crash fault-tolerant consensus algorithm, which guarantees security, tamper resilience, and delivery in arbitrary faults. To participate in the network each entity of the consortium may host multiple nodes, while EVUs and CSs do not have to run full nodes, but lightweight. By running a lightweight client, they can still verify every transaction without maintaining a complete ledger copy. Lightweight clients enable the use of intelligent devices with minimum hardware capabilities, keeping the cost low without sacrificing any of the security and immutability the blockchain network has to offer.

5 Network Design for the EV Charging Ecosystem Requirements

Fig. 2. Overview of the proposed system architecture

Multiple channels enable the interaction between the entities while protecting and isolating personal and commercial data. The proposed system architecture (Fig. 2) includes multiple channels to allow the different organizations to establish contractual relationships. These relationships are clearly defined in the smart contracts and are binding for the organizations involved, while access to the channel is secured through the MSP. Each organization that participates in the channel maintains a copy of the channel’s ledger, where transactions are recorded. A smart contract can interact with other smart contracts, both within the same channel and across different channels, enabling

216

K. D. Afentoulis et al.

them to access data stored in other network channels. The proposed ecosystem includes the registration channel along with other channels that would support the interaction between the different entities, as described below. Registration Channel. The ecosystem includes entities that are service providers, service users, or both. The entities that desire to employ services must request to register with the corresponding service providers. This channel includes a ledger, where the transactions that take place in the channel are recorded. Additionally, it includes the Registration Smart Contract (R) mechanism, which handles the registration process for each entity. The smart contract has different functionality depending on the entity that uses it. For instance, EVUs may use this smart contract, providing all the necessary data, to handle their registration process with the EMSP. They may request registration or cancel or update their registration with the EMSP. While registering, EVUs may choose between different charging plans (e.g., pay as you go, monthly or annual subscription, or custom pricing schemes offered from the different EMSPs). Additionally, EMSPs may use this smart contract to handle registrations (i.e., request, cancel, update registration). Finally, CSPs may use this smart contract to register with the desired EVA. EVU-EMSP Cooperation Channels. This channel type is dedicated to hosting the interaction between an EMSP and the EVUs that are registered to this EMSP. Each EMSP should create this type of channel to support its activities. Apart from the ledger, which stores all the related transactions, this channel includes the smart contract that enables EVUs to request charging services, request a reservation of a specific CS, set the charging preferences, overview the charging process, and pay for the delivered energy. The private and commercial data of the EMSP are isolated and protected from the other competitors. EMSP-CSP Cooperation Channels. The entities that participate in this channel type are an EMSP and a CSP. These entities must cooperate to match the management of the charging infrastructure with the EVUs charging needs. Each channel needs to host the ledger, where the sensitive data are stored, and the contractual agreement, defined in the smart contract, between the considered entities. Every time an EVU requests a charging service, the EMSP must interact with the CSP to ensure user authentication. Since each CSP may offer different services and compensation rates to each EMSP, the separated channel is necessary to protect the sensitive commercial data of the EMSP and the CSP included in the smart contract. CSP-EVA Cooperation Channels. The interaction between the CSP and the EVA includes the exchange of sensitive commercial data. The network must create a new channel for each contractual agreement established between the EVA and the CSP to enable the contractual agreements between the EVA and the CSP. Each EVA may apply its own policy and offer different services and rates to each CSP. Consequently, a channel that is dedicated to this interaction is necessary to protect EVAs sensitive commercial data. These contractual agreements specify the CSs that EVA are granted to send load control signals. Additionally, this channel includes the smart contract that specifies the financial interactions between these entities along with the ledger where the sensitive commercial data is stored.

A Blockchain-Based Smart Contractual Framework

217

CSP-CS Cooperation Channels. The entities that participate in this type of channel are the CSP, the CSs that are registered to the CSP and the ES that supplies energy to the CSP. This channel includes the Charging Station Management Smart Contract. This smart contract is designed to automate the management of the CSs from the entity that is granted to manage them. The entities that may handle the charging process are the CSPs and the EVAs, as described in the CSP-EVA cooperation channel. The EVA produces a schedule for every EV that is connected for charging. This schedule respects the EVUs’ charging preferences and maximizes the revenues that the EVA may receive from participating in the SO flexibility market. This smart contract enables the EVA to control the charging process of every CS. The EVA executes this smart contract, by proving as input the desired schedule for each EV. The smart contract checks if the EVA is responsible for these CSs and enables the EVA to interact with the CSs.

6 Conclusions The impending EV adoption introduced new entities that must cooperate on charging services and demand-side flexibility provision. This work describes the interactions between the ecosystem entities and proposes a blockchain-based platform to handle these interactions. Blockchain-based smart contract platforms are a promising solution for enabling secure and transparent transactions between these entities. Blockchain with distributed ledger technologies could eliminate concerns about confidentiality, non-reputation, and tamper-proof exchange of data. This framework may increase the customer base and the adoption rates and decrease the cost of the delivered services by reducing transaction costs and the total duration of the settlement process. Smart contracts will automatically audit the energy delivered for each event. They will trigger payment from the beneficiaries in near real-time, reducing the time and cost of providing services. Additionally, they could increase the efficiency of the electric vehicle aggregator services by allowing the automation of several required operations. Funding Source. The research project was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the “2nd Call for HRFI Research Projects to support Postdoctoral Researchers” (Project Number: 649).

References 1. European Commission: Clean Energy for All Europeans Package (2017) 2. European Commission: A European Green Deal (2021) 3. European Commission: Make Transport Greener. [Online]. Available: https://ec.europa.eu/ commission/presscorner/detail/en/fs_21_3665 (2021). Accessed 25 Apr 2022 4. Nojavan, S., Ghesmati, H., Zare, K.: Robust optimal offering strategy of large consumer using IGDT considering demand response programs. Electr. Power Syst. Res. 130, 46–58 (2019) 5. CENELEC E-Mobility Smart Charging. [Online]. Available: https://ec.europa.eu/energy/ sites/ener/files/documents/xpert_group1_sustainable_processes.pdf (2015) 6. Basmadjian, R.: Communication vulnerabilities in electric mobility hcp systems: a semiquantitative analysis. Smart Cities 4(1), 405–428 (2021)

218

K. D. Afentoulis et al.

7. Reeh, D., Cruz Tapia, F., Chung, Y.W., Khaki, B., Chu, C., Gadh, R.: Vulnerability analysis and risk assessment of ev charging system under cyber-physical threat. In: ITEC 2019 - 2019 IEEE Transportation Electrification Conference and Expo (2019) 8. Vaidya, B., Mouftah, H. T.: Deployment of secure EV charging system using open charge point protocol. In: 14th International Wireless Communications and Mobile Computing Conference (2018) 9. Open Charge Alliance: Open Charge Point Protocol 2.0. [Online]. Available: https://www. openchargealliance.org/downloads/ (2020). Accessed 25 Apr 2022 10. Rubio, J.E., Alcaraz, C., Lopez, J.: Addressing security in OCPP: protection against manin-the-middle attacks. In: 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS), pp. 1–5 (2018) 11. Drescher, D.: Blockchain Basics: A Non-Technical Introduction in 25 Steps. Apress (2017) 12. IBM: [Online]. Available: https://www.ibm.com/topics/blockchain-security (2022). Accessed 25 Apr 2022 13. Hassija, V., Chamola, V., Garg, S., Krishna, D.N.G., Kaddoum, G., Jayakody, D.N.K.: A blockchain-based framework for lightweight data sharing and energy trading in V2G network. In: IEEE Transactions on Vehicular Technology, pp. 5799–5812 (2020) 14. Okwuibe, C., Li, Z., Brenner, T., Langniss, O.: A blockchain based electric vehicle smart charging system with flexibility. IFAC-PapersOnLine 53(2), (2020) 15. Dorokhova, M., Vianin, J., Alder, J.M., Ballif, C., Wyrsch, N., Wannier, D.: A blockchainsupported framework for charging management of electric vehicles. Energies 14(21), 1–32 (2021) 16. Rubio, J., Alcaraz, C.C., Lopez, J.: Selecting privacy solutions to prioritise control in smart metering systems. In: 11th International Conference on Critical Information Infrastructures Security (2016) 17. Di Silvestre, M., Gallo, P., Ippolito, M., Sanseverino, E., Zizzo, G.: A technical approach to the energy blockchain in microgrids. IEEE Trans. Ind. Inform. 4792–4803 (2018) 18. Hyperledger Fabric: https://www.hyperledger.org/ (2022). [Online]. Accessed 25 Apr 2022 19. Hyperldger Fabric. [Online]. Available: https://hyperledger-fabric.readthedocs.io/en/release2.2/smartcontract/smartcontract.html (2022). Accessed 25 Apr 2022 20. DigiCert. [Online]. Available: https://www.digicert.com/ (2022). Accessed 25 Apr 2022 21. Hyperledger Fabric. [Online]. Available: https://hyperledger-fabric.readthedocs.io/en/rel ease-2.2/network/network.html (2022). Accessed 25 Apr 2022 22. Hyperledger Fabric: Private Data. [Online]. Available: https://hyperledger-fabric.readthedocs. io/en/release-2.2/private-data/private-data.html (2022). Accessed 25 Apr 2022 23. Van Hoye, L., Maenhaut, P.J., Wauters, T., Volckaert, B., De Turck, F.: Logging mechanism for cross-organizational collaborations using hyperledger fabric. In: ICBC 2019 - IEEE International Conference on Blockchain and Cryptocurrency, pp. 352–359 (2019)

Investigating the Option of Developing a Power Supply Network Using Electricity in Greek Islands: The Case of Skiathos Island Ioannis Gagtzas and Giannis Adamos(B) Traffic, Transportation and Logistics Laboratory, University of Thessaly, Pedion Areos, 38334 Volos, Greece [email protected]

Abstract. In recent years, road transportation of goods and people have been constantly increasing. As a result of population growth and economic development, the transportation industry has a need for larger amounts of energy. Many countries, including Greece, are emphasizing the promotion of electric mobility to reduce emissions. Also, a goal to achieve in the future is to produce the electricity needed through renewable sources. In addition, the Sustainable Urban/Island Mobility Plans, Electric Vehicle Charging Systems and other programs such as Daphne and Green Deal are applied to ensure the smooth integration of electric mobility in Greece. The present paper investigates through a questionnaire survey the development of sustainable network for people and goods transportation in Skiathos Island. Based on the sample responses, data analysis was performed with descriptive and inferential statistical analysis. It is concluded that there is a will to purchase an electrical vehicle, taking into consideration the existence of the appropriate infrastructure. Also, respondents were keen on autonomous vehicles regarding public transportation. The sustainable proposals for the transportation of goods seem to be of high interest to the businessmen of the island. Keywords: Sustainable mobility · Island mobility plans · Clean energy · Questionnaire survey · Smart solutions

Thematic Track: Electric and clean energy in transportation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_18

Emerging and Innovative Technologies in Transport: Technological Innovations in Transport and Mobility

Innovative Non-polluting Traffic Light Crossroads Calin Ciufudean(B) and Corneliu Buzduga Stefan cel Mare University, 13 University, Suceava, Romania {ciufudean,cbuzduga}@usm.ro

Abstract. Pollution is a change in the physical, chemical and biological components of the environment that is harmful to humans, natural ecosystems and man-made ones; therefore pollution can be understood as an action by which man degrades his own environment of existence. The issue of urban pollution due to car traffic and especially the pollution of the intersections of main routes, especially in big cities, is an important source of nowadays pollution. The objective of this paper is to present a system for monitoring and improving air quality at road intersections. By giving priority to traffic to polluting vehicles, road intersections will be less polluted. Constructive details and operation mode of the proposed system, as well as suggestions for further development of it are discussed in this paper. Keywords: Air pollution · Carbon dioxide · Microcontroller · Wireless data transmission · Traffic light · Sensors

1 Introduction The term pollution can be defined in several ways. Pollution is a change in the physical, chemical and biological components of the environment that is harmful to humans, natural ecosystems and man-made ones. Pollution manifests itself as a result of the introduction of pollutants into the environment. By pollutant we mean any substance (chemical, biological) solid, liquid, gaseous or vaporous or any form of energy (electromagnetic, ionizing, thermal, sound or vibration radiation) which, when introduced into the environment, alters the balance of its constituents and organisms live and cause damage to property. The term pollution comes from Latin: “polluo-ere” - to pollute, to degrade. In other words, pollution can be understood as an action by which man degrades his own environment of existence. The atmosphere can be affected by a multitude of solids, liquids or gases [1]. Given that the atmosphere is the widest and at the same time the most unpredictable vector of the spread of pollutants, the effects of which are felt directly and indirectly by humans and other components of the environment, it is necessary that the prevention of air pollution is a matter of public, national and international interest. Potentially, air pollution is the most serious problem, as it has short, medium and long term effects. In the short and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_19

224

C. Ciufudean and C. Buzduga

medium term, pollution has negative effects, endangering human health, damaging biological resources and ecosystems, causing economic damage. In the long run, pollution causes changes in the environment through: the greenhouse effect, the destruction of the ozone layer and acid rain. For the air, the current environmental problems are: the greenhouse effect, the destruction of the ozone layer, the acidification, the micro-pollutants, the suspended particles. Among the main chemical pollutants are [7, 8]: The issue of urban pollution due to car traffic and especially the pollution of the intersections of main routes is treated in the literature with palliatives or solutions that look to the more or less distant future instead of immediate solutions with visible effect and easy to implement. Among the solutions found in the literature we mention the following: organizing an alternative traffic of vehicles according to the parity of the registration number; reducing the speed of circulation; supplying the vehicles with LPG or auto-gas; using in the agglomerated centers only hybrid or electric vehicles; reducing traffic, and boosting public transport [2, 3]. The main objective of this paper is to present an innovative non-polluting system (INP-01), patented, capable of controlling a traffic light intersection and to “adapt” to traffic in order to give priority to cars in the direction in which a greater amount of pollutants from combustion engines accumulates. Exhaust gases include sulfur dioxide, lead, hydrocarbons, organic compounds, methane, etc. These substances are harmful to health and the implementation of this system should be carried out primarily at intersections in the vicinity of schools, hospitals, residential area, etc. INP-01 detects any exceedances of the limit values for these substances, as follows: benzene has a permissible value limit of 5 µg/m3 ; carbon monoxide has a permissible limit value of 10 mg/m3 ; Nitrogen dioxide has a permissible limit value of 200 µg/m3 ; Lead has a permissible value limit of 0.5 µg/m3 [4–6]. The idea for greening cross roads was to detect the pollutants at the intersection with the help of gas sensors and, depending on the results transmitted by these sensors, to determine the direction of traffic of vehicles that produce more pollutants and try to maintain them a as short a time as possible at the intersection by reducing the red display time at the traffic light. The sensors used in the project are CO2 sensors. The data provided by the sensor is transmitted to the wireless intersection control unit by means of a transmitter/receiver module, thus eliminating the inconvenience of using cables and giving freedom in choosing the location of the sensor. The transceiver module is a development kit provided by Microchip (rfPIC Development Kit 1). The rfPIC Development Kit 1 provides wireless connectivity for built-in control applications with the rfPIC12F675 Flash microcontroller, UHF RF transmitter, and rfRXD0420 receiver. It consists of transmitter and receiver modules that support a modulation of the transfer handling amplitude - Amplitude Shift Keying (ASK) - at 315 and 433 MHz. The remainder of this paper is organized as follows: Sect. 2 discusses the hardware support of INP-01 system, Sect. 3 displays the operation mode of INP-01 system, and Sect. 4 conclude our work and suggest further possible development of it.

Innovative Non-polluting Traffic Light Crossroads

225

2 INP-01 System Hardware Support The block diagram of the INP-01 system is depicted in Fig. 1.

Fig. 1. Block diagram of the INP-01 system.

According to the block diagram in Fig. 1 the automatic traffic light control system with priority for polluting machines it consists of a module called (Sx) which is composed of the effective sensor and a transmission module (Tx) which converts the signal from analog to digital and its wireless transmission to the receiver module (RX) connected to a command and control unit (UCC) which is the control device for the operation of the traffic light; this module is controlled by the PIC16F648A microcontroller [6]. The transmission module (Tx) has as main element a microcontroller, which contains an A/D converter and realizes the wireless transmission of the information from the CO2 sensors, where the sensors’ accuracy is in the range of 350~10000 ppm. The transmitted data contains in their format: a serial number and a function that helps to identify the sensor, the digital value of the signal taken from the sensor. In order not to overlap the signals at the reception, each transmitter has different transmission times depending on the location near the traffic light, as follows: first they will transmit the sensors closest to the traffic light then they will transmit the sensors placed every 20–100 m. The reception module (Rx) has as main element a microcontroller, which transmits to the command and control unit (UCC) the data received from (Tx). Sensors located at the intersection in the presence of carbon dioxide from vehicle exhaust will provide a current at the input of the conditioning circuit that depends on the detected CO2 concentration. The conditioning circuit is actually an instrumental amplifier containing three operational amplifiers. It picks up the signal from the sensor and displays it amplified at the input of the transmission module. A manual calibration

226

C. Ciufudean and C. Buzduga

of the sensors can also be performed with the help of the conditioning circuit. The transmission module (rfPIC 12F675) contains an A/D converter that first converts the signal from analog to digital [7, 8]. It then processes the data for the radio transmission format and transmits it. The transmitted data contains in their format: a serial number and a function that helps to identify the sensor, the digital value of the signal taken from the sensor. In order not to overlap the signals at the reception, each transmitter has different transmission times, so chosen that the overlapping of the signals takes place for as long as possible. The receiving module first receives the data and then transmits it to the command and control unit. The command and control unit deals with data decoding, data processing, depending on the result obtained the control and command of the traffic light and the interface with the PC. The process of decoding the data consists in identifying the sensor and the value transmitted by it. Then these data are processed and depending on the result obtained we operate on the times associated with the red and green colors of the traffic light. Due to the fact that the control unit of the traffic light is in fact a PIC microcontroller and that it has a limited number of connectors, assigned to the output ports a serial transmission of data controlling the lighting of colors was used (the protocol used being I2 C) and with the help of integrated circuit PCF 8574 (i.e. an I2 C bus expander) performs the conversion of data from serial mode to parallel mode [9, 10]. The ULN2003 circuit contains 7 transistors in Darlington configuration, and its role is to amplify the value of the output signal, provided by PCF8574, used to light the LEDs at the traffic light. The rfPIC12F675 microcontroller is capable of transmitting amplitude modulated (ASK = Amplitude Shift Keying) or frequency modulated (FSK = Frequency Shift Keying) data. The transmitter is an integrated high-frequency one with ASK/FSK modulation and consists of a quartz oscillator, a frequency tracking loop, an open collector output power amplifier and mode control logic. There are three settings for optimizing performance in the most commonly used frequency ranges. The internal structure of the transmitter is shown in Fig. 2 [8, 10]. A Colpitts oscillator generates the reference frequency. The voltage-controlled oscillator converts the voltage on the LF pin to frequency. This frequency is divided by 32 and compared to the quartz reference. If the frequency or phase does not match the reference, the charger corrects the voltage on the LF pin. The output signal of the VCO (voltage controlled oscillator) is also amplified by the power amplifier (PA), whose only final output feeds the user’s antenna. The external components we need are a quartz crystal that sets the transmission frequency, a power filter capacitor and an impedance adapter component to get the most power from the antenna. The two control signals from the microcontroller are connected externally for maximum flexibility. The receiver module fRXD0420 is made in a UHF configuration with ASK modulation, frequency 433.92 MHz with a signal transfer rate of 4800 baud which allows the implementation of telemetry applications with low energy consumption. The antenna length (L) can be calculated with the ratio L [inch] = 2952/f [MHz], where f is the working frequency [10, 11]. The TGS4160 noxious sensor exhibits a very good linear characteristic between the variation of the thermoelectric power (FEM) and the CO2 concentration on a logarithmic scale. The

Innovative Non-polluting Traffic Light Crossroads

227

Fig. 2. The block diagram of rfPIC12F675 microcontroller.

sensitivity curve, according to the catalog, to CO2 shows an accentuated increase of FEM as the concentration of CO2 , CO, benzene increases.

3 Operation of the INP-01 System The operating times of the traffic light are adjusted according to the values received from the sensors. In the absence of these values, the traffic light will operate normally, i.e. the time allocated for the green color will be equal to the time allocated for the red color (we assume that it is 16 s, and depending on the configuration of car traffic we can change it). Just an exemplifying example on how to make decisions based on the values transmitted by the sensors is shown in Fig. 3.

Fig. 3. Example of the operation of the traffic light according to the values delivered by the sensors.

If the values received by the sensors have similar values (the difference between the values is less than 5 µg/m3 ), the traffic light will also operate normally. Assume

228

C. Ciufudean and C. Buzduga

that the values from one sensor are higher than those from the other, then the time associated with the green color will increase and the time associated with the red color will decrease for the traffic light on the direction where the sensor receiving the highest concentration of CO2 . If the ratio between the sensor concentrations is maintained, the time increases/decreases until the maximum/minimum threshold limit values are reached (24 s for maximum and 8 s for minimum). If the values from the sensors equalize, then the times associated with the colors equalize. The organization chart of the principle of dynamic regulation of the times associated with the traffic light colors according to the value of the sensors is exemplified in Fig. 4, where the values delivered by sensor 1 are denoted by S1 and the values delivered by sensor 2 are denoted by S2 . We assume that sensors S1 and S2 are located in incompatible directions of simultaneous circulation, and the traffic light is located on the same circulation sense with sensor 1.

Fig. 4. The organization chart of the principle of dynamic regulation of the times associated with the traffic light colors according to the value of the sensors.

As can be seen from the block diagram, the command and control unit has a serial interface for interconnection with a PC. With the help of the PC you can give various commands to query the command and control unit, and on its display you can view this data. Communication between the PC and the command and control unit is achieved via the RS232 serial transmission protocol. To connect the two systems, use the HyperTerminal utility in the communication accessories package of the Windows operating system. Thus, after opening the program, the New Connection option is chosen from the File menu. Enter a name for the connection and press the OK button. The COM1 serial port will then be selected as the communication port, after which the OK button will be pressed. The next window will set the port configuration parameters. Thus, for the transmission speed, 19,200 bps will be chosen, for the number of bits of the date, 8 will be chosen, without parity bits, 1 stop bit and without flow control [12].

Innovative Non-polluting Traffic Light Crossroads

229

At startup (or after reset), the system displays PW, waiting ~ 5 s for the ‘@U’ command (UART parameters: 19,200, 1, N, None), useful for correcting a wrong serial port (UART) configuration [12–14]. After ~5 s. if no command has been transmitted, the system displays ‘up’ and loads the last configuration from the internal memory (non-volatile). The @ character is used to enter ‘command’ mode; the system returns the prompt >>, and after executing the command, the message ‘ok!’ will be displayed or ‘error!’ as the case). The orders that can be sent are: D Demo or simulation mode in which values received from the sensors can be modified with the help of the console; +, - modifies the value received from sensor 1; P, M changes the value received from sensor 2; S Info, device series; U Upload configuration file (returns the ‘k’ character and waits for the file to be transmitted); V Internal memory view (configuration parameters, hexadecimal format); R Reset; X Initializes the buffer for reception; Z Views buffer contents for reception. Editing the source code was done using the MPLAB program. It also created the hex files, which were written to the memory of the microcontrollers. The files containing the rf12F675 PIC source code are named: FASK_L.asm and ASK_Tx_L.h [13, 15]. The code in these files has been adapted from the code provided by Microchip, according to the needs of the project. The original source code transmitted two values, which represented the values of two voltages present at the input of the analog/digital converters, when a button was pressed. The difference is the introduction of the MainLoop function. This function was introduced to make the transmission expire over a period of time, repeatedly, not when a button is pressed. In the first phase, it is checked if the time until the transmission has expired by testing the Sample_Time flag. Setting this flag means that time has expired and can be transmitted. If the Sample_Time flag is set, it is deleted via the Sample_Time bcf statement. Then the value Funct_S0 is written in FuncBits, the Read_ADC_AN3 function is called, which activates the conversion of the signal from the sensor from the analog signal to the digital signal. The XMIT call is called 5 times to make sure the data reaches the receiver. The files that contain the source code for 16F648 PIC programming are: ASK_433_Rx.asm, ASK_433Rx.h, BCDISO_to_BIN32.asm, BIN32ISO_to_BCD.asm, I2Cbus_soft.asm, ISO_registers.inc, and Semafor.asm. The ASK_433Rx.h file contains the assignment definitions of the registers and flags, the definition of the pins according to the wiring diagram, the mapping of the EEPROM, the selection of the transfer rate for the RS232 protocol, the bus address for the I2C protocol. The ASK_433_Rx.asm file contains the procedures used to decode the data received from the sensors and the procedures used in serial communication according to the RS232 protocol. The files BCDISO_to_BIN32.asm, BIN32ISO_to_BCD.asm, i2cbus_soft.asm are standard files taken from Microchip. The Semafor.asm file contains the source code that defines the running times of the traffic light and the routine of changing them according to the values of the sensors.

230

C. Ciufudean and C. Buzduga

4 Conclusions The objective of this paper is to present a system for monitoring and improving air quality at road intersections. By giving priority to traffic to polluting vehicles, road intersections will be less polluted than at present when waiting times are constant for traffic directions without taking into account their different degree of pollution. The system designed and built in our laboratory was approved and gave good results in the tests performed on the university campus. The INP-01 system is a novelty in the field and has a high chance of being implemented in reality in the future, especially in order to comply with European Union pollution regulations. Also its implementation can bring an improvement of the air quality in the localities, which will bring benefits on the health condition of the population. Another direction of application of the project is the creation of an air quality monitoring system in the neighborhoods of a city and the display of air quality with the help of optic indicators like traffic lights that will display green when the air has a good quality, red for a polluted atmosphere and yellow for average air quality values. Also we believe that INP-01 system can be easily adapted for highways facilities and airports parking in order to optimize the air quality. Due to the fact that the INP-01 system has a connection interface with a personal computer (PC), it is possible to capture the data transmitted by the sensors in a txt file. By using several types of sensors, an analysis of air quality can be made.

References 1. World Economic Forum: The Net-zero Challenge: Global Climate Action at a Crossroads (2019) 2. Wang, J., Guo, X., Yang, X.: Efficient and safe strategies for intersection management: a review. Sensors (Basel, Switzerland) 21(9), 3096 (2021) 3. Cui, H., Yuan, G., Liu, N., Xu, M., Song, H.: Convolutional neural network for recognizing highway traffic congestion. J. Intell. Transp. Syst. 24(3), 279–289 (2020) 4. Andert, E., Khayatian, M., Shrivastava, A.: A dependable detection mechanism for intersection management of connected autonomous vehicles. In: Workshop on Autonomous Systems Design, pp. 7:1–7:13 (2019) 5. Worrawichaipat, P., Gerding, E., Kaparias, I., Ramchurn, S.: Resilient intersection management with multi-vehicle collision avoidance. Front. Sustain. Cities 3 (2021) 6. Andert, E., Khayatian, M., Shrivastava, A.: Crossroads—Time-Sensitive Autonomous Intersection Management Technique, DAC ’17, pp. 1–6, Austin, TX, USA, (2017). https://doi.org/ 10.1145/3061639.3062221 7. British Columbia Road Builders and Heavy Construction Association, Ministry of Transportation and Infrastructure, Reducing Greenhouse Gas Emissions in B.C. Road Building and Highway Maintenance (2011) 8. Success stories within the road transport sector on reducing greenhouse gas emission and producing ancillary benefits. EEA Technical Report, No. 2, ISSN 1725-2237 (2008) 9. Maraqa, M.A., Albuquerque, F.D.B., Alzard, M.H., Chowdhury, R., Kamareddine, L.A., El Zarif, J.: GHG emission reduction opportunities for road projects in the Emirate of Abu Dhabi: a scenario approach. Sustainability 13, 7367 (2021) 10. Johnson, J.H.: Air Pollution, the Automobile, and Public Health. National Academies Press (US), Washington (DC) (1988)

Innovative Non-polluting Traffic Light Crossroads 11. 12. 13. 14. 15.

231

https://www.alldatasheet.com/datasheet-pdf/pdf/74969/MICROCHIP/PIC16F648A.html http://ww1.microchip.com/downloads/en/AppNotes/tb069a.pdf http://ww1.microchip.com/downloads/en/AppNotes/00868a.pdf http://ww1.microchip.com/downloads/en/AppNotes/00515.pdf http://ww1.microchip.com/downloads/en/AppNotes/00845a.pdf

System for Avoiding Traffic Jams of Intervention Vehicles Calin Ciufudean(B) and Corneliu Buzduga Stefan cel Mare University, 13 University, Suceava, Romania {ciufudean,cbuzduga}@usm.ro

Abstract. This paper introduces a new version of the systems already on the market for streamlining traffic at intersections in order to give priority to intervention vehicles. The system consists of two modules, one located on board the vehicle that emits the spatial coordinates converted to binary code and the other is located in the intersection traffic light box and receives the data, interprets them and decides the traffic lights change colors in the direction of travel of the intervention vehicle in green and all other directions will be red, thus giving priority and maximum safety to the intervention vehicles. Keywords: Traffic jam · Traffic light · Intersection · Intervention vehicles · Microcontroller · Digital compass

1 Introduction A very important topic is being dealt with nowadays: the prompt response of fire engines, police, and ambulances vehicles to requests. It is known that not infrequently, a few minutes are extremely important for a person’s life. More specifically, parking in a traffic jam at an intersection of an ambulance often misses crucial moments in a lifetime. Although in a large city hospitals and fire pickets are evenly distributed within the city to reduce response time to requests from certain people in need of help, the rapid population growth leads to a very high density of vehicles in the city’s traffic [1–3]. It is known that vehicles with light or acoustic signals in operation have priority and can drive in the opposite direction, but this becomes impossible when the directions are separated by an obstacle in the middle zone, or when all lanes are occupied by vehicles waiting at the traffic light in which case the intervention vehicles are forced to wait until the traffic participants in front of it start moving. In all these cases, the only solution is to fluidize the intersection using one of the following systems: Global Positioning System (GPS) traffic control system - proposes the use of technology that depends on the international geo-location systems and the global positioning system, built into the vehicle and a network of microcontrollers built into intersections. Both integrated systems can instantly decide the position of the intervention vehicle in advance so that they can cross the intersection safely [4–6]. This system has, like any other, a number of advantages, but it also has disadvantages. Among the advantages, we mention: the rather short time of data processing, high accuracy of location and optimization of the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_20

System for Avoiding Traffic Jams of Intervention Vehicles

233

shortest route. The disadvantages are the following: one of the biggest drawbacks is that the driver has to enter the location of the destination (street name, building number, etc.) in order for them to be identified in the information map system as longitude and latitude, thus being distracted attention from the driving activity of the vehicle. Another disadvantage is the high price of implementing this system. Another way to streamline crowded intersections is to use an infrared light beam from the intervention vehicle and a detector to change the color of the traffic light mounted at the intersection. This device has the disadvantage that the light beam may not fall perpendicular to the detector, in which case the traffic lights will be synchronized with lower accuracy. The light wave can also be obstructed. In terms of cost, hardware support is quite expensive. Last but not least, the security of the system is quite low. Examples of such systems are OPTICOM, Mirt and Tomar/SRTOBECOM [4, 5]. Traffic signaling device based on acoustic signals uses audio sensors to detect the sound waves emitted by the siren of an intervention car. However, this principle is one of rather poor accuracy because sound waves are reflected in all directions and can be misinterpreted as there is another problem concerning the uneven sound of sirens [6, 7]. In order to improve the response time of the intervention vehicles, we patent in Romania and we present the System for Avoiding Traffic Jams of Intervention Vehicles (TIV-01) which is intended to be a system that contains the advantages of the other already existing systems that were previously discussed, but which eliminates their disadvantages. Unlike the system based on acoustic signals and the one based on the emission of light beams, where communication can be erroneous due to the phenomenon of wave reflection, the prototype proposed by us correctly interprets the spatial coordinates emitted by the two digital modules driven by two microcontrollers. The module located at the intersection is set so that it can always be traced by the geographical north coordinate and according to this module the data received from the module mounted on board the vehicle are interpreted. The data is processed in real time, so the intersection has time to be smoothed by changing the colors of the traffic lights and traffic participants have enough time to clear the center of the intersection, without any enforcement as it is usually done. If a comparison is made between the solution chosen for the implementation of this project and the three ways of fluidizing the intersections presented above, it can be said that in terms of precision in interpreting the data the system proposed in this paper is superior to systems based on emission of acoustic or light waves and accuracy is basically the same as to the GPS-based device, as it avoids acoustic pollution and stress induced by acoustic and light signals mentioned above. Under these conditions, the factor that makes the difference is the very low price, less than 120 euro (i.e. costs in Romania), of our system compared to other systems. The remainder of the paper is organized as follows. We provide a description of the hardware support of TIV-01 system in Sect. 2. The software support of TIV-01 system is discussed in Sect. 3. Section 4 concludes the present work and draws possible new approaches for future research.

2 The Hardware Support of TIV-01 System Block diagram of the system to avoid traffic jams of intervention vehicles (TIV-01) is displayed in Fig. 1.

234

C. Ciufudean and C. Buzduga

Fig. 1. The block diagram of TIV-01 system.

The significance of the modules in Fig. 2 is as follows: OS - orientation sensor; MC digital compass; M1 , M2 - microcontrollers; VS - power supply; E - wireless transmitter; R - wireless receiver; TLVS - traffic light supply; S - traffic light. The two microcontrollers that were used to carry out the TIV-01 system are part of the Arduino family. This development platform (microcontroller Atmel ATmega 328) is in fact the core of the system to avoid traffic jams of intervention vehicles [8–10]. These microcontrollers provide the execution of the instructions that are necessary for the proper functioning of the entire system. Arduino Nano microcontrollers are powered by two 9V DC power sources. They communicate with the sender and the receiver, respectively, which transmits and receives the spatial coordinates given by the two digital compasses. The digital compass calculates the geographical coordinates, which are assigned a series of consecutive numbers in order to make a Cartesian system. These are transmitted to the microcontroller in a binary system with a length of 3 bits D0 , D1 and D2 , see Fig. 2. The microcontroller takes the received value and in turn transmits it to the radio module, which retransmits the frequency modulated information until when it receives a confirmation message from the other module installed in the traffic light. The second module takes over the radio signal performing the demodulation. For experiments we performed we used the wireless transmitter and the wireless receiver of 433 MHz modules: FS1000A, respectively XY-MK-5V [11]. The microcontroller interprets the received message and sends a confirmation response to the sender, then depending on the message it transmits signals to change the color of the traffic lights. The LEDs of the traffic lights are also lit properly with the help of the microcontroller, which, depending on the interpretation of the coordinates, determines which of the directions has a green wave and which does not. Basically, the coordinates of the intervention vehicle are obtained from a digital compass that locates the vehicle through eight cardinal points

System for Avoiding Traffic Jams of Intervention Vehicles

235

and transmits an eight-bit code at the output. This code is interpreted by the microcontroller and is transmitted to the crossroad traffic with a transfer rate of 2400bps. When the vehicle is at a distance of 250 m from the traffic light, the receiver at the traffic light takes over the data emitted by the transmitter in the intervention vehicle. In the traffic light there is also a digital compass that transmits the coordinates of the intersection to the microcontroller. The data thus obtained from the compass and the receiver are processed by the microcontroller which will control the color of the traffic light in the direction of travel of the intervention vehicle in green and all other directions will be red, thus giving priority and maximum safety to the intervention vehicles. The operating principle of the RDCM-802 digital compass is based on determining the position by interpreting the 8 cardinal points it distinguishes, namely: N, S, E, W, N-E, N-W, S-E and S-W, see Fig. 2 [12–14].

Fig. 2. Interpretation of coordinates by 3-bit binary code.

This module outputs a 3-bit binary code D0 D1 D2 . RDCM-802 has applicability in robotic systems, in scientific, educational applications and last but not least in the industrial environment. From a constructive point of view, the plate has a miniaturized square shape that does not contain elements in mechanical movement. Based on the effect of the Earth’s magnetic field, the device eliminates the mechanical problems of some similar sensors [15–17]. Another important advantage is the low electricity consumption. The board contains its own microprocessor, which makes it very easy to interface with the higher hierarchical level. The device is powered by a standard 5V DC source and is depicted in Fig. 3.

Fig. 3. The RDCM-802 digital compass.

3 The Software Support of TIV-01 For the proper operation of the traffic jam avoidance system of the intervention vehicles, the Atmega 328 microcontrollers were programmed using the Arduino IDE development

236

C. Ciufudean and C. Buzduga

environment. The Arduino IDE development environment uses a GNU tool chain for AVR libraries to compile programs. The avrdude utility is used to load programs onto the board. The program includes three main components: 1. Initialization, in which all classes, variables, and libraries are initialized. Also in this part of the initialization is the void setup () function, which cannot be missing from the Arduino IDE environment because it describes the initial state of the development platform; 2. The program execution component is actually an infinite loop due to the void loop () function; 3. The third component in which the functions used in the program are defined. Regarding the programming mode of the RDCM-802 digital compass, we mention the coordinate_coding function, which has the role of reading the data from the compass and saving them in a variable. In this case, the RDCM-802 compass uses three pins to represent the coordinates. Thus each pin of the compass, respectively D0, D1 and D2 are connected to three general pins 0, 1 and 2. The function has as input parameters the three pins through which the data will be read. Basically, the compass encodes the coordinates in the form of a binary number, and in the microcontroller they will be saved as a decimal number to be easier to understand and easier to perform certain calculations using these values. The function checks what value each microcontroller pin reads from the compass (“0” or “1”). After this check, the value is saved in a byte variable by moving to the left according to the position (if we talk about pin_0 the value will not be moved, for pin_1 it will be moved by one bit and for pin_2 it will be moved by two positions). After reading the three pins and storing the data in the coordinate variable, proceed to the execution of the next function. Modifying the coordinates of the traffic light according to those received from the intervention vehicle is done using the void change_local_coordonates () function. This function is also ensured by the fact that the value in decimal does not exceed the value 8. The void read_rf_module () function reads the data received from the RF module, then analyzes the message and finds out which traffic light should be set to indicate green and which traffic lights should be set to display red.

4 Conclusions The TIV-01 Intervention Vehicle Traffic Avoidance System is intended to be a new version of the systems already on the market for streamlining intersection traffic in order to give priority to intervention vehicles. The work consists mainly of two modules, one located on board the emitting vehicle emits its spatial coordinates converted into binary code and the second module is located in the traffic light and receives data, interprets them and decides how the LEDs of the traffic light it changes its colors. The traffic intervention vehicle avoidance system consists of two development platforms that are interconnected with a digital compass, a radio transmission module and a radio reception module, respectively. Because it is powered by a low voltage of 9V it can be said that

System for Avoiding Traffic Jams of Intervention Vehicles

237

the system made in this work is a modest consumer of electricity and is not polluting. Among the advantages of the TIV-01 system compared to the already existing systems we mention the precision in locating the intervention vehicle, the small size of the system, the high precision and reliability and last but not least the low cost. Further development of TIV-01 system which performed well in an experimental stand we built on the university campus it includes the addition of a module to identify the address to which the intervention vehicle is moving and thus the construction of an integrated module to avoid traffic jams and guide the vehicle to the intervention point.

References 1. Triyanto, G., Azhari, A.F., Yuspin, W.: Authority of procurement and maintenance of traffic facilities and infrastructure on national roads. Jurnal Ilmu Hukum, Unifikasi, No. 2 (2018). E-ISSN 2580-7382 2. Wang, J., Guo, X., Yang, X.: Efficient and safe strategies for intersection management: a review. Sensors (Basel, Switzerland) 21(9), 3096 (2021) 3. Cui, H., Yuan, G., Liu, N., Xu, M., Song, H.: Convolutional neural network for recognizing highway traffic congestion. J. Intell. Transp. Syst. 24(3), 279–289 (2020) 4. Andert, E., Khayatian, M., Shrivastava, A.: A dependable detection mechanism for intersection management of connected autonomous vehicles. In: Workshop on Autonomous Systems Design, pp. 7:1–7:13 (2019) 5. Worrawichaipat P., Gerding E., Kaparias I., Ramchurn S.: Resilient intersection management with multi-vehicle collision avoidance. Front. Sustain. Cities 3 (2021) 6. Andert, E., Khayatian, M., Shrivastava, A.: Crossroads—Time-Sensitive Autonomous Intersection Management Technique, DAC ‘17, pp. 1–6. Austin, TX, USA (2017). https://doi.org/ 10.1145/3061639.3062221 7. Taale, H., Hoogendoorn, S.: Anticiperende netwerkregelingen. NM Mag 1(4), (2006) 8. Lefebvre, N., Chen, X., Beauseroy, P., Zhu, M.Y.: Traffic flow estimation using acoustic signal. Eng. Appl. Artif. Intell. 64, 164–171 (2017) 9. Gordon, M.: Traffic Device. Institute of Traffic Engineers, Washington (1999) 10. Traffic Signals: http://cityofcarrollton.com/index.aspx?page=259 (2014) 11. Naghiu, M.: Distributed Sensor Systems for Robust Road Traffic Management (in Romanian language). Editura Politehnica Timis, Oara (2009) 12. Collura, J., Willhaus, E. W.: Traffic signal preemption and priority: technologies, past deployments, and system requirements. In: Proceedings of the 11th ITS America, Florida, USA (2001) 13. Margolis, M.: Arduino Cookbook, 2nd edn. O’Reilly Media (2012) 14. Barrett, F.: Arduino Microcontroller Processing for Everyone. Laramie, USA (2013) 15. Gertz, E.: Environmental Monitoring with Arduino: Building Simple Devices to Collect Data About the World Around Us. O’Reily Media (2012) 16. https://quadmeup.com/fs1000a-and-xy-mk-5v-433mhz-rf-modules-overview 17. https://www.geosensory.com/rdcm-802.htm

A Mobile Computing Based Tool for Low-Emission Driving Nikos Dimokas1,4 , Dimitris Margaritis1(B) , Sébastien Faye2 , Ramiro Camino2 , Orhan Alanku¸s3 , and Engin Ozatay3 1 Centre for Research and Technology Hellas, Hellenic Institute of Transport, 6th km

Charilaou—Thermi, 57001 Thessaloniki, Greece [email protected] 2 Luxembourg Institute of Science and Technology Avenue Des Hauts-Fourneaux, L-4362 Esch-Sur-Alzette, Luxembourg 3 Istanbul Okan University, Tuzla Campus, 34959 Tuzla, Istanbul, Turkey 4 Department of Informatics, University of Western Macedonia, Fourka Area, 52100 Kastoria, Greece

Abstract. Recent advances in communications and mobile computing boosted the green mobility. Mobile Computing is the ability to provide computing technology in mobile environments while Green mobility aims to reduce vehicle emissions. The paper introduces a tool consisting of a mobile application and an information system that aims to collect and analyse the user’s driving style, with a specific focus on emission reduction. To achieve that, relevant user information such as accelerometer, gyroscope, location and on-board diagnostics data are being collected transparently and continuously for the sake of developing the driving assistance tool for low-emission driving. Based on the collected information and a scoring algorithm, the user’s driving style is analysed in real time and transcribed in a score. The proposed tool provides recommendations to the drivers when the score is bad and aggregated data to the authorities. The tool offers straightforward recommendations, while the driver is on the way, that can lead to prevention of high-emission driving styles by providing immediate corrective actions. As opposed to that, the information system stores and analyses the driving data that have been gathered to generate a post-driving dashboard for the authorities to monitor overall the driving behaviour and vehicle emissions. The tool has been tested thoroughly and the results indicate a robust performance. Keywords: Green vehicle · Low emissions · Driving · Mobile computing · Intelligent transport system · Mobile application

1 Introduction There have been various efforts aimed at enhancing underlying vehicle and fuel technology, traffic management, and enforcement in response to the significant policy concern regarding the impact of road traffic on local air quality. Although it is appreciated that zero tailpipe emission technologies may solve the problem in the long term, fleet renewal © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_21

A Mobile Computing Based Tool for Low-Emission Driving

239

takes time and current road traffic is clearly dominated by internal combustion fleets with a share of more than 95%. On-road emissions can be significantly different from laboratory measurements, as was revealed by a vehicle emissions testing program funded by the UK Department for Transport in 2016 [1]. The study shows that some cars emit up to 12 times the EU maximum. Real-world emissions take into account factors such as vehicle characteristics, ambient temperature, traffic, road layout and driver behavior. The internet’s and smartphones’ technological advancements, along with the rapid expansion of mobile computing [2, 3], boosted green mobility [4], which attempts to lower vehicle emissions. Fuel consumption, gas emissions and traffic safety are all significantly influenced by driving behavior. The automatic recognition of a driver’s driving behavior is crucial in creating a safety and emission score for that driver, which can improve overall safety and reduce gas emissions and fuel consumption [5–7]. Moreover, tracking driving patterns can meet the demands of several businesses, including fleet management, vehicle insurance, and fuel economy improvement. The proposed tool focuses on creating and implementing a smartphone app-based data collection system that would record variables to profile a driver’s behavior. Additionally, a recommendation system incorporated into the mobile application to automatically advise the user based on the driver behavior perceived. The mobile application exploits the on-board diagnostics (OBD) devices for collecting data. Moreover, the mobile app provides message to the drivers based on the data collected and processed. Except from the mobile application, the tool contains a web dashboard application addressed to public and local authorities, providing them with visualization of the aggregated data collected. The remaining of the paper is structured as follows: We present the previous work in Sect. 2 and the mobile application and the scoring algorithm are described in Sect. 3. Section 4 presents the dashboard application, while Section 5 describes the evaluation. Finally, Sect. 6 concludes the paper.

2 Relevant Work According on specific movements (e.g., turns, lane change, and aggressive acceleration and braking), previous studies have suggested a number of ways for identifying and categorizing driving habits using the fusion-sensor approach of the accelerometer, gyroscope, magnetometer, and GPS [8–12]. Among these, smartphones were frequently utilized to gather information for an analysis of driving behavior. Smartphones have a number of advantages over conventional data collection tools like cameras and telemetry units. First and foremost, the tools that are based on smartphones are affordable, scalable, and easy to upgrade. Second, smartphones are capable of online evaluation and fast driver feedback. Last but not least, cellphones can offer a short path to modern technology due to their quick replacement and development cycles. Numerous studies have been conducted on the classification of driving behavior using smartphone sensing [13–15]. Recent technological advances in communication technology and mobile computing have provided new ways to understand driving behavior. These new tools require setting up in-car sensing systems to collect relevant data and process it. Detections

240

N. Dimokas et al.

performed by such sensing systems can be divided into two categories: participatory sensing and opportunistic sensing. The difference between the participatory sensing and opportunistic sensing is mainly in the user participation. The opportunistic sensing is preferred in most projects since the data collection process is performed automatically and more easily supports large scale deployments. For instance, tri-axial accelerometers have been used alone for several decades to monitor human movement and estimate energy expenditure. Sensing applications can usually address three detection levels depending on existing data-sharing policies [16]. The levels are the individual level, the group level and the community level. Depending on the detection level, different data protection and privacy degrees need to be adjusted, thus requiring the definition of rigorous data treatment systems. The usage of smartphone sensors to analyse driver behavior is persistently shown in the literature and is mostly focused on the individual level. Therefore, the key challenge for conducting such research is selecting the most suitable sensors accepted by the driver (i.e., data collection process should be convenient to the end user, non-intrusive, and should not breach user privacy). The table below presents a comprehensive analysis to identify potential data collected through smartphones and OBD-II interfaces that can be used to analyse driving behavior and to show user acceptance of the sensors and/or operations used for the estimation of driving behavior [17, 18] (Table 1).

3 Mobile Application The mobile application tries to assist drivers to achieve low emissions driving. The application provides recommendations to the drivers without distracting them during travelling. The system architecture is based on modules. There are three major modules. The data collection module that receives data from OBD-II (e.g., engine rpm, vehicle speed), phone sensors (e.g., accelerometer, wireless traces) and the user. The data mentioned previously are used as input to a scoring module, making it possible to create a local representation of the user’s profile and distinguish different behaviours previously identified via laboratory tests and state-of-the-art reviews. This scoring module can compute, on one side, a real-time acceleration profile, and on the other side, a time series of scores, using a classification approach. Input data comes from OBD dongle and phone sensors, while ground truth data would be provided by the user and independent data such as GPS. The third module is the recommendation module that advises the user to adopt attitudes depending on his/her perceived behavior. Based on the above information, the mobile application generates two types of recommendations: • Active recommendations, which are presented to the user while driving. The user’s driving style is analysed in real-time and transcribed into an acceleration score. The user interface and possible actions are limited, so these recommendations need to be simple to preserve the user’s safety. Active recommendations require local data processing and storage. The recommendations have been implemented as visual messages without distracting the driver.

A Mobile Computing Based Tool for Low-Emission Driving

241

Table 1. Data from smartphone and OBD for the driver behavior analysis. Device

Sensor

Data

User acceptance of collected data

Smartphone

Accelerometer Acceleration, vibration, and tilt

High

Gyroscope

Orientation details, rotation, and direction like up/down and left/right

High

Barometer

Air pressure

High

Network traces Passive network data left by High Wi-Fi, Bluetooth and cellular nodes Compass

Magnetic fields

Medium

Camera

Facial Images

Low

Microphone

Loudness of sound

Very Low

GPS

Location

Low

OBD-II dongle –

Real-time parameters: engine Medium RPM, speed, pedal position, airflow rate, coolant temperature, engine load, throttle percentage vehicle identification number Medium (VIN) Status of “check engine” light Medium Emission readiness status

Medium

Oxygen sensor (maximum and, minimum voltage output, and switching rate)

Medium

Diagnostic trouble codes (DTCs)

Medium

Number of miles driven

Medium

Number of ignition cycles

Medium

• Passive recommendations, which are given to the user after a trip. A report is generated, including textual recommendations, statistical analyses and contextual elements. This feedback allows drivers to learn from bad driving habits and improve their emissions reduction. Passive recommendations mean that data processing and storage will be outsourced to an external server. After successful registration and login, the user is able to configure the preferred settings or to start the application by pressing the “play” button (Fig. 1a). Figure 1b

242

N. Dimokas et al.

depicts the screen where the user can fill in the appropriate information about the vehicle like vehicle category, fuel type, fuel consumption, emission label, vehicle weight etc.

Fig. 1. Main screen (a) and vehicle properties configuration (b)

Additionally, the user has a variety of settings (Fig. 2a) that can configure. For example, the user can select the server synchronization option and determine when the application is going to synchronize with the server (e.g. only with Wi-Fi, only manually, etc.), the frequency of the synchronization and the file size. Figure 2b depicts the data collection options. The user can activate the accelerometer, Bluetooth traces, GPS, Gyroscope and OBD (Table 2). The data collected by the mobile application either by the device’s sensors or the OBD dongle and the user input are listed below. 3.1 Scoring Algorithm In this study, the process is focused on collecting smart phone sensor data including GPS and accelerometer and detecting driving events namely acceleration, deceleration,

A Mobile Computing Based Tool for Low-Emission Driving

243

Fig. 2. Settings menu (a) and data collection options (b)

cornering and speeding. The acceleration event is a driving event where the driver speeds up for a certain amount of time and exceeding predefined limit. Similarly, deceleration is an event when the driver slows down either by pressing braking or just coasting with the effect of road topology which can be a straight level road or an inclined road. Cornering is another event when the driver turns the steering wheel and the event is sensed by lateral acceleration values collected from accelerometer sensor. The events are detected in the following order. The original sensor data is collected and filtered. Then, based on accelerometer values the calibration parameters are determined such that the z direction of the calibrated value points to the center of the world, the x direction points to the longitudinal direction of the vehicle and the y direction points to the lateral movement of the vehicle. Having determined the calibration parameters the accelerometer signals are fused with GPS data which has latitude, longitude, speed and direction information. Then, by analyzing the sensor data in real time based on excitation of the signals event occurring regions are determined. The process continues with feature extraction and feature selection steps. Those steps form the input for random forest algorithm which finally categorizes the event as acceleration, deceleration, cornering and speeding driving behaviors.

244

N. Dimokas et al. Table 2. Data collected

Factors

OBD/J1939

Mobile sensors

User

External services

Type of road

X

X

Speed limit

X

X

Road surface conditions

X

Wind

X

Slope

X

X

Congestion

X

X

Light conditions

X

X

Temperature

X

Humidity

X

Raining

X

Snowing

X

Vehicle type

VIN

X

X

Engine type

VIN

X

X

Fuel consumption label

VIN

X

X

Emission label

VIN

X

X

Vehicle weight

VIN

X

X

Tyre information

X

Tyre pressure Vehicle km

X

Use of retrofits Gender

X

Age

X

Use of vehicle

X

Driving experience

X

Acceleration

On-board diagnostics parameter ID 90

X

Deceleration

On-board diagnostics parameter ID 90

X

Speed

On-board diagnostics parameter ID 13

X

Engine torque requirement

X

Engine rpm

On-board diagnostics parameter ID 12

The flow chart for the scoring algorithm is shown below. The system is flexible, training can be performed in line with any objective function such as fuel consumption, emissions or aggressive driving etc., (Fig. 3). All signals were initially linearly interpolated into a constant sample rate during the pre-processing step. The sensor data is then individually standardized to [1] using the maximal and least values. Then, to divide continuously collected sensor data into

A Mobile Computing Based Tool for Low-Emission Driving

245

Fig. 3. Scoring algorithm flowchart

windowed data without overlap, a sliding window is utilized. There are two different label categories in the dataset. The UAH dataset only offers the start timestamp and accompanying label for each class. After the pre-processing phase the feature extraction is taken place. The feature extraction includes: 1. statistics values: For each window, statistical features such as the mean, standard deviation, variance, minimum and maximum values, range, and slope between maximum and minimum are calculated. Different sensor data were applied with no overlap using a sliding window. In addition, jerk (j(t)), which performed well in a prior study [19], is calculated as an extra characteristic.  a(t) = ax (t)2 + ay (t)2 + az (t)2 j(t) =

d |a(t)| dt

2. Automatically extracted features: Both PCA and SSAE are applied to the preprocessed data. In addition to the raw data, acceleration magnitude is included for both UAH. For the UAH dataset, when C ≥ 100, the dimension of each layer is C → C/2 → 30 → 20 → T. The dimension of each layer is C → 30 → 20 → T when C < 100. Here, C is the dimension of input data, T is the dimension of the resulting feature set. The dropout strategy is also used to avoid continually extracting the same features from the training data and to prevent complex co-adaptations on the training data. A tree-based technique called random forest (RF) creates a predetermined number of classification trees without pruning. A random selection of m features from the whole

246

N. Dimokas et al.

feature set M is used to divide the nodes. Each tree is constructed from a bootstrapped random sample taken from the training set. Since it is a collection of many decision trees. Decision trees are prone to over-fitting individually. This leads to high variance and in turn causes errors in prediction. A collection of decision trees will solve the problem of higher variance. Every decision tree is trained on a random subset of data. The errors caused by each decision tree is random and when they are averaged what remains is the actual prediction that was desired. The benefit of random forest is its capacity to produce a metric to rank predictors according to their relative contribution to the predictive efficacy of the model. This is how the prediction is defined. P=

N 1  Ti N n=1

Ti is the n-th tree response of the random forest while N is the number of trees. The average output gives us the most likely driving event. In the given window after extracting the features and corresponding event scoring part takes in place. The division of the data into training and test sets is the last step in the data preparation process. In order to teach the model how to forecast the event from the features, we allow it to know the results during training, in this case, actual driving events. All of the features should be related to the goal value in some way, and the model’s task during training is to discover this relationship. When it’s time to evaluate the model, we ask it to make predictions on a testing set where it only has access to the features and not the responses. Since we do have the real answers for the test set, we can compare these predictions to the true value to determine how accurate the model is. After the data preparation phase, building and training the model with Scikit-learn is straightforward. The RF regression model is imported from skicit-learn, created, and then fitted to the training set of data. Now that it has been trained, the model can recognize the connections between the features and the targets. The next stage is to assess the model’s quality. We use test feature predictions to do this. After that, the following stage includes the comparison of the predictions to the known values. Because we anticipate some of our responses to be low and some to be high, we must use the absolute error when performing regression. Since we are interested in how much our average prediction departs from the actual value, we take the absolute number as we did when establishing the baseline.

4 Dashboard Application The mobile application is complemented by a dashboard web application addressed to public and local authorities, providing them with a statistical overview of the data collected. For each app user, anonymous indicators are transmitted to a dashboard application to collect usage statistics and performance metrics. The latter allows the authorities and the public to understand the benefits of the mobile app, and view statistics by region or type of user. The dashboard application aggregates the data and present them in various graphical representations in order to assist the decision making.

A Mobile Computing Based Tool for Low-Emission Driving

247

The dashboard application is based on the data received and stored and more precisely in some significant and indicative indicators. The indicators are: – Vehicle emissions – Fuel Consumption – Driver aggressiveness based on the information received by the smartphone’s accelerometer and vehicle speed sensors. Thus, the dashboard web application presents aggregated indicators (Fig. 4) about the average fuel consumption and average NOx emissions per emissions label, per vehicle category, per road type, per license category, per driving experience, per gender and per age group and per trip.

Fig. 4. Dashboard web page

The web application provides also aggregated indicators about a specific region. The information presented is similar to the one in dashboard but for a specified geographical area. The dashboard web application’s system architecture follows the N-tier architecture. The application exploits the web services for providing data from/to the database. The web services have been built according to the most popular architectural styles that is Representational State Transfer (REST) architecture. Additionally, the user interface (presentation layer) has been implemented based on the Grafana open-source analytics and isualizeion software. Grafana offers an open-source tool for building dashboards, query data sources, explore, monitor and isualize metrics. The dashboard application requires also a data storage infrastructure in order to generate the aggregated information. The data storage has been implemented with MySQL Relational Database Management System. The data model developed like a relational schema consisting of various tables and the corresponding relations among them.

248

N. Dimokas et al.

5 Performance Evaluation The performance evaluation of the mobile application and the web application includes the usability testing. Twenty users took part in a vast number of studies that we performed. Users responded to questionnaires to provide insightful input on the usability of the web and mobile applications. The evaluation process took place over the course of about two hours, with participants completing specific tasks based on pre-established scenarios that reflected the relevant use cases. Early on, a user-centered strategy was used, therefore getting feedback from possible end users was crucial. Subjective evaluation was focused on the following primary usability components in accordance with ISO directives: • • • •

Efficiency Satisfaction Effectiveness 20 (11 male and 9 female) users took part in the trials.

The most relevant outcomes in terms of usefulness are depicted in Fig. 5. Regarding effectiveness more than 65% of the users found the Android application effective and more than 75% of the users considered the dashboard application effective. However, the perceived usefulness of the applications reached nearly 80%.

Fig. 5. Perceived system usefulness

A Mobile Computing Based Tool for Low-Emission Driving

249

6 Conclusions This paper presents a data collection tool using a mobile application in order to profile a driver’s behaviour. The tool consists of a mobile and a dashboard web application. The mobile application exploits the on-board diagnostics devices for collecting data. Moreover, data have been collected by the users and the sensors of the mobile device. Based on the collected information and a scoring algorithm, the user’s driving style is analysed in real time and transcribed in a score. The mobile application provides recommendations to the drivers according to the score. The dashboard web application provides visualization of the aggregated data collected to the local and public authorities. The performance evaluation of the mobile and the dashboard application attested the usefulness of applications. Both applications were effective with high-perceived usefulness. Acknowledgements. Research supported by MODALES project [20] that is funded by the European Com-mission under the Grant Agreement 815189.

References 1. Department for Transport, Vehicle Emissions Testing Programme. ISBN 9781474131292 (2016) 2. Guan, L., Ke, X., Song, M., Song, J.: A survey of research on mobile cloud computing. In: 10th International Conference on Computer and Information Science (ICIS), pp. 387–392. Sanya, China (2011) 3. Siuhi, S., Mwakalonge, J.: Opportunities and challenges of smart mobile applications in transportation. J. Traffic Transp. Eng. 3(6), 582–592 (2016) 4. Ausubel, J.H., Marchetti, C., Meyer, P.S.: Toward green mobility: the evolution of transport. Eur. Rev. 6(2), 137–156 (1998) 5. Dingus, T.A., et al.: Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proc. Natl. Acad. Sci. 113(10), 2636–2641 (2016) 6. Guo, F., et al.: The effects of age on crash risk associated with driver distraction. Int. J. Epidemiol. 46(1), 258–265 (2016) 7. Klauer, S.G., Guo, F., Simons-Morton, B.G., Ouimet, M.C., Lee, S.E., Dingus, T.A.: Distracted driving and risk of road crashes among novice and experienced drivers. N. Engl. J. Med. 370(1), 54–59 (2014) 8. Koesdwiady, A., Bedawi, S.M., Ou, C., Karray, F.: End-to-end deep learning for driver distraction recognition. In: International Conference Image Analysis and Recognition, pp. 11–18. Springer (2017) 9. Liao, Y., Li, S.E., Wang, W., Wang, Y., Li, G., Cheng, B.: Detection of driver cognitive distraction: A comparison study of stop-controlled intersection and speed-limited highway. IEEE Trans. Intell. Transp. Syst. 17(6), 1628–1637 (2016) 10. Ma, Y., Zhang, Z., Chen, S., Yu, Y., Tang, K.: A comparative study of aggressive driving behavior recognition algorithms based on vehicle motion data. IEEE Access 7, 8028–8038 (2019) 11. Johnson, D.A., Trivedi, M.M.: Driving style recognition using a smartphone as a sensor platform. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1609–1615. IEEE. (2011)

250

N. Dimokas et al.

12. Araujo, R., Igreja, A., De Castro, R., Araujo, R.E., Driving coach: a smartphone application to evaluate driving efficient patterns. In: 2012 IEEE Intelligent Vehicles Symposium, pp. 1005– 1010. IEEE (2012) 13. Xie, J., Hilal, A.R., Kulic, D.: Driving maneuver classification: a comparison of feature extraction methods. IEEE Sens. J. 18(12), 4777–4784 (2018) 14. Eren, H., Makinist, S., Akin, E., Yilmaz, A.: Estimating driving behavior by a smartphone. In: 2012 IEEE Intelligent Vehicles Symposium, pp. 234–239. IEEE (2012) 15. Fazeen, M., Gozick, B., Dantu, R., Bhukhiya, M., Gonzalez, M.C.: Safe driving using mobile phones. IEEE Trans. Intell. Transp. Syst. 13(3), 1462–1468 (2012) 16. Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. IEEE Commun. Mag. 48(9), 140–150 (2010) 17. Faye, S., Bronzi, W., Tahirou, I., Engel, T.: Characterizing user mobility using mobile sensing systems. Int. J. Distrib. Sens. Netw. 13(8) (2017) 18. Hong, J.H., Margines, B., Dey, A. K.: A smartphone-based sensing platform to model aggressive driving behaviors. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 4047–4056 (2014) 19. Bejani, M.M., Ghatee, M.: A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data. Transp. Res. Part C: Emerg. Technol. 89, 303–320 (2018) 20. MODALES European Union Research Project. https://modales-project.eu/ (2022). Accessed 10 April, 2022

Discrepancy Between Hyperpath and Actual Route Choices Based on Smart-Card Data in Shizuoka, Japan Rattanaporn Kaewkluengklom1(B) , Fumitaka Kurauchi1 , and Takenori Iwamoto2 1 Graduate School of Engineering, Gifu University, Gifu 501-1193, Japan

[email protected], [email protected] 2 School of Management, Shizuoka Sangyo University, Shizuoka 422-8545, Japan

[email protected]

Abstract. Understanding passengers’ route choices plays an important role in public transport planning. As an alternative to traditional web-based surveys, smart cards, an emerging technology for fare collection, can be useful to obtain massive amounts of information over a long period of time. This paper determines how smart-card data can help us understand passengers’ travel strategies, by identifying factors influencing route choice behaviour within the bus system of Shizuoka, Japan. We also examine the discrepancy between hyperpath and actual route choices based on smart-card data together with the choice principles proposed by Luo et al. [17] and arrival time-based route assignment. Origin–destination (OD) pairs are analysed with the goal to determine the most appropriate model for the transit assignment problem. We found a discrepancy between hyperpath and actual route choices, which was attributed to the erroneous assumption of random arrivals of bus services; in fact, bus operation in the Shizuoka area is timetable-based. Consequently, passengers are likely to follow the bus schedule. Route choice flexibility was displayed by regular commuters, who did not strictly adhere to a single bus route even though they usually travelled according to the same origin–destination pair. This supports the concept of “hyperpath travellers”. A variety of factors, such as perceived crowding and uncertainty of services (delays), might also affect choices when bus routes are overcrowded and another bus is due shortly. Our findings aim to assist transport planners towards predicting traffic demand more accurately, and therefore enhancing the provided public transport services and determining influential factors for commuters’ travel strategies. Keywords: Actual choice · Hyperpath choice · Route choice model · Smart-card data

1 Introduction In public transport (PT) planning, the typical four-step model of the transit assignment problem estimates passenger demand for target PT services and networks. The ideal © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_22

252

R. Kaewkluengklom et al.

situation is generally assumed when estimating PT demand and travel time for different modes of transport and services [1]. To effectively model transit assignment, it is important to understand passenger decision-making, preferences and perceptions [2]. Travel decisions encompass mode and route choices. Few studies have focused on route choice behaviour because of the complicated assumptions [2]. Nevertheless, understanding passengers’ route choice behaviour is essential, and helps transport planners predict travel behaviour, design feasible PT networks and improve services based on origin–destination (OD) demands. Route choice models are usually limited to certain basic service attributes, including travel time and cost [3]. Other variables related to both the supply of information and passenger preferences are generally ignored. Two popular approaches used in transit assignment modelling are schedule and frequency-based assignment. In schedule-based models, detailed time-dependent and dynamic transit assignment simulations are performed, while frequency-based models are used for planning and provide average passenger distributions over time in a largescale network. The frequency-based approach assumes that passengers select routes based on route information, travel time and the frequencies (“headways”) of services in PT networks lacking a reliable schedule [1, 4]. This is appropriate for bus networks operating on congested roads with varying travel times or very frequent services. In this case, a passenger does not need to check the schedule as a bus arrives at the stop every few minutes [1]. Existing methods make assumptions regarding the regularity of services (distribution of inter-arrival times), arrival distribution of passengers at stops, capacity constraints, information available to passengers, and the underlying structure of the choices made by each passenger [4, 5]. Most approaches adopt the optimal framework developed in [6, 7], and assume stochastically independent and exponentially distributed inter-arrival times, and uniformly distributed passenger arrivals. Passengers are assumed to minimise the expected travel time by considering a set of “attractive” lines (hyperpath strategy), and the first vehicle arriving is assumed to be selected. This assumption is realistic when there is no information about PT arrival times. Many transit operators provide online estimated arrival times in real time. This information is very helpful for passengers planning trips, and influences travel strategies. For example, the first-arriving vehicle may be ignored if a faster one is arriving soon thereafter, while a slower line may be boarded if a faster one is predicted to be delayed. It is important to examine whether passengers choose PT services according to the hyperpath concept or other factors, to obtain information for realistic transit assignment models [5]. Previous studies have used online surveys to investigate factors influencing choice flexibility, such as trip characteristics, users, and the availability of service information [5, 8, 9] and seats [10]. Smart cards provide demand data for route choice models. In-vehicle, waiting and walking times, among other variables such as experience of transfers, crowding, network topology and socioeconomic variables, reportedly influenced route selection of PT users [11]. Kurauchi et al. [12] investigated the flexibility of regular morning commuters in terms of the bus routes chosen, and observed that most commuters did not use the same line every morning, possibly because of uncertainty regarding services. More recently, habitual behaviour, as measured by a stickiness index,

Discrepancy Between Hyperpath and Actual Route Choices

253

regarding route choice was observed across locations and times of day; very frequent commuters were likely to use the same route repeatedly, particularly when the trips were more constrained with respect to time and location [13]. Whether passengers rely on the hyperpath choice strategy or adhere to the same route remains unclear, and other factors may explain route choice behaviour. Against this background, our study examined the factors influencing route choice behaviour, and the discrepancy between hyperpath and actual route choices based on smart-card data, to better understand passenger travel behaviour and obtain insight into their travel strategies.

2 Methodology 2.1 Study Description We analysed the bus system in Shizuoka City, Japan. The bus network comprises 264 routes connecting 1,056 bus stops. About 60% [14] of passengers uses the ‘LuLuCa’ smart card operated by Shizutetsu Company to pay travel costs upon boarding and alighting. Details of smart-card data that provide real-time information were explained in [14, 15]. Smart-card data can be aggregated to determine discrepancies in route choice behaviour. Three months of smart-card data (July–September, 2016) for the weekday morning peak (7.00–9.00) period were analysed, to limit variation of travel behaviour due to uncertainty regarding bus services over a long period. Firstly, we assessed the services in all datasets, and found some service uncertainty. To analyse discrepancies in route choices, direct OD pairs (i.e., no transfers) were selected. The following criteria, as used in [16], were adapted to identify OD pairs: each pair must have at least two alternative routes, and each pair must have data for more than 500 trips. The first criterion allows analysis of trade-offs between alternative routes. The second criterion ensures sufficient travel behaviour data and prevents a large influence of unusual observations. To improve reliability, three OD pairs were analysed (Fig. 1), along with the service attributes of bus routes (Table 1). We first categorised the OD pairs according to the frequency of bus services (frequent vs. infrequent), with frequent services further classified according to route, i.e., bus routes using the same or different corridors. Bus service attributes were extracted from smart-card data, as stated above, and delays caused by traffic were included. The criteria used for data extraction were described in a previous study [15]. 2.2 Analysis Framework The analysis framework for this study is presented in Fig. 2. We addressed the following question: “Do passengers choose a bus route based on the hyperpath approach or other factors?”. The demand for each of the three OD pairs was determined through aggregation of smart-card data, and the probability of selecting a given bus route was determined through a demand-based calculation. We compared our hyperpath-based calculation with two other models, including that of Luo et al. [17], and estimated line choice probabilities in accordance with bus arrival times. Our hyperpath model, and the model

254

R. Kaewkluengklom et al.

Fig. 1. Map showing the bus network in Shizuoka City and origin–destination pairs used for analysis of discrepancies between hyperpath and actual route choices.

Table 1. Service attributes of three origin–destination bus route pairs. Case

Bus route

In-vehicle time (min)

Frequency (veh/h)

3.1

112

19.90

1

110

22.20

4

114

23.62

5

109

24.68

1

3.2

3.3

113

27.48

1

58

26.03

4

57

27.30

1

40

29.97

2.5

121

32.72

2

105

35.25

3

106

39.03

1

92

15.58

2

93

18.13

3.5

of Luo, were used for link weight assignment (“time impedance”) among OD pairs in the PT network in our previous study [15]. The concepts and differences of the two methods were clarified in that paper.

Discrepancy Between Hyperpath and Actual Route Choices

255

Fig. 2. Analysis workflow.

The following procedures were carried out for each OD pair. Firstly, a hyperpathchoice set was obtained by searching for an optimal set of attractive lines [15]. Then, the probability of using each route in the choice set was expressed in terms of frequency, according to accepted convention. In contrast, all alternative bus routes were included in the choice set in Luo’s model. We also computed probabilities based on actual bus arrival times under the assumption that passengers arrive randomly, while bus services follow a schedule. Hereafter, this method is referred to as “arrival time-based assignment”. The line choice probability calculation methods are illustrated in Fig. 3, showing the three bus services on the same OD pair. As for the hyperpath model, the bus routes (B3) with longer in-vehicle time than the expected travel time is excluded from the optimal set of attractive lines. The hyperpath choice set, thus, consists of two bus routes (B1, B2) that are assigned a probability based on their frequency. While the choice set of Luo’s model and arrival time-based assignment method include all alternative bus routes (B1, B2, B3) as abovementioned, the assumption for computing line choice probability depends on its frequency and demand on actual bus arrival times, respectively. The results of four route choice models were compared. Influential factors regarding passenger route choice were also extracted from the smart-card data, as described in the literature [i.e., bus timetable (boarding and alighting times), crowding over time, number of onboard passengers and individual choice sets]. Fare was not different among the bus routes. Figure 4 shows the quantification of crowding on buses over time. Boarding and alighting passengers were distinguished. Average overcrowding for a given bus was calculated based on the number of service days.

3 Comparison of Hyperpath and Actual Route Choices In this section, the three different OD pairs, all with large numbers of trips, are further compared and discussed.

256

R. Kaewkluengklom et al.

Fig. 3. Line choice probability calculation methods.

Fig. 4. Crowding on bus routes over time.

3.1 Bus Routes Using the Same Corridors (Frequent Services) The origin and destination in this case are Shizuoka Station and Shizuoka University, respectively, for which there are five overlapping bus services. Bus routes 110 (R110) and 114 (R114) provide the most frequent services; all of the other bus routes provide only one service per hour (Table 1). Prior to discrepancy analysis, possible factors influencing route choice were considered. The numbers of boarding/alighting passengers between 7 and 9 a.m. are shown in Fig. 5(a) and (b). The boarding distributions of R110 and R114, which provide an hourly service, were consistent with the data in Table 1, and correspond to the 8.30 a.m. arrival time of students at the university. Figure 5(c) shows that the number of onboard passengers increased initially, dropped at Shizuoka Station (middle part of the long line), and finally increased again, indicating that Shizuoka Station is a transfer hub. Crowding was minimal since most passengers alight at Shizuoka Station (station of origin for this OD pair). Figure 5(d) shows a histogram of onboard passengers at consecutive bus stops along the bus routes. The bus licence plate (i.e., the bus number recorded by the smart cards) was used to identify each bus, allowing us to determine the number of passengers onboard each vehicle (pax/veh). Some higher-frequency bus routes had more passengers (y-axis). Overall, the distribution of the number of onboard passengers using smart cards (pax/veh) was highly right-skewed for the overall sample, with a long tail. The maximum value was 55 pax/veh on the R114 and R112 routes, and 40 pax/veh on route R109; a values of 0–20 pax/veh

Discrepancy Between Hyperpath and Actual Route Choices

(a) Boarding time

(b) Alighting time

(c) Degree of crowding

(d) Number of onboard passengers

257

Fig. 5. Factors influencing route choice for services between Shizuoka Station and Shizuoka University.

were observed most frequently. Since the capacity of a bus is about 70 pax, the number of onboard passengers recorded using smart cards was well below capacity; few observations exceeded 40 pax. However, users paying by cash were not recorded. The analysis indicates that when overcrowding is minimal, the first arriving vehicle tends to be selected. Also, the greater apparent uncertainty regarding the boarding time for R112 may be due to delays resulting from the higher ridership of R109. General service attributes can influence route choice probability. Four choices are compared in Fig. 6(a–d). The graph of actual choices (Fig. 6(a)) shows that R114 and R110 were selected most frequently, followed by R109, R112 and R113. The hyperpath choice data in Fig. 6(b) show that the bus routes with the most frequent services (R110 and R114) had a higher probability of being chosen; however, since R113 had an invehicle time exceeding the optimal travel time, it was not included in the set of attractive lines. The choice probabilities calculated by Luo’s model (Fig. 6(c)), considering all services, were similar to the actual choice data; however, is the probabilities were not accurate for the infrequent bus routes of R109 and R113. Thus, a purely frequencybased calculation (hyperpath choice) cannot accurately estimate bus route probability, especially when the arrival time is important (e.g., for school trips). Furthermore, the arrival time-based assignment analysis in Fig. 6(d) clearly shows that the calculated route choice probability is very similar to the actual route choice, implying that when services are provided according to a timetable, the assumption of frequent random bus arrivals is less appropriate for transit assignment models. Additional investigation of this issue is warranted. 3.2 Bus Routes with Different Corridors (Frequent Services) There are three different bus corridors for services bound for Shizuoka City Shimizu Hospital; R57 and R58 have shorter corridors, such that their in-vehicle times are shorter

258

R. Kaewkluengklom et al.

(a) Actual route choices

(b) Hyperpath choices

(c) Choices predicted by Luo’s model

(d) Choices predicted by the arrival time-based assignment model

Fig. 6. Comparison of the results of route choice models for services bound for Shizuoka Station and Shizuoka University.

than those of R40, R112 and R110. The frequency of the services of these bus routes varies. Regarding the number of boarding and alighting passengers (Fig. 7(a–b)), passengers tended to arrive at the hospital before 8.30 a.m., suggesting that the desired arrival time is a significant factor affecting route choice behaviour. Comparing R58 and R106, passengers seemed to avoid the more crowded conditions of R58, even though it has a longer in-vehicle time (Fig. 7(c)). Therefore, overcrowding (and thus seat availability) might also influence route choice behaviour. The first vehicle to arrive might not be selected if passengers perceive it to be overcrowded; they may wait for the next bus (Fig. 7(a)). Figure 7(d) shows that the maximum pax/veh value on R58 was 55, with values of 0–20 pax/veh being the most common among the bus routes, as observed for the previous OD pair. The results in Fig. 8(a–d) indicate that passengers tend to select bus routes with shorter in-vehicle times when overlapping bus routes have different corridors (R58 and R40), although routes with longer in-vehicle times (R121 and R106) were also chosen. However, bus routes with frequent services did not always have a high route choice probability (e.g., R105; three services), because knowledge of the timetable for the first vehicle to arrive may affect the route choice. Regarding the hyperpath choices shown in Fig. 8(b), routes having longer in-vehicle times were not included in the set of attractive routes. Likewise, the probabilities calculated by Luo’s model for all bus route services (Fig. 8(c)) were not in good agreement with the actual route choices. As for the previous OD pair, the arrival time-based assignment model outperformed the other models.

Discrepancy Between Hyperpath and Actual Route Choices

(a) Boarding times

(c) Degree of crowding

259

(b) Alighting times

(d) Numbers of onboard passengers

Fig. 7. Factors influencing route choice for services bound for Shimizu Station and Shizuoka City Shimizu Hospital.

(a) Actual route choices

(b) Hyperpath choices

(c) Choices predicted by Luo’s model

(d) Choices predicted by the arrival timebased assignment model

Fig. 8. Comparison of the results of route choice models for services bound for Shimizu station and Shizuoka City Shimizu Hospital.

260

R. Kaewkluengklom et al.

3.3 Infrequent Bus Services Two bus routes running on the same corridor have Shizuoka Station as their destination R92, is a shorter line than R93, which starts in a residential area (Fig. 1). Buses with relatively large temporal gaps from the previous vehicle (R92 at 7.30 a.m.; R93 at 7.50 a.m.) had higher numbers of boarding and alighting passengers (Fig. 9(a) and (b)). Since these services are infrequent, perceptions of seat availability might not significantly affect the route choice probability for this OD pair. The maximum number of onboard passengers (Fig. 9(d)) was about 50 pax/veh for both services, with values of 0– 10 pax/veh being the most common among bus routes not characterised by overcrowding. The four models produced relatively similar route-choice probabilities, with the arrival time-based assignment model being the most accurate (Fig. 10(a–d)). Bus routes with more frequent services had more passengers, and frequency-based assignment can well-represent route choice selection.

(a) Boarding times

(b) Alighting times

(c) Degree of crowding

(d) Numbers of onboard passengers

Fig. 9. Factors influencing route choice for services between a residential area and Shizuoka Station.

3.4 Discrepancy Analysis Table 2 compares the route choice probabilities of the three route choice models with the actual choices. The average error for the arrival time-based assignment model was 14.81%, which is 58.74% less than that for the hyperpath choice model (73.55%) and 77.47% less than that of Luo’s model (92.28%). Although less accurate than the arrival time-based assignment model, the hyperpath model was superior to that of Luo.

Discrepancy Between Hyperpath and Actual Route Choices

(a) Actual route choices

(b) Hyperpath choices

(c) Choices predicted by Luo’s model

(d) Choices predicted by the arrival time-based assignment model

261

Fig. 10. Comparison of the results of route choice models for services between a residential area and Shizuoka Station.

3.5 Individual Choice Sets A choice set can be generated for each OD pair, comprising routes repeatedly chosen by individual travellers over long periods of time [2]. We used this approach for travellers making a large number of trips. Frequent travellers are regular commuters repeatedly faced with a travel choice. The individual choice sets of the three OD pairs, as shown in Fig. 11(a–d), indicate that more passengers have two compared to one alternative bus route (< 1% for the latter case, for all three OD pairs). Passengers are likely to choose different bus routes for a given OD pair, according to the “common line consideration” [2, 12], which defined as “a given traveller might choose different bus lines between the same pair of stops on different days” [2]. Regarding the bus routes sharing the same corridor (with the university as the destination) shown in Fig. 11(a), most passengers have two alternative routes. On the other hand, passengers tend to randomly choose from among four or five alternative routes in the case of routes with different corridors (and with the hospital as the destination), as shown in Fig. 11(b). Stricter time constraints might explain the behaviour of university commuters [13]. The six bus routes were classified into three different corridors (Fig. 11(c)), and most passengers considered all three corridors (i.e., all three choice sets). This might be due to arrival time being a particular consideration. Also, as the arrival times are similar, they might wait for an upcoming service if they perceive the first vehicle arriving to be overcrowded.

262

R. Kaewkluengklom et al. Table 2. Route proportions predicted by the three route choice models.

OD

Route

Actual choice (%)

3.1

112

10.2

9.1

10.5

8.3

18.0

8.9

12.9

110

30.2

36.4

20.5

33.3

10.4

29.7

1.7

114

41.8

45.5

8.7

41.7

0.4

41.5

0.8

109

13.4

9.1

32.3

8.3

38.0

15.1

12.1

113

4.4

0.0

100.0

8.3

88.9

5.0

12.5

58

29.1

53.3

83.1

29.6

1.7

22.3

23.5

57

2.7

13.3

386.9

7.4

170.5

2.6

4.9

3.2

3.3

Hyperpath Relative choice (%) error (%)

Luo’s model (%)

Relative error (%)

Arrival time-based assignment (%)

Relative error (%)

40

31.0

33.3

7.5

18.5

40.3

25.9

16.6

121

16.5

0.0

100.0

14.8

10.1

22.0

33.5

105

2.6

0.0

100.0

22.2

755.8

3.7

41.9

106

18.0

0.0

100.0

7.4

58.9

23.5

30.4

92

37.9

36.4

4.1

36.4

4.1

37.5

1.1

93

62.1

63.6

2.5

63.6

2.5

62.5

0.6

(a) Individual bus routes in the same corridor

(b) Individual bus routes in different corridors

(c) Pairs of bus routes in different corridors

(d) Infrequent bus services

Fig. 11. Proportions of total trips according to choice set size.

Concerning bus routes using different corridors, it was not clear whether commuters mostly preferred shorter routes. The routes within the choice set were investigated in

Discrepancy Between Hyperpath and Actual Route Choices

263

terms of the relative proportions of trips (Fig. 12(a–d)). Route 1 has the shortest travel distance and in-vehicle time, followed by routes 2 and 3. Figure 12(a) presents the data for passengers using only one route. The graph indicates that route 3 (longest travel distance) was the most popular route, whereas the shortest route (route 1) was the least popular. This contradicts the hyperpath concept, as the route with the shortest travel time must be included in the optimal hyperpath. The route selections for passengers who used two lines are presented in Fig. 12(b–c). The passengers tended to prefer routes 1 and 3 over route 2, possibly due to the lower number of services for route 2. Moreover, as a pair, routes 1 and 3 were selected more frequently than routes 1 and 2, and routes 2 and 3. Figure 12(d) refers to commuters considering all routes. These commuters tended to choose route 3 more frequently than routes 1 and 2. According to the analysis, the bus route with the shortest travel time is not always selected by regular commuters. It is intuitive that the number of services for each route affects the route choice order (route 3 > route 1 > route 2). Routes with more frequent services that accord with the preferred schedule of commuters tend to be chosen over those with shorter in-vehicle times. In other words, regular commuters do not always use the same bus route. Instead, they choose routes on a situational basis; the first bus will be chosen depending on the expected arrival time and overcrowding, assuming that the buses all depart at nearly the same time.

(a) Case 1

(b) Case 2

(c) Case 3

(d) Case 4

Fig. 12. Proportion of total trips for each route.

4 Conclusion and Future Work This paper examined discrepancies between hyperpath and actual bus route choices based on aggregated smart-card data for three OD pairs. Luo’s model and an arrival timebased assignment model were compared to determine the most appropriate model of the

264

R. Kaewkluengklom et al.

transit assignment problem. Additionally, we identified factors influencing passengers’ route choice behaviour through analysis of smart-card data to explain the phenomena of interest. The discrepancy between the hyperpath and actual route choices is attributed to the erroneous assumption of random arrivals of bus services; the buses in Shizuoka actually operate according to a timetable (regular services). Thus, passengers are likely to follow the bus schedule. Route choice flexibility was observed for regular commuters; they do not adhere to a single bus route, although they usually use the same OD pair. This supports the concept of “hyperpath travellers”. However, bus routes with shorter travel times did not always have high usage probabilities due to the effects of perceived crowding and uncertainty of services (delays) on passenger route choices. Bus routes with more frequent services were more likely to be used, although this was not always the case due to the influence of other factors. Furthermore, service attributes (i.e., frequency and in-vehicle time), perceived overcrowding and the temporal gap between buses influenced route choice. A hyperpath search algorithm considering service attributes might be useful for predicting route choices if passengers arrive randomly at bus stops providing irregular services. The hyperpath route choice algorithm should be modified to take these factors into consideration. A “penalty value” could also be implemented. Some limitations of this study should be noted. The in-vehicle times extracted from the smart-card data (which include delay times) affect the hyperpath search algorithm, leading to error in the optimal set of attractive lines. Data from a PT company provide more accurate information for hyperpath search algorithms.

References 1. Oliker, N., Bekhor, S.: A frequency-based transit assignment model that considers online information. Transp. Res. Part C: Emerg. Technol. 88, 17–30 (2018) 2. Kurauchi, F., Schmöcker, J.D. (eds.): Public transport planning with smart-card data. CRC Press (2017) 3. De Dios Ortúzar, J., Willumsen, L.G.: Modelling transport. Wiley (2011) 4. Nökel, K., Wekeck, S.: Choice models in frequency-based transit assignment. In: Proceedings of the European Transport Conference (ETC), held 17–19 October 2007, Leiden, The Netherlands (2007) 5. Fonzone, A., Schmöcker, J.D., Bell, M.G., Gentile, G., Kurauchi, F., Nökel, K., Wilson, N. H.: Do “hyper-travellers” exist? Initial results of an international survey on public transport user behaviour. In: 12th World Conference on Transport Research, Lisbon, Portugal (2010) 6. Spiess, H., Florian, M.: Optimal strategies: a new assignment model for transit networks. Transp. Res. Part B: Methodological 23(2), 83–102 (1989) 7. Nguyen, S., Pallottino, S.: Equilibrium traffic assignment for large scale transit networks. Eur. J. Oper. Res. 37(2), 176–186 (1988) 8. Kurauchi, F., Schmöcker, J.D., Fonzone, A., Hemdan, S.M.H., Shimamoto, H., Bell, M.G.: Estimating weights of times and transfers for hyperpath travelers. Transp. Res. Rec. 2284(1), 89–99 (2012) 9. Fonzone, A., Schmöcker, J.D., Kurauchi, F., Hasson, S.M.: Strategy choice in transit networks. J. Eastern Asia Soc. Transp. Stud. 10, 796–815 (2013)

Discrepancy Between Hyperpath and Actual Route Choices

265

10. Schmöcker, J.D., Shimamoto, H., Kurauchi, F., Fonzone: A seat capacity and hyperpath choice on-board: alight or remain seated? (2010) 11. Raveau, S., Guo, Z., Muñoz, J.C., Wilson, N.H.: A behavioural comparison of route choice on metro networks: time, transfers, crowding, topology and socio-demographics. Transp. Res. Part A: Policy Pract. 66, 185–195 (2014) 12. Kurauchi, F., Schmöcker, J.-D., Shimamoto, H., Hassan, S.M.: Variability of commuters’ bus line choice: an analysis of oyster card data. Public Transp. 6(1–2), 21–34 (2013). https://doi. org/10.1007/s12469-013-0080-x 13. Kim, J., Corcoran, J., Papamanolis, M.: Route choice stickiness of public transport passengers: Measuring habitual bus ridership behaviour using smart-card data. Transp. Res. Part C: Emerg. Technol. 83, 146–164 (2017) 14. Kaewkluengklom, R., Kurauchi, F., Iwamoto, T.: Investigation of changes in passenger behavior using longitudinal smart-card data. Int. J. Intell. Transp. Syst. Res. 19(1), 155–166 (2021) 15. Kaewkluengklom, R., Kurauchi, F., Iwamoto, T.: Applying the hyperpath concept to public transit accessibility evaluation. J. Eastern Asia Soc. Transp. (2021) 16. Shelat, S., Cats, O., van Oort, N., van Lint, J.W.C.: Evaluating the impact of waiting time reliability on route choice using smart-card data. Transportmetrica A: Transp. Sci., 1–19 (2022) 17. Luo, D., Cats, O., van Lint, H., Currie, G.: Integrating network science and public transport accessibility analysis for comparative assessment. J. Transp. Geogr. 80, 102505 (2019)

Developing a Heuristic Route Planning Method to Support Seamless Mobility Solutions Domokos Esztergár-Kiss1(B) , Alireza Ansariyar2 , and Géza Katona2 1 Institute for Computer Science and Control, Budapest, Hungary

[email protected] 2 Budapest University of Technology and Economics, Budapest, Hungary

Abstract. Since travelers seek efficient transnational door-to-door journey planners, seamless mobility solutions and multimodal transport networks connecting distinct systems should be in transport planners and researchers’ focus. Thus, in current research, a method is elaborated to implement a seamless multimodal route planning solution by identifying potential exchange points between various networks, filtering the relevant exchange points, running a routing algorithm, and presenting a utility function for the ranking of the alternatives. Exchange points are discovered by an algorithm using the GPS coordinates of stops. If the coordinates are close, a connection is indicated. To identify the potential exchange points, solely the stops of different local journey planners are considered by the algorithm. Some specific exchange points are chosen for route calculation. The selection is necessary as the number of exchange points is high due to the involvement of international and multimodal networks. By using a heuristic optimization algorithm, a rough estimation of the routes is conducted. The proposed method is flexible; the parameters can be easily updated and enhanced. Therefore, the framework provides an up-to-date and pragmatic implementation in case of changes, too. Furthermore, the developed method is applicable to wide geographical areas and by any traveler information service provider. Keywords: Seamless mobility · Heuristic algorithm · Exchange points · Flexible routing

1 Introduction Nowadays, routing is becoming more and more essential. Due to urbanization, the population in cities and suburban areas is steadily growing, which brings challenges to the existing transport systems. Municipalities, transport planners, and service providers attempt to implement new solutions while encouraging travelers to choose sustainable transport modes. One of the solutions is the introduction of multimodal journey planners, which provide efficient door-to-door services, seamless information flows, integrated options, and adequate comfort for travelers. In 2011, the European Commission presented a general framework for a European multimodal journey planner [1]. Afterward, a standard of Open API (application programming interface) was introduced by the European Committee for Standardization © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_23

Developing a Heuristic Route Planning Method

267

(CEN) [2], which relying on the experience of such implementations as EU-Spirit, JourneyWeb, and Delphi proposed a solution for distributed journey planning. In 2019, a system architecture for multimodal and distributed journey planners was developed [3]; however, the realizations of the solutions were not demonstrated. Several researchers apply analytic and heuristic algorithms to implement advanced routing solutions. Idri et al. [4] are among these researchers because for multimodal transport networks, a goal-oriented single-source and single-destination algorithm is introduced as a time-dependent shortest-path algorithm. As a heuristic, the idea of closeness-to-the-target is applied to focus on the destination. In the study, a dynamic route planning is demonstrated, where a constraint-based shortest-path algorithm over a time-dependent multimodal graph is introduced to identify the optimal path between the origin and the destination. Similarly, Ayed et al. [5] apply a heuristic platform. The multimodal transport networks are divided into smaller graphs, and a unique graph structure, a.k.a. transfer graph, is implemented. Furthermore, the scholars identify the exchange points, and find the best paths for all pairs of nodes. Afterward, the best paths are stored in a database. The users’ requests are processed, and the relevant graphs are developed. Finally, the users’ requests are fulfilled. Furthermore, Zhang et al. [6] develop an application to improve and test a generic multimodal transport network model. The primary attribute of the research is time. From an abstract viewpoint, the scholars model multimodal transport networks and divide them into two groups: private and public modes. By applying transfer links, a generic method is used to formulate a multimodal transport network representation. The transfer links are based on the concept of the super network technique, which integrates various modes into a single network. Delling et al. [7] aim to increase the speed of multimodal route planning by introducing access-nodes. A label-constrained shortest-path problem is applied by the researchers, where the edges are labeled based on their types of transport networks. The basic rule says if certain constraints are accomplished by the labels indicated on the route, the path between the origin and the destination is valid. The scholars demonstrate that access-node routing is faster than the label-constrained routing introduced by Dijkstra. Moreover, for the edges, weight parameters are assigned, as well. In case of road networks, the weights are the average travel time of the road segments. Dibbelt et al. [8] attempt to find the best integrated journey between two points. Primarily, the scholars aim at computing exact multimodal journeys, which can be restricted by specifying the arbitrary modal sequences at query time. Point-to-point queries on a continental network combining with cars, railways, and flights are computed, which is faster than Dijkstra’s algorithm. Another research by Monios and Elbert [9] focuses on the travelers’ modal change at intermodal exchange points. The scholars study the costs and benefits with various socio-economic features. The aim is to analyze the network planning and information use at the exchange points in case of long-distance journeys. Travelers have their own utilities; thus, they can choose to either trust the journey planners and let them assist on the travel or acquire some advice from such travel assistant information systems. At exchange points, travelers have to decide on how to continue their journeys. Travelers aim to reach the next exchange point with the shortest possible travel time. Besides travel

268

D. Esztergár-Kiss et al.

time, travelers are concerned about the travel cost, the convenience, and the walking distance between the exchange points. Zografos et al. [10] study the passenger information and trip planning systems of urban and interurban multimodal trip planning services as well as the real-time travel information through a single access point. A scheme classifying passenger information and trip planning systems is introduced. Additionally, analytic hierarchy process (AHP) is applied to identify the travelers’ exchange points during their journeys. The developed AHP method helps in determining the main features of the journeys. The results demonstrate that travelers are ready to use and can easily use/learn how to use new services. In conclusion, based on the literature, it can be seen that the applied approaches are similar. There is a lack of comprehensive solutions seamlessly connecting separate networks. Thus, current research aims at finding such a suitable and applicable solution, which can provide seamless mobility services and combines distinct services and networks in a distributed routing system. It is worth mentioning that defining the exchange points is automatized for the efficiency of the process. The main achievement of this research is implementing a seamless multimodal route planning solution by recognizing potential exchange points between multimodal transport networks, where filtering the relevant exchange points, creating the basics of a routing algorithm, and presenting suitable exchange points is included in the paper.

2 Methodology The connection of the scheduled public and the non-scheduled individual transport modes provided by a local journey planner (LJP) is the basis of multimodal networks. The connection can be realized by the supplementation of the scheduled public network with the customized traffic, which is virtually accessible all the time and includes all possible routes. While operating a routing system, the access to the stops near the points of departure and destination must be planned. The potential routes must be mapped. By exploring the connections between the stops, further optimization is possible. Thus, individual transport options, such as the combination of taxis, shared systems, or walking, can be developed. This possibility is especially essential when there are changes in the network causing delays or early arrivals, which substantially alter the estimated travel time and thus the arrival time. The following chapters demonstrate how journeys between any origin and destination point of immense transport networks using different transport modes across countries can be handled. Below, there are some necessities identified for the routing process: • Data on the connections of the networks in the LJPs: routes, stops, and timetables for public transport; road networks for individual transport. • A catalogue of the potential exchange points (i.e., those near the network borders) and the main exchange points (e.g., main stations). • The creation and ranking of the most suitable routes based on the utility function.

Developing a Heuristic Route Planning Method

269

2.1 Defining the Exchange Points Exchange points are defined as the nodes of a network connecting other nodes of another network within a preset distance. The nodes demonstrate the connections between the LJPs. Thus, exchange points can be any station, stop, modal change node between two neighboring countries, where the trip leg (i.e., the part of the whole trip realized within the area of one LJP) of one system is linked to the trunk leg (i.e., the part of the whole trip realized across LJPs) of another system and the travelers change their transport modes. Two kinds of exchange points are distinguished. City exchange points are those points, where travelers change their transport modes or service providers within the area of a network border. However, in case of border exchange points, travelers change their transport modes or service providers at the border of two networks. In Fig. 1., the red lines indicate long-distance journeys through the network border, where the exchange points are the major terminals, railway stations, or airports of cities. Generally, travelers cross the network border without switching transport mode. It is essential to define the connections between the location of these exchange points and those within the other network.

Fig. 1. Distinct networks connected by city exchange points.

Fig. 2. Distinct networks connected by border exchange points.

270

D. Esztergár-Kiss et al.

In case of border exchange points, the points are located near the border of countries. It is important to preset the walking distance between the nodes. In Fig. 2, exchange points which are the nodes of different service providers within a preset walking distance are indicated with red lines. In this case, when travelers reach the node of their original LJPs, they continue their journeys on board of a different transport mode or service provider of other LJPs as they cross the border. It is worth noting that along a trunk leg, all stops are potential exchange points. Thus, the main steps of the algorithm used in this research consist of the exchange points filtering and the routes filtering, which are explained in the next subsections. 2.2 Filtering the Exchange Points Precomputing the distances to all potential exchange points is the basis of routing. To be able to take the travelers’ specific parameters into account as a utility function, a mathematical formulation calculating the route is necessary. The utility function might include the travel time, travel cost, emission, health, comfort, and other parameters, as well. Travelers should weigh the parameters. Some specific exchange points, which are used for calculating the route, should be filtered. This process is necessary as the number of exchange points might be huge as immense international and multimodal networks are involved. Moreover, for routing, there should be a threshold as a maximum value (e.g., eight hours as the travel time parameter). Due to calculation purposes, a preset average weight is given to the parameters; however, travelers can change the weights while calculating their journeys. The next step is the calculation of a rough estimation between the exchange points based on the utilities (e.g., travel time). This process is conducted offline before applying the list of the potential exchange points and the information on stops provided by the LJPs. Thus, the developed network is static since there is no real time data presented in it. As a method for calculation, a heuristic optimization algorithm should be considered. Based on the elaborated utility function, the filtering of the exchange points is possible. Thus, by using the threshold, the suitable exchange points are selected from the list of the total potential exchange points. If a route request appears, the options above the threshold based on the estimations are not taken into account. Therefore, for each “TripRequest”, some filtered exchange points are individually suggested through the algorithm. The list of the suggested exchange points is affected by the threshold. 2.3 Filtering the Routes Based on the real-time data from the LJPs and the filtered exchange point options, the exact routes are calculated. Thus, the routing process is based on the results of the trip legs considering the provided exchange points of the LJPs. An example for the realization of the routing process can be seen in Fig. 3. In the figure, there are eight exchange points (Aa, Ba, Bb, Cc, Eα, Dα, dα, eβ), which are chosen by using the preset threshold value. In the green network, where there is the origin point, the LJP provides three possible routes (A, B, C) through three selected exchange points toward the destination situated in the neighboring blue network. In the blue network, there is the destination, and three routes (a, b, c) are found, as well. Moreover, in the green network, two routes (D, E) are

Developing a Heuristic Route Planning Method

271

possible through two selected exchange points toward the destination and the service area of the LJP in the yellow network. In the yellow network, two routes (α, β) are found. However, additional selected exchange points are to be applied between the LJPs of the yellow and blue networks by using routes d and e. The use of the yellow network for traveling from the departure point to the destination point is a realization of a remote use case.

Fig. 3. Routing in the network based on the exchange points.

Fig. 4. Routing in the network based on the exchange points with timetable.

Afterward, a simplified network, demonstrated in Fig. 4, is obtained. This network provides a basis for the filtered routes presented to the travelers. Additionally, the routes are ranked based on the calculations with the utility function.

3 Results In this research, six LJPs from the Central European region are involved in the realization of the routing process. 126.513 stops with 6.654.959.900 connections are found in the database. Java (OpenJDK 15.0.2+7-27 and Eclipse 4.18.0.I20201202-1800) is applied to write the algorithm, while OpenStreetMap (OSM) is used for visualization purposes.

272

D. Esztergár-Kiss et al.

Based on the preset straight-line maximum walking distance, the exchange points at the borders are filtered. Current study analyzes three scenarios: (1) with a walking distance of one km, (2) where the walking distance is two km, (3) with three km as the walking distance. The findings (Table 1.) show that for the scenario with a walking distance of three km, almost seven times more exchange points are found than for the scenario where the walking distance is one km. Table 1. The defined number of exchange points. Max. walking distance (km)

Number SI-AT AT-CZ AT-SK HU-AT CZ-SK SK-HU HU-RO SL-HU of exchange points

1

770

38

672

0

0

50

0

10

0

2

2290

812

1356

34

6

68

4

10

0

3

5406

3082

2102

34

50

110

10

12

6

Figure 5, 6 and 7 demonstrate the highlighted areas around the borders in the examined region based on the identification of the exchange points. It can be seen that between the Czech Republic and Austria, there are the most exchange points. On the other hand, solely some adequate exchange points can be found at the border of Hungary and Slovakia as well as Hungary and Romania. It might be due to the received datasets. In Slovakia, solely the regional bus stops are available, while for Romania, merely the railway stations are to be used in the study.

Fig. 5. The identified exchange points with one km walking distance.

Developing a Heuristic Route Planning Method

273

Fig. 6. The identified exchange points with a walking distance of two km.

Fig. 7. The identified exchange points in case of three km walking distance.

An area at the border between Austria (Angern an der March) and Slovakia (Záhorská Ves) is demonstrated as an example. As Fig. 8 shows, in Slovakia, there is one railway station indicated by a blue triangle in the map, while several bus stops marked by red rectangles can be found in Austria. These are identified as exchange points based on a three km walking distance.

274

D. Esztergár-Kiss et al.

Fig. 8. An example for exchange points within a three km walking distance at the Austrian and Slovakian border.

4 Conclusion The developed algorithm is able to cope with a huge number of exchange points regardless of their locations. There is no limit to particular countries, the proposed method can cover any area. Therefore, the application of the algorithm to the European environment is possible without any technical difficulty. In conclusion, the developed algorithm can handle wide geographical areas and provide a transferable solution, which is available for any traveler information service provider. The feasibility of the defined method is established by experiments on multimodal networks with a huge number of nodes. It would be beneficial to examine the developed algorithm with real-world requests involving various origin and destination points. Acknowledgement. OJP4Danube is co-funded by the European Union under the INTERREG Danube Transnational Programme. The linguistic revision is prepared by Eszter Tóth.

References 1. European Commission—Directorate-General Mobility and Transport: Study “towards a European multi-modal journey planner” (2011) 2. CEN/TC 278: Road transport and traffic telematics, open API for distributed journey planning, WI 00278420 (2017) 3. Detti, A., et al.: Federation and orchestration: a scalable solution for EU multimodal travel information services. Sustainability 11(7), 1888 (2019) 4. Idri, A., Oukarfi, M., Boulmakoul, A., Zeitouni, K., Masri, A.: A new time-dependent shortest path algorithm for multimodal transportation network. In: 8th International Conference on Ambient Systems, Networks and Technologies, vol. 109, pp. 692–697 (2017)

Developing a Heuristic Route Planning Method

275

5. Ayed, H., Khadraoui, D., Habbas, Z., Bouvry, P., Merche, J.F.: Transfer graph approach for multimodal transport problems. Modelling, Computation and Optimization in Information Systems and Management Sciences, pp. 538–547 (2008) 6. Zhang, J., Liao, F., Arentze, T., Timmermans, H.: A multimodal transport network model for advanced traveler information systems. Procedia. Soc. Behav. Sci. 20, 313–322 (2011) 7. Delling, D., Pajor, T., Wagner, D.: Accelerating multi-modal route planning by access-nodes. In: 17th Annual European Symposium, pp. 587–598. Copenhagen, Denmark (2009) 8. Dibbelt, J., Pajor, T., Wagner, D.: User-constrained multimodal route planning. ACM J. Exp. Algorithmics 3(2), 19 (2015) 9. Monios, J., Elbert, R.: Modal shift and logistics integration in intermodal transport networks. Res. Transp. Bus. Manage. J 35(6), 100517 (2020) 10. Zografos, K.G., Androutsopoulos, K.N., Spitadakis, V.: Design and assessment of an online passenger information system for integrated multimodal trip planning. In: IEEE Transactions on Intelligent Transportation Systems, vol. 10, pp. 311–323 (2009)

A Large-Scale Traffic Scenario of Berlin for Evaluating Smart Mobility Applications Karl Schrab1(B)

, Robert Protzmann2

, and Ilja Radusch2

1 Daimler Center for Automotive IT Innovations (DCAITI), Technical University of Berlin,

Berlin, Germany [email protected] 2 Fraunhofer Institute FOKUS, Berlin, Germany {robert.protzmann,ilja.radusch}@fokus.fraunhofer.de

Abstract. Research on novel concepts in the field of smart mobility and ITS requires employing traffic simulations in combination with communication and application simulations. With Eclipse MOSAIC we developed a co-simulation simulation framework to setup holistic system simulations in that very field, by coupling best-in-class simulators from various research domains. One important task here is modeling road traffic, which is non-trivial on a large scale. Traffic for a city-wide area can be modeled on a macroscopic or microscopic level, however, only the later provides realistic vehicle movements which is a requirement for communication and application simulation. Currently, there are only a handful of scenarios that model enough traffic to reliably test smart mobility applications. In this paper, we describe how we created a large-scale simulation scenario depicting a full day of motorized private traffic for the City of Berlin. To achieve this, we created a scenario for the microscopic traffic simulator SUMO, with the traffic demand extracted from an existing MATSim scenario and transferred to SUMO using iterative traffic assignment. Comparing the simulated counts with real data emphasizes that this scenario can model traffic in Berlin close to reality. With more than 2.2 million trips within an area of 800 km2 this is the largest traffic scenario we are currently aware of, and we will provide it for other researchers under an open-source license. Keywords: Road traffic simulations · Smart mobility · Transportation modeling · Urban mobility · Scenario generation

1 Introduction Two key points towards sustainable urban transport systems are electrification of vehicles and the development of novel mobility concepts, such as intelligent ride sharing, smart logistics, or robotaxis. Finding solutions in those fields requires researchers from all over the world to employ holistic system simulations which not only model the movements of individual vehicles, but also further aspects such as communication (e.g., ITS-G5 or LTE/5G), in-vehicle and server applications, electric mobility (battery, consumption, power grids), and more. To achieve this, we developed Eclipse MOSAIC [1] (formerly © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_24

A Large-Scale Traffic Scenario of Berlin

277

VSimRTI) as a multi-domain and multi-scale co-simulation framework, allowing to setup system simulations by coupling models and tools of various domains with each other. One important role plays the MOSAIC Application simulator, which is able to deploy application models of different scale onto individual vehicles and other entities in the simulation. Examples for such applications are intelligent tour planning or ridesharing on a higher abstract level, or safety functions such as Cooperative Adaptive Cruise Control (CACC) on a lower level. This also allows to integrate real applications into the simulation, as shown with traffic estimation and control algorithms in the INFRAMIX project [2]. In order to successfully find and evaluate the impacts of such applications, a realistic model of vehicle movements is required. This can be achieved by using multiple scales as well. For one thing, vehicle simulators such as Carla or PHABMACS [3] model vehicle dynamics using physics models. However, especially investigating efficiency and sustainability usually requires modeling of thousands of vehicles, e.g., covering traffic of a whole city, which is computational unfeasible with such simulation tools. As an alternative, microscopic traffic simulations model the behavior of individual vehicles by reducing the computation to car-following and lane-changing, enabling simulations on a large scale. To configure such simulation, one needs to define a traffic scenario, which includes the paths and departure times of all vehicles. Gathering suitable data for this task is not trivial. Microscopic traffic simulation tools, such as Eclipse SUMO [4], furthermore require a realistic definition of the road network and traffic signaling. While the first is straightforward to get with the aid of OpenStreetMap, data for traffic signal programs is hard to obtain and usually heuristics are used instead. There already exist a handful of large-scale traffic scenarios for SUMO. Depending on the use-case of the application these can or cannot be used. More precisely, there currently exist SUMO scenarios for the Municipality of Turin (1,2 million trips) [5], a rather old version for the City of Cologne (1,2 million trips), the LuST scenario covering the City of Luxemburg [6], and the MoST scenario focusing on multi-modal transport in Monaco based on synthetic demand data [7]. However, in upcoming studies we are interested in evaluating smart logistics and remote-operated driving use-cases in a largescale urban area with several millions of inhabitants, such as Berlin, Germany. In [8] a large-scale traffic scenario for the area of Berlin/Brandenburg in Germany was presented. This scenario, however, concentrated on modeling basic vehicle movements of around 20% of real traffic volume on primary roads and highways rather than realistic inner-city traffic patterns. As another option, the MATSim Open Berlin Scenario provides a calibrated mobility model of a whole day for the area of Berlin/Brandenburg, depicting 10% of real traffic volume [9]. In a most recent study this mobility model has been used to predict the spread of SARS-CoV-2 under certain measures such as contact restrictions [10]. This scenario has furthermore been utilized in various studies related to vehicle traffic [11, 12]. The traffic dynamic models of MATSim, however, do not provide continuous vehicle positions for each agent, which is required for most mobility applications, especially for FCD-based technologies [8]. A similar problem was identified by Triebke et al., who wanted to deduce driving profiles of vehicles [13]. As a solution, they proposed to sequentially couple MATSim with SUMO by converting the Open Berlin Scenario into a simulation scenario for this tool. They also found that various

278

K. Schrab et al.

steps would be required to do so, such as adjusting the road network and re-calibrating the traffic. Due to internal restrictions, they were not able to adjust the road network and were forced to reduce traffic demand to 80% [14], and also reduced the area of interest to a rather small one within Berlin, making their work not directly usable for us. In this paper we describe how we transformed the Open Berlin Scenario – which was calibrated with MATSim – to a microscopic simulation scenario to be executed with SUMO. Firstly, we will explain the main difference between both simulation methodologies and give a brief overview about the Open Berlin Scenario in Sect. 2. In the main part of this paper, we will present an insight into our conversion process (Sect. 3) and how well the generated SUMO scenario performs in comparison to the original scenario in MATSim and real world data (Sect. 4). In the final Sect. 5 we conclude our work and discuss future steps.

2 Simulation Tools Both MATSim and SUMO are multi-agent traffic simulation tools which are available under an open-source license. The feature set is very similar, as both tools can simulate multi-modal traffic scenarios based on real world data. Agents (persons or vehicles) are modeled individually and as results one get macroscopic data such as traffic flows on roads and travel times of agents. The behavior and traffic dynamic models are very different, though. While MATSim uses queue- and cell-based models to move vehicles on the road, SUMO uses car-following and lane-change models to move them continuously. In short, the mobility simulation in MATSim can be described as a mesoscopic traffic simulation, while SUMO is a microscopic traffic simulator. 2.1 MATSim The traffic network model in MATSim consists of nodes and links. Links can take more or less vehicles bound by their capacity. Multi-lane highways have much higher capacities than one-way roads in residential areas. Each link uses a waiting queue to collect cars entering the link. According to the outflow capacity of a link, only a certain number of cars can leave the queue within a timestep. The outflow capacity of links can therefore be used to model traffic signals to a limited extent, but traffic signal programs as found in reality are not realizable. Even though there are extensions to model multi-lanes and traffic signals, the core mobility simulation does not support these features [15]. Thus, the MATSim Open Berlin Scenario was also not created using traffic signals. The mobility simulation is just one part of MATSim. Furthermore, MATSim uses an agent-based concept to model daily plans of individual agents (the traffic demand). These day plans are iteratively adjusted using co-evolutionary algorithms in combination with the mobility simulation until all day plans are stabilized (a user equilibrium is reached) [15]. This way, MATSim generates calibrated traffic demand and supply. 2.2 SUMO SUMO’s traffic network model contains more details, as roads consist of multiple lanes. Furthermore, each junction has multiple paths to pass it, enabling a detailed simulation of

A Large-Scale Traffic Scenario of Berlin

279

right-of-way rules and junction behavior. This also allows modeling of separate turning lanes and complex traffic signaling, including traffic signals which adapt to incoming traffic flow. The traffic dynamics are implemented using car-following models to move vehicles which adapt on the behavior of their leading vehicles, or other events such as speed signs and traffic signals. One can choose between different car-following models, such as Krauss or IDM. In addition, vehicles can change lanes which is realized with an integrated lane-change model [4]. The more detailed simulation of vehicles comes with the price of a higher execution time compared to MATSim. While the MATSim Open Berlin Scenario can be simulated within minutes, the SUMO scenario we created requires several hours to complete. 2.3 The MATSim Open Berlin Scenario The MATSim Open Berlin Scenario is based on synthetic demand data. This traffic demand (i.e., sources and sinks of people’s trips) was created using open data (e.g., demographic statistics) and has been calibrated against real traffic counts. During the calibration with MATSim, for each person the mode of transport and the route was chosen in a way that a user equilibrium was eventually reached. This was achieved by iteratively adapting the transport mode and route until each person could not improve anymore in terms of costs, e.g., travel time. In addition to the individual costs, real traffic counts have been included in the calibration process [9]. In total, the model includes 419,000 agents (persons) depicting 10% of real mobility in the whole Berlin/Brandenburg area, with each agent having various trips planned. Around 225,000 of these agents are living and working within Berlin. The general modal split is 35% car, 16% public transport, 17% bicycle, and 22% walk. When extracting all trips made by car, the mean travel time in the Berlin area (with all commuters excluded) is around 19.7 min with a mean travel distance of 7.3 km.

3 Methodology We present our method of transferring the traffic model from MATSim to SUMO in order to create an urban traffic scenario in the area of Berlin, Germany. Two key aspects need to be considered here: Firstly, the MATSim scenario only models 10% of real traffic which was realized by reducing the capacities of all roads. This method, however, is not applicable for SUMO. This implicates that we need to scale up the traffic demand to 100% to get a realistic traffic model in SUMO as well, resulting in much more agents in the simulation. To cope with this huge amount of traffic participants, we limited the target scenario: We removed all trips which are not taken by car, and we removed all trips which originate or end in Brandenburg, resulting in traffic in the Berlin area only. Secondly, the models for the traffic dynamics differ significantly between MATSim and SUMO, as shown in previous sections. This means, that the actual routes which have been calibrated by MATSim cannot be transferred to SUMO as is. Instead, we propose to extract only the calibrated demand from the MATSim scenario and re-assign the traffic with SUMO by iteratively choosing routes until a user equilibrium is reached.

280

K. Schrab et al.

3.1 Preparing the SUMO Traffic Network Due to the differences in the road network model between MATSim and SUMO (as depicted in Fig. 1) we decided to re-build the traffic network from scratch using OpenStreetMap data with the aid of SUMO’s tool NETCONVERT. The resulting network, though, was far from being perfect after the import. OpenStreetMap data does provide only limited information about roads, leading to certain modeling errors in the road network. Especially the layout of complex junctions differed from their real counterparts, as information about turning lanes and stop lines are not available. Therefore, we adjusted the layout and connectivity (which oncoming lane is connected with which outgoing lane) of several hundreds of junctions using the GUI-based tool NETEDIT bundled with SUMO. Furthermore, various tagging errors in OpenStreetMap data lead to wrong speed limits and wrong number of lanes which were fixed by us using local knowledge, satellite images, and photos from Google Street View. In addition to the junction layouts, traffic signaling is from crucial importance when it comes to a later simulation. As the MATSim scenario does not provide any signaling information due to the lack of a traffic signal model, we decided to use pre-defined traffic signal programs in SUMO generated during the import by NETCONVERT. Many of these generated signal programs resulted in unsatisfying traffic flows and required manual adjustments by following official guidelines (e.g., RiLSA [16]) using NETEDIT. We also set all traffic lights into the actuated mode, meaning that the duration of green phases is dynamically adjusted to the incoming traffic flow. Due to the lack of signaling data, we found no other way than using this option without generating too much congestion in the network.

Fig. 1. Comparison of road networks at Messedamm, Berlin.

3.2 Extracting Traffic Demand In a second step, we transferred the traffic demand from the MATSim scenario to SUMO. This scenario provides a selected day plan for each agent, which contain all activities (duration and type - e.g., home, work, or leisure) and the trips between these activities (mode of transport, origin, destination, and departure time). To extract the demand, we chose all trips which used car as transport mode. We then map-matched the origin and destination location onto the SUMO road network and kept only those trips for which

A Large-Scale Traffic Scenario of Berlin

281

a route could be successfully calculated. This eliminated all trips outside the area of Berlin. To scale up the traffic demand to 100% of real traffic, we introduced nine clones for each trip. We furthermore moved source and target for each clone within an area of 3 km2 randomly and deviated the time of departure by 15 min. In total, we generated 2.25 million individual trips. 3.3 Traffic Assignment with SUMO The goal of traffic assignment is to generate a route for each of the trips in a way, that a rather stable traffic state is reached, i.e., reducing overall travel times and the number of teleports in SUMO. Teleports are a concept in SUMO, which occur if vehicles wait too long due to traffic congestion or even a deadlock. The vehicle is removed at its position and reinserted at a new location along the route with enough space to continue the ride. In [5] the authors proposed to create such routes using the MAROUTER tool of SUMO, which calculates a stochastic user equilibrium without employing simulations. However, we were not able to successfully apply the results calculated by the tool, as many deadlocks and congestion occurred which would require too much manual handwork on the network in combination with executing long lasting simulations. Instead, we decided to follow the classical approach by applying iterative dynamic user assignment. For this purpose, we calculated a set of initial routes for each trip using the Choice Routing algorithm [12, 17], and then used the duaIterate.py script from SUMO which tries to find a user equilibrium by running the simulation and route choice iteratively. We ran this process until we could not see a significant improvement in overall travel time. The improvement in travel time and the reduction of teleports over all executed iterations can be seen in Fig. 2. In the first iterations we aborted the simulation as soon as one million teleports have been executed, as in this state the whole traffic network was flooded with vehicles. In the final iterations we could reduce the number of teleports to roughly 3,000. The whole assignment process required a long time (several weeks) to complete due to the large number of vehicles and routes to process. In summary, this approach was easily manageable since the assignment process automatically eliminates deadlocks and congestion by the iterative route choice.

Fig. 2. Improvement in travel time (left) and number of teleports (right) over iterations.

4 Results In this section we show the results gathered by the current state of the SUMO simulation scenario after almost 60 iterations. The simulation scenario contains 2,248,952 individual

282

K. Schrab et al.

trips. This represents the total traffic volume of motorized private transport of a full day in the city-area of Berlin. Official statistics available for Berlin show very similar numbers [18, 19]. In Table 1 we calculated the total number of vehicle trips in Berlin based on these statistics from 2018, resulting in equal traffic volume as found in our scenario. Table 1. Calculation of the expected total number of vehicle trips. Multiplier Population Berlin

Total number 3,748,148 [19]

% mobile persons

93.3% [18]

3,497,022

Number of trips per person

3.7 [18]

12,938,982

% inner-city traffic

94.0% [18]

12,162,643s

% motorized vehicle traffic

24.0% [18]

2,919,034

Vehicle occupation

1/1.3 [18]

2,245,411

For a more detailed look we present the general statistics in comparison with the MATSim scenario. Firstly, the overall traffic volume over time is depicted in Fig. 3 showing a certain discrepancy between SUMO and MATSim (we exaggerated the traffic count for MATSim by a factor of 10 to match real traffic volumes). At first glance, it seems that the MATSim scenario contains much more vehicles. However, when comparing mean travel times with each other, it can be found that the mean travel time in MATSim is around 19 min, compared to 14 min in the SUMO scenario. Taken this into account, the duration these vehicles are longer in the network results in the higher number of vehicles shown in Fig. 3. For one thing, using adaptive traffic lights in the SUMO scenario instead of fixed signaling might lead to shorter travel times compared to the fixed outflow capacities modeled in MATSim. Furthermore, we did not model passenger crossings or bicycle streams, which in reality could have a significant influence on rightturns and therefore travel times. In general, due to the different models of network and vehicle dynamics in both tools, it is rather difficult to compare the results with each other.

Fig. 3. Number of vehicles over time (MATSim versus SUMO).

A Large-Scale Traffic Scenario of Berlin

283

Therefore, we wanted to understand how well the simulated traffic depicts reality in terms of traffic volumes handled by the roads. For this purpose, we analyzed traffic count data from the City of Berlin, which is publicly available [20]. This data provides hourly traffic counts of each day of the past six years for over 300 counting spots within the city. Additional hourly traffic counts for highways could be obtained from the official highway management in Germany (BASt) [21]. For the analysis we chose a random date from the pre-pandemic era, September 27th of 2018, which was a Thursday representing an average working day. For a visual comparison, we chose eight random spots in the city and two spots on the main highway A100 leading through Berlin. We furthermore selected the corresponding edges and links of the MATSim and SUMO outputs to gather simulated traffic counts. For SUMO, we used the edge dump which was generated during the simulation, containing traffic counts for each edge in the network. For MATSim, we extracted the required data from the link stats output which contains hourly traffic counts for each link of the network. The comparison with real traffic counts shows a good resemblance of traffic patterns at various points in the network. The simulated counts on the highway A100 shows very similar traffic volumes compared to the real data (see Fig. 4). Here we reach 3,000 vehicles per hour in both simulation and real traffic counts. Still, we do not see clear morning or evening peaks in the simulated data, as we see in the real counts. We assume that this is due to the fact that we excluded commuting traffic from the scenario. This highway is frequently used by commuters from/to locations outside of Berlin.

Fig. 4. Vehicle counts (veh/h) on A100 east and west direction.

For inner-city locations we chose various spots spread across the town to gather traffic volumes over time. The comparison of the simulated data in SUMO with real data and MATSim counts is given in Fig. 5. It shows that real counts are very similar to the simulated ones in SUMO throughout the whole day. Again, morning and evening peaks are not clearly visible, e.g., at Siemensdamm. The reason for this could be again the missing commuting traffic, as this location can be found right in front of a highway entry. Nevertheless, we still find locations where real traffic volume cannot be reached by the simulation. This is especially the case for large roads (three or more lanes), such as Straße des 17. Juni or Puschkinallee (see Fig. 6). Here we observe only around 500 vehicles per hour at peak, whereas real data shows that these roads handle up to 1 500 vehicles per hour and more. Interestingly, we found that MATSim shows quite similar traffic counts on these main roads. This artifact might be related to traffic signal timings, which hold back vehicles from entering the large roads. Therefore, future work on this

284

K. Schrab et al.

Fig. 5. Vehicle counts (veh/h) on different inner-city locations.

Fig. 6. Deviating vehicle counts (veh/h) on some locations.

scenario could focus on more realistic traffic light signaling including a synchronization of traffic lights (green waves).

5 Conclusions In this paper we presented the creation process of a large-scale traffic simulation scenario for the City of Berlin, Germany. With around 2.25 million individual trips and covering an area of around 800 km2 it is the largest simulation scenario for the traffic simulator SUMO we are currently aware of (more details see Table 2). To achieve this, we used the calibrated demand definition from the MATSim Open Berlin Scenario by extracting relevant car-based trips and re-calculating all routes. The traffic network for SUMO was created using OpenStreetMap data and has been manually adjusted to eliminate most of the modeling errors. For the traffic assignment we used the method of dynamic user assignment which iteratively runs route calculation and simulation until a user equilibrium is reached. After 60 iterations we found a stable state which represents traffic in Berlin close to reality. Comparing to real traffic counts emphasized that many roads show simulated traffic volumes similar to reality. The results shown in this paper represent the current state of the scenario after executing 60 iterations of the dynamic user assignment process. Still, we assume that with further work on the traffic network, improving signaling programs, and more iterations,

A Large-Scale Traffic Scenario of Berlin

285

an even better state could be achieved which resembles real traffic even more. Especially some of the main roads of Berlin are still underestimated in the SUMO scenario, when comparing to real traffic counts. Also, general travel times are lower than expected due to the simplified modeling of traffic signals. However, we are already very satisfied with this current state and will use this simulation scenario in future work and hope that this scenario is helpful for others, too. The scenario can serve as a baseline for analyzing the effects and impacts novel smart mobility applications can provide. This can be achieved by equipping a share of the vehicles with specific solutions, or by adding additional fleets of vehicles, such as taxis, buses, delivery fleets, or other types. For that purpose, this scenario will be compatible with the co-simulation framework Eclipse MOSAIC [1], which allows to couple the vehicle traffic with other aspects of smart mobility, such as simulators in the field of communication, application, electric mobility, autonomous driving, logistics, and such. In current works we use this scenario with MOSAIC for studying remoteoperated driving with the help of in-vehicle sensors on city scale. Here we use the scenario to steer individual vehicles remotely trough the modeled background traffic to cope with numerous different situations. In this context, we already used this scenario to implement and test a simplified perception module to detect other vehicles in a given field of view [22]. We furthermore plan to synchronize parts of the vehicle movements with vehicle dynamics simulators, such as Carla or PHABMACS, to generate realistic data from LiDAR and camera sensors. In tradition to other SUMO related traffic scenarios, such as LuST, MoST, and TuST, we will provide this scenario under the acronym BeST (Berlin SUMO Traffic) for everyone on GitHub1 under an open-source license. Table 2. Basic characteristics of the final scenario. Number of nodes

27,404

Number of edges

69,234

Number of junctions controlled by traffic signals

2,249

Number of trips

2,248,952

Average duration of each trip

805 s

Average distance of each trip

7.9 km

Overall mean speed compared to speed limits

0.71

Total number of teleports

3,026

Simulation duration on 3,4 GHz CPU (no GUI)

8h

Acknowledgement. This work has been performed as part of the “AI-NET ANTILLAS” project (funding number 16KIS1311), which is funded by the Federal Ministry of Education and Research (BMBF), Germany. 1 See https://github.com/mosaic-addons/best-scenario.

286

K. Schrab et al.

References 1. Eclipse MOSAIC Core Team: Eclipse MOSAIC—a Multi-Domain and Multi-Scale Simulation Framework for Connected and Automated Mobility. https://eclipse.org/mosaic. Accessed 14 July 2022 2. Markantonakis, V., Doko, A., Papamichail, I., Papageorgiou, M., Schrab, K., Neubauer, M., Protzmann, R.: Traffic control algorithms for mixed vehicle traffic—a simulation-based investigation. Transp. Res. Procedia 52 (2021) 3. Massow, K., Thiele, F. M., Schrab, K., Bunk, S.B., Tschinibaew, I., Radusch, I.: Scenario definition for prototyping cooperative advanced driver assistance systems. In: 2020 IEEE 23rd ITSC (2020) 4. Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using SUMO. In: The 21st IEEE ITSC (2018) 5. Rapelli, M., Casetti, C., Gagliardi, G.: Vehicular traffic simulation in the city of Turin from raw data. IEEE Trans. Mob. Comput. (2021) 6. Codeca, L., Raphael, F., Engel, T.: Luxembourg SUMO traffic (LuST) scenario: 24 hours of mobility for vehicular networking research. In: Proceedings of the 7th IEEE Vehicular Networking Conference (2015) 7. Codeca, L., Härri, J.: Towards multimodal mobility simulation of C-ITS: the Monaco SUMO traffic scenario. IEEE Veh Networking Conf. (2017) 8. Protzmann, R., Hübner, K., Ascheuer, N., Raack, C., Bauknecht, U., Enderle, T., Witt, A., Gebhard, U.: Large-scale modeling of future automotive data traffic towards the edge cloud. 20. Fachtagung Photonische Netze Leipzig (2019) 9. Ziemke, D., Kaddoura, I., Nagel, K.: The MATSim open berlin scenario: a multimodal agentbased transport simulation scenario based on synthetic demand modeling and open data. Procedia Comput. Sci. 151, 870–877 (2019) 10. Müller, S., Balmer, M., Charlton, W., Ewert, R., Neumann, A., Rakow, C., Schlenther, T., Nagel, K.: Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based modelling, agent-based simulation, and mobile phone data. Preprint (2021) 11. Wortmann, C., Syré, A., Grahle, A., Göhlich, D.: Analysis of electric moped scooter sharing in berlin: a technical, economic and environmental perspective. World Electr. Veh. J. 12, 96 (2020) 12. Ewert, R., Martins-Turner, K., Thaller, C., Nagel, K.: Using a route-based and vehicle type specific range constraint for improving vehicle routing problems with electric vehicles. Transp. Res. Procedia 52, 517–524 (2021) 13. Triebke, H., Kromer, M., Vortisch, P.: Pre-study and insights to a sequential MATSim-SUMO tool-coupling to deduce 24h driving profiles for SAEVs. In: Sumo User Conference 2020 (2020) 14. Triebke, H., Kromer, M., Vortisch, P.: Calibrating spatio-temporal network states in microscopic traffic simulation on a global level. In: Sumo User Conference 2021 (2021) 15. Horni, A., Nagel, K., Axhausen, K.W.: The Multi-Agent Transport Simulation MATSim. Ubiquity Press, London (2016). https://doi.org/10.5334/baw 16. Forschungsgesellschaft für Straßen- und Verkehrswesen: Richtlinien für Lichtsignalanlagen - RiLSA. Köln, FGSV-Verlag (2010) 17. Hübner, K., Schünemann, B., Radusch, I.: Sophisticated route calculation approaches for microscopic traffic simulations. In: Proceedings of the 8th International Conference on Simulation Tools and Techniques (2015)

A Large-Scale Traffic Scenario of Berlin

287

18. Gerike, R., Hubrich, S., Ließke, F., Wittig, S., Wittwer, R.: Mobility in Cities—SrV: Fact sheet for high-order cities located in Eastern Germany for 2018 (2018) 19. Amt für Statistik Berlin Brandenburg: Einwohnerbestand Berlin. https://statistik-berlin-bra ndenburg.de/kommunalstatistik/einwohnerbestand-berlin (2018) 20. Digitale Plattform Stadtverkehr Berlin: Verkehrsdetektion Berlin. https://api.viz.berlin.de/ daten/verkehrsdetektion (2021) 21. Bundesanstalt für Straßenwesen (BASt): Automatische Zählstellen auf Autobahnen und Bundesstraßen. https://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/ zaehl_node.html (2021) 22. Protzmann, R., Schrab, K., Schweppenhäuser, M., Neubauer, M., Radusch, I.: Implementation of a perception module for smart mobility applications in eclipse MOSAIC. In: Sumo User Conference 2022 (2022)

Emerging and Innovative Technologies in Transport: On-Demand Transport Services

Smart Parking System (SPS): An Intelligent Image-Processing Based Parking Solution Keerthi Lavanyeswari Pasala, Charitha Sree Jayaramireddy, Sree Veera Venkata Sai Saran Naraharisetti, Steven Atilho, Benjamin Greenfield, Benjamin Placzek, Mohamed Nassar, and Mehdi Mekni(B) University of New Haven, West Haven, CT 06564, USA {kpasa3,cjaya1,snara8,satil1,bgree2,bplac2}@unh.newhaven.edu, {mnassar,mmekni}@newhaven.edu

Abstract. The proliferation in the number of vehicles on the road are causing traffic problems. Existing transportation infrastructure and car park facilities are unable to cope with this influx. Drivers struggling to park their vehicles is a usual scenario witnessed at peak parking times causing waste of precious time and energy. Intelligent parking systems for meeting near-term parking demand are a must-have for developing smart cities. Goals of intelligent parking systems include counting the number of parked cars, and identifying the available locations. In this paper, we propose an intelligent parking system using real-time image processing techniques. Features of the proposed system include vacant parking space detection, detection of improper parking, display of available parking spaces, and directional indicators toward different types of parking spaces (vacant, occupied, reserved and handicapped). The system leverages the existing video surveillance infrastructure to capture, process image sequences and provide guidance and information to the drivers. Keywords: Smart cities · Car parking · Image processing · Edge detection · Object recognition

1 Introduction By 2050, with the urban population more than doubling its current size, nearly 7 of 10 people in the world will live in cities [1]. Cities are major contributors to climate change. According to UN Habitat, cities consume 78% of the world’s energy and produce more than 60% of greenhouse gas emissions. Yet, they account for less than 2% of the Earth’s surface [2]. With regard to the United States, about 82.66% of the total population lived in cities and urban areas in 2020 [3]. As an impact of the growth of urban population, the number of land transportation vehicles in US has been increasing significantly [4]. Along with the increase of urban population, traffic jam and the number of parking spaces in many densely populated cities in the US become more problematic. Particularly in public places, the limited parking slots lead car drivers to slowly cruise the city, generating large amounts of exhaust emissions and creating traffic congestion. In © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_25

292

K. L. Pasala et al.

addition, 86% of drivers face difficulty in finding a parking space in multilevel or geographically distributed parking lots [3, 5]. Insufficient car park spaces lead to traffic congestion and driver frustration. Improper parking is also another parking-related issue that occurs when a driver parks on or outside of the lines of a parking space. This matter annoys other drivers and most of the time a driver who wants to park in a small leftover slot will give up and feel frustrated. Although video surveillance systems (also knows as Closed-Circuit TeleVision (CCTV)) help monitor parking, manually inspecting videos to recognize unauthorized parking behaviors is tedious and inefficient [6]. Not only does it obstruct traffic and cause inconvenience, illegally parked vehicles pose economic risks [7]. Moreover, safety in parking and the need for real-time parking monitoring require the employment of applications and tools that track and record continuous activities including traffic and occupancy. Potential solutions, such as adding more infrastructure and parking spaces, are not feasible due to the high cost and limited supply of commercial real estate in cities. Therefore, it is crucial to leverage technology advances and develop smart solutions to help drivers quickly locate unoccupied parking spaces. Implementing such smart systems will help resolve the growing problem of traffic congestion, wasted time, money, and energy. It will provide better public service, reduce car emissions and pollution, improve city visitor experience, increase parking utilization, and prevent unnecessary capital investments. In this paper, we propose an innovative image-processing based Smart Parking System (SPS) for meeting the short-term parking demand. The proposed SPS aims to convert traditional parking lots equipped with video surveillance systems into smart ones. The contributions of our work are: (1) monitor parking space utilization, (2) improve driver experience while decreasing drivers’ frustration, (3) enhance parking lots security through number plate recognition, (4) collect valuable data for efficient parking management and informed decision making, and (5) assist drivers and recommend parking lots with respect to an activity time and location. To validate and verify the proposed SPS, we modeled, analyzed, and experimented it on a selection of parking lots at the University of New Haven. The University serves 6,961 students among which 40% are commuter students, employs 766 staff and faculty, and welcomes 25,000 visitors each year. The main campus counts 25 parking lots and is located on 82 acres in West Haven, Connecticut, United Stated of America. Considering the complex spatial distribution of the parking lots, academic, administrative, service, health, and housing buildings, there is a critical need to provide a user-friendly platform to monitor, secure and efficiently navigate the campus for commuting students, faculty, staff, and visitors. The reminder of the paper is organized as follows: Sect. 2 provides an extensive literature review of existing SPSs. Section 3 details the proposed SPS underlying software requirements engineering and software architecture and design models. Section 4 provides an overview of the obtained results. Finally, Sects. 5 and 6 discuss the proposed SPS, highlight its advantages and limitations and conclude with future work.

Smart Parking System (SPS): An Intelligent Image-Processing

293

2 Related Work In this section, we discuss three main classes of parking monitoring and vehicle detection technology; sensor-based, artificial intelligence-based, and vision-based parking systems. 2.1 Sensor Based SPS – Wireless Sensor Network (WSN) based SPS can be defined as a network of sensor nodes that are spatially dispersed and are dedicated to monitoring different environmental aspects such as sound, temperature, and pressure [8]. Nowadays, WSN has received outstanding traction among the SPS developers for flexibility, scalability, and low deployment cost [9]. – Vehicular Ad-Hoc network (VANET) based SPS are based on a wireless network of mobile devices. An SPS utilizing VANET has three main components: Parking Side Unit (PSU), Road Side Unit (RSU), and On-Board Unit (OBU) [10]. The OBUs are installed on the vehicles, PSUs are installed on parking areas, and RSU’s are installed beside the roads near the parking areas. – Internet of Things (IoT) based SPS rely on computational devices that are connected through Internet and can transfer data without any human intervention [11]. – Global Positioning System (GPS) based SPS is an essential component of several proposed smart parking systems. From GPS data, many systems can forecast parking lot occupancy and road traffic congestion. The GPS data is prone to error if operated inside of a closed parking area. Thus, smart parking systems that use GPS are suitable for open parking lots [12]. 2.2 Artificial Intelligence Based SPS – Machine learning (ML)/Deep learning (DL) based SPS analyzes the data collected from a parking lot to extract and infer its status. Moreover, ML and AI-based SPS can predict parking lot occupancy status of the upcoming days, weeks, or even months and provide a dynamic pricing scheme. ML-based systems can monitor traffic congestion of particular roads and offer a smart solution to smart parking spaces [13]. – Neural Network (NN) based SPS NN is a combination of algorithms that extracts features and underlying relationships from datasets through a process that mimics human brain function [14]. In SPS, NN is used for license plate recognition using realtime video data. In particular, Convolutional Neural Networks (CNN) and machine vision are implemented to detect parking lot occupancy status. – Fuzzy logic based SPS Fuzzy logic is a reasoning method that resembles human reasoning. It uses multi-valued logic, which means there is no absolute truth or absolute false value in fuzzy logic [15]. Fuzzy logic is used in SPS for predicting parking lot occupancy status [16]. The accuracy of the prediction model based on Fuzzy logic is based on the collection of real-time indicators [17].

294

K. L. Pasala et al.

2.3 Computer Vision/Image Processing Based SPS Computer vision/Image processing based SPS uses different types of camera networks to use image data to extract different information such as parking lot occupancy status, license plate recognition (LPR) and face recognition for billing, security issues, and to provide road traffic congestion report [18]. The systems based on computer vision/image processing technologies usually have a high data transmission rate from the camera network to the processing units because these systems are dependent on real-time parking lot video data for feature extraction [19]. These SPSs are usually suitable for open parking areas because a single camera can capture a significant area in the parking lot. However, these systems are prone to occlusion, shadow effects, distortion, and changing of light.

3 Smart Parking System (SPS) In this section, we detail the followed steps to support the Software Development LifeCycle (SDLC) of the proposed Smart Parking System. First, we present the software requirement engineering process and highlight the key system requirements. Next, we provide an overview on the system design and architecture [5]. 3.1 Software Requirements Specification

Fig. 1. SPS use case diagram

The proposed Smart Parking System has been designed from the perspectives of requirements identified by two key actors; (1) User (car driver) and (2) Public Safety (Police). Moreover, with respect to the complexity of SPS, several services are also involved for image processing and data analysis and prediction. The use case diagram depicted in Fig. 1 shows how different users with different roles interact with the system.

Smart Parking System (SPS): An Intelligent Image-Processing

295

Requirements describe the characteristics that a system must have to meet the needs of the stakeholders. These requirements are typically divided into functional and nonfunctional requirements. Functional Requirements [FR] describe how a software must behave and what are its features and functions. Non-Functional Requirements [NFR] describe the general characteristics of a system. They are also known as quality attributes. The following is a selection of functional requirements: – [FR1] SPS shall allow users to view current availability in a parking lot. – [FR2] SPS shall compute and display future predictions to users. – [FR3] SPS shall detect illegal and unauthorized parking, issue Parking Violation Notices, and allow public safety and users to view and process them. – [FR4] SPS shall track parking traffic and identify vehicles for parking safety purposes. – [FR5] SPS shall assist users to identify the best parking considering the location of the scheduled activities. The above listed functional requirements (FR) have been analyzed and validated with stakeholders and the following set of quality attributes non-functional requirements (NFR) has been derived: – [NFR1]Performance: SPS will collect CCTV images, process, analyze, and store them every minute to keep parking information relevant. – [NFR2]Security: SPS will use identification and authentication techniques to read Parking permit registration data and encryption techniques to securely store extracted vehicles identification data. – [NFR3]Portability: SPS will be mobile accessible through apps and web browsers. 3.2 Software Architecture and Design The software architecture style of the proposed SPS relies on a service-oriented architecture. The interactions between services are depicted in Fig. 2. The components of SPS architecture are: – FTP Server: It gathers parking images from surveillance cameras which are installed in the parking lots. – Image Processing: The image processing module collects, analyzes and processes data extracted from the FTP Server. This module has two key services: • The Image Extraction Service: This service acts as medium to extract the images from the recorded videos which are installed in parking lots. • The Object Detection Service: This service is used for the detection of objects. It generates a number n of things and their locations. This service detects and captures the vehicles in the parking spots. Objects include vehicles, parking lines (to detect illegal parking), handicap symbols (to detect unauthorized parking). – Character Extraction: This module focuses on interpreting images and detected objects.

296

K. L. Pasala et al.

• Automatic Number Plate Recognition (ANPR): The ANPR algorithm is applied to extract the plate numbers of the parked cars. The data collection process consists of accumulating of images and bounding-boxes for training the machine learning model. This module uses ML/AI based learning techniques using the collected dataset of plate images taken from different positions. The Optical character Recognition (OCR) approach is used to convert images of text into machine-encoded text. The goal is to build a model that can recognize and localize the plates. – Vacancy/Occupancy Analysis: This service provides information on vacant spots and updates this list as cars enter or leave the parking. – License Plate Verification: The goal of this phase is to verify and validate plate numbers using the public safety database. – Parking Violation Service: This service is responsible to issue parking violation notices when improper (illegal or unauthorized) parking is detected. – Prediction Module: This service extracts the information from the database and predicts the occupancy/availability of the parking based on current status and historical data. – Parking Assistant: This service provides recommendation about the best parking lot to use while maintaining a trade off between availability and distance/time to a specific location.

4 Results WE designed, implemented, and tested our SPS using 2 parking lots out of 25 geographically distributed parking lots on the campus of the University of New Haven. The existing CCTV deployed system uses FTP compatible cameras. We implemented a web-based application to support our students, staff, faculty, and visitors. Our SPS successfully detected and reflected the count of parking spots at a rate of 92% accuracy (Fig. 3a). We built a prediction model for each tested parking lot to provide a live-feed, compute, and visualize occupancy (Figs. 3b, c, and 4a). As far as parking lot monitoring and vehicle identification, we are able to identify and extract local Connecticut license plates information at a 81% success rate (Fig. 4b). Thanks to the vehicle identification information, SPS sends a query to the public safety services to verify if a valid parking permit has been issued. When a parking violation or unauthorized parking is detected, SPS creates a parking violation notice and notifies the public safety services (Fig. 4c). Finally, our SPS proposes a unique feature: Parking Assistant. Considering an event location, a starting time, and a duration, SPS can recommend a parking lot with a convenient walking distance and a high level of availability confidence. An example of events include classes for commuting students or faculty (classroom, start time) or a meeting for a staff or an administrator (meeting room, meeting time).

5 Discussion A number of experiments was conducted and it was confirmed that the image processing algorithms and character extraction techniques work better under good weather conditions, daylight, high contrast between identified objects and their background, and from

Smart Parking System (SPS): An Intelligent Image-Processing

297

Fig. 2: SPS software architecture diagram

certain angles of camera view. In fact, under snow or heavy-rain weather conditions, the object detection accuracy can drop as low as 32%. Similarly, the precision of the license plate recognition algorithm drops to 18%. SPS benefited from a pilot project involving 2 lots out of the available 25 parking lots the campus of the.

(a)

(b)

(c)

Fig. 3. (a) Vehicle detection; (b) Analytical predictions; (c) lot availability prediction

University of New Haven counts. Additional verification and validation will be required to assess the scalability of the proposed solution. Moreover, the machine learning algorithm used to read license plates is trained over a data set of local Connecticut plates. However, since the strategic location of the University of New Haven attracts students from neighboring states such as New York, Massachusetts, Rhode Island, Vermont, Pennsylvania, and New Jersey, it is important to extend the training data set to allow SPS to comprehensively recognize and read US license plates. Although the implemented

298

K. L. Pasala et al.

(a)

(b)

(c)

Fig. 4. (a) Availability/Occupancy; (b) License plate extraction; (c) Parking enforcement dashboard

features of the proposed SPS are quite important and mainly focus on parking lot traffic monitoring, occupancy analysis, vehicle identification and tracking, illegal and unauthorized parking, and illegal parking notice management, there are other value-added features that are still missing such as accident detection in parking lots.

6 Conclusion and Future Work In this paper, an image processing based smart parking system was presented. The proposed image processing based smart parking system has four advantages. First, special hardware infrastructure is not necessary because a CCTV camera can cover large parking spaces. Second, the system can provide an accurate occupancy prediction that is essential for finding the vacant parking. Third, camera-based system can also be applied to the parking lot in the street or residential area. Fourth, the assistant parking feature is a time-space optimization solving that improved parking users experiences. However, such camera-based parking are still vulnerable to accidents that may occur. In this respect, the proposed SPS needs additional features to detect an accident and avoid hit and run situations. Future research following this project will focus on accident detection and processing of parking violation notices. In addition, research to improve the detection accuracy and the processing speed will be performed.

References 1. The World Bank, “The World Bank,” 2022, [Online; accessed January 01, 2022]. [Online]. Available: https://www.worldbank.org/en/topic/urbandevelopment/overview#1 2. United Nation: Climate Action, “Cities and Pollution,” 2022, [Online; accessed January 01, 2022]. [Online]. Available: https://www.un.org/en/climatechange/climate-solutions/cit ies-pollution 3. Statista, “Degree of urbanization in the United States from 1970 to 2020,” 2022, [Online; accessed January 01, 2022]. [Online]. Available: https://www.statista.com/statistics/269967/ urbanization-in-the-united-states/ 4. Samara, F., Ondieki, S., Hossain, A. M., Mekni, M.: Online social network interactions (osni): a novel online reputation management solution. In: 2021 International Conference on Engineering and Emerging Technologies (ICEET), pp. 1–6 (2021) 5. Mekni, M.: An artificial intelligence based virtual assistant using conversational agents. J. Softw. Eng. Appl. 14(9), 455–473 (2021)

Smart Parking System (SPS): An Intelligent Image-Processing

299

6. Mekni, M., Jayan, A.: Automated modular invertebrate research environment using software embedded systems. In: Proceedings of the 2nd International Conference on Software Engineering and Information Management, pp. 85–90 (2019) 7. Chowdhury, I. H., Abida, A., Muaz, M. M. H.: Automated vehicle parking system and unauthorized parking detector. In: 2018 20th International Conference on Advanced Communication Technology (ICACT). IEEE, pp. 542–545 (2018) 8. Portocarrero, J.M., Delicato, F. C., Pires, P. F., G´amez, N., Fuentes, L., Ludovino, D., Ferreira, P.: Autonomic wireless sensor networks: a systematic literature review. J. Sens. 2014 (2014) 9. Samara, F., Ondieki, S., Hossain, A. M., Mekni, M.: Online social network interactions (osni): A novel online reputation management solution. In: 2021 International Conference on Engineering and Emerging Technologies (ICEET). IEEE, pp. 1–6 (2021) 10. Tsukada, M., Madokoro, H., SatoUnsupervised and adaptive category classification for a vision-based mobile robot. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–6 (2010) 11. Kumar, S., Swaroop, S.:Collateral development of invasive pulmonary aspergillosis (ipa) in chronic obstructive pulmonary disease (copd) patients. In: Recent Developments in Fungal Diseases of Laboratory Animals, pp. 111–118. Springer (2019) 12. Guo, J., Liu, Y., Yang, Q., Wang, Y., Fang, S.: Gps-based citywide traffic congestion forecasting using cnn-rnn and c3d hybrid model. Transportmetrica A: Transport Sci. 17(2), 190–211 (2021) 13. Veres, M., Moussa, M.: Deep learning for intelligent transportation systems: a survey of emerging trends. IEEE Trans. Intell. Transp. Syst. 21(8), 3152–3168 (2019) 14. Amato, G., Carrara, F., Falchi, F., Gennaro, C., Vairo, C.: Car parking occupancy detection using smart camera networks and deep learning. In: 2016 IEEE Symposium on Computers and Communica- tion (ISCC), pp. 1212–1217 (2016) 15. Kammoun, H.M., Kallel, I., Casillas, J., Abraham, A., Alimi, A.M.: Adapt-traf: an adaptive multiagent road traffic management system based on hybrid ant-hierarchical fuzzy model. Transp. Rese. Part C: Emerg. Technol. 42, 147–167, 2014. [Online]. Available: https://www. sciencedirect.com/science/article/pii/S0968090X14000692 16. Xie, X., Wang, C., Chen, S., Shi, G., Zhao, Z.: Real-time illegal parking detection system based on deep learning. In: Proceedings of the 2017 International Conference on Deep Learning Technologies, pp. 23–27 (2017) 17. Costa, D.G., Collotta, M., Pau, G., Duran-Faundez, C.: A fuzzy-based approach for sensing, coding and transmission configuration of visual sensors in smart city applications. Sensors 17(1), 93 (2017) 18. Baroffio, L., Bondi, L., Cesana, M., Redondi, A.E., Tagliasacchi, M.: A visual sensor network for parking lot occupancy detection in smart cities. In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), pp. 745–750 (2015) 19. Dangi, R. S., Kuvelkar, A., Maity, S.K., Wandhekar, S.: Efficient and robust Indian number plate recognition through modified and tuned lprnet. In: ICDSMLA 2020, pp. 115–129. Springer (2022)

The Impact of Total Cost of Ownership on MaaS System Appeal Using an Agent-Based Approach Carolina Cisterna(B) , Federico Bigi, and Francesco Viti University of Luxembourg, 2 Av. de L’Universite, 4365 Esch-Sur-Alzette, Luxembourg [email protected]

Abstract. Despite the interest in the MaaS system is growing fast within the scientific community, it remains uncertain if MaaS could be a potential tool able to reduce car ownership. This study aims to capture the impact of the total cost of ownership (TCO) on MaaS demand by endogenizing the MaaS choice and the TCO within the users’ travel choice in an agent-based model. We simulate different TCO price range starting from a benchmark cost in the literature and embed a specific type of MaaS plan which gives unlimited access to the services. Results show a significant growth of MaaS demand when TCO rises, in particular MaaS members are car users who shift their mode choice to public transport by travelling within more trips but in a shorter time slot. In contrast, MaaS users employ public transport for short trips while they still employ cars reducing their travel time but employing the same number of trips when TCO decreases. Results suggest that MaaS might become a more sustainable service by developing specific subsidies to discourage car ownership and by increasing mobility accessibility. Keywords: MaaS membership · Agent-based model · Car ownership

1 Introduction and State of Art The mobility as a service (MaaS) concept keeps gaining the scientific community’s attention since 2014 when it was first introduced by Heikkila and Hietanen [1, 2]. MaaS is mainly described in the literature as subscription-based system in which customers purchase several customized mobility plans through a digital platform [3]. This new mobility concept aims to satisfy customers’ mobility needs by offering a package of specific mobility services at a fixed cost [4, 5]. For this reason, MaaS is intended to capture a broad range of demands including multiple user profiles with various mobility requirements, which underlines how discovering population mobility tastes, needs, constraints and competitive alternatives they have as options is essential for successful MaaS implementation. The grand ambition of the MaaS system is to progress towards social and sustainability goals by increasing multimodal choice and reducing car ownership and usage [6]. In this context, MaaS has been individualized as a potentially efficient tool with future environmentally and sustainable outcomes able to reduce car ownership [3, 7]. In the literature, many studies have undertaken the MaaS topic employing different methods © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_26

The Impact of Total Cost of Ownership on MaaS

301

and providing distinct results. Due to the lack of data, since the MaaS system is not available on the market yet, one of the main approaches to study the attitude of potential users is the stated and revealed preference survey, in which participants are invited to claim their willingness to subscribe to hypothetical customized MaaS scenarios. Findings have shown how public transport users are the main MaaS potential users since they are more apt to subscribe than car users who are reluctant to shift their mobility choice to an aggregated service as MaaS system [3, 8, 9]. Fioreze et al. have further observed that regular car users are likely to underestimate the cost of MaaS, and therefore they are less apt to subscribe than public transport users [10]. However, MaaS seems to be a potential substitute for the second vehicle household whereas car ownership seems to be a barrier to MaaS adoption [11, 12]. Another approach widely used in the literature is by running and collecting data from pilot projects, many of these have taken place to study MaaS market appeal in different contexts by selecting specific participants and MaaS packages [13, 14]. Pilot projects outcomes have underlined that the new service might be a complement rather than a substitution for the private car, while public transport users remain the main potential customers of the MaaS plan [15, 16].Overall, the shift from car ownership to MaaS seems very difficult to achieve; Several studies in the literature have discussed it suggesting how making users more aware of their current travel expenditure might increase the MaaS appeal [17]. Hensher et al. have underlined that notifying a potential saving in terms of traveling costs by subscribing MaaS might increase the new system market share [18]. In conclusion, findings in the literature seem still far from individualizing a general trend for MaaS potential customers; survey approaches mainly rely on participants’ answers about hypothetical scenarios implemented following the specific costs and the mobility services available in the running area. Additionally, interviewing people about a future mobility service even providing a specific description of it to them might not guarantee a realistic and reliable data set. Whereas, pilot projects are developed by choosing specific niches of customers and contexts which are not representative of the whole potential MaaS demand. A general model able to capture potential MaaS demand starting from a heterogeneous population with various mobility habits and supplying different mobility services to them including a MaaS system seems to be currently missing in the literature. In this context, this study aims to develop a tool able to capture MaaS potential demand by embedding MaaS choice in the agent’s daily mobility options by using an agent-based model. The agent-based modelling approach allows agents to experience the MaaS service in a synthetic reality in terms of the trade-off between the new service subscription fee and time-linked mobility service costs. Furthermore, the total cost of ownership (TCO) of the private car is embedded in the agents’ car mode choice to make them conscious of their travel expenditure while the new MaaS subscription option is available. Through the new tool, this study aims to individualize the sensitivity of MaaS demand across TCO price range, starting from a TCO benchmark cost from the literature [19]. Whereas, the supply side is fixed in the model, as much as the simulated MaaS plan business type which allows unlimited usage of services within the packages and it is simulated within a benchmark fixed cost from the literature [20].Finally, the authors analyse the sensitivity of the MaaS demand across the TCO

302

C. Cisterna et al.

variety in terms of the modal split, the average of travel time and the number of trips per mobility service within the MaaS package.

2 Methodology This study employs an agent-based modelling approach, which provides a microscopic representation of individuals’ (or agents’) daily mobility choices by giving as input the spatial and temporal distribution of the demand [21]. The advantage of employing an agent-based model is the possibility to capture a dynamic demand response towards supply change which affects the agents in terms of activities performed and travel costs. We adopt the agent-based MATSim software which simulates agents’ ehavior by running a configurable number of iterations, schematically represented by the conceptual framework in Fig. 1. In our approach, a new event (5.1) is computed. Each agent has a memory containing fixed numbers of daily plans, where each plan represents a specific activity schedule with an associated score or economic function. The agent-based model allows agents to maximize their score to achieve their plans through the iteration process which is repeated until the agents’ average score stabilizes and the system equilibrium is reached [21].

Fig. 1. MATSim conceptual framework.

The conceptual framework loop can be summarized through the following iteration steps: – A plan is selected from the user’s memory plans as initial demand; (1–2 Events) – Mobsim executes the selected plan in a synthetic reality where agents travel on the network and us the selected modes (3–4 Events) – The actual performance of the plan is taken to compute the experienced plan and its specific score, S plan,experienced as it is shown in Eq. 1 (5 Event). The user’s score is given by the sum of all daily activities performed or utilities Sact,q and travel (dis)utilities Strav,(mode)q , where q indicates both the generic daily activity and the trips: S plan,experienced =

N −1  q=0

Sact,q +

N −1  q=0

Strav,(mode)q

(1)

The Impact of Total Cost of Ownership on MaaS

303

where the travel part is calculated as follows: Strav,q,cs = αcs + βc,cs ∗ (ct ∗ tr + cd ∗ d ) + βt,walk ∗ (ta + te ) + βt,cs ∗ t

(2)

Strav,q,mode = αmode + βt,mode ∗ t

(3)

Equation 2 represents the daily score of travelling by carsharing [22], where: • αcs is the carsharing-specific constant; • βc,cs ∗ (ct ∗ tr + cd ∗ d ) is the portion of the score (disutility) regarding reservation time and travel distance; • βt,walk ∗ (ta + te ) is the portion of the score (disutility) regarding access and egress time, by assuming that access/exits are made by walk; • βt,cs ∗ t is the portion of the score (disutility) regarding travel time experienced via carsharing. • Equation 3 is the generic score specification for all other available modes, where: • αmode is the mode-specific constant; • βt,mode ∗ t is the part of the score (disutility) for the time spent travelling on that mode. – The S plan,MaaS (Eq. 4) is calculated modifying just the travel (dis)utilities part of S plan,experienced under specific constraints which allows to experience the MaaS subscription (Event 5.1). The assumption that gives MaaS accessibility is that the potential users subscribe according to their predetermined or experienced travel mode choices specifically, if the experienced users’ plan includes at least 1 of the services in the MaaS  bundle. Once the accessibility to the MaaS system is given, the MaaS package cost −CostMaaS,package is added to the score and the cost included in the MaaS membership (Costincluded ) is subtracted, by selecting the time-linked cost of the mobility services within the MaaS bundle in a scenario where users pay as much as they travel. Scoreplan,MaaS(t) = Splan,experienced (t) + Costincluded − CostMaaS,package 

q,car

−TOC ∗

Iq,car

(4)

q=0

Moreover, a total cost of ownership euro-per-km (−TCO) is embedded in agents’ travel expenditure, independently from the MaaS subscription by counting the number of the trip made by car (Iq,car ). – Replanning section instead defines how agents can change their travel behaviour to maximize their score by reducing their generalized travel costs. The Event 7 is defined by following predefined strategies which allow re-routing and changing of transport mode, in this way agents have the chance to choose among all the sets of mobility services simulated in the synthetic network (Event 7). – Finally, the greatest plan score between S plan,MaaS and S plan,experienced is stored in the user’s memory.

304

C. Cisterna et al.

3 Case Study This study employs the synthetic population of 25560 agents generated by census data, with heterogenous socio-demographic characteristics and travel patterns in the synthetic network of the city of Berlin [23, 24]. Moreover, the following mobility services are implemented in the supply network: free-floating carsharing, two-way carsharing, public transport, bike, walk and private car. In the carsharing supply, a total number of 62 twoway stations have been simulated, with two available cars per each of them whereas a fleet of 160 cars for free-floating carsharing are simulated and spatially distributed within specific service areas in the city of Berlin. Furthermore, the carsharing costs are simulated following one of the current company prices in the city of Berlin, while for the public transport we kept the cost simulated and validated in the previous calibrated scenario of Berlin [25, 26]. The MaaS plan business simulated in the network is instead an unlimited MaaS package in which the potential subscriber has unlimited time access and usage to the services included in the bundle which are the following: free-floating, two-way carsharing services and public transport. The sensitivity analysis of MaaS demand across TCO variation is calculated starting from a benchmark value of 0.30 e/km following Eisenmann and Kuhnimhof’s study [19]. The TCO scenarios are 21 obtained varying the price by 10% from −100% with TCO equals zero to + 100% in which TCO has the double cost of the benchmark equal to 0.60 e/km. In all the scenarios the unlimited MaaS bundle price is settled following Caiati et.al study who estimated a positive parameter of monthly price equals 150 e in the Netherlands which has been divided by 20 working days obtaining a 7.50 e as a MaaS daily fee for the current paper [20]. In order to understand the MaaS demand behaviour, the NoMaaS (or Pay-as-you-go) scenario has also been simulated in which users pay for each service as much as they travel by experiencing separable trip-based costs per each mobility service simulated. We start from a set of parameters previously estimated and validated in the literature by following the score specification reported in Eqs. 1–3 and MATSim general framework in Fig. 1 without implementing the new Event 5.1 [26]. In the NoMaaS scenario either TCO per car trip or MaaS option are simulated. However, the time-linked costs of carsharing services and public transport are equal to the TCO scenarios to allow the comparison in terms of travel costs between scenarios.

4 Result The Table 1 shows the MaaS demand across the TCO price range, the number of MaaS users is around 16% at the benchmark price of 0.30 e/km and it increases up to 30% as TCO rises 0.60 e/km. Whereas, MaaS demand remains almost constant at around 15% when the TCO decreases from the benchmark until it becomes free of charge. Figure 2 displays the total number of trips per transport mode in each TCO scenario among MaaS members; at the benchmark cost (0.30 e/km) trips made by car represent almost 8% of the total trips, while public transport covers more than 80% of them. The number of trips made by car increases up to 19% of the total number of trips when the TCO is equal to

The Impact of Total Cost of Ownership on MaaS

305

Table 1. MaaS demand variation in terms of number of members and percentage across TCO (e/km) scenarios TCO range

0

Demand (%) 15

0.03

0.06

0.09

0.12

0.15

0.18

0.21

0.24

0.27

0.30

15

14

14

14

14

14

14

15

15

16

Members

3802 3741 3679 3644 3692 3606 3621 3641 3753 3810 4049

TCO range

0.30

Demand (%) 16 Members

0.33

0.36

0.39

0.42

0.45

0.48

0.51

0.54

0.57

0.60

16

18

19

20

21

23

24

26

28

30

4049 4152 4486 4802 5089 5375 5861 6179 6666 7194 7614

zero, while the number of trips employed by using public transport decreases following an inverse trend from car mode (around 70%).Active modes such as walking and cycling are not employed by Maas members when the TCO is lower than the benchmark cost. In contrast, the total number of trips made by car and public transport remains almost constant in the scenarios in which TCO is higher than the benchmark cost, while the percentage of trips made by active modes slightly increases. The number of trips employs by carsharing instead, remains almost constant among all TCO scenarios. Table 2 shows the average travel time per transport mode of MaaS agents across TCO scenarios; a linear increase of travel time by using public transport is displayed, in which MaaS users travel on average almost 20 min longer in the 0.60 e/km scenario than the benchmark one. The travel time employed using public transport does not change consistently when the TCO decreases till being equal to zero, whereas the travel time using car modes decreases almost linearly with the rise of TCO; from more than 30 min on average as the TCO is given for free, up to less than 10 min as TCO is the double benchmark price (0.60 e/km). Unexpectedly, travel time spent by travelling by freefloating service generally decreases with the rise of TCO, while travel time employed by using active modes increases. In contrast, there is no evident trend of travel time employed using active modes when TCO decreases. Two-way service instead seems not to have any travel time trend across TCO scenarios. In the same way, the average of the number of trips per transport mode is analysed and as expected for public transport, it rises following the growth of TCO, an inverse trend instead, is shown for car and free-floating modes in which the number of trips decreases as TCO rises. To further understand the MaaS modal shift demand, we analyse MaaS users’ travel behaviour in the NoMaaS scenario. We capture the total number of trips per mode in the NoMaaS scenario among MaaS agents in each TCO scenario, the differential in terms of average of travel time and the number of trips per mode between MaaS members in each TCO scenario and the NoMaaS scenario. Figure 3 displays the mode choice among MaaS members, in the NoMaaS scenario across TCO scenarios. Almost 70% of the trips are represented by car mode in every scenario which rises up to 76% when TCO increases from the benchmark cost of 0.30 euro/km. Whereas public transport is employed as much as bike mode by representing almost 10% of the

306

C. Cisterna et al.

Fig. 2. MaaS members trips distribution across TCO scenarios

Table 2. The average of MaaS agents’ travel time (min) per transport mode across TCO (e/km) scenarios TCO

0

0.03

0.06

0.09

0.12

0.15

0.18

0.21

0.24

0.27

0.30

PT

89

89

89

95

92

92

93

97

94

87

93

TW

44

42

46

46

45

46

43

44

45

36

35

FF

31

29

31

32

29

30

26

26

28

27

29

CAR

33

29

27

23

22

19

17

16

14

13

13

WALK

90

149

106

122

129

125

112

85

105

55

68

BIKE

46

39

31

30

36

26

41

20

34

17

21

TCO

0.30

0.33

0.36

0.39

0.42

0.45

0.48

0.51

0.54

0.57

0.60

PT

93

100

106

108

110

110

113

110

111

114

111

TW

35

44

44

42

43

42

41

41

40

39

38

FF

29

30

28

28

27

25

25

23

22

23

22

CAR

13

13

12

13

12

12

11

10

10

10

10

WALK

68

85

87

94

95

105

100

105

109

120

121

BIKE

21

29

31

30

38

32

39

36

44

41

43

total trips, in contrast, MaaS users seem to barely use any carsharing service when MaaS and TCO are not simulated. Table 3 represents instead the differential in terms of travel time between MaaS and NoMaaS scenarios which increases with the TCO price when agents travel by car and the trend follows a linear correlation in which the differential decreases with the decrease of TCO.

The Impact of Total Cost of Ownership on MaaS

307

A similar inclination is shown for free-floating mode, while public transport mode occurs in the opposite direction in which the differential decreases with the TCO rise. Equally, the differential of the number of trips per mode shows public transport agents increase their number of trips, while on the other hand car mode is less employed by MaaS agent as TCO increases. Carsharing usage has an opposite trend instead, freefloating service use goes down when TCO rises, while two-way service does not have a defined direction among scenarios.

Fig. 3. MaaS members trips distribution across TCO scenarios in NoMaaS scenario. Table 3. Differential in terms of average of travel time (min) per agent among TCO (e/km) scenarios TCO 0

0.03

0.06

0.09

0.15

0.18

0.21

PT

−10.5 −9.3

−8.8

−12.4 −11.6 −7.6

−6.6

−10.9 −5.4

FF

−5.2

−8.8

−8.9

−14.8 −17.9 −15.2 −16.0 −19.2

−7.3

0.12

−10.3 −6.3

0.24

0.27

0.30

−11.6 −3.9

CAR −11.3 −13.4 −14.7 −19.1 −18.7 −21.6 −23.2 −25.1 −26.2 −23.9 −28.8 TCO 0.30

0.33

0.36

0.39

0.42

0.45

0.48

−13.9 −16.2 −17.5 −17.6 −13.3 −7.8

0.54

0.57

0.60

−11.6 −6.4

0.01

−3.4

−5.5

PT

−3.9

FF

−19.2 −16.7 −14.8 −20.7 −17.6 −22.4 −23.2 −17.0 −19.4 −22.7 −18.2

CAR −28.8 −32.9 −36.4 −36.3 −38.5 −38.3 −40.1 −38.3 −39.0 −40.0 −38.9

5 Discussion and Conclusion The study analysed the impact of TCO on MaaS demand by employing an agent-based model able to endogenize MaaS system in users’ choice set and TCO cost. The MaaS

308

C. Cisterna et al.

demand grows with the rise of TCO capturing 30% of the whole sample, while the number of agents remains constant at around 15% when the TCO reduces showing how potential customer does not seem to be affected by its reduction. Findings show two main different MaaS customers’ behaviour; the first occurs for high TCO in which Maas members are car users who shift their mode choice to public transport by travelling within more trips but in a shorter time slot. MaaS users drastically reduce car usage both in terms of average of the number of trips and of travel time in order to reduce their TCO daily expenditure. Free-floating instead seems to be unexpectedly employed in the same modality of the car mode; rather than having an increase in terms of travel time and the number of trips as TCO rises. A further result of the TCO growth, MaaS demand seems to reduce the active modes usage in favour of MaaS service, while public transport use follows the opposite direction increasing with the TCO rise. Whereas two-way service does not have a defined direction. On the other hand, another travel behaviour occurs for a TCO reduction in which MaaS members seem to be car users who shift to public transport and carsharing services without employing any active modes during their daily trips. MaaS users employ public transport for short trips while they still employ cars reducing their travel time but employing the same number of trips due to the reduced effect of TCO on the agents. In contrast with the other type of MaaS members, as TCO decreases the usage of free-floating increases in terms of both the number of trips and travel time. Two-way usage instead, does not show any difference in terms of usage in each TCO trend probably due to the station-based nature of the service in which the user must drop the car off in the same picking up station. Conversely, free-floating usage appears overturned since its rise might be expected as TCO increases due to its versatile and similarity with private cars [27, 28]. The free-floating usage distribution might be due to a demand-supply unbalance, in fact as TCO rises many users want to book free-floating cars to substitute their cars and as a result, too many booking requests are involved and the system is saturated and not able to satisfy the customers. On the other hand, as TCO decreases the availability of carsharing fleets is higher than in high TCO scenarios due to the reduced number of bookings in the network. In this study, we should also not forget that the simulated MaaS bundle provides unlimited time access to the services which might affect their usage among members. Furthermore, the supply side is a simulation input set up following specific services space distribution and tariffs in the city of Berlin and within these specifics, the result might not be generalized. In this context, one of the main limitations in this paper is the MaaS unlimited plan simulated that might encourage the overuse of the services within the plan while embedding different MaaS business models might improve the distribution of the supply capacity. A possible bundle model which provides a time limit access or a discount per each trip made by the transport modes could further help the balance of the supply side. Moreover, differentiating MaaS bundle might discourage generalized undesirable effects such as the shift from active modes to MaaS subscription.

The Impact of Total Cost of Ownership on MaaS

309

A possible future study might be focused on simulating different MaaS business models to capture MaaS appeal and identify further characteristics of potential customers. Moreover, by including new mobility services in the MaaS packages it might increase the MaaS appeal in the market. In conclusion, simulating Maas thought an agent-based model using different validated MATSim scenarios,and comparing them might increase their reliability and reproducibility.

References 1. Heikkilä, S.: Mobility as a Service—A Proposal for Action for the Public Administration Case Helsinki, p. 94 2. Hietanen, S.: Mobility as a service The New Transport Paradigm, p. 18 3. Kamargianni, M., Matyas, M.: The Mobility-as-a-Service Business Ecosystem, p. 19 (2016) 4. Sochor, J., Arby, H., Karlsson, I.C.M., Sarasini, S.: A topological approach to Mobility as a Service: a proposed tool for understanding requirements and effects, and for aiding the integration of societal goals. Res. Transp. Bus. Manag. 27, 3–14 (2018). https://doi.org/10. 1016/j.rtbm.2018.12.003 5. Jittrapirom, P., Caiati, V., Feneri, A.-M., Ebrahimigharehbaghi, S., González, M.J.A., Narayan, J.: Mobility as a service: a critical review of definitions assessments of schemes, and key challenges. Urban Plan 2(2), 13 (2017). https://doi.org/10.17645/up.v2i2.931 6. Reck, D.J., Hensher, D.A., Ho, C.Q.: MaaS bundle design. Transp. Res. Part Policy Pract. 141, 485–501 (2020). https://doi.org/10.1016/j.tra.2020.09.021 7. Becker, H., Balac, M., Ciari, F., Axhausen, K.W.: Assessing the welfare impacts of shared mobility and Mobility as a Service (MaaS). Transp. Res. Part Policy Pract. 131, 228–243 (2020). https://doi.org/10.1016/j.tra.2019.09.027 8. Hoerler, R., Stünzi, A., Patt, A., Del Duce, A.: What are the factors and needs promoting Mobility-as-a-Service? Findings from the swiss household energy demand survey (SHEDS). Eur. Transp. Res. Rev. 12(1), 1–16 (2020). https://doi.org/10.1186/s12544-020-00412-y 9. Ho, C.Q., Hensher, D.A., Mulley, C., Wong, Y.Z.: Potential uptake and willingness-to-pay for Mobility as a Service (MaaS): a stated choice study. Transp. Res. Part Policy Pract. 117, 302–318 (2018). https://doi.org/10.1016/j.tra.2018.08.025 10. Fioreze, T., de Gruijter, M., Geurs, K.: On the likelihood of using Mobility-as-a-Service: a case study on innovative mobility services among residents in the Netherlands. Case Stud. Transp. Policy 7(4), 790–801 (2019). https://doi.org/10.1016/j.cstp.2019.08.002 11. Pickford, A., Chung, E.: The shape of MaaS: the potential for MaaS Lite. IATSS Res. 43(4), 219–225 (2019). https://doi.org/10.1016/j.iatssr.2019.11.006 12. Alonso-González, M.J., Hoogendoorn-Lanser, S., van Oort, N., Cats, O., Hoogendoorn, S.: Drivers and barriers in adopting Mobility as a Service (MaaS)—a latent class cluster analysis of attitudes. Transp. Res. Part Policy Pract. 132, 378–401 (2020). https://doi.org/10.1016/j. tra.2019.11.022 13. Sochor, J., Karlsson, I.C.M., Strömberg, H.: Trying out Mobility as a Service: experiences from a field trial and implications for understanding demand. Transp. Res. Rec. J. Transp. Res. Board 2542(1), 57–64 (2016). https://doi.org/10.3141/2542-07 14. Storme, T., De Vos, J., De Paepe, L., Witlox, F.: Limitations to the car-substitution effect of MaaS. Findings from a Belgian pilot study. Transp. Res. Part Policy Pract., S0965856418312564 (2019). https://doi.org/10.1016/j.tra.2019.09.032 15. Strömberg, H., Karlsson, I.C.M., Sochor, J.: Inviting travelers to the smorgasbord of sustainable urban transport: evidence from a MaaS field trial. Transportation 45(6), 1655–1670 (2018). https://doi.org/10.1007/s11116-018-9946-8

310

C. Cisterna et al.

16. Tsouros, I., Tsirimpa, A., Pagoni, I., Polydoropoulou, A.: MaaS users: Who they are and how much they are willing-to-pay. Transp. Res. Part Policy Pract. 148, 470–480 (2021). https:// doi.org/10.1016/j.tra.2021.04.016 17. Vij, A., Ryan, S., Sampson, S., Harris, S.: Consumer preferences for Mobility-as-a-Service (MaaS) in Australia, p. 8 (2018) 18. Hensher, D.A., Ho, C.Q., Reck, D.J.: Mobility as a service and private car use: evidence from the Sydney MaaS trial. Transp. Res. Part Policy Pract. 145, 17–33 (2021). https://doi.org/10. 1016/j.tra.2020.12.015 19. Eisenmann, C., Kuhnimhof, T.: Some pay much but many don’t: Vehicle TCO imputation in travel surveys. Transp. Res. Procedia 32, 421–435 (2018). https://doi.org/10.1016/j.trpro. 2018.10.056 20. Caiati, V., Rasouli, S., Timmermans, H.: Bundling, pricing schemes and extra features preferences for mobility as a service: sequential portfolio choice experiment. Transp. Res. Part Policy Pract., S0965856418309534 (2019). https://doi.org/10.1016/j.tra.2019.09.029 21. ETH Zürich, Horni, A., Nagel, K., Berlin, T.U., Axhausen, K.W., ETH Zürich.: Introducing MATSim. In: ETH Zürich, Horni, A., Nagel, K., Berlin, T.U. (eds.) The Multi-Agent Transport Simulation MATSim, pp. 3–8. Ubiquity Press (2016). https://doi.org/10.5334/baw.1 22. Ciari, F., Bock, B., Balmer, M.: Modeling station-based and free-floating car sharing demand: test case study for Berlin. Transp. Res. Rec. J. Transp. Res. Board 2416(1), 37–47 (2014). https://doi.org/10.3141/2416-05 23. Ziemke, D., Nagel, K.: Development of a fully synthetic and open scenario for agent-based transport simulations—The MATSim Open Berlin Scenario, p. 11 24. Giorgione, G., Ciari, F., Viti, F.: Dynamic pricing on round-trip carsharing services: travel behavior and equity impact analysis through an agent-based simulation. Sustainability 12(17), 6727 (2020). https://doi.org/10.3390/su12176727 25. ‘Stations—Stadtmobil Berlin’. Accessed: 06 March 2020. [Online]. Available: https://berlin. stadtmobil.de/privatkunden/stationen/ 26. Giorgione, G., Ciari, F., Viti, F.: Availability-based dynamic pricing on a round-trip carsharing service: an explorative analysis using agent-based simulation. Procedia Comput. Sci. 151, 248–255 (2019). https://doi.org/10.1016/j.procs.2019.04.036 27. Le Vine, S., Polak, J.: The impact of free-floating carsharing on car ownership: early-stage findings from London. Transp. Pol. 75, 119–127 (2019). https://doi.org/10.1016/j.tranpol. 2017.02.004 28. Cisterna, C., Giorgione, G., Cipriani, E., Viti, F.: Supply characteristics and membership choice in round-trip and free-floating carsharing systems. In: 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 1–7. Cracow, Poland. https://doi.org/10.1109/MTITS.2019.8883360

A Recommendation Engine for a Smart Parking Ecosystem Spyros Kontogiannis1,3 , Nikos Zacharatos2 , and Christos Zaroliagis2,3(B) 1 Department of Computer Science & Engineering, University of Ioannina, Ioannina, Greece

[email protected] 2 Department of Computer Engineering & Informatics, University of Patras, Patras, Greece

{zacharato,zaro}@ceid.upatras.gr 3 Computer Technology Institute and Press “Diophantus”, Patras, Greece

Abstract. Due to waste of time and unnecessary pollution when seeking for a parking space in urban areas, the lack of parking spaces has a serious social and environmental impact. To address this problem, we developed the SocialPARK ecosystem which engages a community of interacting citizens, parking vendors and municipalities. SocialPARK revolves around a crowdsourcing scheme that aggregates parking information, for free public spaces reported by commuters, and for commercial parking spaces offered by parking vendors, in a single integrated platform. SocialPARK offers a variety of services, including “Park-andRide” options, making the city centers more accessible with less pollution. Apart from commuters’ participation, a central challenge is the involvement of as many parking vendors as possible, to offer a multitude of parking alternatives to the commuters. We present a business-oriented recommendation engine, developed as part of SocialPARK, to motivate parking vendors to participate in the envisioned open market of parking-related services. The engine is composed of two modules: the first module gathers usage information about the utilization of parking houses within a certain area; (ii) the second module exploits this information to exhibit statistical data and create targeted recommendations for parking houses, rendering their businesses more competitive and improving the services they offer. Keywords: Recommendation systems · Parking

1 Introduction Cars provide an unrivaled combination of speed, autonomy, and privacy, which is why people prefer to use them on a daily basis. The congregation of cars in big cities that have not been designed to withstand the load, creates many problems and parking is one of the most severe ones (Ibrahim, 2017). Parking issues have prevailed in cities and metropolitan regions and constitute arguably the most widely debated concern among both the public and professionals. The main challenge is an imbalance between parking offer and demand. Therefore, a parking ecosystem should be an important part of the urban-life system, and its absence is linked to traffic congestion, accidents, and pollution (Shoup, 2006). Although an © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_27

312

S. Kontogiannis et al.

effective parking system can improve urban transportation, the city’s environment and inhabitants’ quality of life, parking is a rather overlooked aspect of urban planning. Urban planners should explore more efficient and imaginative solutions to the parking problem, on the level of administration, planning, and design (Mingardo, van Wee, & Rye, 2015) (Lin, Rivano, & Le Mouël, 2017). SocialPARK tackles the problem by providing technologically innovative solutions to make it easier for commuters to search for free parking spots. Its goal is to maintain a digital platform providing personalized parking-related information and services, i.e., a parking-as-a-service platform. The challenge of SocialPARK is twofold: (i) to improve the commuters’ quality of life by dealing with the loss of time and the environmental repercussions; (ii) to facilitate the monitoring of parking houses of the involved parking vendors and support them in their decision-making process towards improving their business exploitation plans. This paper focuses on the provision of a parking-business oriented recommendation engine that digests historical information, along with stakeholders’ business profiles, to provide targeted and explainable recommendations which would lead to better exploitation plans for their own parking houses. The paper is organized as follows. Section 2 presents an overview of the SocialPARK ecosystem’s architecture. Section 3 presents a literature overview for parking-related recommender systems. It also presents our approach for a parking-business oriented recommender system. Section 4 reports some representative experimental results. Finally, Sec. 5 concludes the paper and briefly discusses directions for future work in the area.

2 SocialPARK Architecture The high-level architecture of the SocialPARK ecosystem is depicted in Fig. 1. It consists of the following conceptual modules: (a) Parking Information Brokerage Module: This module is in charge of storing and maintaining all parking-related and profile information in an appropriate database, while ensuring privacy and anonymity for all the involved stakeholders. The parking-related data originates from two sources: parking owners, who use automated parking availability notification techniques; and commuters, who spontaneously provide crowdsourced parking-availability information. Due to the variety of the data to be collected, a NoSQL database was chosen, mainly to cover the needs for scalability and simplicity of managing unstructured data from diverse sources (e.g., fully/partially automated updates, voluntary crowdsourced information, etc.). In particular, MongoDB was our technological choice, which is an open-source, incredibly efficient, and volatile database that also supports the JSON language for data transmission. Because the SocialPARK ecosystem keeps individualized data for commuters, parking houses and parking vendors, data protection and anonymization are particularly critical. An anonymization method is used to obscure commuters’ actual identities when interacting with the system, on a per-session basis, to avoid leakage of IDs and everyday commuting habits. Furthermore, before being granted access to view and edit their personal profile, data or information relevant to parking company regulations

A Recommendation Engine for a Smart Parking Ecosystem

313

and strategic goals, all the involved stakeholders – i.e., commuters and parking owners – must verify themselves.

Fig. 1. Overall architecture of SocialPARK

In addition, the brokerage module includes the required functions for conducting statistical analysis of aggregated parking data, as well as a business-oriented recommendation engine for making targeted suggestions how parking vendors could optimize their business strategies. (b) Personalized parking & routing: This module takes over the task of monitoring parking house availability and/or providing booking services, as well as allowing closed self-organized groups of commuters (e.g., disabled), to share their own parking spaces. Specialized rewarding schemes are engaged to motivate voluntary participation of commuters, both in the crowdsourced aggregation of parking related information and in self-organized groups. One rewarding scheme is for the general public, to encourage participation in crowdsourcing, and the other is to ensure the survival of self-organized closed groups of commuters sharing their parking places within the group. Commuters can also use the module to get point-to-parking-spot routing and realtime guidance. (c) Commuter frontend API: Commuters interact with the full SocialPARK ecosystem via this module, which is a mobile API. Due to its popularity in the smartphone market, the implementation is for Android OS.

314

S. Kontogiannis et al.

(d) Parking owner frontend API: This module is a web-based API implemented as a unified dashboard that gives parking owners (and parking house managers) access to all the functionalities related to the management of their profiles, the visualization of the results of statistical analyses for parking-related historical data, and the provision of business-improvement recommendations. 2.1 Brokerage Module This module refers to parking owners of SocialPARK. The front-end is used by the owners to register and log-in to the system, add their parking houses with all their features, and access the module’s back-end system. The back-end system collects static and dynamic data from the parking houses participating in SocialPARK, which is then analyzed and presented to the parking vendors, while also proposing ideas for better commercial exploitation of their own spaces, towards becoming more competitive. Statistical data concerning information related to parking-usage is collected using two methods. The first method is automated and receives information via requests to the Data Interconnection Unit, which in turn draws them from SocialPARK’s database, per parking house, at regular intervals (sixty minutes was initially chosen, which can be easily increased/decreased in the future depending on the needs of the owners). More specifically, for each parking house, occupancy data is collected for each parking-space’s subcategory (general, disabled, elderly, pregnant women/parents with prams), as well as for the total occupancy (in absolute values and percentages). The second method concerns real-time information updates, regarding parkingavailability, that is, customers entering or leaving the parking houses. The method is provided via a remote procedure call, provided that the parking house supports an automated mechanism for recording the incoming and outgoing vehicles. Alternatively, in addition to the automated mode, authorized parking employees are provided with the option to manually update parking-availability data through a web interface. Regardless of how each provider chooses to update their parking-availability information, the data updates occur as follows. When a vehicle enters the parking house, the Brokerage Unit is informed, through an http-post request. The body of the request includes the license plate of the car, the unique ID of the parking house and an alphanumeric ID indicating that this is an entrance. The Brokerage Unit stores the hashed license plate of the vehicle (until the same vehicle leaves the parking house), the parking house ID and the current time (timestamp). When a vehicle leaves the parking house, the Brokerage Unit is informed with a similar http-post request indicating that this is an exit from the parking house. After matching the hashed license plate and the parking house ID with those of a previous entrance (i.e., ensuring that the same vehicle still resides in the parking house), the stored license plate is deleted and the time difference between these two posts is saved. From these real-time updates of parking-house availability information, we obtain the parking durations and arrival-times of customers which, apart from providing vital data, will later help us build a representative operational “fingerprint” per parking house.

A Recommendation Engine for a Smart Parking Ecosystem

315

3 Parking-Related Recommender Systems Recommender systems (Ricci, Rokach, & Shapira, 2022) predict ratings (or preferences) of users for items which they have not yet considered, in order to provide meaningful suggestions of new items to users. There are different types of recommender systems, each with its own characteristics, pros and cons (Son, Kim, Kim, & Cho, 2015). The major categories are content-based recommender systems, collaborative-filtering recommender systems, demographic-based recommender systems, knowledge-based recommender systems and hybrid recommender systems. 3.1 Review of Related Literature We present some of the relevant work that has been carried out on parking-related recommender systems. Commuters typically could get recommendations about parking spots as a result of a (commuters, parking-spots) relation. Providers, on the other hand could get recommendations about pricing policies and types of services to invest on, as a result of historical data analysis for their parking-business exploitation. The literature on parking-related recommender systems almost exclusively focuses on commuters. In (Rizvi, Zehra, & Olariu, 2019), a recommendation system for Smart Cities is proposed, which takes the commuters’ preferences (e.g., type of parking) and constraints (e.g., maximum price to pay, or maximum walking time to destination) into consideration. The Agent-oriented Smart Parking Recommendation System for Smart Cities (ASPIRE) acts like an on-demand parking lookup service, matching the drivers’ preferences with the best matching spots. The availability of parking spots in the various parking houses is updated in real time, using IoT sensors. The data collected by ASPIRE also provides local governments with parking demand and supply statistics for specific geographical areas, aiding them in the decision-making process of building new parking houses. In (Tsai & Chen, 2021), a parking recommendation system for Smart Cities is presented, which recommends the best parking house to the commuter based on the current traffic flow and parking house information. A “time gap” is taken into account between the commuter’s query-time and the actual arrival-time at the parking lot. E.g., popular parking lots are usually either fully occupied or impose a long waiting line upon arrival, although they appeared empty at query time. A queueing model is used to estimate the probability to obtain a free space upon arrival at a parking lot, by utilizing historical and real-time data. The real-time updates, regarding parking-space availability, are performed using again IoT sensors. In (Saleem, Rehmani, Crespi, & Minerva, 2021) the anonymity and privacy of commuters’ data is considered. Two solutions are proposed to preserve their privacy in parking recommender systems, while analyzing the parking history using anonymization and differential privacy techniques. An experimental evaluation is carried out with a data set constructed from real parking measurements, to evaluate the trade-off between privacy protection and utilization of the parking spaces. In (Rahaman, et al., 2021) a multi-criteria parking recommender system is proposed, which takes into consideration criteria like fare, parking rules, walking distance to destination, travel-time and likelihood of a parking spot being unoccupied at arrival time, in

316

S. Kontogiannis et al.

order to create recommendations for the users. The novelty of this recommender system is that these criteria are dynamic, may change over time and sometimes conflict with each other. In (Montgomery, 2005) a pricing decision support system (PDSS) is proposed, targeting supermarket retailers, with a goal to suggest optimal pricing and promotional strategies based on historical data. The proposed system forecasts movement, revenue and profit in real-time using the current prices and the offers as variables. Moreover, it manipulates prices of (groups of) products. Finally, it creates promotional offers and price strategies about products and even warns users about bad pricing strategies that are already implemented. To our knowledge, the only recommendation systems targeted for parking vendors are the ones proposed in (Lei & Quyang, 2017) and (Tian, Yang, Wang, & Huang, 2018). These systems dynamically change the prices of parking spots in real time, based on reservations and occupancies per parking house. These two systems look alike our system, but they have two main downsides. First, they rely on bookings/reservations information, which are neither predictable by every customer, nor supported by all parking houses. Second, they ignore historical data of both parking houses and geographical areas, but only increase or decrease the prices based on current vacancies of each parking house. Moreover, no similarity metric is considered for the parking houses that would exploit their business profiles (e.g., whether they target at commuters seeking for shorttime parking spots, or for residents within their geographical area seeking for overnight parking-spots), towards providing focused suggestions for business improvement to the parking house. 3.2 Our Approach: Parking-Business Recommender System Our Parking-Business Recommender System (PBRS), implemented within SocialPARK, targets at parking vendors and suggests ways to increase the utilization of their parking houses. It can be classified as a decision support system (DSS) (Keen, 1980) that uses recommendation systems’ techniques in order to correlate the parking houses and provide targeted suggestions to parking vendors, on a per-parking-house base. In a nutshell, PBRS is based on the following axes, in order to provide recommendations for a parking house H: (i) exploitation of the historical parking-related data of H; (ii) consideration of aggregated parking-related historical data from the broader geographical area around H; and (iii) comparison of H’s utilization with that of a targeted group of competing parking houses within its own geographical area, which have a similar business profile with H. The methods implemented within PBRS are a combination of: • Similarity metric for parking houses: Each parking house is described via a “parking fingerprint”, i.e., a vector describing its spatiotemporal usage by diverse types of commuters (e.g., shoppers, workers, residents, etc.). Consequently, the “similarity” for pairs of parking houses is computed, i.e., pairs of houses that focus on the same types of customers and are located in a nearby geographical area, according to the cosine similarity of the corresponding parking fingerprints.

A Recommendation Engine for a Smart Parking Ecosystem

317

• Knowledge-based suggestions for pricing policies: They are proposed to the parking vendors, based on detailed parking data of their own parking houses. • Collaborative-filtering: A parking vendor (or parking house administrator) gets targeted recommendations for improving the utilization of a particular parking house, taking into account successful policies of their direct competitors (similar houses within their geographic area) for common groups of customers. • Parking-related aggregated historical information: A parking vendor gets recommendations for altering the business plan of a particular house (e.g., change targeted groups), based on aggregated historical parking information of the corresponding geographical area. The “fingerprint” of each parking house acts as its characteristic vector. The parking durations, the customers’ arrival-times, and the occupancy levels do not constitute a representative profile of the house on their own. For example, it would be incorrect to assume that two parking houses are similar because their typical customers park for the same duration, since they could arrive at completely different hours of the day. To avoid this, we create parking-houses’ fingerprints from spatiotemporal data of parking-occupancies. In particular, the fingerprint of a parking house is created as follows. Customers are separated in three categories depending on the duration of their stay: Shoppers that park up to four hours; workers that park five to eleven hours; and residents that park for at least twelve hours. For each hour of the day, we maintain a triplet of counters, one per customer category, which shows how many new customers of that type arrived at the parking house. The array C stores 24 triplets of counters (i.e., 72 = 24 · 3 integers) per day, with the new entries per hour and per category of customers, for the particular parking house. E.g., if the 17th triplet of the array (corresponding to the interval 17:00–17:59) is [14, 5, 3], this indicates that 14 shoppers, 5 workers and 3 residents entered the parking house during this interval. Since we do not know beforehand the parking times of customers, C is updated upon their departures from the parking house. Moreover, an array D of similar structure (three counters per hour) stores the number of customers residing at a parking house during a given time-interval. E.g., if the 17th triplet of the array (which corresponds to the time interval 17:00–17:59) is [34, 12, 6], this indicates that, during this interval, there were 34 shoppers, 12 workers, and 6 residents in the parking house. Using the aggregated D arrays, we can compare the occupancies of different parking houses for a specific category of customers. PBRS provides a recommendation for a parking house P as follows: All the data related to parking houses are retrieved from the system database. They are distinguished in two groups, based on their Euclidean distance from P. The ones geographically close to P, and those far away from P (which are ignored). For each parking house Q close to P, the aggregated C array of Q is computed by averaging the corresponding C arrays over all available days. This aggregated C array is perceived as the “fingerprint” of Q and it is used for comparing it with P, according to the cosine similarity metric. Q is then perceived as competitive to P, if its similarity with P is greater than a given threshold. PBRS produces recommendations to P based on three distinct levels of analysis:

318

S. Kontogiannis et al.

(a) Type-A recommendations: From P’s occupancy data alone, the system can already create recommendations about subcategory spaces (e.g., groups of spots for elderly, disabled, parents with children, etc.) that are underutilized or overutilized. E.g., if the “elderly” subcategory has occupancy over 90% during morning-hours, while another subcategory (e.g., “disabled”) has low occupancy for the same time interval, the system would recommend some rearrangement of parking spaces between these two subcategories for the particular interval. (b) Type-B recommendations: From P’s nearby parking houses’ data, the system creates recommendations about the geographical area’s most dominant customer category. E.g., if P’s most active category of customers does not coincide with the dominant category of customers within the geographical area of P, PBRS would consider lowering a certain price (e.g., long-stay charges), or to promote a targeted offer for specific time slots (e.g., 12:00–14:00), to attract more customers from the dominant category. (c) Type-C recommendations: The performance of P is now compared against the most competing parking houses within P’s geographical area, i.e., those targeting similar customer categories with P. This is done by aggregating the D arrays of each parking house and comparing the occupancy of their most profitable customer category and their pricing policy. Then PBRS would recommend targeted pricing policy fluctuations, based on aggregated offer and demand among P’s competing houses, in the flavor of user-based collaborative filtering.

4 Experimental Evaluation The city of Thessaloniki was chosen by SocialPARK as its main case study. Thessaloniki is the second largest city of Greece with population of 324,766 inhabitants in the municipality 1.12 million inhabitants in the metropolitan area. The densely populated environment of Thessaloniki and the limited public space to serve all urban functions with the consequent impact on the environment and the society, have necessitated an effective utilization of parking space. It is commonplace that one way of devaluing public space in urban environments is by occupying them with parked vehicles, and in particular by abusive/illegal parking, or by vehicles that make more trips while searching for free parking spots. The aforementioned reasons make Thessaloniki an ideal testbed for the SocialPARK ecosystem and our approach for a parking-business oriented recommendation engine. Five private parking houses participated in SocialPARK and provided real-time occupancy data during the experimental evaluation. All of them reside in the center of Thessaloniki, in close distance to each other. We used the data of those parking houses to create recommendations for a hypothetical new parking house P, also supposed to be located in the same geographical area. The data of P were altered so as to try the different scenarios. The experimental evaluation was executed in three phases. In every phase one additional feature of PBRS was added for testing. In the first phase (type-A recommendations), the system created recommendations using only P’s own occupancy data (i.e., a purely content-based approach) and it suggested internal parking-spot rearrangements between underutilized and overutilized subcategories of parking spaces within P.

A Recommendation Engine for a Smart Parking Ecosystem

319

In the second phase (type-B recommendations), the system considered the parkingoccupancy data from the geographical area around P (i.e., a purely demographic approach). Apart from parking-spot transfers between underutilized and overutilized subcategories of spots, the system also recommended to P some changes in targeted categories of customers (after calculating the area’s most dominant categories) and price fluctuations (depending on the occupancies and prices of the parking houses in the geographical area). In the third and final phase (type-C recommendations), the system created recommendations using the data from the area around P, while also computing the relevance of the most competing parking houses of P (i.e., a hybrid method that combines P’s content with demographic data and user-based collaborative filtering). This resulted in more targeted price fluctuation recommendations, since PBRS compared similar parking houses within P’s geographical area. The following example illustrates the first phase. Assume that P has 100 conventional spots, 30 disabled spots and 5 elderly spots. Figure 2 shows the mean occupancy percentage of P’s spot subcategories. PBRS recommended a transfer of spots from the disabled (the lowest occupancy at the peak hours) to the conventional subcategory, since conventional spots have more than 95% occupancy at peak hours. More specifically, PBRS computed the maximum value of the lowest subcategory at the peak hour (the disabled with 65% in this example), and recommended transferring 8 spots (90% − 65% = 25%, i.e., about 8 out of 30) of the disabled subcategory to the conventional subcategory.

Fig. 2. Average occupancies of subcategories in P. The horizontal axis denotes the hours of the day, while vertical axis denotes the occupancy percentages.

The next example illustrates the second phase, where the data of the area around P is also taken into account. P was assumed to be in the city-center of Thessaloniki, close to all the other parking houses. Figure 3 shows: (up-left) shows the mean occupancy of P (red line) and the parking houses in its area (blue line); (up-right) the average parking duration of P (in hours). PBRS deduced that, obviously, P’s target group was not the same as that of a typical customer in its area. The dominant category of the area is

320

S. Kontogiannis et al.

Fig. 3. Left: Mean Occupancies of P (red) and the area around it (blue) throughout the day. Right: Average customer parking duration of P.

shoppers. Therefore, PBRS suggested P to target the dominant customer category within this area, by lowering the price of short-parking durations. The recommended price drop was based on the pricing policy of the most successful parking house for this customer category. In our example, the most successful parking house had a short-parking charge of 3e/hour, while P had 4e/hour. Had the short-stay price been at most equal to the most successful parking house, PBRS would suggest creating a targeted offer during the middle of the day (hours 9:00–16:00), where the difference between P’s and the area’s average occupancies is the greatest, towards attracting more customers. The third example illustrates the final phase of our experimental evaluation. Let us use the same scenario for P as in the previous example. P’s most profitable customer category concerns residents (who park for more than 12 h), who constitute 49% of its clientele. In addition to the recommendations of the previous phase, PBRS would create a recommendation based on the performance of P and its competitors and their pricing policies on that specific customer category. Out of the five parking houses, only one has a similar strategy with P (with their cosine similarity being greater than a given threshold, e.g., 75%). Figure 4 shows their occupancies (upper left chart), their most profitable customer category occupancies (upper right chart) and their pricing policies for longstay charges (bottom chart). Since P, compared to its competitor, has lower occupancy and higher long-stay charge for residents, PBRS recommended a price decrease for longstay charge (e.g., to 17 euros in the example of Fig. 4, which matches that of the most successful parking house.

5 Conclusions In this paper, we presented a parking-business oriented recommendation system, aimed towards parking owners and their parking houses. The spatiotemporal occupancy vectors for each parking house and each geographical area’s corresponding aggregated data are considered as parking-house “fingerprints”, in order to create targeted recommendations based both on historical data and collaborative-filtering techniques, rendering them more competitive and profitable. In the future a more complex way of calculating the competitive parking houses will be considered, instead of the traditional collaborative filtering with cosine similarity.

A Recommendation Engine for a Smart Parking Ecosystem

321

Fig. 4. Upper Left: General mean occupancies of P and its similar competitor houses throughout the day. Upper Right: Mean Occupancies of most profitable customer category throughout the day. Lower Middle: Price policy of their most profitable category.

Acknowledgment. This work was supported by the Operational Program Competitiveness, Entrepreneurship and Innovation (call Research – Create – Innovate, co-financed by EU and the Greek State) under contract no. T1EDK-03616 (project SocialPARK).

References Ibrahim, H.-D.: Car parking problem in urban areas, causes and solutions. In: 1st International Conference on Towards a Better Quality of Life. SSRN (2017). https://doi.org/10.2139/ssrn. 3163473 Keen, P.G.: Decision support systems : a research perspective. Cambridge: Massachusetts Institute of Technology, Center for Information Systems Research, Alfred P. Sloan School of Management (1980) Lei, C., Quyang, Y.: Dynamic pricing and reservation for intelligent urban parking management. Transp. Res. Part C: Emerging Technologies, 226–244 (2017). https://doi.org/10.1016/j.trc. 2017.01.016 Lin, T., Rivano, H., Le Mouël, F.: A Survey of Smart Parking Solutions. IEEE Trans. Intell. Transp. Syst. 3229–3253 (2017). https://doi.org/10.1109/TITS.2017.2685143 Mingardo, G., van Wee, B., Rye, T.: Urban parking policy in Europe: a conceptualization of past and possible future trends. Transp. Res. Part A: Policy Pract. 268–281 (2015). https://doi.org/ 10.1016/j.tra.2015.02.005

322

S. Kontogiannis et al.

Montgomery, A.L.: The implementation challenge of pricing decision support systems for retail managers. Appl. Stoch. Models Bus. Ind. 367–378 (2005). https://doi.org/10.1002/asmb.572 Rahaman, M., Shao, W., Salim, F. D., Turky, A., Song, A., Chan, J., … Bradbrook, D.: MoParkeR: Multi-objective parking recommendation. In: SSDBM 2021: 33rd International Conference on Scientific and Statistical Database Management, pp. 237–242 (2021) Ricci, F., Rokach, L., Shapira, B: Recommender Systems Handboom (third edition). Springer (2022). https://doi.org/10.1007/978-1-0716-2197-4 Rizvi, S.R., Zehra, S., Olariu, S.: ASPIRE: an agent-oriented smart parking recommendation system for smart cities. IEEE Intell. Transp. Syst. Mag. 48–61 (2019). https://doi.org/10.1109/ MITS.2018.2876569 Saleem, Y., Rehmani, M., Crespi, N., Minerva, R.: Parking recommender system privacy preservation through anonymization and differential privacy. Eng. Rep. 3(2) (2021). https://doi.org/ 10.1002/eng2.12297 Shoup, D.C.: Cruising for parking. Trans. Policy 13(6), 479–486. Elsevier, Los Angeles (2006). https://doi.org/10.1016/j.tranpol.2006.05.005 Son, J., Kim, S., Kim, H., Cho, S.: Review and analysis of recommender systems. J. Korean Inst. Ind. Eng. (2015). https://doi.org/10.7232/JKIIE.2015.41.2.185 Tian, Q., Yang, L., Wang, C., Huang, H.-J.: Dynamic pricing for reservation-based parking system: a revenue management method. Transp. Policy 71, 36–44 (2018). https://doi.org/10.1016/j.tra npol.2018.07.007 Tsai, T.-C., Chen, Y.: An IoT based parking recommendation system considering distance and parking lot flow. Int. Conf. Inf. Commun. Technol. Convergence (ICTC), 978–983 (2021). https://doi.org/10.1109/ICTC52510.2021.9620850

A Crowdsourcing Framework for Reporting Available Parking Spots in Urban Areas Grigorios Christainas1 , Dionysios Kehagias1(B) , Athanasios Salamanis1 , Pavlos Spanidis2 , Menelaos Kyrkoy2 , and Dimitrios Tzovaras1 1 Centre for Research and Technology Hellas, Information Technologies Institute, Thessaloniki,

Greece [email protected] 2 Centre for Research and Technology Hellas, Hellenic Institute of Transport, Thessaloniki, Greece

Abstract. Given the concentration of the majority of population in urban areas and the lack of available public space, car parking has evolved as a major problem for big cities in recent years. To address the issue, several approaches have been proposed including incentivisation mechanisms for increasing the use of public transport, carpooling for reducing the number of vehicles in the urban traffic network and crowdsourcing for reporting in real-time available free parking spots in the network. Towards the last direction, in this paper we introduce a novel crowdsourcing framework for reporting and allocating available free parking spots in an urban traffic network. The framework provides the necessary services for a user to report an available free parking spot discovered in the network, it distributes in real-time to all registered users the information about all available free parking spots in the whole network, as well as it evaluates the credibility of the received crowdsourcing information using an advanced probabilistic algorithm. Moreover, the proposed framework places special emphasis on the visualization of the parking availability (i.e., reported from and to the users) using appropriate coloring schemes with varied opacity levels. The proposed crowdsourcing framework is part of the platform developed in the context of the national research project Social Park. Keywords: First keyword · Second keyword · Third keyword

1 Introduction The concept of crowdsourcing firstly appeared in 2006, in a Wired magazine article “The Rise of Crowdsourcing” [1], by Jeff Howe in order to utilize the full potential of a large number of people in order to undertake a variety of tasks and make an effective contribution to solving complex problems, as well as to the creation of new products. In a later attempt to give an accurate definition of the term, researchers Estellés-Arolas and González-Ladrón-De-Guevara [2] analyzed existing definitions based on their common characteristics and defined the concept of crowdsourcing as an online activity in which © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_28

324

G. Christainas et al.

a certain number of people volunteer for a job, while offering heterogeneous skills and resources to complete it. As a reward, they receive a kind of a prize whose nature varies between financial reward, social recognition and personal satisfaction. Crowdsourcing can be materialized in various forms such as Crowd funding, crowd creation, crowd voting, crowd wisdom, etc. In recent years there has been an increasing number of companies that turn to the utilization of the crowd through payroll to outsource their work and solve their individual problems, the nature of which varies and extends to different sectors. Some illustrative examples of population-based applications are presented in the next section. Intelligent Transportation Systems (ITS), are associated with the use of information and communication technologies in the field of transport. The research work of Małecki, Iwan and Kijewska [3] states that ITS are characterized by complex architectures that incorporate different types of technologies and include several functions in areas such as continuous information and traffic management, public transport as well as emergencies. Efficient and fast collection of real-time information is one of the main points for the implementation of ITS. However, the wide coverage of the application areas of ITS makes it necessary to have a large number of sensors and cameras, which significantly increases the cost of implementation and the reliability of these systems. In addition, unforeseen emergencies are not easy to detect, as these technologies mainly detect changes in vehicle speed and traffic flow. Among the solutions that tend to be utilized for this purpose is the population tourism, with its use increasing more and more in both commercial and research fields [4–6]. The integration of GPS in all smart devices combined with technological developments in communication networks, have facilitated the collection of information by users in real time. This information, in combination with static information, can enhance the operation of an ITS. In addition, users are given the opportunity to provide real-time information on emergencies on the road network, helping to inform other users to avoid congested routes and delays. This paper describes how to integrate crowdsourcing for the declaration of availability of freely accessible parking spaces in the SocialPARK ecosystem. Specifically, it describes the process of a crowdsourced declaration and confirmation of availability of parking spaces by users, by listing a usage scenario of the SocialPARK application that has been developed for mobile devices. In addition, a description is provided of how the population report is recorded and stored in the database, and finally the process of verifying the reliability of the information collected by users is described. The following section outlines a list of representative crowdsourcing applications for ITS. Section 3 describes in sufficient detail the crowdsourcing application developed in the context of the SocialPARK project. Section 4 describes the theoretical foundation and evaluation results of the information validation mechanism that we developed in order to validate the credibility of the information provided by the crowdsourcing mechanism. Finally, Sect. 5 concludes the paper.

2 ITS Related Crowdsourcing Applications In what follows, we present some examples of ITS, which have integrated the crowdsourcing model into their operation. For a more exhaustive list of crowdsourcing applications in ITS, the interesting reader may refer to [7].

A Crowdsourcing Framework for Reporting Available

325

Moovit [8] offers comprehensive mobility and transportation information services in more than 3,000 cities in 94 countries around the world. Combining data from public transport services as well as information provided by users, the application provides opportunities to find the optimal route to a destination and provide real-time information on the arrival times of means of transport (bus, metro, etc.), but also emergency alerts. The data collection relies heavily on the community of volunteers (known as “Mooviters”), who contribute to updating the app’s data by adding stations, routes, and means of transportation. Waze [9] is a free navigation application that provides real-time updates on a wide range of traffic issues through the use of the crowdsourcing model. More specifically, users are given the opportunity to provide real-time information ranging from updates on traffic conditions, roadblocks due to project execution and the existence of an accident, to fuel prices at local gas stations. All the information provided is used by the application in order to inform users and redirect them when necessary. Navmii GPS World [10] (Navfree) is a free navigation application from Navmii, which provides additional, real-time information to drivers about the conditions prevailing in the traffic network. The collection of necessary map data and traffic information is done utilizing existing technologies such as OpenStreetMap [11], but also population data provided by users in the application. Users can report an event they have encountered at some point along the way, while the application provides real-time information to other users as well as the ability to redirect their route to avoid any delays. One of the most popular applications for providing real-time public transport data is Transit [12]. It started operating in 2012 and was based on the massive collection of public transport data (e.g., bus and train locations, arrival delays at stops, etc.) by users, and their feedback to other users. In particular, by boarding a vehicle, users are able to to transmit their location in real time (by automatically sending the current location of their device), thus making it possible to accurately determine the location of the vehicle. In addition, the application allows someone to view a list of users who provide public transport data, while providing access to additional transportation options such as bike-sharing, car-sharing, etc. The SpotAngels application [13] is the most relevant to our application, as its aim is to facilitate users in the process of finding available parking spaces. The application presents both freely available and available parking spaces, while at the same time providing information to users about the applicable parking regulations (e.g. existence of controlled parking on a road, permitted parking time limits, etc.), sending alerts in case their vehicle needs to be moved during parking. The data of the current parking regulations is collected through bulk reports from the community of users of the application, who can inform during their parking for any renewals in case the validity of the regulations has changed. When a user is parking, the app automatically saves user’s location using the vehicle’s Bluetooth sensor or the GPS position it receives from the mobile phone. When the application is restarted, users are asked about possible available spaces next to their parked vehicle, when they leave the parking space, which is automatically shown as available to the other users of the application who are searching for a parking space. Furthermore, the application allows for booking and purchasing parking spaces, through

326

G. Christainas et al.

third-party online booking platforms. Screenshots of the application’s GUI are shown in Fig. 1.

Fig. 1. Find and book parking in the SpotAngels application.

3 The SocialPARK Crowdsourcing Application Given the parking space availability information, provided through its crowdsourcing mechanism, the SocialPARK platform offers a set of useful services to its end users. These services include, booking a public or private parking spot, routing and navigation to the selected parking spot, rewarding based on the reporting frequency, analysis of spatiotemporal parking data, promotion of offers for private parking spaces, co-management of private parking spaces (along with the parking spaces’ providers) and Park-n-Ride. A more detailed description of the SocialPARK integrated platform can be found in [14]. The crowdsourcing mechanism is materialized in the SocialPARK mobile application through a set of UIs and a corresponding cloud-based backend that is accessible by the app through a RESTful API. From the functionality point of view, the end user selects to report on the SocialPARK mobile app a new parking spot identified while driving in an urban area. The specific information reported by the end user includes the location of the parking spot (identified by providing the specific address), a comment in textual form describing the spot, and a photo (captured by the user’s mobile phone). The location of the parking spot is stored in the internal data repository as GeoJSON object, while the textual description and the photo as plain text and a media file split in chunks using the MongoDB GridFS mechanism, respectively. Figure 2 depicts the main steps of the crowdsourcing process through the relevant UI. The main menu screen of the SocialPARK mobile application presents a set of options

A Crowdsourcing Framework for Reporting Available

327

to the user. Of these, the “Parking Space Registration” allows the sharing of information about an available parking space identified by the user. Through this option the screen shown in Fig. 2 (b) is shown up. At this step the user can fill in relevant information about the parking spot they wish to register in the system. Information may include: postal address, either by typing it or by automatically retrieving it through GPS, and comments in natural language. The process of registering an available parking space is completed by tapping on the “Confirm” button. The user is then notified with a successful login message and the application redirects them back to the main menu. The registered parking space is now available by any other user connected to the application. Through the “Search destination” option of the main menu, any other user can come across the previously registered parking space, by utilizing the search facility provided by the app, as shown in Fig. 2 (c). After that, the user may proceed on booking the available parking space.

Fig. 2. Registration of available parking spaces through the SocialPARK application: (a) Main menu, (b) selection of an available parking spot, (c) other users can come across the parking spaces that have been registered by crowdsourcing users.

4 Crowdsourced Information Validation Mechanism One of the main shortcomings of similar crowdsourcing application, as the ones mentioned in Sect. 2, is the lack of an appropriate validation mechanism in order to evaluate and validate the credibility of the information that is being pushed by the user. One

328

G. Christainas et al.

common approach for tackling this shortcoming is to introduce a user rating mechanism that would allow users to rate too low other users who inject false information into the network. However, such a mechanism would not prevent malicious users from forming coalitions in order to materialize a Denial of Service type of cyber-attack. Hence more efficient mechanisms are necessary. In order to handle this, we utilize our previous work on probabilistic validation models [15], which was proven to exhibit robust performance both in real and simulated scenarios of users who are moving in a city both as drivers or pedestrians and report various traffic and weather related events in real time. In the context of the SocialPARK project, we extend the application of the previous framework as a means for characterizing the trustworthiness and reliability of the parking space that has been registered through the crowdsourcing mechanism, taking into account both the credibility of the user and the geographical location of the parking space with respect to the one of the reporting user. In addition to that, we further evaluate the performance of our probabilistic framework in various simulated scenarios. Furthermore, SocialPARK utilizes two rewarding schemes developed in the context of the project, which incentivize users to behave truthfully [16–18]. Both schemes use credits that users can earn as rewards for providing parking availability information. 4.1 Theoretical Background As described in detail in [15], Eq. (1) calculates the probability that a report e submitted by a crowdsourcing user is true, given a set of N conditions d 1 , d 2 , …, d N , p(e|d1 , d2 , . . . , dj , . . . , djk , . . . , dL ) = 1 − A

N 

p(dl |e)p(e)

(1)

l=1

In Eq. (1) the notation d jk describes two possibly correlated conditions, p(e) is the probability that event e is false and A is given by Eq. (2). A=

1     p(d1 )p(d2 ) . . . p dj . . . p djk . . . p(dN )

(2)

After assigning a reliability value to a user’s report, the reliability validation mechanism assigns a reliability score L to the registered parking space, which is calculated according to Eq. (3): k L=

i=1 Si ri

+ Wa k + Wb m , Wtot

(3)

where k is the total number of reports of all users in the current period relating to the reliability assessment for that particular parking space, m is the total number of unique users who have reported for that particular parking space in the period of time relating to the evaluation, S i is the reliability value of the user who made the report i, r i the reliability value of the report i and W a and W b parameters that are properly determined when configuring the algorithm to give the corresponding weight to the variables k and

A Crowdsourcing Framework for Reporting Available

329

m, respectively. The variable W tot is derived by Eq. (4). Wtot =

K 

Si + Wa k + Wb m

(4)

k=1

After the above calculations are completed, the reliability verification algorithm is executed according to the following steps. 1. We specify the exact time t 0 , which is considered the starting point of the reliability verification algorithm. 2. Every t = 1 h, evaluation of all user reports submitted in the previous hour [t-1, t] is conducted against their reliability, from Eq. (1) and (2). The reliability score value that is calculated will also be the reliability score of the reports that a user will submit in the SocialPARK ecosystem for the next period [t, t + 1]. The specific value of each reference is stored in the corresponding entry in the database. 3. Every V = 24 h, the evaluation of the total reliability S of the users themselves is conducted. The updated trust value is stored in the user profile in the database. 4. Every T = 15 min, or on every new user report submitted within the specified time period, the reliability score L of the registered parking space is calculated from Eq. (3). The updated reliability value is stored in the database entry for the parking space. In the special case in which a new user has not yet submitted any previous parking space registrations, a time period of 48 h is defined, during which the evaluation of the users’ reliability is carried out under the following conditions: • If the number of reports made by the user is greater than a customizable upper limit of reports within one hour, then these are considered false (for example, entering more than one hundred reports within one hour will be considered malicious behavior) • If the first condition does not apply, a check is performed if the number of confirmed reports (reports used by other users of the app to park) is less than a predefined threshold (for example, if the confirmed reports are less than 30% of the total number of reports made by the same user within one hour), then the user’s behavior is considered malicious. After the end of the specified period, the reliability assessment is carried out normally taking into account all the parameters presented above. 4.2 Evaluation Results In this section we present results after conducting evaluation of the aforementioned crowdsourcing information validation mechanism. For this purpose, we designed a five simulation scenarios that cover the most common cases, of malicious user behavior, as shown in Table 1. In each one of them, we assume a certain number (small or large) of reports, i.e., parking space registrations, generated and submitted by a user suspicious

330

G. Christainas et al.

of being malicious, and a corresponding number of positive confirmations by the other users on the validity of the registered information. Each simulation is considered to be performed for a period of 15 days and at the end of each day the reliability value is calculated for the user based on his behavior is calculated and presented. Also, in order to capture potential changes in the behavior of malicious users, we introduce a turning point in time (e.g. on day 7), after which the malicious users alter their behavior, i.e. users who, initially behaving correctly, begin to behave maliciously from that point on. Table 1 describes the details of each simulation scenario. Table 1. Simulation scenarios for capturing malicious behaviour. No.

Behavior before the turning point

Behavior after the turning point

1

Small number of user reports and large number of confirmations by other users

Large number of user reports and small number of confirmations by other users

2

Large number of user reports and small number of confirmations by other users

Small number of user reports and large number of confirmations by other users

3

Small number of user reports and small number of confirmations by other users

Large number of user reports and large number of confirmations by other users

4

Large number of user reports and large number of confirmations by other users

Small number of user reports and large number of confirmations by other users

5

Small number of user reports and large number of confirmations by other users

Large number of user reports and zero number of confirmations by other users

For the creation of the simulation data we are based on observations on data generated by real users during the pilot phase of the project. As real data do not include malicious reports, we had to create synthetic data. We did so, by performing random selection with uniform distribution within plausible intervals (based on observations on real data) of low and high numbers of reports of one user and the corresponding confirmations submitted by other users. The charts plotted in Figs. 3, 4, 5, 6 and 7 illustrate for each scenario the user reliability score for every day and for the whole period of 15 days that overall simulation lasted. By the reliability scores shown in Figs. 3, 4, 5, 6 and 7, it is seen that reliability verification algorithm manages to respond correctly to the various changes of user behaviors. It is worth noting how the algorithm performs in scenario 5. As it is shown in Fig. 7, that scenario simulates malicious users who after the turning point of 7 days, they start creating a large number of reports, which nevertheless are not confirmed by any other user. In this case, the reliability score is quite low, as opposed to the first half of the curve that has a value above average.

5 Summary and Conclusions This paper presented the crowdsourcing mechanism that was developed in the context of the SocialPARK project in order to handle registration of available parking spaces by

A Crowdsourcing Framework for Reporting Available

331

Fig. 3. Reliability score of malicious users according to scenario 1, when the turning point is set to 7 days.

Fig. 4. Reliability score of malicious users according to scenario 2, when the turning point is set to 7 days.

Fig. 5. Reliability score of malicious users according to scenario 3, when the turning point is set to 7 days.

332

G. Christainas et al.

Fig. 6. Reliability score of malicious users according to scenario 4, when the turning point is set to 7 days.

Fig. 7. Reliability score of malicious users according to scenario 5, when the turning point is set to 7 days.

the crowd. As opposed to other similar ITS applications, which exhibit crowdsourcing mechanisms for massively collecting data generated by the interconnected users, our crowdsourcing framework is equipped with a validation process based on a previously defined theoretical probabilistic framework. In this paper we extend our validation mechanism, which comprises a core component of a newly designed reliability verification algorithm, in order to validate the parking spaces that users register as available within the SocialPARK application. We also conduct evaluation of the verification mechanism and the reliability score that is produced for each user report, using synthetic data. Evaluation results show that the proposed validation mechanism properly captures changes in user behavior, when e.g. a member of the crowd turns into a malicious user injecting false data in the network. This shows the practical value of the proposed mechanism in the general context of ITS crowdsourcing applications, based on its inherent qualities that make it more effective compared to other validation mechanisms. In particular, the proposed model is far from simplistic since it requires the calculation of the reliability of all submitted reports during a specific period in order to calculate the reliability of a user.

A Crowdsourcing Framework for Reporting Available

333

This is in contrast with many simple user reputation systems used in real-world systems. On the other hand, from a computational point of view, the calculations involved in the proposed validation mechanism are essentially sums and products that can be efficiently performed in both off-line and on-line (e.g., whenever a new report is submitted to the system) settings. In addition, the proposed model takes into account the opinion of all registered users for the calculation of the reliability of reports submitted by a particular user during a specific time period, and not only the rough number of submitted reports. This means that in the SocialPARK platform, it is not enough for a user to just submit reports frequently in order to maintain a high reputation within the system, but the submitted reports should also be verified and validated by the SocialPARK community. However, one of the main difficulties of this mechanism is related to the absence of an effectively large number of initial user reports, which will then be used for their further evaluation. Thus, an appropriate “cold start” period, in which users’ evaluation will be less stringent, should be selected carefully. Future work includes experimentation with real data and further testing with more complex malicious user attack scenarios, in comparison with similar systems. Acknowledgments. Work presented in this paper was co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation (call RESEARCH–CREATE–INNOVATE) under contract no. T1EDK-03616 (project SocialPARK).

References 1. The Rise of Crowdsourcing. Accessed 26 April 2022. (2006) https://www.wired.com/2006/ 06/crowds/ 2. Estellés-Arolas, E., González-Ladrón-de-Guevara, F.: Towards an integrated crowdsourcing definition. J. Inf. Sci. 38(2), 189–200 (2012) 3. Małecki, K., Iwan, S., Kijewska, K.: Influence of intelligent transportation systems on reduction of the environmental negative impact of urban freight transport based on Szczecin example. Procedia Soc. Behav. Sci. 151, 215–229 (2014) 4. Ali, K., Al-Yaseen, D., Ejaz, A., Javed, T., Hassanein, H.S.: Crowdits: crowdsourcing in intelligent transportation systems. In: 2012 IEEE Wireless Communications and Networking Conference (WCNC), pp. 3307–3311 (2012) 5. Misra, A., Gooze, A., Watkins, K., Asad, M., Le Dantec, C.A.: Crowdsourcing and its application to transportation data collection and management. Transp. Res. Rec. 2414(1), 1–8 (2014) 6. Wan, X., Ghazzai, H., Massoud, Y.: Mobile crowdsourcing for intelligent transportation systems: real-time navigation in urban areas. IEEE Access 7, 136995–137009 (2019) 7. Wang, X., Zheng, X., Zhang, Q., Wang, T., Shen, D.: Crowdsourcing in ITS: the state of the work and the networking. IEEE Trans. Intell. Transp. Syst. 17(6), 1596–1605 (2016) 8. Moovit Homepage. Accessed 26 April 2022. https://moovit.com/ 9. Waze Homepage. Accessed 26 April 2022. https://www.waze.com/ 10. Navmii Homepage. Accessed 26 April 2022. https://www.navmii.com/ 11. OpenStreetMap Homepage. Accessed 26 April 2022. https://www.openstreetmap.org/about 12. Transit Homepage. Accessed 26 April 2022. https://transitapp.com/ 13. SpotAngels Homepage. Accessed 26 April 2022. https://www.spotangels.com/

334

G. Christainas et al.

14. Christainas G., Kampyli A.-M., Kehagias D., et al.: SocialPARK: an integrated Parking-asa-Service ecosystem. In: Proceedings of the 14th ITS European Congress, Toulouse, France, 30 May–1 June 2022 (in print) (2022) 15. Salamanis, A., Drosou, A., Michalopoulos, D., Kehagias, D., Tzovaras, D.: A probabilistic framework for the reliability assessment of crowd sourcing urban traffic reports. Transp. Res. Procedia 14, 4552–4561 (2016) 16. Zaroliagis, C.: SocialPARK: an incentivizing crowdsourced parking ecosystem. IEEE Smart Cities eNewsletter (2021) 17. Kampyli, A., Kontogiannis, S., Kypriadis, D., Zaroliagis, C.: Incentivizing truthfulness in crowdsourced parking ecosystems. In: IEEE International Smart Cities Conference—ISC2, pp. 1–7 (2021) 18. Kampyli, A., Kontogiannis, S., Zaroliagis, C.: Rewarding schemes for crowdsourced parking ecosystems. In: 31st European conference on operational research—EURO, p. 216 (2021)

A Review of Use Cases of Gamification in Mobility Systems and Services Luís Barreto1(B)

, António Amaral2,3

, Teresa Pereira4

, and Sara Paiva1

1 ADit-Lab, Instituto Politécnico de Viana Do Castelo, Rua Escola Industrial E, Comercial

Nun’Álvares, 4900-347 Viana Do Castelo, Portugal [email protected] 2 Instituto Superior de Engenharia Do Porto, Rua Dr. António Bernardino de Almeida, 431, 4249-015 Porto, Portugal [email protected] 3 INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal 4 Departamento de Sistemas de Informação, Universidade Do Minho, Campus de Azurém, 4804-533 Guimarães, Portugal [email protected]

Abstract. Nowadays, Mobility, in all its dimensions (transport mobility, sustainable mobility, active mobility, and Mobility as a Service (MaaS)), is an essential dimension in sustainable development goals, allowing to increase in the quality of life, the health, the social inclusion and to reduce climate action in any society. To increase the citizens’ awareness and promote a true behavioral change, the citizens need to feel part of the process. Gamification has proved to be effective in raising citizens’ awareness, encouraging their participation, and promoting a gradual but profound behavior change in various areas such as participatory governance, tourism, culture, education, etc. Gamification can also propel a Smart Living Society 5.0 among the younger groups of the society, especially in the context of academic communities that are more knowledgeable and eager to foster a healthier, more sustainable, and more inclusive society. Smart Living Society 5.0 is an activity in the scope of the TECH - Tecnologia, Ambiente, Criatividade e Saúde - a project of NORTE 2020, focusing on creating an Academic MaaS (AMaaS). At this stage, it is essential to know about gamification use cases related to new mobility solutions and practices. The paper presents successful cases of mobility systems and services that consider gamification to promote and incentivize their use concerning active mobility and sustainable mobility; it discusses the potential of gamified systems to achieve a gamification proposal approach to implement in the AMaaS under development. Keywords: Mobility · Maas · AMaaS · Gamification · Smart living

1 Introduction Urban and suburban areas are growing significantly, throughout time, and this tendency has been consolidated. Commonly, these areas offer more economic perspectives and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_29

336

L. Barreto et al.

job opportunities, as well as access to cultural and leisure activities. However, at a certain point, these areas’ performance will be affected by mobility policies, physical infrastructures, and technological solutions available. When such mobility options are not adequate, individual motorized traffic (IMT) increases, introducing new problems such as more energy consumption and more greenhouse gas emissions. Thus, more sustainable, healthier, and socially fair mobility is needed [1], towards enduring a prosperous and sustainable development of cities. The new generations are changing the way mobility is perceived. The use of shared bicycles and cars, for example, by using ride-sharing services, is increasing in Europe. Car ownership is becoming less critical, and car use is also decreasing in some regions [2]. Thus, younger generations are crucial elements in changing mobility behaviors within a society [3]. However, according to [4] mobility choices of younger people are not always influenced by a more sustainable vision or healthier habits but are strongly influenced by parents, and underlying values such as ‘Freedom’ and ‘Comfortable life’. It is vital to induce more sustainable and healthier mobility through the shift in attitudes amongst this type o public. Higher Education Institutions (HEI), having a single or multiple location campus, can have an essential role in shaping the way their students foresee mobility, especially in commuting trips, and in creating mechanisms for changing their students’ mobility behavior. Students usually make commuting trips, during the week between their household or their school week location, to the main HEI location thus it is essential to promote that such trips can be made by a combination of different modes of transport, such as public transport, riding, and sharing a bicycle, sharing a car, walking and also be prepared for future transportation modes that are not invented yet [5]. Nowadays, Mobility, in all its dimensions (transport mobility, sustainable mobility, active mobility, and Mobility as a Service (MaaS)), is a vital dimension towards the attainment of sustainable development goals, allowing the increase in the quality of life, the health, the social inclusion and to reduce climate action in any society. To increase the citizens’ awareness and promote real behavioral change, the citizens and the communities need to feel part of the process. Although has been proof those changing behaviors is a very difficult and challenging task, HEI should invest in mechanisms that can propel a more sustainable and healthier behavior amongst its academic communities. New paradigms for changing attitudes and behaviors have emerged, and gamification is one such method. Gamification can thus propel a Smart Living Society 5.0 (SLS 5.0), among the younger groups of the society, especially in the context of academic communities, that are more knowledgeable and eager to foster a healthier, more sustainable, and more inclusive society. Gamification can be defined, in a simple manner, as applying game design principles to a non-gaming context [6]. Aware of such challenge the Polytechnic Institute of Viana do Castelo (IPVC), in Northern Portugal, has designed a Smart Living Society 5.0 an activity in the scope of the TECH - Tecnologia, Ambiente, Criatividade e Saúde - a project of NORTE 20201 , with the focus to create an Academic Mobility-as-a-Service (AmaaS). This research activity is a collaborative activity, participating two Portuguese HEI, the Polytechnic of Viana 1 https://www.norte2020.pt/.

A Review of Use Cases of Gamification in Mobility

337

do Castelo and the Polytechnic of Porto. The defined AmaaS aims to propel a change in the students’ behavior and mobility choices, that can be more sustainable and healthier by using, for example, active mobility services and systems. It needs to be pointed out that the HEI teachers and other employees can also embrace the AmaaS as members of the academic community. Thus, it is essential to know about gamification use cases related to new mobility solutions and practices. The paper presents successful cases of mobility systems and services that consider gamification to promote and incentivize their use, concerning active mobility, MaaS, and sustainable mobility; discusses the potential of gamified systems, in combination with new mobility solutions, in terms of citizens’ engagement and behavior change and current limitations and future research challenges are also discussed, to achieve a gamification proposal approach to implement in the AmaaS under development. The paper is structured as follows. First, in Sect. 2, a theoretical background is presented to establish concepts. Then, Sect. 3 summarizes the successful cases of mobility systems and services that consider gamification to promote and incentivize their use, concerning active mobility and sustainability. The penultimate section, Sect. 4, discusses the potential of gamified systems to achieve a gamification proposal approach to implement in the AmaaS under development. Finally, Sect. 5 presents the conclusions and future developments.

2 Theoretical Background 2.1 Sustainable Mobility Since the EU introduced the concept of sustainable mobility in its 1992 Green Paper on Transport [1], Sustainable mobility has become an emerging and essential research theme. It incorporates trade-offs between intrinsic characteristics, such as freedom of movement, economic competitiveness, and environmental protection [7]. A sustainable mobility system should use walking and bicycle pathways, inclusive transport infrastructure, public transport, car-sharing systems, and autonomous (electric) vehicles. The main goal of sustainable mobility is to address mobility issues, reduce the use of the private car, and promote access to mobility using different means of transport [8]. Therefore, to foster sustainable mobility, environmental, social, and economic consequences should be reduced (air pollution, noise pollution, road congestion, accidents, and travel costs [9]. A sustainable mobility system or service should foresee the reduction of the environmental impact with the focus on making travel more effective and faster. The main tools that allow achieving sustainable mobility are technology, innovation, and people’s behavior (ICT tools) [10]. It also needs to be pointed out that sustainable transport and mobility are fundamental elements to progress in realizing the promise of the 2030 Agenda for Sustainable Development and in achieving the 17 Sustainable Development Goals (SDGs) [11].

338

L. Barreto et al.

2.2 Active Mobility Active mobility can be defined as being the mobility mode that uses physical activity for a single trip or within a journey combined with public transport [12]. When mentioning active mobility, it is understood that walking and cycling and their variants, like skateboarding, roller skating, and using a wheelchair, are the primary means of travel [13]. Active mobility thus refers to non-motorized forms of transport. Currently, active mobility is recognized as a mobility category, having the same level of importance as public transport and motorized private transport. Active mobility also supports transport planning ambitions, considering that walking and cycling are space-efficient; these modes of transport are flexible; they cause low individual and societal costs; and, in combination with public transport, they are able to cover almost all mobility needs [14]. The main impacts of active mobility, as a result of physical activity, are improved health and enhanced quality of life. Active mobility can also be a tool to achieve the recommendations of The World Health [15]: at least 150 min of moderate-intensity physical activity per week for adults and at least 60 min of daily moderate to vigorous physical activity for children. This can allow reducing the risks of several non-communicable diseases such as cardiovascular diseases, type 2 diabetes, cancer, dementia, depression, and reduced life-expectancy [15]. Some drawbacks of active mobility are the increased collision risks and higher exposure to air pollution, especially in megacities [16] There are studies that also consider potential harms from increased noise exposure [17]. According to [14] urban planning, transport planning, and public health could and should collaborate for fostering active mobility. Whist they have different goals, but they all have a strong interest in achieving higher active mobility levels. 2.3 Mobility as a Service Mobility as a Service (MaaS) is a concept/paradigm that tries to embrace a new holistic model for mobility that promotes the unification of all modes of transport and mobility services together in a single service/system [18]. A MaaS system intends to reduce traffic volumes, emissions, and congestion in urban areas and increase efficiency in rural areas, as well as create a cost-effective, time-efficient and organized transport system [19]. MaaS’s main goal is to demote the use of the private car and to promote more multimodal, flexible, sustainable, and healthier mobility options, supported by ICT tools. 2.4 Gamification As previously stated, gamification can be defined, in a simple manner, as applying game design principles to a non-gaming context [6]. Gamification has emerged, in the recent years, in many different applications, namely in education, in health, and also in mobility/traveling [20]. Gamification uses interactive online design that influences on people’s competitive instincts and also makes use of some type of reward to drive action, like virtual rewards such as points, payments, badges, discounts, “free” gifts; and status indicators such as friend counts, retweets, leader boards, achievement data, progress bars, and the ability to “level up.”. When using such rewards, gamification could also be

A Review of Use Cases of Gamification in Mobility

339

called Action Gamification [21]. Gamification, thus, has potentially wide applications in contexts such as healthcare, sustainability, government, transportation, and education, among others. A gamification system should use clear progression paths with achievable goals, levels, and rewards, giving players intervention over their actions, making use of strategy and novelty to engage players, providing feedback, making use of social comparison or competition, encouraging cooperation between players, or combining various of these principles [22]. Gamification can be an important tool to create an environment in which individuals are inherently motivated to participate in a subject where behavior change is desirable [23]. Incentives that promote intrinsically rather than extrinsic motivation are considered more effective as they can encourage behavior change as a personal reward. Thus, gamification is used in combination with both psychologically orientated, and design-oriented elements. The combination of such elements can be used to promote new mobility concerns and attitudes, propelling healthier and more sustainable mobility patterns. As previously mentioned, in a simplified view gamification tries to bond two different views: the game design view and the user view; aiming to sustainably promote new attitudes. The bond of those different views, or perspectives, can be described through game elements, game components, game mechanics and game dynamics. According to [24] they can be organized into a hierarchy, in which game elements consist of components, mechanics, and dynamics (see Fig. 1).

Fig. 1. The game element hierarchy [25].

According to [26], the Dynamics are the abstractions related to the task that is being gamified and are used to create the motivation to perform the task and are manifested via Mechanics. The Mechanics are the processes used to drive the users’ actions like goals, rules, settings, types of interactions, and the boundaries of the situation to be gamified, and are presented through the Components, which are extrinsic rewards and feedback features. Game mechanics include: competition, cooperation, challenges, win states, chance and feedback. Game dynamics include: the narrative (good and engaging

340

L. Barreto et al.

storyline), progression, relationship, status, self-expression and altruism. Common components include: points, badges, leaderboards, virtual goods and spaces, achievements and quests. Another important Gamification feature described by Goethe [27] are game aesthetics. Aesthetics include the graphics, animations, realism that are incorporated in the game, according to [27] relates to the sensory phenomena that the player encounters in the game (visual, aural, haptic, and embodied).

3 Use Cases Nowadays, it is understood that mobility, in all its dimensions (transport mobility, sustainable mobility, active mobility, and Mobility as a Service (MaaS)), is an essential dimension in fostering the sustainable development goals, being an important means to increase the quality of life, the health, the social inclusion and to reduce climate action in any society. To promote changes in people’s behaviors and increase their awareness and engagement, they need to feel part of the process. Gamification can then be a driver to improve new attitudes and behaviors regarding mobility choices. It is possible to develop a variety of use cases that promote and engage new mobility patterns using gamification. In this section, we present real-world scenarios that demonstrate gamification’s application to promote new mobility behaviors. For the purpose of this study, we used as methodology an exploratory and descriptive research, thus allowing to study gamification used in the context of mobility. The scenarios were chosen based on their similar characteristics such as: gamification, promotion of mobility changes, promotion of sustainable mobility, lifetime, with a broad variety of users and well documented. 3.1 trafficO2 App The trafficO2 [28] is a smartphone app which aims to improve the urban traffic conditions with the involvement of social networks supported by smartphone technologies. In a sense, it is a Decision Support System. As referred in [28] it is serious game for sustainable mobility, to reduce traffic and pollution, offering citizens a convenient agreement for everyone: prizes in exchange for sustainable moves. Gamification is used to set customized goals based on the users’ mobility habits and earn O2 points that can be exchanged for prizes and discounts from partners and sponsors. It is presented a study regarding the use of the trafficO2 App in the Palermo’s University Campus. The study evidence that the use of the app promotes a real change the students’ mobility choices for their commuting trips, using more sustainable and healthier options for traveling. The trafficO2 App started to be developed in 2012, as a research project under the call “Smart Cities and Communities and Social Innovation” promoted by the Italian Ministry of Education, University and Research. During the first three years, the App and platform were developed supported in three trials, that lasted one month each, and were called Sustainable Urban Values Challenge (SUV Challenge). These trials allowed to collect interesting information about interactions among users, allowing to improve the dynamics proposed by the service. According to [28], the trials represented more than 310 users, that is University students’, that recorded than 9000 routes, with the number

A Review of Use Cases of Gamification in Mobility

341

of sustainable kilometers increasing by 84%. Another important conclusion, shows that eliminating material prizes, within the gamification context, represented a reduction in about 70% of the active users. The trafficO2 App development and research was concluded in 2016. However, the APP is freely available to be tested in other cities and contexts2 . 3.2 Bella Mossa BetterPoints App In 2017, the city of Bologna decided to change its approach reducing CO2 emissions. The city authorities were using penalties to try to reduce CO2 emissions, but that method was not successful. So, the authorities decided to partner with BetterPoints [29] to create Bella Mossa [30] a mobility system with incentives and gamification to encourage people to reduce their daily reliance on car use. The system uses a smartphone app, a digital management platform, and client-facing dashboards. In the beginning, in 2017, Bella Mossa only offered rewards for making journeys by foot, bicycle, bus, train, or carsharing and set up leaderboards for businesses to run workplace challenges. In 2018, they also included different ways to earn points. Users could form teams outside the workplace to compete against friends and family, join new monthly challenges, and participate in a unique challenge for the summer holiday. Bologna’s public transport authority (BPTA) then partnered with more than 100 Local, national, and International brands, providing a great variety of rewards and discounts. The reward points can also be donated to charity. These diverse partnerships were really important for the success of Bella Mossa. According to [31], between March and October 2017, 15,000 people recorded 895,000 sustainable transport journeys, and it was calculated that those journeys mitigated 728 tons of CO2 emissions. Also, according to [31], Bella Mossa appealed to younger people, with 59% of participants between the ages of 18 and 35. This is an important result as a University Campus is mainly frequented by people between 18 and 35 years. To test if it was possible to increase the user engagement, BPTA decided to include more gamification, and it was registered an increase in the user active journeys, demonstrating the power of gamification [31] to reduce CO2 emissions and a tool to create a healthier lifestyle and a more sustainable environment. 3.3 Viaggia Play&Go App Viaggia Play&Go [32] is also a mobile application that promotes smarter and greener mobility choices for a trip, allowing a user to choose between different mobility means and also to use combinations of mobility means, such as walking, riding a bike, using public transport or using private cars. The smarter and greener a trip is, the more points the user earns. If a user wants to earn more points, he/she can participate in personalized challenges allowing to improve his/her score and leaderboard position. According to [32], the APP is freely available and the last Play&Go challenge was available between March and May of 2021. 2 https://www.wepush.org/en/projects/traffico2/.

342

L. Barreto et al.

The application uses gamification for motivational purposes. The app rewards each registered trip with Green Leaves points. The points obtained depend on the kilometers traveled, and the level of sustainability of the transportation means (walking, riding a bike, using a bus and a train) used. The most profitable way of moving is by walking, which gives 10 points per kilometer, followed by the bike, which awards 5 points per kilometer [33]. The app also suggests personalized and weekly challenges, announces final and weekly prizes and uses weekly and global leaderboards as motivational elements. The evaluation of Viaggia Play&Go, available on [32], concludes that very significant results were achieved, with more than 1000 app installations, over 700 active players, over more than 54000 sustainable trips (of which more than 38,000 zero impact ones) were recorded, with more than 2755 million calories spent and 11.1 tons of CO2 emissions saved, reinforcing the promotion of healthier habits and more sustainable trips. The results also show the ability to support citizen participation in long-lasting games and change player behavior: 27% of players were active until the end of the game, and 45% of players tried at least one new sustainable means of transportation. 3.4 greenApes Platform The greenApes platform [34] includes frontend applications, a backend application for the management and an interface with third-party apps and services. With the greenApes app, users can earn points according to their positive behaviors. The points earned, called BankNuts, can then be spent to access rewards offered by local businesses, for fun and learning (while earning extra points), exchanging ideas and best practices with other users, and completing particular challenges. One interesting feature of greenApes is its ability to connect to other apps like Google Fit and Up2GO. The first greenApes implementation was realized as pilot tests in the cities of Florence and Essen in the year 2016. The pilot tests results showed that 72% of participants stated that the use of the platform made them adopt new sustainable behaviors. In comparison, 65% reported they had discovered new sustainable local businesses and initiatives [35]. In 2018, greenApes was chosen by the city of Milan for its “Sharing Cities” project. The main goal of the greenAPes use was to incentivize sustainable living in a city district. At the end of the project, more than 2,000 citizens were registered, who shared 6,000 best practices and claimed more than 70 rewards for their actions. Since 2020, greenAPes has been used in a project, in the Province of Siena that uses gamification with playful competitions between schools and classes, for promoting healthy and sustainable meals in corporate and university canteens [35]. The greenApes platform is successfully being used since 2018 in the city of Prato, as part of the Prato Urban Jungle (PUJ) project that aims to regenerate selected districts of Prato in a sustainable and socially inclusive way by developing urban jungles [36].

4 Discussion The projects mentioned in the previous section show how gamification and gameful experiences can facilitate the adoption of sustainable behaviors and healthier lifestyles.

A Review of Use Cases of Gamification in Mobility

343

Table 1. Apps/Platforms characteristics. Apps/Platforms Characteristics Rewards Challenges Leaderboards Choice Progression Achievements Badges Cooperation trafficO2

Material and virtual

Yes

Yes

No

No

Yes

Yes

No

Bella Mossa

Material and virtual

Yes

Yes

Yes

Yes

Yes

Yes

Yes (teams)

Viaggia Play&Go

Material and virtual

Yes

Yes

No

Yes

Yes

No

No

greenApes

Material and virtual

Yes

Yes

Yes

Yes

Yes

Yes

No

An overview of the main features/of the apps/platforms previously presented is available at Table 1. The Polytechnic Institute of Viana do Castelo is promoting the Smart Living Society 5.0 activity in the scope of TECH- Tecnologia, Ambiente, Criatividade e Saúde - a project of NORTE 2020, with the focus to create an Academic MaaS (AmaaS) application. The AmaaS app is a Mobility as a Service application with the primary goal to promote a more sustainable HEI campus and healthier lifestyles for the students, teachers, and workers between intra campus and also inter campus within the different HEI that compose APNOR3 - North Region Polytechnics Association. The AmaaS app will allow promoting trips with different means of transportation, from walking, riding a bicycle, using public transport (bus and train), and car-sharing amongst and between the campus community, giving reward points for each situation. The commuting trips of the campus students are the most crucial factor with more environmental and health consequences. Thus, it is essential to use, and especially with this kind of public, that generally are between 18 years and 30 years, innovative solutions to promote and propel a natural behavior and attitude change in terms of personal and group mobility. The solution that can be a game-changer is the use of Gamification. Considering the uses cases referred, for the real success of the AmaaS gamification, different stakeholders need to be involved, like regional business, national business, local authorities, and the HEI authority. Also, considering the results of the use cases presented, and to foster more student engagement, the AmaaS app should provide a set of alternative weekly and monthly challenges with different rewards, allowing the users to choose the one they are more comfortable with. This can also be a strategy to improve the retention of active users. Rewards are also a vital element of the AmaaS gamification environment. HEI and users can be rewarded. Users can be rewarded, for example, by changing points by prizes or discounts that are available from the different stakeholders (cinema tickets, theater tickets, museum tickets), by substantial reductions in HEI fees, by smartphones 3 https://www.apnor.pt/pt_index.html.

344

L. Barreto et al.

and tablets and laptops, by healthy meals in the HEI’s canteens. This will induce many commuting students to adopt more sustainable behaviors. The HEI could also be rewarded as more sustainable commuting trips to the campus represent less CO2 emissions and more quality of the air, promoting a healthier environment that can also be returned economically as natural capital credits [37], and then afterward used and exchanged to improve the campus services. The increase in sustainable commuting trips also implies that the campus’ users will opt for more active means of transportation, like walking, cycling, skating, roller-skating, meaning that the number of calories burned by users, will also rise; this having an impact in the health of the users and also an economic impact as such users will decrease their expenses in health-related services, allowing the public health system to reduce costs and improve its services. The health information obtained with the app can also be utilized to produce a report with the correspondent MET (Metabolic Equivalents) of the user’s activity [38], contributing to the prevention of cardiovascular diseases, type 2 diabetes, cancer, dementia, depression, and reduced life-expectancy.

5 Conclusions and Future Work Nowadays, there is a tremendous pressure to have more sustainable cities. Mobility behavior plays a vital role in promoting socially, economically, and environmentally sustainable cities. Also, HEIs have an essential part within their campus to promote more sustainable and healthier mobility behaviors, especially in the commuting trips of the campus’ users. It is thus important to use the technologies to and encourage that mobility behavior change. One of the mechanisms that can be a behavior game changer is the use of gamification. According to the findings discussed in Sect. 4, gamification could be used providing alternative weekly and monthly challenges, with different rewards, allowing to improve the retention of active users – which is sometimes the most critical issue. This engagement through gamification will also allow to increase the use of more sustainable and active mobility means, and consequently improve the users’ health. In this paper, we present successful cases of mobility systems and services that consider gamification to effectively promote and incentivize their use, which contributed to changing the mobility behaviors of their users towards more active and sustainable choices, and as a consequence is also healthier. The paper also discusses the potential of gamified systems to achieve a gamification proposal approach to implement in the AMaaS under development. To evaluate the engagement effect of the AMaaS under development for changing mobility patterns and behaviors within the community, we are planning, as future research, to carry out a real-world scenario test within the IPVC’s campus. There is a significant limitation in this work that could be addressed in future research, that is not having an AMaaS application fully developed with the gamification feature that could allow studying how the gamification system is promoting the engagement, for how long the engagement remains, and what mobility options are being used. Simultaneously, a profile assessment will be conducted to determine each user’s level of competitiveness towards choosing the strategies that could better result for each individual or a group, to identify the proper type and amount of stimulus that could increase the odds of the intended behavioral change.

A Review of Use Cases of Gamification in Mobility

345

Acknowledgments. This work is funded by the European Regional Development Fund (ERDF) through the Regional Operational Program North 2020, within the scope of Project TECH Technology, Environment, Creativity and Health, Norte-01–0145-FEDER-000043.

References 1. UN. Habitat: Planning and Design for Sustainable Urban Mobility: Global Report on Human Settlements. UN Habitat (2013) 2. Heinonen, J., Czepkiewicz, M., Árnadóttir e J. Ottelin, Á.: Drivers of car ownership in a car-oriented city: a mixed-method study. Sustainability 13, 1–26 (2021) 3. Casadó, R.G., Golightly, D., Laing, K., Palacin e L. Todd, R.: Children, young people and Mobility as a Service: opportunities and barriers for future mobility. Transp. Res. Interdisc. Perspect. 4 (2020) 4. Johansson, M.: Mobility as a Service: exploring young people’s mobility demands and travel behavior (Dissertation). http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215509 5. Gebhardt, L., Krajzewicz, D., Oostendorp, R., Goletz, M., Greger, K., Klötzke, M., Wagner e D. Heinrichs, P.: Intermodal urban mobility: users, uses, and use cases. Transp. Res. Procedia 14, 1183–1192 (2016) 6. Deterding, S., Dixon, D., Khaled, R., Lennart, N.: From game design elements to gamefulness: defining “gamification”. In: Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments (MindTrek ‘11), pp. 9–15 (2011) 7. Whitmarsh, L., Gerger Swartling, Å., Jäger, J.: Participation of experts and non-experts in a sustainability assessment of mobility. Environ. Policy Gov. 19, 232–250 (2009) 8. Schröder, P., et al.: Advancing sustainable consumption and production in cities - A transdisciplinary research and stakeholder engagement framework to address consumption-based emissions and impact. J. Clean. Prod. 213, 114–125 (2019) 9. Gallo, M., De Luca, G., De Martinis, V.: The effects of urban traffic plans on noise abatement: a case study. WIT Trans. Ecol. Environ. 191, 583–594 (2014) 10. Böhm, M., Flechl, B., Frötscher, A.: ICT concepts for optimization of mobility in smart cities. Publications Office of the European Union, 213 11. High-level Advisory Group on Sustainable Transport, “MOBILIZING Sustainable Transport for DEVELOPMENT,” United Nations (2016) 12. Gerike, R., de Nazelle, A., Nieuwenhuijsen, M., Panis, L. I., Anaya, E., Avila-Palencia, I., Boschetti, F., Brand, C., Cole-Hunter, T., Dons, E., Eriksson, U., Gaupp-Berghausen, M., Kahlmeier, S., Laeremans, M., Mueller, N., Orjuela, J.P., Racioppi, F., Raser, E., RojasRueda, D., Schweizer, C., Standaert, A., Uhlmann, T., Wegener, S., Götschi, T.: Physical activity through sustainable transport approaches (PASTA): a study protocol for a multicenter project. BMJ Open 6 (2016) 13. Held, M., Schindler, J., Litman, T.: Cycling and active mobility—establishing a third pillar of transport policy. em Cycling Futures. From Research into Practice, pp. 209–225 (2015) 14. Koszowski, C., Gerike, R., Hubrich, S., Götschi, T., Pohle, M., Wittwer, R.: Active mobility: bringing together transport planning, urban planning, and public health. In: em Towards User-Centric Transport in Europe, Lecture Notes in Mobility. Springer (2019) 15. World Health Organization: Prevention and control of non communicable diseases in the European Region: a progress report. World Health Organization (2014) 16. Mueller, N., Rojas-Rueda, D., Basagaña, X., Cirach, M., Cole-Hunter, T., Dadvand, P., Donaire-Gonzalez, D., Foraster, M., Gascon, M., Martinez, D., Tonne, C., Triguero-Mas, M., Valentín e M. Nieuwenhuijsen, A.: Urban and transport planning related exposures and mortality: a health impact assessment for cities. Environ. Health Perspect. 125, 89–96 (2017)

346

L. Barreto et al.

17. Méline e B. Chaix, J.: Mobility, personal exposure to noise, and blood pressure in hypertensives in the Paris region. Eur. J. Public Health 25 (2015) 18. Aapaoja, A., Eckhardt, J., Nykänen, L., Sochor, J.: MaaS service combinations for different geographical areas. In: 24th World Congress on Intelligent Transportation Systems. Montreal, Canada (2017) 19. Barreto, L., Amaral, A., Baltazar, S.: Mobility in the Era of Digitalization: Thinking Mobility as a Service (MaaS). In: Jardim-Goncalves, R., Sgurev, V., Jotsov, V., Kacprzyk, J. (eds.) Intelligent Systems: Theory, Research and Innovation in Applications. SCI, vol. 864, pp. 275– 293. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38704-4_12 20. Çeker e F. Özdaml, E.: What “Gamification” is and what it’s not. Eur. J. Contemp. Educ. 6, 221–228 (2017) 21. Chou, Y.-k.: Actionable Gamification: beyond points, badges, and leaderboards. Octalysis Media (2015) 22. Sharma, S., Siu, K.W.M.: Gaming as a driver for social behaviour change for sustainability. In: Advances in human factors in wearable technologies and game design. AHFE 2017. Advances in intelligent systems and computing, vol. 608, pp. 258–266. Springer, Cham (2017) 23. Wee, S.-C., Choong, W.-W.: Gamification: predicting the effectiveness of variety game design elements to intrinsically motivate users’ energy conservation behavior. J. Environ. Manage. 233, 97–106 (2019) 24. Werbach, K., Hunter, D.: For the win: how game thinking can revolutionize your business. Wharton Digital Press (2012) 25. Werbach, K., Dan, H.: The gamification toolkit - dynamics, mechanics, and components for the win. Wharton Digital Press, Philadelphia (2015) 26. Toda, A.M., Klock, A.C.T., Oliveira, W., Palomino, P.T., Rodrigues, L., Shi, L., Bittencourt, I., Gasparini, I., Isotani, S., Cristea, A. I.: Analysing gamification elements in educational environments using an existing Gamification taxonomy. Smart Learn. Environ. 6(16) (2019) 27. Goethe, O.: “Visual Aesthetics in Games and Gamification”, em Gamification Mindset, vol, pp. 85–92. Springer International Publishing, Human-Computer Interaction Series (2019) 28. PUSH design lab: TrafficO2. [Online]. Available: http://www.traffico2.com/en/ 29. BetterPoints Ltd.: BetterPoints Ltd—Behaviour change technology. [Online]. Available: https://www.betterpoints.ltd/ 30. Bella Mossa: Bella Mossa—Vivere con lo sport e sostenibilità. [Online]. Available: https:// www.bellamossa.it/ 31. BetterPoints: Bella Mossa: One Italian city, two massengagement programmes—a comparison (2019) 32. Viaggia Trento e Rovereto Play&Go.: Viaggia Trento e Rovereto Play&Go—Smart Community. [Online]. Available: https://www.smartcommunitylab.it/apps/viaggia-trento-e-roveretoplaygo/ (2019) 33. Marconi, A., Ferron, M., Loria, E., Massa, P.: Play&Go, an urban game promoting behaviour change for sustainable mobility. Interac. Des. Architect. J. 40, 24–45 (2019) 34. Fireware: greenApes—The sustainability app,” [Online]. Available: https://www.greenapes. com 35. Fireware: SMART CITIES AND ENVIRONMENTAL SUSTAINABILITY—Sustainable living app welcomes new cities onto digital platform. FIWARE Foundation (2021) 36. Prato, C. d.: Prato Urban Jungle. [Online]. Available: https://www.pratourbanjungle.it/

A Review of Use Cases of Gamification in Mobility

347

37. Leach, K., Grigg, A., O’Connor, B., Brown, C., Vause, J., Gheyssens, J., Weatherdon, L., Halle, M., Burgess, N.D., Fletcher, R., Bekker, S., King, S., Jones, M.: A common framework of natural capital assets for use in public and private sector decision making. Ecosyst. Serv. 36 (2019) 38. Jetté, M., Sidney, K., Blümchen, G.: Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity. Clin. Cardiol. 13(8), 555–565 (1990)

An Innovative Mobile Application for Booking Parking Spots Pavlos Spanidis1(B) , Nikos Dimokas1,3 , Mary Panou1 , George Christainas2 , Athanasios Salamanis2 , and Dionysios Kehagias2 1 Centre for Research and Technology Hellas, Hellenic Institute of Transport, 6th km

Charilaou – Thermi, 57001 Thessaloniki, Greece [email protected] 2 Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou – Thermi, 57001 Thessaloniki, Greece 3 Department of Informatics, University of Western Macedonia, Fourka Area, 52100 Kastoria, Greece

Abstract. Given the concentration of the majority of population in urban areas and the lack of available public space, car parking has evolved as a major problem for big cities in recent years. To address the issue, several approaches have been proposed including incentivization mechanisms for increasing the use of public transport, carpooling for reducing the number of vehicles in the urban traffic network and crowdsourcing for reporting in real-time available free parking spots in the network. Towards the last direction, in this paper we introduce a novel crowdsourcing mobile application for reporting and allocating available free parking spots in an urban traffic network. The mobile application is based on a framework providing the necessary services for a user to report an available free parking spot discovered in the network, it distributes in real-time to all registered users the information about all available free parking spots in the whole network, as well as it evaluates the credibility of the received crowdsourcing information using an advanced probabilistic algorithm. Moreover, the proposed framework places special emphasis on the visualization of the parking availability using appropriate coloring schemes with varied opacity levels. The mobile application encapsulates diverging services to provide a complete solution to the end user of the city. Keywords: Mobile application · Parking · Crowdsourcing · Intelligent transport system · Urban mobility

1 Introduction The rapid technological development in the fields of telecommunications and networks during the past years together with the growing interest in implementing solutions based on crowdsourcing, have led to the development of a large number of crowdsourcing applications [1–5] that cover a wide range of fields. The development of Web 2.0 and the ease of connecting users via the Internet have played a distinctive role in it, allowing their quick and easy support as well as the utilization of their possibilities and ideas for © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_30

An Innovative Mobile Application for Booking Parking Spots

349

the benefit of a common goal. One of the most important challenges, where the existence and the success of the crowdsourcing is based on, is the attraction and participation of a sufficient number of users. Attracting users should be based on appropriate rewards for their participation, either for financial or personal gain. Additionally, proper management and information on the exact scope of the work assigned is required to attract a large number of people and ensure the success in the completion of the project. At the same time, the concentration of the population in large urban centers is increased. The use of technological development by cities for the better provision of services to support the wellbeing of their citizens led to the concept of the “Intelligent City” [6–9]. In addition, the Intelligent Transportation Systems (ITS) are associated with the use of information and communication technologies in the field of transport [10]. The range of technologies used includes a network of sensors and cameras that offer better and faster information to users about traffic conditions and events, while aiming to reduce traffic congestion, as well as the optimal management of transport infrastructure. However, the wide coverage of the areas of application of ITS requires a large number of sensors and cameras, which significantly increases the implementation and application cost. Furthermore, emergencies that may occur cannot easily been detected, as these technologies mainly detect changes in vehicle speed and traffic flow. The everyday use of private vehicles evolved a major problem on the traffic network of the cities. This problem enlarges according to the size of the city. The larger the city, the larger the lack of available parking spaces. According to studies [22], 30% of a city’s traffic is due to drivers looking for a parking space, which contributes to air pollution. Thus, parking management became a major problem and smart parking [23, 24] gains significant interest for both researchers and urban planners. This mobile application aims to effectively address the problem of finding available parking spaces, through the creation of an integrated digital platform for the provision of personalized parking services. This platform unites (in a mutually beneficial way) citizens, parking rental companies and municipalities. The parking availability information is based both on the crowdsourcing real-time contribution of the users and on the promotion and availability of parking services of the commercial users. The rest of the article is organized as follows: Sect. 2 describes the relevant work, while the Sect. 3 presents the evaluation and rewards scheme of the crowdsourcing reports. Section 4 presents the mobile application and Sect. 5 presents the evaluation of the mobile application. Finally, Sect. 6 concludes the article.

2 Relevant Work The efficient and fast collection of information in real-time is of the key aspects of ITS application. Among the solutions that tend to be utilized for this purpose is the crowdsourcing. These days, its use is rapidly increasing in both commercial and research fields [11–13]. The integration of GPS in all smart devices combined with technological development in communication networks, have facilitated the collection of information by users in real-time. This information can enhance the operation of an ITS system in combination with static information. In addition, users are given the opportunity to provide real-time information on emergencies on the road network, helping to inform other users to avoid traffic jams and delays.

350

P. Spanidis et al.

The concept of ITS is not limited to the use of the car as the only mean, but extends to the field of public transport. More specifically, the existence of services for informing the citizens about itineraries and access points and for finding the best route, has enabled the effective saving of time and the improvement of the traveling experience. Through the crowdsourcing, citizens can further contribute by providing real-time information on network conditions, whether they are related to changes in the time of arrival of the means of transport or their completeness. Furthermore, they can provide information on alternative modes of transport, such as access points to stations that offer bicycle-sharing or car-sharing in the area. Additionally, an alternative mode of transportation to which the crowdsourcing is successfully applied is carpooling, that refers to the possibility of a common journey of a large number of people to a destination using the same vehicle. In most carpooling applications, users can state the availability of seats in their vehicles as well as their destination, and share the route with other users of the application who wish to travel to the same or a nearby destination. Moovit application [14] offers comprehensive mobility and transportation information services in more than 3,000 cities in 94 countries around the world. The application provides capabilities to find the optimal route to a destination and provide real-time information on the arrival times of means of transport (bus, metro, etc.) and alert for emergencies by combining data from public transport services as well as information provided by users. The data collection is basically relied on the community of volunteers (known as “Mooviters”), who contribute to updating the application’s data by adding bus stops, routes, and arrival times. Waze [15] is a free navigation application that provides real-time updates on a wide range of traffic issues through the use of the crowdsourcing. In more detail, users are given the opportunity to provide real-time information, from updates on traffic conditions, roadblocks due to project execution and the existence of an accident, to fuel prices at local gas stations. At the same time, the Navmii GPS World (Navfree) application is a free navigation application from Navmii [16], which provides additional, in real-time, information to drivers about the conditions in the traffic network. The collection of necessary map data and traffic information is made utilizing existing technologies such as OpenStreetMap, but also population data provided by users in the application. One of the most popular applications for providing real-time public transport data is Transit [17]. Its operation is based on the general collection of public transport data (e.g., bus and train locations, delays in arrival at stops, etc.) by users, and the feedback of this data to other users by format of updates. In addition, the application allows the user to view a list of users who provide public transport data, while providing access to additional transportation options such as bike-sharing, car-sharing, etc. SpotAngels [18] utilizes crowdsourcing to facilitate users in the process of finding available parking spaces. The application presents both free of charge and commercially available parking spaces, while providing information to users about the applicable parking regulations (e.g., the existence of controlled parking on a road, permitted parking time limits, etc.), sending notifications in case the movement of their vehicle is required during the parking.

An Innovative Mobile Application for Booking Parking Spots

351

The proposed application tries to address the parking spots’ cooperative management. According to our knowledge, there were no other applications available that comanages (a) parking spots of private parking companies – commercial users together with (b) parking spots dedicated to people with disabilities together with (c) crowdsourcing real-time contributed parking spots from the actual users of the system.

3 Evaluation and Reward Scheme of the Crowdsourcing Report The users who update the system about parking spaces availability receive two kind of rewards, a small one when making the statement to the system, and a greater when another user utilizes the information. The implemented approach is similar to the one proposed in [19]. The reward points held by the users are divided into two categories, the points of the common users and the points of the members of the group with disabilities. Common user’s reward points are derived from public parking updates, and all users can access them. The points of the members of the group with disabilities are only for the members of this same group and the common group of users cannot obtain them. The points of the common category users are used by the users for obtaining discounts or additional services in the private parking spaces. The points of the group with disabilities are used to reserve seats for other disabled group users. The main issue that arises in crowdsourcing applications is that of the reliability of information derive from users. The system should reward users who give reliable information and “punish” users who provide false information for personal gain. In a case that a user informs the system that he/she has vacated a position, the speed of the position should also correspond to this description in regards to the verification of the availability of the provided information. The control is completed by monitoring the GPS position for some time after the update. An example for a not reliable information may be when the user informs that he has released a parking space and then his position is either stationary, or moving at a very low speed (e.g., 2km/h), or moving very fast (e.g., at a speed higher than the speed limit of the specific road) for the duration of the inspection. In case the user informs that he/she has parked in a public place, the speed of his position must be low (either he is stopped or he is walking) for some time after the update. Once again, the check is performed by monitoring the GPS position for a short time after the update. Two different time limitations have been introduced for the proper use of the system. • The first limitation concerns the actions “Parking in a previously searched location” and “Vacancy”. These actions should be at least ten minutes apart, so that the malicious user can not inform the system that he has parked, and immediately after he has vacated the place (in order to gain two rewards instead of one). • The second limitation concerns the “Position Declaration of availability” (as an observer). A user’s availability statements should be at least ten minutes apart. This prevents the malicious user from constantly updating the system (possibly with false information, which is not verifiable) to get the small reward.

352

P. Spanidis et al.

3.1 Evaluation of the Crowdsourcing Report Evaluating the crowdsourcing availability of free parking spaces by users also includes delivering a reliability value to each user. The price is determined based on the model proposed in [20]. More specifically, the reliability value of a user is derived from the following equation: S = wf Rf + Wc Rc + Wr Rr

(1)

The Rf parameter refers to the frequency with which the user reports on the platform, based on the premise that users, who report more often, should be rewarded more than those who report less often. The Rc parameter represents the average probability that a user’s report is true, and depends on the circumstances prevailing at the time the report is submitted to the system. These conditions as well as the exact way of calculating the parameter are described below. Additionally, the parameter Rr represents the verification of one user’s reports by other users. Considering that the total number of reports submitted to the platform by all users up to the present time t is M, and the total number of reports submitted by a particular user sub-rating is N, the parameter Rf is given by the following equation: Rf =

N M

(2)

The Rc parameter refers to the degree of user reliability. If a user has submitted N references e1 , . . . , eN to the platform with true probabilities p(e1 ), . . . , p(eN ), then factor Rc is calculated from the following equation: N p(ei ) (3) Rc = i=1 N Finally, the parameter Rr represents the verification of one user’s reports by other users. In particular, a report i of a user may receive Ci confirmations and Ri rejections as to its truth, from other users. If Ci ≥ Ri , is valid then then the report is considered as confirmed by the rest users of the platform. Otherwise, it is considered as rejected. If a user submits to the platform a total of N reports up to the current time t, of which C is confirmed and R is rejected, then the parameter Rr is given by the equation: Rr =

C C = N C+R

(4)

3.2 Rewards Scheme The rewarding procedure of users who provide correct-true information is as follows. The user who shares information to the system, gets extra points if another user utilizes this information. In this way it is confirmed that the information provided was true and the first user is rewarded accordingly. For example, if a user is parked in a public place that another user has informed that he has already released, the latter will receive points when the location is notified and when the first user confirms that he has parked. The earned system points are listed below:

An Innovative Mobile Application for Booking Parking Spots

• • • • •

353

Position Availability Statement (as an observer): 1 point. Parking in a previously sought location: 1 point. Utilization of information for public parking by another user: 7 points. Admission to the disabled group: Each new user of the group receives 50 points. Concession of a private position: (only for the group with disabilities) 5 points/hour of use by another member of the group. The points earned by users are analyzed as follows:

• For private parking spaces, the percentage of the discount and the amount of points that will cost is done in consultation with the respective provider (for example of the order of 15% with 60 points, without the cumulative utilization of more points for the same use). • Private reservation: (only for the disabled group) 5 points/hour.

4 Mobile Application Once the user has successfully logged in to the application, the initial menu appears (Fig. 1a), which includes the options “Destinations”, “Destination Search”, “My Location” and “Parking Availability Statement”. Just before using any of the available service, the user’s profile must be filled in with the required information. In more detail, the user should fill in the tabs “GENERAL” with the general details of the user, “VEHICLE” with the details of the user’s vehicle and “MY PLACE” (Fig. 1b) with the details of his/her parking place. The “Destination Search” option is intended to serve the immediate needs of a driver for parking in the immediate vicinity of his location or other desired address. The search screen consists of a map of the user’s location, which shows the available parking spaces within a radius of five hundred (500) meters, as well as a search criteria box. The radius may change through the sliding line and can be from a few meters up to one kilometer. The criteria include an address entry field (initially, the user’s geographical location is used based on the GPS technology of the mobile device), as well as two switches to filter the search results. The first switch restricts the search to only on-street parking spaces, which have been registered through the public availability declaration function, while the second switch displays only the available parking spaces of private or municipality companies (Fig. 2). The availability check is performed based on the user’s profile and the parking needs the user has registered (disabilities, elderly, parent with child or conventional, if not specifically defined), using the type: Availability = {Large, if (available > X ∗ total) AND (available > 5 Small, for the rest of the cases available = 0 None, for available = 0 where X = 10% is the percentage of total places below which availability is considered low and the available and total places correspond to conventional, disabled, parent with child or elderly.

354

P. Spanidis et al.

Fig. 1. Main menu and location

By selecting a specific point on the map, the application displays the information box of the corresponding parking space. The information of the free crowdsourcing spaces consists of the title/type, the address of the parking space together with the date and time of declaration. For parking spaces by providers (private or municipality companies) the information consists of the title/type, address, availability of seats per driver category (conventional, disabled, young parent with child, elderly), and the pricing information. The user is redirected to the booking screen of the respective location by selecting the relevant information box. The information screen of a free parking space (as seen in Fig. 3a), declared through crowdsourcing, includes information like position type, address, listing date, and optional comments and photograph of the location. When the user presses the “Show Interest” button, he/she is redirected to the “Active Destination” screen where he/she can manage this position. The parking information screen from providers (private or municipality companies) includes the presentation of information about the specific provider, the facilities and the services it provides (Fig. 3b). This information includes: availability, availability of privileged positions, costing and acceptable

An Innovative Mobile Application for Booking Parking Spots

355

Fig. 2. Destination search

payment methods, opening hours, acceptable vehicle types, services provided, offers, general policy and contact information. The “Active Destination” screen (Fig. 4a) displays information about the destination stated by the user, such as the address and the date the report was made. The “Routing” button displays to the user, routing instructions to the selected destination. By selecting “Parking” the user declares his successful parking at the location, which is removed from the “Active Destination” window and placed in the “My Location” window where the user’s current parking location is displayed. The “Release position” button gives the user the option to cancel his reservation or declare his departure from the specific parking space. After the user clicks, the post is released and made available to other users. The option “Declaration of Parking Location” allows the registration of a new free parking space to the user (Fig. 4b). To register a new parking space, the user must enter the address of the location in the search field or state it through the coordinates of his location or enter a pointer to the specific location by long-clicking on the map. Optionally, the user can add information to the post, such as comments or a photo.

356

P. Spanidis et al.

Fig. 3. Booking of parking space

5 Evaluation Twenty users participated in the experiments to evaluate the usability and effectiveness of the mobile application. The evaluation process carried out in two rounds. Each user filled in questionnaires to provide information and feedback after extensive use of the mobile application. A number of scenarios have been introduced to the users to familiarize them with the main functionality of the mobile application before the evaluation process. A user-centralized approach has been adopted on the early stages of the design making the gathering of feedback essential for the development of the application. The evaluation of the mobile application focused on: • Effectiveness • Efficiency • Satisfaction The most significant outcomes regarding effectiveness and usefulness are depicted in Figs. 5 and 6. Figure 5 depicts the usefulness, while Fig. 6 depicts the effectiveness of the application. In case of effectiveness around 85% of the users found the mobile

An Innovative Mobile Application for Booking Parking Spots

357

Fig. 4. Active destination and parking spot registration

application effective. However, the perceived usefulness of the application approaches almost 95%.

6 Conclusions The mobile application exploits the “wisdom of the crowd” for providing parking recommendations which achieve an effective utilization of the entire publicly available parking space within an urban environment. Moreover, the mobile application provides personalized parking services to citizens and groups with special needs, for discovering in real-time and possibly also reserving (whenever this is possible) of free parking positions, as well as navigate-to-park and park-n-ride services. The application supports location search, reservation, cancellation of reservation, processing of personal profile data, implementation of the destination together with the function of co-management of parking spaces of sensitive social groups. Private parking is also supported. All the desired changes of the users during the testing of the application were evaluated and many of them were implemented in order to increase the

358

P. Spanidis et al.

Fig. 5. Usefulness of the mobile application

Fig. 6. Effectiveness of the mobile application

user experience but also to make the application easier and more attractive to the end user. The availability of parking spaces is based on the real-time contribution of the user community, enabling on the one hand the utilization of the freely available parking spaces by the citizens, and on the other hand the more efficient promotion and availability of their parking services. Commercially usable parking spaces. The mobile application proposes an evaluation and reward scheme of the crowdsourcing reports. Acknowledgements. Research supported by SocialPark national research project [21].

An Innovative Mobile Application for Booking Parking Spots

359

References 1. 2. 3. 4. 5. 6. 7. 8.

9. 10.

11. 12. 13. 14. 15. 16. 17. 18. 19.

20.

21. 22. 23. 24.

Topcoder. https://www.topcoder.com/. Accessed 10 April 2022 Freelancer. https://www.freelancer.com/. Accessed 10 April 2022 Crowdspring. https://www.crowdspring.com/. Accessed 10 April 2022 Amazon Mechanical Turk. https://www.mturk.com/. Accessed 10 April 2022 NASA Solve. https://www.nasa.gov/solve/index.html. Accessed 10 April 2022 Caragliu, A., del Bo, C., Nijkamp, P.: Smart cities in Europe. J. Urban Technol. 18 (2011) Giffinger, R., Gudrun, H.: Smart cities ranking: an effective instrument for the positioning of cities? Architect. City Environ. 4 (2010) Washburn, D., Sindhu, U., Balaouras, S., Dines, R.A., Hayes, N.M., Nelson, L.E.: Helping CIOs Understand “Smart City” Initiatives: Defining the Smart City, Its Drivers, and the Role of the CIO. Forrester Research Inc, Cambridge, MA (2010) Harrison, C., Eckman, B., Hamilton, R., Hartswick, P., Kalagnanam, J., Paraszczak, J., Williams, P.: Foundations for Smarter Cities. IBM J. Res. Develop. 54 (2010) Małecki, K., Iwan, S., Kijewska, K.: Influence of intelligent transportation systems on reduction of the environmental negative impact of urban freight transport based on Szczecin example. Procedia Soc. Behav. Sci. 151 Ali, K., Al-Yaseen, D., Ejaz, A., Javed, T., Hassanein, H.S.: CrowdITS: Crowdsourcing in intelligent transportation systems. IEEE Wirel. Commun. Networking Conf., WCNC Misra, A., Gooze, A., Watkins, K., Asad, M., Le Dantec, C.: Crowdsourcing and its application to transportation data collection and management. Transp. Res. Rec. 2414 (2014) Wan, X., Ghazzai, H., Massoud, Y.: Mobile Crowdsourcing for intelligent transportation systems: real-time navigation in urban areas. IEEE Access 7 (2019) Moovit. https://company.moovit.com/. Accessed 10 April 2022 Waze. https://www.waze.com/. Accessed 10 April 2022 Navmii. https://www.navmii.com/. Accessed 10 April 2022 Transit. https://transitapp.com/. Accessed 10 April 2022 SpotAngels: Retrieved November 28, 2019, from https://www.spotangels.com/ (2019) Hoh, B., Yan, T., Ganesan, D., Tracton, K., Iwuchukwu, T., Lee, J.-S.: TruCentive: a gametheoretic incentive platform for trustworthy mobile crowdsourcing parking services. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 160–166 (2012) Salamanis, A., Drosou, A., Michalopoulos, D., Kehagias, D., Tzovaras, D.: A probabilistic framework for the reliability assessment of crowd sourcing urban traffic reports. Transp. Res. Procedia 14 (2016) SocialPark Research Project. https://www.socialpark.upatras.gr/. Accessed 10 April 2022 Shoup, D.: The High Cost of Free Parking. Planners Press (American Planning Association) (2005) Fahim, A., Hasan, M., Chowdhury, M. A.: Smart parking systems: comprehensive review based on various aspects. Heliyon 7(5) (2021) Samir, E.-S., Hosam, E.-S., Islam, T.-E.: Towards the development of smart parking system using mobile and web technologies (2016). https://doi.org/10.1109/IMCTL.2016.7753762

AI Driven Adaptive Scheduling for On-Demand Transportation in Smart Cities Veneta Markovska1 , Margarita Ruseva2 , and Stanimir Kabaivanov2(B) 1 University of Food Technologies, Plovdiv, Bulgaria 2 Plovdiv University “Paisii Hilendarski”, Plovdiv, Bulgaria

[email protected]

Abstract. Artificial intelligence algorithms can be used to automate and improve various processes in public transportation. Using a combination of data sources like positioning devices, ticketing and sales notifications and video surveillance we can obtain details on the load and utilization of transportation network segments. These results can be used not only to improve marketing campaigns and increase quality of transportation services, but also to provide better on-demand transportation options with flexible schedules. In this paper we discuss one such automation system that benefits from delay analysis and real-time processing of video streams. Keywords: Adaptive transportation schedules · AI in transportation · Real-time video processing · Monte Carlo simulation

1 Theoretical Background 1.1 Transportation Scheduling In order to provide efficient schedule for public transportation vehicles, one needs to answer several crucial questions: • What exactly is the reasonable set of criteria in order to consider a solution to be optimal? • How often can we assume that restrictions and parameters used during our optimization procedures will remain unchanged, or at least close to the values we have observed? • How feasible and execution-friendly will be the solution? The list of questions can be extended to include also special needs and restrictions, depending on the city and area where optimization is done. As a result, it is possible that some technically feasible and theoretically sound solutions, do not meet all acceptance criteria and fall short on providing sufficiently adequate answers to all entries in the list. We can greatly benefit from existing studies that focus on optimization with regard to common factors like available transfers [1–3], use of new IoT devices [4, 5] and contemporary traffic management systems [6, 7]. Yet we believe there is a substantial © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_31

AI Driven Adaptive Scheduling for On-Demand

361

potential in providing better decisions, due to the fact that focusing solely on the optimization techniques and used devices can lead to skipping a very important part of the issue – customer satisfaction and convenience of transportation network. If we consider that public transportation also provides a social and welfare support, it is evident that in addition to technical and purely economic criteria we should also consider this special case. There are several directions in which more traditional optimization techniques can be improved, and we use them to suggest an AI-powered solution: • Combine different sources of information and decision criteria. Use of various information sources imposes a technical difficulty as that would require to integrate several systems and to be able to handle distinct inputs and data types. But the gains may more that outweigh the efforts, due to the fact that use of events and notifications from independent sources can improve the accuracy and on-time delivery of crucial inputs (like for example traffic management and notification system and accident reporting can help avoid bottlenecks and choose alternative route whenever possible). • Real-time processing of information. For a public transportation system that operates on a fixed schedule (or one that is updated at sufficiently large periods – typically few months) real-time processing of information is still beneficial, although not crucial. As a result, such a system would exhibit regular delays of different magnitude. Even if schedules are updated frequently, timing issues will still be present due to irregular traffic jams, changes in road and weather conditions. • Use of consumer behavior models and preferences. Consumer-centric approach in transportation schedule optimization is a key factor for generating results that are not only utilizing resources efficiently but also help improve citizen experience and wellbeing. Due to the fact that public transportation system impacts also economic development of individual cities and regions, accounting for consumer behavior and preferences can also boost economic growth and improve long-term development prospects. • Efficient dissemination of information on changes. Changes that aim for better transportation schedules need to be communicated in an efficient and convenient way. Otherwise they will simply not be considered by end users and their positive impact will be reduced. While contemporary means of communication and IT systems can spread information quickly, automating this process and development of proper visual representation can significantly improve the overall use experience and benefits from transportation optimization efforts. Artificial intelligence algorithms can support each of these transformations due to their ability to adapt to changing environment conditions and provide flexible solutions.

362

V. M. Ruseva et al.

These advantages come at a cost, that typically AI systems are harder to understand – this being especially true for deep learning algorithms, and even harder to certify [8]. To overcome this problem, we suggest several easy-to-understand and visualize key performance indicators that allow to benchmark the results of the AI optimization.

2 Building a Model A high-level overview of the model is presented on Fig. 1 and Fig. 2, where we have split the process into two steps: • Data processing and fusion of different information sources (like traffic control and monitoring systems, ticketing). The main function of this step is to combine information from different sources (vehicle location systems, ticketing sales and use, traffic control and video surveillance) into one stream of inputs. At the end of this step the system is able to provide intermediate estimates on the load of the public transportation (thus optimize the available in accordance with it) as well as short-term estimates on the demand.

Fig. 1. Overview of data inputs and first stage of the AI-supported scheduling.

• Use of intermediate load estimates from the first processing steps to create dynamic schedules and monitor their execution. Dynamic schedule generation and calculation of benchmarking KPIs is the second step of the model. Here artificial intelligence algorithms can support the process in speeding-up the optimization, accessing robustness [9] and simulating special conditions to facilitate proactive public transportation management. By combining both stages, as demonstrated on Fig. 3, it is possible to create schedules that track user demand and traffic conditions. Unlike static allocation of resources, this makes it possible to react to changes in customer behavior, and when short-term forecasts are available to even pro-actively deploy available vehicles.

AI Driven Adaptive Scheduling for On-Demand

363

Fig. 2. Overview of the second state of the AI-supported scheduling.

Fig. 3. Combination of different model stages to provide dynamic scheduling

Therefore, dynamic scheduling can help meet the demand with a smaller number of transportation vehicles. As a result, available funds can be re-allocated to acquiring more modern (thus environment-friendly) equipment and introduction of new means of transportation. 2.1 Traceability of Passengers As argued in [8], to be able to get the most of such systems it is necessary to extend the analysis of collected information. No matter how detailed the inputs are, if their processing is limited to simply calculating aggregated values (like for example means or deviations), the benefits will remain limited. To achieve a better segmentation and true understanding of public transportation needs, it is necessary to process in more intelligent way collected data and constantly monitor the changes in the results. In our model, the ability to model and analyze consumer behavior and preferences is a key requirement for economically sound and efficient decisions. Regardless of the decision criteria, if the ultimate need is to achieve sustainable and usable transportation system then end user preferences should be studied at any given moment. But to achieve traceability of passengers we have to define the fine balance between collecting data that can at the same time:

364

V. M. Ruseva et al.

• Comply with regulations about privacy and data protection – e.g., does not violate the General Data Protection Regulation (GDPR) rules. • Is easy to collect and does not require excessive costs for collecting and storing. • Allows to identify groups of public transportation users that are of interest for the analysis. While the list is quite specific and demanding, it is often the case that public transportation monitoring authorities and municipalities already have a lot of the necessary details. However, they are also often spread around and kept in different IT systems. As a result, the process of gaining traceability of passengers is actually the one of integrating different systems holding necessary data pieces. Table 1 shows major sources of information as well as the inputs that could be used to retrieve them. Table 1. Data types, sources and availability Data type and characteristics

Source/Availability

Routes and location of public transportation vehicles

Dynamic information with high frequency (often in the range of few seconds – 10 to 30) from GPS systems of the respective AVL devices

Planned schedules and frequency of different Information that is relatively constant and types of public transport. Location of stops could be found in the respective schedules and and links between different types of transport municipality decisions/local legislation Preprocessed inputs which indicate the discrepancies between planned (scheduled) locations (resp. arrivals and departures) and actual times

Depending on the feature set of the software system controlling ALVs such information may already be available, or may be easily calculated from the GPS inputs

Ticketing and fare information

Summary of ticketing and fare information is typically available from the fare system database

Passenger location and movement information

Such information cannot be obtained due to violation of GDPR and privacy protection. However mobile networks can give anonymized and aggregated information on movement of mobile devices to match against utilization of public transport network

To avoid violation of privacy regulations, all the information used should be anonymized and only aggregated (e.g. preprocessed) output should be made available to users of the traceability analysis.

AI Driven Adaptive Scheduling for On-Demand

365

2.2 Identification of Fundamental Needs Traceability of passengers alone is not sufficient to select the best policies and deploy them to improve public transportation. As there are multiple ways to optimize the public transportation services, it is very important to define appropriate goals and translate them into optimization criteria. Our study has been inspired by Innovative demand responsive green public transportation for cleaner air in urban environment (INNOAIR) project, which aims to provide alternative transportation methods and improve air quality by reducing traffic pollution. Financing from EU Urban Innovative Actions initiative, makes it possible to test innovative and creative ways to address problems in modern cities. Establishing on-demand transport, as one of the major targets for INNOAIR, needs to fit inside the existing public transportation framework. To guarantee this, goals must be in line with general regulations for the public transportation in Sofia, as well as to be measurable. Metrics and goals presented in Table 2 are defined to be S.M.A.R.T. – e.g., they are specific, measurable, achievable, relevant and time-bound [13]. Table 2. Important goals and metrics used to identify needs for public transportation Goals/Optimization criteria

Metrics involved

Adaptive schedule that considers congestion and expected load (including traffic and number of passengers)

Time between consecutive trips (in hours/minutes) for different weekdays, weekends, and official holidays Delays and advances, compared to established schedules Load and number of tickets validated for a specific period

Adaptive bus lanes, that consider passenger point of entry and destination

Load and number of tickets validated for a specific period Time series of people entering or leaving a specific area

Fixed lanes optimization and bus stop location optimization

Top destinations based on passenger location and movement over time (generalized and not individual data) Top origin-destination pairs based on selectable scale (station, lane, residential area, city-level)

Traffic lights and special rules for public transportation vehicles

Delays and advances, compared to established schedules over time Time to stay on every stop and the number of people entering/leaving the vehicle Traffic light cyclogram data

Traffic data and bus location is available from the tracking systems that vehicles have and use to signal their location (as shown on Fig. 4). A typical application of GPS data

366

V. M. Ruseva et al.

is to check that all scheduled courses have been completed in time and without missing bus stops. While this information is very important, it does not reflect any patterns in passenger behavior. Figure 4 demonstrates how a usual report looks like and indicates that the information contained in it is purely technical.

Fig. 4. Example technical output, showing distance travelled, GPS coordinate quality and average speed between different stops.

2.3 Measuring the Impact of Changes Measuring changes in accessibility in specific areas can be useful for local planning or for promoting project-based coordination between different public agencies. Transportation projects, by themselves, cannot create denser, mixed-use, and active neighborhoods, but they can be catalysts for redevelopment and create conditions for improved economic development. Due to the stochastic nature of process, the impacts on individual passengers vary, however in an aggregated way passenger mainly experience the following three effects: (1) Impacts on duration of travel time components, being in-vehicle time and waiting time, which lead to arriving early or late. (2) Impacts on passenger perception of the public transport mode depending on the variability of travel time components, being departure time, arrival time, in-vehicle time and waiting time, which lead to uncertainty of the actual travel time. (3) Impact on the probability of finding a seat and of crowding, affecting the level of comfort of the journey. Reliability is important for operators and passengers alike. For operators, unreliable services cause difficulties in timetabling and resource planning. Also, unreliable services are typically more unevenly loaded, causing issues of passenger overloading and possible breaching of loading licenses. For passengers, unreliable services cause adjustments in an individual’s desired trip making behavior to account for the possibility of a service not operating ‘as normal’. Variable departure times force the traveler to arrive earlier at the service and create uncertainty and anxiety about whether the service has arrived. Variable arrival times cause travelers to arrive at their destination late and force them to take an earlier service.

AI Driven Adaptive Scheduling for On-Demand

367

In-vehicle time variability causes the traveler to experience uncertainty and anxiety about how long they will have to spend in the service. Valuations of reliability can be estimated using revealed and stated preference data. However, most valuations are undertaken using stated preference techniques, where a survey asks respondents about hypothetical situations. 2.4 Benchmarking and Key Indicators The first and most obvious performance area for public transport relates to the portion of travelers using the services. Although it is not a direct measure of the quality of a public transport system it is a definite indicator of its popularity or in some cases the patron’s dependency on it for essential travel. Performance measures/indicators are used in both performance measurement and benchmarking. The performance measures are normally a quantitative measure or index that numerically expresses a specific activity. In the context of this study, reference is made to key performance indicators (KPIs), as the aim is not to measure a complete set of performance measures, but rather focus on some key ones that will provide a sufficient understanding of relative comparison in the process. The challenge in defining KPIs is to select the appropriate ones that will give a sufficient understanding of overall performance. The KPIs should also be practical in terms of data availability and understandable to the audience. Useful KPIs can normally be associated with the S.M.A.R.T principle: • Specific – A KPI must cover concisely one aspect of the activity. • Measurable – KPIs must be quantifiable as subjective measures, e.g., a rating scale, could lead to distorted comparisons. • Achievable – Available data and common items normally measured must be used for KPIs. It would not be useful to develop sophisticated KPIs for which data are unobtainable. • Relevant – The KPI must be relevant to the activity being considered. • Sometimes a different KPI is used to indicate or estimate a different activity. For example, one can use fuel consumption as a surrogate of CO2 emission if no actual emission data exist and Timebound – KPIs of similar timeframes need to be used to be an effective comparison tool for benchmarking. The measures used are related to the factors affecting access, safety, efficiency, and affordability to public transportation system: (1) Comfort and safety – overall experience; safety; security; walking infrastructure; public transport infrastructure; operational performance; impact of traffic on walkability. (2) Service demand – daily trips. (3) Connecting destinations – access to public transport stops; access to jobs and services. (4) Support and encouragement – information, affordability; incentives.

368

V. M. Ruseva et al.

3 Algorithm Usage Artificial intelligence algorithms can be applied in many different forms and contexts. Not all of them are suitable for managing on-demand transportation and dynamic scheduling, as some are too demanding to implement or too complicated to explain and calibrate on the available data. If we consider that on-demand transportation is often implemented as pilot projects with a limited scale, there is also little data available to train large deep learning systems. Therefore, we have decided to use the simplest possible solutions as a start, in order to simplify initial implementation and reduce the required amount of information for training. Table 3. Sample application of intelligent algorithms to support data analysis Algorithm

Usage information

Step 1 Hybrid neural network for anomaly detection

Based on research in [12], anomalies and outliers in data sets are detected to generate special events and warnings on unusual conditions, that can help in maintenance and provide for on-time reaction of potential issues

Data profiling for high frequency inputs with deep learning neural network

This pre-processing step helps to reduce the dimension of data and speed-up the analysis

Clustering and classification of inputs on This step helps to reduce significantly the customer satisfaction with K-Means clustering amount of data stored for processing in the with Mahalanobis distance [13] next step. Since classification is done with pre-defined groups, the only input passed on is the group identifier, instead of all initial consumer details Step 2 Short term time series forecasting with neural networks

Multilayer neural networks provide good accuracy and efficiency when analyzing time series. Since necessary forecasts are limited in short-term, we can also benefit from avoiding issues like vanishing gradient problem

Jump-diffusion process calibration with adaptive re-calibration in case of outlier detection

We have used jump-diffusion processes to model bus delays [14], but when combined with anomaly detection, process parameters can be re-estimated in case of outlier detection or when enough arguments pointing toward environment conditions change are collected

Geospatial analysis of on-demand requests identifies use frequency and patterns in requests

In addition to request analysis, geospatial data can be used to cluster routes and improve efficient use of available transportation fleet

AI Driven Adaptive Scheduling for On-Demand

369

The list presented in Table 3 can be extended to include domain- and city-specific solutions, depending on requirements and problems to be solved. Once the results of both steps are available, it is possible to continue analyzing the schedules under different simulated conditions. Depending on the context, different parameters can be modified in order to estimate how generated schedules will behave and under what scenarios they will become obsolete. Due to the modular and flexible structure of the suggested steps, it is possible to perform such simulations locally (for example modifying delays at single point – like individual bus stops) or globally, by introducing simultaneous delays or special events across the city area. The latter option is only limited by the available computational resources and time that can be spent on Monto Carlo simulations. Local simulations on the other hand, can be applied to critical parts of the public transportation network. The main benefit of such restricted options is that they require much less computational power and can bring results faster.

Fig. 5. Simulated delays with jumps in case of event that causes traffic jam

Figure 5 shows one such simulation, where a traffic jam event could cause different delays, because on reaction, number of vehicles and incident severity. In this particular scenario random behavior of delays have been assumed with potential for jumps, but other patterns can be applied as well. This provides additional flexibility and can improve proactive management of transportation networks.

370

V. M. Ruseva et al.

4 Conclusions Transportation services are very important factor for driving economic development of individual cities or entire regions. They require careful planning, long-term investments and complex maintenance processes. Due to the fact that transportation services are important for both economic development and supporting specific social groups, there are various ways to analyze, manage and improve them. Based on experience gained in INNOAIR project, we have suggested a model where artificial intelligence algorithms can be used to improve existing transportation planning and scheduling algorithms, by considering different sources of information, real-time data processing and pay special attention to consumer preferences. There are several key areas, where AI-supported data analysis can make a significant difference and add value to traditional transportation scheduling methods: (1) Pre-processing of visual records and information on traffic flows, jams and potential delays. (2) Analysis of passenger behavior and adaptive profiling of different customer groups, their needs and demands. (3) Delay analysis and proactive maintenance of fleet, assets and transportation support systems. In combination with simple dashboards and carefully selected key indicators, it is also possible to overcome one of the significant drawbacks of AI-supported systems – their complexity and lack of transparency. People involved in the decision-making process do not need to dig into the details of the algorithms but only to make sure that performance indicators are adequate to the quality of service provided by the transportation system. Acknowledgement. This research was supported by UIA05–202 “INNOAIR - Innovative demand responsive green public transportation for cleaner air in urban environment”, funded by the European Union initiative - Urban Innovative Actions. (UIA).”

References 1. Adamski: Transfer optimization in public transport. In: Computer-Aided Transit Scheduling. Berlin, Heidelberg, (1995) 2. Jansen, L.N., Pdersen, M.B., Nielsen, O.A.: Minimizing passenger transfer times in public transport timetables. In: 7th Conference of the Hong Kong Society for Transportation Studies, Transportation in the information age. Hong Kong (2002) 3. Ceder: Urban mobility and public transport: Future perspectives and review. Int. J. Urban Sci. 25(4), 455–479 (2021) 4. Dharti, P., Narmawala, Z., Tanwar, S., Kumar, S.P.: A systematic review on scheduling public transport using IoT as tool. Smart Innovations in Communication and Computational Sciences, pp. 39–48 (2019) 5. Kuppusamy, P., Kalpana, R., V.R.P.V.: Optimized traffic control and data processing using IoT. Cluster Comput. 22(1), 2169–2178 (2019)

AI Driven Adaptive Scheduling for On-Demand

371

6. Bhouri, N., Mayorano, F.J., Lotito, P.A., Salem, H.H., Lebacque, J.P.: Public transport priority for Multimodal urban traffic control. Cybern. Inf. Technol. 15(5), 766 (2015) 7. Wang, Y.Z.D., Hu, L., Yang, Y., Lee, L.H.: A data-driven and optimal bus scheduling model with time-dependent traffic and demand. IEEE Trans. Intell. Transp. Syst. 18(9), 2443–2452 (2017) 8. Tambon, F., et al.: How to certify machine learning based safety-critical systems? A systematic literature review. Autom. Softw. Eng. 29(2), 1–74 (2022). https://doi.org/10.1007/s10515022-00337-x 9. Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transp. Res. Part C: Emerg. Technol. 137(103566) (2022) 10. Iliopoulou, Kepaptsoglou, K.: Combining ITS and optimization in public transportation planning: state of the art and future research paths. Eur. Transp. Res. Rev. 11(27) (2019) 11. Bogue, R.: Use S.M.A.R.T. goals to launch management by objectives plan. TechRepublic (2018) 12. Kabaivanov, S., Markovska, V.: Hybrid deep-learning analysis for cyber anomaly detection. IOP Conf. Ser. Mater. Sci. Eng. 878(1), 012029 (2020) 13. Kabaivanov, S., Roberts, K., Kovacheva, S.: Machine learning assisted social system analysis: youth transitions in five south and east Mediterranean countries. AIP Conf. Proc. 2333, 030002 (2021) 14. Kabaivanov, S., Markovska, V.: Data driven public transportation delay modelling. INNOAIR Project, Sofia (2021) 15. Apache Software Foundation: Apache NiFi. [Online]. Available: https://nifi.apache.org/ (2021). Accessed 11 November 2021 16. influxdata: InfluxDB Telegraf. [Online]. Available: https://www.influxdata.com/time-seriesplatform/telegraf/ (2021). Accessed 15 11 2021 17. Apache Software Foundation: Apache Airflow . [Online]. Available: https://airflow.apache. org/ (2021). Accessed 15 11 2021 18. Apache Software FoundationApache Spark MLib. [Online]. Available: https://spark.apache. org/mllib/ (2021). Accessed 18 11 2021 19. Grafana Labs: Grafana. [Online]. Available: https://grafana.com/ (2021). Accessed 22 11 2021 20. Beirao, G., Cabral, J.A.S.: Understanding attitudes towards public transport and private car: a qualitative study. Transp. Policy 14(6), 478–489 (2007) 21. Steg, L.: Car use: lust and must. Instrumental, symbolic and affective motives for car use. Transp. Res.: Part A: Policy Pract. 39(2–3), 147–162 (2005) 22. Solomon, M.R.: Consumer Behavior: Buying, Having, and Being. Pearson Prentice Hall (2004) 23. Gaoa, Y., Rasoulib, S., Timmermansb, H., Wang, Y.: Trip stage satisfaction of public transport users: A reference-based model incorporating trip attributes, perceived service quality, psychological disposition and difference tolerance. Transp. Res. Part A 118, 773 (2018) 24. De Vos, J., Mokhtarian, P.L., Schwanen, T., Van Acker, V., Witlox, F.: “Travel mode choice and travel satisfaction: bridging the gap between decision utility and experienced utility. Transportation 43(5), 771–796 (2016) 25. Carrel, Mishalani, R.G., Sengupta, R., Walker, J.L.: In pursuit of the happy transit rider: dissecting satisfaction using daily surveys and tracking data. J. Intell. Transp. Syst. 20(4), 345–362 (2016)

Decision Intelligence Based on Big Data for User-Oriented Trip Planner Development Alise Dinko(B) , Irina Yatskiv Jackiva, and Evelina Budilovich Budiloviˇca Transport and Telecommunication Institute, Lomonosova 1, Riga 1019, LV, Latvia [email protected]

Abstract. Decision intelligence is a wide-range term covering a broad latitude of decision-making techniques, uniting traditional and modern disciplines for the development and process of decision models. It transforms the uncertainty of multiple travel choices into the opportunity to provide safe and optimal travel options. Developing a user-oriented trip planner (TP) should complement decision intelligence. This possibility enables travellers to make more informed decisions since they will have greater visibility of what is happening at their chosen travel destinations. On the other side, it will be based on using a wide range of big data and analytics, improving user experience. Authors analyse the challenging aspect of Big Data (BD) fusion being used by a person, where extraction of information across multiple data sources for travel planning is required. The research aims to develop a personalised trip planner concept for Riga city, which considers all aspects of public transport service quality. The offered concept of the user-oriented TP allows the creation of customer-oriented, safe and sustainable recommendations based on personal preferences and presented in a ranking of possible travel routes. Keywords: Trip planner · Decision making · Traveller behaviour · Big Data · Data sources · Open data

1 Introduction Decision intelligence (DI) is one of Gartner’s top strategic technology trends for 2022 [1]. It is a wide-range term covering a broad latitude of decision-making techniques, uniting traditional and modern disciplines for the development and process of decision models. According to Gartner, DI can support and speed up human decision-making through augmented analytics, simulations, and artificial intelligence. DI transforms the uncertainty of multiple travel choices into the opportunity to provide safe and optimal travel options. DI should complement the development of a useroriented trip planner. This possibility enables travellers to make more informed decisions since they will have greater visibility of what is happening at their chosen travel destinations. On the other side, DI will be based on using a wide range of big data and analytics, which will improve user experience. Authors analyse the challenging aspect of BD fusion being used by a person, where extraction of information across multiple data sources for travel planning is required. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_32

Decision Intelligence Based on Big Data

373

Social media (SM) usage has a history of over 20 years. As mentioned in the “Social media guide for Latvia” [2], in 2011, social media usage in Latvia was 55%, putting Latvia in second place among European countries. A recent publication in Statista [3] shows that in 2021 around 83% of the population in Latvia will be social media users. This number will increase by 2026, but keeping in mind SM usage, we need to consider its spread across the channels. Accordingly to GlobalStats [4], in April 2022, the usage of Facebook worldwide was 75.04%, Twitter had 7.15%, Instagram had 5.74%, and Reddit had only 0.64%. For Latvia, in April 2022, usage of Facebook was 63.12%, Twitter − 16.21%, Instagram −5.75% and Reddit −1.25%. The high volume of SM usage and volume, velocity, and variety of produced data justifies that it represents BD and requires specific technology and analytical methods to transform into valuable and usable information [5]. SM data is frequently used for travel behaviour studies. In order to measure willingness to travel by train for nonwork-related purposes were used data from Yelp for seven cities in North America and Europe [6]. To explore the effect of Twitter announcements of metro disruption on bicycle use in Paris, France, Twitter data were used [7]. Before SM came onto the BD scene, the only data source for collecting traveller’s feedback was surveyed. However, unfortunately, they provided outdated data with a strictly defined number of questions, and the sample size usually was limited. SM provides the opportunity to collect already generated data by society and generate and launch specific analytical surveys, where results can be received in days or even hours. The fusion of SM, mobile phones and survey data increases the volume of available data for analytics and behaviour studies [8]. The research first described the concept of a user-oriented trip planner and provided key findings of previous research dealing with it. Then, it defined the research area and presented the applied Social Media sentiment algorithm. These were followed by the obtained results for the Riga case study, discussion and concluding remarks with directions for future research.

2 User-Oriented Trip Planner A sustainable trip planner (TP) should provide reliable and life information for optimal trip planning, like schedule, traffic conditions, road accidents, road works, accidents, safety, weather, air pollution and any other personal information related to trip planning information ranking. To fulfil and enrich TP and provide the traveller with broader customization options, TP will be complemented with additional information on punctuality, reliability, pollution, comfort and accessibility characteristics (Table 1). Riga city is a case study of this applied research. One of the urban PTS problems – is the absence of the TP for citizens and guests, as described by the authors in [9]. Moreover, for TP development is necessary to define and analyse the public transport (PT) quality indicators (Table 1) and the authors in [10] provided the quality indicator and possible data source analysis. The availability component should include a publicly available timetable and network information. Accessibility will show what ticketing options are available for ticket purchasing. General information will contain information about public transport mode,

374

A. Dinko et al.

Table 1. Usage of information about public transport system (PTS) quality for TP ((a) realised, (b) rarely realised, (c) not realised). PTS quality components*

Availability of information for TP

Components

Characteristics

Availability

(a) network

Information about the use of a sequence of several modes of transport, information about PT services, and transport networks for private transportation

(a) timetable

TP uses PT timetables to determine the availability of particular journeys at specific times

(b) external interface

Ability to set up traveller preferences based on the personal profile and collect information from other open data sources

(b) an internal interface

Ability to collect information on travel on fly-by, providing changes to traveller on delays etc

(b) ticketing

Option to purchase a single travel ticket easily based on the selected travel route

(a) general information

Usually always available basic travel information

(b) travel information – normal conditions

Current realisations of TP take into account only usual road conditions

Accessibility

Information

(c) travel information – abnormal conditions This information should consist of data about what is happening on the selected journey, especially if it will be related to travel time and safety Time

(a) journey time

Total time of travel

(b) punctuality

If the transport mode is usually on time or it is late

(c) reliability

How reliable is the calculated time of arrival to the destination?

Customer care (c) commitment

If there is any commitment from the riding service provider

(a) customer interface

TP interfaces are already realised user-friendly and very intuitive from a usage perspective

(c) staff

Information about available staff for assistance is not present

(c) physical assistance

A driver assists, but there are still present transport modes that are physically impossible to implement

(b) ticketing options

Information about possible ticketing options is not shared within travel planning activities

(continued)

Decision Intelligence Based on Big Data

375

Table 1. (continued) PTS quality components*

Availability of information for TP

Components

Characteristics

Comfort

(c) ambient conditions

No instant update option is available. The delay will happen if there is an accident\anything else related directly to travel time during the journey

(b) facilities

Usually, such data is not collected and shared for public transport modes

(c) ergonomics

How ergonomically comfortable for the traveller are travel mode seats and standing places

(c) ride comfort

Data about ride comfort could be from social network analysis or objectives about specific transport mode details

(c) safety from crime

Limited availability, not shared with the public

(c) safety from accident

Data regarding roads safe from accidents are regularly collected but not used as TP journey selection criteria

(c) perception of security

It is subjective, but information can be collected from social networks and analysed

(c) natural resources

What resources can be saved if a traveller chooses one mode over another?

(c) infrastructure

Data about available infrastructures, like tunnels and pedestrians and others

(b) noise

Noise information is not collected and shared if the transport mode is very noisy due to the engine specifics or perfectly silent as an electric car

(c) pollution

Information about the most polluted air and streets is available but not shared with TP

Security

Environment

the number of seats available, standing places number and others. And also information regarding total travel time for normal and abnormal travel conditions. Time component consists of multiple quality components, such as selected journey total travel time, punctuality, how selected transport mode arrives on selected route point and reliability, and how reliable estimated arrival time and total travel time are. Customer care will share available assistance and options for travel of disabled citizens and ticketing options available in the transport and on the stop. Comfort information will represent transport usage comfort, for example, low floor transport and the model of the transport mode. Safety can be regularly collected as subjective information from social network information and stored based on the need. The environment quality component will contain expert and objective information about air pollution levels and how many resources travellers would use by selecting a particular travel mode and infrastructure.

376

A. Dinko et al.

For each traveller, the importance of each characteristic is subjective, based on individual preferences, age, sex, and other personal aspects; based on that, the route grading algorithm can be used in the multi-attribute decision-making methodology methods. In order to satisfy traveller needs and for TP to become user-focused, there is a need to implement an initial travel indicators ranking, which will be done by the traveller when the profile is set up. Ranking will include all travel quality components used for travel route evaluation. Authors use the Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method that Hwang and Yoon [11] proposed. The main idea came from the concept of the compromise solution to choose the best alternative nearest to the positive ideal solution (optimal solution) and farthest from the negative ideal solution (inferior solution). Then, choose the best sorting, which will be the best alternative [12]. An example of the application of this approach for Riga Trip Planner is presented by authors in TRA2022 [13]. The next step is to enrich the characteristics by using Social Media Data.

3 Decision Intelligence Supported by Social Media Data SM are interactive technologies and digital channels that facilitate sharing of information, ideas, interests, and other forms of expression through virtual communities and networks [14]. Many SM websites produce a tremendous amount of unstructured and semistructured data by humans every second. The most popular are Facebook and many others, such as TikTok, WeChat, Instagram, QZone, Weibo, Twitter, Tumblr, Baidu Tieba, and LinkedIn. This work focuses mainly on the information obtained from posts for specific public transportation topics. The aim is to enrich user-oriented TP with insights based on SM posts and hashtags for security, safety, and cleanliness topics. Based on the data analysis results, adjustments for suggested route rank will be made for the traveller, considering traveller profile preferences settings. 3.1 Social Media Sentiment Algorithm Overview The purpose of gathering and extracting data from SM is to analyse SM comments to extract travellers’ sentiments about particularly defined features of public transport usage in Riga city. The steps involved in the data collection algorithm and sentiment analysis preparation are visualised (Fig. 1). Firstly, SM sources, data time frame and topics for which data should be collected from several SM sources (e.g., Instagram, Reddit and others) should be defined as input parameters. Input parameters should be passed to the SM scraper tool. It is an automatic scraping tool which allows extracting data from SM channels. There are many options and limitations, as free open source solutions have their limits, but paid solutions depend on the usage of their service (Software as a Service). As a result of this step, there should be a loaded and saved dataset with data from SM on the topic defined as input parameters. Before SM’s comments and posts can be used, the following algorithm step is to clean the content and provide the best possible data (Fig. 2). This step aims to clean the text from stop words, remove noise from the text, and perform lemmatisation and

Decision Intelligence Based on Big Data

Pass input Parameters

Crawler collects data from SM

Load scraped data

Save scraped data

Word Embedding

Save Clean data

Text Processing

Read data

Model Training

Model EvaluaƟon

Trained Model

SenƟment scores

377

Fig. 1. Social media data collection and preparation for sentiment analysis

word classification. This step can be used following libraries like spark-nlp [15], spark.ml [16]. Mentioned libraries are very well documented, have good reviews, have pre-trained models and are open source.

1st flow stage, for filtering Nouns, Verbs and Adjectives Select data based on Detect Read data Save sentence the Ɵmeframe

Tokenize

Convert to string

Filter adjecƟves

Filter nouns

Filter verbs

Tag text

Save

Tokenize

Normalize

Remove Stopwords

LemmaƟze

2nd flow stage of data cleaning Fig. 2. Data cleaning flows with 2 stages

After the data cleaning step, natural language should be made understandable for the machine learning model. This step is called word embedding (Fig. 3). Word embedding converts words and phrases into a numeric or vector representation. Exist several techniques for word embedding. The most popular are Word2Vec (studying neighbours),

378

A. Dinko et al.

TF-IDF (measuring importance) and GloVe (counting word co-occurrence). The final decision on which algorithm should be used in the final version will be made based on the accuracy and model training time between Word2Vec and GloVe.

Source LemmaƟzed Data

Build Word2Vec/ GloVe Model

Find Keywords

Find Synonyms/ build Graph Data

Save results

Fig. 3. Word embedding flow steps

Synonyms in the described algorithm are the words close to each other in the vector space, meaning distances between the words. That also means that the words cannot mean the same thing, but they are synonyms in the vector space and are used in similar contexts. The final step is sentiment analysis, which is a supervised learning problem from the machine learning point of view. The dataset is labelled and already should contain answers. The model should be trained and evaluated, and then it is ready to be used for sentiment classification on the love data. 3.2 Challenges for Social Media Data Usage for the Riga Case The main challenge for SM data usage for the Riga case is mentioned above: the Natural Language Processing (NLP) algorithm is data preparation and cleaning because the languages used by travellers are not only Latvian, Russian, and English (for quests), but also “Crazy Russian” or Translit (a method of encoding Cyrillic letters with Latin letters), Latgalian and the combination of all above together. There can be a combination of two or three languages in the same post, bringing sentiment analysis to the next level of complexity. Unfortunately, Facebook disallows any scrapping activity on its pages. According to its robot.txt file, the file specifies a set of rules on how to interact and states that all automated scrapers are prohibited. Besides that, Facebook’s Application Programming Interfaces (API) are locked too. There are still alternative SM data sources like Twitter, Reddit and Instagram.

4 Riga Case Study: Approach Testing The research case study is Riga city. Riga is one of the biggest capitals among Baltic states, with 605802 inhabitants [17]. The absence of the TP is the actual problem for the sustainable urban transport system. The scenario for the trip approach demonstration from one city side to another using the different transport modes: tram (Tram Nr. 1), bus (Bus Nr. 21) and trolleybus (Trolleybus Nr. 14) were developed and analysed from the PT route reliability perspective [18]. For sustainable TP development, as was mentioned above, it is necessary to know and analyse the Biga Data from social media. The research aims to analyse the citizens’

Decision Intelligence Based on Big Data

379

comments regarding the routes and the traveller’s opinion of the trip. However, mostly the comments about the PT operator work - Riga Urban PT operator is a municipal limited liability company “R¯ıgas Satiksme”. Data for the time frame from January 2020 until June 2022, location – Latvia and for posts in all languages were collected from SM sources. One thousand five hundred eighty-four posts related to the “Rigas Satiksme” topic were identified. The main sources were Web pages – 42%, Twitter - 32.2% and 25.8% were collected from the News pages. Distribution by language is presented in Table 2. 240 posts were related to the “bad service” in PT, 73.5% from the News pages, 11.6% from Twitter and 15% from other Web pages. Table 2. Collected SM data language distribution Language

Percentage (%)

Latvian

80

Russian

12.8

English

3.2

German

1.8

Lithuanian

0.3

Sentiment analysis for mentioned PT routes is compiled in Table 3. Table 3. SM Sentiment analysis results for selected PT routes and the number of cases. Route

Positive 2018–2022

Negative 2018–2022

Neutral 2018–2022

Positive 2021–2022

Negative 2021–2022

Bus Nr. 21

1

3

68

1

2

Trolleybus Nr.14

0

2

27

0

2

Tram Nr. 1

3

4

174

3

2

In order to make TP more user-focused, the characteristics of PTS quality components (Table 1) should be enriched by adding SM sentiment analysis results. In the final step, to support the traveller’s decision to provide the best alternative, all travel quality components will be ranked, and the TOPSIS methodology will be used for result calculation. Incorporating more SM data sources with cleaned data will allow getting more feedback from society fast and on different PT-related topics, depending on the current needs. Based on the sentiment analysis results (Table 3), it could be suggested that all PT routes have equal negative sentiment volume. However, on the other side Tram Nr.1 got the most of all positive feedback and will get rank 1, Bus Nr. 21 will get rank two and Trolleybus Nr. 14 ranks 3, for the 18 months - from 01.01.2022 till 15.06.2022.

380

A. Dinko et al.

Table 4. TOPSIS recalculation results in high importance of SM sentiment characteristic Scenario

Performance score

Rank

Previous rank

1

0.55363

1

1

2

0.49236

3

2

3

0.51581

2

3

After adding SM positive sentiment ranking to already existing characteristics evaluation in the TOPSIS scenario analysis model, results became more precise, and PT route ranks changed in Table 4.

5 Conclusion and Future Research Data collection from SM for the Riga PT routes exercise for sentiment analysis showed that the most popular PT is the tram. On the other side, many SM comments and posts contained an informative set of data related to the particular PT route and were marked as “Neutral”. Results can be incorporated into user-focused TP algorithm enrichment for sharing more information with the traveller. The shortest PT routes, like Trolleybus Nr. 14 had a small number of posts because the route itself is relatively short compared with others listed in Table 3. It is driving accordingly to the schedule, with very small and rare delays. Filtering on the region excluded part of the posts where the region was unidentified, but if it would not be used, data would be inaccurate for the Riga case study. The current study covered only such SM as Twitter, Web pages and News pages. Unfortunately, the enormous SM information base – Facebook, has restrictions due to GDPR. However, connecting business pages and regularly collecting post feedback is possible. It could be carefully thought if the PT provider decides to collect or share this information as an open data initiative. For future research, finding and connecting more SM data related to PT in Riga is essential. The most optimal could be sourced where data can be collected with the help of APIs.

References 1. Gartner: Gartner Top Strategic Technology Trends for 2022. [Online]. Available: https://www. gartner.com/en/information-technology/insights/top-technology-trends (2022) 2. “Social media guide for Latvia,” [Online]. Available: https://businessculture.org/eastern-eur ope/latvia/social-media-guide/ 3. Statista: Statista Research Department. [Online]. Available: https://www.statista.com/statis tics/568969/predicted-number-of-social-network-users-in-latvia/ (2022) 4. GlobalStats:“statcounter,” GlobalStats. [Online]. Available: https://gs.statcounter.com/socialmedia-stats (2022) 5. De Mauro, A., Greco, M., Grimaldi, M.: A formal definition of big data based on its essential. In: Library Review, pp. 122–135 (2016)

Decision Intelligence Based on Big Data

381

6. Mondschein, A., King, D., Hoehne, C., Jiang, Z., Chester, M.: Using social media to evaluate associations between parking supply and parking sentiment. Transp. Res. Interdisc. Perspect. 4. ISSN 2590-1982 (2020) 7. Klingen, J.: Do metro interruptions increase the demand for public rental bicycles? Evidence from Paris. Transp. Res. Part A: Policy Pract. 123, 216–228 (2019) 8. Ruiza, T., Mars, L., Arroyo, R., Serna, A.: Social networks, big data and transport planning. Transp. Res. Procedia 18, 446–452 (2016) 9. Dinko, A., Jackiva (Yatskiva), I., Budilovich, E.: Trip planner challenges in the era of fast changing requirements. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. Lecture Notes in Networks and Systems, vol. 195, pp. 485–497. Springer (2020) 10. Dinko, A., Yatskiv, I., Budilovich, E.: Data sources analysis for sustainable trip planner development for riga city. Transport Telecommun. 22(3), 321–331 (2021). https://doi.org/10. 2478/ttj-2021-0025 11. Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag, New York (1981) 12. Borawska, A.: Multiple-criteria decision analysis using topsis method for interval data in research into the level of information society development. Folia Oeconomica Stetinensia 13(2) (2014). https://doi.org/10.2478/foli-2013-0015 13. Yatskiv, I., Dinko, A., Budilovich, E.: User-focused trip planner with an expanded suite of features: Riga case study.(accepted for presentation). In: Transport Research Arena (TRA) Conference. Lissbon (2022) 14. “Wikipedia,” [Online]. Available: https://en.wikipedia.org/wiki/Social_media 15. SPARK-NLP. https://www.johnsnowlabs.com/: https://nlp.johnsnowlabs.com/docs/en/ann otators#postagger-part-of-speech-tagger 16. SPARK.ML. https://spark.apache.org/docs/latest/ml-pipeline.html 17. The Central Statistical Bureau of Latvia. https://stat.gov.lv/lv/statistikas-temas/iedzivotaji/ied zivotaju-skaits/247-iedzivotaju-skaits-un-ta-izmainas?themeCode=IR (2022) 18. Dinko, A., Yatskiv, I., Budilovich, E.: Sustainable trip planner enriched by trip reliability In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2021. Lecture Notes in Networks and Systems, vol. 410. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-96196-1_35

A Framework for Urban C-ITS GLOSA Evaluation Thomas Otto(B) , Michael Klöppel-Gersdorf, and Ina Partzsch Fraunhofer IVI, Institute for Transportation and Infrastructure Systems, Dresden, Germany {thomas.otto,michael.kloeppel-gersdorf, ina.partzsch}@ivi.fraunhofer.de

Abstract. The use of the C-ITS Day1-service GLOSA - green light optimized speed advisory - in urban networks depends on various environmental and temporal constraints. While GLOSA generally is said to improve traffic flow and decrease emissions and delay, the availability may be actually very low in real world scenarios due to the aforementioned constraints. In this paper, a framework to evaluate the effectivity of GLOSA (measured as percent of the cycle time where it is available with high confidence) is proposed, considering the 1. current signal program parameters like cycle time or proportion of green per cycle, 2. traffic flow parameters like traffic load or queue length and 3. limitations on the lowest and highest possible speed advised by GLOSA as well as the range of communication depending on the communication technology. The paper presents the framework and shows its application to evaluate typical urban intersections equipped with V2X communication. For this network, the effectivity of GLOSA will be evaluated based on the access lanes of signalized intersections, and it will be shown, at which intersection and under which circumstances GLOSA makes sense. Furthermore, it will be estimated what level of service is achieved for GLOSA. The proposed framework to assess the C-ITS service GLOSA serves as a basis for evaluating the possibilities for connected and automated driving in other cities and urban corridors. Keywords: C-ITS · GLOSA · V2X · CCAM · Urban traffic flow · Framework

1 Introduction The C-ITS Day1.0 service GLOSA - green light optimized speed advisory - is probably one of the services with the greatest potential in the discussions of recent years. The aim is, on the one hand, to improve the traffic flow by increasing speed and reducing the number of stops as well as increase traffic safety, and, on the other hand, to reduce the negative impact on the environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_33

A Framework for Urban C-ITS GLOSA Evaluation

383

The concept of the GLOSA service can be described as follows: By means of a targeted speed specification, road users are informed of the optimal strategy for approaching a traffic light via V2X (Vehicle-to-Everything) communication. The traffic light system determines the remaining red and green times by knowing the signal states, and an optimal approach speed can be calculated based on the spatio-temporal position of the vehicles in the approaches. This principle enables the traffic light controller to optimize the traffic control in a new adaptive manner. In addition to adapting the clearance times, as is already the case today, it is now also possible to specifically influence the approach behavior of road users. GLOSA is the solution where today’s adaptive signal programs reach their limits. Current deadlock situations, e.g., the prioritization of two simultaneously arriving public transport vehicles in conflict, can be solved in the future by transmitting targeted speed specifications via V2X-communiacation. Regardless of the advantages outlined above, there are reasons why the GLOSA service, which has been under discussion for more than 15 years, has not yet been implemented network wide. In addition to the aspects of standardization of communication and agreement on a communication standard, where we have already made considerable progress today, there are also fundamental traffic conditions that either accelerate the use of GLOSA or, in some cases, slow it down. The progress in traffic management and control in the 1980s, 90s and 2000s was characterized by a progressive implementation of complex intelligent traffic-adaptive signalizations. These control systems react adaptively to the existing traffic volume and thus control the different needs of the traffic participants with complex logics. In principle, this trend is contrary to the GLOSA service since it is based on a constant forecast of remaining time. Furthermore, these new possibilities also place new demands on the control of traffic lights. Today, it is no longer only a question of efficient control of individual traffic. Rather, other constraints such as the integration of VRUs (Vulnerable Road Users, e.g., pedestrians and cyclists) or public transport are essential integral optimization goals. The paper presents the framework and shows its application to evaluate real typical urban intersections equipped with V2X communication. For this, the effectivity of GLOSA will be evaluated based on the access lanes of signalized intersections, and it will be shown, at which intersection and under which circumstances GLOSA makes sense. Furthermore, it will be estimated what level of service is achieveable for GLOSA.

2 C-ITS Services and Test Sites 2.1 C-ITS Test Sites As part of the European C-ROADS project, several C-ITS services are currently being deployed for urban and non-urban areas. From 2016 to 2021, the goal was to implement these C-ITS services primarily on rural roads and highways. Since 2019, the deployment of C-ITS in urban areas started within over 43 European cities. As part of the C-Roads platform [1, 2], the C-Roads Germany - Urban Nodes project will contribute to the implementation and operation of the three different urban nodes Hamburg, Hessen/Kassel and Dresden [3]. The project promotes the large-scale deployment of C-ITS service in urban

384

T. Otto et al.

areas, providing an implementation pattern of C-ITS services in Germany in accordance with EU regulations and standards and in line with the recommendations of the C-Roads platform. Expected benefits include reduction of accidents and travel times. Within the German C-ITS Test Sites, the following services will be deployed: • • • • • • • • • • •

PVD Probe Vehicle Data RWW Road Works Warning SWD Shock Wave Damping TJW Traffic Jam Ahead Warning VRU Vulnerable Road User Protection MVW Maintenance Vehicle Warning IVS In-Vehicle Signage EVA Emergency Vehicle Approaching TSP Traffic Signal Priority Request Route Advice – connected cooperative navigation GLOSA Green Light Optimal Speed Advisory

By deploying new or expanding existing C-ITS services, the national activities within C-Roads Germany and C-Roads Germany - Urban Nodes promote the future rollout of C-ITS throughout Germany and Europe [4]. With the results of the implementation and operation of a total of eleven different C-ITS services, Germany participates as an EU member state in the Europe-wide C-Roads cooperation. The C-ITS services are implemented across the various pilot sites and harmonized by the German Federal Highway Research Institute (BASt). 2.2 C-ITS Service GLOSA The GLOSA service is one of the most important C-ITS services for urban areas. The aim of the GLOSA (Green Light Optimal Speed Advisory) service is to predict the green phases of traffic signals (LSA) and to use this information for efficient and comfortable driving. By predicting the remaining time until the stage change, e.g., the change from red to green or from green to red, the optimal speeds can be calculated based on the spatial position of the vehicles within the approach. Via V2X connection and cooperation, drivers, cyclists, pedestrians, and public transport receive this information and can adjust their speeds optimally. In the future, automated and autonomous vehicles will also be able to process this information and adapt their driving functions accordingly. All C-ITS services and in particular the C-ITS service GLOSA pursue three goals: 1. Increase of traffic safety by avoiding accidents at signalized intersections and traffic lights [5] 2. reduction of negative environmental impacts by reducing pollutants, saving fuel and reducing noise emissions [5, 6] 3. increase of traffic efficiency by stabilization of traffic flow and harmonization of driving behavior [7–10]

A Framework for Urban C-ITS GLOSA Evaluation

385

3 Framework The availability of the GLOSA service at traffic lights, considered over the entire day, depends on many spatial-temporal criteria. The intersection where the traffic signal system is located cannot be considered separately. Rather, the context in the road network must be considered. Road users move from intersection to intersection. Some, for example, drive along the main road along the green wave constructed by planning. Others turn into the main road or turn onto the side road. For these participants, too, it may be useful to communicate speed recommendations. Other road users, such as public transport, bicycles, or pedestrians, for example, move at completely different speeds than individual traffic. These simple examples show the complexity of the analysis. To structure this complexity, the problem is divided into subproblems in the framework presented in the paper. The effectivity of GLOSA (measured as percent of the cycle time where it is available with high confidence) is analyzed, considering • the current signal program parameters like cycle time or proportion of green per cycle, • traffic flow parameters like traffic load or queue length and • limitations on the lowest and highest possible speed advised by GLOSA as well as the range of communication depending on the communication technology.

Fig. 1. Framework for urban C-ITS GLOSA evaluation.

The signal program parameters of traffic lights probably have the greatest influence on the efficiency of the GLOSA service. Here, too, different parameter sets have to be distinguished. The first differentiation is based on the type of flexibility of the signal control system: fixed-time programs are to be distinguished from slightly adaptive programs, trafficdependent controls and full-traffic-dependent controls. The simplest hypothesis is that the more rigid the program sequence, the better the signaling can be predicted, thus, the more accurately control decisions can be predicted. In the case of extremely flexible control systems, such as the fully traffic-dependent control system, a fully reliable prediction is only possible in the areas of interstage times. Slightly traffic-dependent control systems, whose variance are subject to certain conditions, e.g., by coordinating one of

386

T. Otto et al.

the directions, can also be used well for the GLOSA service under certain circumstances. Thus, for a reasonably reliable forecast, certain recurring events are advantageous. Figure 1 shows the deviation of green times per signal group and cycle compared to the level of flexibility of the signal control system starting with • no flexibility (1): fixed time program, • via a slightly traffic-dependent program, e.g., for green time extension within coordination (2): adaptive program • up to a fully flexible control (3): fully adaptive program. As an example, a cycle time of 60s was chosen here. The green time in the fixed time program is 15s and is shortened or lengthened in the adaptive programs according to the traffic conditions (Fig. 2). (1) fixed time (2) adaptive (3) fully adaptive

Fig. 2. Green time deviation depending on the type of signalization.

Here it is clearly evident that the predictive ability of future control states depends on the degree of flexibility. If a forecast were to be based solely on historical data - which is not the norm - statements about future switching states would be difficult to make for fully flexible control. Figure 3 shows this picture, which becomes more and more diffuse as a result of flexibility. (1) fixed time (2) adaptive (3) fully adaptive

Fig. 3. Forecast quality depending on the type of signalization.

In recent years, many research projects have addressed how to ensure prediction of signal states with varying levels of control flexibility. Here we will refer to [11–13]. In [14] it is shown that in 80% of all cases signal changes could be predicted 15s in the future with a high enough accuracy to enable GLOSA for adaptive traffic lights. It is precisely this effect that offers great potential for increasing the efficiency of inner-city traffic in the future when using the C-ITS service GLOSA in conventional traffic but also in the area of CCAM (Connected, Cooperative and Automated Mobility). Figure 3 only shows the forecast quality resulting from the long-term forecast. The forecasts are

A Framework for Urban C-ITS GLOSA Evaluation

387

much more precise if this long-term forecast is overlaid with a short-term forecast. This takes into account all detector inputs, prioritization of public transport, coordination and other traffic boundary conditions. To sum up, the most stable forecasts are possible on the basis of fixed-time programs, even over longer periods of time. As a result of the traffic boundary conditions, as well as the required transition times and interstages of the signalization, high confidences can also be achieved in adaptive signal control in the short-term forecast. For the proposed framework it is relevant to consider the amount of flexibility and the accuracy of the forecast on short and long term time scales. 3.1 Traffic Flow Parameters The capacity utilization of a road, i.e., the ratio of traffic volume to available capacity, is a decisive factor in determining the traffic flow in inner-city road networks. In most cases, the bottlenecks are signalized intersections. Several effects can be observed, which will be shown as examples: Effect 1: The higher the traffic volumes in inner-city networks, the less flexible the control systems are [11]. This is even the case with highly flexible control systems. The reason lies in the underlying control principle, where the requirements of a wide variety of signal groups mean that there are almost no more degrees of freedom available for the control system. For the framework for urban C-ITS GLOSA evaluation, the utilization of the green times of the traffic lights is thus decisive in order to make a decision as to whether the application of GLOSA makes sense. With the overlay of the classification of the flexibility of the control, it can be reasonable that vehicles approach the traffic lights without GLOSA, because the traffic lights give green per se (e.g., green wave). Effect 2: With increasing traffic volumes, the queue length at the intersections typically also increases. Today’s conventionally planned coordination typically work up to a utilization of 80–85% of the green time [9]. After that, they break down. Further, the pulks break down due to vehicles turn off, so you always have to stop vehicles in front of traffic lights briefly after a longer distance to keep the pulks dense. The dynamic C-ITS service GLOSA manages to keep the pulk dense by individual information and to extend the operational conditions for coordination significantly. This leads to an increase in efficiency in the network. Effect 3: Different modes of transportation have different constraints for implementing GLOSA. In this context, motorized individual transport is often discussed. However, great effects are also achieved in public transport and bicycle traffic. The authors of [15] show for buses that GLOSA could avoid unnecessary stops significantly. Compared with non-GLOSA buses, the GLOSA buses save waiting time at the target intersection by 98.95%. Public transport offers the great advantage that stopping times can be shifted to the stops and thus be available, for example, for an extended passenger exchange. Figure 4 shows a plot of the travel time of a public transport vehicle at two different intersections. The individual travel times are represented by points. On the abscissa, the daily times are clustered according to hourly slices, the ordinate shows the travel time in seconds. The graph illustrates the 50% median.

388

T. Otto et al.

Fig. 4. GLOSA versus TSP bus at signalized intersection. Real world data taken from an intersection in Frankfurt/Main (Germany).

The left graph shows a very clear picture of a traffic light without or extremely poor prioritization of public transport. The average travel time varies strongly between a minimum value and something less than the cycle time of the traffic lights. Variations over the day with regard to the peak hours in the morning and in the afternoon can be seen. A correlation to the cycle times of the running signal programs is well observable. The C-ITS service GLOSA would be able to achieve its full impact here. The right graph, on the other hand, shows a traffic light with very good prioritization of public transport throughout the day. The variations in travel times when passing the intersection are minimal. Due to the high quality of prioritization, the C-ITS service GLOSA becomes obsolete at this traffic light. For bicycle traffic, the advantage is that due to the relatively low speeds, even small speed adaptations lead to a smooth passing of the traffic lights. And especially for cyclists, stops are extremely energy-intensive and severely limit comfort. To sum up, traffic parameters such as traffic volume and queue length significantly impact the quality and level of service the C-ITS application GLOSA. The conditions of use of static coordination, which today have physical limits, can be significantly extended by dynamic individual speed information. The framework for urban C-ITS GLOSA evaluation does not only consider the motorized individual traffic, but offers an integral consideration of all traffic types takes place. On parallel traffic routes, static green wave information often fails today due to the different speed levels of the multiple traffic types. This problem can be solved by providing targeted information. 3.2 Limitations of Speed Advice and Communication Range The communication range has a decisive influence on the availability of the GLOSA application and on the possibilities from when a RSU (Road Side Unit) can provide individual information to a vehicle. Figure 5 shows an example of this at an intersection with an intersection distance of 1000 m and communication ranges of 300 m, 500 m and 1000 m. Real measurements are also compared in the right-hand part, which allow different radio propagations depending on the building development, geometry and other factors.

A Framework for Urban C-ITS GLOSA Evaluation

389

Fig. 5. GLOSA effectiveness depending on communication range.

The higher the range, the more likely the speed can be influenced with GLOSA service. Based on the findings of [16], most GLOSA simulation studies are too optimistic in terms of communication performance. Stahlmann et al. [16] give a recommendation on how real-world GLOSA systems can be further improved to support a sufficient level of performance. However, [17] present results from extensive field tests with almost 200 traffic light approaches: two-hop dissemination of signal phase and timing information from traffic lights increases the maximum information distance by around 35% and is able to support continuous updates even in challenging environments. Current hardware developments show that the ranges of RSUs in the ETSI-G5 area have improved significantly with the new generation of hardware. However, another important correlation arises from the effectiveness of the GLOSA as a function of the distance to the traffic light and thus the time remaining until green in combination with the accepted speed range of the GLOSA. The range of parameters of the accepted speed recommendation for vehicle traffic is based on [18]. This correlation is much more complex. Figure 6 shows the position of the green time in an access road with a length of 1000 m spatially ablated. The light green area indicates the effectiveness of the GLOSA application under different boundary conditions. green in 10s for 20s speed: 50km/h green in 10s for 20s speed: 40-50km/h green in 10s for 20s speed: 3050km/h green in 20s for 20s speed: 50km/h green in 20s for 20s speed: 40-50km/h green in 20s for 20s speed: 3050km/h green in 30s for 20s speed: 50km/h green in 30s for 20s speed: 40-50km/h green in 30s for 20s speed: 3050km/h

Fig. 6. GLOSA effectiveness depending on vehicle position and communication range.

Two effects can be clearly identified: Effect 1: The higher the accepted speed interval of the GLOSA, the better the vehicle can be fitted in the range of the GLOSA. If, theoretically, extremely low speeds were also accepted, the vehicle could be influenced in all spatio-temporal areas so that it does not have to stop at the traffic light. Effect 2: The earlier a vehicle can be influenced, the higher the achieved effect of GLOSA. Here, the available communication range is decisive.

390

T. Otto et al.

Effect 3: The higher the traffic volumes at an intersection, the longer the cycle time typically is. This is due to the fact that the proportion of interstage times separating one traffic flow from the other is then lowest in the cycle [11]. In most cases, long cycle times with identical release times also lead to longer red stage. With constant communication ranges, this has the consequence that the GLOSA service cannot be available for the entire time in the approach within the communication range. Theoretically, this would be possible, but since arbitrarily small speed recommendations are not accepted by the road users, it is not reasonable to implement. Effect 4: It does not make sense to provide the GLOSA service in all circumstances. It is important to ensure that different routes do not interfere with each other. For example, because a left turn vehicle receives a lower recommended speed and thus blocks a vehicle going straight ahead because it is not possible to overtake. Under the various boundary conditions, the C-ITS service GLOSA can thus develop its full potential. Xie et al. [13] shows in terms of traffic flow, that crossings with GLOSA can reduce time usage by 41.62 and 35.21%, respectively, compared with crossing in the fixed paths. Environmental compatibility is also significantly improved. Numerical analyses showed [15] that a higher fuel efficiency and lower carbon dioxide (CO2 ) emissions were achievable, even under high-density traffic conditions, by combining the full and partial assistance levels of GLOSA. To sum up, the framework for urban C-ITS GLOSA evaluation has to consider the communication range in any case. For this reason, a tool was integrated that uses machine learning to determine and learn ETSI-G5 communication ranges and uses these results as the basis of a planning tool for analyzing G5 radio coverage. In addition to the planning components, the tool has a live monitoring of the communication including signal strength, communication partners and hopping of messages. For a complete analysis, the traffic-related parameters are combined with the signal-related parameters of the radio. This completes the framework and provides a comprehensive overview of where and when the C-ITS service GLOSA can best be used, or with which other C-ITS service (e.g., PVD or TSP) a combination makes sense.

4 Conclusions and Outlook The framework for urban C-ITS GLOSA evaluation clearly shows that the C-ITS service can exploit large untapped potentials. The key to this is a comprehensive individual cooperation by exchanging information between the infrastructure and the vehicles (V2I) and between the vehicles themselves (V2V). The limits of the systems are also clearly shown. It is obvious, however, that dynamic linear individual information has much more potential for improving traffic flow, safety and environmental impact than stationary collective speed recommendations. C-ITS does not consist of the GLOSA service alone. Another factor in the success of C-ITS will be the integral consideration of all services and information in the area of cooperative mobility. This involves the aspects of informing, warning, navigating and control. In terms of traffic lights, the TSP service is a key factor here. Today’s singular prioritization of public transport will be replaced in the future by a linear prioritization

A Framework for Urban C-ITS GLOSA Evaluation

391

of all road users. Several stakeholders are facing challenges in this regard. What we have already achieved, however, is to provide standardized messages for harmonized services regardless of the communication technology. Whether ETSI-G5, C-V2X or 5G as mobile communications is irrelevant here. In order for these services to be made available, adaptations and expansions are necessary across the entire municipal infrastructure, the hardware of the traffic lights, the RSU and OBU in the vehicle. In addition, completely new approaches are required in the planning tools for traffic lights, traffic management or, for example, tools for planning public transport. Only a few of today’s complex traffic management and control systems can fulfill the necessary real-time requirements, as they were originally designed for monitoring purposes. The increase in penetration rates for the roll-out of C-ITS has been systematically overestimated in recent years. The reason for this is that, in addition to the private automotive industry, the public sector will implement a large part of the measures, and this requires funding, a roadmap and time. In the context of CCAM, the driving function of highly automated vehicles today no longer relies entirely on information from the sensor-ego view but is instead dependent on information from other road users and, above all, from the infrastructure, especially in the complex urban environment. While C-ITS provided information to road users, the level of security is now higher. For CCAM, the information from the infrastructure is directly integrated into the assistance function or automated driving function, which leads to a shared safety responsibility between the communication partners. For this, the systems including cooperation must be expanded or redesigned with regard to both functional safety and SIL conformity. The implementation of signed messages and PKI alone will not be sufficient here. With the C-Roads project, we have succeeded for the first time in deploying harmonized services across Europe. This included the motorways in the first step and is currently being followed by the “urban nodes” pilots. This initial step was necessary and is currently leading to a consolidation of activities. As a result, deployment is taking place more slowly than was assumed 10 years ago, but the stabilization is already leading to the development of initial potential in some areas. For many services, it is irrelevant whether they are offered via the backends of OEMs or municipal transport systems or are communicated directly. What is important is that the communication path and the interfaces match the time criticality, trustworthiness and reliability of the respective service. With regard to the GLOSA service, there are very different implementation options in Germany, Europe and worldwide under the boundary conditions presented in the framework of this paper here. Acknowledgements. This research is financially supported by the European Union Connection Europe Facility (C-Roads Urban Nodes).

References 1. Bohm, M.: Europe’s C-Roads platform: paving the ground to make cooperative, connected and automated vehicles (CCAV) a reality. Routes/Roads (373) (2017)

392

T. Otto et al.

2. Lokaj, Z., Srotyr, M., Vanis, M., Broz, J.: Technical part of evaluation solution for cooperative vehicles within C-ROADS CZ project. In: 2020 Smart City Symposium Prague (SCSP), pp. 1–5. IEEE (2020) 3. Strobl, S., Klöppel-Gersdorf M., Otto T., Grimm, J.: C-ITS Pilot in Dresden—designing a modular C-ITS architecture. In: 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 1–8 (2019). https://doi.org/10. 1109/MTITS.2019.8883376 4. Reiff, T., Godarzi, F., Cheung, S., Hirschberger, M., Pei, Y., Luepges, C.: C-Roads Germany. In: 26th World Road CongressWorld Road Association (PIARC) (2019) 5. Katsaros, K., Kernchen R., Dianati M., Rieck, D.: Performance study of a green light optimized speed advisory (GLOSA) application using an integrated cooperative ITS simulation platform. In: 2011 7th International Wireless Communications and Mobile Computing Conference, pp. 918–923 (2019). https://doi.org/10.1109/IWCMC.2011.5982524 6. Haberl, M., Cik, M., Fellendorf, M., Otto, T., Luz, R., Roth, P.: Emission minimizing adaptive signal control: a multimodal optimization approach. In: Proceedings 22 ITS World Congress, Bordeaux, France 7. Kloeppel, M., Grimm, J., Strobl, S., Auerswald, R.: Performance evaluation of GLOSAalgorithms under realistic traffic conditions using C2I-communication. In: Conference on Sustainable Urban Mobility. Springer, Cham, pp. 44–52 (2018) 8. Kloeppel, M., Grimm, J.: Evaluating suitable glosa-algorithms by simulation considering realistic traffic conditions and V2X-communication. Transport Telecommun. J., 303–310. https://doi.org/10.2478/ttj-2020-0025 9. Otto, T.: Kooperative Verkehrsbeeinflussung und Verkehrssteuerung an signalisierten, Knotenpunkten. Dissertation am Fachgebiet für Verkehrstechnik und Transportlogistik, Universität Kassel (2011) 10. Xie, F., Naumann, S., Czogalla, O.: Speed control system for pedestrians crossing signaled intersections time optimally. IFAC-PapersOnLine 52(24), 82–87 (2019) 11. Otto, T., Weichenmeier, F.: Self-learning algorithm and signal state prognosis at traffic lights for V2I applications. In: Proceedings 20th ITS World Congress ITS Japan (2013) 12. Weisheit, T., Hoyer, R.: Support vector machines–a suitable approach for a prediction of switching times of traffic actuated signal controls. In: Advanced Microsystems for Automotive Applications, pp. 121–129 (2014) 13. Xie, F., Sudhi, V., Ruß, T., Purschwitz: A dynamic adapted green light optimal speed advisory for buses considering waiting time at the closest bus stop to the intersection 14. Bodenheimer, R., Brauer, A., Eckhoff, D., German, R.: Enabling GLOSA for adaptive traffic lights. IEEE Veh. Networking Conf. (VNC) 2014, 167–174 (2014). https://doi.org/10.1109/ VNC.2014.7013336 15. Suzuki, H., Marumo, Y.: A new approach to green light optimal speed advisory (GLOSA) systems for high-density traffic flowe. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 362–367 (2018). https://doi.org/10.1109/ITSC.2018.856 9394 16. Stahlmann, R., Möller, M., Brauer, A., German, R., Eckhoff, D.: Exploring GLOSA systems in the field: technical evaluation and results. Comput. Commun. 120, 112–124 (2018) 17. Stahlmann, R., Tornatis, A., German, R., Eckhoff, D.: Multi-hop for GLOSA systems: evaluation and results from a field experiment. IEEE Veh. Networking Conf. (VNC) 2017, 175–178 (2017). https://doi.org/10.1109/VNC.2017.8275617 18. Otto, T., Hoyer, R.: Operating conditions of on-board displayed green wave speeds via V2Icommunication. In: Proceedings fovus-Network for Mobility (2010)

Emerging and Innovative Technologies in Transport: Co-creating Innovative Technologies in Transport

Pursuing Technological Solutions for Tourists’ Urban Travel Behavior Change in the Post COVID-19 Era; The SUSTOURISMO App Kornilia Maria Kotoula(B)

, Glykeria Myrovali , and Maria Morfoulaki

Centre for Research and Technology-Hellas, Hellenic Institute of Transport (CERTH/HIT), 6th km Charilaou – Thermis, 57001 Thessaloniki, Greece {nilia,myrovali,marmor}@certh.gr

Abstract. Seeking for a positive insight in the Covid-19 emergency situation, it can be well said that from the beginning of the pandemic spread, impacts on transport and tourism sector and adopted policy measures were investigated, as cities noticed a behavioral change towards alternative transport modes. In most cases, governments recognized that the promotion of walking and cycling could reduce the risks of contagion, protect public health and achieve an effective modal split in favor of active modes, hoping to remain after the pandemic. In the need for a fast reaction, the transport and tourism sectors displayed great tolerance for experimentation and a willingness to support innovative technological solutions for promoting safer green trips and visits in the post-COVID era which appears to be coming soon enough. Such solutions limited or even banned the development of expensive and costly infrastructural interventions and encouraged suitable behavioral change of people habits. In this line, the development of smart mobile applications is undoubtedly one of the most vital aspects for both the transport and the tourism industry. Considering the fact that the pandemic effects demand a re-evaluation of travel solutions, the current paper presents the SUSTOURISMO mobile application, developed to meet the tourists’ needs and boost sustainable mobility in touristic regions. So far, the results arise from the testing phase of the app, confirm that such useful technology driven tools can be used as urban policy interventions in an effort to obtain a more sustainable mobility in urban environments. Keywords: Smart mobility applications · Sustainable mobility · Sustainable tourism

1 Introduction The global COVID-19 health pandemic that violently invaded in our lives since the late 2019, caused significant implications and consequences for the society, environment and economy worldwide, showing that modern communities were not well prepared to undertake the various challenges arose in these sectors [1]. It redefined our daily lives and our interactions between organizations, introducing new ways of working [2] and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_34

396

K. M. Kotoula et al.

providing services of different characteristics such as educational, mobility, touristic if any within the lockdown periods, etc. During these two years that Covid -19 haunted our lives, researchers had the opportunity to examine a wide range of its effects on different dimensions of sustainability and sustainable mobility [3], [4], tourism [5], [6], energy [7], [8], climate change [9], etc. Seeking for a positive insight in this emergency situation, it can be well said that the pandemic rapidly generated a demand for designing, developing and adapting technologically innovative solutions to face the negative impacts which arose in our everyday living [10], while it also raised new opportunities for evolving existing information technology solutions [11] by leveraging previous experience and knowledge. Teleworking and on-line education are only two of several paradigms that reminded us of the crucial role digital technology can play during and even after the pandemic, in case of other crucial situations emerge [12]. The need to engage citizens and visitors in safe and sustainable solutions is critical for any industry seeking to achieve its full potential and transport as well as tourism are no exceptions. From the very beginning of the pandemic spread, impacts of COVID-19 on the transport and tourism sector and adopted policy measures have been investigated, as cities around the world noticed a behavioural change towards alternative transport modes. In most cases, governments recognized that the promotion of walking and cycling could reduce the risks of contagion, protect the public health [13, 14] and achieve an effective modal split in favour of active transport modes, hoping to remain after the pandemic. In the tourism sector, the use of active mobility should also be combined with effective measures to manage destinations and avoid overcrowding in the popular points of interest of each touristic area. In the need for a fast reaction, the transport and tourism sectors displayed great tolerance for experimentation and a willingness to support innovative solutions for promoting safer green trips and visits in the post-COVID era which appears to be coming soon enough. Such solutions limited or even banned the development of expensive and costly infrastructural interventions and encouraged suitable behavioral change of people habits. For example, ‘travelers’ guidance in adapting green mobility behavior’ could be achieved by information campaigns, highlighting how active transportation have a positive effect on urban air quality during the pandemic [15], or by the so-called persuasive technologies, which underly the active role that both residents and visitors of an urban environment can play in this procedure [16]. In this line, Information and Communication Technology (ICT) exploitation as well as smart mobile applications are undoubtedly one of the most vital aspects for both the transport and the tourism industry and also part of our modern networked societies. The fact that travel apps are the seventh most downloaded mobile applications [17], essentially amplifies all the more the latest trend of developing and promoting such applications for achieving sustainable mobility [18]. Considering the fact that the pandemic effects demand a re-evaluation of travel solutions, the current paper presents the development of a mobile application to be used within urban policies for modifying the mobility behaviors of tourists mainly, towards more sustainable choices. The application as presented in the next sections was developed in the framework of the SUSTOURISMO Interreg ADRION project, aiming to support shift to sustainable

Pursuing Technological Solutions for Tourists’ Urban Travel Behavior

397

mobility options when visiting specific Adriatic – Ionian Region (ADRION) touristic areas and enhance the participatory tourism & mobility planning approach.

2 Research Approach The biggest challenge for tourists travelling is mobility management. From one hand, understanding tourists’ mobility patterns allows local authorities to better manage touristic flows in overcrowded touristic areas, while on the other hand paves the way for the design of services that satisfy them and at the same time divert attention from private vehicles’ use to more sustainable mobility alternatives such as walking and bicycling. In response to the threats of Covid - 19 spread, this need has become more than imperative nowadays, as the disease is expected to have long-term impacts on the transportation system of both developed and developing countries [19]. Considering from one hand that sustainable mobility principles have brought out mobility pattern changes and from the other that social inequalities have been noticed within the pandemic period, the idea that Covid -19 crisis could be a great opportunity to change transport choices through new solutions, promoted the so called “responsible transport”. According to the concept of responsible transport, users should be more aware of their transport choices consequences, not only on environmental level, but also on possible impacts of other users’ public health, due to the pandemic effects. Based on that, the idea of the SUSTOURISMO app came up in order to be used as a tool for promoting responsible transport mobility and easing users’ life visiting ADRION touristic areas. Due to the fact that the app was developed within the Covid- 19 pandemic period, considerations regarding its effect on tourism and mobility were considered in all development stages. The app’s conceptualization and design followed a user-centered approach, focusing on tourists’ needs and requirements. For that, a questionnaire survey was designed and conducted to a large sample of tourists (n = 2623) visiting specific ADRION touristic areas, in order to understand which features of provided mobile services would be the most helpful and valued in the context of sustainable tourism mobility. The current research focuses on Thessaloniki city case, Greece, presenting initial results of the questionnaire survey that were taken into consideration from the first stages of the app’s development. In total, 301 visitors (both domestic and international), participated in the survey conducted with face-to-face interviews and held on September 2020 in main tourist entrance/exit points of the city. Regarding the sample, almost 7 out of 10 tourists have visited Thessaloniki in the past, only 7% traveled using an organized package tour whereas the vast majority (93%) organized their trip on their own and 69% reached the city by plane as it is considered as the faster way. The main reason for choosing Thessaloniki for their visit appears to be the option “Visiting friends or family” (38%), while the “Sea and sun” and “Outdoor activities” options follow concentrating 31% and 12% respectively. 2.1 Thessaloniki Case Study Thessaloniki is the capital of the Region of Central Macedonia, the second largest city in Greece numbering approximately one million residents within the metropolitan area.

398

K. M. Kotoula et al.

It is a strong political, economic, industrial centre in northern Greece and an attractive tourism destination. Within the last decade, a rapid development of the tourism sector in the city has been noticed. The existing statistical data collected before the pandemic outbreak, present a 12% increase of international arrivals at the airport (2017–2018), while compared to 2014 the increase recorded, climbed to 38%. Moreover, an increase of 26% of international overnight stays in hotels has been noted during 2014–2018. This trend is expected to continue, in the post COVID-19 recovery era. One of the main findings of the survey, focused on the gaps and difficulties tourists encountered while travelling within the city. According to their responses the sense of unsafety due to Covid – 19 pandemic is the third difficulty tourists encountered concentrating 17% of the sample. The inadequate public transport services provision and the heavy traffic within the city road network found to be the main difficulties concentrating 31% and 22% respectively.

Fig. 1. Difficulties encountered by tourists during their travel within the city of Thessaloniki

The above results in combination with the major percentages that both private and rented vehicles as well as taxis concentrate (66% in total), verify somehow the tourists’ considerations regarding the Covid-19 impacts and reveal their preference in favor of private vehicle use, instead of other transport modes available in the city. The low percentages noticed regarding the use of alternative transport modes (bicycle and walking), come to confirm this trend in a large degree. Noticing that similar results came out from the questionnaire survey that took place in the rest ADRION touristic areas, it can be well said that a transport system re-evaluation should be scheduled in short term period following the sustainable urban mobility principles (Figs. 1 and 2). Regarding the provided by the app services, these were designed taking into account real tourists’ requirements as these were identified through specific questions. Indicatively the below are mentioned; “In what kind of information would you like to have access through a mobile application regarding the area you are visiting”, “What kind of rewards would you mainly prefer in case of using the app”, “Would you like to have

Pursuing Technological Solutions for Tourists’ Urban Travel Behavior

399

Fig. 2. Transport modes mostly used by tourists for trips within the city of Thessaloniki

the possibility through the app to express your complaints regarding touristic and transport services”. An example of the kind of information tourists visiting Thessaloniki city declared is depicted in Fig. 3.

Fig. 3. Tourists’ preferences regarding the information to be provided by an app

According to their responses, tourist services regarding accommodation, gastronomy, shopping and activities, information regarding the transport modes available in the area, information regarding the events organized to take place during their staying in the city and information regarding cultural sites and museums are the most preferable. The app included also information categories, as these were defined from the rest questionnaire surveys’ results and described in the next section. The survey also examined the possibility of integrating in the app specific Touristic Packages for tourists in an effort to further promote sustainable tourism through sustainable mobility. Based on the above, the SUSTOURISMO app was developed and presented in the following section.

400

K. M. Kotoula et al.

3 The SUSTOURISMO App System Overview The SUSTOURISMO app operates in both Android and iOS systems. It integrates functionalities found spread out in separate tools and services. The final version, provides information about touristic attractions and points of interest within and outside a visited touristic area, information regarding the availability and operation of ‘green’ modes, in the area, events organized to take place in forthcoming periods, as well as thematic routes to be followed on foot or by bicycle. Through the app, well designed and integrated touristic packages are provided, giving tourists the opportunity to get familiar with the visited area while using alternative transport modes. It consists of two main categories each of which provides several touristic services as depicted in Fig. 4. A more detailed description of the app’s services is provided in the next sub- sections.

Fig. 4. The SUSTOURISMO app main services categories

3.1 Information About the Area The ultimate scope of this service is the provision of a fully detailed and descriptive presentation of all relevant tourism information in an attractive way, for satisfying and motivating tourist towards sustainable travel choices. For achieving that, the SUSTOURISMO app proposes to tourists several options for visiting the preferable points of interest, by providing useful information regarding the available ‘green’ transport modes nearby their current location. The service provides the below informative categories: • Points of Interest, including information regarding the city’s points of interest and how to reach them using sustainable transport modes.

Pursuing Technological Solutions for Tourists’ Urban Travel Behavior

401

• Tourist Packages, informing the tourist a bout available touristic packages offered in the visited area • Mobility info, including information regarding the available transport mode systems in the area • Events, providing any information about ongoing or upcoming events • Weather real time conditions • Useful information links (Emergency Service, Tourist Police, Pharmacies and Hospitals, Ambulance Service, Police, etc.)

Fig. 5. The Information about the city sub-services

The sections above, follow the same design and user experience principles. The user selects category, sub category and all the available items are displayed in a list view or in a map view in order to select the most interesting one. For each selected point of interest, the user has access to relevant texts, photos and contact information. In addition, and by selecting the “Guide me” button the user can get information from a scroll down list which includes all the available sustainable transport modes near the current location. Thus, the user can select the preferable one and get information on how to use it in order to get to the final destination (Fig. 5). 3.2 Contribution and Win All services included in this category aim to increase the users’ engagement, learning and contributing on directing their behavior towards sustainability. More specifically: Through the Trip’s recording and evaluation service, useful data related to tourists’ trips is collected, as the visitor declares a specific trip followed by filling in specific

402

K. M. Kotoula et al.

information such as information about the Origin and Destination points, the transport mode used for the trip and the reason for selecting the specific transport mode, the trip day, the start and end time of the trip. After declaring the trip, the user is asked to rate the transport mode used, scoring it through a specific scale (use of a five-point scale 1–5, where 1 corresponds to not at all satisfied and 5 corresponds to very satisfied). Following, the user is asked whether the chosen transport mode was a first option and in case the answer is ‘No’ a scroll down list is displayed to the user asking to declare the transport mode preferred to have been used and for which reason it was eventually not used. In this case, all touristic areas included in the app collect useful information regarding the existing provided transport services which in turn can be used by Public Authorities and transport operators in order to upgrade the area’s transportation system. Through the Participation in Touristic packages service, the users participate in specific touristic packages free of charge and are rewarded by collecting points in an e-wallet (function of this service). For attracting tourists and engage them in the whole process of being part of a touristic package, a rewarding point system has been developed and integrated within the app as an extra service. For all the app’s services, the user automatically collects points which can redeem through specific rewards and offers. As part of the points’ collection system, the service provides to the user an integrated QR code system which can be used during his participation in the “tourist packages”. Each service (e.g., bicycle rental, completion of a specific trip using a ‘green’ transport mode, use of Public Transport System, etc.) contains a different set of reward points, while the overall points collected are depicted to the user through a personalized digital wallet (Figs. 6 and 7).

Fig. 6. Contribute and Win sub-services; trip’s submission and Participation in Touristic Packages

Pursuing Technological Solutions for Tourists’ Urban Travel Behavior

403

Fig. 7. The Contribute and Win sub-services; proposals and complaints/counting steps

Through the Proposals and Complaints service, tourists are encouraged to propose solutions for the provided tourism and mobility services found not to be satisfied enough during their stay in one of the touristic areas included in the app. Through a predefined list, the user is able to select the relevant category (e.g., cultural sites, transport modes, etc.) and declare a complaint through the selection of specific proposals (predefined drop-down menu) and also describe it in more detail using a free text box. Finally, through the Counting Steps service, tourists are encouraged to visit main points of interest of the city by promoting walking. Therefore, tourist’s steps are tracked during a trip. The user, having activated the GPS function declares the trip’s start/end through a start/end button. The system counts the tourist’s steps throughout the trip, while at the end the user is informed for the exact number of steps and collects the relevant points.

4 Discussion The current paper presented a tourist-oriented mobile application, developed in the framework of the SUSTOURISMO Interreg ADRION project, in an effort to highlight the capabilities of smartphone applications on serving tourists’ needs and supporting efficient decision making in terms of sustainable mobility. Focusing on Thessaloniki city case, it presented main insights acted as the foundation for the app’s basic structure. Main findings were also verified by the other ADRION touristic areas participated in the project. The so called SUSTOURISMO app is an innovative solution, developed under the spectrum of the Covid -19 pandemic, which

404

K. M. Kotoula et al.

eventually seems to act as a great opportunity for an overall behavioral change towards the use of alternative transport modes. The questionnaire survey conducted in Thessaloniki allowed to identify gaps and difficulties tourists face while travelling within the city. Similar difficulties were identified in the rest participating touristic areas. According to the survey’s results the sense of unsafety due to Covid -19 pandemic, seems to complicate the travel decision procedure and the tourism experience as a whole. This fact in combination with the particularly high rates the use of private and rented vehicle concentrate, pointed out the need of a solution able to turn tourists towards the use of environmentally friendly transport modes providing at the same time useful information regarding the visited area. The app’s development and the first results came up from the testing phase, verified main thoughts supporting that the use of such technology driven tools can be used for engaging tourists in the decision-making process and drive them to the right direction of selecting alternative transport modes for trips within touristic areas. So far approximately 50 visitors of Thessaloniki city have downloaded the SUSTOURISMO app, even though it has been tested for one-month time period. The fact that Covid 19 restrictions are no longer in force in combination with the high touristic period launch for Thessaloniki city, is expected to bring more downloads and registrations in the app. According to the initial feedback received from the users, the app seems to cover its initial objectives so far. The dedicated users’ satisfaction questionnaire that has been integrated in the app confirms that tourists who have already used it, provided positive feedback and declared their satisfaction from the offered services and support that the app, offers them the opportunity to get familiar with the visited area using alternative transport modes such as walking and bicycling, not only through the provided information, but also through the touristic packages’ experience. Due to the fact that the hard core of the testing phase is expected to be completed within this summer, a detailed analysis regarding the app’s use and acceptance will follow, as a natural step in this work’s continuation. Acknowledgements. This research was developed in the framework of SUSTOURISMO ADRION 2014–2020 project (https://sustourismo.adrioninterreg.eu/) co-financed by the European Regional Development Fund.

References 1. Politis, I., et al.: Mapping travel behavior changes during the COVID-19 lock-down: a socioeconomic analysis in Greece. Eur. Transp. Res. Rev. 13(1), 1–19 (2021). https://doi.org/10. 1186/s12544-021-00481-7 2. Hiselius, L.W., Arnfalk, P.: When the impossible becomes possible: COVID-19’s impact on work and travel patterns in Swedish public agencies. Eur. Transp. Res. Rev. 13(1), 1 (2021). https://doi.org/10.1186/s12544-021-00471-9 3. Bodenheimer, M., Leindenberger, J.: COVID-19 as a window of opportunity for sustainability transitions? Narratives and communication strategies beyond the pandemic. Sustain. Sci. Pract. Policy 16(1), 61–66 (2020). https://doi.org/10.1080/15487733.2020.1766318

Pursuing Technological Solutions for Tourists’ Urban Travel Behavior

405

4. Pirlone, F., Spadaro, I.: The resilient city and adapting to the health emergency. TE-MA-J. Land use, Mobility Environ. (2020). https://doi.org/10.6092/1970-9870/6856 5. Ioannides, D., Gymothy, S.: The COVID-19 crisis as an opportunity foe escaping the unsustainable global path. Tourism Geogr. 22, 624–632 (2020). https://doi.org/10.1080/14616688. 2020.1763445 6. Romagosa, F.: The COVID-19 crisis: opportunities for sustainable and proximity tourism. Tourism Geogr. 22, 690–694 (2020). https://doi.org/10.1080/14616688.2020.1763447 7. Kanda, W, Kivimaa, P.: What opportunities could the COVID-19 outbreak offer for sustainability transitions research on electricity and mobility? Energy Res. Soc. Sci. 68 (2020), Article 101666. https://doi.org/10.1016/j.erss.2020.101666 8. Kuzemko, C., Bradshaw, M., Bridge, G., Goldthau, A, Jewell, J., Overland, I., Scholten, D., Vande Graaf, T., Westphal, K.: Covid-19 and politics of sustainable energy transitions. Energy Res. Soc. Sci. 68 (2020), Article 101685. https://doi.org/10.1016/j.erss.2020.101685 9. Markard, J., Rosenbloom, D.: A tale of two crisis: COVID-19 and climate. Sustainability: Sci. Pract. Policy, 16, 53–60 (2020). https://doi.org/10.1080/15487733.2020.1765679 10. O’ Leary, D.E.: Evolving information systems and technology research issues for COVID-19 and other pandemics. J. Organ. Comput. Electron. Commer. 30(1), 1–8 (2020). https://doi. org/10.1080/10919392.2020.1755790 11. Sein, M.K.: The serendipitous impact of COVID 19 pandemic. A rare opportunity for research and practice. Int. J. Inf. Manage. 55 (2020), Article 102164 12. Richter, A.: Locked down digital work. Int. J. Inf. Manage. 55 (2020), Article 102157 13. Slater, S.J., Christiana, R.W., Gustat, J.: Recommendations for keeping parks and green space accessible for mental and physical health during COVID-19 and other pandemics. Preventic Chronic Dis. 17, E59 (2020). https://doi.org/10.5888/pcd17.200204 14. World Health Organization: Supporting healthy urban transport and mobility in the context of COVID-19. World Health Organization. https://www.who.int/publications-detail-redirect/ 9789240012554 (2020) 15. Dingil, A.E., Esztergár-Kiss, D.: The influence of the Covid-19 pandemic on mobility patterns: the first wave’s results. Transp. Lett. 13(5–6), 434–446 (2021). https://doi.org/10.1080/194 27867.2021.1901011 16. Anagnostopoulou, E., Bothos, E., Magoutas, B., Schrammel, J., Mentzas, G.: Persuasive technologies for sustainable mobility; state of the art and emerging trends. Sustainability 10, 2128 (2018). https://doi.org/10.3390/su10072128 17. https://www.mindinventory.com/blog/advantages-of-having-mobile-apps-for-your-traveland-tourism-business 18. Myrovali, G., Morfoulaki, M., Vassilantonakis, B.M., Mpoutovinas, A., Kotoula, K.M.: Travelers-led Innovation in Sustainable Urban Mobility Plans. Periodica Polytechnica Transportation Engineering 48(2), 126–132 (2020).https://doi.org/10.3311/PPtr.11909 19. Henser, D.A.: What might Covid-19 mean for mobility as a service (Maas)? Transp. Rev. 40, 551–556 (2020)

Stakeholders’ Survey on the Introduction of Connected and Automated Vehicles in Greece Evangelia Gaitanidou1(B)

, Evangelos Bekiaris1

, and Panagiotis Papaioannou2

1 Centre for Research and Technology Hellas/Hellenic Institute of Transport, 6th Km

Charilaou-Thermi Rd, 57001 Thessaloniki, Greece [email protected] 2 School of Civil Engineering, Aristotle’s University of Thessaloniki, Thessaloniki, Greece

Abstract. Connected and Automated Vehicles (CAV) have been among the most emerging topics in automobility during the past decade. Technologically, the advances show its feasibility and numerous pilot applications worldwide imply its operability in urban and highway contexts. However, what is of utmost importance is to achieve a smooth and effective deployment, in order to ensure that the transportation system - and society in broader terms - shall benefit from their positive implications (among which fostering road safety) and avoid shortcomings and side-effects. In Greece, road safety has been a traditionally critical issue, in which significant progress has been achieved in the latest years. However, apart from small-scale piloting, limited CAV deployment activities have been undertaken so far. In order to identify how CAV could optimally be introduced in the Greek transportation reality, a survey has been designed, addressing stakeholders from different fields (academics, researchers, local authorities, transport operators, etc.). The aim of the survey is primarily to investigate the stakeholders’ opinion on the processes, actions and frameworks towards the deployment of CAV in Greece while, at a later stage, these results will facilitate the definition of deployment scenarios. The survey has been undertaken online, with the use of a specifically designed structured questionnaire, including questions varying from general opinion and acceptance of CAVs, to specific issues regarding, technological, legislative and regulatory issues. In total 47 questions were addressed to the participants and 21 answers received so far. This paper is presenting the survey, along with preliminary results and insights. Keywords: Autonomous · Connected · Automated vehicles · Stakeholders · Survey · Deployment · Transportation

1 Introduction Connected and automated vehicles (CAV) is by many the “next big thing” in transportation and a great challenge for industry, research, authorities, practitioners and the society to develop, assess, deploy, accept and use this new form of mobility. At EU level, it has been more than a decade during which research has been focusing on CAV, both in technological and user perspectives [1]. Moreover, extensive literature exists © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_35

Stakeholders’ Survey on the Introduction of Connected

407

on the implications that the adoption of this form of mobility could bring in diverse areas, primarily in road safety, but not less significantly on mobility, economy, society, employment and the environment, to name a few [2–7]. One of the characteristics of CAV is the variety of their possible applications: from private vehicles and Mobility as a Service (MaaS) to public transport, freight and logistics [8]. In Greece, small scale pilots have been performed in the past [9, 10] or are ongoing [11, 12], however there is not yet a specific strategy in place, planning for the actual deployment of CAV. In this view, and within the framework of the PhD thesis of the main author, a survey has been undertaken, aiming to identify the views and opinions of different affected stakeholders, in terms of the CAV deployment in Greece. Stakeholder analysis has primarily been undertaken by the author, investigating relevant literature [12, [13], in order to specify which are the ones directly involved in the decision making and implementation process.

2 Methodology To investigate the opinions of the stakeholders, a structured questionnaire was used, elaborated by the author. The questionnaire contained in total 47 questions and was divided in 3 sections. The first section (7 questions), upon requesting consent for the use of the data for the needs of the research, aims to gather information about the respondents, like name, email, represented organization, etc. In the second section (12 questions), general questions on the view of the participants regarding CAVs are included, like whether the organization they represent has been (or should be) dealing with CAV related issues, their personal knowledge of such issues, their opinion on necessary changes needed regarding CAV introduction, the issues that are important or could be an obstacle for the introduction of CAVs in Greece, potential benefits for the users, etc. Finally, the third section (28 questions), addresses some more specific questions on AV deployment, regarding organizational, legislative, social, technical and infrastructural aspects. For the construction of the questionnaire, related surveys addressed to stakeholders have been considered in terms of the structure and the contents as well as the topics to be addressed [14–17]. Moreover, policy documents and publications related to the deployment of AVs have also been considered, as the White Paper I of the EU-US Symposium on Automated Vehicles [18], the EC communication “On the road to automated mobility: An EU strategy for mobility of the future” [19] and many others. In the questionnaire design, different types of questions were used, depending on the content and qualitative value of result. Thus, the questionnaire included questions of multiple choice, net promoter score, free text, Yes/No, alternatives prioritization, Likert scale, while the participants were requested to answer all questions. The selection of participants was done with the aim of representing different groups of stakeholders that are related to the deployment of CAVs in the country and are already somehow accustomed to CAV-related issues, according to relevant stakeholder analysis that was performed in the context of the first author’s PhD. In this concept, the respondents’ list includes academics, researchers, transport operators and transport infrastructure managers, representatives of municipalities and transportation authorities.

408

E. Gaitanidou et al.

The survey was performed online, using Microsoft Forms and was conducted during 2020–2021. For the analysis of results Microsoft Excel was used.

3 Results In this section an overview of selected results is presented. These results came from the analysis of the participants’ responses to the questionnaire; in total 21 responses were received. The section is divided in three parts, following the structure of the questionnaire itself, in order to present the results of each of the questionnaire sections in turn. 3.1 Section 1 – Participants’ Data Upon requesting consent from each participant, in terms of the use of the data provided in their responses for the needs of the research (main author’s PhD) some personal information was collected for communication reasons. This related to their name, email and phone number, their professional affiliation, the organization they represent and its type. Out of the 21 respondents, 7 were representing Universities (33.3%). 3 represented Research Centres (14.3%), 5 participants (23.8%) were representing operating companies of transport services and/or transport infrastructure, 2 participants came from transport management authorities (9.5%), while the remaining 4 (19%) were representing Municipalities. 3.2 Section 2 – General Questions In the second section, including 12 questions, the participants are asked to respond regarding general issues on driving automation. These include topics like the responsibility of represented organisations on relevant issues and whether they have already worked on them, how do the participants rate their personal knowledge on driving automation, areas in which changes should occur for CAV deployment and priorities towards this goal. Also, the most significant obstacles and benefits from the introduction of CAVs, as well as how some early problems could affect the industry and the public opinion. In the following we are presenting some indicative results. At institutional level, 67% (14 respondents) declared that their organization has already been involved in issues related to automated driving, while 90.4% (19 respondents) believe there are issues within the responsibility of their organization that are related to driving automation. For the optimal deployment of CAV, certain changes will be needed in various sectors. The participants were asked to express their opinion on the sectors and the level of change they consider necessary. The results show that the respondents believe that what should be totally changed is primarily the management of infrastructure use, as well as the contracts with 3rd parties and the development of relevant supporting Intelligent Transportation Systems (ITS) infrastructure. Significant changes are needed in legislation, as well as in standards, organizational/institutional framework, while for the rest some or few changes are considered as necessary (Fig. 1).

Stakeholders’ Survey on the Introduction of Connected

409

Sectors and level of changes needed Management of infrastructure use (e.g. dedicated lanes for AVs) Development of relevant supporng ITS infrastucture (e.g. 5G, G5, IoT) Construcon/Instaling/Maintenance of infrastructure and equipment Controlling methods (e.g. law enforcement, Road Safety Audits) Internal processes (e.g. personnel training) 3rd party contracts (e.g. insurance) Protocols and procedures (e.g. drivers' training) Standards Organisaonal/Instuonal framework (e.g. responsibilies assignment) Legislaon (e.g. traffic code) 0

3

6

9

12

15

18

21

Should be totally changed Very significant changes/addions should be made Some changes/addions should be made A few changes/addions should be made No change is needed, could remain as is

Fig. 1. Sectors where changes are needed and at which level

The priorities that should be put and the obstacles that are expected to be faced should carefully be considered when designing CAV deployment. Communication issues (Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and vehicle connectivity) are considered as top priority by most of the participants, followed by policy and regulatory issues, Human Machine Interface (HMI) for CAVs and cybersecurity, user privacy and data security, vehicle data management issues. CAV business ecosystem was rated as the least important issues for the smooth CAV deployment (Fig. 2). On the other hand, in the question regarding which is considered as the greatest obstacle for the deployment of AV, reservations on road safety, lack of regulatory framework and infrastructure issues were considered the most critical. Less of a problem were considered the reservations on personal data privacy, the lack of digital mapping platforms with easy update functionality and cybersecurity (Fig. 3).

410

E. Gaitanidou et al.

Priories for the smooth deployment of driving automaon in Greece Cybersecurity, User privacy and data security, Vehicle data management (incl. vehicle mapping and localisaon) V2I, V2V and vehicle connecvity issues (incl. 5G) Policy and regulatory issues

AV Business ecosystem

AVs HMI

ADAS 0%

20%

40%

Very important

Important

Neither important nor unimportant

Unimportant

60%

80%

100%

Very unimportant

Fig. 2. Priorities for CAV smooth deployment in Greece

3.3 Section 3 In the third section of the questionnaire, the participants are asked to express their opinion on more specific issues regarding CAV adoption. The questions can be thematically grouped in terms of the broader issue they address. In particular, there are questions addressing the use of CAV, the regulations and corresponding framework, the infrastructure needs, the business and financial issues and their actual operation and related restrictions. Use of CAV Starting with the use of CAV, the participants were asked to define priorities on what would be their most efficient use. Public transport was the first one in the ranking, followed by shared mobility schemes, while as least efficient use is considered the mixed passenger/freight transportation. The following question refers to whether people that normally are not able to drive a conventional vehicle (e.g., very young, very old, impaired or under substance influence) should be allowed to ride alone an autonomous vehicle. 43% among the respondents answered yes, while a significant 24% is against it. Some suggestions coming from the

Stakeholders’ Survey on the Introduction of Connected

411

Which do you see as the biggest obstacle in the deployment of CAV? 0

3

6

9

12

15

18

21

Reservaons on road safety Cost of investment Infrastructure issues (appropriate road infrastructure) Lack of regulatory framework Reservaons on security Cybersecurity Reservaon on personal data security Lack of digital mapping plaorms for cies with the possibility of easy update 1st choice

2nd choice

3rd choice

4th choice

5th choice

6th choice

7th choice

8th choice

Fig. 3. Prioritization of possible obstacles in CAV deployment

participants indicated that there should be differentiation according to the category of user, that it could be allowed after some training and licensing as well as that legislation should also be adequately reformed to foresee this. Moreover, 12% of the participants did not express an opinion on this issue. Regulations and Framework The following group of questions address the regulations and corresponding framework that would be necessary to effectively introduce CAV. Regarding who should monitor the different regulatory issues related to CAV (EU or National authorities) the answers of the participants indicate that topics related to design, construction and operation of CAV and liability (including insurance) should be monitored at EU level, while licensing, training and enforcement are mostly relying on National level monitoring. Road safety is the only topic for which the answers showed that it should be monitored both at National and EU level. In this view, the respondents also expressed their expectations regarding the upcoming regulations on CAVs at EU level. Thus, they were asked to declare their agreement on whether in the next 5 years there will be a regulatory framework at EU level on autonomous vehicles. Only 5% expressed disagreement, while the remaining 95% of answers where either positive or neutral.

412

E. Gaitanidou et al.

Infrastructure For CAVs to operate smoothly, the infrastructure should meet some requirements, e.g. in terms of lane markings. The participants were asked whether the public authorities should apply more uniform standards to the road and roadside environment, with the majority (85%) agreeing.

5G network Internet of Things (IoT) Data centres for analysis of big data coming from the vehicles Wireless connecvity with the infrastrucutre - V2I Wireless connecvity between vehicles V2V 0 1st choice

2nd choice

5

3rd choice

10 4th choice

15

20

25

5th choice

Fig. 4. Priority infrastructure data needs in smart cities for conducting CAV pilots

Before broad deployment, it is necessary to perform some pilot applications in order to identify possible deficiencies, unexpected impacts as well as measure the user acceptance. In this concept, the respondents rated the priorities in requirements of infrastructure data for pilot testing to be possible (Fig. 4). In the first places we meet wireless connectivity, either between the vehicle and the infrastructure or between vehicles, followed by 5G network and big data analysis centers. Operation The next series of questions refer to how CAVs should operate in different road environments. As seen in the results illustrated in Fig. 5, for urban and rural networks the most preferred option was to use CAV in selected sections (restricted areas), with second option being the operation in the entire network. For highways, the use in all highways came first in the participants’ preferences, with lane separation as second option. It was also suggested to gradually introduce CAVs in the networks, starting from restricted areas, moving to separate lanes and finally allowing them in all public roads, while it was also highlighted that the operation environment is depended on a series of factors, mainly regarding the ability of the vehicle to merge with the existing traffic and road conditions. After specifying the operation mode in each type of network, the appropriateness of CAVs for operation in public roads and the criteria that would define it, were the subject of the next question (Fig. 6). The priority criteria, as expressed in the responses, were certification by responsible authorities, possibility for communication with the

Stakeholders’ Survey on the Introduction of Connected

413

40% 30% 20% 10% 0% Urban In all public roads

Rural

Highway

In selected secons (restricted areas)

In a separate lane

Fig. 5. CAV operation environment per road type

infrastructure and the level of automation. As of least priority, this was the speed limiter and the presence of “driver” or supervisor. Another issue that has been widely discussed regarding CAV deployment is the operating speed, depending of course on the road network category. This was addressed to the participants regarding urban, rural and highway networks. In all three cases the majority of the respondents believe that the speed limit should be the same as for the rest of the vehicles, while the second choice in all cases was the maximum of the suggested speed limits (40, 60 and 90km/h respectively).

Criteria for a CAV to be appropriate for operaon in the road network 0

3

6

9

12

15

18

Level of automaon

Maximum speed (limiter) Possibility for communicaon with infrastructure Cerficaon by responsible authority Existence of "driver" in or out of the vehicle (control center) able to monitor and/or intervene 1st choice

2nd choice

3rd choice

4th choice

5th choice

Fig. 6. Criteria for a CAV to be appropriate to operate in public roads

21

414

E. Gaitanidou et al.

Market Introduction The last group of questions refer to the market introduction of CAV. The participants were asked to rate the top priority challenges for the introduction of CAVs in the market among the provided options. From the answers, we see that the first priority challenge by far is the safety of users, followed by restrictions on liability issues and the merging of national and EU regulatory authority. Marketing and false perceptions/attitudes of user are considered as the least challenging among the provided options (Fig. 7). As for the use of new management and business models for facilitating the V2I cooperation and the optimal functioning of CAVs, the majority of participants are in favour, while in the question of which would be the most appealing CAV technologies to invest in the next 5 years, 5G technology comes as the first choice, followed by Advanced Driver Assistance Systems (ADAS) and communication technologies (V2X, G5) (Fig. 8).

Priority challenges for the market introducon of CAVs 0

3

6

9

12

15

Safety of users Restricons on liability issues (e.g. insurance coverage, responsibility of the driver/manufacturer/operator) Merging of naonal and EU regulatory authority Slow evoluon of infrastructure development (improvements in roads and bridges) Sales/business models (both in private nad professional vehicles) Cybersecurity

False percepons/atudes of the users

Markeng

1st choice

2nd choice

3rd choice

4th choice

5th choice

6th choice

7th choice

8th choice

Fig. 7. Priority challenges for CAVs market introduction

18

21

Stakeholders’ Survey on the Introduction of Connected

415

Finally, the last question investigated the opinion of the participants on whether the pubic authorities should provide motivation to the people to buy and use CAVs. 48% among the respondents answered positively, while it was noted that this should vary according to the vehicle use (as private vehicles; as PT or for shared use) and the expected payback to society.

0

3

6

9

12

15

18

21

5G technology Advanced Driver Assistance Systems (in vehicle) Communicaon technology between vehicles and between vehicle and infrastructure (V2X, G5) Driving data analysis and machine learning Connecvity and entertainment applicaons

1st choice

2nd choice

3rd choice

4th choice

5th choice

Fig. 8. Priority CAV technologies for investment in the next 5 years

4 Discussion The results of the survey highlight that CAV deployment in Greece is a process still in initial stages, however with great potential and interest from the affected stakeholders and organizations, as most of them have good knowledge on related issues, while they have already recognized and are involved with those that lie within their responsibilities, such as development of algorithms, infrastructure improvement, fleet management, legislation, etc. Among the sectors affecting the optimal deployment of CAV the management of infrastructure use and development of supporting ITS infrastructure are recognized as the ones in need for radical change, along with significant modifications suggested for legislation, standards and the organizational/institutional framework. These changes will depend on the final architecture that will be selected (separate or mixed flows, level of automation, communications protocols, etc.) while their timing is considered as dependent mostly on technological process rather than the market penetration. Also, the changes are expected to vary according to the type of network and road environment, while it has been underlined that user acceptance, impact assessment and ethics are some

416

E. Gaitanidou et al.

critical topics to be considered in the process, which should be facilitated by relevant awareness and user engagement campaigns. Communication issues (both V2I and V2V), ADAS and vehicle connectivity, along with the establishment of sound policy and regulatory frameworks are set as the top priorities for smooth deployment, while as major obstacles are considered the reservations regarding road safety, the lack of regulatory framework and issues related to the infrastructure. The use and the users of CAV is an intriguing topic. CAV applications vary from personal vehicles to public transport, shared mobility vehicles, freight and mixed purpose. Moreover, as these vehicles (at higher levels of automation) do not require the cooperation of a driver, it is opening discussion on whether people who do not hold a driver’s license (under or over aged, license-suspended) or are not capable to drive (due to disability or under substance influence) could use such vehicles alone (as a personal vehicle or on demand). From the survey results, public transport and shared mobility schemes are assumed as the CAV use applications that should be prioritized in terms of deployment, while regarding users the use of CAV of driving-restricted persons is generally seen positively, however pointing out that there should be differentiations per user category as well as an appropriate legislative framework to cover this option. Regulations and a relevant appropriate framework are repeatedly mentioned as key parameters for smooth deployment. These regulations should, apart from being established, be continuously monitored and updated. The responsibility to do this, according to the survey findings, should lie at national or EU level depending on the topic. Thus, issues like the design, construction and operation of CAV and liability should preferably be monitored at EU level, while licensing, training or enforcement could be better tackled at national level; interestingly, shared responsibility is suggested for road safety issues, highlighting the importance and significant impacts expected in this area. Other issues (apart from the ones suggested in the survey) that were put forward were ethics (at National level) and the development of relevant infrastructure as well as pricing (EU level for equipment – national level for operation) as requiring shared EU/National level responsibility. Moreover, the majority of the participants expressed their certainty that, in a 5-year horizon, there will be an EU level regulatory framework for CAV in place, thus showing that there is awareness that this is an upcoming evolution and its proximity is imposing the need for preparedness. Infrastructure is one of the sectors where the introduction of this new mobility reality will have significant impact. Even before official deployment, pilots are already taking place, for which the infrastructure should meet some minimum prerequisites. In this sense, it is recognized that more uniform standards should be applied for the road and roadside environment, while there is a great need for infrastructure data, with priorities on data related to wireless connectivity (V2I or V2V), 5G network and big data analysis centers. These are considered as minimum parameters for pilot implementation on CAV in a smart city framework. In terms of the operation environment of CAV in different contexts (urban, rural or highway) interestingly for both urban and rural cases operation in restricted areas is the most preferred option, while lane separation the least appealing one. In the case of highway, operation on all roads is the top choice, with lane separation lacking in small

Stakeholders’ Survey on the Introduction of Connected

417

difference. These answers are reasonable also in the context of a series of challenges that were highlighted for each context. For instance, in urban environment, there are issues regarding conspicuity, road users’ behavior and customization with CAV, traffic management, liability, technological reliability, coexistence with other modes and types of vehicles, to name a few, that are critical for the deployment of CAV in all roads, together with the rest of the traffic, facing an overall challenge to ensure effective mobility in a safe environment through a holistic AV operational system development. In the rural network on the other hand, challenging issues in deployment are the need for a forgiving road environment, which would imply significant reconstruction and maintenance on the network, as well as the need for installation of appropriate equipment. As for the highways, a common framework between different highway operators would be necessary, to ensure interoperability, along with the need for funding regarding network upgrade in terms of equipment, maintenance and traffic management. An overall challenge in terms of operation in the entire network is identified in the shifting between different road categories (rural to highway to urban) to what concerns the adaptability of the vehicle and the interoperability between the networks. Here again raises the need for uniform standards for the roads and roadside environments. Regarding the operating speeds in the different road categories, it is commonly accepted that in all cases CAV should follow the same speed limits as the rest of the traffic, while to what concerns the criteria for a CAV to be appropriate for operation in the network, certification by responsible authorities, connectivity and V2I communication as well as the level of automation were suggested as priorities. Finally, market introduction is a parameter that should be put forward when discussing deployment. The priority challenges that are expected to be met is first and utmost the safety of the users, with liability and regulatory issues coming right after. Novel management and business models should be applied to facilitate V2I cooperation and CAV optimal functioning, while, in terms of CAV technologies that would be most appealing for investment in the short term, 5G, ADAS and communication technologies are at the top of the list. Incentives and motivation for use and purchase of these vehicles should be provided to the public by the authorities, not necessarily in monetary terms, varying according to the vehicle use (PT, shared, private) and the expected payback to society.

5 Conclusions With this overview of the survey results, it is becoming evident that the potential of CAV deployment in Greece is an issue of interest and endeavor of related stakeholders in the country. Of course, a lot of work still needs to be done in legislative, regulatory, operational and implementation level, along with economic and societal parameters that should carefully and effectively be targeted. Some steps towards the direction of an automated mobility framework have already started, with adaptation of legislation and some first piloting activities, while more are foreseen in the near future, fostered also by the latest 5G connectivity coverage expansion. This paper includes some of the main results of the survey undertaken among stakeholders for identifying the parameters for an effective introduction of CAV in Greece. It

418

E. Gaitanidou et al.

should be noted that the present paper includes the overall survey results, while separate analysis and discussion per user group is currently undertaken. The final results shall constitute the basis for the next step of the research in the framework of the main author’s PhD, on defining appropriate implementation scenarios for CAV in the context of the Greek road network.

References 1. CCAM website. https://www.connectedautomateddriving.eu/projects/findproject/. Accessed 16 April 2022 2. Penttinen, M., van der Tuin, M., Farah, H., de Almeida Correia, G.H., Wadud, Z., Carsten, O., Kulmala, R.: Impacts of connected and automated vehicles—State of the art. MANTRA Project, Deliverable 3.1. (2019) 3. Bagloee, S.A., Tavana, M., Asadi, M., Oliver, T.: Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. J. Mod. Transp. 24(4), 284–303 (2016). https://doi.org/10.1007/s40534-016-0117-3 4. Kopelias, P., Demiridi, E., Vogiatzis, K., Skabardonis, A., Zafiropoulou, V.: Connected and autonomous vehicles—environmental impacts—a review. Sci. Total Environ. 712, 135237 (2020). ISSN 0048–9697. https://doi.org/10.1016/j.scitotenv.2019.135237 5. Fagnant, D., Kockelman, K.: Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp. Res. Part A: Policy Pract. 77 , 167–181 (2015). ISSN 0965–8564. https://doi.org/10.1016/j.tra.2015.04.003 6. Duarte F., Ratti C.: The impact of autonomous vehicles on cities: a review. J. Urban Technol. 25(4), 3–18 (2018). https://doi.org/10.1080/10630732.2018.1493883 7. Frisoni, R., Dall’Oglio, A., Nelson, C., Long, J., Vollath, C., Ranghetti, D., McMinimy, S.: Research for TRAN committee—self-piloted cars: the future of road transport?. Directorate-General for Internal Policies Policy Department B: Structural and Cohesion Policies, Transport and Tourism. European Parliament. (2016) 8. Pisarov, J.L., Mester, G.: The use of autonomous vehicles in transportation. Tehnika. 76(2), 171–177 (2021). https://doi.org/10.5937/tehnika2102171P(2021) 9. Alessandrini, A., Cattivera, A., Holguin, C., Stam, D.: CityMobil2: Challenges and Opportunities of Fully Automated Mobility. In: Meyer, G., Beiker, S. (eds.) Road Vehicle Automation. LNM, pp. 169–184. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05990-7_15 10. FABULOS project website. https://fabulos.eu/lamia-pilot/. Accessed 14 April 2022 11. 5G-MOBIX project website. https://www.5g-mobix.com/newsandevents/events/5g-mobixgreece-turkey-cross-border-corridor-demo-event. Accessed 14 April 2022 12. SHOW project website. https://show-project.eu/mega-sites-trikala/. Accessed 14 April 2022 13. Herrmann, A., Brenner, W., Stadler, R. Stakeholders.: Autonomous Driving, Emerald Publishing Limited, Bingley, pp. 171–178 (2018). https://doi.org/10.1108/978-1-78714-833-820 181020 14. Baker & McKenzie International.: Global Driverless Vehicles Survey 2018, Baker and McKenzie (2018) 15. Foley & Lardner.: 2017 Connected Cars & Autonomous Vehicles Survey. Foley & Lardner LLP, USA (2017) 16. TU Automotive, Tractica.: The Connected and Autonomous Vehicles Survey. Tractica. 2018. USA (2018) 17. Perkins Coie: AUVSI. 2019 Autonomous Vehicles Survey. Perkins Coie LLP, USA (2019)

Stakeholders’ Survey on the Introduction of Connected

419

18. Shladover, S., Bishop, R.: EU-US Symposium on automated vehicles. White Paper I, road transport automation as a public-private enterprise. Third EU-U.S. transportation research symposium. In: Transportation Research Board Conference Proceedings, 52, Transportation Re-search Board, USA (2015) ISSN: 1073-s1652 19. European Commission.: On the road to automated mobility: an EU strategy for mobility of the future. EC communication, COM (2018) 283 final. Brussels, Belgium (2018)

Autonomous Mobility as a Means of Innovation Diffusion: The Case of Trikala, Greece Georgios Kalogerakos1(B) and Nikolaos Gavanas2 1 PhD candidate, Department of Planning and Regional Development, School of Engineering,

University of Thessaly, 38334 Volos, Greece [email protected] 2 Assistant Professor, Department of Planning and Regional Development, School of Engineering, University of Thessaly, 38334 Volos, Greece [email protected]

Abstract. The city of Trikala, Greece was one of the first cities in Europe that deployed Autonomous Vehicle (AV) trials in public streets, along dedicated (not separated) path. The implementation of the project faced numerous technical, legislative, administrative and practical challenges. However, it has achieved a significant added value for the city. First, it supported the engagement of local community with smart city projects. Second, it provided the city authorities with valuable knowledge on the development and operation of AV and smart mobility systems. Third, it contributed to the enhancement of the city’s profile as innovator in digital transition. Moreover, after the completion of the project, the AV corridor was converted to a bicycle lane, serving many highly attractive destinations in the city. This paper addresses the challenges in the implementation of the project, the effect of the project in the attitudes of local community towards smart city applications, the knowledge gained and the impact regarding innovation diffusion in the city in two periods. The first evaluation takes place one year after the completion of the project (02/2016) and focuses on its immediate impact and perceptions of the public towards it. Five years later, taking also into account the corona crisis, the second evaluation takes place in 2021 mostly focusing on the knowledge gained and on the innovation diffusion effects. The evaluation is based on literature review, with focus on the examined AV project, as well as on thorough structured interviews with the project implementation authority e-Trikala in a 5-year period. Keywords: Autonomous mobility · Innovation diffusion · Sustainable mobility

1 Introduction In 2016, the city of Trikala was the first city in Greece and one of the first cities globally that tested autonomous public bus along a dedicated path (through the implementation of the CityMobil2 in the context of the European Union’s (EU) 7th Framework Programme) [1]. The city of Trikala is well known for its continuous pursuit for the implementation and engagement to promote smart city solutions and new technologies. However, it © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_36

Autonomous Mobility as a Means of Innovation Diffusion

421

could be stated that the complexity of the above mentioned project for the deployment of the autonomous bus set it as a milestone for the innovation tradition of the city and for mobility research in the country. Based on this statement, in the current paper two assessments of this project in the short and the medium term take place. The first assessment took place in 2017, one year after the completion of the project and the second in 2022, five years afterwards. The first evaluation aims at illustrating how the project affected the plans for further experimentation on new technologies and how it influenced the interest of key stakeholders of the city in sustainable mobility. The purpose of the second evaluation is to illustrate how the new possibilities and the potential deriving from the testing of the autonomous transport system were exploited, regarding the implementation of autonomous mobility, the effect on sustainable transport planning and the overall diffusion of innovation. Emphasis is given on the twofold impact of the COVID-19 pandemic crisis. This refers to the disruption in many development prospects caused by this unforeseen event, on the one hand, and to the acceleration it brought to smart technologies deployment, on the other hand. The objective of this paper is to evaluate the contribution of the examined project on the strong innovation prioritization of Trikala and its potential effect on the development of autonomous and sustainable mobility in the city.

2 Methodology The current paper is a case study regarding the evaluation of the effect from the demonstration of autonomous mobility in the city of Trikala on the promotion of smart and sustainable transport and innovation diffusion. The impact from the autonomous bus trial on innovation diffusion and sustainable mobility in the city of Trikala is examined through the comprehensive literature review on the implementation of autonomous mobility in cities and the overview of the main urban characteristics and previous autonomous mobility projects in Trikala. Furthermore, the summary of results is presented, deriving from existing questionnaire surveys to assess the public opinion from the Trikala’s autonomous mobility demonstration. In addition, two sets of interviews were conducted in two periods, regarding the short-term impact immediately after the trial, and the midterm (see Annex). The first set of interviews took place in March, 2017 with three representatives of E-Trikala and of the CityMobil2 project. The second interview took place in December, 2021 with the participation of a representative from E-Trikala, elaborating on mobility projects. Finally, the challenges and prospects in implementing autonomous mobility in Trikala in the short and the medium term have been researched.

3 Implementing Autonomous Mobility in Cities Autonomous mobility can provide groundbreaking mobility services by filling the gaps of the existing urban transport system [2], the reduction in driver’s costs and the capability to be used by travelers with no driving license. These services refer to demand-responsive transport, neighborhood mobility, last mile services, intra-campus services and so on. These new ways of mobility have significant spatial, economic, social and environmental effects [3]. However, there is still very little experience in the deployment of autonomous

422

G. Kalogerakos and N. Gavanas

mobility (and predominantly on a trial basis) [4], hence it is very hard to accurately assess its effects [5]. Therefore, extensive experimentation on autonomous mobility in real-life situations is required, so that further knowledge on its modus operandi is gained [6]. Nowadays, various autonomous mobility demonstration projects have been conducted, aiming at showing the possibilities of this new technology for urban transport and public space, in different environments and for multiple use cases [7]. In particular, their main purpose is to address the technical, regulatory and public perception related challenges in the implementation of autonomous mobility [8]. Moreover, it is aimed to encourage local communities to embed this kind of mobility in their transport systems and to help them to define the suitable way for implementing autonomous mobility in the local context. At the same time, the spatial, environmental, development and social effects of this technology are examined, thus creating further knowledge on this field. Many of these projects take place on the urban space, due to the increasing part of the population living in cities [9] and to the critical need for tackling climate change effects [10]. At the same time, autonomous mobility gains greater acceptance in cities, compared to rural areas due to weaker automobile dependence in daily travel [11] and the lack of congestion and pollution issues [12].

4 Description of the Study Area 4.1 Demographics Trikala is located in central Greece and has a population of 81,355 inhabitants. It belongs to the Trikala Municipality, which lies within the boundaries of Region of Thessaly. Residents of Trikala are 50.4% females and 49.6% males. Regarding the education level, 16.3% of the population has a university degree, 31.2% has finished junior or senior high school (11.5% and 19.7% respectively) and 25.8% has finished primary school. The economically active population represents 39.7% of the total population. The vast majority of the population (80.4%) is employed [13]. 4.2 Urban Mobility Trikala is a medium-size city, therefore a large share of urban trips is of small or medium length. The above fact in conjunction with the flat terrain of the city has led to high shares of active transport, compared to the Greek context. In specific, walking is estimated to count for 33% of daily trips in the city, while 9% of circulating vehicles are bicycles [14]. Urban transport system consists of privately operated bus lines (Astiko KTEL) and taxis. Bus lines mainly connect the centre with the outskirts and nearby urban areas and villages. Public transport serves 3,500 passengers daily or 1,000,000 annually, 180,000 of them being students moving between home and school [14]. The city centre is developed around Iroon Polytechneiou square and Asklepiou street (partially pedestrianized), where a large share of the city’s services and shopping facilities are located. Another important element of the city centre is Litheos river, which ccommodates a significant number of recreation facilities and constitutes a rather valuable part of the city’s natural environment. Varousi district, at the northeast of the city centre, is where a significant part of recreation facilities of the city is developed.

Autonomous Mobility as a Means of Innovation Diffusion

423

The car fleet in the Regional Unit of Trikala consists of 41,252 vehicles [15]; it is estimated that 60% of them are registered within the Municipality of Trikala [16]. As in most Greek cities, car use is rather popular, even for short distances. In particular, on a typical day, around 19,800 cars are moving in the city centre [14]. This leads to extensive negative spatial, environmental, economic and social effects. Furthermore, car use accounts for a noticeable percentage of households’ income. Simultaneously, individual motorized transport weakens social inclusion by reducing the ability of citizens to be a part of city’s practices. Therefore, an important objective of city and mobility planning strategies should be to reduce car use. Parking lots and spaces occupy a large part of the public space, in the expanse of other uses, such as green spaces, or active transport infrastructure. There are 1,100 roadside parking spots in Trikala, 350 of them being located within the controlled parking area. There are also four parking places, two of which are free of charge. On working days, approximately 20 min are needed on average to find a parking spot in the city centre, while the cost of private parking reaches 1.60 e per hour [16, 17]. Illegal parking is also observed, affecting the efficiency of the transportation network negatively. The analysis of [18] in 24 Greek Municipalities, including Trikala, found an average of 8.8 illegally parked vehicles per 100 m of urban streets and a maximum of 30 illegally parked vehicles per 100 m, where the 37.95% were two-wheelers (motorcycles, bikes etc.). Traffic congestion is not extensive, yet it is still observed along streets leading to the city centre from the main entry points of the city and inside the central area. Peak hours are usually between 8:15–9:00 and 14:00–15:00 and count for 10% of total daily vehicle movements [14]. In 2019, 12 road accidents were reported within the Municipality, resulting in 1 fatality and 15 injured people [19]. 4.3 Innovation Experience Trikala has obtained great experience in technological innovation due to the numerous European and national projects that have taken place in the city in the last 15 years. These involve smart applications in the domains of mobility, health, governance, economy, public participation, food management etc. [20]. Innovation projects are carried out by Trikala Municipality’s development company E-Trikala SA, with the collaboration of a large number of local stakeholders and authorities. According to the interviewees, the ambition of the city’s policy makers for Trikala is to become a test-bed for new technologies.

5 Evolution of Autonomous Mobility Experience in Trikala The projects involving autonomous mobility and implemented in Trikala are described below. Furthermore, the summary of results from existing surveys on the public and stakeholders is presented. 5.1 Projects on Autonomous Mobility CityMobil2

424

G. Kalogerakos and N. Gavanas

CityMobil2 was an EU funded project of the 7th Framework Programme and the first project on autonomous mobility in Greece. The project’s demonstration in Trikala involved a circular, fully autonomous bus route of 2.4 km along a dedicated lane with 9 stops in the city centre (Fig. 1), which was tested in the period between November 1, 2015 and February 29, 2016. More specifically, the demonstration took place during the Christmas festival of Trikala, which attracts more than 900,000 visitors per year. This way, both the publicity of the project were increased and the city’s tourism was benefited [16, 21]. In total, 1,490 trips were made, carrying 8.15 passengers on average and 12,138 passengers in total. The most important innovation of the project was the use of public streets under real-life conditions, as relevant projects around the world in this period were usually implemented in closed or protected areas, like university campuses, airports or hospital complexes. The route was chosen so that maximum visibility and accessibility were gained as well as a large number of traffic scenarios was being tested [16].

Fig. 1. Autonomous bus demonstration route [16].

The demonstration involved six small buses (Fig. 2), each of them supervised at all times by a remote operator and an attendant on board. The project was widely communicated prior to the demonstration, thus gaining acceptance by the local community and attracting a large number of visitors (Interviews). The successful demonstration received considerable publicity in the media at the local, national and European level (Interviews).

Autonomous Mobility as a Means of Innovation Diffusion

425

Fig. 2. CityMobil2 autonomous bus [23].

AVINT AVINT (Autonomous Vehicles INTegrated within the urban context) is a national project (funded by the Programme EPAnEK 2014–2020 – Action “Research-Create-Innovate”). The project began in 2018 and will be completed by the end of 2022 [22]. The project involves testing of an autonomous bus between the intercity bus station (KTEL) and the city centre. The bus will operate for 2 months in 2022. The bus will run on public streets, like Citymobil2, but every remote operator will supervise four buses, instead of one (Interviews). Interviewees supported that this is a rather important difference, as it makes autonomous mobility beneficial in terms of both innovation diffusion and cost savings. SHOW SHOW (Shared automation Operating models for Worldwide adoption) is a project funded by the EU Horizon Europe Programme for the period 2020–2023. Autonomous buses will operate between the intercity bus station and the city centre, like AVINT. However, in SHOW both regular and demand responsive services are tested. Moreover, in SHOW it is intended that autonomous vehicles will not only operate in fixed routes but will move flexibly in larger parts of the city. Urban logistics are also included in the objectives of the project [24]. 5.2 Public Perception of Autonomous Mobility in Trikala After the first demonstration of autonomous mobility in the city, public attitudes towards autonomous mobility were depicted in various surveys, embedded into the ex-post evaluation report of Citymobil2, namely: Passenger ex-post evaluation and Stated Preference

426

G. Kalogerakos and N. Gavanas

(SP) surveys, stakeholder survey and survey to the wider public in Trikala [16]. Concerning the demographic characteristics of respondents in these surveys, the percentage of female respondents ranged from 49 to 52.5% and of male respondents from 47.5% to 51% respectively. Age distribution across the groups 18–24, 25–34, 35–44 and so on was proportionate, with a high participation (51%) of young people (18–34) in the passenger ex-post evaluation. University degree holders represented 41–46% of the respondents across the four surveys and 33–48.5% of the respondents were secondary education graduates. The majority of the respondents had experienced autonomous mobility at least once. These respondents showed higher acceptance of autonomous mobility. The main findings are summarized below: • The largest part of the sample in all surveys considered autonomous vehicles as equally safe or safer than the conventional ones. It should be highlighted that safety is considered as a main factor affecting acceptance of autonomous mobility [21, 25]. Concerns about safety were expressed, particularly regarding emergency management. This explains why 67% of the respondents in the wider public survey were in favor of the presence of staff on board the bus, in line with results from other projects [8]. • Security was assessed as satisfactory; yet concerns were stated about nighttime services. The 38% of interviewees expressed the need for staff on board during nighttime services, even if it comes at a higher fare cost. At the same time, around 1/3 of the sample would completely take the staff out of the vehicle if this would come at an increase in the ticket price. Furthermore, concerns were expressed regarding software malfunctions, such as hacking or navigation failure. • The potential regarding the usefulness and comfort of autonomous mobility was positively assessed. Comfort is considered as a critical factor for the acceptance of autonomous mobility [26]. It should be mentioned that, in the period of the demonstration, unexpected autonomous driving behavior was a common weakness in many demonstrations [27]. • Economic benefits, as a result of the absence of driving cost, or in the form of lower insurance rates are considered by the respondents as a key issue for adopting autonomous mobility. At the same time, concerns exist about increase in vehicle purchase cost in the autonomous mobility era. Reduced energy consumption and gas emissions as well as improved mobility services are believed by the sample as the major impacts of autonomous mobility in the urban transport system. These findings are in line with [28], where economic issues were found to play a key role in the acceptance of autonomous vehicles by the public. Energy efficiency and enrichment of the urban transport system are also considered as major driving forces in adopting autonomous mobility [2]. According to the people participated in the survey, improvements autonomous mobility will bring in the urban transport system include increased mobility for the elderly and the disabled, the possibility for on demand services and larger transportation space. Land savings due to limited parking space requirements were also stated as important in the stakeholders’ survey. On the other hand, congestion increase and legal liability in case of an accident were the primary concerns about autonomous mobility.

Autonomous Mobility as a Means of Innovation Diffusion

427

• The majority of respondents would welcome autonomous mobility on a permanent basis, even at a slightly higher price. Most preferred uses for autonomous vehicles would be service in tourist zones, feeder to public transport and night services. • The 67% of the respondents in the stakeholders’ survey stated that autonomous buses should operate on roads with motorized traffic (on shared or dedicated lane), instead of having them on roads shared with cyclists and pedestrians. This is in line with [25], who suggest that streets used by motorized transport can be regarded as more suitable for autonomous mobility testing than pedestrianized areas, due to the rather intense interaction of the bus with pedestrians in the latter case. • Higher education levels and employment were related to more positive perceptions towards autonomous mobility, in line with [12] and [25]. Male respondents showed slightly higher acceptance to this technology. In contrast to other studies, age [11, 29] and income [25] did not play a significant role in shaping preferences towards autonomous mobility. Another questionnaire survey was carried out by [30] online through local webpages and social media. Similar to the previous surveys, the research illustrated high potential acceptance of the autonomous bus but a weaker willingness to pay for autonomous mobility than in previous survey, thus highlighting the importance of economic incentives. It is worth mentioning that the above positive perceptions were achieved in spite of the fact that, according to the corresponding ex-ante stated preference survey in 2013 [31], 62% of the 208 respondents preferred the introduction of a conventional minibus service within the Trikala city centre, instead of an autonomous bus. From the respondents who chose the autonomous bus, 33% acknowledged the impact of autonomous buses on the innovation tradition and the prestige of the city, 19% believed they would offer high reliability services and 19% would choose them out of curiosity and interest to follow the new technological trends.

6 Challenges and Opportunities for Trikala: Short and Mid-term Assessments The main challenges and opportunities for the city of Trikala by the autonomous bus demonstration are examined through the two rounds of interviews in 2017 (short-term) and 2021 (mid-term) and the on-site research in 2017. Challenges are divided into institutional and technical-technological; two fields where significant efforts were required for the project to take place. Opportunities are sought in the fields of sustainable mobility and innovation diffusion, where this demonstration may have the greatest contribution. 6.1 Challenges Institutional challenges Short term assessment

428

G. Kalogerakos and N. Gavanas

In the period of the demonstration, legislation about autonomous vehicles was not existing in Greece, while according to Vienna Convention on Road Traffic, the existence of a driver was necessary for the movement of any vehicle [32]. Therefore, the legal framework had to be designed from scratch, for the demonstration to take place. Then, Greek State issued law 4313/2015 (article nr.48), which was followed by the ministerial decision 50308/7695/2015 allowed testing of autonomous vehicles for research purposes [33, 34]. According to the interviewees, the concept of this law was that the “driver” would not be abolished but would be placed in a control room, having all “senses” available, like visibility, sound etc., as they would have if they were inside the vehicle. Vehicles could run for a specific period, on a particular route, maximum at 25 km/h and should be supervised at all the time. Before the beginning of the demonstration for the public, testing of the vehicle without passengers had to take place. Insurance of autonomous vehicle also happened here for first time in Greece. Furthermore, detailed description of the project and of the safety measures had to be provided to the Municipality, the Regional authority (Decentralized Administration) and the traffic police in order for the project to be approved. Mid-term assessment In the period 2017–2022, other projects have been focusing on autonomous mobility, such as the aforementioned projects: AVINT and SHOW. As this technology has been significantly developed and spread since 2017, these projects would embed and further expand its current possibilities. Therefore, as interviewees stated, the update of the legislative framework was necessary for these projects to further test and improve this kind of mobility. Thus, law 4784/2021 was published to allow more innovation in autonomous mobility, compared to the initial law 4313/2015 [35]. In specific, it allows the supervision of four vehicles by one operator, instead of the one-to-one rate, posed by the first law. The interviewees indicated that this largely enhances the economic efficiency of the autonomous systems and comprises a critical component for further deployment. The new law enables logistics services, which is also a domain where autonomous mobility can provide notable economic, environmental and spatial benefits. In addition to the new law, the interviewed stakeholders noted that the previous experience increased the awareness and readiness of Trikala’s local authorities in decision making regarding autonomous mobility. Technical and technological challenges Short-term assessment Autonomous mobility is intrinsically linked with information technologies (IT) and telecommunication systems. These are used for the movement of the bus itself as well as for the communication of the bus with the infrastructure and the control room (Vehicle to X, V2X). According to the interviewees, apart from the 4G network which was already sufficiently developed in Trikala in the period of the autonomous bus demonstration, other related smart applications were implemented for the first time for the needs of the demonstration. These include fiber optics, smart traffic lights, the control room for the autonomous bus fleet management, and all necessary software. After the completion of

Autonomous Mobility as a Means of Innovation Diffusion

429

the demonstration, this infrastructure was made available to the public. In specific, fiber optics and Wi-Fi were used to improve the telecommunications of the city. The bus’s autonomous driving and maneuvering along a dedicated lane on the same street with motorized vehicles, bicycles and pedestrians is characterized as the main challenge by the interviewees. The bus lane had to be separated from the rest of the traffic by columns shorter than around 20 cm, otherwise the bus would recognize them as obstacles; so, “cat’s eye” road studs were chosen. However, as this type does not practically prevent access of other vehicles in the bus lane, success of the demonstration was based on the respect by the other road users. The bus ran without significant technical problems during the entire demonstration period, apart from a deviation from its normal course once, due to an interference to its telecommunication system, and other minor incidents due to the sporadic malfunction of sensors because of glaring sunlight and other reasons [16]. After the completion of the demonstration, the bus lane was converted to a bicycle lane. Mid-term assessment During the last five years, extensive improvements of the telecommunication system have taken place; also facilitated by COVID-19 pandemic. In specific, Trikala is the first city in Greece, where 5G network is about to be implemented across the entire area. This is a major driving force for the implementation of autonomous mobility, as it enables transmission of far higher amounts of information, compared to 4G. More specifically, the 5G network enables better remote supervision of the vehicle, improves the effectiveness of autonomous driving and the road safety conditions, and offers the potential to manage a larger fleet of vehicles. Furthermore, the autonomous bus trial highlighted the significance of high-quality telecommunication networks for the deployment of smart technologies in the city. The contribution of the CityMobil2 project in this process was important, as the technological patterns and technical approaches of the project are adjusted to current conditions and applied for the conduction of the two current projects on autonomous mobility, i.e. AVINT and SHOW. 6.2 Opportunities Sustainable mobility Short-term assessment The autonomous bus demonstration in 2016 paved the ground for embedding autonomous mobility in the sustainable mobility agenda of the city of Trikala. It showed the possibilities of this new technology and made visible the potential benefits in the promotion of sustainable urban development. This contribution was highlighted in the first round of interviews in 2017 and confirmed in the second round in 2021. Regarding the potential for the deployment of an autonomous bus system on a permanent basis, the interviewees stressed that the CityMobil2 project has conducted a cost-benefit analysis for a system connecting four Park and Ride points with the city centre, one of them being at the intercity bus terminal. The analysis showed that such a service would be financially sustainable and beneficial to the mobility system of the

430

G. Kalogerakos and N. Gavanas

city. In particular, it is estimated to increase the public transport share, reduce traffic in the city centre, as well as reduce accidents and have significant environmental benefits [16]. Mid-term assessment Through the AVINT and SHOW projects, the city aims to develop and test an autonomous bus system for the connection of the intercity bus terminal to the city centre. This route has a length of 3.8 km and is important, as it is one of the main entry corridors of the city. The selected route is one of the four routes examined in the cost-benefit analysis of the predecessor of AVINT and SHOW projects, i.e. the CityMobil2 project. According to the interviewees, based on the existing background and experience, a new feature that is currently being tested in the context of the above mentioned projects comprises the use of a shared (not separated) path. This way, by apitalizing on new autonomous driving technologies, valuable findings about the challenges from the full implementation of autonomous mobility in real-life, mixed traffic conditions will be produced. Moreover, on demand services will be implemented, so as to provide more effective and attractive services and to improve the overall performance of the urban transport system. At the same time, there will be fewer remote operators for the vehicles, according to the above mentioned national law of 2021, in order to substantially enhance the economic efficiency of the service. In the framework of the SHOW project, autonomous vehicles will be tested for urban logistics, where the technology is believed to have the potential to produce considerable environmental, economic and spatial benefits. It is considered that the familiarity of the local community with autonomous mobility and other “smart city” technologies and services will play an important role in the success of the projects. Although the buses will operate on a trial basis, the equipment and interventions that will be implemented for the needs of the demonstration, such as the smart infrastructure, the traffic regulatory measures and the street and pavement modifications, may be used for a regular autonomous vehicle service in the near future. According to the interviewees, the involvement of the city in these projects was rather facilitated by the previous experience. The experience from the CityMobil2 and the benefits of autonomous mobility that were illustrated during the first demonstration were a major motivation for further experimenting on this technology, while valuable knowledge on how to deal with the challenges of the implementation of autonomous mobility was gained. In the interviews it is highlighted that the pioneering role and the successful completion of the city’s first autonomous mobility trial are main drivers for local authorities and actors to integrate this new kind of mobility in their vision for the city and to collaborate in order to realize this vision. This is reflected on the Sustainable Urban Mobility Plan (SUMP) of Trikala, where the introduction of autonomous buses is included in its action plan [36]. Promotion of technological innovation Short-term assessment Apart from the specific experience and know-how in the implementation of autonomous transport technologies, the first trial of the autonomous bus system in 2016 provided the city with valuable knowledge on how to deal with projects of technological innovation

Autonomous Mobility as a Means of Innovation Diffusion

431

within a complicated institutional framework and uncertain socio-economic impact, while it constituted a major part of the city’s developing tradition in innovation. This fact in combination with the overall positive feedback from the public contributed to the creation of a fertile ground for city experimentation and the further investment in “smart city” infrastructure and services. Mid-term assessment According to the interviewees, the CityMobil2 project was one of the projects that paved the way for the establishment of an innovation tradition in Trikala city, not only in the field of autonomous road transport. This tradition seems to remain unaffected by the lasting the economic crisis in Greece and COVID-19 pandemic. Indeed, smart technologies obtained a steadily developing role during the pandemic, as they allowed the continuation of everyday activities under the regime of social distancing [37]. Apart from the above mentioned AVINT and SHOW projects that focused on autonomous road transport, other indicative examples of innovative projects in Trikala focus on implementation of drone transport for improved accessibility to remote areas, shared electric vehicles, shared mobility services in rural space, real time and periodical collection of traffic, mobility and urban infrastructure data, as well as participatory planning approaches. The interviewees highlighted that the cooperation networks between various stakeholders, which were established in the previous autonomous mobility projects, have acted ever since as core driving forces to continue promote smart and sustainable urban development. Finally, the pioneering role of Trikala in promoting autonomous mobility presents a significant competitive advantage from the place branding and urban tourism attractiveness perspectives.

7 Conclusions Since 2016, the demonstration of the autonomous bus in the city of Trikala could be regarded as a milestone for the development of autonomous mobility in Greece and for the whole of Europe. Technological, technical innovation was developed and implemented in order to conduct the trial, thus producing significant know-how for local stakeholders and place branding benefits. For this reason, the successful conduction of the specific demonstration was a major contribution to the innovation tradition of the city. In addition, the demonstration generated new knowledge for the international scientific community. The pilot testing of the autonomous bus system in the city introduced autonomous mobility in the sustainable urban and mobility planning agenda at the national level. In this framework, legislative and institutional innovations were achieved and pave the road for this new type of mobility to be further experimented and applied in various cities across the country. On the other hand, the city of Trikala is one of the European cities with the potential to integrate autonomous mobility into a smart and sustainable urban transport system on a permanent basis. In the context of the current research, the conduction of two assessments in the short and the medium term, i.e. one year and five years after the autonomous bus trial, conclude that the above positive impacts still remain. It is worth mentioning that the interviewed stakeholders continue to support these arguments in both assessments, in

432

G. Kalogerakos and N. Gavanas

spite of the persisting economic crisis and the COVID-19 crisis that intervened in the period 2017–2021. This can be an indication of the constantly increasing interest of the society for smart technologies in mobility and other sectors as well as for autonomous vehicles, independently from the instabilities of global environment that have occurred in the meanwhile. It is noteworthy that even pandemic crisis had unforeseen negative economic effects, above tendency not only was maintained but was also strengthened. Therefore, the promotion of autonomous mobility contributed to the innovation diffusion at the local, national and European level in the field of new technologies and innovation with focus on smart and sustainable urban mobility. Acknowledgements. The authors would like to thank the representatives of E-Trikala SA Mr. Odysseas Raptis, CEO; Mrs. Christina Karaberi, member of the Department of Research and Communication; Mr Loukas Vavitsas, Project Manager; and Dr. Elena Patatouka, Senior Project Manager and Researcher for their valuable contribution to this study.

Annex: Interview structure First round – 2017 • • • • • • • • •

Which were the main difficulties in bringing the autonomous bus in the city? How difficult was to run the autonomous bus in mixed traffic? Which was the technical stuff and the infrastructure used for the project? How was your collaboration with local stakeholders? Do you believe this project contributed to the establishment of sustainable mobility concept in people’s minds? Does the project have any impact on the development prospects of the city? How do you envision future of mobility, both in the city of Trikala and generally? Which are the primary differences between CityMobil2 and CityMobil4 [next autonomous mobility projects]? What kind of infrastructure should be created for autonomous mobility to be further implemented in the city?

Second Round – 2021 • How did the first autonomous mobility project affect perceptions of the public towards this technology? • How did the project provide the city with technical-technological and institutional know-how in the fields of autonomous/smart mobility and smart technologies in general? • Which were the place branding effects of the project to the city? • Did the project contribute to publicity and networking prospects of the city? • How did the project contribute to investment attraction in the fields of autonomous/smart mobility and smart technology? • Which was the influence of COVID-19 pandemic in the execution of autonomous/smart mobility and smart technologies projects?

Autonomous Mobility as a Means of Innovation Diffusion

433

• How was the infrastructure created for the project used for the city’s needs and for attracting more autonomous/smart mobility and smart technologies projects? • Did the project contribute to the promotion of sustainable mobility in the city? • Was there any impact of the project on the promotion of autonomous mobility on national scale?

References 1. CORDIS webpage. https://cordis.europa.eu/project/id/314190. Accessed 18 April 2022 2. Meyer, J., Becker, H., Bosch, P.M., Axhausen, K.W.: Autonomous vehicles: the next jump in accessibilities? Res. Transp. Econ. 62, 80–91 (2017) 3. Cyganski, R., Gade, K., Steck, F. and Kolarova, V.: Automated vehicles—game changer for urban mode choice?. In: Transport Research Procedia, 24 (= International Scientific Conference on Mobility and Transport, Urban Mobility, Shaping the Future Together, mobil.TUM 2018, 13–14 June, 2018, Munich, Germany), 433–435 (2019) 4. Paddeu, D., Parkhurst, G., Shergold, I.: Passenger comfort and trust on first-time use of a shared autonomous vehicle. Transp. Res. Part C 115, 102604 (2020) 5. Gavanas, N.: Autonomous road vehicles: challenges for urban planning in European cities. Urban Sci. 3(2), 61 (2019) 6. Lopez-Lambas, M.E., Alonso, A.: The driverless bus: an analysis of public perceptions and acceptability. Sustainability 11, 4986 (2019) 7. Salonen, A.O., Haavisto, N.: Towards autonomous transportation. Passengers’ Experiences, perceptions and feelings in a driverless shuttle bus in Finland. Sustainability 11, 588 (2019) 8. Nesheli, M.M., Li, L., Palm, M., Shalaby, A.: Driverless shuttle pilots: lessons for automated transit technology deployment. Case Stud. Transport Policy 9, 723–742 (2021) 9. Eurostat: Urban Europe. Statistics on Cities, Towns and Suburbs. 2016 Edition. Luxembourg: Publications office of the European Union (2016) 10. da Mata Martins, M.C., da Silva, A.N., Pinto, N.: An indicator-based methodology for assessing resilience in urban mobility. Transp. Res. Part D 77, 352–363 (2019) 11. Nordhoff, S., Arem, v., B., Merat, N., Madigan, R., Ruhrort, L., Knie, A., Happee, R.: User acceptance of driverless shuttles running in an open and mixed traffic environment. In: 12th ITS European Congress: ITS Beyond Borders, 19 June, 2017, Strasbourg, France, TS27 (2017) 12. Hudson, J., Orviska, M., Hunady, J.: people’s attitudes to autonomous vehicles. Transp. Res. Part A 121, 164–176 (2019) 13. Hellenic Statistics Authority: General Census of Population (2011) 14. Karaseitanidis, G., Lytrivis, P., Ballis, A., Amditis, A and Raptis, O.: Local transport plans reviewed and automated road transport system assessment. CityMobil2 Project, WP9, D9.1 (2013) 15. Hellenic Statistics Authority: Motor vehicles in circulation, by category, use and region in Greece (2020) 16. Portouli, E., Karaseitanidis, G., Lytrivis, P., Karaberi, X., Gorgogetas, G., McDonald, M., Piao, J., Valerio, M., Site, P.D., Pietroni, F., Sessa, C.: Trikala ex-post evaluation report. CityMobil2 Project, WP25, D25.4 (2016) 17. Municipality of Trikala webpage. https://trikalacity.gr/cat-building/parking/. Accessed 21 April 2022 18. Zoika, S., Tzouras, G.P., Tsigdinos, S., Kepaptsoglou, K.: Causal analysis of illegal parking in urban roads: the case of Greece. Case Stud. Transport Policy 9, 1084–1096 (2021)

434

G. Kalogerakos and N. Gavanas

19. Hellenic Statistics Authority: Road Traffic Accidents and Injured People per Regional Unit and Municipality, Depending on the Kind of the Accident (2019) 20. E-Trikala webpage. https://www.e-trikala.gr/. Accessed 12 April 2022 21. Kalogerakos, G.: Driverless mobilities: understanding mobilities of the future. Master’s Thesis. Aalborg, Denmark: Aalborg University (2017) 22. AVINT webpage. https://www.avint-project.eu/index.php/el/. Accessed 8 April 2022 23. Representative of E-Trikala SA (2017) 24. SHOW webpage. https://show-project.eu/. Accessed 8 April 2022 25. Rosell, J., Allen, J.: Test-riding the driverless bus: determinants of satisfaction and reuse intention in eight test-track locations. Transp. Res. Part A 140, 166–189 (2020) 26. Paddeu, D., Tsouros, I., Parkhurst, G., Polydoropoulou, A.,Shergold, I: A study of users’ preferences after a brief exposure in a shared autonomous vehicle (SAV). In: Transportation Research Procedia, 52 (= 23rd EURO Working Group on Transportation Meeting, EWGT 2020, 16–18 September 2020, Paphos, Cyprus), pp. 533–540 (2021) 27. Eden, G., Nanchen, B., Ramseyer, R., Evequoz, F.: Expectation and experience: passenger acceptance of autonomous public transportation vehicles. In: Bernhaupt, R., Dalvi, G., Joshi, A., Balkirshan, D.K., O’Neill, J., Winckler, M. (eds) Human-Computer Interaction—INTERACT 2017 (= 16th IFIP TC 13 International Conference, Mumbai, India, September 25–29, 2017, Proceedings, Part IV), pp. 360–363. Springer, Switzerland (2017) 28. Paddeu, D., Shergold, I., Parkhurst, G.: The social perspective on policy towards local shared autonomous vehicle services (LSAVS). Transp. Policy 98, 116–126 (2020) 29. Hulse, L.M., Xie, H., Galea, E.R.: Perceptions of autonomous vehicles: relationships with road users, risk, gender and age. Saf. Sci. 102, 1–13 (2018) 30. Papadima, G., Genitsaris, E., Karagiotas, I., Naniopoulos, A., Nalmpantis, D.: Investigation of acceptance of driverless buses in the city of Trikala and optimization of the service using conjoint analysis. Utilities Policy 62, 100994 (2020) 31. Stam, D., Delle Site, P.: City Study Comparative Evaluation. CityMobil2 Project, D14.1 (2014) 32. Vienna Convention on Road Traffic, United Nations (1968) 33. Government Gazette nr. 261A/2014: Law nr. 4313—Regulations about Transportation, Telecommunication and Public Works Issues and Other Provisions. Athens: National Printing House (2014) 34. Government Gazette nr. 1837B/2015: Ministerial Decision nr. 50308/7695—Terms and Conditions for Operation of Driverless Urban Bus. Athens: National Printing House (2015) 35. Government Gazette nr. 40A/2021: Law nr. 4784—Greece in Move: Sustainable Urban Mobility—Micromobility—Regulations for Modernization, Simplification and Digitalization of Ministry of Infrastructure and Transportation Processes and Other Provisions. Athens: National Printing House (2021) 36. Municipality of Trikala: Sustainable Urban Mobility Plan—Action Plan (2019) 37. Pereira, S.C., Veloso, B., Durao, N., Moreira, F.: The influence of technological innovations on international business strategy before and during COVID-19 pandemic. In: procedia computer science, 196 (= CENTERIS – International Conference on ENTERprise Information Systems / ProjMAN—International Conference on Project Management/HCist—International Conference on Health and Social Care Information Systems and Technologies 2021), pp. 44–51 (2022)

Autonomous Vehicles: Impact on Human LifeA Statistic and Descriptive Overview of Research Results, Using the Delphi Method Ioannis C. Matsas(B) , George Mintsis, Socrates Basbas, and Christos Taxiltaris School of Rural and Surveying Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece [email protected]

Abstract. Autonomous vehicles are being introduced into citizens’ daily life. Their introduction will have a positive impact for the city, the citizens and the Economy. But further to the advantages/pros of their introduction are there any disadvantages/cons? What is the expected impact of their presence on people’s quality of life and on the free exercise of the fundamental human rights of individuals? Which aspects of their existence may be affected? A research using the Delphi method approach was carried out, using questionnaires addressed to experts across various scientific fields. The areas of interest covered in the relevant questionnaires include traffic, transportation, road safety, environment, cybersecurity, employment, ethical and social issues, privacy, human rights and the rule of Law. The results of this research, as came out from the last Delphi method’ round, were elaborated for the purposes of this paper and displayed as a statistic and descriptive overview of the experts’ views. Based on the, largely common, views of the participants of this research, a strong focus on ethical issues needs to be considered prior to the circulation of autonomous vehicles and their imminent impact on humans. Keywords: Autonomous vehicle · Transport · Safety · Human rights · Privacy · Ethics · Delphi method

1 Introduction 1.1 AVs on the Way to Our Lives With the advent of the 21st century, among other changes and reforms, there has been a change in the approach to citizens’ mobility and transport. New technologies and applications, such as Intelligent Transport Systems (ITS), e-mobility, car-sharing, carpooling etc. are already here to offer solutions and approaches to improve of people’s mobility services, while they also serve the goals of policies related to the environment, businesses and quality of life. Undoubtedly, however, the widespread use of the autonomous vehicle is expected to be the culmination of the penetration of technology in the transport sector. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_37

436

I. C. Matsas et al.

The benefits are expected to be multiple, evident and highly noticeable. Reduction of accidents, traffic improvement, reduction of pollution, better service of displaced persons, improvement of the citizens’ quality of life. But on the counterside of this evolution, there are concerns about its impact on the citizens’ daily life, and privacy and, consequently, on the shaping of modern ethics and, furthermore, on the functioning of the society. 1.2 Are AVs Going to Influence Our Lives? These concerns, while today they are “potential” threads and dilemmas, can easily be turned into real threats for the human being and the society. Substantial technological, social and ethical questions that arise, are: • Should the research community and the business world continue to move towards the evolution of the autonomous car or prevent it, under the weight of the possible mutation of the human condition? • Is it, after all, good for the man, in his capacity as homo sapiens, to accept the technologies of artificial intelligence, as they are applied in the autonomous car? • What kind of assurances should be given to him? Failure to address these may call into question the basic human acquis, as well as the citizen’s relationship with the private car. In order to assess the aspects of the human-autonomous vehicle relationship and their interactions, it is considered as essential to explore the views of experts on the issues that arise, with a view to developing a “requirements map” that should be met for a smooth transition from the circulation of conventional vehicles, to that of autonomous ones, in the light of guaranteeing the fundamental human rights. 1.3 The Delphi Method: A Means for Disclosure In order to pursue, in the most thorough and suitable way, the inquiry of the views of the participating experts on the subject presented above, which is characterized by complexity, due to its interdisciplinary form, the Delphi research method was selected as the most appropriate technique. The advantages of the method, taken into consideration for its selection, were its simplicity, anonymity, flexibility, adaptability, easiness to complete in short time, while geographically dispersed experts ring together and rapid consensus is reached, without influencing participants. On the other hand, the unclear criteria for selecting the participant experts, the results’ dependence on the adequacy of the participants, the unclear guidelines for determining when consent has been reached, the researcher’s bias affection on the results, and the extended duration of the process can be considered as the method’s disadvantages. This research methodology is based on the prediction of subjective assessments expressed from a panel of experts on the subject under examination, and is widely used in a number of scientific fields. It seeks to reach the maximum possible consent of a

Autonomous Vehicles: Impact on Human Life- A Statistic

437

pre-selected group of experts on a topic, by gradual convergence, after providing them with a series of consecutive structured questionnaires [2, 3]. Key features of the Delhi methodology are: • anonymity, as ensured through the provision of questionnaires, where each participant is not worried about his “profiling” within the group, which can be shaped by his answers, • repetition, which results from the re-assignment of structured questionnaires and so the participant can re-express his views, • controlled feedback, which takes place between rounds of questions, in the form of a simple statistical summary, and • statistical analysis carried out at the end of the process, in which group judgment is usually expressed as an indicator of consent. Delphi includes a series of data collection “rounds” during which questionnaires are distributed to the expert group, aiming to formulate proposals through the trustworthy consent of the participants [1].

2 The Research 2.1 Proceeding to the Delphi Method for AVs In order to apply the Delphi methodology for the identification of the optimal but also more beneficial requirements to the society and the human, in the light of the introduction of autonomous vehicles in traffic, the following stages were planned: • setting up of the research management team, • identification of the fields of expertise/professional background of the persons who would be invited to participate in the research, • selection,by the research management team, of individuals/experts who, according to their involvement with transport, vehicle traffic or citizen service field of expertise, could contribute to the research. Special care was taken, so that experts from all fields of expertise in question, participate in the research, • preparation of an initial questionnaire, with predefined answers, to confirm the characteristics of the participants, but also to identify the participants’ views on transport, traffic and the autonomous vehicle (13 questions), • invitation sent by AUTH e-mail, to the said experts, being informed about the object of the research as well as the research method to be followed and invited to express their interest to participate in the research, by e-mail. In parallel, they were asked to fill in the aforementioned questionnaire, • statistical processing of the answers, in order to form the research identity, • compilation of an “open” questionnaire (1st round) with specific questions and open answers, so that participants are free to express their anonymous views on the questions asked (18 questions), • grouping of answers, without any further processing,

438

I. C. Matsas et al.

• compilation of a questionnaire (2nd round) with predefined answers/positions, as emerged and grouped, from the 1st round (24 questions), • statistical processing of the answers of the 2nd round, by identifying the areas/positions of convergence between the participants, • preparation of a final questionnaire (3rd round), with predefined answers/positions, among those that presented convergence elements in the 2nd round (16 questions), • further statistical processing of the answers of the 3rd round, from which convergence for the positions expressed on the questions of this round is expected. 2.2 The Identity of the Method After consulting with the research management team, it was deemed appropriate to address an invitation to a group of 80 experts from several fields (scientists and other professionals- related, directly or indirectly, to the autonomous vehicle). 34 of the above experts responded to the invitation and participated in the research. Among them: • 26 were permanent residents of Greece (76%) • 6 were women and 28 men (approx. ratio 1:5) The professional background of the participants was as shown in Fig. 1.

14 12 10

12

8 6 4 2

6

7

1

3

1

1

3

0

Fig. 1. Distribution of participants in all phases of the Delphi method/professional activity.

Autonomous Vehicles: Impact on Human Life- A Statistic

439

The research focused on impacts and other concerns related to the AV on 4 major topics: • • • •

Mobility Environment and Economy Society’ s and humans’ development Human Rights and Ethics

2.3 Structuring of the Questionnaires As mentioned before, it was decided to complete the Delphi method after applying a set of 4 rounds of questionnaires.1 The first questionnaire was structured in the form of multiple choice, requesting in its’ first part, information on the identity of the participants, namely age group, gender, educational background and professional level, country of residence and, in its’ second part, data on their transport habits as well as the degree of familiarity with the autonomous vehicle. The second questionnaire, which was, in effect, the 1st round of the Delphi method, participants were asked to freely express their views on specific questions regarding the AV. Through these, the participants were asked to express their concerns, focusing on the ethical dimension of the autonomous vehicle circulation, the resulting safety for the traveler, the liability of its “behavior”, its’ legal status, its’ effect on work, its’ expected influence on individual and collective social behavior, on the exploitation of the data produced by it, on its’ upright use and/or misuse, on its effects on the urban environment and on the ecosystem, on research, education, technology and its’ related applications and, finally, on the established, through international treaties, individual rights and freedoms. According to the expressed views, remarkable positions emerged, which were grouped without any further elaboration and constituted the content of the next round’s questionnaire. It is noted that their grouping led to the formulation of 24 questions, each of which corresponded to 3 or more positions, in which participants were asked to state the probability of a potential event or the degree of necessity of securing a prerequisite, before the autonomous vehicle is put into wide circulation. The answers of the participants in the 3rd round, led to convergent views that clearly ruled out some possibilities while reinforcing the need to make specific predictions.

1 In order to eliminate the disadvantage of unclear guidelines of Delphi method for determining

when consent has been reached so that to conclude the research, it was decided to put an acceptance threshold. In particular the rounds would stop when in the 80% of the questions of a certain round.• in each position, the average for one of the views of the participants (“Yes”, “No”, “Maybe”) exceeds 50%.• the views of the participants on the positions expressed should be clear (“Yes” or “No”).

440

I. C. Matsas et al.

As a result of the processing of the answers and further grouping of the most probable predictions, positions and contingencies, without any bias by the researcher2 , the questionnaire of the 4th and final round of the Delphi method was drawn up. This included 16 questions concerning the effect of the autonomous vehicle’s circulation on road safety, traffic itself, mobility, the environment, the Economy, development, society, the role of the State, the privacy of commuters, its liability, employment and ethics. For each of these questions, corresponding positions were put forward, in which participants were asked to state their agreement or disagreement. The resulting views and positions of the participants, as came out from this last (4th) round of the research, are presented below. 2.4 Overview of the Results As mentioned before, the 16 questions forwarded in the last phase (4th Delphi method round) of the research focused on four areas, namely mobility, environment and the Economy, the development of society and people, human rights and ethics. For each of them, answers on predetermined positions were indicated, as they emerged from previous phases of the Delphi method. According to the participating experts’ views on these positions, both direct and secondary conclusions were drought. Answering in all questions was obligatory. Some of the questions asked and the participants’ views on the formulated positions are presented as follows. The first question concerned road safety and what is likely to happen with the introduction of AVs, regarding it. • 44% of the participants are of the opinion that the operation of the AVs will affect the rights of the travelers, if they do not have the ability to intervene in those decisions made by the AVs, which have adverse consequences for them and 41% express doubts on this issue. • 47% believe that driving delinquency and traffic errors will be eliminated, with a corresponding adjustment of the relevant institutional framework, as the operation of the AV will be independent of its driver, and, therefore, road safety indicators will be significantly improved and only 18% are not in favor of this position. • 50% believe that the degree of freedom of movement of the persons with mobility limitations will increase, resulting in a burden on the road network, in terms of traffic load, while 38% are not sure about it. In the 2nd question “Which of the following do you think should be set prior to the release of AVs, for the safety of road network users?” there were five positions. According to them, the majority of the participants (>90%) believe that:

2 As far as it concerns the possibility of researcher’s bias affection on the results, neither the

researcher, nor the research management team did not interfere in the definition of the positions and the extended duration of the process can be considered as the method’ s disadvantage.

Autonomous Vehicles: Impact on Human Life- A Statistic

441

• The institutional framework for AVs has to be established prior to their release, and must envisage that their security systems are designed and implemented so as to ensure, above all, the protection of human life. • Their security systems must ensure the ongoing protection of their users, through mechanisms for predicting, identifying, dealing with and resolving problems during their circulation. • Security measures and operating principles for the AVs, have to be established for the safe movement of persons in an environment of conventional and autonomous vehicles, through an updated institutional framework. • In order to significantly reduce road accidents the strict application of proper traffic rules by AVs has to be ensured. In what concerns the last position, that is to provide for the possibility of timely recovery of AV’s control by a driver in case of emergency, in order to prevent accidents due to illegal actions or failures of AVs, was supported by 71%, but a 23% of the participants expressed their hesitation to give a clear answer (maybe) and only 6% replied negatively. Regarding those who voted “maybe”, considerate is deemed that either they did not have an opinion on the issue or they are not sure about the content of the position itself, i.e., the recovery of control of the autonomous vehicle, in cases of emergency, such as in the case of emerging accident or in the event of a stand-alone failure of a functional part of the vehicle, is useful or feasible. The 7th question “In which of the following ways do you think that the introduction of AVs will contribute to the development of the Economy?” was followed by 4 positions. The negative answers to these, range between 3 and 8%. In the following 3 positions the positive answers were over 60%: • The introduction of AVs in traffic will contribute to the optimization of movement, in terms of time and fuel consumption costs as well as to the release of parking spaces. • Specific economy sectors will be strengthened, such as insurance services and the automotive industry. • The development of modern infrastructure for AVs and the introduction of new transport services and models (e.g., mobility as a service-MaaS, cooperative and multimodal transport systems) will strengthen the Economy. But in the position “The financial impact of the many road accidents will be reduced” an equilibrium between positive answers and uncertainty of the participant experts was noted. The next question also pertaining to the Economy, resulted as follows: “In which of the following ways do you think the introduction of AVs will affect growth?”. The 4 relevant positions are divided in 2 groups: The first group suggests that the introduction of AVs will enhance mobility’s sustainability, improve the quality of life and, therefore, enhance urban development and that the circulation of AVs will lead to the development of the regional residential tissues, at the expense of the cohesive, in urban and economic terms, city.

442

I. C. Matsas et al.

The dominant answer was “maybe” (50%), followed by a positive answer between 35% and 44%. In the 2nd group, positive reactions represented 53% for the position: When the cost of buying and using AVs becomes affordable for the general public, their production and marketing will contribute to growth, while the position “with investments for the appropriate adaptation of the road infrastructure, so that people traveling is safe in an AV environment”, was considered likely by the 88% of the participants. The 9th question focused on prerequisite factors that should be ensured prior to the introduction of AVs. All three positions, that is: • appropriate policies must be envisaged, in order to eliminate, in the long run, any concerns about AVs, so that their introduction becomes accepted by the societies, • information and training on the benefits and risks of the AVs must become available, adapted to the local communities’ specific features, • for the widespread use of AVs, equal opportunities must be given to all societies, through incentives as well as with the development and operation of appropriate digital infrastructures, were welcomed by the participants in a range between 70 and 85%, while the “maybe” answer represented 15%. From the next question, related to AV impact to society, came out that a percentage of 62% do not believe that the AVs’ use will lead to the deterioration of human relations and to social isolation. It was deemed uncertain, in a percentage of 47%, whether the introduction of AVs will create social inequalities, due to their high cost of purchase and/or use, in combination with the absence of state intervention, while positive or negative answers on this position are almost equal. In addition to the above, 44% of the participants believe that, unless the State ensures equal access to AV services and a sense of justice and social equality, uneven social stratification is expected, while a 38% are not sure if this is going to happen. The 11th question refers to the following ways that the State should intervene with an appropriate institutional framework, before the introduction of the AVs: • by establishing measures to protect citizens when using functional applications related to AVs and, • by establishing an institutional framework for the legal liability during AVs operation. The vast majority of the participants (>80%) agree that both establishments have to be applied, with predominant the need to establish the later (97%). The next question, considered as a critical one, focuses on the ways that privacy of individual travelers using AVs, should be safeguarded. There were 5 positions and the dominant belief of the participants is that all of them should occur.

Autonomous Vehicles: Impact on Human Life- A Statistic

443

More precisely, the respondents believe that the aforementioned target can be achieved by enabling the free movement of persons, regardless of their financial ability and by prioritizing their will, over the choices made by the AVs (71%) with only 9% voting against this opinion. The vast majority of the participants are in favor of the following positions on this question: • By providing protection against accessing the travelers’ personal data, disclosing the details of their travel, its control and influence (91%). • By establishing institutional provisions and strict standards, against the unauthorized access of the AVs to any personal data, -without the permission of their owner or creator- as well as their processing, utilization, exploitation and disclosure to third parties (91%). • By establishing strict rules of law and standards for the certification of personal data security (85%). • By establishing and implementing provisions, during the design of AVs and their supporting technologies, for the safe processing, distribution and authorized use of personal data (94%). The 13th question focused on the entities that have legal responsibility when using the AV, and their ranking, depending on the degree of importance of their responsibility. Eight possibly involved entities were identified, namely the owner of the vehicle, the manufacturer (of the vehicle/hardware/software), the service provider and/or the operator of the AV, the telecommunications provider, the provider and/or the administrator of data, the infrastructure administrator, the one who can legally take control of the vehicle and the AV user. The answers given by the participants, for each entity ranged between 1 and 8, with “1” being the most important with a higher degree of responsibility and “8” the least important. The answers are presented in Fig. 2 As shown in the above diagram, according to the participating experts and considering that ranking between 1 and 2 corresponds to high responsibility, while between 3 and 6 corresponds to medium and 7–8 to low, the rendered level of responsibility is: • • • • • • • •

the owner of the AV is considered as low, the manufacturer of the vehicle is considered as high, the service provider and or the AV operator is considered as medium, the telecommunications provider is considered as medium, the data provider or administrator is considered as high, the infrastructure administrator is considered as medium, the one who can legally take control of the vehicle is considered as high, and the user of the AV is considered as low. Resulting from the above, it can be concluded that the responsibility level is highly rendered to the manufacturer and the one who can legally take control of the vehicle, while the AV user and/or owner are considered not to be responsible as much as the other factors.

444

I. C. Matsas et al.

4

9

2 6

5 1

3

4

3 1

4

6

8 0 1

3 1

6

3

4

6

5

5

4

5

6

3

3

7

7

8

4

3

6

4

3

2

1 3

1 3

5

6

2

12

8

6

2

3 1

5

4

6

5

2

5

7 0 2 2 3 0 3

3 3

3 4 5 6 7 The user of the vehicle The one who can legally take control of the vehicle The infrastructure administrator The provider and / or the administrator of data The telecommunicaons provider The service provider and/or the operator of the AV The manufacturer (of the vehicle/ hardware/ soware) The owner of the vehicle

17

8

Fig. 2. Ranking of the entities that have legal responsibility when using the AV

•

• • •

The next question focused on the impact on employment from the introduction of AVs. There were 4 positions, each of which was faced differently by the participants. More specifically: The fact that the introduction of AVs will affect jobs related to conventional vehicles and their uses with a consequent increase in unemployment, has been supported by 47% of the participants and only 15% were convinced that an increase in unemployment is unlikely, 82% believe that new professional sectors will be created, related to the AV and its uses, resulting in the rearrangement of the working structure, 21% of the participants believe that the balance, in terms of strengthening employment, will be tilted in favor of the AVs, while 73% are not sure about that, and 45% believe that the AVs will allow the utilization of travel time in favor of the work, by subtracting it from the time of the required personal presence at the workplace, while 56% are not sure about this likelihood.

Autonomous Vehicles: Impact on Human Life- A Statistic

445

Getting to the 15th question, through which the participants were asked to assess the proposed necessary actions to be taken before the introduction of AVs, the vast majority (>85%) believe that a strict institutional framework of measures and penalties in case an AV violates human rights and/or human ethics needs to be established, as well as the legal framework for the use of AVs must allow for an equal, non-discriminatory, safe and free movement of all citizens, regardless of their status. The last question referred to the AVs’ “ethics”. More precisely the participants were asked to express their opinion about what is likely to happen at the introduction of AVs, concerning the relation of human and AV ethics. • 44% of the participants believe that human rights and commonly accepted human morality will not be affected, while 24% believe the opposite. • 65% believe that human ethics will be adapted to the AVs’ “ethics” and 23% are of the opinion that this is not going to happen. • 29% believe that ethical issues will arise when it comes to valuing human life, while 50% are not sure that this is going to occur. • Finally, 62% believe that AVs, as an application of artificial intelligence, will not weaken the importance of human in the functioning of the society, while 26% are not sure about this possible outcome.

3 Conclusions Following a statistical analysis of the Delphi method results, it was found that the expected convergence was achieved, as regards the positions expressed in the key questions. More specifically, in terms of an overall statistical average of the corresponding averages to each question, it was found that 57% of the participants provided positive answers on the positions expressed, while 32% did not express either a positive or a negative opinion. As a result of a critical review of the participants’ answers, important findings emerged that characterize their outlook on the autonomous vehicles, this enormous evolution that is expected to become the future of transport. The views of the participants are deemed of particular value, as the statistical sample was not randomly assembled; they were in fact carefully selected experts from fields directly related to or influenced by transport. Accordingly, with the aim to highlight the forthcoming impact of autonomous vehicles on the daily lives of citizens, the interpretation of the experts’ views expressed in our areas of interest is hereby endeavored. 3.1 Regarding Mobility 1. The answers confirm the positive effect on road safety that autonomous vehicles’ circulation is expected to cause, but not to the expected extent, since one of the main advantages anticipated is the elimination of road accidents. It is therefore concluded that the use of AV for the purpose of road safety is desirable but probably not attainable, taking into account the negative and uncertain answers. The reason for this uncertainty is attributed to the following factors:

446

I. C. Matsas et al.

• Autonomous vehicle traffic is experimental at the current stage and the so far demonstrated approaches on level 5 of autonomy have shown a significant degree of failures, while many of them have also led to an accident. On the contrary, most car manufacturers, experimenting with a realistic time approach on technologies that will lead to their safe operation with level 5 of autonomy, estimate their time-to-market at the decade of 2030–2040. Therefore, participants, while acknowledging the contribution of the autonomous vehicle to road safety, are unable to confirm its overall contribution to it. • The answers were provided by experts whose’ age, in their majority, is over 50 years old and is based on the reality that they currently experience with conventional vehicles’ circulation; therefore, any assessment made relies exclusively on the existing experimental data or estimates. It is therefore expected that the views of experts will be restrained. • The prevailing views on the role that the human will be called to play in the traffic of the autonomous vehicle as its passenger, as a commuter in its environment but mainly as an entity that is likely to take over control of the vehicle, in case of emergency. • The “maturity” of the existing road infrastructure to serve autonomous vehicles’ traffic, given that, for their uninterrupted and reliable operation, the upgrading of infrastructure and the investment of significant funds are necessary, in order to ensure safe interoperability. • The required amendment of the legislation, in order to introduce a new entity, with the corresponding rights and obligations. 2. The views of the participants on the effect that driverless vehicle’s traffic is expected to have on the Law, show that the disengagement of the vehicle from the driver is, from one point of view, expected to create new rules of law that may lead to a safer driving environment. This probabilistic approach is obviously based on the uncertainty as to whether the delinquency and errors in the movement of a vehicle will be eliminated and on the correlation of this uncertainty with the “new” Law to be established. The answers also show the uncertainty of the participants on the level of importance that driving delinquency and the mistakes of the drivers have, as compared to that of the traffic rules, the Law for preventing and punishing the driver’s delinquency, the safety of the road infrastructure and the vehicles’ manufacturing integrity, in terms of safety. Also, as expected, there will be an extended period of “coexistence” of conventional and autonomous vehicles in road traffic, for which special regulatory measures should be provided, in order to achieve optimal road safety in an environment where interaction between person-drivers and driverless vehicles will occur. 3. The participants argue that the protection of human life should be the dominant factor in the introduction of new rules of law, and in fact these rules should not only cover the effect of the traffic of autonomous vehicles but also include structural aspects, such as the construction and programming of the vehicles, the infrastructure, the vehicle-infrastructure interoperability, the citizens’ behavior towards its operation, whether they are its passengers or commuters in its traffic environment.

Autonomous Vehicles: Impact on Human Life- A Statistic

447

4. The traffic of autonomous vehicles is expected to increase citizens’ and products’ mobility. The yielded benefits for people’s mobility, such as the convenience of transportation, its facilitation, especially through intelligent applications for multimodality, time savings and enabling the movement of persons who are not at ease with the conventional means of transport, are balanced by the increase in traffic, the transportation costs, the change of mentality from the belief that a person is responsible for the trip, but also from the fear of the unknown reaction of the autonomous vehicle to unpredictable road (or other) events. Respectively, the expected increase of the movement of goods and products will improve Market supply, in terms of transport time, speed of responding to demand, transport security and probably also costs, but unless rational demand management and smart logistics applications, road redesign and support-freight infrastructure are in the frame, the above can lead to traffic congestion, increased goods’ consumption and even environmental burden, as a result of the growing need to increase production. 3.2 Regarding the Environment and the Economy 1. According to the positions expressed by the participants, the contribution of the autonomous vehicle to the reduction of environmental burden, whether it concerns air or noise pollution, is clear, but there is a significant degree of uncertainty which is estimated that is due to the vehicle’s power source. It is a fact that the use of fossil fuels, despite the foreseen reduction of emitted pollutants, clearly lags behind hydrogen fuel, other gaseous fuels as well as accumulators’ generated power. It is also important to assess the environmental impact holistically, ie from fuel production to waste management, instead of dealing with it disjointedly, focusing only on the impact of pollutants emitted as a result of the vehicles movement. In terms of noise pollution, it is clearly decreased, but this reduction may have secondary unwanted consequences in a pedestrian environment, for example by increasing road accidents. Furthermore, the increased mobility, if not addressed with approaches that will enhance the combined use of public transport, is likely, as estimated by the participants, to affect the environment as a result of the use of private autonomous vehicles. Evidently, intelligent technologies integrated in the autonomous vehicle and also those that serve its traffic environment, contribute to the reduction of environmental pollution, but since these technologies are being gradually introduced in use and are constantly evolving, their contribution cannot be clearly estimated and moreover, their exploitation for commercial or other purposes may affect the environment. As a result, the participants’ uncertainty about the degree to which the environment is affected by the foreseen increased mobility, is prevailed. 2. The introduction of autonomous vehicles into circulation will affect the Economy in various ways, with a balance that, according to the views of the participants, is not currently predictable. On one hand, the travel costs may increase, through the share of the cost of the autonomous vehicle and its infrastructure, corresponding to the commuters and the transport professionals, but at the same time, travel/transport times and probably also direct travel costs are expected to be reduced. The introduction of autonomous vehicles will induce unemployment to several conventional

448

I. C. Matsas et al.

sectors but also growth to existing and new ones, such as the automotive industry, the services’ and the insurance sector. 3. Furthermore, significant state support will be required and incentives to be provided for the transition from conventional to autonomous vehicles, during the simultaneous circulation of both types of vehicles, as well as during the exclusive circulation of autonomous vehicles. Interventions should be made for the adaptation of infrastructure, the strengthening of public transport, the modernization of legislation, but also for the training of the society in the new transport environment. 4. According to the prevailing opinion of the participants, the financial (direct and indirect) benefit from the reduction of road accidents, due to the traffic of autonomous vehicles is not estimated to be as significant as it was anticipated, based on the more optimistic estimations of the initial round of questions. 3.3 Regarding the Society and Human Development 1. The majority of the participants estimate that the circulation of autonomous vehicles is likely to enhance urban development as well as the peripheral residential tissue. Consequently, improvement of the quality of life is expected, but also the weakening of the cities’ cohesion, in economic and urban terms, are foreseen. 2. The participants generally conclude that, as societies are not ready to welcome the circulation of autonomous vehicles, it is up to the State and the social partners to establish and implement policies, informative and educational approaches addressed collectively to targeted social groups, as well as to citizens, individually, in order to eliminate any remaining concerns regarding the necessity, usefulness and safety of use of autonomous vehicles, while as well providing relevant incentives. 3. The prevailing opinion of the participants is that the advent and circulation of autonomous vehicles will not significantly affect society and citizens, given that, on one hand, people are not expected to be socially isolated as a result of their increased interaction with the autonomous vehicle and, on the other hand, as long as there is coordinated and fair state control over its marketing, circulation and use, any probable inequalities will be of such an extent that could not lead to disruption of the social equality. 3.4 Regarding Human Rights and Ethics 1. The overwhelmingly prevailing view of the participants is that the State must play a prominent role in the protection of human rights. In this regard, it must, on one hand, establish measures to protect citizens in the use of applications related to autonomous vehicles and, on the other hand, take action to adapt the institutional framework governing their circulation, so that legal liability is clear and distinct. In addition, the State must ensure the free movement of citizens, regardless of their financial situation. Another important factor in safeguarding human rights is the prioritization of the will of the passengers versus the one of the autonomous vehicle, as well as the protection of personal data provided from, or transmitted through, the autonomous vehicle and its surrounding infrastructure. Through these protective

Autonomous Vehicles: Impact on Human Life- A Statistic

449

measures, the safeguarding of the passengers’ will must be ensured, against any influence for exploitation or utilization that may be attempted by the autonomous vehicle, the infrastructure or the managers of the vehicle’s applications. Also, according to the participants, the State must ensure the establishment of a strict framework for the assessment of the conformity of vehicles, of the applications they use and of those involved, directly or indirectly, in its operation, to regulatory, technological or service standards, accordingly. Furthermore, public authorities must ensure the establishment and implementation of a strict institutional framework for the protection of human rights and ethics, against any possible breaches by or because of the autonomous vehicle, but also for an equal, non-discriminatory, safe and secure basis for the free movement of citizens. 2. As the protection of human life and personality is one of the principal human rights, it is essential to the user of the autonomous vehicle or to the person moving in its’ environment, to know who has the legal liability, during its use, for any likely failures, accidents and/or damages, breaches of personal data management and protection principles, influence on the circulation or transport choices. The opinion of the participating experts is the obvious, that is, that the owner of the vehicle has the least responsibility, as compared to the manufacturer of the vehicle and the authority in control of the operation of the autonomous vehicle. There is also relevant responsibility of the communication service provider (between vehicle and infrastructure), the infrastructure manager and the user of the vehicle. However, the positions expressed on the question regarding the autonomous vehicle’s liability may need to be further examined, depending on the reason for which the legal liability will be invoked. 3. The circulation of autonomous vehicles is expected to affect the right to work, as it is estimated that many industries are about to be affected, leading an already skilled part of workforce to unemployment and/or retraining, in order for them to be able to ensure employment. At the same time, however, new professions will be generated, related to the autonomous vehicle as a product, as a means of transport, but also as a factor of transport. However, the participants are not convinced that the advent of autonomous vehicles will contribute to employment. 4. Taking into account: • the negative disposition of the experts participating in this research, regarding the possible impact of human rights and of the acceptable human ethics from the circulation of autonomous vehicles but also as concerns the weakening of the importance of human and social functions, • the expressed uncertainty of the participants towards the possibility that human ethics will adapt to the “ethics” of the autonomous vehicle and that ethical issues will arise, regarding the evaluation of the value of human life, • the limited positive views of the participants on the possible impact of the autonomous vehicle on human rights and ethics, it is easy to conclude that the participants do not have a clear picture of the potential impact of the driverless vehicle on human rights and on the acceptable human ethics.

450

I. C. Matsas et al.

In order to approach the participants’ uncertainty, as expressed by the overall picture of their positions in the research, two major aspects should be considered which could also be perceived as its weaknesses: • the participants’ characteristics, as the fact that the majority were permanent residents of Greece, or the fact that there percentage of participating males was 5 times more than female participants and the overall identity of the sample under question, where the majority of the participant experts were academics, • the participants’ defensive behavior against AVs, based on their hesitation towards a technology that is currently under development, effectively its’ parameters, on the absence of the required infrastructure environment and operating culture and on the lack of elaboration of the related institutional and technical requirements for its operation.

References 1. Linstone, Ha., Turoff, M.: The Delphi method: techniques and applications, pp. 3–14, AddisonWesley Publishing Co, Massachusetts (1975) 2. Powell, C.: The Delphi technique: myths and realities. J. Adv. Nursvol. 41, 376–382 (2003) 3. Mi, Y.: Using experts’ opinions through Delphi technique. Pract. Assess. Res. Eval. 12, 1–8 (2007)

Big Data Analytics for Modelling Consumer Preferences and Satisfaction in Public Transportation Yulia Dzhabarova, Aygun Erturk, and Stanimir Kabaivanov(B) Plovdiv University “Paisii Hilendarski”, Plovdiv, Bulgaria [email protected]

Abstract. In this paper we develop a model to estimate and analyze consumer preferences and satisfaction from public transportation services. Unlike many other studies in this area, our approach is based on use of big data from multiple sources and allows to achieve continuous and precise estimation of consumer behavior. These results can be used then to adjust parameters of the transportation plans, schedules and asset allocation. We build the model with available data from INNOAIR project in Sofia. Keywords: Big data analytics · Consumer behavior · Customer satisfaction

1 Introduction and Methodology 1.1 Literature Review The European Standard classified quality criteria of service in public passenger transport into the categories of: availability, accessibility, information, journey time, customer care, comfort, safety and environmental impact [1]. There are a large set of service quality attributes. Examples can be given as network design, service supply and reliability, comfort, fare, information, safety, relationship with personnel, customer preservation, environmental protection, quality of exterior, but there is the necessity to quantify the importance of each one as each of them has a different weight [2]. Also, demographic characteristics play a direct role on perceived service quality [3]. There are many service quality measure techniques and the evaluation can be done by customers satisfaction, expectation of the customer for statistical analysis, indexes for measuring or linear and non-linear models. A popular one is SERVQUAL a 22-item instrument for assessing customer perceptions in service and relating organizations, where tangibles, reliability, responsiveness, assurance, and empathy are the dimensions that has an impact on consumers evaluate service quality [4]. The alternative of SERVQUAL as SERVPERF which is a performance-based measure [5]. Customer Satisfaction Index points out to customization and customer expectations and pays attention to the fact of quality than the price for customer satisfaction [6]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_38

452

Y. Dzhabarova et al.

There are also linear and non-linear models as structural equation model (SEM) which has become one of the most applicable methods in public transport [7]. Generally service quality measures for public transport are dependent on the perception and expectations of the customer. 1.2 Analytical Framework Consumer behavior is a very broad topic and can be addressed from different points of view. We adhere to the definition that consumer behavior is the study of how people decide on taking actions that satisfy their needs, wishes and desires. It should be noted that this definition refers to a broader set of options than just making purchases or spending money. For the purpose of INNOAIR1 project, it is justified to address also actions that do not result in immediate economic spending, due to the fact that public transportation plays a very special role in providing services and support for different social groups. In accordance with the definition of passenger behavior, used in previous reports, that “A passenger behavior is the way that passenger think, feel, reason, make judgement and select different products and services, directly related to their travel.” We only select consumer behavior aspects related to public transportation: • Public transportation and the potential of establishing a green on-demand transportation system. • Congestion and load on the city road infrastructure. • Air pollution and emissions in different urban areas. • Attitude toward public transportation. All these items have direct impact on the quality of service (QoS) of the public transportation. Only two levels of headings should be numbered. Lower level headings remain unnumbered; they are formatted as run-in headings. The aim of this study is to distinguish passenger segments with similar behavior, and to identify the public transport they tend to use, and thus to characterize their preference patterns. Based on previous studies on understanding passenger patterns in different countries and on different occasions, the most important characteristics that distinguish passenger behavior have been determined. The passenger profile is a significant determinant. Which lays in the overall behavior, e.g. consumer needs, motives/preferences, attitudes and expectations. After interlinking these variables, we will be able to obtain in-depth understanding of passenger insights and thus to design an empirical model. The study will be focused on the average traveler in the piloting residential districts of Manastirski Livadi and Buxton in Sofia. Different scenarios will be drawn, and appropriate recommendations on adapting “innovative green mobility solutions” to traveler’s behavior will be elaborated. In order to capture different points of view on consumer behavior, we have conducted a brainstorming session with the aim to build a mind map of the targeted issue and 1 https://innoair-sofia.eu/en/.

Big Data Analytics for Modelling Consumer Preferences

453

potential ways to solve it. To make sure that sufficient diversity in expertise involved is indeed present, Table 1 provides a summary of special areas of knowledge present in the discussion. Table 1. Experts involved in the mind mapping and brainstorming session. Experts

Details and remarks

Marketing

Inputs on standard methods used for marketing study of consumer behavior

Economics

Inputs on economic and financial aspects of consumer behavior and metrics available to assess them

Urban development

Inputs on contemporary aspects of urban development as well as on metrics available to clarify existing challenges

Software development

Inputs on technical solutions and required data to estimate and track down selected metrics

Public transportation services Inputs on viability of suggested metrics and mapping to already collected information for public transportation system

Three major areas of interest have been identified with regard to consumer behavior, in the context of INNOAIR project goals: • Consumer characteristics and profiling. Consumer characteristics and segmentation goal follows well-defined and commonly used methods and inputs to analyze passenger choices and spending. In addition to traditional tools and features used to study customer decisions, we suggest applying state-of-the-art machine learning methods to improve segmentation results and handle special cases (e.g. outliers). • Technical aspects. Technical aspects of the output aim at finding the most relevant and precise sources of data. While it is often sufficient to conduct representative surveys of consumer preferences, such an approach is limited in terms of providing adequate real-time information. For the purpose of accuracy, it is better to combine different sources of information and merge survey results with objective and real-time feeds like fleet management data, anonymized mobile network load and coverage information and ticket sales. • Implementation details Implementation details refers to the use of appropriate technologies and employing open source packages in order to achieve sustainable and economically sound effect. This part follows from the previous two and despite playing supportive role, it is important to make sure that implementation can fulfill all required actions and process inputs in a timely and accurate manner. Apart from concerns regarding data analysis, it is of great importance to ensure that different sources can be used together and can be addressed in a uniform way. Due to the fact that inputs often come from

454

Y. Dzhabarova et al.

separate organizations, with different level of technical readiness and degree of process automation, being able to integrate all existing solutions with ease is crucial.

2 Data Inputs and Theoretical Background 2.1 Demographic Characteristics For appropriate consumer profiling, and based on analysis of previous research on public transport in different countries, [8–10] the following key demographic features have been selected: age structure, household size, occupation, personal income, education, vehicle availability. Age structure of the passenger profile represents types of activities according to the life cycle stages. According to the set-up purposes the passengers are grouped as follows: 15–24, 25–29, 30–40, 41–50, 51–64, and 64 +. By 2021 the population of Sofia is 1 307 439 people, and most of them are between 20 and 64 years old (825 370 people) [11], the population projection for Sofia for 2025 is to reach 1,350,054 people [12], while the projected age dependency ratio for Sofia for 2025 is 48.84 while for Bulgaria it is 59.26 [13]. Age as a factor provides the opportunity to divide life-cycle stages into: education, work, and retirement, and to describe the contrast between the financial opportunities, passenger preferences, and to reveal more precisely the purposes and frequencies of traveling. This division also specifies the travel purpose, the presence of an additional passenger, car ownership, the ability to use alternative modes of travel. Generation differences have also a significant role on passenger preferences. There is a new trend as slow mobility especially among ageing population and interest in modern, healthy, technological and green mobility among the younger ones. Household size is used as main indicator for the ownership and dependency on personal cars, as it shows the number of working members of the household and appears to explain the choice of different types of transport. The average household size is decreasing and in 2020 both for the European Union and Bulgaria was 2.3 people per household ( [14]). The tendency for the number of cars per household is increasing, but the last findings display a decrease in the stock of vehicles in Bulgaria from 3,667,787 in 2015 to 3 339,725 in 2019 [15]. Meanwhile, in Sofia the number of cars is increasing, there are 663 cars per 1000 inhabitants in 2020 [16]. For the purposes of the research, the household size will be divided into the next groups: a single person, two-person household, three-person household, four-person household, and more, in order to describe the socio-economic groups and their transport preferences. Occupation of the passenger modifies the time preferences, type of transport, frequency, and also the ticket type as it describes the regular and habitual activities. Occupation options include the next categories: full or part-time employee (managers; professional technicians and associate professionals; clerical support workers and armed forces occupations; agricultural, craft and related trades, service and sales workers, machine operators, and assemblers) unemployed person, pupil, student, housewife, and retiree. The availability of public transport and the time-lasting of traveling are among the key factors for accepting a job offer. The passenger occupation profile is an indicator and prerequisite to schedule the public transport route [17].

Big Data Analytics for Modelling Consumer Preferences

455

Personal income is used to shape the social demographic structure and is an evidence for special fare usage and car ownership ratio. The structuring of the attitude towards public transport and accordingly passenger preferences, depends on the income range. The passenger belonging to the low, middle, and high-income group will have explanatory value for his habits and attitudes. As a common belief, bus riders rather belong to the lower-income group than the passengers of metropolitan. In Sofia, there is a significant difference between the percentage of metropolitan use between 2011 (9.7%) and 2020 (24.3%) [16]. The passenger income prevails the preference between owning a private car, using public transport or using other free transport types as walking or cycling, and moreover, it motivates the passenger willingness to pay for better quality. Education level is another main demographic shaping the consumer profile related to the average habitant level. The groups are outlined as: Bachelor level or higher, high school, less than high school education, secondary school. Generally, metropolitan riders are considered as more educated than bus riders. Furthermore, the general aim of the public transport usage is to make it preferable mainly for students and pupils. A distinguishing feature of pupils is that they travel mainly to school and typically travel at a shorter distance. Education level has to be considered for the recommendations seeking to change the general attitudes. Vehicle availability in the household also effects the passenger preferences of public transport. The increasing level of car ownership is in the basis of the infrastructure and traffic problems. Dependency on own car is influenced by the changing life style and the generation differences. 2.2 Behavioral Characteristics Although previous research results estimate trip purpose, the age and duration as major factors, other ones, including psychological factors, underline flexibility, safety, comfort and environmental concerns as central factors [18]. In this research passenger profiling implies particular transportation features as frequency and reasons for public transport use, types of sources of information, access method [8, 19]. – Important aspect of the frequency of using public transport is to make contrast between routine commute behaviors and random on-occasion transport demand. – The reasons for using public transport as explanatory for behavioral motives to design a need or preference base model can be found in: saving money and time, avoiding traffic, environmental concerns, convenience. – Access mode to reach the bus stop indicates if the riders walk, drive, bike or use another transit. – The travel routine of passengers is described by trip the duration and trip road. – Information sources are important to predict the pre-travel planning behavior of passengers. Real Time Passenger Information is one of the facilitating systems, pre-travel information has also positive psychological effect on the passengers [20]. 2.3 Social Class and Lifestyle Transport practices are ranked among the most habitual behaviors and the older generations are supposed to adhere to car ownership and valuing the old pattern of “car

456

Y. Dzhabarova et al.

pride” as using car as a symbol of status and prestige and the drivers are not reluctant to prefer public transport [21]. Yet in EU the belief is that youngest generations will be more interested in technological gadgets and social networks rather than owning car [9]. Ultimately there is tendency of using the alternative technological and green mobility in urban areas with regard to healthier and sporty lifestyles and increasing health and environmental awareness. The outdated pattern that the public transport is used by lower income group is challenged by generation lifestyle change and use of metro. Although the existing interdependence between the quality of public transport and income level of passenger, the image campaign, adequate guidance, and information systems play a significant role in changing this pattern [22]. Our previous passenger behavior analysis report put the major goals to identify needs for public transportation as adaptive schedule and bus lanes, fixed lanes optimization and bus stop location optimization; traffic lights and special rules for public transportation vehicles. This improvement in public transport quality would attract the higher income population with definite requirements. 2.4 Behavioral Determinants (Passenger Insights) For the purpose of the analysis we use several variables that determine passenger behavior, such as: consumer needs, motives and preferences, attitudes and expectations. Based on the consumer insight approach, and according to the specifics of the distinguished segments we aim to reveal the personal drivers for a concrete behavior. Needs and motives In the last years the lifestyle patterns have substantially changed, generating diversified travel needs. People become more concerned about the implications from transport congestion and pollution. The now-a-day consumers tend to be more sensitive to their personal and family health-being, social welfare and ecological issues. Thus, the implementation of holistic behavioral approaches, satisfying new-consumer demands, and gaining social, economic and ecological benefits is getting more and more insistent. According to the existing requisites the needs can be evaluated as utilitarian or hedonic, Under the utilitarian need lays the desire to achieve practical (functional) benefits, e.g. to sustain innovative mobility solutions, to contribute to the city carbon reduction, to save money, to obtain a quick access to a definite place/point, to avoid traffic conjunction, to care about personal or family’s health, etc.; The hedonic needs refer to the emotional striving, e.g. to enjoy the travel, to share the experience of travel with other passengers, to be eco-friendly, to demonstrate a typical lifestyle or social class affiliation, to have personal sentiments to the district/city as a resident or as a place of birth, etc. In the usual case the consumer need contains both utilitarian and hedonic features, and it can be satisfied in different ways, as the personal choice depends on a specific set of experiences and personal characteristics (demographic, social or cultural, for example the cultural values he has been raised). Hence, the traveler will perform his choice, driven by his want to satisfy a certain need, to attain a concrete goal. To be more precise, we also have to consider the concrete reason for a travel: how it performed – on a regular basis as traveling from home to work/school, or on a specific occasion – to visit friends, to sport, to go for a sight-seeing or amusement, etc. The traveler’s occasion will influence

Big Data Analytics for Modelling Consumer Preferences

457

the frequency of using different modes of transport – public, private, alternative, etc., and a combination between them. In order to enhance the public transport use, we need to know the motives that drive passenger behavior, on one hand, and the barriers that prevent him. Table 2 provides a summary of some important motivating and de-motivating factors that influence the decision whether and how to make use of available public transportation system. We have paid more attention to the barriers as most of them are easier to quantify and assess. Table 2. Motivations and barriers to public transport use Motivations

Barriers

Better service Be certain that the timetables are performed Direct transport from home to work More information available and easy to understand Save money Not having a parking space More comfort and air-conditioning on vehicles Contribute to a better environment

Not having alternative to car Lack of direct transport Lack of availability of buses Long travel time Buses’ unreliability Do not known what to expect Need for multiple journeys Poor information Not frequent enough Bus stop too far Buses are smelly and crowded Feeling of personal insecurity Having to use more than one transport Bad waiting conditions Negative feeling towards public transport Habit of driving

Source Beirao and Cabral [23]

Preferences The choice of transport is influenced by several factors, such as individual characteristics and lifestyle, the type of journey, the perceived service performance of each transport mode and situational variables [23]. Nowadays people perform a private car dependence, not only as a mode of travel, but also to express their social status and hedonic desires, such as feelings of sensation, power, freedom, status and superiority [24]. In order to make a shift from private cars to other travel modes, they need alternatives, to be convinced in their benefits and corresponding to their personal values and lifestyle. Moreover, innovations in improving the quality of public transport will satisfy the specific traveler’ needs and sustain a modern urban transportation system. Regarding the type of mode, a passenger could choose among 4 options of public transport in Sofia: bus, trolleybus, tram and metro lines. Additional options as taxi, shuttle and electric scooters could be used as well. In order to perform their choice, passengers evaluate the characteristics of the different modes, make their preferences and actually their choices.

458

Y. Dzhabarova et al.

Some of the most significant transport characteristics that determine passenger preferences and choice, and influence his satisfaction are the following: – – – – – – –

Convenience; Safety; Connectivity; Reliability; Service quality; Fastness; Information-equipped.

All these choice determinants can be integrated by implementing modern transport systems with improved traffic management. Specific solutions as introduction optimization routes and bus schedules, new eco-bus lines, creation of bus only lanes, innovations and incentives for passengers, digitalization of passenger tickets, etc. may sustain the passenger modern needs and desires. As long as there are many alternatives, bonded with appropriate information, a passenger will be more facilitated and convinced in his decision. In order to boost this process targeted information campaigns in social media could promote the benefits of sustainable travel modes as eco-friendly, as easily accessible and reliable. Attitudes Beliefs and attitudes lay in the entire process of consumer decision making. They are determined by (1) the existing knowledge from the obtained information and gained experience, (2) the emotions evoked by the explored situation, and (3) the beliefs related to the concrete object. The mutual performance of the three variables, as: cognition, affect and behavior, determine the consumer/passenger attitude, by which we could predict the forthcoming reaction. In the ABC model Michael Solomon [25] explains the relative impact of the three components as hierarchy of effects, where each hierarchy specifies that a fixed sequence of steps occurs on the route to attitude, e. g. An attitude based on cognitive information process, an attitude based on behavioral learning process. And an attitude based on hedonic consumption. In other words, passenger behavior could be predicted when the attitudes are evaluated as an output of the hierarchical process of the effects. In this way two scenarios could be performed: (1) beliefsaffect-behavior, or (2) beliefs-behavior-affect. In the first case consumer is motivated to search for a lot of information and carefully evaluates the offered alternatives, and thus comes to a thoughtful decision. In the second case a passenger may not be particularly informed/knowledgeable about the different transport options and public modes, then he may have an emotional response. In this hierarchy a person does not initially have a strong preference for one over another alternative. Even more, when a passenger has “bonds” with a certain transport mode over time, he is not easily persuaded to experiment with other options. Additionally, we have to consider the passenger motives (rational and emotional), the existing knowledge and previous experience to model his attitudes towards the different travel options (public and private) and the different public travel modes. In order to make consumers more involved and conscious about their decision

Big Data Analytics for Modelling Consumer Preferences

459

making, the marketing stimuli, affecting the whole process, have to be addressed properly and adequately. Taking in mind that attitudes are time-lasting, a special focus should be given on the communication set of tools, influencing cognitional learning rather than behavioral learning. Considering the Expectancy-Value Attitude model (multi-attribute attitude model), the passenger attitude towards the different modes of public transport might be determined by  salient beliefs about the values possessed and their eval scaling two factors: uation Aobi = i=1..n bei . The personal evaluation might regard a certain transport mode in general, as it consists of particular evaluation of the demanded characteristics. Expectations and satisfaction Usually, passenger satisfaction is conceptualized as a function of the gap between expected and experienced service delivered. Travel satisfaction in transportation research has been predominantly measured as a function of objective or subjective attribute levels [26]. The discrepancy between expected and experienced service, as: waiting time, invehicle time, perceived service quality, as well as the passenger personality, sociodemographic and behavioral characteristics might have a significant influence on the level of satisfaction. Loyalty to public transport, price affordability, seat availability, in-vehicle environment, enjoyment mood, etc. lead to higher ratings of trip stage satisfaction. Some studies of trip satisfaction found that gender has no significant effect on satisfaction with public transport services [27, 28], others [26] in reverse state that satisfaction ratings of men are higher than satisfaction ratings of women. 2.5 Technical Aspects (External Input) There are multiple data sources that can be used to analyze passenger behavior and use of public transportation system. But not all of them are equally important and useful. In order to find the balance between usability, accessibility and information value, we have separated different options with regard to: • Type of data inputs (numerical, categorical and nominal); Giving preference to numerical inputs allows us to apply wide variety of analytical tools and provide more accurate and timely forecasts, conclusions and recommendations. While some sources are by virtue either categorical or nominal, we have tried in our report to focus on numeric sources and extract as much useful details as possible. • Frequency of new data that is made available; Making strategic decisions takes time and preparations. Yet there are some daily changes that have to be made and that require to have information quickly, or its value will simply fade away before its even taken into consideration. In order to support both strategic and tactical decisions, our consumer behavior analysis approach relies on data inputs that arrive at different frequency – from real-time data of movements and fleet positions, to periodic surveys that take several weeks to complete and process.

460

Y. Dzhabarova et al.

• Accessibility and control of municipal authorities on the quality of methodology of data collection. Accessibility of data is a twofold issue – it depends on both legal and practical factors. For example, tracking movement of people is a very valuable input, but on the other hand it violates their privacy and intrinsic human rights. To eliminate issues with GDPR and access to sensitive information, in our study we rely on anonymized and pre-processed information that prevents identifying any individual based on the collected data. With regard to practical concerns, we choose inputs that cannot be manipulated, have negligible margin for error (mostly due to the fact that they are automatically generated and do not require human intervention) and that can be reproduced and verified. The last two characteristics are of particular important in order to make sure that obtained results are valid and sustainable. Table 3 contains a detailed list of inputs from the following categories: • Mobile network data; Mobile network data is not freely available and requires close cooperation with mobile service providers. Use of anonymized or preprocessed information on the other hand makes it much easier to cope with legal restrictions and avoid violation of individual rights. To minimize the investment of time and resources on mobile providers’ side, we plan to use information on density of users and summary of movements in specific areas, that are relevant to INNOAIR scope. • Ticketing system data; Ticketing system data is already available and used to estimate revenues and cash flows. In our case it’s more important to figure out relative weight of different products (ticket and prepaid travel cards), rather than the absolute amounts. With the introduction of electronic ticketing and check-in system, required inputs are readily available and can be provided with minimal efforts. To analyze the consumer behavior, it is required to have only timing and general information, thus avoiding potential conflicts with GPDR and use of personal data. • Mobile application statistics; Mobile application inputs are focused on INNOAIR experimental services and green transportation. Therefore, its relevance is extremely high. Mobile application offers a direct channel of communication with passengers that rely on the service, can rate it and give suggestions on how to improve the quality of offered services. Our goal is to use this data with gradually putting more weight on it, as the number of application users increases.

Big Data Analytics for Modelling Consumer Preferences

461

• Traffic and transportation control data. Traffic and transportation control data is crucial to mapping available resources to demanded services and improving customer satisfaction. This information serves analytical purposes and supports decision making process. In our model, traffic and transportation data is first used to assess current situation and compare it with user expectations. As gradually new services start to gain popularity, estimate that its role will be primarily as a support tool when changes have to be made to meet new requirements and consumer needs. Ingestion of different sources and analyzing the data can be split into several steps, as shown on Fig. 1. Integration of multiple data sources (which normally deliver inputs with disparate frequencies) is handled first. By abstracting this initial (pre)processing, we are able not only to support series with different frequencies, but also different communication protocols. Therefore, this step is crucial in putting together legacy systems and data producers managed by separate legal entities. Having a separate data ingest layer also helps in simulating some inputs that are not yet available or developed. Pipeline and flows are central to our way of consumer behavior analysis and monitoring. Representing a sequence of tasks (pre-processing, analytical and data persistence ones), they are capable of wrapping up different algorithms and methods. By sticking to the idea of flow, that takes predefined inputs, processes them (which could involve multiple steps of different complexity) and stores and/or presents the results, we are able to create building blocks for consumer behavior modelling and link these blocks in very creative ways. This approach can provide the much-needed flexibility and improve sustainability of suggested solutions, as we are always able to modify or adapt some part of them, without rebuilding the complete system. In addition, the ability to immediately present the results is very important for maintaining real-time notifications and “live” view on consumer behavior. The last part represents the graphical front-end of the system. It is responsible for providing consumer behavior analysis in a readable and convenient way. To highlight the dynamic nature of the built system we have implemented this component as usercontrolled and user-configurable dashboard solution. This provides for interactive output and customization based on specific end-user needs. 2.6 Implementation Details Implementation of consumer behavior relies on software packages that fulfill the following generic requirements: • • • • •

open source and no licensing spending required for use; support by major cloud providers and easy scalability; integration with common scripting languages and in particular with R and Python; support for common authentication methods and state-of-the-art security mechanisms; support for machine learning and AI algorithms.

462

Y. Dzhabarova et al. Table 3. Data inputs and sources.

Data input

Description

Mobile network inputs Anonymized position

Used for heat maps and summarized tracking of movement in different hour zones

Speed of movement

Used for rough estimates and classification of means of transportation

Time of appearance

Entry and exit time, calculated from available movement data as proxy of transportation use and load estimates

Ticketing system inputs Transportation cards

Used to track down established route and sustainable interest in particular lines or areas of transportation. Inputs regarding sales of transportation cards are important not just for economic planning and financial forecasting, but also for separating long-term interest in particular routes

Tickets sold/checked-in

Tickets sold and/or checked-in can be used to track down in real-time the load of transportation network. It can be used for adaptive scheduling and planning of resource allocation. This information is also very interesting with regard to traffic control and pollution monitoring

Mobile application statistics Number of requests

In addition to number of requests (which is used to heat map the activity and user needs at given time), the distribution of these requests is very useful in resource optimization

Frequency of use

Used as a proxy for customer satisfaction and segmentation of frequent/devoted users of the services offered

Feedback and quality study

While this is a voluntary input (not obligatory to rate the service and application) and it provides an assessment of the complete service, quality feedback is a very strong (though irregular) input

Traffic and transportation control center Schedules

Fixed schedules are used to compare demand for transportation services with supply. They are also the foundation of providing more flexible and efficient allocation of assets and forming dynamic schedules

Delays

Delays are very important in reducing consumer satisfaction (as pointed out in D5.3.1) but also as indicator of problems and deficiencies in planning and implementation of transport network

GPS live positioning

Live positioning information on vehicles (busses and other means of transportation) is vital for providing quality service and also map consumer behavior to availability of offered services

Big Data Analytics for Modelling Consumer Preferences

463

Fig. 1. Data processing and analytics.

Figure 2 provides an overview of common packages used to implement different stages of our passenger behavior analysis. There are multiple variants and alternatives to selected software, but we have decided to bet on proven and easy-to-use options.

Fig. 2. Software packages used to support analysis.

Table 4 contains a list of selected packages, followed by a short description. Only specialized packages are listed, where common applications (like spreadsheets or survey automation packages) are not explicitly described.

464

Y. Dzhabarova et al. Table 4. Software packages selected for implementation.

Selected solution

Description

Ingestion stage Apache NiFi [29]

Data routing and transformation in NiFi is especially valuable for integrating legacy systems and various inputs

InfluxDB Telegraf [30]

Used to collect data and metrics from different devices, which do not need special handling and preprocessing

Pipeline/Flows Apache AirFlow [31]

Used to orchestrate different tasks and worker threads of the analysis

Storage InfluxDB [30]

Used to store time series data and do simple calculations over it

File system

Used to store configuration and binary objects

Analytics Apache Spark MLib [32]

Used to provide reference machine learning algorithms

Custom Python scripts

Used to customize and automate tasks, that cannot be easily handled by other software packages

Presentation Grafana [33]

Used to create interactive dashboards that present study findings as well as any real-time outputs

2.7 Preliminary Results Due to the fact that collecting sufficient number of observations requires a significant amount of time, our initial tests were conducted with a lot of simulated inputs, aiming mostly at confirming that implementation can meet flexibility and sustainability expectations. Our preliminary results of the system agility can be summarized as follows: • Thanks to the multi-stage processing and separation of different steps, the overall load can be split up and a lot of different device inputs (vehicles, selling points, satisfaction check points) can be processed without noticeable delay. In our simulated tests, up to 250 devices sending information at intervals of 1–10 s were checked, without resulting in significant system load. • Use of configurable processing blocks can improve a lot system flexibility and facilitate use of dashboards that present the output. • Parallel processing and use of orchestration frameworks can help in maintaining sequence of different actions and trigger processing of inputs, only when it is necessary.

Big Data Analytics for Modelling Consumer Preferences

465

3 Conclusion Consumer behavior analysis is extremely important in providing adequate, economically sustainable and efficient transportation services. Due to its significance for local community and important social aspects, municipal transportation system offers some special and unique products. In this report we have highlighted approaches and algorithms that can be used to collect information, study consumer behavior, process it in appropriate way and provide outputs that can support various stages of the municipal authorities decision-making processes. Considering the goals and stages of the INNOAIR project, our suggestions can help greatly in planning green transportation and adapting it to meet in full consumer needs. There are several important characteristics of consumer behavior analysis presented in the report: • First of all, it is considered as a continuous process that should provide updates with various frequency – starting from real-time monitoring of behavior to conducting periodic in-depth surveys of attitude toward public transportation system. • Consumer behavior can be studied from different points of view, but to gain full insight on what drives people in choosing different transportation options, we need to combine data inputs from various sources and of various types. • Machine learning algorithms can support the analysis, but only when properly combined with expert knowledge. This is particularly important for representing the outputs in a way that can be beneficial to people with different level of knowledge in technical details. • Flexibility and adaptability are important characteristics in the scenarios regarding modes and services. In this way, the transport system will adjust to peoples’ needs and to the way in which the urban environment is constantly changing in order to meet the needs and expectations of people. We suggest a new, flexible and versatile software system, that relies exclusively on open source components and is capable of processing large amounts of information related to consumer satisfaction. This approach can provide a solid foundation for intime feed of both technical and management information that improves operation of existing transportation networks and facilitates new approaches like those at the hearth of INNOAIR project. Acknowledgement. This research was supported by UIA05–202 “INNOAIR - Innovative demand responsive green public transportation for cleaner air in urban environment”, funded by the European Union initiative - Urban Innovative Actions. (UIA).”

466

Y. Dzhabarova et al.

References 1. Standardization, E.C.f.: European Standard, EN 13816. [Online]. Available: https://tpbi.ro/ file/2021/02/EN-13816-standard-Service-Quality-Definition-Targeting-and-MeasurementEU-2002.pdf (2002) 2. Mazzulla, G., Eboli, L.: A service quality experimental measure for public transport. Eur. Transport 34, 42–53 (2006) 3. Yaya, L.H., Fortià, M.F., Canals, C.S., Marimon, F.: ervice quality assessment of public transport and the implication role of demographic characteristics. Public Transport 7(3), 409–428 (2015) 4. Parasuraman, V., Zeithaml, V.A., Berry, L.: SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality. Retailing: critical concepts 64(1), 140 (2002) 5. Cronin, J.J., Jr., Taylor, S.A.: SERVPERF versus SERVQUAL: reconciling performancebased and perceptions-minus-expectations measurement of service quality. J. Mark. 58(1), 125–131 (1994) 6. Fornell, C., Johnson, M.D., Anderson, E.W., Cha, J., Bryant, B.E.: The American customer satisfaction index: nature, purpose, and findings. J. Mark. 60(4), 7–18 (1996) 7. De Oña, J., De Oña, R., Eboli, L., Mazzulla, G.: Perceived service quality in bus transit service: a structural equation approach. Transp. Policy 29, 219–226 (2013) 8. Clark, H.M.: Who rides public transportation. APTA- American Public Transportation. https://www.apta.com/wp-content/uploads/Resources/resources/reportsandpublications/Doc uments/APTA-Who-Rides-Public-Transportation-2017.pdf (2017) ˇ 9. Gogola, M., Sitanyiová, D., Cernický, L., Veterník. M.: New demand patterns for public transport due to demograhic change, Interreg Central Europe. https://www.interreg-central.eu/ Content.Node/working-paper--New-demand-patterns-for-public-transport-due-.pdf (2018) 10. Sim¸ ¸ seko˘glu, Ö., Nordfjærn, T., Rundmo, T.: The role of attitudes, transport priorities, and car use habit for travel mode use and intentions to use public transportation in an urban Norwegian public. Transport Policy 113–120, Elsiever (2015) 11. NSI: Population by districts, age, place of residence and sex (2022) 12. NSI: Population projections by districts and sex (2018) 13. NSI: Projected age dependency ratio by districts and sex until 2080 (2018) 14. Statista: Average number of persons per household in selected European countries in 2020 (2022) 15. Eurostat: “2021,” Stocj of vehicles by category and NUTS 2 regions 16. Sofiaplan: Infographics (2021) 17. Bastiaanssen, J., Johnson, D., Lucas, K.: Does better job accessibility help people gain employment? The role of public transport in Great Britain. Urban Studies 2021, 1–22 (2021) 18. Han, Y., Li, W., Wei, S., Zhang, T.: Research on passenger’s travel mode choice behavior waiting at bus station based on SEM-logit model. Sustainability (1996) 19. Godwin, Y., Cottrill, C., N. J.: Understanding factors influencing public transport passengers pre-travel information-seeking behaviour. Public Transport, pp. 135–158 (2019) 20. Sweeney, B.: An analysis of the role of real time passenger information on bus users in a European City: The Case of Dublin, Ireland, ITRN, Dublin (2012) 21. Legal, J., Meyer, T., Csillik, A.: Goal Primign, public transportation habit and travel mode selection: the moderating role of trait mindfulness. Transport Res. F-Traffic (2016) 22. Sumaedi, S., Bakti, I., Yarmen, M.: The empirical study of public transport passengers behavioral intentions: the roles of service quality, perceived sacrifice, perceived value, and satisfaction (case study: paratransit passengers in Jakarta, Indonesia). Int. J. Traffic Transport Eng., 83–97 (2012)

Big Data Analytics for Modelling Consumer Preferences

467

23. Beirao, G., Cabral, J.A.S.: Understanding attitudes towards public transport and private car: a qualitative study. Transp. Policy 14(6), 478–489 (2007) 24. Steg, L.: Car use: lust and must. Instrumental, symbolic and affective motives for car use. Transp. Res. Part A: Policy Pract. 39 2(3), 147–162 (2005) 25. Solomon, M.R.: Consumer Behavior: Buying, Having, and Being. Pearson Prentice Hall (2004) 26. Gaoa, Y., Rasoulib, S., Timmermansb, H., Wang, Y.: Trip stage satisfaction of public transport users: a reference-based model incorporating trip attributes, perceived service quality, psychological disposition and difference tolerance. Transp. Res. Part A 118, 773 (2018) 27. De Vos, J., Mokhtarian, P.L., Schwanen, T., Van Acker, V., Witlox, F.: Travel mode choice and travel satisfaction: bridging the gap between decision utility and experienced utility. Transportation 43(5), 771–796 (2016) 28. Carrel, A., Mishalani, R.G., Sengupta, R., Walker, J.L.: In pursuit of the happy transit rider: dissecting satisfaction using daily surveys and tracking data. J. Intell. Transp. Syst. 20(4), 345–362 (2016) 29. Apache Software Foundation: Apache NiFi. [Online]. Available: https://nifi.apache.org/. Accessed 11 Nov 2021 30. influxdata: InfluxDB Telegraf. [Online]. Available: https://www.influxdata.com/time-seriesplatform/telegraf/. Accessed 15 Nov 2021 31. Apache Software Foundation: Apache Airflow. [Online]. Available: https://airflow.apache. org/. Accessed 15 Nov 2021 32. Apache Software Foundation: Apache Spark MLib. [Online]. Available: https://spark.apache. org/mllib/. Accessed 18 11 2021 33. Grafana Labs: Grafana. [Online]. Available: https://grafana.com/. Accessed 22 Nov 2021

Citizen and Stakeholder Engagement in the Development and Deployment of Automated Mobility Services, as Exemplified in the SHOW Project Delphine Grandsart1(B) , Henriette Cornet2 , Matina Loukea3 , Stéphanie Coeugnet-Chevrier4 , Natacha Metayer4 , Anna Anund5 , and Anna Sjörs Dahlman5 1 European Passengers’ Federation (EPF), 9000 Ghent, Belgium

[email protected] 2 Union Internationale Des Transports Publics (UITP), 1080 Brussels, Belgium 3 Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece 4 Institut VEDECOM (VEDECOM), 78000 Versailles, France 5 Swedish National Road and Transport Research Institute (VTI), 58195 Linköping, Sweden

Abstract. The H2020-funded SHOW project (SHared automation Operating models for Worldwide adoption) supports the deployment of connected, cooperative and automated mobility (CCAM) through real-life pilot demonstrations taking place in 20 cities across Europe. While CCAM has the potential to bring great benefits to citizens and society, user acceptance is a crucial challenge to address. In this paper, we explore the importance of citizen and stakeholder engagement in the development of new mobility services, and how such aspects have been integrated and applied in SHOW. User acceptance surveys are being conducted at different stages in the project. In addition, dedicated citizen and stakeholder engagement activities are organized, including Ideathons and Hackathons. By engaging both citizens as potential end-users and stakeholders in the development process, we aim to ensure that SHOW services meet their needs and requirements and to increase the positive impacts on society. Keywords: Automated mobility · Automated vehicles · CCAM · Citizen engagement · Stakeholder engagement · User acceptance

1 Introduction Connected, cooperative and automated mobility (CCAM) represents a disruptive change to the existing mobility system. While CCAM has the potential to bring great benefits to citizens and to society, by making transport more affordable, safe, inclusive and sustainable, its deployment comes with challenges that must be addressed. One such challenge is how citizens will react, potentially accept, and use such new services, as user demand will be a critical success factor for CCAM. In addition, it is important to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_39

Citizen and Stakeholder Engagement in the Development

469

think about how to prevent any unintended adverse effects (e.g., increased single-person use of automated vehicles leading to a modal shift away from public transport, walking and cycling). User acceptance and assessment of medium- and long-term impact of automated and connected driving are listed as research priorities in ‘On the Road to automated mobility: An EU strategy for mobility of the future’ [8]. Similarly, in the Strategic Research and Innovation Agenda (SRIA) of the CCAM Partnership (a public-private multi-stakeholder partnership aiming to align CCAM R&I efforts to accelerate CCAM implementation in Europe), “insufficient demand as society does not yet understand the potential benefits” and lack of knowledge about “the long-term implications, benefits and impacts of integrating CCAM solutions into the mobility system” are identified as a major problem driver hindering the uptake of CCAM. Hence, one of the R&I clusters proposed in the SRIA focuses on ‘Societal aspects and user needs’ [4]. Another R&I cluster refers to the organisation of large-scale demonstrations, as in SHOW. In this paper, we explore the importance of citizen and stakeholder engagement in the development of new mobility services. By engaging both citizens and stakeholders (i.e., practitioners, local authorities, operators and service providers) in the development process, mobility services are designed to meet their needs and requirements and to increase the positive impacts on society. To validate our engagement method in an applied context, we implemented it in the SHOW project1 (Shared automation Operating models for Worldwide adoption) [21], which supports the deployment of fleets of Automated Vehicles (Avs) for shared mobility through real-life pilot demonstrations in 20 cities across Europe. SHOW use cases are diverse and cover the deployment of Avs in mixed traffic flows including features such as demand-responsive transport (DRT), Mobility as a Service (MaaS) and Logistics as a Service (LaaS) schemes.

2 Involving Citizens and Stakeholders in the Development of New Mobility Services The development and deployment of new mobility services implies the involvement of multiple stakeholders motivated by interests that are not per se aligned. Local authorities are driven by elections and therefore target the guarantee of the general interest, while industry and practitioners’ decisions are mostly benefit-driven [25]. At the citizen level, in everyday life, people’s decisions are governed by quality of life, defined as the interaction of basic human needs and personal wants on the one hand, and subjective perception of their fulfilment on the other hand (so-called “subjective wellbeing”) [17]. In order to design and develop sustainable and successful new mobility services (such as CCAM), both end-user needs and requirements from the different other stakeholders involved in the transport and mobility ecosystem therefore need to be considered. Assessment of the users’ and more generally stakeholders’ needs is prompted by a great number of interests, such as a better quality of the final service thanks to better adequacy to the expectations of the users [11], a more precise delimitation of the goal of 1 SHOW has received funding by the European Union’s Horizon 2020 research and innovation

programme under Grant Agreement number 875530.

470

D. Grandsart et al.

the new service/product [18], targeting of solutions on the market [9], a positive effect on the use of the service/product in the short and medium-term [3] and general user satisfaction and a higher level of perceived usefulness of the service/product [13]. Studying acceptance of a new technology or service makes it possible to understand and predict the user behaviour towards it. Technology or services acceptance refers to the subjective judgments that make the technological object or service attractive, usable, useful and even essential for users [26]. Acceptance is increasingly considered in the strategies of decision makers in order to “prevent” disputes and to ensure successful deployment [7]. However, as pointed out by Stilgoe, the use of the term ‘acceptance’ “implies a problem, locates it outside technology, and suggests that the role of the public is reactive and necessarily fatalistic” [22]. Therefore, rather than targeting acceptance as a reactive process, we should consider the involvement of citizens as a possibility to familiarize them at an early stage with new services or products. During SHOW, involving the citizens and stakeholders from the beginning increases the familiarity with the new mobility services. This familiarity may then lead to more authentic behaviours towards the use of new services, which then leads to the gathering of reliable insights. Later, this familiarity may lead to acceptance as assumed by KPMG, which links ‘consumer acceptance’ with the proportion of the population living in areas where AVs are being tested [16]. Different theoretical models exist that aim to categorise different forms of citizen engagement, such as Arnstein’s ladder of participation [2]. Informing and consulting are activities that are situated on the low end of the participation ladder, whereas concepts such as co-creation, co-design, living labs, etc. are characterized by a higher level of participation, empowering people and sometimes even placing the final decision-making in the hands of the public. For instance, using Citizen Dialogues to engage citizens on complex and technical topics like CCAM can be helpful in eliciting opinions and insights. In the case of Citizen Dialogues on Driverless Mobility [24] conducted in 2018–2019 in nine countries in Europe, North America and Asia, the educational element inherent to the method allowed participants to first learn about driverless mobility, thus providing informed and richer insights that would not otherwise have been possible with traditional methods (e.g., surveys and interviews) [5]. Whereas the importance of stakeholder involvement and citizen engagement is increasingly recognized – it is for example a key element in the development of Sustainable Urban Mobility Plans (SUMPs) –, real co-creation in transport planning seems to be still a rather new phenomenon, as reflected by a low volume of academic articles to be found on the subject [19]. Nevertheless, European projects can be seen as the ideal platform to bring stakeholders together with researchers and citizens for developing and deploying inclusive and meaningful mobility products and services. The topic of citizen and stakeholder engagement has already been addressed in several European-funded and CCAM-related projects, in many cases with a direct link to user acceptance, e.g. PasCal [20], SuaaVE [23] and Drive2theFuture [12]. PasCaL

Citizen and Stakeholder Engagement in the Development

471

aims to develop a multidimensional map of public acceptance of higher levels of Connected and Autonomous Vehicles (CAV) using large-scale questionnaires and experiments involving a variety of users “with specific attention to minority groups and those who currently experience some issues with their mobility”. With its Human-Driven Design (HDD) approach, SuaaVE (2022) integrates the user at an early stage considering emotions for designing Human-Machine Interfaces. The results feed into simulations for public acceptance and guidelines to support Public Authorities. Drive2theFuture wishes to “prepare future travellers and vehicle operators to accept and use connected, cooperative and automated transport modes” and at the same time, it aims at helping the industry of these technologies to understand and meet people’s needs and wants through simulations and user consultation. Within the SHOW project, a large number of activities are planned with a diversity of participants: e.g., interviews, surveys, focus groups, workshops. Likewise, several citizen engagement activities are planned, ranging from a low level of participation (informing people about the AV service, SHOW user acceptance surveys) to a higher level of participation (such as Citizen Dialogues, Ideathons, Hackathons). SHOW has conducted a literature review to identify the prioritized needs, wants and expectations of AV end-users and stakeholders (Chap. 3). In addition, user acceptance surveys are conducted at all pilot sites – i.e., the cities where the AV fleets are deployed and tested (Chap. 4). Furthermore, each SHOW pilot site has developed its own customized engagement strategy and plan, which includes co-creation activities such as Ideathons and Hackathons (Chap. 5).

3 Ecosystem Actors’ Needs, Wants and Priorities An important work conducted as one of the first steps in the SHOW project consisted of identifying all relevant stakeholders, both internal (i.e., SHOW project partners) and external, and to define the main wants, needs and priorities, per stakeholder group. The findings – based on desktop research through a literature review of deliverables of EU and non-EU projects, as well as papers and publications, focusing specifically on AV user and stakeholder acceptance – have been compiled in a deliverable of the SHOW project “D1.1 – Ecosystem actors needs, wants and priorities and user experience exploration tools” [7] and are summarized hereinafter. The following stakeholder categories were identified: • Original Equipment Manufacturers (OEMs) and transport/mobility operators; • Tier 1 suppliers, telecom operators, technology providers, Small or Medium Enterprises (SMEs); • Research & academia; • Passengers and other road users encompassing Vulnerable to Exclusion (VEC); • Umbrella associations/Non-profit organisations; • Road operators, Authorities (Cities, Municipalities, Ministries) and policy makers. For AV logistics use cases, the following additional stakeholder groups were identified: • Senders

472

D. Grandsart et al.

• Receivers • Delivery service providers. A comparison of findings across the different stakeholder groups has allowed us to point out common gaps, views, etc. as well as main differences. A major commonality amongst the involved stakeholders is the fact that they are “not familiar with automation”, which relates to mistrust, safety and security concerns, as well as poor user acceptance. On the other hand, there are also crucial differences in the prioritization of needs and wants among stakeholders, especially when comparing passenger and freight transport. In both cases, safety is of course a top priority. However, in passenger transport, improving the service in terms of comfort, reliability and cost is an important issue, whereas, on the other hand, in freight transport the “value for money” aspect is an important point, taking into account also environmental impacts, other restrictions and legal obligations (e.g., in terms of environmental sustainability and footprint). Considering passenger transport (which is the focus of this paper), almost all research outcomes point towards prioritization of safety, “fear of the unknown” being the main reason of concern, inconvenience and doubt on behalf of not only the passengers, but also authorities, public transport operators, network and technology providers. This intrinsic mistrust is reinforced by the fact that the legal framework has so far been somewhat unclear regarding the share of responsibility in case of an accident involving an AV. In addition, the willingness to pay is a major issue for everyone, dealing mainly with who is going to be burdened with the initial investment and how affordable the new automated passenger transportation system will be for the average user. Service providers and operators are also interested in compliance with regulations on environmental friendliness, while environmental preservation is ranked high for public authorities as well. Furthermore, accessibility is a recurrent topic, referring not only to physical accessibility – which is expected to be addressed already in the design phase – but also to the need for easy and equal access to services by all potential users, regardless of the level of their familiarization with new technologies and including vulnerable to exclusion citizens. Finally, users appear reluctant to share their personal data and processing of such personal data should be done in compliance with the General Data Protection Regulation (GDPR). As a next step, the outcomes of this research are complemented by a set of surveys and interviews at all SHOW pilot demonstration sites. The SHOW user acceptance surveys are discussed in more detail in the following chapter.

4 User Acceptance Surveys in SHOW Acceptance is a process that takes place on a temporal continuum in three steps [26]. The first step corresponds to the a priori acceptance (before the first use). A priori acceptance is the consequence of a comparison judgment between reality and its known alternatives, i.e., the possible benefits generated by the new technological device or the new service. The other two steps correspond to the acceptance in the use of the technology (from the first uses to six months of use) and appropriation (established use in everyday life). The acceptance step takes place from the first uses to a real experience with the technology.

Citizen and Stakeholder Engagement in the Development

473

The step of actual appropriation of the technology characterizes the step of adequacy between the user’s real needs and the technology which becomes a real component of her/his identity. A frequent approach used for collecting information on user needs consists of building experience or user journey maps (see for example [10, 15]). Maps are precious tools to better understand and anticipate interactions between users and a specific product or service. They are often used to make projections in the future users’ or stakeholders’ minds. After drawing the typical journey of a user, both qualitative and quantitative methods can be deployed to collect feedback referring to the users’ needs in each journey step [6]. Overall, six main categories may be defined: 1. Information and signage (e.g., display at bus stops, means of recognizing the correct vehicle, information after the journey about congestion or disruption, …); 2. Service request (e.g., reservation on an application, tickets, included in a subscription card, on-demand or continuous with a timetable, service available all day and/or night, …); 3. Identification and boarding (e.g., validation, code, automatic doors, privacy, …); 4. Service start (e.g., automatic start or with a button press, …); 5. On-board experience and activities (e.g., speed and position of the vehicle, available connections, available space per person, infotainment, shared activities, control on the driving and/or on the vehicle, view of the cameras, safety, …); 6. Descent (e.g., automatic stop, doors opening, satisfaction assessment, feedback after the journey, and support in the event of lost luggage and complaints, …). Based on Nielsen’s model [18], four dimensions could explain most of the practical acceptance of a mobility service: the utility of the mobility services and/or vehicles, the usability of the mobility services and/or vehicles including the satisfaction, the compatibility of the mobility service and the reliability perceived by users. The previous elements allowed us to construct the methodological bases of the assessment of the stakeholder needs and to plan the different surveys in SHOW. We proposed to conduct two types of surveys: • an extensive online survey conceived as a baseline measurement, to collect information on users’ a priori acceptance of and familiarity with AV services (open to the general public, i.e. anyone who wishes to share their thoughts); • a shorter survey (on-site and/or online) to monitor the actual user experience at different times – at the beginning, near the middle and at the end – during the SHOW pilot activities (targeting a representative pool of travellers at each SHOW site). In addition, a one-question satisfaction ‘survey’ is foreseen at all SHOW pilot sites, to allow a very quick assessment. Besides these surveys, stakeholder interviews are planned in SHOW, both before and at the end of the pilot activities, involving all stakeholders and travelers’ cohorts per site. Table 1 presents a synthesis of all planned user acceptance instruments. The a priori acceptance survey is based on a user journey, presenting different categories of interest (see examples in Table 2). The acceptance surveys used during the

474

D. Grandsart et al. Table 1. Synthesis of the surveys and related tools

User/stakeholder

Campaign and instrument

When

Administration

Tool

Traveler (passenger / driver)

Needs/wants, a priori acceptance & intention to use long questionnaire

Before the implementation of the pilots

Online via invitations

Typeform, SurveyMonkey, socsurvey, etc

Acceptance a posteriori & intention to (re)use short questionnaire (15-questions)

On-site during the automated services piloting (3 measurement times: end of the pre-demo, at the midterm of the demo, at the end of the demo)

Asked by personnel entering stops or the public transport vehicle – contextually appropriate with high face validity

Same as above via a tablet or mobile phone, QR code, etc

Satisfaction 1-question

On-site during the automated services piloting

Travelers respond Feedback strips directly in the vehicle

Needs/wants, acceptance & intention to deploy interview

On-site during Face to face the automated services piloting (2 measurement times: end of the pre-demo, at the end of the demo)

OEMs, Operators, authorities, infrastructure operators, Tier 1 service providers, etc

Hard copy/ tablet/ recordings

real-life pilot demonstrations are shorter, so they can be filled in directly in the vehicle, during the service. Based on the previously cited Nielsen’s model [18], only one item per tested dimension was retained (instead of 3 or 4 in the long version). The survey aims at assessing the context of the journey and the acceptance of the vehicle/service. It includes items about mobility context (day/night, reason of the journey, duration of the journey or line start/stop, problem encountered on the journey), acceptance in the use (e.g., satisfaction, perceived utility, usability, easy to learn, perceived safety and comfort) and socio-demographical data (age, gender). To understand the acceptance progression and the perception change, three measurement times will be performed during the pilot demonstration period: at the beginning, in the middle, and at the end. In addition to the surveys, interviews are conducted with stakeholders. The interview grid is adapted according to the stakeholder groups. Nevertheless, for each group, it is expected to collect the following data:

Citizen and Stakeholder Engagement in the Development

475

Table 2. Examples of the questionnaire structure Main categories

Examples of items

Socio-demographic data

Age, gender, education, household structure, employment, category of stakeholder

Technology and automated vehicles knowledge and relationship

Experience with automated vehicles, technology savviness

Prerequisites and conditions of deployment

Shared or individual vehicle, traffic area

Information and signage

Information for the first use, means of recognizing the correct vehicle

Service request

Reservation on an app, ticketing

Identification and boarding

Validation, code, automatic doors

Service start

Waiting for other passengers

On-board activities

Available space by person, infotainment, shared activities, comfort, cleanliness

Descent

Automatic or stop button, satisfaction assessment

Generic expectations

Facilitating conditions, safety, perceived risk, data privacy

• Expectations regarding the results of the assessment of the automated service and key points to be addressed after the pilots; • Perception and assessment of the automated service; • New or remaining needs to address; • Pain points and difficulties encountered during the implementation of the automated service and planned solutions; • Personal acceptance in use. An online software was used to implement the surveys, which were translated into the local languages of each pilot site. The a priori questionnaire is being disseminated through SHOW partners’ networks and those of other stakeholders in each site’s ecosystem, through targeted e-mailing and use of social media mainly, and in some cases supported by printed materials (e.g. flyers, posters, etc.) with a QR code linking to the survey. The shorter acceptance surveys can be filled in during the service, in the vehicle. At most sites, QR codes (on the vehicles, on printed materials, at the shuttle stops etc.) are used, linking to the survey, while several sites also provide a paper form. Staff (safety drivers, SHOW personnel, other) alert passengers to the survey and invite them to fill it in. The one-question satisfaction survey is conducted continuously during the pilot demonstration phase, preferably through the use of a tablet with smileys ranging from happy to sad with at least five steps. The results of the surveys and interviews (at the time of writing not yet available) will feed into the overall SHOW evaluation framework. For this, a methodology has been created denoted M3ICA (multi-impact, multi-criteria, and multi-actor) that allows

476

D. Grandsart et al.

for the consistent analysis and evaluation of pilots and simulations. The surveys and interviews are meant as evaluation tools to collect also ‘subjective’ data dealing with experience, usability, user acceptance, trust and socio-economic variables [1].

5 User Engagement Activities in SHOW Within the SHOW project, a specific task is dedicated to user engagement and co-creation activities. The aim is to support the SHOW pilot sites in reaching out to end-users and other stakeholders and to monitor their engagement efforts. A Framework and Guidelines for a successful stakeholder engagement process have been developed to this purpose, guiding the SHOW sites in developing their own customized engagement strategies and plans. In addition, dedicated activities – Ideathons and Hackathons – are being organized to recognize gaps and collect solution-oriented ideas to improve the services proposed by SHOW. Ideathons – co-creation workshops with citizens and core stakeholders – are conceived as creative brainstorming sessions focusing on end-user needs. The solutions that come up during these Ideathons are implemented in practice by organizing follow-up Hackathons during which the best ideas coming from the Ideathons are further developed. The following sections present in more detail the approach and results of the first SHOW Ideathon and Hackathon (5.1). We also elaborate on the customized engagement strategy developed at the SHOW site in Linköping, as a good example on how to tackle the challenge of user engagement in a project like SHOW (5.2). 5.1 SHOW’S First Ideathon and Hackathon The first SHOW Ideathon took place on 15th January 2021. 39 people attended the event, representing a diversity of stakeholders (authorities, policy makers, municipality agencies, road operators; OEMs and transport/mobility operators; research and academia; Tier 1 suppliers, telecom operators, technology providers and services companies; passengers and other road users) – with special focus on end-user representatives as we want to ensure that the SHOW solutions take into account end-users’ needs. After a short introduction to the SHOW project, we launched a few ‘warm-up’ questions using an online poll tool (What do you think are the main benefits that Automated Vehicles can bring for end-users? & In your opinion, what are the most important user needs to address in order to obtain or improve user acceptance of Automated Vehicles?). Safety, accessibility, flexibility, ‘no more private cars’ and transport availability were highlighted by the participants as the main potential benefits of CCAM. Then, we presented four scenarios that would be the topics of parallel brainstorming sessions, as well as four mobility personas (adapted from [14]) which might help to envision different user profiles when thinking about user needs in different scenarios. After a short break, participants were asked to move to their parallel sessions to which they had been assigned beforehand to ensure an optimal balance of stakeholder group representatives per session. Four scenarios were discussed:

Citizen and Stakeholder Engagement in the Development

1. 2. 3. 4.

477

Driverless shuttle for first/last mile Door-to-door delivery of persons and goods Mass transit with driverless buses Shared on-demand Robotaxis. Each break-out session followed the same procedure:

• Tour de table, during which participants were asked to briefly introduce themselves • ‘Wild’ question: Imagine your future in 10–20 years. What would mobility look like by then? • Presentation of the scenario in some more detail • Considering the scenario from the end-user perspective, in terms of user needs (with special focus on: safety; ease of use; environmental impact; transport equity/inclusion/accessibility; speed/travel time). After the break-out sessions, all participants moved back to the plenary for the ‘enrichment’ phase, in which selected ideas from each brainstorm session were discussed in more depth. Some questions that we kept in mind for guiding the enrichment were: • Does the solution take into consideration special needs of users? • Does the solution imply new forms of interaction between different users or stakeholders? • Is the solution easy to use for all users? • Are there any barriers that might prevent the users’ acceptance of the solution? • What potential improvements would you suggest? • Which other applications would you see? • … After the Ideathon, the SHOW team assessed all ideas in terms of (i) their impact on user experience and user acceptance; and (ii) their feasibility, business potential and potential for SHOW. In the end, we selected three so-called ‘killer ideas’ to be taken forward to the first SHOW Hackathon: • 24/7 surveillance on board and stand-by human assistance • Flexibility to adapt capacity to increased demand and potential impact on bus depots • Accessibility, audio-visual messaging and assistance for PRMs (persons with reduced mobility) across the whole trajectory. In addition to Ideathons, SHOW will also organise Hackathons with participants coming both from within the project consortium as well as externals from each pilot site and beyond. Designers, developers and scientists of diverse backgrounds are invited to work closely together with business analysts and user representatives (transport service operators, travellers, etc.), utilizing project tools to develop added value services that: • meet end-users’ (unfulfilled) needs related to the use of CCAM; • can be utilised by the project (e.g., to be included in the SHOW marketplace);

478

D. Grandsart et al.

• can ideally be deployed in one or more SHOW pilot sites. The first SHOW Hackathon took place from 21st to 23rd March 2022 in Thessaloniki, Greece, and welcomed more than 20 developers, technology providers, etc. who formed three teams, working on innovative solutions to the three challenges that came out of the 1st SHOW Ideathon: • A first team, ‘GUSTAV’, worked on the topic of “How To Make Safer and More Secure Driverless Automated Vehicles for Passengers and People on the Road”; • A second team, ‘assistIO’ worked on finding a solution to improve “Accessibility and Assistance to Persons with Reduced Mobility”; • Team three, ‘DeFORUS’ worked on “A Project for Adapting/Demanding Capacity to Handle Demand in a Flexible Way”. According to the designated panel of judges that were faced with a tough choice, the Gustav and AsistlO teams were announced to be joint first-place winners. However, regardless of the “winning team”, all applicable outcomes of this Hackathon will be hosted in the SHOW Marketplace, which is a side outcome of the SHOW project, serving as a one-stop-shop of all types of CCAM products: algorithms, services, UIs, datasets, architectures, apps, case studies, etc. 5.2 Customized Engagement Strategies – Linköping as a Best Practice Each SHOW site has developed its own customized engagement strategy and plan, adapted to the local context and taking into account the specificities of each pilot in terms of objectives, stakeholders involved, user groups addressed, and factors affecting user acceptance. These strategies are conceived as a ‘living document’, to be updated regularly as the project progresses, and cover the following core questions: • • • • •

Who are the local stakeholders, who are the end-users? Which communication channels and tools can we use to reach out to them? How can we engage and involve stakeholders and end-users in SHOW? How can we encourage people to try out the SHOW services? How can we exploit synergies with planned SHOW events – tools – actions?

To illustrate the user engagement process, we describe below the activities undertaken in the SHOW pilot in Linköping, as an inspiring best practice on how to turn user engagement into a success story. The SHOW pilot in Linköping is running with three AV shuttles in the Campus Valla Area, which is connected to the Science Park with Ericsson, Combitech and 370 more companies as well as schools, elderly people’s residencies, child-care facilities and residential areas. The operation will also be extended to cover a residential area called Vallastaden. Specific user groups targeted in Linköping include children and elderly people, for whom dedicated engagement activities have been set up. Kick-off events. When the shuttle service first opened for the general public, a kickoff event was organized for students and employees. For the first ride, ‘golden tickets’

Citizen and Stakeholder Engagement in the Development

479

were printed. Anyone wishing to try out the shuttle could write their names on a piece of paper and drop them in a box, and then names were drawn at random. Also, the local demonstration board (with representatives from all important stakeholders, such as the public transport operator, the municipality, etc.) presented itself to the public. A follow-up event took place when the 3rd AV shuttle was put into service on the 2nd part of the route. This event was held outside at the public transport hub ‘Nobeltorget’. There were balloons, and popcorn and soft drinks were served to the attendees, which included employees from the companies in the area, but also local citizens living in the residential area Vallastaden and commuters arriving at Nobeltorget. Target-group specific engagement activities. The Linköping AV service targets basically anyone who wishes to travel in the pilot area, with a special focus on current car users, who can use the shuttles as a first and last mile solution to complement public transport. The majority of engagement activities targeted two key user groups: children and elderly people. In December 2021, several engagement activities were organized in cooperation with the school, e.g. having the children make drawings to visualize the future of mobility with AVs. Try-out sessions were held where the children had the opportunity to take a ride and familiarize themselves with the shuttles, talk to the safety driver and researchers. Engagement activities combining a try-out session with a focus group session afterwards, were also conducted, with a group of teenagers and with a group of people with a visual impairment (blind or almost blind), so as to gather these specific user groups’ views on how to make the service more attractive and accessible to them. A name-giving contest was also organized, both with the elderly people living at the residential home and the persons with cognitive impairments visiting the centre for daily activities, and with the children at the daycare centre. The three shuttles were baptized Hjulia, Reza and Busse. User satisfaction. To be able to engage users but also to understand acceptance and need for improvements, a customer satisfaction screen is installed in all three shuttles. This provides the users with an opportunity to rate their experience. In addition, the safety operator can interact and discuss with the travellers to engage them even more. The role of the safety operator is different from a normal bus driver’s role and (s)he is seen as a guarantee to safe and secure travel. Lessons learned from Linköping show that continuous work to engage users is needed. This was even more important when restrictions due to Covid-19 were in place. People need to know that the service is open for them, feel invited and engaged. User engagement is a continuous activity. New residents, children and elderly will arrive and need to be engaged. The more mature the operation gets the less user engagement activities are most likely needed.

6 Conclusions This paper investigated the need for citizen and stakeholder engagement while developing and deploying CCAM services. Prior to the actual engagement, we conducted an extensive literature research in SHOW in order to map the known needs and requirements of the CCAM ecosystem from a diversity of sources. Considering SHOW’s citizen and stakeholder engagement strategy, the user acceptance surveys’ method has been presented as well as the insights coming from the first SHOW Ideathon and Hackathon.

480

D. Grandsart et al.

In both cases, an emphasis was put on safety especially considering vulnerable users. Finally, a closer look at one SHOW site, Linköping, highlighted the need for close engagement with the local community in order to increase the degree of familiarity, which can expectably lead to an increased acceptance of CCAM. Overall, this paper provides a methodological framework for deploying a citizen and stakeholder engagement strategy in CCAM projects, which could be generalized to other initiatives aimed at providing new (mobility or other) services to citizens and requiring the collaboration of several stakeholders. In order to design and develop sustainable and successful new mobility services, including CCAM, a user-centric approach is needed, focusing on end-users’ needs and wants, while also taking into account the requirements of other stakeholders (such as operators, authorities, service providers etc.). Rather than thinking about citizen and stakeholder engagement as a one-off exercise, governance could in fact be rethought as taking place ‘in dialogue’, demanding ongoing conversations with the citizens and local stakeholders [22]. Three decades ago, Wynne mentioned already that “reflexive institutions needed to place science-public interactions on a more constructive footing” [27]. European projects such as SHOW are a suitable setting to enable the emergence of these ‘reflexive institutions’ where decision makers (funding agencies – the European Commission for SHOW – and the local authorities involved in the project), practitioners and citizens interact throughout the project. Acknowledgements. The results included in this paper are part of SHOW, a project which received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 875530.

References 1. Anund, A., Dahlman, A.S., Rombaut, E., Robinson, D., Vanhaverbeke, L., Jany-Luig, J., Schauer, N., Hülsermann, J., Heinen, M.: SHOW D9.2: Pilot experimental plans, KPIs definition and impact assessment framework for pre-demo evaluation, available https://show-pro ject.eu/media/deliverables/. Accessed 04 Dec 2022 2. Arnstein, S.: A ladder of citizen participation. J. Am. Plann. Assoc. 35(4), 216–224 (1969) 3. Baroudi, J.J., Olson, M.H., Ives, B.: An empirical study of the impact of user involvement on system usage and information satisfaction. Commun. ACM 29(3), 232–238 (1986) 4. CCAM Strategic Research and Innovation Agenda (SRIA) Version 1.4, 17/03/2022, https:// www.ccam.eu/our-actions/sria/. Accessed 04 Dec 2022 5. Chng, S., Kong, P., Pei Yi Lim, Cornet, H., Cheah, L.: Engaging citizens in driverless mobility: insights from a global dialogue for research, design and policy. Transp. Res. Interdisc. Perspect. 11 (2021) 6. Coeugnet, S., Bel, M., Kraiem, S., Malin, S., Sanmarty, G., Souliman, N.: Ecomobility by Autonomous Vehicles on the Paris-Saclay territory—Collection of needs. Deliverable L56 EVAPS project. VEDECOM, France (2018) 7. Coeugnet, S., Métayer, N., Alcaraz, G., Britsas. B., Dias, P., Dimakopoulos, N., Gemou, M., Loukea, M., Marteau, J., Merlhiot, G., Panou, M., Touliou, K.: SHOW D1.1: Ecosystem actors needs, wants and priorities and user experience exploration tools (2020), available https://show-project.eu/media/deliverables/. Accessed 12 Apr 2022/04/12

Citizen and Stakeholder Engagement in the Development

481

8. COM (2018) 283 final: Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee, the Committee of the Regions: On the road to automated mobility: An EU strategy for mobility of the future 9. Cooper, R.G., Kleinschmidt, E.J.: New product performance: What distinguishes the star products. Aust. J. Manag. 25(1), 17–46 (2000) 10. Crosier, A., Handford, A.: Customer journey mapping as an advocacy tool for disabled people: a case study. Soc. Mark. Q. 18(1), 67–76 (2012) 11. Damodaran, L.: User involvement in the systems design process: a practical guide for users. Behav. Inf. Technol. 15(6), 363–377 (1996) 12. DRIVE2THE FUTURE project. https://www.drive2thefuture.eu/. Accessed 04 Dec 2022 13. Foster, S.T., Jr., Franz, C.R.: User involvement during information systems development: a comparison of analyst and user perceptions of system acceptance. J. Eng. Tech. Manage. 16(3–4), 329–348 (1999) 14. Kong, P., Gill, A., Cornet, H., Frenkler, F.: Mobility Personas for Singapore Cards. Tech. Rep., TUMCREATE Ltd., Singapore (2019). Available: http://bit.ly/MPScards 15. Kong, P., Vallet, F., Al Maghraoui, O., Cornet, H., Frenkler, F.: Seeking emotions in mobility experience elicitation: a Singapore-France comparison. In: Proceedings of the International Association of Societies of Design Research Conference 2019. Manchester, United Kingdom (2019) 16. KPMG Global: Autonomous Vehicles Readiness Index. Available at: https://home.kpmg/ xx/en/home/insights/2020/06/autonomous-vehicles-readiness-index.html. Accessed 04 Dec 2022 17. Max-Neef, M.: Development and human needs. In: Ekins, P., Max-Neef, M. (eds.) Real life Economics: Understanding Wealth Creation, pp. 97–123. Routledge, London (1992) 18. Nielsen, J.: Usability engineering. Academic Press, Boston (1993) 19. Pappers, J., Keserü, I., Macharis, C.: Co-creation or Public Participation 2.0? An Assessment of Co-creation in Transport and Mobility Research. In: Müller, B., Meyer, G. (eds.) Towards User-Centric Transport in Europe 2. LNM, pp. 3–15. Springer, Cham (2020). https://doi.org/ 10.1007/978-3-030-38028-1_1 20. PASCAL project. https://www.pascal-project.eu/. Accessed 04 Dec 2022 21. SHOW project. https://show-project.eu/. Accessed 04 Dec 2022 22. Stilgoe, J., Cohen, T.: Rejecting acceptance: Learning from public dialogue on self-driving vehicles. Sci. Public Policy 48(6), 849–859 (2021) 23. SUAAVE project. https://www.suaave.eu/. Accessed 14 Dec 2022 24. Tomorrow, our lives with driverless mobility. https://themobilitydebate.net/. Accessed 04 Dec 2022 25. Tukker, A., Charter, M., Vezzoli, C., Stø, E., Andersen, M.: System innovation for sustainability. Perspectives on radical changes to sustainable consumption and production. Greenleaf Publishing Ltd., Sheffield (2008) 26. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003) 27. Wynne, B.: Public uptake of science: a case for institutional reflexivity. Public Underst. Sci. 2(4), 321–337 (1993)

Transportation Systems of Asia: Investigating the Preferences for Their Implementation in Greece with the Use of the Maximum Difference (MaxDiff) Scaling Method Melpomeni Mokka1 , Georgios Palantzas1,2 , Ioannis Politis1 and Dimitrios Nalmpantis1,2(B)

,

1 School of Civil Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, PO

Box 452, 541 24 Thessaloniki, Greece [email protected] 2 School of Applied Arts and Sustainable Design, Hellenic Open University, Parodos Aristotelous 18, 263 35 Patras, Greece

Abstract. Our modern era is characterized by swift and continuous technological development in a pressing and competitive environment, where time, timely decisions, and the risk of the adoption of new technologies and innovations are of crucial and decisive importance. In such an environment, we pursue to adopt pioneering innovations that will be able to exceed all previous efforts and will prevail both at a technological and an economic level. This paper focuses on the technology transfer process of transportation innovation from a financially advanced part of the world, that of Southeastern Asia, to Greece. By using the Maximum Difference Scaling (MaxDiff) method, a questionnaire survey was conducted in which 195 people who live in Greece participated. Through this questionnaire, the preferences among several alternative transportation innovations of the transportation system of Southeastern Asia were estimated. Smart parking, high-speed trains, and autonomous vehicles were selected as the top choices. The least preferred innovations were the parking spaces only for women, the flying cars, and the flying taxis. By analyzing the results, we concluded how positive the residents of Greece are toward new transportation technologies in combination with demographic criteria. Through the MaxDiff method, we recorded these preferences analytically for any future applications of these innovations at a national level. Keywords: Innovation · Economic development · Technological development · Technology transfer · Questionnaire survey · Maximum difference scaling (MaxDiff) method

1 Introduction “Innovation” means “the transformation of an idea into a new, radically different product, process or service” [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_40

Transportation Systems of Asia: Investigating the Preferences

483

Innovation is “born” in the form of a new idea, product, or technology and is adopted by businesses, organizations, and consumers. In short, the process of innovation is the production and adoption of a new product or process [1]. The role of innovation in the dynamics of socio-economic development and economic growth is not easily disputed due to the existence of an extensive literature. Innovation actively contributes to the evolution of humanity through original inventions that are a source of social, technological, and cultural change. Innovation is characterized as a multidimensional concept whose boundaries are determined by the perspective of different disciplines [2]. The purpose of this paper is to introduce the reader to the transportation innovations and new ideas applied in Asian countries and to highlight the preferences in Greece. Asia is the largest, most populous, and most diversified continent in the world. It is more a geographical term than a homogeneous continent, and the use of the term to describe such a vast area always carries the potential to obscure the enormous diversity between the regions it encompasses. Asia has the highest and lowest points on the Earth’s terrain, has the longest coastline of any continent, is generally subject to the widest climatic extremes in the world and, as a result, produces the most diverse forms of vegetation and animal life on Earth. In addition, the people of Asia have established the widest variety of human adaptations found on any of the continents [3]. In this paper, an attempt was made to investigate the preferences of the people of Greece with the use of Maximum Difference Scaling (MaxDiff) method for the future application of new ideas and technologies to transportation systems “coming” from Asia, and more specifically, from its more developed part, i.e., Southeastern Asia. It is original research as no similar research has been conducted earlier. Despite the fact that this research is original, it builds upon the experience gained from earlier research of some of the authors regarding transportation innovation [4–9], with the use of Multi-Criteria Decision Analysis (MCDA) [10–12], Conjoint Analysis [13–15], and MaxDiff [16].

2 Methodology For the purpose of this study, the MaxDiff method, an effective tool for covering a large number of features, was used. The Discover software platform from Sawtooth Software was used to implement the method. After selecting the characteristics of the innovations to be studied, a questionnaire was structured consisting of two parts. The first part included 13 general questions of demographic/general content, while in the second part, the respondents were asked to answer by selecting the more important and the less important of the 20 innovations, which were presented in 12 different slides, being five (5) different in each task. The 20 innovations included in this questionnaire survey provided information on the preferences of Greek citizens. Through the questionnaire survey, there will be a clear picture of which innovations are considered important based on the features most emphasized by the users of the transportation systems. MaxDiff was initially developed by Jordan Louviere and his colleagues. The MaxDiff method is ideal for obtaining priority/importance scores for many items such as brand names, product attributes, etc. [17]. The questions in a MaxDiff questionnaire are simple to understand and can be answered by children to adults with different educational and cultural backgrounds to

484

M. Mokka et al.

provide reliable data. The respondents are asked to select the item they consider best and worst among a set of items. They do not express their strength of preference using a numerical scale, thus avoiding the opportunity for scale bias [17]. The MaxDiff method displays a set (subset) of possible items in the exercise and asks respondents to indicate (among this subset) the most and least important (best and worst) items. Not all items are displayed at once; instead, they are divided into subgroups of four (4) or five (5) possible items. The respondents completed eight (8) to fifteen (15) such MaxDiff questions. Item combinations are very carefully designed so that each item appears an equal number of times, and item pairs appear an equal number of times as well. The respondent typically sees each item two (2) or more times in the MaxDiff sets [17]. The MaxDiff method focuses on collecting preferences and estimating importance ratings for about 15–40 items, although hundreds of items could be accommodated in advanced applications. The resulting scores are also easy to interpret, as they are placed on a standard scale of 0–100 points, with a sum of 100 [17]. In summary, the choice of the MaxDiff method is based on its ease of use by the respondent with diverse educational and cultural backgrounds, strong discrimination between items, reduction/elimination of scale use bias, and is easier to use and applied in a wider variety of research situations, such as measuring the importance of product attributes, cross-cultural research studies, testing advertising claims, package design, market segmentation based on needs. In this research, data were collected from anonymous questionnaires and processed in an aggregated manner to evaluate the preferences of the sample as a whole rather than individually. This eliminates the possibility of linking specific responses to specific individuals. In this survey, 195 transportation system users in Greece evaluated according to their preferences the following 20 transportation innovations found mainly in Southeastern Asia. Electric taxis (reduced CO2 emissions). Electric garbage trucks (reduced CO2 emissions). Hydrogen cell buses (environmentally friendly, source of energy in case of disasters, facilitating passengers with strollers and wheelchairs, safety of transport). 4. Electric buses (reduced CO2 emissions, same transport costs as conventional buses, fast battery charging 24 h a day). 5. Electric bus routes (wireless charging during the bus journey, time-saving, CO2 reduction). 6. Electric car parks for passenger cars (wireless charging, timesaving). 7. Flying cars (reduce traffic congestion, quiet, take-off and landing on top of a building). 8. Flying taxis (reduce traffic congestion, economical and fast transport option, use for remote islands, natural disasters and emergency needs). 9. Autonomous vehicles (access to mobility for people who are physically unable to, e.g., elderly, people with mobility difficulties, reducing accidents). 10. Autonomous buses (driverless, minimize waiting time for passengers, reduce congestion). 1. 2. 3.

Transportation Systems of Asia: Investigating the Preferences

485

11. Autonomous cargo vehicles (nighttime operation, reduction of traffic congestion). 12. Autonomous waste collection vehicles (nighttime operation, reduce congestion). 13. Magnetic levitation trains Maglev (trains that do not touch the rails, impossible train collision and derailment, turbulence-free movement, absence of exhaust fumes, high speed in a short time). 14. High-speed trains (safety, high speed, precise timing, low noise level, environmentally friendly, passenger control by a special device at each seat). 15. Smart parking (information via electronic devices for the availability of parking in real time, reduction of traffic congestion, time-saving, integrated information on parking spaces, e.g., for food outlets and other public transport facilities in short distances from the parking place, ensuring accurate charging). 16. Automatic underground bicycle parking (secure parking under the ground, easy and quick to use, encourages bicycle use). 17. Women-only parking spaces (longer, wider, closer to mall entrances and surveillance systems, high security). 18. DiDi system (mobility platform via electronic devices, provision of real-time traffic management information, time-saving, cooperation with local police). 19. Advanced Traveler Information System (ATIS) using Geographic Information Systems (GIS) (providing real-time traffic information, safety advice for dangerous situations, warning messages, and suggestions for optimal routes). 20. Variable Message Signs (VMS) (road signs, information on the road, real-time information to drivers on speed restrictions, conditions at other general roads, safety information, messages about the travel time). In questions from 1 to 8, the respondents were asked to answer demographic questions such as gender, age, educational level, employment status, marital status, number of adults/children in the household, and population of the town or village where they live. In questions 9 to 13, the respondents were asked to answer general questions such as which means of transportation they own, what is their most frequent reason for traveling, which mode of transportation they choose for their travel, and how positive they are about the application of new technologies in transportation and which characteristic(s) most influence their choice of means of transportation for their travel. Then they were asked to answer 12 MaxDiff questions/tasks. Each task presented a list of five (5) innovations out of the total twenty (20) transportation presented in the survey. The respondents had to indicate which one they considered most important and which one they considered least important for their future implementation in Greece.

3 Results and Discussion 3.1 Demographics The questionnaire was answered by 195 people. 33% of the respondents were male, and 67% were female. There was 1% who selected “Other,” while the response “I prefer not to answer” was not selected.

486

M. Mokka et al.

The majority of respondents were between 19 and 25 years old, with 42%. This was followed by ages 26–35 with 17%, 36–45 with 16%, and 46–55 with 12%. Ages 56–67 made up 9% of the total sample. The lowest percentages were for ages < 19 years with 3% and > 67 years with only 2%. 55% of the respondents were students of Higher Education Institutions (HEI) and Technological Educational Institutions (TEI). 24% were postgraduate degree holders, and 3% were PhD holders. 11% and 3% were high school and middle-high school students, respectively. Finally, 5% were students at a Vocational Training Institute (VTI). The largest proportion of 34% belonged to pupils/students. 20% of the respondents were public servants, and 17% were private employees. Freelancers accounted for 13% of the total sample, while 5% were retired. Of the total respondents, 7% were unemployed, and 4% chose the answer “Other.” The largest percentage (61%) indicated that they were residents of cities with a population of more than 300,000. 15% were residents of cities with populations of 100,001–300,000 and 9% were residents of cities with 20,001–100,000 population. 10% were residents of areas with 1,001–20,000 residents, and for less than 1,000 residents, the percentage was 4%. 65% reported owning a car, 35% owned a bicycle, 7% owned a motorcycle, and 1% owned an electric scooter. 24% reported owning no means of transportation. The most common reason for travel among respondents was work, with 49%. This was followed by entertainment at 19% and shopping at 12%. Education and other obligations occupied percentages of 11% and 9%, respectively. When asked about their mode of transportation on a daily basis, respondents chose car with 50%, bicycle with 9%, motorbike with 2%, and electric scooter with 1%. 17% of the total sample chose public transportation for their daily commute, while 21% commuted on foot. 27% and 44% of respondents were respectively very and extremely positive about the implementation of new technologies in transportation, while only 2% said they were not positive at all. 22% and 6% were respectively moderately and somewhat positive. The majority of respondents indicated cost and speed as most important at 59% and 56%, respectively. This was followed by comfort at 48%, followed by how environmentally friendly the vehicle is at 38%, and distance at 38%. Finally, 32% of respondents chose timetable, and 16% chose infrastructure. 3.2 MaxDiff Results The results of the total sample were sorted by preference in the figure below (Fig. 1). An innovation with a score of 2.00 is twice as preferable as an innovation with a score of 1.00 [17]. Considering the results of Fig. 1, the comparison between innovations is easier. Large variations between innovations are observed, and it is worth noting that many innovations that were considered more popular were rated lower than others by user preference. The five (5) most popular innovations among respondents are smart parking (8.41), autonomous vehicles (7.82), high-speed trains (7.79), electric buses (7.62), and hydrogen cell buses (7.48) (Fig. 1).

Transportation Systems of Asia: Investigating the Preferences

487

In contrast, the lowest scores were for flying taxis (2.27), flying cars (2.14), and women-only parking spaces (1.54) (Fig. 1). Based on the top choice of respondents, the ranking does not change much as smart parking takes the top position with 16.41%, followed very closely by autonomous vehicles with 13.85%, high-speed trains with 12.31%, hydrogen cell buses with 10.77%, and electric buses with 7.69%. The lowest percentage of 0.51% belonged to the DiDi system, while electric garbage trucks had 0% (Fig. 2). Among the top preferences of men, the highest percentages were found for highspeed trains (15.63%), electric buses (12.50%), and smart parking (12.50%), while the lowest percentages, 0%, was found for electric garbage trucks, electric roads for buses, and electric parking for passenger vehicles (Fig. 3). Women’s top preferences include smart parking (18.46%), autonomous vehicles (16.92%), and hydrogen cell buses (11.54%). The DiDi system, women-only parking spaces, and electric garbage trucks were not selected as a top preference by the female population (0%) (Fig. 4). Respondents who selected the “Other” response to the gender question had highspeed trains as their top preference at 100%. Respondents residing in cities with populations greater than 100,000 were “Moderately,” “Very,” and “Extremely” positive with the highest percentage towards these transportation innovations, while areas with less than 20,000 population had the highest percentages for the “Not at all” option. It is important to note that in the “Somewhat” positive option, respondents residing in cities with a population of more than 300,000 touched 72.7%, while they also occupied a large percentage in the “Not at all” positive option. Based on their mode of transportation, respondents gave their top preferences as seen below: • Pedestrians (N = 41): respondents who indicated pedestrian in their mode of travel gave smart parking as their top preference, with 24.39% (Fig. 5). • Bicycle (N = 17): Electric buses ranked as the top preference, at 17.65%, for bicycle users (Fig. 6). • Electric scooter (N = 1): the choice of the only respondent using electric scooter was automatic underground bicycle parking with a 100% rate. • Motorbike (N = 4): Electric taxis, flying taxis, high-speed trains, and automatic underground bicycle parking tied in this category with 25%. • Car (N = 98): respondents who mostly use car chose autonomous vehicles as their top choice with 17.35%. Smart parking came in second place with 14.29% (Fig. 7). • Public transport (N = 34): Finally, in the category of traveling with public transport, the top choice was tied with hydrogen cell buses and smart parking with 20.59% (Fig. 8).

488

M. Mokka et al.

4 Conclusion and Future Work With the completion of the research on the inventory of transportation innovations in Southeastern Asia and the exploration of preferences for their implementation in Greece, it was observed that transportation users in Greece have diverse preferences for fulfilling their needs. This survey collected and presented to the respondents state-of-the-art transportation innovations in Southeastern Asia that could potentially be implemented in Greece. By applying modern preference analytics tools, in particular, the MaxDiff method, the different innovation preferences of the respondents were presented using multiple criteria. Based on the results of the survey, it was found that Greeks are very positive toward new technologies related to transportation and mobility, a necessary condition for their realization in the near future. Moreover, the answers given by the 195 respondents are considered a satisfactory sample as it includes percentages from all the categories presented, although it would be better if the number of women was similar to the number of men. The innovations that attracted the most preferences are smart parking, autonomous vehicles, and high-speed trains, while the innovations that received the least preferences of all are women-only parking spaces, flying cars, and flying taxis. The positive attitude shown in the sample indicates that transportation system users are ready to change their habits and adapt to new and innovative travel patterns. The promotion of innovative technologies will benefit, improve, and facilitate the daily life of citizens. The completion of this survey has provided a clear picture of the preferences of the sample of 195 citizens/users with regard to transportation innovations. However, it is proposed to conduct further research in the future and collect qualitative data through other tools, such as Choice-Based Conjoint (CBC) analysis, in order to give more weight to the preferences of the different categories of the sample. Future research could address the advantages and disadvantages of implementing transportation innovations in Greece, as well as in which sectors and aspects of daily life they would have a greater impact. It would be equally important to mention the negative consequences of their implementation in order to have a comprehensive and unbiased view. Furthermore, the survey could include the incentives and funding given by the government concerned to promote and develop innovative ideas in the transportation sector so as to provide a guide for the common good of the citizens. The demographic and geographical conditions, culture, and specificity of each place could form part of a new questionnaire that would give a more comprehensive picture of the preferences of the inhabitants. The direct involvement of citizens in the design of a new transportation system and the development of an innovative environment is an important aspect of improving research results.

Transportation Systems of Asia: Investigating the Preferences

489

MaxDiff Results Women Only Parking Spaces Flying Cars Flying Taxis Automac Underground Bicycle Parking Electric Garbage Trucks Autonomous Cargo Vehicles Didi System Autonomous Buses Electric Taxis Autonomous Waste Collecon Vehicles Electric Car Parking Variable Message Signs VMS Electric Bus Routes Magnec Levitaon Trains Maglev Advanced Traveler Informaon Systems ATIS using GIS Hydrogen Cell Buses Electric Buses Autonomous Vehicles High Speed Trains Smart Parking

1.54 2.14 2.27 3.51 4.14 4.06 4.03 4.04 4.67 4.53 4.98 4.81 5.15 5.21 5.83 7.48 7.62 7.82 7.79 8.41

Fig. 1. MaxDiff results.

MaxDiff Results: First Choice Electric Garbage Trucks Didi System Women Only Parking Spaces Flying Taxis Flying Cars Electric Bus Routes Electric Car Parking Variable Message Signs VMS Autonomous Cargo Vehicles Autonomous Buses Autonomous Waste Collecon Vehicles Electric Taxis Automac Underground Bicycle Parking Magnec Levitaon Trains Maglev Advanced Traveler Informaon Systems ATIS using GIS Electric Buses Hydrogen Cell Buses High Speed Trains Autonomous Vehicles Smart Parking

0.00% 0.51% 1.03% 1.03% 1.54% 1.54% 2.05% 2.56% 2.56% 2.56% 3.59% 3.59% 4.62% 5.64% 6.15% 7.69%

Fig. 2. MaxDiff results for the top choice.

10.77% 12.31% 13.85% 16.41%

490

M. Mokka et al.

MaxDiff Results: Top Choice for Men (N=64) Electric Bus Routes Electric Car Parking Electric Garbage Trucks Variable Message Signs VMS Didi System Autonomous Buses Flying Taxis Flying Cars Women Only Parking Spaces Autonomous Waste Collecon Vehicles Autonomous Cargo Vehicles Magnec Levitaon Trains Maglev Electric Taxis Advanced Traveler Informaon Systems ATIS using GIS Autonomous Vehicles Automac Underground Bicycle Parking Hydrogen Cell Buses Smart Parking Electric Buses High Speed Trains

0% 0% 0% 1.56% 1.56% 1.56% 1.56% 1.56% 3.13% 3.13% 3.13% 4.69% 4.69% 6.25% 7.81% 9.38% 9.38% 12.50% 12.50% 15.63%

Fig. 3. MaxDiff results for the top choice for men (N = 64).

MaxDiff Results: Top Choice for Women (N=130) Didi System Women Only Parking Spaces Electric Garbage Trucks Flying Taxis Flying Cars Automac Underground Bicycle Parking Autonomous Cargo Vehicles Electric Bus Routes Variable Message Signs VMS Electric Taxis Autonomous Buses Electric Car Parking Autonomous Waste Collecon Vehicles Electric Buses Advanced Traveler Informaon Systems ATIS using GIS Magnec Levitaon Trains Maglev High Speed Trains Hydrogen Cell Buses Autonomous Vehicles Smart Parking

0% 0% 0% 0.77% 1.54% 2.31% 2.31% 2.31% 3.08% 3.08% 3.08% 3.08% 3.85% 5.38% 6.15% 6.15% 10.00% 11.54%

Fig. 4. MaxDiff results for the top choice for women (N = 130).

16.92% 18.46%

Transportation Systems of Asia: Investigating the Preferences

MaxDiff Results: Top Choice for Pedestrians (N=41) All the rest Didi System Women Only Parking Spaces Autonomous Waste Collecon Vehicles Autonomous Buses Electric Car Parking Magnec Levitaon Trains Maglev Electric Buses Advanced Traveler Informaon Systems ATIS using GIS Automac Underground Bicycle Parking High Speed Trains Electric Taxis Autonomous Vehicles Hydrogen Cell Buses Smart Parking

0% 2.44% 2.44% 2.44% 2.44% 2.44% 4.88% 4.88% 7.32% 7.32% 7.32% 7.32% 12.20% 12.20% 24.39%

Fig. 5. MaxDiff results for the top choice for pedestrians (N = 41).

MaxDiff Results: Top Choice for Bicycle Users (N=17) All the rest Advanced Traveler Informaon Systems ATIS using GIS Automac Underground Bicycle Parking Smart Parking High Speed Trains Magnec Levitaon Trains Maglev Autonomous Waste Collecon Vehicles Flying Taxis Flying Cars Electric Bus Routes Electric Taxis Variable Message Signs VMS Autonomous Vehicles Electric Buses

0% 5.88% 5.88% 5.88% 5.88% 5.88% 5.88% 5.88% 5.88% 5.88% 5.88% 11.76% 11.76%

Fig. 6. MaxDiff results for the top choice for bicycle users (N = 17).

17.65%

491

492

M. Mokka et al.

MaxDiff Results: Top Choice for Car Users (N=98) All the rest Women Only Parking Spaces Flying Cars Electric Car Parking Electric Bus Routes Electric Taxis Variable Message Signs VMS Automac Underground Bicycle Parking Autonomous Buses Autonomous Cargo Vehicles Advanced Traveler Informaon Systems ATIS using GIS Magnec Levitaon Trains Maglev Autonomous Waste Collecon Vehicles Electric Buses Hydrogen Cell Buses High Speed Trains Smart Parking Autonomous Vehicles

0% 1.02% 2.04% 2.04% 2.04% 2.04% 3.06% 3.06% 3.06% 4.08% 5.10% 5.10% 5.10% 8.16% 9.18% 13.27% 14.29% 17.35%

Fig. 7. MaxDiff results for the top choice for car users (N = 98).

MaxDiff Results: Top Choice for Public Transport Users (N=34) All the rest Autonomous Cargo Vehicles Autonomous Buses Electric Car Parking Electric Buses Advanced Traveler Informaon Systems ATIS using GIS Magnec Levitaon Trains Maglev Autonomous Vehicles High Speed Trains Smart Parking Hydrogen Cell Buses

0% 2.94% 2.94% 2.94% 5.88% 8.82% 8.82% 8.82% 17.65% 20.59% 20.59%

Fig. 8. MaxDiff results for the top choice for public transport users (N = 34).

References 1. Gupta, M.: The innovation process from an idea to a final product: a review of the literature. Int. J. Comp. Manage. 1(4), 400–421 (2018). https://doi.org/10.1504/ijcm.2018.096731 2. Edwards-Schachter, M.: The nature and variety of innovation. Int. J. Innov. Stud. 2(2), 65–79 (2018). https://doi.org/10.1016/j.ijis.2018.08.004 3. Spencer, J.E., Pannell, C.W., Clifton P.W., Ryabchikov, A.M., Chandrasekhar, S., Alexeeva, N.N., Narasimhan, C.V., Chapman, G.P., Gourou, P., Sengör, ¸ C.A.M., de Beaufort, L.F., Leinbach, T.R., Yefremov, Y.K., Owen, L.: Asia. In: Encyclopedia Britannica Webpage, https:// www.britannica.com/place/Asia (2021). Accessed 20 Apr 2022

Transportation Systems of Asia: Investigating the Preferences

493

4. Alevras, D., Zinas, D., Palantzas, G., Genitsaris, E., Nalmpantis, D.: Micromobility in Thessaloniki, Greece, and Madrid, Spain: a comparative study. IOP C. Ser. Earth Environ. 899(1), 012061. https://doi.org/10.1088/1755-1315/899/1/012061 5. Genitsaris, E., Amprasi, V., Naniopoulos, A., Nalmpantis. D.: Mapping and analyzing the transport innovation framework of the Region of Central Macedonia, Greece. In: Nathanail, E., Adamos, G., Karakikes, I. (eds.) Advances in Mobility as a Service Systems. CSUM 2020. Advances in Intelligent Systems and Computing, vol. 1278, pp. 368–378. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-61075-3_36 6. Papanaoum, D., Palantzas, G., Chrysanidis, T., Nalmpantis, D.: The impact of megatrends on the transition from car-ownership to carsharing: a Delphi method approach. In: Nathanail, E., Adamos, G., Karakikes, I. (eds.) Advances in Mobility as a Service Systems. CSUM 2020. Advances in Intelligent Systems and Computing, vol. 1278, pp. 515–524. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-61075-3_51 7. Akac, A., Anagnostopoulou, A., Nalmpantis, D.: Digitalization in freight transport services: Balkan area. In: Nathanail, E., Adamos, G., Karakikes, I. (eds.) Advances in Mobility as a Service Systems. CSUM 2020. Advances in Intelligent Systems and Computing, vol. 1278, pp. 1056–1065. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-61075-3_101 8. Stamelou, A., Genitsaris, E., Nalmpantis, D., Naniopoulos, A.: Investigating potential synergies among social entrepreneurship and public transport through experts’ consultation in Greece. In: Nathanail, E., Karakikes, I. (eds.) Data Analytics: Paving the Way to Sustainable Urban Mobility. CSUM 2018. Advances in Intelligent Systems and Computing, vol. 879, pp. 496–503. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02305-8_60 9. Genitsaris, E., Stamelou, A., Nalmpantis, D., Naniopoulos, A.: A criteria-based evaluation framework for assessing public transport related concepts resulted from collective intelligence approaches. In: Nathanail, E., Karakikes, I. (eds.) Data Analytics: Paving the Way to Sustainable Urban Mobility. CSUM 2018. Advances in Intelligent Systems and Computing, vol. 879, pp. 529–537. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02305-8_64 10. Nalmpantis, D., Genitsaris, E., Amprasi, V., Akac, A., Anagnostopoulou, A.: Hierarchizing the importance of the attributes of an online shared freight transportation service platform with the use of Multi-Actor Multi-Criteria Analysis (MAMCA). IOP C. Ser. Earth Environ. 899(1), 012059. https://doi.org/10.1088/1755-1315/899/1/012059 11. Kouta, M., Nalmpantis, D.: Siting of safe and secure truck parking areas in Greece and definition of their security level with the use of multi-actor multi-criteria analysis (MAMCA). IOP C. Ser. Earth Env. 899(1), 012060. https://doi.org/10.1088/1755-1315/899/1/012060 12. Nalmpantis, D., Roukouni, A., Genitsaris, E., Stamelou, A., Naniopoulos, A.: Evaluation of innovative ideas for public transport proposed by citizens using multi-criteria decision analysis (MCDA). Eur. Transp. Res. Rev. 11(1), 1–16 (2019). https://doi.org/10.1186/s12 544-019-0356-6 13. Nalmpantis, D., Genitsaris, E., Amprasi, V., Akac, A., Anagnostopoulou, A.: Optimization of an online shared freight transportation service platform with the use of conjoint analysis. IOP C. Ser. Earth Environ. 899(1), 012058. https://doi.org/10.1088/1755-1315/899/1/012058 14. Papadima, G., Genitsaris, E., Karagiotas, I., Naniopoulos, A., Nalmpantis, D.: Investigation of acceptance of driverless buses in the city of Trikala and optimization of the service using conjoint analysis. Util. Policy 62, 100994 (2020). https://doi.org/10.1016/j.jup.2019 15. Tsoukanelis, A., Genitsaris, E., Nalmpantis, D., Naniopoulos, A.: Conjoint analysis for the optimization of a potential flexible transport service (FTS) in the region of Zagori, Greece. In: Nathanail, E., Karakikes, I. (eds.) Data Analytics: Paving the Way to Sustainable Urban Mobility. CSUM 2018. Advances in Intelligent Systems and Computing, vol. 879, pp. 478– 486. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02305-8_58

494

M. Mokka et al.

16. Tsafarakis, S., Gkorezis, P., Nalmpantis, D., Genitsaris, E., Andronikidis, A., Altsitsiadis, E.: Investigating the preferences of individuals on public transport innovations using the maximum difference scaling method. Eur. Transp. Res. Rev. 11(1), 1–12 (2019). https://doi. org/10.1186/s12544-018-0340-6 17. Sawtooth Software Homepage. https://sawtoothsoftware.com/. Accessed 20 Apr 2022

Emerging and Innovative Technologies in Transport: Cooperative Intelligent Transport Systems

Digital Infrastructure Service Role and Functional Model for Urban ITS Service Applications Junichi Hirose(B) Highway Industry Development Organization, Tokyo 1120014, Japan [email protected]

Abstract. Emerging ITS service applications such as parking (including AVPS: Automated valet parking systems), CAV (connected and automated vehicle) (including LSAD: Low speed automated driving), Kerb operations needs digital infrastructure supports for secured and safety operations. And there are several independent related ongoing standardization work items within ISO/TC204, such as HD (high definition) maps, METR (Management for Electronic Traffic Regulations), GDD (Graphic Data Dictionary). Therefore, there is a need of a guidebook style technical report. Creation of such technical report to have a definite need for how those independent standardization works fit in a prospected digital infrastructure service role and functional model for smart city ITS service applications. This role model concept must be extended by authorities/communities to be applied to other smart city services such as energy and telecommunication network. Keywords: Digital infrastructure · ITS · CAV · Kerb · LSAD

1 The Purpose of Defining Role Model 1.1 The Trend Toward Smart City Currently, more than 70% of the world’s people live in cities. The proportion of people living in cities is rising around the world as civilizations develop and congregate around cities where employment opportunity most arises. Societies develop more innovatively and more rapidly in cities, adding to their attraction, finally cities present better entertainment opportunities. All adding to their attraction and popularity. Hence the continuing trend. The Economist magazine forecast that by 2045, an extra two billion people will live in urban areas. Due to the concentration of the population that this causes, various issues arise, such as road congestion due to increase in vehicle population, environmental pollution due to exhaust gas and tire erosion. This has caused to increases in the number of delivery trucks and taxis and city center traffic. It is further exacerbated by obstacles to effective use of urban space due to private ownership of cars (parking lots, street parking).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_41

498

J. Hirose

1.2 Emerging Issues in the City The pressures caused by scientific advice that significant action and change of behavior is necessary to ameliorate the adverse effects of climate change require more environmentally friendly use of the transport system. We recognized that there is also road infrastructure deterioration, lack of provision of information on the use of public transportation, driver shortages due to the increase in the number of elderly people, and inconvenience of multimodal fare payments, and action to improve this situation is urgently needed. The International Data Corporation forecasts that of the USD81 billion that is spent on smart city technology in 2020, a quarter will go into fixed visual surveillance, smart outdoor lighting, and advanced public transit. Eventually, this is likely to mean high speed trains and automated driving cars. Consultancy McKinsey forecasts that up to 15 per cent of passenger vehicles sold globally in 2030 will be equipped with fully automated functions, while revenues in the automotive sector could double to USD 6.7 trillion thanks to shared mobility (car-sharing, e-hailing) and data connectivity services (including apps and car software upgrades). Changing consumer tastes are also calling for new types of infrastructure. Today’s city dwellers, for example, increasingly shop online and expect ever faster delivery times. To meet their needs, modern urban areas need the support of last-minute distribution centers, backed by out-of-city warehouses. 1.3 ITS Standardization Needs In recent years, studies on the development of ITS mobility integration standards have been active to solve urban issues. There are various movements around the world making efforts to address these issues. We are using ITS technology to try to solve these urban problems, as in the Smart City Pilot Project. Columbus, Ohio was selected by USDOT as a smart city pilot project. Important key factors here are the core architectural elements of smart cities, and urban ITS sharing of probe data (also called sensor data), connected cars, and automated driving. In addition, contemporary issues have been recognized with the introduction of the connected car to the real world in respect of privacy protection, the need to strengthen security measures, big data collection, and processing measures, which are becoming important considerations. In terms of effective use of urban space, we expect that the introduction of connected cars and automated driving can significantly reduce the requirements for urban parking lots (redistribution of road space). If technology can eliminate congestion, city road area usage can also be replanned - reallocated (space utilization improvement) to improve the living environment of/ quality of life in, the city. In addition, the environment around the road must be improved by authorities’ enforcement (e.g., overloaded vehicles). On the other hand, even in rural areas, it is possible to introduce automated driving robot taxis and other shared mobility that saves human load (and is therefore more affordable) and improves the mobility of elderly people. To achieve this requires the realization of various issues. Some examples are as follows.

Digital Infrastructure Service Role and Functional Model

499

• Cooperation with harmonization of de-jure standards such as ISO and industry de facto standards • Recognition of the significance of international standardization (for example, to reduce implementation costs) • Recognition of the significance of harmonization activities by countries around the world. • Cooperation and contribution between ISO/TC22, ISO/TC204 and ISO/TC268. As mentioned above, automated driving mobility is expected to play a key role both in cities and in rural areas. The main effects are, as described above, reduction of traffic accidents, reduction of environmental burden, elimination of traffic congestion, realization of effective use of urban space. ITS technology is an essential element for realizing ‘smart’ cities, and it is important to clearly understand the role model of ITS service applications when developing standards to achieve these objectives. 1.4 ISO Standardization Activities The published Technical Report ISO/TR 4445 is an important guidebook for the objective with the consideration of emerging direction of mobility electrification, automated driving, and the direction of an environmentally friendly society and incorporating other urban data such as traffic management into the city management can improve the mobility of urban society. The experts in ISO/TC204 recognizes that it is important to create the document describing digital infrastructure service role and functional model which adding such role into ISO/TR4445 role model as supplementary part as emerging ITS service applications such as parking (including AVPS), CAV (including LSAD), kern operations need infrastructure supports for secured and safety operations. That document describes how ITS sensor data can be structured into valuable data cluster presented on the map data so that ITS service provider can provide services such as automated driving, parking, kerb operations. That document does not describe smart city use cases for ITS data in any detail, nor does it describe in detail any specific ITS use-cases; but it is focused on the generic role model for digital infrastructure service.

2 Role Model of Smart City 2.1 ISO/TR4445 As shown in Fig. 1, there are three key actors in the role model of TR4445. – the service users; – the service provider(s); – for any regulated applications: the jurisdiction(s).

500

J. Hirose

The role model provides the general attributes and the responsibilities of the parties. The authority is the body that has official power to make legal decisions and impose regulations. How this operates varies from country to country according to their constitution or legal structure. Countries have a single authority or delegate such authorities to their constituent states or, independent states concede part of their independent national authority to a common authority union (e.g., European Union) to achieve common goals and interoperability within common conditions, while retaining independent authority in other matters. Regardless of the differences between jurisdictions is the concept that at any specific location, and time, there is a single authority that has official power to make legal decisions and where it deems applicable to impose regulations in respect of the regulation of ITS service applications. ITS service applications and smart city applications vary. In jurisdictions, some application services are mandatory or voluntary (but if they are implemented in a specific way). Most services envisaged are safety services, mobility-related services, or commercial services. A service provider can be described as a party which is providing safety, commercial or regulated ITS or smart city services. Application services are certified by the certification authority (regulatory). 2.2 Needs to Add Digital Infrastructure to TR4445 The Technical Report TR4445 describes the roles and responsibilities of the classes and actors involved in the basic role model in ITS services. To provide emerging ITS services such as automated driving mobility, another role of digital infrastructure and map service provider becomes necessary and creation of TR7872 has been created by ISO/TC204.

3 Digital Infrastructure For the creation of TR7872 digital infrastructure, the work has been created in ISO/TC204. 3.1 TR7872 Emerging ITS service applications such as parking (including AVPS: Automated valet parking systems), CAV (connected and automated vehicle) (including LSAD: Low speed automated driving), Kerb operations needs digital infrastructure supports for secured and safety operations. And there are several independent related ongoing standardization work items, such as HD (high definition) maps, METR (Management for Electronic Traffic Regulations), GDD (Graphic Data Dictionary). Therefore, there is a need of create a guidebook style technical report TR7872 which have a definition guide how those independent standardization works fit in a prospected digital infrastructure service role and functional model for smart city ITS service applications. It is especially important work item.

Digital Infrastructure Service Role and Functional Model

501

Fig. 1. Role model defined in ISO/TR4445

This will help to lead ITS service to digital twin operation for smart city; create digitality formed society twining real physical world to process big data and analysis to send out data stream to real world. In actual deployment, distributed security technology such as block chain can effectively be adopted in application services, and it will introduce for efficient and speedy transactions. That document suggests investigating ITS as a component part of a smart city and that the ITS data can focus on data originated by ITS components and available for sharing with other smart city services and commercial interests. 3.2 Concept of TR7872 The big data are connected to other smart city data entities and share the data for the efficient smart city operation in a manner approved and authorized by the authority. This role is required to configure complete roles of actors to support privacy requirements and to fairly manage any business case issues. The data aggregator will provide timely and value-added data to service provider for its ITS (intelligent transport system) service application provisioning. Data collected for sharing is not forwarded in the same formats or data timing so there is a need to have an entity that can provide standardized data to service provider in a standard data format and data timing. AI can be deployed in application services to create such structured value-added data for service providers.

502

J. Hirose

The word “open architecture platform map data” is meant to be used as “Map data that anyone can use for free”, “Map data that is paid/subscribed but has no usage restrictions”, “Map data that anyone can modify and re-provide”. The map service will provide an open architecture platform map data and digital infrastructure receives probe data, OEM (original equipment manufacturer the car maker) cloud data, from data aggregator. It combines such data into map and provides map data (such as HD map) cluster in a standard format such as GDF, NDS, etc. for ITS application services. 3.3 Digital Infrastructure Service Provider The digital infrastructure service provider will receive map data cluster in a standard format from map service provider and receive public infrastructure and enforcing regulation data, such as METR, GDD. From jurisdictions/road authority/municipals. It combines such data into map as data cluster, digital infrastructure data which are consist of dynamic and static data. Digital infrastructure service provider provides those to service provider who performs provisioning of ITS services such as CAV, parking (AVPS), Kerb. Service provider will utilize AI/edge computing tools in some use cases for low latency safety service applications. The conceptual view of digital infrastructure of a role and functional model is as shown in Fig. 2.

Fig. 2. An image of role model and functional model of digital infrastructure servicer

4 Extended Application Other Than ITS Services The role model concept described here must be extended by authorities/communities to overall integration of other smart-city infrastructure services such as energy and

Digital Infrastructure Service Role and Functional Model

503

telecommunication networks. Defining role model before planning such services gives boost start to the projects.

Reference 1. ISO/TR 7872 Intelligent transport systems—Mobility Integration—Digital infrastructure service role and functional model for urban ITS service applications

Innovative Technologies and Systems for Urban Mobility: The Case of Padua Marco Mazzarino1

, Luca Braidotti2(B) and Teresa de la Cruz3

, Beatriz Royo3

,

1 Università IUAV di Venezia, Ca’ Tron - Santa Croce 1957, 30135 Venezia, Italy

[email protected] 2 Venice International University, Isola di S. Servolo, 30133 Venice, Italy

[email protected] 3 Zaragoza Logistics Center, Avenida de Ranillas 5, edificio 5A (EXPO), planta baja, 50018

Zaragoza, Spain

Abstract. The city of Padua in Italy aims to strategically redesign the urban mobility and logistics network as part of the new sustainable urban mobility plan (SUMP). Several strategies and business models – running on IT platforms and apps – will be assessed and deployed in this framework. This paper focuses on the current development and deployment of NEXT - a mixed freight/passenger transport system (so-called cargo hitching) of autonomous electric pods - in the urban area of Padua. Cargo hitching allows the integration of freight and passenger transport systems, thus, improving the operational, socio-economic and environmental performance of the urban mobility systems. The pods are capable to join and detaching while running. Its modularity and flexibility are relevant features leading to resources optimization. This paper presents the SPROUT Project (H2020- GA 814910) approach to define a city-led (co-created) policy response to harness the impacts of this new urban mobility solution in Padua. It includes the methodological approach adopted for the ongoing implementation in Padua and discusses the main cutting-edge features of the NEXT systems and related technologies. Keywords: SPROUT · NEXT · Cargo hitching · Electric vehicles · Autonomous vehicles · Modular pods · Policies

1 Introduction Nowadays, urban mobility regulations appear to be unable to appropriately handle the changes taking place in the urban transportation environment. To overcome this issue the Sustainable Policy RespOnse to Urban mobility Transition (SPROUT) project aims to provide a new city-led innovative and data-driven policy response to address the impacts of the emerging mobility patterns, digitally-enabled operating & business models, and transport users’ needs. The starting point of this approach is understanding the transition currently taking place in urban mobility and what are the elements driving this change. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_42

Innovative Technologies and Systems for Urban Mobility

505

SPROUT has defined the impacts at the sustainability and policy levels of different new mobility solutions tested in the project, for both passengers and freight transport, to later harness these through a city-led innovative policy response. The knowledge generated will be used to build cities’ data-driven capacity to identify, track and deploy innovative urban mobility solutions. It will be also used to navigate future policy by channelling project results at local, regional, national and EU levels. In this context, the city of Padua (Italy) has tested an innovative urban mobility technology: the NEXT system [1]. It is an electric and modular mobility system based on vehicles capable of coupling and uncoupling, even on the move, to modulate the transport capacity based on real-time demand [2]. In particular, the Padua pilot explores the opportunity of using NEXT for cargo hitching [3]. The term refers to the planning and management of people and freight mixed flows, i.e. cargo that hitches a ride on a vehicle transporting persons or persons hitching a ride on a vehicle transporting cargo. This creates attractive business opportunities because the same transportation needs can be met with fewer resources and assets, including vehicles and infrastructures. If properly deployed, the NEXT system can disrupt the current approach to urban mobility and logistics, lowering the traffic and related pollution. Due to the significant innovation level of the technology, only the definition of proper policies developed by the administration together with stakeholders and potential users will enable its widespread application. The present paper explores this topic. First, the methodology adopted within the SPROUT project for the evaluation of new technologies/solutions for urban transport is described. Then, after a description of the NEXT system, its application in Padua is presented including the description of trials and the selected policies.

2 Methodology Introducing innovative city level mobility solutions to mitigate transport externalities requires a systematic approach that supports policymakers in (1) understanding the effects of introducing this new element on a short scale and, (2) balancing stakeholders’ interests, (3) using the information to consistently define the city led policy response that may leverage the positive impacts to scale up the adoption. The SPROUT Evaluation Framework (EF) proposes a methodological pipeline to build a robust city-led policy response for harnessing the impact of new mobility systems and facilitating the adoption and scalability to the city level. The methodology consists of a five-step process (Fig. 1).

Fig. 1. SPROUT evaluation framework: methodological pipeline. [4]

506

M. Mazzarino et al.

2.1 Step 0: Implementation Plan and Preparation Phase The preliminary step or S0 refers to preparatory work and corresponds to the FESTA’s implementation plan [5], context and preparing phase (Fig. 2). It also includes the communication strategy and the ethical and legal issues management protocol. The SPROUT EF gives clear guidelines to cover FESTA points and describes the data acquisition and analysis methods for the four consequent steps as detailed in the following subsections.

Fig. 2. FESTA methodology [5]

2.2 Step 1: Barriers and Challenges Identification During this period, the aim is to collect useful data from the transport system and field data from the stakeholders and users with a twofold objective: (1) assess the sustainability impacts, the operational feasibility and the operator’s financial sustainability; (2) identify the barriers and challenges for enhancing the adoption that can be mitigated with new instruments or policies. The SPROUT EF is an ex-post evaluation that gives guidelines for assessing the operator’s financial sustainability and operational feasibility and the city’s sustainability impacts (Fig. 3). For the assessment of financial and economic aspects of the demonstration projects, it suggests conducting a Cost-Benefit Analysis (CBA) based on the Economic Net Present Value (ENPV) and the Financial Net Present Value (FNPV). The latter is to assess the financial sustainability of the operator (financial analysis). The former allows conducting an economic analysis considering the social and environmental impacts or benefits of introducing the new transport system (sustainability assessment). For including

Innovative Technologies and Systems for Urban Mobility

507

Fig. 3. SPROUT EF evaluation pillars [4]

social and environmental costs, the methodology considers the guidelines provided by the Handbook, 2019 and additional desktop research for covering new vehicles (bikes, autonomous vehicles) default values gaps (see Table 1). More specifically, the calculation of the sustainability impacts according to the SPROUT EF requires: 1. Identifying the type of vehicle for the situation “AS IS” or before the implementation of the mobility solution from the Handbook and compiling the kilometres travelled during the time frame considered for the calculation of the baseline. 2. Identifying the vehicle used for the “TO BE” situation or considered for the implementation of the mobility solution from the Handbook or Table 1 and compiling the kilometres driven during a considered time frame. 3. The total kilometres travelled for the situation “AS IS” and “TO BE” are multiplied by the corresponding cost factor (i.e. column 5 in Table 1). For the operational feasibility, as all the SPROUT mobility solutions rely on some digital component, the SPROUT EF suggests assessing the operational feasibility of the pilot as a whole following the ISO/IEC 25010 Product Quality Model and the Quality in Use Model [10]. 2.3 Step 2: Supportive Package of Policies: Benefits and Drawbacks After Step 1, the stakeholders with some influence in urban mobility need to explore more in-depth the main problems encountered during S1 (baseline scenario) and evaluate to what level the implementation of one or more regulatory instruments or policy measures, coined here as a package of measures, could be beneficial to favour their interests (alternative scenario). To this end, the SPROUT EF proposes the Stakeholder-Based Impact Scoring (SIS) methodology [11] that involves seven different steps: 1. Formulation of the problem and identification of alternative solutions. To perform an SIS, there should minimally be one baseline, and one alternative to the baseline. 2. Stakeholder identification. The stakeholders affecting or affected by the project need to be identified. 3. Formulation of stakeholder criteria. These criteria represent the objectives of the stakeholder with regard to the problem and the identified alternative solutions. 4. The effects of the alternative in terms of each criterion when compared to the baseline scenario are assessed through a performance score ranging from +1 (very positive) to −1 (very negative).

508

M. Mazzarino et al. Table 1. SPROUT EF external transport costs factors (Source: [4])

Category

Description

SPROUT vehicles

EF source

Cente/vkm

Tank to wheel emissions – TTW

Emissions produced by the effect of transport GHG emissions that contribute to climate change

Bike active

0

Self driving pods

Active mobility and electric vehicle do not produce GHG emissions during the consumption phase

Emissions from upand downstream processes related to transport that contribute to climate change

Bike active

Active mobility is zero 0

Electric bike

[6] as electric motorbikes

0

E-skooter Self driving pods

Not available

–

Result of the cost impacts on health care, crop losses, managerial and building manage and biodiversity loss

Bike active

0

Self driving pods

Active mobility and electric vehicle do not produce GHG emissions during the consumption phase

Result of the cost impacts on ischemic heart disease, stroke, dementia, hypertension, annoyance

Bike active

Active mobility is zero 0

Electric bike

[7]

1

Self driving pods

Not available

–

Impedance vehicles impose on each other, as the traffic flow approaches maximum capacity. Calculated for both delay and deadweight loss costs

Bike active

[8]

0

Self driving pods

Not available

–

Result of the cost impacts on human, medical costs and administrative costs; production losses and material damages

Bike active

[9]

10.6

Well to tank emissions - WTT

Air pollution

Noise

Traffic congestion (delay or deadweight loss)

Accidents

Electric bike E-skooter

Electric bike E-skooter

E-skooter

Electric bike E-skooter

Electric bike E-skooter

Assumption: as bikes

Self driving pods

Not available

–

5. Attribution of weights to their criteria by the stakeholders, to evaluate the relative importance of each of the criteria. 6. Impact score calculation of each alternative for each criterion, for each stakeholder. This is done by multiplying the weight of a criterion, as attributed in step 5, with the impact, as assessed in step 4. This impact score will be either positive or negative and will fall between +1 and −1. 7. Calculation of the aggregate positive impacts and the aggregate negative impacts.

Innovative Technologies and Systems for Urban Mobility

509

2.4 Step 3: Alternative Policy Response Definition After exploring the benefits of enhancing the baseline scenario with additional policy measures, the stakeholders participating in S2 contribute to the identification of potential alternative policy responses and evaluate their implementation feasibility and user acceptance. The identification of the list of alternatives followed the three steps below: 1. SPROUT experts identified a list of supportive policy measures for the pilots use cases. 2. SPROUT partners evaluated the lists and selected the top three ones during a workshop. 3. The SPROUT pilots fine-tuned or adapted the proposed lists to provide S3 with four supportive measures, either exclusive or not. 2.5 Step 4: Robust City-Led Policy Response The final step explores the policy measures’ user acceptance and implementation feasibility. The process requires high commitment from the stakeholders participating in the previous SPROUT EF steps (Fig. 4). They participate in a two-stage process. First, they fill an online survey translated into the local language with the specific policies. Second, they have to respond to concrete questions to identify the reasons for misalignments, unacceptance and mitigation strategies. The pilot in Padua slightly modified the proposed data collection methods and conducted a prior workshop to explain to the stakeholders the reason for filling out the questionnaire and the questions themselves. After identifying the responses, stakeholders were interviewed to identify the causes for misalignments and approaches to reduce the barriers.

3 Application in Padua Within the framework of the SPROUT project, the city of Padua along with the University of Venice tested a disruptive technology for urban transport, i.e. the NEXT system. 3.1 The NEXT System The NEXT system consists of innovative vehicles and business models based on cuttingedge technologies carrying both passenger and freight (cargo hitching). Cargo hitching solutions are achieved by employing a modular transport system [1, 2] based on swarms of electric self-driving pods (Fig. 1). Each module can join with and detach from other modules while in motion on standard city roads [12]. When joined, modules can be built up into a bus-like vehicle. Modules carrying passengers and goods can be combined based on estimated flows, which are calculated in real-time by algorithms considering different final destinations by users and freight [13–15]. Hence, the NEXT system represents a disruptive innovation to effectively address major urban mobility challenges, in particular air pollution, congestion and overall urban

510

M. Mazzarino et al.

Fig. 4. A NEXT electric pod tested in Padua

sustainability. In fact, its application can provide significant benefits in terms of dramatic reductions in traffic levels, travel times and emissions by consolidating urban traffic flows (both passenger and freight), thus optimizing urban transport capacity [16, 17]. Modularity enables a strong reduction of traffic thanks to the fleet management system., which adapts in real-time to the request and can combine, in door-to-door service, several passengers sharing the same destination. Furthermore, electrification of public transport leads to a reduction of pollution and increased efficiency, compared to internal combustion vehicles and electric buses and fleets of taxis. It is noteworthy to underline that modularity significantly helps to improve the electrification feature, limiting the electric consumption [2]. Finally, another significant feature of the NEXT pods is the technical possibility for the implementation of self-driving. However, since this feature is not the main innovation of the project and currently autonomous vehicles are not allowed by Italian legislation, this feature has not been considered during SPROUT. The major barriers and challenges implied by the introduction of the NEXT system consist of the adaptation of the existing regulatory framework (and partly infrastructure requirements as well) to deal with such disruptive mobility innovation (authorizations, approvals, etc.). This is why during the SPROUT project focus has been made on the policy framework that can foster the real application of the NEXT system in the urban environment. 3.2 Relevance in the Current Policy Framework The Padua Municipality is constantly updating an innovative policy framework within the adopted Sustainability Urban Mobility Plan (SUMP). The SUMP defines the guidelines for the evolution of Padua mobility up to 2030, starting from the current reference scenario. Critical urban mobility related issues and problems identified within the existing

Innovative Technologies and Systems for Urban Mobility

511

policy framework, represent key goals of the forthcoming SUMP.Among the strategies, the city of Padua aims focus on innovation of urban transport, using ITS (Intelligent Transport System)/big data, both for passenger and freight as an opportunity for mitigating urban mobility negative externalities reaching optimal transport efficiencies and sustainability targets. In this context„ the NEXT system will contribute to reducing traffic levels, travel times and emissions by (dynamically) consolidating urban flows for both passenger and freight. Therefore, the NEXT system trials have been included in the SUMP overall strategies. The testing phase results and the policies identified and evaluated according to SPROUT EF (Sect. 2) will be valuable input for the roadmap for achieving SUMP’s goals. 3.3 NEXT System Tests The Padua Pilot aims to demonstrate the efficiency and effectiveness of the NEXT system as a urban transport system for people, goods and in a mixed solution (cargo hitching). To this end, the Padua municipality along with the University of Venice tested the NEXT system in a real urban ecosystem. The pilot focused on selected areas of the city consisting of Longhin Street and a larger area comprising Stanga district, the Fair and the railway/bus station as shown in Fig. 5.

Fig. 5. Small scale pilot test (G.A. Longhin) and route for wider area simulation assessment.

NEXT system assessment has been divided into two stages

512

M. Mazzarino et al.

1. Real-life-testing (trials) to assess the technical performance of the transport system in a selected urban area (Longhin Street); 2. Simulation scenario assessment in a wider urban area (Fair/Autobus station route) where the proposed transport option is supposed to be implemented to show the financial and socio-economic feasibility. The selected area for trials, Longhin Street, is inserted in the context of the city’s directional/commercial area, closed to the Padua industrial area. The selection criteria were the following. First, it is closed to the industrial area, thus, trials would have limited impacts on local traffic. Furthermore, in Longhin Street the realization of a dedicated lane for trials with sustainable infrastructure costs was made it possible. Finally, the area is closed to large park areas that ease NEXT pods’ manoeuvring and will encourage the adoption of the NEXT service. Based on the trials’ results in Longhin St., the brand-new innovative business model was simulated and assessed in a wider urban context. In detail, the deployment of the NEXT service is supposed to include some strategic urban areas – the Fair and the bus/railway station – which would benefit from a regular cargo hitching service. Considering freight transport, the area is mostly related to e-commerce deliveries (small parcels). Thus, in the simulations, the Fair has been envisaged as a potential urban logistics “micro-hub”. These assumptions, May result in a relevant policy response leading to the reconfiguration of the existing urban logistics network. In this context, the creation of a logistic micro-hub may require an agreement between the Municipality and the Fair. Furthermore, an additional stop is envisaged nearby a supermarket closed to the Fair. Therefore, customers could benefit from the innovative urban mobility solution as well. In conclusion, simulations and scenario assessment allowed proving how the NEXT system can be integrated into the existing local public transport network (in particular for reaching the bus/railway station) and urban logistics network, thus, supporting local policy responses in line with the strategic goals of the forthcoming SUMP. 3.4 City-Led Policy Response After the testing phase, the objective was to assess the feasibility and user acceptance of NEXT system introducing a proper set of policy responses on a city-specific scale. To this end, a set of relevant stakeholders were involved to work on a set of alternative policy responses. The stakeholders included the public administration (Padua municipality, Environment Department, Mobility Department, Public Works Department, Padua Local Police), the local logistic operator (Cityporto), the local public transport operator (Busitalia Veneto - BIV) and the main mobility service provider responsible for city parking, car sharing and shared mobility in Padua (APS Holding). Following SPROUT methodology (Sect. 2), the set of alternative policy measures were defined and stakeholders’ opinions were collected through a survey. This process helpedidentify the most critical aspects of policy implementation and user acceptance. The following alternative policies applicable to Padua pilot were selected in agreement with the stakeholders: • PM1 Integration of NEXT with Local Public Transport (LPT) and development of modal shift

Innovative Technologies and Systems for Urban Mobility

513

• PM2 Development of innovative solutions as support for logistic operators • PM3 New function/office dedicated to the development and management of freight logistics and Local Public Transport • PM4 Set-up of specific procurement procedures for innovative mobility solution. The preferred policy measures were analysed using pairwise relationship in Table 2. Finally, the pilot conducted the feasibility assessment of the policy measure, analysed the reasons of considering some policy measures unfeasible and identified mitigation strategies (Table 3). Table 2. Alternative policy measures (PM) pairwise relationships. PM1

PM2

PM3

PM4

PM1

X

The implementation of the corresponding PM2 can increase the benefits to passenger transport if the integration will be put in place

The integration of NEXT with existing services must necessarily pass through an analysis of the demand, and the transport needs. It is necessary to manage and coordinate a fixed system (traditional lines) with a flexible system (the NEXT)

The integration with local public transport requires the development of new updated procedures that include innovations in terms of transport mobility, and improve the efficiency of the synergy between public and private

PM2

The implementation of X the corresponding PM1 can increase the benefits for freight if the integration in the city centre or the “last-mile” will be put in place

The creation of a dedicated office can ease the implementation of PM2. The management of logistic aspects requires specialized resources and robust know-how, both in technical and economic administrative subjects

The development of innovative solutions for logistic operators requires the development of new updated procedures that include innovations in terms of transport mobility, and improve the synergy between public and private

PM3

The integration of NEXT with LPT requires resources, skills, know-how, data management and relationships between the different stakeholders. This highlights the need for coordination, to be implemented with a dedicated office

X

The definition and implementation of specific procurement procedures require specialized resources and robust know-how, both in technical and economic administrative matters. This process can be enhanced through a dedicated office

The development of PM2 requires resources, skills, know-how, data management and relationships between the different stakeholders. This highlights the need for coordination, to be implemented with a dedicated office

(continued)

514

M. Mazzarino et al. Table 2. (continued)

PM4

PM1

PM2

PM3

PM4

The integration with LPT requires the development of new updated procedures that consider innovations in transport mobility and improve the synergy between public and private

The implementation of innovative solutions for logistic operators requires the development of new updated procedures that consider innovations in transport mobility and improve the synergy between public and private

Staff/personnel with proper skills, know-how and competencies can define specific procurement procedures for innovative mobility solutions

X

Table 3. Implementation feasibility, second stage: responses to misalignments PM

Dimension criteria

Questions

Second stage responses

PM1

Acceptability: urban logistics operator

What are the reasons for unacceptability?

The logistics operator (Cityporto) considered the integration of LPT an independent measure from logistics. BIV did not provide an answer as the policy does not directly concern LPT operators. The low acceptability score in this sense underlines how the logistics operators are affected by the adoption of this policy. In other words, the stakeholder does not result directly involved in the adoption of the measure

Measures for overcoming/reducing the acceptability barriers

Technically, there are no barriers to overcome. Simply PM1 and PM2 were considered independent during the evaluation. The evaluation of the acceptability of the different types of stakeholders highlights who is the strategic player (for PM1, BIV). In general, the application of PM4 is considered fundamental to ease the implementation of PM1

(continued)

Innovative Technologies and Systems for Urban Mobility

515

Table 3. (continued) PM

PM2

Dimension criteria

Questions

Second stage responses

Acceptability: public sector stakeholder

What are the reasons for unacceptability?

Similar consideration can be done for public stakeholders’ acceptability (Local Police, Firefighters, Civil protection). They are not significantly affected by the adoption of this policy. The low score has to be intended not as a negative assessment, but rather as a sort of “involvement score” in the implementation of the policy

Measures for overcoming/reducing the acceptability barriers

Technically, there are no barriers to overcome. Simply PM1 and PM2 were considered independent in the evaluation. The evaluation of the acceptability of the different types of stakeholders highlights who is the strategic player (for PM1, BIV). In general, the application of PM4 is considered fundamental to ease the implementation of PM1

What are the reasons for unacceptability?

As for PM1, the aforementioned public stakeholders are not strategic players in the adoption of this measure. The low score has to be intended not as a negative assessment, but rather as a sort of “involvement score” in the implementation of the policy

Measures for overcoming/reducing the acceptability barriers

The evaluation of the acceptability of the different types of stakeholders highlights who is the strategic player (for PM2, Cityporto). Evaluation of different new scenarios: to increase the score in this area, further potentialities involving other public players, previously unexplored since they are outside the scope of the project, could be investigated. For example, these could involve the use of NEXT at the service of the logistics of health care material logistics for COVID-19 emergency. Other potential contexts could be those related to the procurement process for the public furniture/materials, or the needs of the Culture Sector (museums, theatres, libraries,). In both cases, the big size of the elements is a limiting factor

Acceptability: public sector stakeholders

(continued)

516

M. Mazzarino et al. Table 3. (continued)

PM

Dimension criteria

PM3

Feasibility: resources availability Why there are not resources available?

Questions

Second stage responses Despite Administration’s commitment, it is not easy to create a new office and hire new staff, due to the bureaucracy (resource allocation and selection process). Inside the stakeholders’ organization, there is already an internal equilibrium, with consolidated job roles. A new office in this sense would represent additional costs, which should be adequately justified. The question shows that it is more likely that this policy will be implemented within the perimeter of the Municipality. Indirectly, the scores express this, even in this case low score doesn’t represent a negative assessment, but an estimation of these difficulties

Is there any chance to make Yes. Within the Administration, the resources available? How? challenges to be faced are represented by political will and bureaucratic complexity, but there are several chances to make it available. For financing, it could also be conceivable to find external private resources through a public-private synergy (to be evaluated) Financial: indirect costs

What are the indirect costs? The question has been misunderstood by most of the stakeholders since they expressed an assessment about the entity of the costs and not on their impact. The low average score therefore clearly identifies reasonably limited costs. The indirect costs are additional non-financial impacts, for example, man-hours required to interface Municipality and logistics (and therefore, resources for creating contacts, synergies, etc.), indirect costs related to the home-work transit of people who will work in this office (e.g. traffic-congestion, possible accidents, etc.) Will these costs be outbalanced by the benefits

Definitely yes, as emerges also from indirect benefits assessments

(continued)

Innovative Technologies and Systems for Urban Mobility

517

Table 3. (continued) PM

Dimension criteria

PM4

Feasibility: resources availability Why there are no resources available?

Questions

Second stage responses Similar considerations of the previous point can be applied to this policy response. Difficulties are related to new human resources equilibrium needed to be reached among offices that will be in charge to participate in the set-up process and following its implementation

Is there any chance to make Yes. Within the Administration, the resources available? How? challenges are represented by political will and bureaucratic complexity of resources allocation, which must be set up and approved in advance Financial: fixed/operational & maintenance costs

What are the fixed/operational costs?

The question has been misunderstood by most of the stakeholders since most of them expressed an assessment about the entity of the costs and not on their impact. The adoption of this policy reveals an increase in the operative costs in terms of the time of the administrative and technical staff. This will lead to an increase in worked hours. Fixed costs could be represented by the purchase cost of new management software with a regular fee, the economic resources to be necessarily dedicated to the training of personnel in this specific area, possible costs related to the use of new office spaces required for the function or functions that deal with the set-up. Furthermore, external consultancy costs or the implementation of an internal management system can be classified as fixed costs

Will these costs be outbalanced by the benefits

Definitely yes, as emerges also from indirect benefits assessments

In conclusion, the stakeholders considered feasible the integration of the NEXT system in the LPT (PM1) and logistics (PM2), although some differences were highlighted between the 2 main stakeholders (BIV, Cityporto). They considered LPT and logistics independent of each other, thus, additional effort is required to make cargo hitching operational in Padua. From a financial point of view, it was pointed out by the LPT operator that NEXT system deployment requires a robust initial investment, but, considering its operation, excellent benefits and convenient operating costs are envisaged. Regarding user acceptance, stakeholders concluded that all the policies well fit the social and personal aims of users. The creation of a new office (PM3) and procurement procedures

518

M. Mazzarino et al.

(PM4) have been identified as “key measures” to deploy the NEXT system in Padua. However, the excessive and rigid bureaucracy, typical of Italian Administrations, has been identified as the main obstacle. Furthermore, the implementation of PM3 and PM4 directly requires political commitment and the allocation of resources.

4 Conclusions Padua Municipality is committed to fostering the adoption of environmental-friendly transport modes, developing new e-mobility systems to reduce pollutant emissions, fossil fuel consumption and, thus, mitigating climate change. Other key goals stated in the SUMP are the reduction of private cars and the improvement of the effectiveness and efficiency of urban logistics. To this end, during the SPROUT project the NEXT modular transport system has been tested aiming at its deployment as a regular freight/passenger transport service in Padua. The expected integration of NEXT in the existing urban mobility system has been positively considered by most of the stakeholders, especially regarding the improvement of accessibility and connectivity along with the reduction of traffic and air pollution. To foster its deployment, several policy measures have been identified and discussed with stakeholders in dedicated workshops. In conclusion, the NEXT system showed promising results during the testing and assessment phase for improving passenger and freight transport efficiency. The flexibility and modularity of the system are the key factors to going beyond the current state. The policy measures identified will harness the adoption of this disruptive technology. Furthermore, although it is still necessary to deepen investigations about cargo hitching to fit the goals set by Padua’s SUMP, the present work is a valuable contribution for defining the roadmap. Acknowledgments. The work described in this article has been developed in the framework of the European Project “SPROUT: Sustainable Policy Response to Urban Mobility Transition”, funded by the European Commission under the European Union’s Horizon 2020 research and innovation program. Grant agreement No 814910.

References 1. NEXT Future Mobility: https://www.next-future-mobility.com/. Last accessed 2022/03/31 2. Gecchelin, T., Webb, J.: Modular dynamic ride-sharing transport systems. Econ. Anal. Policy 61, 111–117 (2019) 3. Mazzarino, M., Rubini, L.: Smart urban planning: evaluating urban logistics performance of innovative solutions and sustainable policies in the Venice Lagoon: the results of a case study. Sustainability 11, 4580 (2019) 4. SPROUT D4.1 SPROUT Pilots Evaluation Framework (2020) 5. FOT-Net FESTA Handbook 6. EU: European Commission Handbook on the External Costs of Transport. European Commission, Brussels (2019)

Innovative Technologies and Systems for Urban Mobility

519

7. Jochem, P., Doll, C., Fichtner, W.: External costs of electric vehicles. Transp. Res. Part D Transp. Environ. 42, 60–76 (2016) 8. Sælensminde, K.: Cost-benefit analyses of walking and cycling track networks taking into account insecurity, health effects and external costs of motorized traffic. Transp. Res. Part A Policy Pract. 38, 593–606 (2004) 9. Gössling, S., Choi, A.S.: Transport transitions in Copenhagen: comparing the cost of cars and bicycles. Ecol. Econ. 113, 106–113 (2015) 10. ISO ISO/IEC 25010:2011(en), Systems and software engineering. Available online: https:// www.iso.org/obp/ui/#iso:std:iso-iec:25010:ed-1:v1:en 11. te Boveldt, G.: All aboard? A New Evaluation Approach for Institutionally Complex Transport Projects. VUBPress, Brussels (2019) 12. Wu, J., Kulcsár, B., Selpi, Qu, X.: A modular, adaptive, and autonomous transit system (MAATS): an in-motion transfer strategy and performance evaluation in urban grid transit networks. Transp. Res. Part A 151, 81–98 (2021) 13. Pei, M., Lin, P., Du, J., Li, X., Chen, Z.: Vehicle dispatching in modular transit networks: a mixed-integer nonlinear programming model. Transp. Res. Part E 147, 102240 (2021) 14. Caros, N.S., Chow, J.Y.J.: Day-to-day market evaluation of modular autonomous vehicle fleet operations with en-route transfers. Transportmetrica B: Transport Dyn. 9(1), 109–133 (2021) 15. Dakic, I., Yang, K., Menendez, M., Chow, J.Y.J.: On the design of an optimal flexible bus dispatching system with modular bus units: Using the three-dimensional macroscopic fundamental diagram. Transp. Res. Part B 148, 38–59 (2021) 16. Chen, Z., Li, X., Zhou, X.: Operational design for shuttle systems with modular vehicles under oversaturated traffic: continuous modeling method. Transp. Res. Procedia 38, 359–379 (2019) 17. Zhang, Z., Tafreshian, A., Masoud, N.: Modular transit: using autonomy and modularity to improve performance in public transportation. Transp. Res. Part E 141, 102033 (2020)

Using C-ITS for Shockwave Damping and Preventing on Highways Marina Kouta(B) , Konstantina Marousi, and Athanasios Koukounaris Department of Civil Engineering, University of Patras, 265 04 Rio, Greece [email protected]

Abstract. Shockwaves are traffic pulses that mainly propagate upstream of traffic flow; commonly found on highways. The phenomenon is created due to the fact that vehicles move at different speeds and many drivers tend to change their speed abruptly; resulting to a temporary limited length tail. This can be damped or prevented using Connected Intelligent Transportation Systems (C-ITS). It is possible to damp or avoid shockwave formations by transmitting relevant on time messages to drivers, which will suggest optimal driving behaviours. In this direction, a method for damping/avoiding traffic pulses (shockwaves) is developed and presented in detail in this paper. After analyzing traffic data from the Attikes Diadromes S.A. highway, Artificial Neural Networks (ANNs) were developed and trained to detect incidents when there is a high probability of shockwave formation. The ANNs were evaluated with real-world data from video analysis. In view of the evaluation, an optimal ANN for detecting shockwaves and an optimal ANN for detecting shockwave forerunners are proposed. The messages to be sent to drivers when detecting such incidents were analyzed. Final messages are proposed based on the available technology and equipment. Next, the C-ITS architecture is developed to support the above services. It was ascertained that the developed ANNs detect shockwaves and shockwave forerunners very efficiently based on specific Key Performance Indicators (KPIs). When the detection is made, the relevant messages are transmitted to drivers in real time. The impact of shockwave preventing and damping messages can be tested through pilot activities. Keywords: Connected intelligent transportation systems (C-ITS) · Artificial neural networks (ANNs) · Shockwave damping (SWD) · C-ITS messages

1 Introduction Shockwave Damping (SWD) is a Cooperative Intelligent Transport System (C-ITS) service that mainly aims to smoothen traffic flow in dense traffic conditions by giving optimal speed recommendations or other driving suggestions and displaying them in a vehicle via a Human Machine Interface (HMI) [1]. That way, an optimal usage of the capacity of the roads is expected to be reached. Little abnormalities in dense traffic can contribute to upstream-moving shockwave generation. In general, traffic waves, also known as stop waves, ghost jams or traffic shocks, are disturbances that spread to vehicles, and are common on highways. In particular, shockwaves are traffic pulses that mainly © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_43

Using C-ITS for Shockwave Damping and Preventing on Highways

521

propagate upstream or downstream the traffic flow [2]. Upstream-moving shockwaves can spread and lead to long-term disruption of the traffic flow and an increased probability of incidents and accidents [2]. This can be encouraged by less proactive ways of driving and acceleration, which can lead to new shockwaves or a standstill. The objective of this service is to contribute to harmonization of traffic flow, and to prevent the formation of upstream-moving shockwaves or mitigate their development [3]. Mitigating of such shockwaves includes actions that can achieve their dampening. To dampen shockwaves, there is a need to detect shockwaves and conditions that could lead to shockwaves. Once a shockwave or a pre-shockwave condition is detected, appropriate messages to the involved drivers can contribute to shockwave dampening. This service aims at estimating and detecting shockwaves on highways, with a view to mitigation, i.e., damping and complete avoidance through communicating suitable messages to drivers [4]. In the case of shockwaves propagating upstream, drivers recognize shockwaves as a continuous transition from motion to lack of movement in traffic flow. Shockwaves often occur in partly congested traffic. Primarily, they are developed since vehicles move at different speeds and many drivers tend to change their speed at unexpected rates [5]. As a result of shockwaves moving upstream, a temporary queue, of limited length can be formed. If untreated, this queue retains or increases its length [6]. When the first vehicle accelerates and moves away from the queue, another vehicle slows down approaching the rear of the queue.

2 SWD Methodology 2.1 Introduction This section presents the SWD algorithms utilized for the development of a system that monitors traffic data, in order to detect shockwave incidents. The system performs two tasks. In the context of the first task, a potential shockwave at an arbitrary point in space and time is detected; a condition in which a potential shockwave has been detected is called a Watch Condition, and the algorithm that aims to detect such conditions is called Shockwave Watch Algorithm. In the context of the second task that the system performs, an occurring shockwave at an arbitrary point in space and time is detected; a condition in which an occurring shockwave has been detected is called a Warning Condition, and the algorithm that aims to detect such conditions is called Shockwave Warning Algorithm. Both the Shockwave Watch Algorithm and the Shockwave Warning Algorithm are implemented by the system. Lastly, tests that evaluate the performance of the system in terms of well-established metrics (i.e., Detection Rate and False Alarm Rate) are presented. 2.2 Technical Preliminaries Input Data: The available input data are collected from a set of inductive loops. The duration of the sampling time interval of each inductive loop is 20 s. At the end of every sampling time interval, an inductive loop provides the following three quantities1 : 1 Note that the available input data may contain missing or invalid values, when a communication

error or hardware breakdown occurs; in such cases the corresponding values are ignored.

522

M. Kouta et al.

• The number of vehicles N detected during the sampling time interval.2 • The time occupancy oc of the loop (%). • The time mean speed ut (km/h). The data generated from all the inductive loops, at a specific sampling time interval, are stored to an XML file located to an SFTP server of Attikes Diadromes. Range of Interest: Only the sections of a specific range of interest are used; in particular, sections E18 – E38 (towards Elefsina) and A18 – A38 (towards Airport). Note that each section is uniquely identified by a numerical value, called detectorID. Data Flow: A Python script, which essentially implements the developed system, is hosted on a server. The system constantly monitors an SFTP server of the road provider, for new XML files (input data). Once a new XML file arrives, the system loads the appropriate data, and evaluates them. The output of the system is an output message (in a JSON format) for a Watch and/or Warning Condition, which is published back to the road provider. Algorithms Architecture: The system is built around two Artificial Neural Networks (ANNs), which evaluate (classify) the Watch/Warning Conditions, based on a patternrecognition approach (see Fig. 1). Artificial Neural Networks: Each ANN of Fig. 1 is a four-layer (one input layer, two hidden layers and one output layer), feed-forward, fully connected neural network. The input layer of an ANN, through which the input data is fed, has 3 neurons, which correspond to the three quantities that an inductive loop generates at the end of every sampling time interval; namely, N, oc, and ut . Each of the two hidden layers has 160 neurons, where each neuron is equipped with a rectifier activation function. The output layer, at which the output of the ANN is generated, contains a single neuron that uses a sigmoid activation function in order to produce a probability output in the range of 0 to 1, for binary classification (as the occurrence of a particular incident is classified). This probability, essentially, expresses the ANN’s confidence in whether an incident indeed takes place. In order to specify whether the output of an ANN indicates an incident or not, a proper classification threshold should be adopted. If the output of the ANN is greater than the specified threshold, then it is considered that an incident indeed takes place (True State); otherwise, it is not (False State). Persistence: The output of each ANN passes a persistence check, according to which a Watch/Warning output message is generated only when a True State is observed for a number of successive sampling time intervals (see Fig. 1). As it will be shown subsequently, this number of successive samples was opted to be equal to 3, for both the Watch and the Warning Conditions. Recall that the sampling time interval of each inductive loop is 20 s; hence, a Watch/Warning output message is generated only when a True State is continuously observed for a time interval of 1 min (60 s). Output Filter Rules: Before a Watch/Warning output message is sent to the road operator, additional output filter rules are applied (see Fig. 1). The applied rules can take a variety of forms; an indicative rule encodes the policy according to which no Watch 2 To be precise, the available data provide the number of vehicles detected during an hour; hence,

in order to calculate the number of vehicles N detected during the sampling time interval of 20 s, the provided quantity has to be divided by 180.

Using C-ITS for Shockwave Damping and Preventing on Highways

Input

523

Output

[0 – 1]

Persistence ( 1min )

Threshold

Persistence ( 1min )

Warning

ANN 2 (Warning)

Threshold

Watch

[0 – 1]

Output Filter Rules

ANN 1 (Watch)

Fig. 1. Architecture diagram of the system.

output message is sent, if a Warning output message has been sent recently (e.g. in the last 60 s). With slight modifications, several other useful policies can be also encoded. Training: The two ANNs were trained on real-world labeled data of the following form: [ [ N 1 , oc 1 , ut1 ] , 0, [ N 2 , oc 2 , ut2 ] , 1, [ N 3 , oc 3 , ut3 ] , 1, [ N 4 , oc 4 , ut4 ] , 0, ... ]

Each 3-tuple [N, oc, ut ] is associated with a binary label, where 1 denotes that an incident had been occurred (during the data-collection phase), and 0 denotes that the incident had not been occurred (during the data-collection phase). Furthermore, the values of each 3-tuple [N, oc, ut ] are normalized to a 0–1 range, before entering the input layer of an ANN. Prior training, the weights of each ANN are initialized to a small random numeral (between 0 and 0.05), generated from a uniform distribution. The training procedure was implemented using the logarithmic loss function, which constitutes the preferred loss function for binary classification problems, as well as the efficient Adam optimization algorithm for gradient descent. Evaluation: The two ANNs were evaluated on real-world labeled data. The evaluation of ANN 1 revealed an Accuracy of approximately 65%, whereas the evaluation of ANN 2 revealed an Accuracy of approximately 74%.3 Apart from the Accuracy metric, 3 Accuracy is defined as the fraction of correct predictions that an ANN made.

524

M. Kouta et al.

the system is evaluated, for different classification thresholds, in terms of the Detection Rate (DR) and the False Alarm Rate (FAR) metrics, defined below. Detection Rate(DR) = Incidents correctly detected/All incidents

(1)

False Alarm Rate(FAR) = False alarms/All the evaluated samples

(2)

Ideally, a classifier is expected to have a high DR and, at the same time, a low FAR. Classification Threshold Specification: As earlier stated, in order to specify whether the output of an ANN indicates an incident or not, a proper threshold should be adopted. On that premise, if the output of the ANN is greater than the specified threshold, then it is considered that an incident indeed takes place (True State); otherwise, it is not (False State). The specification of the classification threshold, both for ANN 1 and for ANN 2, is carried out by evaluating the DR and FAR metrics for different values of classification thresholds. We start our analysis with the specification of the classification threshold for ANN 1, which evaluates the Watch Condition. Accordingly, Fig. 2 (left) depicts the DR and FAR metrics versus the classification threshold. Evidently, by lowering (resp., increasing) the classification threshold, ANN 1 classifies more samples as True States (resp., False States), thus, both DR and FAR are increasing (resp., decreasing). The curve of Fig. 2 (right) plots DR versus FAR at different classification thresholds.

Fig. 2. DR & FAR versus classification threshold (watch) (on the left) & DR versus FAR rate at different classification thresholds (watch) (on the right).

As it is apparent from Fig. 2, the choice of the classification threshold should be done in view of the trade-off between the DR and FAR metrics. Against this background, for ANN 1, a classification threshold of 0.35 was opted, at which we have DR ≈ 74% and FAR ≈ 42%; this choice of classification threshold is in favor of DR over FAR. We, then, turn to the specification of the classification threshold for ANN 2, which evaluates the Warning Condition. Accordingly, Fig. 3 (left) depicts the DR and FAR metrics versus the classification threshold. The curve of Fig. 3 (right) plots DR versus FAR at different classification thresholds. As in the case of Fig. 2 (right), the choice of the classification threshold should be done in view of the trade-off between DR and FAR. Against this background, for ANN 2, a classification threshold of 0.285 was opted, at which we have DR ≈ 6% and FAR ≈

Using C-ITS for Shockwave Damping and Preventing on Highways

525

Fig. 3. DR & FAR versus classification threshold (warning) (on the left) & DR versus FAR rate at different classification thresholds (warning) (on the right).

0.5%; contrary to the case of ANN 1, this choice of classification threshold is in favor of FAR over DR. 2.3 Shockwave Watch Algorithm Subsequently, the Shockwave Watch Algorithm is presented; details on the specific tasks that the algorithm performs have been discussed earlier. Input: A (new) XML file, located to the SFTP server of the road operator, containing the measured data of a sampling time interval. Output: A message for a Watch Condition, if such a condition is detected; nothing, otherwise. 1. WHILE TRUE DO. 2. Connect to the SFTP server of the road operator. 3. For the range of interest, load data (i.e., Road name, detectorID, N, oc, ut ) from a (new) XML file of the SFTP server of the road operator. 4. Normalize N, oc, ut to 0–1 range. 5. Feed the normalized N, oc, ut to ANN 1 for evaluation. 6. Evaluate under the specified threshold. 7. Store the output of ANN 1 (True State/False State) for persistence check. 8. For each detectorID, if 3 consecutive True States have been occurred, and no Warning message has been sent recently (i.e., during the last 60 s), then send a message for Watch Condition to the road operator; otherwise, do not send anything. 9. Store the sent message (if any) to a backup hard disk drive. 2.4 Shockwave Warning Algorithm Subsequently, the Shockwave Warning Algorithm is presented; details on the specific tasks that the algorithm performs have been discussed earlier. Input: A (new) XML file, located to the SFTP server of the road operator, containing the measured data of a sampling time interval. Output: A message for a Warning Condition, if such a condition is detected; nothing, otherwise.

526

M. Kouta et al.

1. WHILE TRUE DO. 2. Connect to the SFTP server of the road operator. 3. For the range of interest, load data (i.e., Road name, detectorID, N, oc, ut ) from a (new) XML file of the SFTP server of the road operator. 4. Normalize N, oc, ut to 0–1 range. 5. Feed the normalized N, oc, ut to ANN 2 for evaluation. 6. Evaluate under the specified threshold. 7. Store the output of ANN 2 (True State/False State) for persistence check. 8. For each detectorID, if 3 consecutive True States have been occurred, then send a message for Warning Condition to the road operator; otherwise, do not send anything. 9. Store the sent message (if any) to a backup hard disk drive. Note that each of the aforementioned algorithm runs an infinite while-loop, which ensures continuous operation (detection). The evaluation of the information of an XML file is performed in approximately 0.5 s, both for a Watch and a Warning Condition; this running-time is much shorter than the duration of the 20-s sampling time interval of each inductive loop. Lastly, if an error (exception) occurs during the execution of the Watch and/or Warning algorithm (e.g., due to a problematic connection with a server), the algorithm continues to run until the corresponding issue is resolved. 2.5 Algorithms Verification Persistence. This section is devoted to the verification of the implemented system with respect to persistence. In particular, the performance of the system, in terms of the DR and FAR metrics, is evaluated for different values of persistence. We start our analysis with the case of ANN 1, which evaluates the Watch Condition. Accordingly, Fig. 4 (left) plots DR versus FAR at different classification thresholds, parametrized by the value of persistence; notice that the curve corresponding to a 60-s persistence is identical to the curve of Fig. 2 (right).

Fig. 4. DR versus FAR rate at different classification thresholds, parametrized by the value of persistence (watch) (on the left) & DR versus FAR rate at different classification thresholds, parametrized by the value of persistence (warning) (on the right).

It is evident that all five curves of Fig. 4 (left) are approximately identical; on that premise, and given the inaccuracy of the labeled data used in the training of ANNs,

Using C-ITS for Shockwave Damping and Preventing on Highways

527

which in turn results in a deviation of the curves, the choice was in favor of a 60-s persistence. We, then, turn to the case of ANN 2, which evaluates the Warning Condition. Accordingly, Fig. 4 (right) plots DR versus FAR at different classification thresholds, parametrized by the value of persistence; notice that the curve corresponding to a 60-s persistence is identical to the curve of Fig. 3 (right). It is evident that all five curves of Fig. 4 (right) are approximately identical; on that premise, and given the inaccuracy of the labeled data used in the training of ANNs, which in turn results in a deviation of the curves, the choice was in favor of a 60-s persistence. Video analysis. This section is devoted to the verification of the implemented system with the use of data which corresponds to real shockwave incidents, recognized by video analysis. The videos were recorded by machine vision cameras installed in Attiki Odos. Moreover, data from non-incident situations were used for improving the system based on the set KPIs. As expected, the new data sets improved significantly system’s effectiveness, as shown Figs. 5 and 6. For ANN 1-Watch Algorithm, Fig. 5 on the left depicts the DR and FAR metrics versus the classification threshold, for 60-s persistence. Figure 5 on the right shows that a classification threshold ca. 0.36 was considered, at which we have DR ≈ 60% and FAR ≈ 35%; the choice of classification threshold was made in favor of a low FAR over an adequate DR. Accordingly, for ANN 2-Warning Algorithm, Fig. 6 on the left depicts the DR and FAR metrics versus the classification threshold, for 60-s persistence. Figure 6 on the right shows that a classification threshold ca. 0.36 was considered, at which we have DR ≈ 70% and FAR ≈ 2%. As in the case of ANN 1, the choice of classification threshold was made in favor of a low FAR over an adequate DR.

Fig. 5. DR & FAR versus classification threshold (watch) (on the left) & DR versus FAR rate at different classification thresholds (watch) (on the right), for 60-s persistence.

3 C-ITS Service Architecture This research focuses on the implementation of the C-ITS service for SWD which aims at damping or dissolving shockwaves on the roadmap with help of C-ITS concepts and integrates the Shockwave Watch Algorithm and the Shockwave Warning Algorithm.

528

M. Kouta et al.

Fig. 6. DR & FAR versus classification threshold (warning) (on the left) & DR versus FAR rate at different classification thresholds (warning) (on the right) for 60-s persistence.

SWD can be achieved by properly issuing Shockwave Alerts. Shockwave Alerts include Shockwave Watches and Shockwave Warnings. Shockwave Watches are issued for broader areas where conditions are favorable and ripe for the development of shockwaves. Then, driving recommendations are sent to drivers to stay alert. Shockwave Warnings are issued for highly localized areas where shockwave conditions have been confirmed and a Shockwave is imminent or has been detected. Then, driving guidance recommendations are sent immediately to drivers to stay safe. To reduce the number of Shockwaves on highways, apart from Shockwaves, we suggest detecting Shockwave forerunners. Thus, Shockwaves can be foreseen and prevented. Watch Shockwave algorithm detects these forerunners and raises “Watch Alert” for specific parts of the highway. These parts are temporarily called “Alert Zones”. “Alerts Zones” are also used when a Shockwave is detected by Shockwave Warning algorithms. When a watch or warning alert is raised, suitable messages are disseminated to the drivers. The Traffic Control Center (TCC) (or a server) sends Interface Definition Language (ILD) data (inductive loop detector data) to the ICS subsystem. The ICS subsystem feeds Shockwave Damping Watch and Shockwave Damping Warning Algorithms with ILD data. The algorithms discover relevant situation (detection of shockwave or of shockwave precursor) and then, generate cooperative output messages. Road Side Units (RSU) are used for the transmission of the output messages. RSUs send these data to the Vehicle On-board Units (OBUs) of the vehicles in In-Vehicle Information (IVI) form. The Human Machine Interface (HMI) receive the recommended messages and/or other advice for SWD in IVI form. The vehicle driver is expected to adapt own driving compliant to applicable driving regulations and messages provided. The messages and their content are analysed in the following chapter of this manuscript. The Traffic Control Center (TCC) of the road operator keeps collecting traffic flow, time occupancy and instantaneous speed data for predefined sections at frequency ≥0.05 Hz, i.e. at least every 20 s. The operator can display general messages via Variable Message Signs (VMS), supporting personalised messages.

4 Results In this section, the result of the algorithm implementation with real loop data and the corresponding C-ITS messages are presented. The form of the output messages of the

Using C-ITS for Shockwave Damping and Preventing on Highways

529

system is, also, presented. An output message contains the timestamp of the incident, the duration of the validity of the output message (i.e., how long the output message is considered valid, set to 60 s), the type (flag) of the incident (namely, Watch/Warning Condition), the detectorID of the section at which the incident has been observed, as well as a free text field which could contain arbitrary text (used, e.g., for internal purposes). Regarding the arbitrary text, for “Watch Alerts” the messages’ content should assure that shockwave is not developed, despite the great probability of it developing. Thus, the suggested message for “watch alerts” is, “Drive carefully. Keep your lane. Dense Traffic Ahead”. For “Warning Alerts” the messages’ content should result in shockwave stopping propagating backwards as soon as possible. Thus, the suggested message for “warning alerts” is “Keep constant speed. Maintain safe distance. Traffic Jam Ahead”. For the successful implementation of the suggested C-ITS service, the drivers’ acceptance is prerequisite [7]. Several researchers have examined the level of acceptance and the correlation between the message type and the driver response, suggesting that message content is an important control variable for improving system performance without compromising the integrity of the information provided [8, 9]. From these researchers, it was proven that the drivers prefer messages that convey real-time information about exceptional or dangerous situation, and they do not want to receive periodic or programmed notifications.

5 Conclusions This paper presents in detail a SWD system and a suggested C-ITS Service Architecture aiming to contribute to harmonization of traffic flow, and to prevent the formation of upstream-moving shockwaves or mitigate their development. The SWD system is composed of two modules. In the first module, a potential shockwave at an arbitrary point in space and time is detected, namely a Watch Condition. Then, the Shockwave Watch Algorithm is activated. In the second module, an occurring shockwave at an arbitrary point in space and time is detected, namely a Warning Condition. Then, the Shockwave Warning Algorithm is activated. Once a shockwave or a pre-shockwave condition is detected, appropriate messages are proposed to the involved drivers. For the efficient transmission of the proposed messages, a C-ITS Service Architecture has been introduced, involving several components (TMC, ICS, RSU, OBU, HMI). However, C-ITS are mainly implemented in pilot activities and the number of the connected vehicles is still very low. Thus, it is important that these messages are communicated not only to connected vehicles, but also to non-connected vehicles. For the non-connected vehicles, the information about detection of Shockwave Damping Watch or Warning situation can be transmitted through National Access Point (NAP) and a service provider, with the use of a mobile application. The SWD service contributes significantly to a homogenous traffic flow without traffic jams/congestion. This leads to a number of benefits: 1. Economic benefits (saved resources, money and time, road utilization closer to capacity); 2. Social benefits (traffic safety, reduced incidents, reduced loss of life); 3. Personal benefits (more comfortable driving, increased quality of life) and; 4. Environmental benefits (reduced CO2 emissions and reduced environmental pollution). Further research on SWD C-ITS service will focus on the real-time implementation of the system, the users’ acceptance through evaluation methods, including a set of

530

M. Kouta et al.

Key Performance Indicators (KPIs) to measure driving behaviour advisory messages effectiveness and the final impact on the traffic conditions. The acceptance is the most crucial parameter. Thus, the acceptance by the user will be examined and an ethical evaluation of the services will be carried out. Acknowledgement. Special thanks to Attikes Diadromes S.A., partner of the C-ROADS Greece project under the Connecting Europe Facility (CEF) - Transport Sector Programme which is co-funded by the European Union and implements pilot activities of C-ITS services.

References 1. Ling, S., Yameng, L., Jian, G.: Architecture and application research of cooperative intelligent transport systems. Procedia Eng. 137, 747–753 (2016) 2. Daganzo, C.F.: The cell transmission model: a simple dynamic representation of highway traffic. Transp. Res. Board 28, 269–287 (1994) 3. Javed, M.A., Zeadally, S., Hamida, E.B.: Data analytics or cooperative intelligent transport systems. Veh. Commun., pp. 63–66 (2014) 4. Liu, K., Ng, J.K.Y., Lee, V.C.S., Son, S.H., Stojmenovic, I.: Cooperative data scheduling in hybrid vehicular ad hoc networks: Vanet as software defined network. IEEE/ACM Trans. Net. 24(3), 1759–1773 (2016) 5. Stephanedes, Y.J., Golias, M., Dedes, G., Douligeris, C., Mishra, S.: Challenges, risks and opportunities for connected vehicle services in smart cities and communities. In: 2nd IFAC Conference Cyber-Physical & Human Systems Proceedings, Miami, Florida, USA (2018) 6. Badillo, B.E., Rakha, H., Rioux, T.W., Abrams, M.: Queue length estimation using conventional vehicle detector and probe vehicle data. In: 15th International IEEE Conference of Intelligent Transportation Systems, pp. 1674–1681 (2012) 7. Motamedidehkordi, N., Margreiter, M., Benz, T.: Shockwave suppression by vehicle-to-vehicle communication. Transp. Res. Procedia 15, 471–482 (2016) 8. Zhang, Y., Yan, X., Li, X.: Effect of warning message on driver’s stop/go decision and redlight-running behaviors under fog condition. Accid. Anal. Prev. ISSN 0001-4575 (2021) 9. Yan, X., Liu, Y., Xu, Y.: Effect of audio in-vehicle red light-running warning message on driving behavior based on a driving simulator experiment. Traffic Inj. Prev. 16(1), 48–54 (2015)

Relationship and Differences Between Entrepreneurship and Research in the CrowdMapping Project for Crowdsourced Urban Data Mátyás Szántó(B)

and László Vajta

Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary [email protected]

Abstract. The increasing interest and the amount of capital invested in the technology of autonomous vehicles in urban and suburban environments resulted in a surge of research activities sustained around urban mobility in the competitive as well as the academic sector. The CrowdMapping project at the Department of Control Engineering and Information Technology at the Budapest University of Technology and Economics is one of such endeavors aiming at developing the necessary technology backbones for crowdsourced map data acquisition for autonomous traffic and transportation. In this article, we are introducing the differences we came to understand between the mindset and the aims of an entrepreneur versus a researcher and show how we attempted to manage these distinctions during the 2021 EIT Jumpstarter competition. Keywords: Entrepreneurship versus research · CrowdMapping · EIT Jumpstarter · Urban mobility

1 Introduction The CrowdMapping (CM) framework – as described in our previous publications ([1] and [2]) – is an architecture whose primary aim is to provide a framework for autonomous driving aiding database creation and updating. The foundation of this project – which was started in 2018 at the Budapest University of Technology and Economics Department of Control Engineering and Information Technology – was based on the increasing need that appeared in and around the topic of autonomous vehicle development in the past decade. However, the activities surrounding the CM project were mainly focused on research activities, and the aims have always been prioritized by student interests and research requirements. On the one hand, the project has been proven successful with numerous scientific results already published, and ongoing student projects. On the other hand, the requirements of founding a startup with the same driving idea behind it are profoundly different than that of a research project as the authors of this publication found. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_44

532

M. Szántó and L. Vajta

In this section, we introduce our aims and the basic technological and historical background of this paper. This section is constructed as follows: first, we give an overview of the CrowdMapping (CM) research project; afterwards, the Jumpstarter program of the European Institute of Innovation and Technology (EIT) is introduced. 1.1 The CrowdMapping Research Project The framework designed for the CM platform can be seen in Fig. 1. The CrowdMapping project’s architecture as published in [1] The project consists of two main sections, the local and the remote level. Both levels encompass numerous features and points of interest for research activities primarily around the topics of computer vision and database management. The idea behind the research project is based on the assumption that many modern vehicles have the capability to perceive their surroundings – i.e., the road infrastructure, the pavement, and the other members of traffic – via the wide variety of sensors deployed onboard.

Fig. 1. The CrowdMapping project’s architecture as published in [1]

The primary use for these sensors is to supply vital data for the functionality of Driver Assistance Systems (DAS). A special kind of DAS – ones using vision-based sensors, such as cameras or Light Detection and Ranging sensors (LIDARs) – is widely referred to as ADAS [3] (Advanced Driver Assistance Systems). The data generated for the functionality of ADAS solutions is – in most cases – used only locally for instantaneous decision making and then disregarded. A lot of data that otherwise could be utilized is lost as a result. By selected image processing techniques, the visual data sourced from the onboard cameras of ADAS-containing vehicles can be used for database

Relationship and Differences Between Entrepreneurship

533

creation. If enough vehicles take part in the data acquisition process – thus becoming the individual data acquisition agents of a crowdsourced information gathering system [4] – vast amounts of useful data can be collected and processed. Moreover, the collected data can be used to create traffic databases or – more specifically – maps, which can be updated with low latency. The CM project builds on the assumptions described above. It aims at creating databases answering the needs of modern vehicles with self-driving capabilities. These are currently available vehicles with ADASs, which can provide level 2 or level 3 driving automation as described by SAE J3016 Levels of Driving Automation [5]. The architecture of the CM framework (Fig. 1) consists of two levels: 1. The local level consists of activities on the level of the individual data acquisition agents – i.e., the vehicles taking part in the crowdsourcing efforts. Many activities shall be carried out at the local level: unifying the acquired information – i.e., heterogeneous images –, filtering unnecessary data via image processing algorithms, sensor-fusion, and data packaging. 2. The remote level is then responsible for all the processes that yield the databases – in this case the maps, which can then be used again by the individual data users. Activities include image-to-pointcloud conversions, map building, and updating processes via advanced neural mapping and monitoring using error calculation. The results achieved within the scheme of CM at a research and education level can best be demonstrated by the publications and finished theses stemming from the project. • Publications: papers describing the framework ([1] and [2]) have already seen international scrutiny, while student research activities ([6] and [7]) were presented at the Hungarian Scientific Students’ Association Conference. Numerous publications are in the pipeline for 2022. • Theses: a total of 5 Bachelor and Master theses have been written by students working with a respected aspect of the platform. • Numerous lab projects and individual experiments and measurements have been designed and carried out by students at the Budapest University of Technology and Economics. 1.2 The Jumpstarter Program The Jumpstarter program [8] is an annually organized competition held by the European Institute for Innovation and Technology (EIT). This program aims at creating a sustainable impact in the Central, Eastern and the Southern regions of the European Union (EU). The program intends to find the innovation-potential nested in the researchers and professionals in the countries of these regions1 with the hope to meet the needs of the 1 Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Greece,

Hungary, Italy, Latvia, Lithuania, Malta, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Montenegro, Republic of North Macedonia, Serbia, Turkey, Guadeloupe, French Guiana, Réunion, Martinique, Mayotte and Saint-Martin (France), the Azores and Madeira (Portugal), and the Canary Islands (Spain).

534

M. Szántó and L. Vajta

innovation ecosystem and the demands of the private sector of their respected countries and fields of expertise. The program has seen an ever-increasing interest since its start in 2017. The competition welcomes original business ideas from a variety of industrial sectors, which are collected around the six Knowledge and Innovation Communities (KIC) of EIT: 1. 2. 3. 4. 5. 6.

EIT Health EIT RawMaterials EIT Food EIT InnoEnergy EIT Manufacturing EIT Urban Mobility.

The program spans a total of 7 months, it is built up of several rounds: the idea submission and selection; the bootcamp stage; the local joint training stage; and the “Grand Final”. As a result of the increasing attention for the program, the selection process as well as reaching the later stages and the Grand Final all got progressively more difficult. In 2021, more than 540 teams applied from 25 countries, but only 220 were selected for participation. In the 2021 rendition of the competition, the CM team applied for the program, as we realized that with the project being founded on a very actual trend in the automotive world, there may very well be room on the market for a venture and for establishing a business building on the ideas behind the crowdsourcing-based map creation framework. We aimed at taking part in the competition as one of the projects in the section running under the Urban Mobility KIC. CM managed to get accepted and take part in the Jumpstarter competition.

2 CrowdMapping at Jumpstarter This section provides an overview of the activities and achievements of the CM project during the 8-month-long Jumpstarter program. We provide an overview of the different sections and rounds of the program and how the activities involved benefited the development of the business plan of CM. We also introduce the information and tasks that were provided for the participating teams that managed to advance to the ongoing round. 2.1 The Team The CM team was initially composed of 4 people, all alumni of the Budapest University of Technology and Economics: • • • •

Mr. Mátyás Szántó as founder of the project and lead designer; Dr. László Vajta as senior advisor; Mr. Tamás Mészéget˝o as designer, and business-developer; Mr. Olivér Horváth as head of development.

Relationship and Differences Between Entrepreneurship

535

2.2 The Competition The CrowdMapping team received a call for application for the 2021 Jumpstarter competition in February 2021. We entered the competition that consisted of several phases: Application and Selection Phase, the Bootcamp Round, Round of Local Joint Trainings, and the Grand Final. In this section, the consecutive phases of the event are listed together with the results that our team managed to achieve during them. Application and Selection Phase. In the application phase, the teams had to submit an application, which had to include an introduction of the project or idea, the team’s motivation, the possible market, and future plans. For the CM team, the preparation of the paperwork already helped in realizing some of the shortcomings of the project as a business venture and the possible opportunities of the participation in the Jumpstarter program. The team managed to submit the paperwork on 04.30.2021. The organizers found that our project had the potential of becoming a future venture successfully answering the demands of modern urban challenges. The CM team’s application received a positive decision after evaluation by EIT on 24.05.2021. The project was accepted to take part in the Urban Mobility section of the project together with 29 further business ideas and projects. Bootcamp Round. The goal of this round was for each team to create a pitch deck by the end of the bootcamps and present it to a committee of judges. The selected teams were invited to take part in 3 online training sessions and were assigned a mentor, who helped in the identification of the answers for some key questions with regard to the viability and business potential of the project. The pitch deck focused on 8 key aspects of the project: 1. The Deal, which is the initial business model and a value flow diagram of the product or service that the project envisioned. In the case of CM, we came to the realization, that our project accomplishes a business-to-business (B2B) model, with ADAS technology providers as the customers. 2. Market segmentation, which provided an overview of the Total Addressable Market (TAM), Serviceable Addressable Market (SAM), and the Beachhead Market (BM). This latter would provide the first customers of a business venture. In case of CM, we identified our Beachhead Market as European Tier 1 suppliers of automotive Original Equipment Manufacturers (OEM). 3. Customer value proposition, which had to show the possible value gain that the customer would realize, if they chose to adopt the service or product of the startup in question. For CM, we realized that this value originates in the low-cost mapping solution our crowdsourced approach provides. 4. Product, which served as an overview of the most important aspects of the product or service of the business venture. In this section of the pitch, we presented the CM framework described above in a nutshell. 5. Initial financial prognosis, which presented a very early and basic prediction of the yearly profit of the project. 6. KIC impact, which had to show the points in which the project could be aligned with the objectives of the Knowledge and Innovation Community by which the project was selected. For CM, we managed to find numerous aspects, in which the successful fruition of the project could benefit the goals of EIT Urban Mobility:

536

M. Szántó and L. Vajta

– – – –

Potentially speeding up the implementation of higher-level traffic automation; Increased traffic safety; Advancement in Mobility for All; etc.

7. Competitive advantage, which presented the edge provided by the idea behind the project over other ventures targeting the same market segment. In the case of CM, we highlighted some of the key assets of the framework, including the low cost and the low latency possibilities. 8. Team and dream, which introduced our initial goals and the members of the team. The CM team managed to prepare and submit the pitch deck on 15.07.2021 and thus take part in the pitch event held on 21.07.2021, where a total of 24 teams presented their pitches. The jury decided that the CM venture was valuable enough to advance to the next round, the round of local joint trainings. Round of Local Joint Trainings. The selected teams had the opportunity to take part in local joint trainings, which have been organized in numerous cities across Europe. The goal of this round was to provide the necessary knowledge and tools for the teams to be able to prepare a well-founded and sound business plan (BP). The local joint training that the CM project participated in, was held in Budapest. It was a particularly condensed schedule over the span of 5 days. On the first day, the importance of a good team was emphasized, and good leadership attributes were introduced through numerous self-assessment exercises. The following three days were focused on developing market penetration techniques through persona analysis, market research, and validation. Many truly useful tools, such as the Lean canvas, were introduced and demonstrated by professional members of the EIT Urban Mobility KIC. Marketing, sales, and finance topics were also heavily scrutinized, and team members – especially founders – had to constantly develop and adopt the strategies of their respected projects with the help of the methods and tools introduced. The fifth day was about developing the presentation skills of the team members with the help of a public speaking expert. A 3-min presentation had to be prepared based on the developed aspects of the business plan and the pitch deck finalized by the end of the previous round and then presented to a small audience of other teams’ members several times. The business plan had to be finalized based on the insights and knowledge gained during the local joint training and the validation and market research activities of the teams. During the creation of the BP document, another member joined the CM team: • Mr. Gergely Deák as head of strategy. The resulting BP had to contain the features of the business to be established led by an executive summary then grouped into the following sections: 1. 2. 3. 4.

Needs/Problems Solution and value proposition Market Revenue streams

Relationship and Differences Between Entrepreneurship

537

5. Cost structure 6. Implementation plan 7. Team. Although similar to the pitch deck, the business plan was required to contain in-depth analysis and emphasis put on individual topics concerning the viability of the project as a business. The submitted document was an 8-page summary of the CM venture and the plans for the first year of its existence. The most important sections and our provided elaborations are detailed below. The needs/problems section provides a description of the current trends and demands of the market that the business proposal aims at. This section also includes the analysis of the provided solution’s impact towards EIT KIC challenges. For CM this market is the ADAS segment of the automotive market, especially driver assistance system developers in the Central and Eastern European (CEE) region. The need comes from the constant data-hunger of these companies. The business plan elaborated the contents of the KIC impact slide of the pitch deck described previously: These included Urban Mobility challenges, such as Safe transport for people and goods (C4) and Boosting the competitiveness of the mobility industry (C6) [9]. The solution and value proposition section includes experience gathered from using the tools supplied by EIT during the local joint trainings, as well as the results of validation activities. Validation meant surveying the industry for feedback on whether the envisioned business venture was capable of answering substantive demands. In the case of CM, the numerous discussions and meetings with industry players (including Robert Bosch GmbH, AImotive Kft, and others) assured the team members that the venture was indeed viable in case of finding the right balance between pricing and quality. In this section of the of the plan, an analysis is also required of the intellectual property (IP) and legal considerations regarding their projects as well. For CM, the need for IP protection and for the legal and ethical factors regarding the anonymization of crowdsourced data have been realized early on and are listed in the business plan. The market section of the document provides an in-depth analysis of the markets, the user needs and the unique selling propositions (USPs) relevant for the project. For CM, we identify our key market and target customers as the technical decision makers of ADAS technology providers, and provide a list of USPs that our project provides to answer their “pain-points”. For the revenue streams section, the main revenue sources and flows are identified and proposed for the business. This includes the customer value proposition, the current solutions available on the market, trends and preferences for payment and proposed revenue streams. For CM, we list our proposed answers for our key customer personae’s needs. Based on this, and our market research for current solutions, we identify key revenue streams sourced from automatically labelled images and low-latency data updates answering current development needs of ADAS technology developers. The cost structure section of the business plan regards costs of the venture’s operation in the first few years of existence. A preliminary expenditure and revenue balance was developed for CM, an abstract of which is included in the BP. The implementation plan section must describe the first few months of the execution of the business plan including reaching some well-defined milestones and deliverables.

538

M. Szántó and L. Vajta

For CM, these milestones were the validation of the business plan, the implementation of the minimal viable product (MVP), and completing the first sale before the end of the first year of operation. Finally, the team section included the current members of the project as well as the competencies where the project needs to mature. In the case of CM, we realized that the onboarding of sales, financial controlling and legal experts were inescapable. The CM project team prepared a business plan and submitted it on 15.10.2021. A total of 17 projects submitted their business plans, out of which 6 teams were chosen to take part in the Grand Final for the Urban Mobility KIC. The CM team did not advance to the final round of the 2021 Jumpstarter competition. The judges’ feedback was utterly useful however, as it showed the reasons why the business plan was graded for 49/70 points. It also showed key insights as to what aspects of the proposal should be modified or expanded. Grand Final. During the final event, all teams that advanced to this round had the chance to present their BP in the form of a pitch presentation. The projects were scored based on a committee’s judgement, and the top 3 teams were selected. These teams received an award, in the form of a one-time money prize.

3 Discussion In this section, we introduce the key lessons we managed to learn from our participation in the Jumpstarter competition. We focus on the key disparities we identified between a project being examined from a research and from a venture perspective. We also discuss our planned future efforts for pursuing the realization of CM as a business. 3.1 The Differences Between Research and Venture Activities As the CM project first started out as a research endeavor, it was only a basis for experimentation and testing of scientific theses. However, it has always been a point of interest for the founders – i.e., the authors of this article – to see whether the project would be able to hold up to the requirements and demands of the industry, especially a profit-focused automotive development firm. The Jumpstarter program therefore turned out to be the perfect forum for testing the capabilities of the project and its management team. In the literature, the differences between academic and professional research has been published before. Burgers et al. [10] argue that the main contrast lies in understanding the differences between technical knowledge versus market knowledge. According to the findings of Dougherty [11], the process of fruitful New Business Development (NBD) heavily depends on the successful linking of these two types of knowledge. However, the results presented in the literature present helpful points, they focus mainly on NBD activities from the perspective of corporations already in existence, and not necessarily startups. Clarysse et al. [12] compare University and Corporate spin-offs based on the amount of technical knowledge available to them, and find that corporate spin-offs tend to grow faster than their University counterparts, as a result of – amongst other reasons – technical knowledge not being sufficient for the success of a technology-based company.

Relationship and Differences Between Entrepreneurship

539

Vohora et al. [13] list the most crucial junctions a University spin-off (such as CrowdMapping) shall face. According to the categorization described in this article, our project went through the Opportunity Phase, wherein the founders first have to mitigate the uncertainties surrounding the project and – e.g., technical, market, resource, and entrepreneurial concerns. This categorization held true for the CM project in the Jumpstarter competition. However, meeting these points of dilemma can still be unexpected. During the 8-month span of the Jumpstarter competition, we realized that our approach must drastically be changed for the project to be turned into a viable business or a startup company. The key differences that we identified between our project as a research and as a business project are listed below: 1. Real demand in focus: One of the most important distinctions we identified early on during the competition is the importance of understanding the market and replying to solid demands and needs. Although, CM was always meant to give a realistic answer to one of the key problems that appears in current times in the automotive industry, namely that the development of high accuracy and robust Driver Assistance Systems requires immense amounts of training data, it has never searched for a solid foundation in the market of the automotive suppliers. This latter characteristic is a key realization that every venture that wants to stay alive on any market must make their primary consideration. 2. Market analysis: For a business venture to be successful, it must identify its market precisely, and determine the players on it, understand its dynamics and most importantly pinpoint their available entry point well. Without laying down the accurate foundations of the market strategy, a business venture will not be able to survive in the industry. In contrast, a research project does not need to consider the market, or the possible size of it, as generating profit is not one of the concerns when experimenting with a novel scientific method or conducting a literature review. 3. Key performance indicators (KPIs): A vital realization for us was the difference between the KPIs of a venture and a scientific project. On the one hand, for any scientific research activity, the judgement of the performance is measured in peer recognition i.e., the perception of the scientific results a publication is met with by the scientific society. On the other hand, for a for-profit business, the most important KPI is the customer’s satisfaction – without that, any product or service will remain unused, and the venture will eventually go out of business. 4. Quality: Medium, and large enterprises tend to have rather strict rules on the quality of products or services they will buy. This is partly because of the quality management systems employed by them, and partly because of their intention to keep up their reputation. For the automotive industry, the quality requirements towards suppliers tend to reach even higher levels. This issue can-not be neglected when a startup is trying to integrate into a supply chain, however, these concerns are rare considerations to be taken into account in the case of research projects. 5. Competitive advantage: One of the key considerations to be made during the preparation of the business plan was to identify and understand the possible competitive advantages that CM as a business could provide to its potential customers. This difference in mindset is something that is closely related to the understanding of

540

M. Szántó and L. Vajta

the market and identifying the competition. It is improbable to replace previously proven suppliers with a new venture on the market – in this case CM – without a clear indication of the advantages the switch will provide. 6. Financial, sales, and marketing activities: The functionality and success of a venture highly depends on some of the key competencies that are seldom considered when running a research project. Here, we only listed a few of these areas, but it is a rather important consideration, to take the cost of these activities into account during the calculation of a venture’s cost structure. 7. Best practices: There is a considerable difference between obtaining previously tried techniques and methods for research and business activities. Business ventures are much more practical in this sense: most of the knowledge can be gained via learningby-doing. Receiving business advice, and directions is much more commonly managed through human interactions. In contrast, for research activities it is hard to find an expert that can provide advice in a specific scientific field. Besides these points, an argument can be made that the margin of forgivingness is a lot lower for ventures. While both activities can be considered as a series of trial-anderror actions, it is generally true that for research endeavors, the iterations are signs of moving forward, while for ventures, this might not be the case. However, we found that both approaches to a project – be that research or business – entails an immense amount of learning, and both have their points of beauty within. 3.2 Continued Efforts At the time of writing this paper, the CM project remained a research endeavor at the Budapest University of Technology and Economics. However, largely resulting from the knowledge gained during the Jumpstarter competition, the founders of the project intend to continue their efforts in turning CM into a venture. The positive responses as well as the criticisms received during the program showed us that it is worth pursuing the establishment of our project as a business in the future.

4 Conclusion In this paper, we have introduced the CrowdMapping research project and the CM team as a competitor in the 2021 Jumpstarter competition organized by the European Institute of Innovation and Technology. We listed the different stages of the program and showed the results of our efforts conducted during them. We enumerated the most important conclusions we came to during the preparation of the deliverables for each round of the Jumpstarter competition. With these conclusions and findings, we aim at introducing the scientific community to some of the most pressing obstacles, and considerations a project must be facing during the transition from purely academia-oriented research to an actual venture. For urban mobility related research topics, the listed points are ever so vital, since the demand for research-oriented projects in this market is constantly growing, and academia-founded projects will inevitably meet difficulties, where finding the right

Relationship and Differences Between Entrepreneurship

541

answers and solutions might prove to be more straightforward given the knowledge of our observations. Another possible use of our findings can be adopted during the founding of research projects. Given the differences listed above, research project with results possibly applied in a for-profit scenario can be better built from their starting point to fit some of the requirements of the markets. Acknowledgement. This research has been supported by the European Commission H2020 Program under the IoTAC Research and Innovation Action, under Grant Agreement No. 952684.

References 1. Szántó, M., Vajta, L.: Introducing CrowdMapping: a novel system for generating autonomous driving aiding traffic network databases. In: 2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO), 2019, pp. 7–12. https://doi.org/10. 1109/ICCAIRO47923.2019.00010 2. Mészéget˝o, T., Tass, B., Szántó, M.: Motion based masking of a moving vehicle’s environment. In: 2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO), 2019, pp. 25–30. https://doi.org/10.1109/ICCAIRO47923.2019.00013 3. Shaout A., Colella, D., Awad, S.: Advanced driver assistance systems—Past, present and future. In: 2011 Seventh International Computer Engineering Conference (ICENCO’2011), 2011, pp. 72–82. https://doi.org/10.1109/ICENCO.2011.6153935 4. Estellés-Arolas, E., González-Ladrón-de-Guevara, F.: Towards an integrated crowdsourcing definition. J. Inf. Sci. 38(2), 189–200 (2012). https://doi.org/10.1177/0165551512437638 5. SAE: Taxonomy and Definitions for Terms Related to Driving Automation Systems for OnRoad Motor Vehicles (Surface Vehicle Recommended Practice: Superseding J3016 Jan 2014) SAE International. September (2016) 6. Mészéget˝o, T.: Haladó Járm˝u Környezetének Adaptív Maszkolása Optical-Flow Technikák Használatával. In: National Conference of Scientific Students’ Associations (2021) 7. Bogár, G.R.: Mélységbecsl˝o- És Szemantikus Szegmentáló Mély Neurális Hálók Fejlesztése És Alkalmazása. In: Conference of Scientific Students’ Associations (2021) 8. EIT Jumpstarter homepage: https://eitjumpstarter.eu. Last accessed 2022/04/29 9. EIT—Our Communities—Urban Mobility. https://eit.europa.eu/our-communities/eit-urbanmobility. Last accessed 2022/05/02 10. Burgers, J.H., Van Den Bosch, F.A., Volberda, H.W.: Why new business development projects fail: coping with the differences of technological versus market knowledge. Long Range Plan. 41(1), 55–73 (2008) 11. Dougherty, D.: A practice-centered model of organizational renewal through product innovation. Strateg. Manag. J. 13(S1), 77–92 (1992) 12. Clarysse, B., Wright, M., Van de Velde, E.: Entrepreneurial origin, technological knowledge, and the growth of spin-off companies. J. Manage. Stud. 48(6), 1420–1442 (2011) 13. Vohora, A., Lockett, A., Wright, M.: Critical junctures in the growth of university high-tech spin-out companies. In: The International Conference on Business & Technology Transfer, 2002.1(0), 12–17 (2002)

Repurposing Open Traffic Data for Estimating the Mobility Performance Špela Verovsek1(B) , Tadeja Zupanˇciˇc1 , Matevž Juvanˇciˇc1 , Lucija Ažman Momirski1 , Miha Janež2 , and Miha Moškon2 1 Faculty of Architecture, University of Ljubljana, Ljubljana, Slovenia

[email protected] 2 Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

Abstract. Traffic monitoring and advanced urban analytics play an important role in city planning and in attaining sustainable mobility through data-driven decisionmaking. With the advent of open-sourced data initiatives, new data-sharing technologies and software, also analytical methods, and data integration techniques are forced to subsequent levels. In this paper, we present two pilot studies of a recently conducted national project, joining vehicle counts and the travel times to present roadway traffic flows. We introduce a targeted selection of indicators and demonstrate the applicability of travel time metrics and the vehicle count measures, by combining different open datasets and transferable analyses to advance the interpretation strength of the data. Concretely, we apply regression methods based on the cosinor model, which allows us to analyze the rhythmic behavior of travel time and congestion trends. Furthermore, following the principles of data integration and data reusability we examine, using regression modeling and cross-validation, the possibilities to interchangeably use the governmental roadway counting database, vehicle counting by WeCount Ljubljana Telraam database, and the travel times records sourced by Google Direction API. The data analyzed indicate the possible interchangeability in selected scenarios and confirm the prospective to be used as complementary systems in the city monitoring and for urban sustainability assessment. Keywords: Traffic monitoring · Urban analytics · Travel time · Traffic counts

1 Introduction The research on the connections between the urban environment, its organization, and sustainability targets has increased substantially in recent years, linking the broad interests among policymakers, research initiatives, and neighborhood communities to assure more sustainable, high quality and livable cities [1–3]. The mobility aspect here plays an important role: infrastructure planning and renewal, planning of the amenities and services, conducting people’s habits, and routines, managing the public transport, or planning for higher walkability are important factors in this initiative. However, timely

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_45

Repurposing Open Traffic Data for Estimating the Mobility

543

reactions require a different approach in integrated planning practices [4]; it entails continuous and adaptive monitoring, as well as the assessment of the existing situations based on frequent and solid data infrastructures. Data-driven urbanistic practices are at the core of these endeavors. The rapid evolution in information and communication technologies has brought the potential of urban analytics and assessments onto the subsequent levels and consequently better prospects for well-informed decisions, also on the bases of the existing data, its repurposing, and reuse. The evolution towards a data-based society, where data from different domains and activities can be publicly accessed, reused, and integrated, is one of the strong European goals.1 The increasing availability of new forms of data stemming from smart sensors and our interactions with large socio-technical systems, such as social media platforms or mapping applications (e.g. Twitter, Instagram, Strava, etc.) and ubiquitous devices such as smartphones or activity trackers (e.g. smartwatches), allows us to use data science and artificial intelligence (AI) to better understand the cities [5, 6] peoples’ needs and expectations, as well as peoples’ behavior, movements, and to find relations between these and the urban form and organization [7]. Not only can urban planners tackle urbanistic challenges to create healthier and more sustainable environments [8], but they can nowadays examine socio-environmental systems on a finer level and with the enormous support of human-gathered or geo-location data evidence. Moreover, the analytical methods developed and applied in other domains have proven to offer possible synergies among the examined problems and the methods used. For example, today, more than ever, different types of rhythmic datasets are being captured, thus, the rhythmicity analysis2 has also become an important aspect in other fields of research [9], also by interpreting sustainability and efficiency trends, such as traffic flows and their oscillation, walkability patterns, energy consumption patterns, buildings’ performance variations. However, in combining heterogeneous data to solve the cross-cutting urban issues it is common to encounter syntactic and semantic discrepancies [10], mainly due to spatial, temporal, or thematic diversities, different techniques of capturing, and institutional dispersion of the studied datasets. In particular, national and municipality-related institutions create and operate datasets based on specific purposes which are designed for problems at hand and cover specific areas of interest (from mobility, accessibility, and commuting, noise and air pollution, to energy consumption, waste management, or sociodemographic data, indicators of peoples’ commitment, their habits, and patterns of behavior, etc.). This diversity in data subjects results also in sparse and incompatible datasets, discontinued or disconnected time series, and reciprocally incompatible data queries characterized by diverse data models and storage structures [11–13]. Despite the huge amounts of data generated, paradoxically, obtaining micro-urban, and fine-grained records that correspond to higher spatial resolutions and eloquence, still often bring significant data scarcity [14] and thus requires advanced techniques of data 1 Directive (EU) 2019/1024 of the European Parliament and of the Council on open data and

the re-use of public sector information: https://eur-lex.europa.eu/legal-content/EN/ALL/?uri= CELEX:32019L1024. 2 Detection and analysis of rhythmic patterns were initially introduced and developed, especially in the field of biology and medicine [9].

544

Š. Verovsek et al.

integration, repurposing, or introducing new ways of data interchangeability. For this reason, feasible and serviceable data sources are increasingly being extended also towards citizens- and the crowds- data harnessing. Here, the miscellaneous, location-stamped data, is collected by engaged citizens or end-users. Different citizen science activities also create new learning opportunities, increase scientific legibility among the public and allow civic participation in important decisions or to foster important sustainability goals [15]. With a continuously growing number of smart and wearable devices or other sensors, the feasibility and the richness of crowdsourced and citizens collected data is increasing [16]; however, it brings additional difficulties in the aggregation process with other data repositories, and the necessity to introduce solid indicators and validation techniques for further processing of data queries. In this paper, we present selected results of the two pilot studies within the national research3 to process and interchangeable use three different data sources (i.e., Google Directions API [17], governmental road vehicle monitoring, and the We Count Ljubljana Telraam database [18]), two basic indicators (i.e., traffic counts and travel times), and several different analyses (i.e., cosinor analyses, travel time reliability analyses, regression modeling) to assess the roadway traffic flows on certain strategical routes/trips in the city of Ljubljana, Slovenia. Even though services, such as Google Directions API [17], can be used to assess short-term traffic conditions, these cannot be directly applied to the assessment of traffic trends and travel time reliabilities, which presented the focus of our analyses. Our research entailed a specific focus on the network performance of individual motorized traffic on the six routes connecting the three neighborhoods with important points in the city by suggesting vehicle counts analyses and travel time metrics for the assessment. We extract and present, for the purpose of this paper, the data sources and the analyses applied as well as interpretation techniques used in two studies of this research to showcase the research, benefits attained, and difficulties encountered. Selected detailed results have been published in Janež et al. [16] and Verovšek et al. [19]. In this paper, we first briefly explain the three different sources of the roadway flow performance used in the study. We further outline the methodological contexts of the study; the geographical and time frames and describe the data variables and the necessary data pre-processing. We continue with the description of the types of analyses and measures applied in the study and the demonstration of results presented by different interpretation techniques, followed by a short discussion and conclusion.

2 Three Sources of Roadway Flow Performance Commonly, the network travel performance and the efficiency of road systems have been estimated by roadway flow rates directly rendered from the vehicle counts. The stateof-practice procedures on counting propose stationary on-road or over-road counting devices, among which the inductive loop counters (ILC) embedded in the pavements are by far the most widely used in conventional traffic control systems [20, 21]. We obtained historical traffic count data from the Slovenian Ministry of Infrastructure and the Municipality of Ljubljana (MOL). 3 Slovenian Research Agency [J5-1798, 2019–2022].

Repurposing Open Traffic Data for Estimating the Mobility

545

Technical advances of the last decade have attested a strong development in sensorbased solutions. Coupled with the new initiatives of citizens science, cost-efficient sensors have been developed and promoted for traffic counting. As part of the H2020 citizen science project WeCount Ljubljana,4 which has also been extended to WeCount the Littoral and WeCount Novo mesto in Slovenia, engaged citizens have placed WeCount Telraam sensors on the windows of their homes and offices to count traffic flows on the city’s streets. The project features an open-source WeCount Ljubljana Telraam platform, developed with support from the Belgian federal government’s Smart Mobility Belgium fund and the European Union’s Horizon 2020 research and innovation programme as part of the WeCount project [22], and collects data captured by a low-resolution sensor with a Raspberry Pi module that processes the sensor inputs and sends the count data to the central database [18]. As the count is based on visual input, the count can only be done during the day. The advances and presence of cellular networks have also evolved significantly in the last decade which has also enabled continuous tracking of vehicles and floating car data [23]. With that, the roadway performance can be measured by travel times on the selected routes and the traveling reliability referred to that. These two measures and their derivatives are by far more intuitive and bring a different understanding of the traffic situations and patterns. In our study, we used Google Directions API’s data [17] to render the trip durations on the selected routes in the city. The Directions API provides real-time traffic data and modeled estimations for travel times and directions between selected locations, to enable real-time vehicle routing. One of our endeavors was to repurpose and effectively couple these measures with the traffic counts to estimate flow performances in the long run, and eventually enable interchangeable use of both sources if needed. The possible prediction of trip durations (and reliability) from traffic counts represent an added value for the assessment. There have been several studies employed to improve the assessment strategies in this regard e.g. [24–26], however, the comparison of the research settings is problematic due to different input data targeting, different information outputs, or travel modes examined (e.g. bus public transport), different geographical contexts, etc.

3 Methodological Context 3.1 Location and Periods We demonstrated the proposed analysis in different residential districts of Ljubljana, the capital of Slovenia with approximately 280,000 residents. We selected routes connecting different strategic destination points of the city. The routes were based on car-based trips (Fig. 1). The city center and a widely popular shopping center located on the city boundary were selected as the main strategic points. A visual representation of the established routes is available on the interactive map (link). For each route, travel time rates and traffic counts were evaluated in two different periods. In the first study, the observation period covered 4 weeks in October 2020 with the additional periodand location- extensions for traffic counts examination in our second study. We applied 4 https://we-count.net/networks/ljubljana.

546

Š. Verovsek et al.

available count data of WeCount Ljubljana Telraam counters in 2021 and coupled it with the ILC counters within equivalent periods and fitting locations.

Fig. 1. An example of a route setting visualized by the Google Directions API interface [17]. The route connects the selected neighborhoods on the outskirts of the city with one of the two strategical destination points, i.e., the city center.

3.2 Data Variables and Pre-processing Three types of aforementioned traffic data were engaged: (i) travel times data obtained on selected routes using Google Directions API; (ii) vehicle count data captured from on-road traffic sensors (magnetic loops) provided by governmental and municipal traffic services, and (iii) WeCount Ljubljana Telraam vehicle count data publicly available under the CC-BY-NC license.5 Travel times were normalized to obtain the variables of average pace (in seconds per meter) and speed (in meters per second) for each route at a given time. We filtered the data to remove the outliers, namely, we removed the 5 Data ownership: All intellectual property rights in the traffic statistics collected by Telraam

and the data.bases it contains belong to the Telraam Alliance, in this case to the WeCount Ljubljana team from University.of Ljubljana Faculty of Architecture.The WeCount Ljubljana Telraam sensors data are openly available under the CC-BY-NC license (https://creativec ommons.org/licenses/by-nc/4.0/legalcode; https://telraam.zendesk.com/hc/en-us/articles/360 056754532-Telraam-Data-License). The creator(s) of the Licensed Material is: WeCount Ljubljana Telraam. A copyright notice is: ©WeCount Ljubljana Telraam. A notice that refers to this Public License is: All intellectual property rights from the WeCount Ljubljana Telraam belong to the University of Ljubljana Faculty of Architecture, WeCount Ljubljana team. Funding: This work was part of the WeCount: Citizens Observing UrbaN Transport project. The project received financial support from the EU Research and Innovation Framework Programme Horizon 2020 under Agreement number 87274.

Repurposing Open Traffic Data for Estimating the Mobility

547

measurements deviating more than three standard deviations from the mean. Traffic data were augmented with weather data (iv), which we obtained using the Visual Crossing Weather API [27]. Weather data were classified into two categories, namely normal and adverse weather conditions as described in [19]. All four datasets were aligned at a given timepoint to further assess their correlations and mutual impacts. Another factor that was included in the analyses was the type of day. Days were classified into two categories, namely workdays and weekends as these reflect different traffic patterns as already described in the previous studies [28, 29].

4 Interpretation of Data 4.1 Types of Analyses and Measures In the two pilot studies of the national research project, we used several different analytical approaches to interpret the data series described in the following section. Cosinor analyses. Travel time data were examined using the rhythmicity analyses by the set of cosinor regression models [9, 30]. We presumed a 24-h main period and assessed the number of components in a model for each dataset using the extra sumof-squares F-test, as described in [9]. We employed the selected model to identify the locations, heights, and number of peaks and troughs repeating with a 24-h rhythm. Moreover, we used the constructed models to compare different scenarios on the same route, i.e., differences between workdays and weekends and differences between normal and adverse weather conditions. Travel time index (TTI) and the planning time index (PTI). In the subsequent step of the travel times analysis, we introduced two existing measures of travel time reliability, i.e. the travel time index and the planning time index, both calculated on the hourly terms. TTI is defined as the observed average travel time divided by the constant of the free-flow travel time rate on the observed route [31], whereas the PTI in its formulation uses 95th-percentile travel time to represent the near-worst case travel time [32, 33], thus, in general, is more sensitive to rare events, particularly to accidents. The advantage of the TTI and PTI here is the possibility to directly compare them on the equivalent numeric scales. Since both measures are based on the free-flow6 factor, they enable comparable assessment in the case of different road types. Regression of ILC data. We observed eight road segments with the current matching ILC and WeCount Ljubljana Telraam counters (the overlapping location, equivalent direction of counting, and matching timestamp), and estimate their interchangeability based on the regression analyses. We tested different regression models using different sets of features to find the best-performing model for each scenario (the quality of predictions was assessed using the R2 – coefficient of determination metric). The regression models were trained on 70% of the data and tested on the remaining 30% of the data. Grid search cross-validation was applied to identify the optimal values of hyperparameters for each of the models. The whole framework was implemented in Python relying on the scikit-learn library [35]. 6 The free-flow travel time rate in our case was defined, as suggested by [34], by the 85th

-percentile travel time during overnight hours (10 p.m. to 5 a.m.).

548

Š. Verovsek et al.

Predicting travel times using count data. We analyzed the possibilities to predict the travel times on a route using the count data. Firstly, we identified the counters located on a selected route. Secondly, we assessed the Pearson and Spearman correlation coefficients between the travel times and count data. Furthermore, we employed a simple linear and exponential model to predict the travel time data from vehicle counts for a route. The prediction quality was again assessed using the R2 metric obtained with 10-fold cross validation. 4.2 Representation of the Results The results of the two pilot studies of the national research overviewed here were presented by different interpretation techniques. The actual detailed results are not in the scope of this paper; however, we present several selected aspects of the results. Distributions of travel times. Analysis of distributions of travel time values is vital since different distributions might require different steps in further quantitative analyses. We used frequency distributions visualization with different boxplot-type and violin-plot-type graphs. Violin-plots enable the assessment of the multimodality in the distributions and allow for estimating the basic differences between the routes concerning the median, quartiles and outliers, distribution width, and skewness. To normalize the travel time values with regard to the length of a route, we used space-mean speed (in meters per second) or the average travel time rate – pace, which present the exact inverse of each other. In the next step, we rescaled the obtained pace measurements to 24 h and plotted their distributions in dependence on the day of the week (i.e., workday or weekend) and the weather conditions (i.e., normal or adverse) – Fig. 2. The assessed distributions of travel times not only guide further analyses but also present a baseline for defining travel time reliability metrics. From a measurement perspective, reliability is thus quantified, for a given trip over a significant timespan. It may be viewed from different perspectives, which include the focus on the travel time distributions within the course of a day, from day-to-day, or even within a month or a season of the year [31]. A balanced summary of travel time measures and reliability performance comes from [36], recommending a specific, e.g. 95th, percentile travel time, which also corresponds to the buffer index. Reliability metrics also include on-time measures such as the percentage share of trips completed within a travel time threshold or failure measures like the percent of trips that exceed a travel time threshold [37]. Many metrics are expressed relative to the free-flow travel time, i.e., travel time in low traffic-flow conditions, which is becoming the benchmark for travel time and reliability analysis. Moreover, while the distributions show actual travel times, their values are commonly normalized to obtain travel rates that enable comparative analyses across routes of different lengths (Fig. 3). Rhythmicity trends. We analyzed the rhythmicity trends, i.e., trends repeating with a predefined period (in our case this was set to 24 h), of travel times more precisely by applying cosinor models with different complexity to the data obtained on the workdays and weekends, and to normal and adverse weather conditions. Figure 4 summarizes the results of the cosinor model obtained on Route 4 on different types of a day (i.e., workdays or weekends). Measured travel times are plotted against the fitted curves to determine the level of consistency between a model and the underlying data. We assessed

Repurposing Open Traffic Data for Estimating the Mobility

549

Fig. 2. An example of the preliminary analyses: pace distributions on all six routes divided by workday/weekday (right boxplot) and normal//adverse weather (left plot). Figure adapted from [19]. All the segments share similar distributions except segment 5, which presents a highway road segment. It is evident that the type of the day (workday/weekday, right plot) has a significantly stronger effect on traffic than the weather conditions (left plot).

the overall significance values of each fit using the F-test. Quantile-quantile (Q-Q) plots were also analyzed to assess the goodness of fit of each model [34].

Fig. 3. An example of the travel time index and planning time index during; the 24-h course for the Route 4; a comparison of normal and adverse weather situations. The shaded areas represent the corresponding 95% confidence intervals. Two peaks are expressed during the day, both in adverse and normal weather situation, however, the travel time variability is evidently higher in the case of adverse weather.

TTI and PTI trends. TTI and PTI values can be employed to systematically assess the travel time trends, and to compare the pervasiveness of “normal” delays with the “exceptional delays” (Fig. 3). Typical delays together with the normal trip durations can be assessed using the TTI. On the other hand, TTIs can be extended with the surplus delays using the PTIs. In this context, the surplus delays present the unexpected delays caused by non-recurrent events. Pair-plotting: travel times and traffic counts. We compared the trip durations with the vehicle counts per hour for the equivalent period utilizing correlation analyses. Figure 5 illustrates the correlations between the average pace [s/m] and the network flow

550

Š. Verovsek et al.

Fig. 4. An example of the travel time rate (pace) distributions on Route 4; a comparison between workdays (above) and weekends (below) with the residual Q-Q visual check plots. While the cosinor model can satisfactorily describe the observed data during the weekends, its goodness-offit is lower on the workdays. The obtained results indicate that the observed data can be described with the selected cosinor models.

[vehicles/hour] captured by stationary ILC counters (0174-1, 0855-1, 1010-1) on Route 5. Pair-plotting: ILC counts and WeCount Ljubljana Telraam counts. We matched and compared the ILCs with WeCount Ljubljana Telraam counters on the observed road. Since WeCount Ljubljana Telraam counters might face several issues (e.g., due to low light conditions or obstruction), we employed different machine learning regression models, which are more robust to data anomalies and do not presume specific distributions of input data. Regressing ILC data. We further estimated the interchangeability of the ILC counters with WeCount Ljubljana Telraam counters in different scenarios by regression analyses. Figure 6 presents the results of the regression analysis using the top five performing models on a selected road in two different directions. The majority of the presented models reflected a relatively strong predictive capability, which indicated that pre-trained regression models could be used to induce the count data of a counting source with an alternative counting infrastructure.

Repurposing Open Traffic Data for Estimating the Mobility

551

Fig. 5. An example of the correlations between the average pace [s/m] and the network flow [vehicles/hour] on Route 5. r P and r S denote Pearson’s and Spearman’s rank correlation coefficient, respectively. Figure adapted from [19]. The obtained results indicate that the average pace if highly correlated with all of the observed counters (Spearman’s rank correlation values equal to 0.88).

5 Discussion and Conclusion Our results indicate that the observed datasets can be used interchangeably in certain cases. Count data can be obtained using different types of counting infrastructure. Afterwards, we can employ the count data to assess the travel times on a selected route. Even though the travel times can be expressed with traffic counts using relatively simple regression models, the substitution of one type of count data with another requires precise tuning of regression models, which limits its generalization. For example, our analyses were performed in selected parts of a specific city in a specific season. To transfer the obtained models to different cities, neighborhoods or even the same road segments in different conditions, the models would require additional training using the additional input data. This presents the main limitation of the presented approach, which

552

Š. Verovsek et al.

Fig. 6. The predictive power of top five regression models on a road segment (Ižanska cesta) in different directions. R2 (test) presents the coefficient of determination evaluated on the testing. Figure adapted from [16]. Our results indicate that the ILC data can be accurately predicted using the WeCount Ljubljana Telraam counters with the majority of the selected models.

we aim to address in our future work. We could partially address this problem with the application of unsupervised machine learning approaches, such as clustering of sensors and their locations. The main benefit of such (unsupervised) approach is that it reduces the requirement for prior training of the models. However, its feasibility needs to be verified on the proposed testbed or beyond. One of the crucial questions that arise here is the actual feasibility of the diverse citizens’ science databases (present in different geographical environments, capturing data of different scopes and with diverse dispersion degrees) to be used in scientific research. Dense geographical resolution and solid distribution of citizens’ sensors can enable more accurate results with better prediction capabilities. Namely, the broader use of a large number of less reliable sensors to improve the accuracy of obtained data has been employed in different engineering disciplines (see [38]). An example vividly illustrating the concept of increasing the accuracy by redundancy was reported by Weis et al. [39] demonstrated this concept by employing several imprecise watches to obtain a highly accurate clock. Using an adequate number of less reliable Telraam sensors might be able to provide more accurate results in relation to the appurtenant ILC counter, as possible cut-outs could be replaced by redundant sensors in direct vicinity. In our pilot case, we were able to obtain relatively accurate predictions of count data on Ižanska and Dunajska road by employing a single or two Telraam counters. More than 1700 Telraam sensors are in operation today, mostly in Europe. The highest density of sensors is in Belgium and the Netherlands. Smaller concentrations are also found around Dublin, Cardiff and Slovenia. Telraam is wholly owned and maintained by Rear Window BV (BE0762.549.266), a spin-off initiative of TML, Mobiel 21 and Waanz.in [18]. During the WeCount project, project partners from Leuven, Madrid/Barcelona, Cardiff, Dublin and Ljubljana have upgraded the already established

Repurposing Open Traffic Data for Estimating the Mobility

553

Telraam sensor and platform with a focus on usability and the aim to make the technology friendlier to reach a wider range of Telraam users. A variety of interactions between the project teams and local citizens supported this process with a range of engagement methods, guidelines and recommendations to identify and promote local communities. Given the different cultural contexts and approaches to recruiting and engaging citizens, as well as the different urban structures in the cities, all of which influence the effectiveness of the sensor distributions, remain the most important and demanding aspect of such a citizens science approach. Acknowledgments. We would like to thank the Slovenian Ministry of Infrastructure, and the Municipality of Ljubljana for granting access to the inductive loop counters data. We would also like to acknowledge the WeCount Ljubljana Telraam project team (https://www.we-count.net, specifically Tomaž Berˇciˇc) for enabling Open Access to the collected data. This work was supported by the Slovenian Research Agency [J5-1798, 2019–2022; P5-0068, 2017–2023; P2-0359, 2013–2023] and H2020 WeCount-project, under grant agreement No. 872743. The funding body had no role in the design of the study and collection, analysis, and interpretation of data nor in writing the manuscript.

References 1. Batty, M.: Smart cities, big data. Environ. Plan. B Plan. Des. 39(2), 191–193 (2012) 2. Garau, C., Pavan, V.M.: Evaluating urban quality: indicators and assessment tools for smart sustainable cities. Sustainability 10(575), 1–18 (2018) 3. Zheng, H.W., et al.: Neighborhood sustainability in urban renewal: an assessment framework. Environ. Plan. B Urban Anal. City Sci. 44(5), 903–924 (2017) 4. Erdeli´c, T., et al.: Estimating congestion zones and travel time indexes based on the floating car data. Comput. Environ. Urban Syst. 87(1), 1–22 (2021) 5. Bailey, D.E., et al.: We are all theorists of technology now: a relational perspective on emerging technology and organizing. Organ. Sci. 33(1), 1–18 (2022) 6. Dodge, S.: A data science framework for movement. Geogr. Anal. 53(1), 92–112 (2021) 7. Tigran, H., Littke, H., Elahe, K.: Urban form and human behavior in context of livable cities and their public realms. Sch. J. Psychol. Behav. Sci. 3(4), 325–339 (2020) 8. Barton, H., Grant, M.: Urban planning for healthy cities: a review of the progress of the European healthy cities programme. J. Urban Health. 90(Suppl 1), 129 (2013) 9. Moškon, M.: CosinorPy: a python package for cosinor-based rhythmometry. BMC Bioinform. 21(1), 485–498 (2020) 10. Psyllidis, A., et al.: A platform for urban analytics and semantic data integration in city planning. Commun. Comput. Inf. Sci. 527(1), 21–36 (2015) 11. Costin, A., Eastman, C.: Need for interoperability to enable seamless information exchanges in smart and sustainable urban systems. J. Comput. Civ. Eng. 33(3), 1–12 (2019) 12. Lützkendorf, T., Balouktsi, M.: Assessing a sustainable urban development: typology of indicators and sources of information. Procedia Environ. Sci. 38(1), 546–553 (2017) 13. Massaro, E., et al.: Ontology-based integration of urban sustainability indicators. In: Binder, C., et al. (eds.) Sustainability Assessment of Urban Systems. Cambridge University Press (2020) 14. Verovsek, S., et al.: Widening the scope and scale of sustainability assessments in built environments: from passive house to active neighbourhood. Acad. J. Interdiscip. Stud. 7(1), 129–138 (2018)

554

Š. Verovsek et al.

15. Turrini, T., et al.: The threefold potential of environmental citizen science—Generating knowledge, creating learning opportunities and enabling civic participation. Biol. Conserv. 225(1), 176–186 (2018) 16. Janež, M., et al.: Citizen science for traffic monitoring: investigating the potentials for complementing traffic counters with crowdsourced data. Sustainability 14(622), 1–18 (2022) 17. Google Inc.: The Directions API Overview | Google Developers. https://developers.google. com/maps/documentation/directions/overview. Last accessed 2021/04/18 18. Telraam: https://telraam.net/; https://telraam-api.net/. Last accessed 2022/03/25 19. Verovšek, Š, et al.: An integrative approach to neighbourhood sustainability assessments using publicly available traffic data. Comput. Environ. Urban Syst. 95(7), 101805 (2022) 20. Jain, N.K., et al.: A review on traffic monitoring system techniques. Adv. Intell. Syst. Comput. 742(1), 569–577 (2019) 21. Tasgaonkar, P.P., Garg, R.D., Garg, P.K.: Vehicle detection and traffic estimation with sensors technologies for intelligent transportation systems. Sens. Imaging 21(1), 1–28 (2020). https:// doi.org/10.1007/s11220-020-00295-2 22. WeCount Project: https://www.we-count.net/. Last accessed 2022/03/24 23. Astarita, V., et al.: A review of traffic signal control methods and experiments based on floating car data (FCD). Procedia Comput. Sci. 175(1), 745–751 (2020) 24. Abdi, A., Amrit, C.: A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities. PeerJ Comput. Sci. 7(1), e689 (2021) 25. Martínez-Díaz, M., Soriguera, F.: Short-term prediction of freeway travel times by fusing input-output vehicle counts and GPS tracking data. Transp. Lett. 13(3), 193–200 (2020) 26. Zhu, G., et al.: A kind of urban road travel time forecasting model with loop detectors. Int. J. Distrib. Sens. Networks. 12(2), 9043835 (2016) 27. Visual Crossing Inc.: Free Weather API | Visual Crossing. https://www.visualcrossing.com/ weather-api. Last accessed 2021/04/18 28. Silvano, A.P., Bang, K.L.: Impact of speed limits and road characteristics on free-flow speed in urban areas. J. Transp. Eng. 142(2), 1–17 (2016) 29. Stogios, Y.C., et al.: Incorporating Reliability Performance Measures into Operations and Planning Modeling Tools. Transportation Research Board. The National Academies Press (2014) 30. Cornelissen, G.: Cosinor-based rhythmometry. Theor. Biol. Med. Model. 11(1), 1–16 (2014) 31. Büchel, B., Corman, F.: Review on statistical modeling of travel time variability for road-based public transport. Front. Built Environ. 6(1), 1–14 (2020) 32. Kittelson, W., Vandehey, M.: Incorporating Travel Time Reliability into the Highway Capacity Manual. Transportation Research Board. National Academies Press (2014) 33. Pu, W.: Analytic relationships between travel time reliability measures. Transp. Res. Rec. J. Transp. Res. Board. 2254(1), 122–130 (2011) 34. Chen, Z., Fan, W.: Data analytics approach for travel time reliability pattern analysis and prediction. J. Mod. Transp. 27(4), 250–265 (2019). https://doi.org/10.1007/s40534-019-001 95-6 35. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(1), 2825–2830 (2012) 36. Vandervalk, A., et al.: Incorporating Reliability Performance Measures into the Transportation Planning and Programming Processes: Technical Reference. National Academies Press, Washington DC (2014) 37. OECD: Managing Urban Traffic Congestion. Organisation for Economic Cooperation and Development (OECD), Paris (2007) 38. Jafari, M.: Optimal redundant sensor configuration for accuracy increasing in space inertial navigation system. Aerosp. Sci. Technol. 47(1), 467–472 (2015)

Repurposing Open Traffic Data for Estimating the Mobility

555

39. Weiss, M.A., et al.: Smart clock: a new time. In: Conference Record IEEE Instrumentation and Measurement Technology Conference, pp. 38–41. Institute of Electrical and Electronics Engineers (IEEE) (2003)

Evaluating the Quality of Public Spaces Using Crowdsourcing Data: The Case of the Metropolitan Area of Thessaloniki Alexandros Sdoukopoulos1(B) , Nikolaos Gavanas2 , and Magda Pitsiava-Latinopoulou1 1 Department of Civil Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece

[email protected] 2 Department of Urban Planning and Regional Development, University of Thessaly, Volos,

Greece

Abstract. One of the critical differences that distinguish the traditional from the sustainable transport planning approach in urban areas is how roads and public spaces are considered and managed. Traditional planning treats roads as linear elements (links) that exclusively serve motor vehicles’ movement and thus are cut off from the rest of the public space. In contrast, the sustainable approach recognises the integration of mobility in the urban environment and therefore prioritises the existence of quality urban infrastructure and attractive public spaces, promoting thus sustainable urban mobility. In this framework, the current paper introduces a reliable, simple to implement, and low-cost methodological approach for assessing the perceived quality of public spaces such as parks, squares, and pedestrian areas, using crowdsourcing data. More specifically, the presented approach utilises data from Google Places API and employs Geographic Information Systems and spreadsheets to calculate an overall score for public spaces within a given spatial unit, such as a municipality. Given that the transition from theory to practice underlines issues of scalability, usability, and credibility, the developed methodological approach was applied in the seven municipalities that constitute the Metropolitan Area of Thessaloniki, namely the Municipalities of Ampelokipi-Menemeni, Kalamaria, Kordelio-Evosmos, Neapoli-Sykies, Pavlos Melas, Pylaia-Chortiatis, and Thessaloniki. The pilot implementation highlighted interesting findings for the above-mentioned areas and in addition, it validated the applicability and value of the developed methodological approach which thus can be used as a reliable tool for assessing the quality of public spaces. Keywords: Perceived quality · Public spaces · Crowdsourcing data · Google Places API · Metropolitan Area of Thessaloniki

1 Introduction Public spaces constitute a vital component of every liveable, thriving, great, and sustainable city [1]. They comprise areas in the built environment with multi-functional roles © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_46

Evaluating the Quality of Public Spaces Using Crowdsourcing Data

557

that improve the quality of life, give identity to a city, enrich the urban landscape, create a sense of belonging, shape the cultural “character”, and encourage economic prosperity, environmental regeneration, and social inclusion [1–4]. More explicitly, public spaces support the local economy of urban areas [1, 2]. They are significant generators of income, investment, and wealth, while they deliver considerable benefits to entrepreneurship and businesses [1]. Also, public spaces act as promoters of environmental sustainability, helping cities alleviate the negative externalities. Accordingly, public spaces are considered an essential element of climate change mitigation strategies and enablers of sustainable development goals [4]. Regarding their social role, public spaces foster equity and inclusion by serving all citizens and prioritising the needs of vulnerable and disadvantaged groups [1, 2, 4]. Furthermore, public spaces play a pivotal role in improving well-being and citizens’ mental and physical health, as the relaxing atmosphere they provide encourages people to lower stress levels, walk, play, communicate, and enjoy the environment [1, 2, 5]. Additionally, vibrant and busy public spaces during all times of day increase both actual and perceived levels of security [1, 2]. Moreover, public spaces constitute an ideal field for strengthening citizens’ active participation and involvement in the planning process [1]. At the same time, they contribute to the shape of urban culture and encourage civilised behaviour and attitude [1, 2]. Finally, public spaces enhance the public realm, promote common goals, and create a sense of citizenship [1, 2, 5]. In this context, the presence of quality public spaces could also motivate a paradigm shift from private cars toward more sustainable mobility patterns [1, 2, 6]. Unlike the traditional transport planning approach that neglected the relationship between mobility and public spaces and treated roads narrowly as linear elements (links) exclusively serving motor vehicles’ movement, the sustainable urban mobility recognises the integration of mobility in the urban environment and therefore prioritises the existence of quality urban infrastructure and attractive public spaces [7–11]. Taking into consideration the bidirectional interaction between public spaces and sustainable urban mobility, the current paper introduces a reliable, scalable, simple, and low-cost methodological approach for assessing the perceived quality of public spaces such as parks, squares, and pedestrian areas. Instead of relying on typical surveys and other traditional yet costly and time-consuming data collection methods, the developed methodological approach utilises crowdsourcing data. The methodological approach proposed in this paper constitutes a valuable tool both for urban designers and transport planners, that could also be easily implemented in the framework of the Sustainable Urban Mobility Plans (SUMPs). The remainder of this paper is organised as follows. Section 2 focuses and elaborates on the concept of crowdsourcing. Section 3 describes the developed methodological approach. Section 4 presents the results of the pilot implementation in the seven municipalities that constitute the Metropolitan Area of Thessaloniki. Finally, Sect. 5 discusses the strengths and the limitations of the proposed methodological approach, while it also summarises the major findings and recommendations for future studies.

558

A. Sdoukopoulos et al.

2 Crowdsourcing Although crowdsourcing is not a novel concept as it has existed in the form of “wisdom of the crowds” for hundreds of years, it is only in the recent digital era that the advancement in smartphone technology and Web 2.0 reshaped it into a valuable and with great potential data collection method [12]. Crowdsourcing is a participatory bottom-up online method of building a dataset with the voluntary help of communities or large groups of individuals of varying characteristics, knowledge, and heterogeneity [12–16]. Through crowdsourcing, authorities and researchers gain access to collective intelligence, and obtain valuable and plentiful information, ideas, experiences, attitudes, and knowledge [14–17]. Compared to the traditional data collection methods, crowdsourcing is characterised by substantially lower time and costs, massive sample sizes, improved accuracy, increased/disaggregated research scale, and finally, the opportunity for applying enhanced data analysis tools [12, 13]. Considering its potential prospects as well as the benefits mentioned above, crowdsourcing is gaining attention and popularity in research at an astonishing rate. As indicated in Fig. 1, which exploits data from Scopus, the use of crowdsourcing in research papers has grown significantly during the last decade, with the number of relevant papers (1747) in 2020 being over ten times higher than the respective (136) in 2010. Based on the same Scopus dataset, Fig. 2 illustrates the distribution of research papers mentioning the term “crowdsourcing” in various scientific fields.

Fig. 1. Radial bar chart showing the annual number of research papers that include the term “crowdsourcing” in their keywords based on the Scopus database.

Even though crowdsourcing is applied to nearly all scientific fields, as it can be concluded from the figure above, many authors believe it is still underutilised [18–20]. In this context, the research papers employing crowdsourcing to measure or assess a parameter or an element of the built environment, such as the public spaces, are rather limited. Shi et al. [21] introduced a methodological approach to exploit tourism crowding in public spaces based on the analysis of a large crowdsourcing dataset from Weibo that included

Evaluating the Quality of Public Spaces Using Crowdsourcing Data

559

Fig. 2. Sankey diagram presenting the distribution of research papers mentioning the term “crowdsourcing” in various scientific fields based on the Scopus database.

446,273 check-ins in the city of Shanghai [21]. Their study revealed a number of interesting findings, such as the seasonality, origin, and spatial distribution of crowdedness; the relationship between crowdedness and popularity; and the identification of visitors’ travel patterns in Shanghai [21]. Spyratos and Stathakis [22] developed two new indicators using the Foursquare place database to estimate citizens’ satisfaction regarding urban facilities [22]. The key concept of their work was that the higher the number of places of a facility or service is on social media, the higher citizens’ satisfaction regarding this type of facility or service will be [22]. In this context, the authors evaluated the validity of their assumption using the Eurobarometer survey data as a reference [22]. Based on their findings, statistically significant relationships exist between the percentage of satisfied citizens and the categories of sports facilities, cultural facilities, streets, and buildings [22]. Bakogiannis et al. [23] introduced a methodological approach utilising crowdsourcing data to illustrate residents’ and visitors’ mobility behaviour in the urban environment and implemented it in two medium-sized Greek cities [23]. Song and Zhang [24] collected and analysed 3314 Instagram posts referring to the period 2015–2017, aiming to answer “how is the Seattle Freeway Park used as a public space and what are the users’ emotional ties to this specific built environment” [24]. They concluded that rough materials such as concrete should be used reasonably as, on the one hand, they “invoke a strong sense of discovery”, while on the other, they are correlated to less happy visitors’ moods [24]. Bai and Jiao [25] analysed 4100 e-scooters parking violation reports in Austin, crowdsourced from the local non-emergency service request system, and found out that sidewalks along with other public space intrusions were the two most common violations [25]. Moreover, based on their findings, they designed a crowdsourcing-based shared responsibility framework for the main actors involved in shared micro-mobility management [25]. Finally, Mohamed and Stanek [26] followed a methodological approach exploiting crowdsourcing data and space syntax analysis to examine the empirical relationship between the configuration of the street network and the harassment in the Central Business District of Cairo [26]. According to their findings, harassment incidents are positively correlated with highly accessible street segments featuring significant volumes of foot traffic [26]. In this framework, the present paper focuses on the perceived quality of public spaces and introduces a crowdsourcing based methodological approach that relies on users’ ratings on Google Places (Maps) database.

560

A. Sdoukopoulos et al.

3 Methodological Approach The methodological approach presented in this paper utilises crowdsourcing data from Google Places API and employs Geographic Information Systems (GIS) and spreadsheets to capture the perceived quality of the public spaces located within the boundaries of a given spatial unit and calculate an overall score. As illustrated in Fig. 3, the methodological approach includes three main steps consisting of multiple discrete actions. More specifically, in the first step, crowdsourcing data referring to public spaces, i.e., location (coordinates), name, id, number of ratings, and mean rating, is collected through HTTP requests in Google Places API. As a part of the Google Maps Platform, Google Places belongs to the broad category of Location-Based Social Networks (LBSNs), i.e., “the intersection of social media and Location-Based Services”, in which users post their opinions, perspectives and presence in various urban spaces [21, 27]. More explicitly, Google Maps users have the ability to evaluate and rate a location, a business or a point of interest on a scale from one star (1, “hated it”) to five (5, “loved it”), while they could also add a photo or post a comment [28]. Subsequently, Google calculates the mean value of all users’ ratings and adds the result to the attribute table of each location [29]. Given the great variability of spaces included in the Google Places database, resulting thus in nearly 100 different types, the HTTP requests are highly recommended to focus on the broad type entitled “park” that encloses all the relevant to the scope of this research public spaces such as parks, squares, pedestrian areas etc. Moreover, as the HTTP requests also involve the selection of the coordinates of a starting/reference point and a search radius, the iteration of the whole process, including multiple requests with different inputs, should be considered to cover the examined area adequately. In the second step, the multiple returned .JSON files containing the public spacesrelated data are initially inserted in a single spreadsheet to perform a first data clean up. The scope of this process is to remove any duplicates as well as the irrelevant to the examined public spaces locations. Next, using the included in the edited spreadsheet coordinates, the public spaces are easily mapped in a GIS environment such as the ESRI ArcGIS Desktop or Q-GIS, aiming to exclude those located outside the boundaries of the examined area. Finally, in the third step, tables and figures illustrating the perceived quality of every public space are generated, while an overall score summarising the whole picture in the examined area is also calculated. This score is calculated as shown in Eq. 1 below. 1 Rti N N

PQPSsc =

(1)

i=1

where: PQPS sc is the overall perceived quality of the public spaces in the examined area, N is the number of public spaces taken into consideration, and Rt i is the mean rating of each public space (i) in the Google Places database. As the mean rating of every public space ranges between 1.0 and 5.0 stars, it is clear that the final value of the PQPSsc falls into the same interval. However, it should be highlighted here that the extreme value of 5.0 stars is a rather theoretical score that

Evaluating the Quality of Public Spaces Using Crowdsourcing Data

561

Fig. 3. Flow chart of the proposed methodological approach.

could hardly be achieved even under the best circumstances. According to an indicative study that focused on 100 emblematic public spaces with thousands of ratings on 10 European capitals featuring high quality of life standards, none of the examined cases managed to get the ultimate score of 5.0 stars [30]. Moreover, as many researchers in other fields such as business and e-commerce point out, 4.5 stars seem the most preferable, commonly adopted, realistic, and ideal goal [31–33]. Hence, based on the aforementioned, in the framework of this research, the PQPSsc values above 4.5 stars are considered an entirely satisfactory, or in other words, a sustainable performance. On the contrary, given the limited number of locations in the Google Places database having a mean rating between 1.0 and 2.0 stars, as well as that Google describes the score of

562

A. Sdoukopoulos et al.

2.0 stars with the phrase “disliked it” [28, 34], PQPSsc values below 2.0 stars represent an unsatisfactory, unsustainable performance.

4 Pilot Implementation: The Metropolitan Area of Thessaloniki, Greece Given that the transition from theory to practice highlights scalability, usability, applicability, and credibility issues, the described methodological approach was applied as a pilot in the municipalities that comprise the Metropolitan Area of Thessaloniki. The Metropolitan Area of Thessaloniki is the second-largest in Greece in terms of area, population, and GDP [35–37]. At the same time, it constitutes the capital of the region of Central Macedonia and an important commercial centre for the Balkans [37]. The Metropolitan Area of Thessaloniki consists of seven municipalities, namely Ampelokipi-Menemeni, Kalamaria, Kordelio-Evosmos, Neapoli-Sykies, Pavlos Melas, Pilea-Chortiatis, and Thessaloniki. It should be noted here that as the administrative boundaries of Pilea-Chortiatis extend far beyond the functional boundaries of the Metropolitan Area, only the urban part of this municipality consisting of the municipal units of Pilea and Panorama was considered. Based on the proposed methodological approach, 254 public spaces were identified within the boundaries of the seven municipalities, and the respective data were collected in November 2020. As indicated in Table 1 which presents basic metrics for the examined cases, almost half of the considered public spaces are located in the Municipality of Thessaloniki, while a relatively lower number refers to each of the other six municipalities. Although, at first glance, the availability of public spaces based on the collected data seems to be quite different between the examined municipalities, the consideration of municipality size in terms of the population reveals a more or less similar picture. Table 1. Basic metrics. Municipality

Ampelokipi-Menemeni

Population

Number of identified public spaces

Number of public spaces per 105 inhabitants

Users’ ratings Total number

Max

52,127

12

23

523

219

325,182

110

34

112,337

28,496

91,518

30

33

2442

917

101,753

31

30

1890

715

Neapoli-Sykies

84,741

22

26

730

175

Pavlos Melas

99,245

32

32

3681

2221

Pilea-Chortiatis

52,069

17

33

2239

1154

Thessaloniki Kalamaria Kordelio-Evosmos

Evaluating the Quality of Public Spaces Using Crowdsourcing Data

563

As far as user ratings are concerned, the vast majority refer to the public spaces that are located within the boundaries of the Municipality of Thessaloniki. This finding indicates a considerable higher interest and popularity as well as the presence of public spaces that act as significant tourist attraction poles (e.g., “Aristotelous Square” and the “Square of the White Tower” featuring 28,496 and 26,539 users’ ratings, respectively). The popularity of the examined public spaces in terms of number of users’ ratings is illustrated in Fig. 4. Regarding the perceived quality, the mean rating of each of the examined public spaces is presented in the scatter map in Fig. 5. Furthermore, a violin plot describing the distribution of these ratings in each of the examined municipalities is presented in Fig. 6. Finally, the calculated PQPSsc scores for the seven municipalities are given in Fig. 7. Based on the figures above, the municipalities featuring the higher quality public spaces are located on the east side of the Metropolitan Area, i.e., Pilea-Chortiatis, Thessaloniki, and Kalamaria. This finding is consistent with the higher land values characterising these three areas, while it is also indicative of the inequalities that exist between the two sides of the Metropolitan Area. On the other hand, the other four municipalities located on the northwest side and especially the Municipalities of Pavlos Melas and Kordelio-Evosmos, although their performance is considered generally satisfactory, need to emphasise on further improving the quality of the public space. This finding could initiate the adoption of a new strategy in these areas with the main scope to improve the liveability of the public spaces and enhance citizens’ quality of life. At the same time, it should also be considered when developing the local visions in the context of their impending SUMPs.

5 Conclusions The current paper introduced a methodological approach for evaluating the perceived quality of public spaces such as parks, squares, and pedestrian areas and subsequently presented its pilot implementation to the seven municipalities that comprise the Metropolitan Area of Thessaloniki. The main feature of this methodological approach is that it utilises crowdsourcing data gathered through the Google Places API instead of relying on typical questionnaire surveys and other traditional data collection methods. The presented approach constitutes a valuable tool both for urban designers and transport planners as, in a simple, easy, and reliable manner, gives the ability to capture not only through a single score the perceived overall quality of all public spaces within the boundaries of a given spatial unit (aggregated level) but also the individual assessment of each one (disaggregated level). The aforementioned double functionality, which, as it should be pointed out, could not be achieved easily through the traditional data collection methods, is of great importance as it could lead not only to the formulation of a new holistic strategy regarding the design of the public space in an area but also to targeted actions aiming to improve specific low-quality spaces. Moreover, this methodological approach could also contribute to the direction of multiple other secondary tasks, including identifying the precise location (coordinates) of each public space, evaluating the

564

A. Sdoukopoulos et al.

Fig. 4. Popularity of the examined public spaces in terms of number of users’ ratings.

availability of the public spaces in an area in terms of absolute numbers, and indicating their popularity. Furthermore, sentiment analysis of the comments following the users’ ratings could also be conducted, thus gathering more detailed data. As far as the main limitations of this study are concerned, given that crowdsourcing data still introduces sampling issues with specific groups being generally underrepresented (e.g., older people, people with limited access to technology), it is suggested that

Evaluating the Quality of Public Spaces Using Crowdsourcing Data

565

Fig. 5. Scatter map presenting the mean rating of every public space.

the results of the presented methodological approach are not used in an absolute, rigorous manner. Moreover, as the personal demographic and socioeconomic characteristics of the Google Maps users are not available, further analysis that could potentially lead to a deeper understanding of the needs and perceptions of different groups of people (e.g., residents vs tourists, young vs older people), currently could not be applied.

566

A. Sdoukopoulos et al.

Fig. 6. Violin plot illustrating the distribution of the mean ratings in each of the examined municipalities.

Fig. 7. PQPSsc scores in the examined municipalities.

References 1. United Nations Human Settlements Programme (UN-Habitat): Global Public Space Toolkit: From Global Principles to Local Policies and Practice. UN-Habitat, Nairobi (2016) 2. United Nations Human Settlements Programme (UN-Habitat): Public Space in Asia Pacific. UN-Habitat, Nairobi (2017)

Evaluating the Quality of Public Spaces Using Crowdsourcing Data

567

3. Project for Public Spaces Homepage, Great Public Spaces: What Makes a Place Great? https:// www.pps.org/article/public-space-benefits. Last accessed 2020/04/15 4. Vukmirovic, M., Gavrilovic, S., Stojanovic, D.: The improvement of the comfort of public spaces as a local initiative in coping with climate change. Sustainability 11, 6546 (2019). https://doi.org/10.3390/su11236546 5. Mehta, V.: Evaluating public space. J. Urban Des. 19(1), 53–88 (2014). https://doi.org/10. 1080/13574809.2013.854698 6. Papagiannakis, A., Vitopoulou, A.: An urban strategy in time of crisis: mobility management and low-cost public space design. Spatium 33, 1–7 (2015). https://doi.org/10.2298/SPAT15 33001P 7. Rupprecht Consult: Guidelines for Developing and Implementing a Sustainable Urban Mobility Plan, 2nd edn. European Commission (2019) 8. Sdoukopoulos, A., Pitsiava-Latinopoulou, M., Basbas, S., Papaioannou, P.: Measuring progress towards transport sustainability through indicators: analysis and metrics of the main indicator initiatives. Transp. Res. Part D 67, 316–333 (2019). https://doi.org/10.1016/j.trd. 2018.11.020 9. Svensson, Å. (ed.): Arterial Streets for People: Guidance for Planners and Decision Makers When Reconstructing Arterial Streets (2004) 10. Grindlay, A.L., Ochoa-Covarrubias, G., Lizárraga, C.: Urban mobility and quality of public spaces: the case of Granada, Spain. WIT Trans. Built Environ. 200, 37–48 (2020) 11. Ravazzoli, E., Torricell, G.P.: Urban mobility and public space. A challenge for the sustainable liveable city of the future. J. Public Space 2(2), 37–50 (2017). https://doi.org/10.5204/jps.v2i 2.91 12. Wazny, K.: “Crowdsourcing” ten years in: a review. J. Glob. Health 7(2), 1–13 (2017). https:// doi.org/10.7189/jogh.07.020601 13. World Bank Homepage: Dime Wiki. https://dimewiki.worldbank.org/Crowd-sourced_Data. Last accessed 2020/04/15 14. Bai, S., Jiao, J.: From shared micro-mobility to shared responsibility: using crowdsourcing to understand dockless vehicle violations in Austin, Texas. J. Urban Aff. 1–13 (2020). https:// doi.org/10.1080/07352166.2020.1798244 15. Brabham, D.C.: Crowdsourcing as a model for problem solving: an introduction and cases. Convergence. Int. J. Res. into New Media Technol. 14(1), 75–90 (2008). https://doi.org/10. 1177/1354856507084420 16. Smith, A.: Crowdsourcing for active transportation. Inst. Transp. Eng. J. 85(5), 30–36 (2015) 17. Staleti´c, N., Labus, A., Bogdanovi´c, Z., Despotovi´c-Zraki´c, M., Radenkovi´c, B.: Citizens’ readiness to crowdsource smart city services: a developing country perspective. Cities 107, 102883 (2020). https://doi.org/10.1016/j.cities.2020.102883 18. Osella, M.A.: Multi-dimensional Approach for Framing Crowdsourcing Archetypes. PORTO Publications Open Repository Torino, Politecnico di Torino (2014) 19. Zhao, Y., Zhu, Q.: Evaluation on crowdsourcing research: current status and future direction. Inf. Syst. Front. 16(3), 417–434 (2012). https://doi.org/10.1007/s10796-012-9350-4 20. Saxton, G.D., Oh, O., Kishore, R.: Rules of crowdsourcing: models, issues, and systems of control. Inf. Syst. Manag. 30, 2–20 (2013). https://doi.org/10.1080/10580530.2013.739883 21. Shi, B., Zhao, J., Chen, P.-J.: Exploring urban tourism crowding in Shanghai via crowdsourcing geospatial data. Curr. Issue Tour. 20(11), 1186–1209 (2017). https://doi.org/10.1080/136 83500.2016.1224820 22. Spyratos, S., Stathakis, D.: Evaluating the services and facilities of European cities using crowdsourced place data. Environ. Plan. B: Urban Analytics City Sci. 1–18 (2017). https:// doi.org/10.1177/0265813516686070

568

A. Sdoukopoulos et al.

23. Bakogiannis, E., Siti, M., Athanasopoulo, K., Vassi, A., Kyriakidis, C.: Crowdsourcing and visual research methodologies to promote data collection for sustainable mobility planning. In: Nathanail, E.G., Karakikes, I.D. (eds.) CSUM 2018. AISC, vol. 879, pp. 215–222. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02305-8_26 24. Song, Y., Zhang, B.: Using social media data in understanding site-scale landscape architecture design: taking Seattle Freeway Park as an example. Landsc. Res. 45(5), 627–648 (2020). https://doi.org/10.1080/01426397.2020.1736994 25. Bai, S., Jiao, J.: From shared micro-mobility to shared responsibility: using crowdsourcing to understand dockless vehicle violations in Austin, Texas. J. Urban Aff. (2020). https://doi. org/10.1080/07352166.2020.1798244 26. Mohamed, A.A., Stanek, D.: The influence of street network configuration on sexual harassment patterns in Cairo. Cities 98, 102583 (2020). https://doi.org/10.1016/j.cities.2019. 102583 27. Martí, P., García-Mayor, C., Serrano-Estrada, L.: Identifying opportunity places for urban regeneration through LBSNs. Cities 90, 191–206 (2019). https://doi.org/10.1016/j.cities. 2019.02.001 28. Google My Business Help Homepage: Score ratings for local places. https://support.google. com/business/answer/4801187. Last accessed 2021/01/10 29. Heitzman, A.: Google Reduces Star Rating Threshold: Why Businesses Should Take Notice (2017). https://www.searchenginejournal.com/google-reduces-star-rating-thresholdbusinesses-take-notice/185947/ 30. Sdoukopoulos, A.: A Methodological Framework for Assessing the Progress Towards Sustainable Urban Mobility. Doctoral Dissertation. Aristotle University of Thessaloniki, Greece (2021) 31. Maslowska, E., Malthouse, E.C., Bernritter, S.F.: Too good to be true: the role of online reviews’ features in probability to buy. Int. J. Advert. 36(1), 142–163 (2017). https://doi.org/ 10.1080/02650487.2016.1195622 32. Moellers, B.: Spiegel Research Reveals 4.5 Stars Are Better Than 5. Spiegel Research Center (2015). https://spiegel.medill.northwestern.edu/news/are-4.5-stars-better-than-5-.html 33. Uberall: The Reputation Management Revolution: A Global Benchmark Report (2019) 34. Murphy, R.: Google Reviews Study: How Many Reviews Do Local Businesses Need? (2018). https://www.brightlocal.com/ 35. Hellenic Statistical Authority Homepage: https://www.statistics.gr/. Last accessed 2020/10/3 36. IMF Homepage: https://www.imf.org/external/index.htm. Last accessed 2020/10/3 37. Sdoukopoulos, A.: A methodological approach for estimating urban green space: the case of Thessaloniki, Greece. In: Nathanail, E.G., Adamos, G., Karakikes, I. (eds.) CSUM 2020. AISC, vol. 1278, pp. 728–738. Springer, Cham (2021). https://doi.org/10.1007/978-3-03061075-3_71

Emerging and Innovative Technologies in Transport: Vehicle Automation and Smart Equipment

Application of Smart Windows Equipped with Radiant Internal Curtains to Improve Thermal Comfort in Urban Transport Vehicles Eusébio Conceição1(B) , Mª Inês Conceição2 , Mª Manuela Lúcio1 João Gomes3 , and Hazim Awbi4

,

1 FCT—Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal

[email protected] 2 Instituto Superior Técnico, Av. Rovisco Pais, 1049-001 Lisboa, Portugal

[email protected] 3 CINTAL, Campus de Gambelas, 8005-139 Faro, Portugal

[email protected] 4 School of Built Environment, University of Reading, Reading RG6 6AW, UK

[email protected]

Abstract. In this work the application of radiant curtains in a train composition, for improving thermal comfort conditions, in winter season, is made. In this study the train compositions consider one occupied space with 32 passengers. The evaluation of thermal comfort conditions, using the Predicted Mean Vote index, is made by the Human Thermal Modelling numerical model. This model, that works in transient conditions and simulates simultaneously a group of persons, considers the body divided in 24 cylindrical and 1 spherical element, each element is divided in 4 parts and each part sub-divided in several layers. The body is protected by several clothing layers. This numerical model considers the human body and clothing thermal system, based on mass and energy integral equations system, the human body thermo-regulatory system, the heat exchange between the body and the environment and thermal comfort. The numerical test, when the train is subjected to solar radiation in the right side, is made with internal curtains. In these simulations tests are considered a uniform convective environment without significant air velocity, the internal air temperature, the air relative humidity and the Mean Radiant Temperature that each body element are subjected calculated numerically. In accordance with the obtained results, the internal warm curtains guarantee, in general, internal acceptable thermal comfort conditions. Keywords: Vehicle thermal modelling · Human thermal modelling · TMR · Thermal comfort

1 Introduction In this work the application of smart windows equipped with radiant internal curtains to improve thermal comfort in urban transport vehicles is made. This numerical study © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_47

572

E. Conceição et al.

considers a software consisting of three numerical models, namely, a Vehicle Thermal Modelling, a Passenger Thermal Modelling and a Computational Fluids Dynamics numerical models. The Vehicle Thermal Modelling calculates the temperature distribution in all surrounding vehicle surface and the airflow rate that the passengers are subjected. This numerical model calculates the mean value of the thermal comfort level (see Fanger [1], ISO 7730 [2], ASHRAE 55 [3] and Conceição et al. [4]) and the indoor air quality level (see ASHRAE 62 [5]), that occupants are subjected inside the passenger compartment. This numerical model considers energy balance integral equation (see Conceição et al. [6]), mass balance integral equation, three dimensional geometry (see Conceição and Lúcio [7]), airflow rate calculated using tracer gas techniques (see Conceição et al. [8] and [9]), Heating, Ventilating and Air Conditioning (HVAC) system (see Conceição et al. [10]) and other sub-models. The Passenger Thermal Modelling calculates the temperature distribution in the passenger and clothing bodies. This numerical software calculates the thermal comfort level (see Fanger [1], ISO 7730 [2] and ASHRAE 55 [3]), that each occupant is subjected, and the Draught Risk (see ISO 7730 [2] and ASHRAE 55 [3]), that the occupant is subjected in each human body section. This numerical model considers energy balance integral equation (see Conceição et al. [6]), mass balance integral equation, three dimensional geometry (see Conceição and Lúcio [7]), human thermoregulatory system, and other sub-models. This numerical model can be seen in Conceição and Lúcio [11], using experimental and numerical values, Conceição et al. [12], using a coupling methodology with the CFD, and Conceição et al. [13], using experimental data as input data. In the last topic, other authors also develop different methodologies in order to simulate numerically the human thermo-physiology (see Stolwijk and Hardy [14]). The mathematical model, based on energy and mass balance equations, human thermoregulatory system, heat and mass transfer coefficients, and other constants, can be found, as example, in Tanabe et al. [15] and in Ying et al. [16]. The non-uniform environments conditions and the transient conditions are also used in the numerical simulation conditions. The thermal sensations for non-uniform environments were analyzed, as example, by Jin et al. [17], while the thermal comfort in transient conditions was studied, as example, by Kaynakli and Kilic [18]. However, models for non-uniform and transient environments simultaneously can be analyzed in Zhang et al. [19–21]. The Computational Fluids Dynamics numerical models calculates the airflow inside the vehicle (see an example in Conceição et al. [22]) and around the occupants (see an example in Conceição et al. [7]). This numerical model calculates the indoor air quality (see ASHRAE 62 [5]), that the occupant is subjected in the respiration area, and the Draught Risk (see ISO 7730 [2] and ASHRAE 55 [3]), that the occupant are subjected in each human body section. Finally, in the heat exchange between the human body sections and the environment, different authors presented some contributions. A greater number of radiant cooling systems and combination of radiant cooling with convective systems were analyzed in the last years using numerical or experimental means. Some examples of this kind of studies can be found in, as example, Zmrhal et al. [23], Tian and Love [24], Kim et al. [25], Lim

Application of Smart Windows Equipped with Radiant Internal

573

et al. [26] and Olesen [27]. In these kinds of studies, in general, the radiant surface temperature and the environment variables are measured. This information, with a comfort model, is used to evaluate the thermal comfort level that occupants are subjected. The model developed and applied in this work, present several innovative methodologies, namely, the passengers and trains geometry, the equation resolution and the coupling of different numerical models. The geometry developed in this work is important to obtain the view factors, between the occupants and the surrounding surfaces, between the different occupants and between the different sections of each occupant. These view factors are used to evaluate the Mean Radiant Temperature and consequently the heat exchange by radiation. In the equation system resolution, the Runge-KuttaFelberg method, with error control, is used. This resolution method control the time increment in order to the method do nor diverge. Finally, the coupling between the different models, used as input and output between them, is important to evaluate more real situations. The main objective of this work is to evaluate the thermal comfort that the passengers of a train compartment are subjected. The study is made in winter season and considers the application of radiant curtains in a train compartment, for improving thermal comfort conditions. In the study two curtain temperature were considered.

2 Numerical Model The software applied in this work considers the Vehicle Thermal Modelling numerical model and the Human Thermal Modelling numerical model. The Vehicle Thermal Modelling numerical model, that works in transient conditions, is based on first order integral energy and mass balance equations. The energy equations take into account the convection, conduction evaporation and radiation phenomena. The mass equation considers the convection and the diffusion phenomena. The first order integral energy balance equations are used to evaluate: • • • •

the air temperature inside the spaces; the temperature of the transparent bodies; the temperatures of the opaque bodies; the temperatures of the indoor bodies. The first order integral mass balance equations are used to evaluate:

• the mass of contaminants (CO2 concentration or other gases); • the mass of water vapor. In the resolution of all equations, the Runge–Kutta–Fehlberg Method with error control, is used. The Passenger Thermal Modelling numerical model, that works in steady state and transient conditions, is based on first order integral energy and mass balance equations. The energy equations take into account the convection, conduction, evaporation and radiation phenomena. The mass equation considers the convection and the diffusion

574

E. Conceição et al.

phenomena. The numerical model considers also the human thermoregulatory system, in order to control the human temperature distribution. The first order integral energy balance equations are used to evaluate: • the temperature of the tissue; • the temperature of the arterial and venous blood; • the temperature of the clothing. The first order integral mass balance equations are used to evaluate: • the mass of water vapor in the skin surface; • the mass of water vapor in the clothing. In the resolution of all equations, the Runge–Kutta–Fehlberg Method with error control, is used.

3 Numerical Methodology In this work the thermal comfort level, that the passengers are subjected inside a train compartment, is evaluated. The study is made in winter conditions, for two different thermal conditions, namely: • The first Case studied is made with internal curtain with surface temperature of 28 °C, with the passengers not subjected to solar radiation; • The second Case studied is made with internal curtain with surface temperature of 38 °C, with the passengers not subjected to solar radiation. In both Case studied the numerical simulation considers the following thermal conditions, namely: • • • •

The uniform convective environment without significant air velocity; The internal air temperature is 18 °C; The air relative humidity is 50%; The Mean Radiant Temperature that each body element is subjected is calculated numerically.

The analysed train composition considers one compartment occupied by 32 passengers (see Fig. 1). The passengers train compartment is built with 112 train main surfaces, while the passenger is divided in 24 cylindrical and one spherical elements. Each of the 32 occupants has 1.70 m of height, 70 kg of weight, 1.2 met. of activity and 1 clo. of clothing. In the view factors determination, all human body sections and the trains compartments surfaces are divided in small areas. In the numerical simulation the numerical model calculates the following view factors:

Application of Smart Windows Equipped with Radiant Internal

575

• View factors between the 25 elements and the surrounding human bodies sections; • View factors between the 25 elements and the train compartments surfaces. In this work, that will be evaluate the passenger thermal comfort level, the occupants should be identified sequentially with a number. Thus in Fig. 1, the grid generation around the passengers and surrounding surfaces is presented, while in Fig. 2 the identification of the passenger number is made.

Fig. 1. Grid generation in the passenger compartment surfaces and around the passengers. Passenger compartment with 32 passengers.

4 Results and Discussion In this section, in the compartment with 32 passengers, the Mean Radiant Temperature and the skin temperature that each passenger body section is subjected are presented. The Mean Radiant Temperature, that each body section of the 32 passengers is subjected, calculated by the numerical model, in the two tests are presented in Figs. 3 and 4, when the internal curtains are closed with surface temperature of, respectively, 28 and 38 °C. In Figs. 5 and 6 the skin temperature for the 32 passengers is presented. In Fig. 5 the window curtain temperature with 28 °C is closed and the passengers are not subjected to solar radiation, while in Fig. 6 the window curtain temperature with 38 °C is closed and the passengers are not subjected to solar radiation.

576

E. Conceição et al. 29 30 25

31

26

32 28

27

22

21

23

18

24

20

19 16

17 14

13 9

15

10

12

11 8

5

6

7

2

4

1

3

Fig. 2. Identification of the passenger number. Compartment with 32 passengers.

Finally, the Predicted Mean Vote (PMV) for the 32 passengers are presented in Figs. 7 and 8. In Fig. 7 the curtain surface temperature is 28ºC and the passengers are not subjected to solar radiation, while in Fig. 8 the curtain surface temperature is 38 °C and the passengers are not subjected to solar radiation. 40

MRT(ºC)

34

T-1

T-2

T-3

T-4

T-5

T-6

T-7

T-8

T-9

T-10

T-11

T-12

T-13

T-14

T-15

T-16

T-17

T-18

T-19

T-20

T-21

T-22

T-23

T-24

T-25

T-26

T-27

T-28

T-29

T-30

T-31

T-32

28

22

16

Body Sections

Fig. 3. Mean Radiant Temperature (MRT) for 32 passengers, when the window curtain temperature with 28 °C is closed and the passengers are not subjected to solar radiation.

Application of Smart Windows Equipped with Radiant Internal

577

40

MRT(ºC)

34

T-1

T-2

T-3

T-4

T-5

T-6

T-7

T-8

T-9

T-10

T-11

T-12

T-13

T-14

T-15

T-16

T-17

T-18

T-19

T-20

T-21

T-22

T-23

T-24

T-25

T-26

T-27

T-28

T-29

T-30

T-31

T-32

28

22

16

Body Sections

Fig. 4. Mean Radiant Temperature (MRT) for 32 passengers, when the window curtain temperature with 38 °C is closed and the passengers are not subjected to solar radiation. 40

T-1

T-2

T-3

T-4

T-5

T-6

T-7

T-8

T-9

T-10

T-11

T-12

34

T-13

T-14

T-15

T-16

32

T-17

T-18

T-19

T-20

30

T-21

T-22

T-23

T-24

T-25

T-26

T-27

T-28

T-29

T-30

T-31

T-32

TSkin (ºC)

38

36

Body sections

Fig. 5. Skin temperature for 32 passengers, when the window curtain temperature with 28 °C is closed and the passengers are not subjected to solar radiation.

It can be verified that the average skin surface temperature with curtains at 28 °C is similar in all occupants and in all parts of the body. It was verified that the neck, the right and left hands are the parts of the body that present the lowest skin temperature.

578

E. Conceição et al. 40

T-1

T-2

T-3

T-4

T-5

T-6

T-7

T-8

T-9

T-10

T-11

T-12

34

T-13

T-14

T-15

T-16

32

T-17

T-18

T-19

T-20

30

T-21

T-22

T-23

T-24

T-25

T-26

T-27

T-28

T-29

T-30

T-31

T-32

T Skin (ºC)

38

36

Body sections

Fig. 6. Skin temperature for 32 passengers, when the window curtain temperature with 38 °C is closed and the passengers are not subjected to solar radiation

It can be seen that the average skin surface temperature with curtains at 38 °C does not suffer great variations, with occupants 5, 13, 21 and 29 having a slightly higher skin temperature in the upper left part of the body. 2,1

PMV

1,4

0,7

0

-0,7 0

4

8

12

16

20

24

28

32

Occupant Number

Fig. 7. PMV index for 32 passengers, when the curtain surface temperature is 28 °C and the passengers are not subjected to solar radiation.

Application of Smart Windows Equipped with Radiant Internal

579

2,1

PMV

1,4

0,7

0

-0,7 0

4

8

12

16

20

24

28

32

Occupant Number

Fig. 8. PMV index for 32 passengers, when the curtain surface temperature is 38 °C and the passengers are not subjected to solar radiation

For curtain temperatures of 28 and 38 °C, all occupants are within the limit of thermal comfort due to cold. However, the comfort level for the curtain temperature of 38 °C is slightly higher than the comfort level for the curtain temperature of 28 °C. However, both situations result in negative values of the PMV index. In future works the numerical models will be applied to evaluate the influence of differents Heating, Ventilation and Air-Conditioning (HVAC) Systems. The study will be applied in a virtual chamber similar with a experimental chamber existing in the laboratory.

5 Conclusions In this work the application of smart windows equipped with radiant internal curtains to improve thermal comfort in urban transport vehicles is made. In accordance with the obtained results, when the warm curtains increase the temperature value the MRT and the skin temperature asymmetry increases and the thermal comfort level increases, by negative PMV values. However, the thermal comfort level is acceptable in accordance with the international standards. Thus, in winter conditions, the warm curtains, heated by the solar radiation, as example, improve the thermal comfort level that the occupants are subjected. Acknowledgement. The authors would like to acknowledge to the project (SAICTALG/39586/2018) from Algarve Regional Operational Program (CRESC Algarve 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) and the National Science and Technology Foundation (FCT).

580

E. Conceição et al.

References 1. Ole Fanger, P.: Thermal Comfort. Analysis and Applications in Environmental Engineering. Copenhagen Danish Tech. Press (1970) 2. ISO: ISO 7730: Ergonomics of the thermal environment analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria. Management (2005). https://doi.org/10.1016/j.soildyn.2004.11.005 3. ASHRAE-55: Thermal Environmental Conditions for Human Occupancy. ANSI/ASHRAE Stand.—55 (2017) 4. Conceição, E.Z.E., Nunes, A.R.L., Gomes, J.M.M., Lúcio, M.J.R.: Application of a school building thermal response numerical model in the evolution of the adaptive thermal comfort level in the Mediterranean environment. Int. J. Vent. 9(3) (2010). https://doi.org/10.1080/147 33315.2010.11683887 5. ANSI: ANSI/ASHRAE Standard 62.1-2010, Ventilation for Acceptable Indoor Air Quality. Ashrae (2007) 6. Conceição, E.Z.E., Da Silva, M.C.G., André, J.C.S., Viegas, D.X.: Thermal behaviour simulation of the passenger compartment of vehicles. Int. J. Veh. Des. 24(4) (2000). https://doi. org/10.1504/IJVD.2000.005199 7. Conceição, E.Z.E., Lúcio, M.M.J.R.: Numerical study of the influence of opaque external trees with pyramidal shape on the thermal behaviour of a school building in summer conditions. Indoor Built Environ. 19(6) (2010). https://doi.org/10.1177/1420326X10377546 8. Conceiçäo, E.Z.E., Silva, M.C.G., Viegas, D.X.: Air quality inside the passenger compartment of a bus. J. Expo. Anal. Environ. Epidemiol. 7(4) (1997) 9. Conceição, E.Z.E., Farinho, J.P., Lúcio, M.M.J.R.: Evaluation of indoor air quality in classrooms equipped with cross-flow ventilation. Int. J. Vent. 11(1) (2012) 10. Conceição, E.Z.E., Lúcio, M.M.J.R., Ruano, A.E.B., Crispim, E.M.: Development of a temperature control model used in HVAC systems in school spaces in Mediterranean climate. Build. Environ. 44(5) (2009). https://doi.org/10.1016/j.buildenv.2008.06.015 11. Conceição, E.Z.E., Lúcio, M.: Numerical and subjective responses of human thermal sensation. In: Proceedings of the BioEng, 2001 12. Conceição, E.Z.E., Santiago, C.I.M., Manuela, Mª., Lúcio, J.R., Awbi, H.B.: Predicting the air quality, thermal comfort and draught risk for a virtual classroom with desk-type personalized ventilation systems. Buildings 8(2) (2018). https://doi.org/10.3390/buildings8020035 13. Conceição, E.Z.E., Lúcio, M., Farinho, J.P.: Experimental and numerical study of personalized of ventilation in classrooms desks. In: Proceedings of the 10th International Conference in Rooms, Room Vent, Helsinki, Finland, 2007, pp. 13–15 14. Stolwijk, J.A.J., Hardy, J.D.: Temperature regulation in man—A theoretical study. Pflugers Arch. Gesamte Physiol. Menschen Tiere (1966). https://doi.org/10.1007/BF00412787 15. Tanabe, S.I., Kobayashi, K., Nakano, J., Ozeki, Y., Konishi, M.: Evaluation of thermal comfort using combined multi-node thermoregulation (65MN) and radiation models and computational fluid dynamics (CFD) (2002). https://doi.org/10.1016/S0378-7788(02)000 14-2 16. Ying, B.A., Kwok, Y.L., Li, Y., Yeung, C.Y., Li, F.Z., Li, S.: Mathematical modeling of thermal physiological responses of clothed infants (2004). https://doi.org/10.1016/j.jtherbio. 2004.08.027 17. Jin, Q., Li, X., Duanmu, L., Shu, H., Sun, Y., Ding, Q.: Predictive model of local and overall thermal sensations for non-uniform environments. Build. Environ. (2012). https://doi.org/10. 1016/j.buildenv.2011.12.005 18. Kaynakli, O., Kilic, M.: Investigation of indoor thermal comfort under transient conditions. Build. Environ. (2005). https://doi.org/10.1016/j.buildenv.2004.05.010

Application of Smart Windows Equipped with Radiant Internal

581

19. Zhang, H., Arens, E., Huizenga, C., Han, T.: Thermal sensation and comfort models for non-uniform and transient environments, part III: whole-body sensation and comfort. Build. Environ. (2010). https://doi.org/10.1016/j.buildenv.2009.06.020 20. Zhang, H., Arens, E., Huizenga, C., Han, T.: Thermal sensation and comfort models for nonuniform and transient environments, part II: local comfort of individual body parts. Build. Environ. (2010). https://doi.org/10.1016/j.buildenv.2009.06.015 21. Zhang, H., Arens, E., Huizenga, C., Han, T.: Thermal sensation and comfort models for nonuniform and transient environments: part I: local sensation of individual body parts. Build. Environ. (2010). https://doi.org/10.1016/j.buildenv.2009.06.018 22. Conceição, E.Z.E., Vicente, V.D.S.R., Lúcio, M.M.J.R.: Airflow inside school building office compartments with moderate environments. HVAC R Res. 14(2) (2008). https://doi.org/10. 1080/10789669.2008.10391003 23. Drkal, F., Zmrhal, V., Hensen, J.: Modelling and simulation of a room with a radiant cooling ceiling. In: International, Eighth Conference IBPSA, 2003 24. Tian, Z., Love, J.A.: An integrated study of radiant slab cooling systems through experiment and building simulation (2005) 25. Kim, T., Kato, S., Murakami, S., Rho, J.W.: Study on indoor thermal environment of office space controlled by cooling panel system using field measurement and the numerical simulation. Build. Environ. (2005). https://doi.org/10.1016/j.buildenv.2004.04.010 26. Lim, J.H., Jo, J.H., Kim, Y.Y., Yeo, M.S., Kim, K.W.: Application of the control methods for radiant floor cooling system in residential buildings. Build. Environ. (2006). https://doi.org/ 10.1016/j.buildenv.2005.01.019 27. Olesen, B.: Radiant floor cooling systems. ASHRAE J. (2008)

Urban Transport Vehicles Equipped with HVAC Based on Ceiling-Mounted Air Distribution Systems Eusébio Conceição1(B) , João Gomes2 , Mª Inês Conceição3 Mª Manuela Lúcio1 , and Hazim Awbi4

,

1 FCT—Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal

[email protected] 2 CINTAL, Campus de Gambelas, 8005-139 Faro, Portugal

[email protected] 3 Instituto Superior Técnico, Av. Rovisco Pais, 1049-001 Lisboa, Portugal

[email protected] 4 School of Built Environment, University of Reading, Reading RG6 6AW, UK

[email protected]

Abstract. This paper presents a numerical study on the airflow and indoor air quality influencing the occupants of an urban transport vehicle equipped with a HVAC (Heating Ventilating and Air Conditioning) system based on ceilingmounted air distribution systems. This study is carried out in a virtual chamber, which simulates the one that exists in the laboratory, occupied by twentyfour seated occupants. Above each occupant there is located an air inlet directed towards the occupants’ breathing zone. The exhaust is also located close to the ceiling between every two occupants seated side by side. Numerical simulation is performed using research software founded on a set of numerical models, one that simulates the vehicle’s thermal response, Vehicle Thermal Modelling, and a coupling between one that simulates the airflow around the occupants, Computational Fluid Dynamics, and another that simulates the temperature distribution in occupants, Human Thermal Modelling. The performance of the HVAC system is evaluated for an inlet air velocity of 3 m/s. In this study the air velocity and the carbon dioxide concentration field are presented. The results show that the airflow presents a downward path in the inlet towards the breathing zone. In the area located between the occupants, the airflow presents an upward path towards the exhaust area. In accordance with the air velocity field, the CO2 concentration is removed from the breathing zone and is transferred to the exhaust area. Keywords: HVAC · Indoor air quality · Numerical simulation

1 Introduction The major objective of Heating, Ventilation, and Air Conditioning (HVAC) systems is to supply a healthy and comfortable environment for occupants indoors. In addition, it © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_48

Urban Transport Vehicles Equipped with HVAC Based

583

must also ensure that the spread of contagious diseases, due to respiratory processes, such as the influenza or COVID-19, is limited as much as possible. In this sense, it is important to develop ventilation systems that provide clean air directly to the breathing area of each occupant in order to improve the quality of the air there, as well as efficiently extracting contaminants to the outside. The improvement of the indoor air quality in the breathing area is associated with the inlet airflow location. However, the improvement of the contaminant in the breathing area and inside the space removal is associated with the extraction airflow location. In the article by Liu et al. [1] a review study was presented on different ventilation systems, their advantages, disadvantages and implementation costs in buildings, as well as an analysis of their performance from Computer Fluid Dynamics (CFD) applications. Several studies show that it is possible to create microclimate with improved air quality around each occupant by using a low-momentum personalized ventilation system [2], hybrid ventilation system [3], and other types of personalized ventilation systems [4–6]. In this work, it is numerically analyzed a new HVAC system based on a ceilingmounted ventilation system, consisting of individual air inlets directed towards the occupants’ breathing zone and exhaust outlets installed next to the ceiling in the area located between occupants seated side by side. The performance of this system is evaluated using research software made up of a set of numerical models, namely the Vehicle Thermal Modelling, Human Thermal Modelling and CFD. The model developed and applied in this work, presents several innovative methodologies, namely, the passenger geometry, the urban transport and HVAC geometry and the coupling of different numerical models. The passenger geometry, that considers the passenger posture, coupling the human thermal response with the airflow topology around the passenger. The urban transport and HVAC geometry, that considers the inlet and outlet airflow, is important to promote an efficient airflow around the occupants. Finally, the coupling of the three numerical models permits information exchange between them. Thus, using these innovative methods, in this work new HVAC system based on a ceiling-mounted ventilation system was proposed. Air inlets were installed above each occupant and facing their breathing zone. Between every two occupants seated side by side, an exhaust outlet was installed close to the ceiling and located slightly ahead of the air inlets. The Vehicle Thermal Modelling is used to calculate temperatures distribution in all surrounding surfaces of the vehicle as well as the airflow rate provided for occupants. It can also be used to evaluate thermal comfort [7] and indoor air quality [8] levels according to international standards. This numerical model takes into account energy and mass balance integral equations as well as the three-dimensional geometry of the spaces [9–13]. The Human Thermal Modelling is used to calculate temperatures distribution in the bodies and clothing of occupants (passengers). It can also be used, e.g., to evaluate the thermal comfort in each body section of each occupant, and the average thermal comfort of each occupant [14]. This numerical model works in a coupling methodology with CFD. The CFD is used to evaluate the airflow inside the vehicle cabin [15], and around

584

E. Conceição et al.

the passengers [11]. It can also be used to evaluate the indoor air quality in the breathing zone obtaining the CO2 concentration distribution around the occupants. The aim of this work is to evaluate the performance of the new HVAC system mentioned above through the airflow and CO2 concentration distributions numerically obtained around the occupants of an urban passenger vehicle.

2 Numerical Methods The numerical model used in this work considers the Vehicle Thermal Modelling and the coupling of CFD and Human Thermal Modelling. These three numerical models are used to assess the indoor air quality existing in the breathing area of the occupants and the airflow distribution inside the vehicle cabin. The Vehicle Thermal Modelling, that works in transient conditions, is based on first order integral energy and mass balance equations. The first order integral energy balance equations are used to evaluate the air temperature inside the spaces, temperature in the glazing surfaces, temperatures in the opaque and indoor bodies. These equations take into account in the left side the heat accumulation term and in the right side the heat flux term: • Convection between the opaque, transparent and interior bodies and the surrounding environment; • Conduction inside the opaque bodies, namely between the several layers; • Evaporation between the opaque, transparent and interiors bodies and the surrounding environment; • Radiation phenomena, namely the incident solar radiations and heat exchange inside each space. The first order integral mass balance equations are used to evaluate the mass of contaminants (CO2 concentration) and water vapor. These equations consider the convection and the diffusion phenomena. In the resolution of all equations, the system uses the Runge–Kutta–Fehlberg Method with error control. The Human Thermal Modelling and CFD work in a coupling methodology [16]. The Human Thermal Modelling, which works in transient and steady-state conditions, is based on a first order integral energy and mass balance equations system. The first order integral energy balance equations are used to calculate the temperature of the tissue, the temperature of the arterial and venous blood and the temperature of the clothing. These equations take into account in the left side the heat accumulation term and in the right side the heat flux term: • Convection between the skin tissue and clothing and the surrounding environment and between tissue and the arterial and venous blood; • Conduction inside the tissue and the clothing, namely between the several layers; • Evaporation between the skin tissue and the clothing and the surrounding environment; • Incident solar radiations and heat exchange inside each space, namely in the skin tissue and the clothing;

Urban Transport Vehicles Equipped with HVAC Based

585

• Heat exchange by radiation between the skin tissue and the clothing or between the several clothing layers. The first order integral mass balance equations are used to calculate the mass of water vapor in the skin surface and in the clothing. The resolution of all equation systems is also done using the Runge–Kutta–Fehlberg method with error control. The CFD, which works in steady-state, is based on a second order differential balance equation system, consisting of: • The differential Navier–Stokes balance equations, which are used to evaluate the three directional components of air velocity; • The differential energy balance equations, which are used to evaluate the air temperature; • The differential mass balance equations, which are used to evaluate the CO2 concentration; • The differential RNG turbulence model balance equations, which are used to evaluate the turbulent kinetic energy and turbulent energy dissipation. The CFD input data are the room surrounding surface temperatures, surface temperatures of the occupants, and the inlet airflow conditions, such as the air velocity, air temperature, air turbulence intensity, and CO2 concentration. The CFD output data are, among others, the three components of air velocity, the air temperature and the CO2 concentration. Thus, CFD is used to obtain the air velocity field and the CO2 concentration field, useful to establish the air quality level in the occupant breathing zone.

3 Methodology The numerical study is carried out in a virtual camera (Fig. 1), identical to an experimental one available in the laboratory, which simulates a section of a bus occupied by twentyfour virtual passengers. The HVAC system is based on a ceiling-mounted ventilation system. For each occupant there is an air inlet located close to the ceiling and directed towards the occupant’s breathing zone (see green arrows in Fig. 1). Between every two occupants seated side by side there is an exhaust outlet located also close to the ceiling and in a slightly advanced position in relation to the air inlets of these occupants (see yellow arrows in Fig. 1). The numerical simulation was done for winter conditions with an outdoor air temperature of 0ºC, an indoor air relative humidity of 50%, and an outdoor CO2 concentration of 500 mg/m3 . The inlet air velocity was 3 m/s. In the numerical simulation, the Vehicle Thermal Modelling is used to calculate the bus transparent, opaque and interior bodies’ temperature. This numerical software also calculates the airflow rate in the inlet and exhaust airflow. This information is used by the CFD and by the Human Thermal Modelling as input data. The Human Thermal Modelling, using information of the CFD (air velocity and temperature around the occupants) and the Vehicle Thermal Modelling (surrounding temperatures), calculates the passenger and clothing temperatures.

586

E. Conceição et al.

Fig. 1. Virtual chamber used in the numerical simulation: at the top, with ceiling-mounted ventilation system; at the bottom, with the signaling of the inlet airflow (green arrows) and the exhaust airflow (yellow arrows).

Urban Transport Vehicles Equipped with HVAC Based

587

The CFD, using information of the Human Thermal Modelling (skin and clothing temperatures around the occupants) and the Vehicle Thermal Modelling (surrounding temperatures and inlet and outlet airflow), calculates the carbon dioxide concentration, air temperature, air velocity, turbulence air intensity, among others, around the passenger and inside the space. Finally, the three numerical models define the geometry: the Vehicle Thermal Modelling the surrounding surfaces, the CFD the occupants (using boxes) and the interiors bodies and the Human Thermal Modelling the occupants (using cylinders and sphere). The numerical simulation results are obtained in three vertical planes arranged in the XZ-directions: • one located in the air intake area of the occupants seated by the window; • another located in the air intake area of the occupants seated next to the aisle; • a third located in the area of the exhaust outlets located between every two occupants seated side by side. In this study all passengers present the same dimensions and activity and clothing levels. The environmental variables, in all inlet system, present also the same inlet air velocity, air temperature, air turbulence intensity and carbon dioxide concentration.

4 Results and Discussion In this study some plans of the air velocity fields and the CO2 concentration fields are showed. The air velocity fields in vertical planes along the X and Z direction were obtained, namely the: • plane for Y values equal to 0.075 m (Fig. 2), • plane for Y values equal to 0.125 m (Fig. 3) • plane for Y values equal to 0.175 m (Fig. 4). CO2 concentration fields in vertical planes along the X and Z direction were also obtained, namely the: • plane for Y values equal to 0.075 m (Fig. 5), • plane for Y values equal to 0.125 m (Fig. 6) • plane for Y values equal to 0.175 m (Fig. 7). The vertical plane in the Y direction equal to 0.075 m is located in the area of the air intake of the occupants seated by the window. The vertical plane in the Y direction equal to 0.175 m is located in the area of the air intake of the occupants seated next to the aisle. Between these two vertical planes is the vertical plane in the Y direction equal to 0.125 m located in the exhaust zone. The results show that the air velocity presents the highest values close to the air inlet and the lowest values in the lower part of the trunk. The airflow has a downward path

588

E. Conceição et al.

Fig. 2. Air velocity field in a vertical plane for Y equal to 0.075 m. Vertical plane located in the area of the air intake of the occupants seated by the window.

Fig. 3. Air velocity field in a vertical plane for Y equal to 0.125 m. Vertical plane located in the exhaust area.

from the air inlet towards the breathing zone, where it removes contaminants. In the area located between the occupants, it is verified that the airflow presents an upward path towards the exhaust with an increasing air velocity. Therefore, this ventilation system provides an airflow distribution favorable to the extraction of contaminants from an occupant’s breathing zone and their exhaust without influencing the breathing zone of another occupant.

Urban Transport Vehicles Equipped with HVAC Based

589

Fig. 4. Air velocity field in a vertical plane for Y equal to 0.175 m. Vertical plane located in the area of the air intake of the occupants seated next to the aisle.

Fig. 5. CO2 concentration field in a vertical plane for Y equal to 0.075 m. Vertical plane located in the area of the air intake of the occupants seated by the window.

The results obtained from the distribution of CO2 concentration show that CO2 is rapidly removed from the breathing zone, whose values decrease, along with the occupant, reaching the lowest value in the lower trunk zone. It can be seen that the concentration of CO2 accumulates in the area between the occupants seated side by side and that its value decreases as it approaches exhaustion. It is therefore confirmed that

590

E. Conceição et al.

Fig. 6. CO2 concentration field in a vertical plane for Y equal to 0.125 m. Vertical plane located in the area of the exhaust air.

Fig. 7. CO2 concentration field in a vertical plane for Y equal to 0.175 m. Vertical plane located in the area of the air intake of the occupants seated next to the aisle.

this ventilation system is able to remove contaminants in order to maintain an acceptable level of air quality in the occupants’ breathing zone, that is, below 1800 mg/m3 [8].

Urban Transport Vehicles Equipped with HVAC Based

591

5 Conclusions In this article a new HVAC system based on a ceiling-mounted ventilation system was proposed. Air inlets were installed above each occupant and facing their breathing zone. Between every two occupants seated side by side, an exhaust outlet was installed close to the ceiling and located slightly ahead of the air inlets. The performance of this HVAC system is numerically analyzed through the distributions of air velocity and CO2 concentration around the occupants obtained by CFD. The air velocity distribution shows that the airflow follows a downward path from the air inlet towards the occupant’s breathing zone, then moving towards the zone located between the occupants. In this zone it takes an upward path to exhaustion. Consequently, the concentration of CO2 in the occupants’ breathing zone decreases to values within those recommended by the standard (below 1800 mg/m3 ). Thus, it is concluded that this system is effective in removing contaminants from the occupants’ breathing zone, providing an acceptable air quality inside the vehicle. Acknowledgements. The authors would like to acknowledge to the project (SAICTALG/39586/2018) from Algarve Regional Operational Program (CRESC Algarve 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) and the National Science and Technology Foundation (FCT).

References 1. Liu, J., Zhu, S., Kim, M., Srebric, J.: A review of CFD analysis methods for personalized ventilation (PV) in indoor built environments. Sustainability 11, 4166 (2019) 2. Danca, P., Cosoiu, C., Nastase, I., Bode, F., Georgescu, M.: Personalized ventilation as a possible strategy for reducing airborne infectious disease transmission on commercial aircraft. Appl. Sci. 12, 2088 (2022) 3. Chang, Z., Yi, K., Liu, W.: A new ventilation mode of air conditioning in subway vehicles and its air distribution performance. Energy Built Environ. 2, 94–104 (2021) 4. Melikov, A., Ivanova, T., Stefanova, G.: Seat headrest-incorporated personalized ventilation: thermal comfort and inhaled air quality. Build. Environ. 47, 100–108 (2012) 5. Mazanec, V., Kabele, K.: Effect of the personalized ventilation to a human thermal comfort. IOP Conf. Ser.: Earth Environ. Sci. 290, 012146 (2019) 6. Katramiz, E., Ghaddar, N., Ghali, K.: Novel personalized chair-ventilation design integrated with displacement ventilation for cross-contamination mitigation in classrooms. Build. Environ. 213, 108885 (2022) 7. ISO 7730: Ergonomics of the Thermal Environments—Analytical Determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria. International Standard Organization, Geneva, Switzerland (2005) 8. ANSI/ASHRAE Standard 62-1: Ventilation for Acceptable Indoor Air Quality, American Society of Heating, Refrigerating and Air-Conditioning Engineers: Atlanta, GA, USA (2016) 9. Conceição, E., Nunes, A., Gomes, J., Lúcio, M.: Application of a school building thermal response numerical model in the evolution of the adaptive thermal comfort level in the Mediterranean environment. Int. J. Vent. 9(3), 287–304 (2010) 10. Conceição, E., Silva, M., André, J., Viegas, D.: Thermal behaviour simulation of the passenger compartment of vehicles. Int. J. Veh. Des. 24(4), 372–387 (2000)

592

E. Conceição et al.

11. Conceição, E., Lúcio, M.: Numerical study of the influence of opaque external trees with pyramidal shape on the thermal behaviour of a school building in summer conditions. Indoor Built Environ. 19(6), 657–667 (2010) 12. Conceição, E., Silva, M., Viegas, D.: Air quality inside the passenger compartment of a bus. J. Expo. Anal. Environ. Epidemiol. 7(4), 521–534 (1997) 13. Conceição, E., Farinho, J., Lúcio, M.: Evaluation of indoor air quality in classrooms equipped with cross-flow ventilation. Int. J. Vent. 11(1), 53–67 (2012) 14. Conceição, E., Santiago, C., Lúcio, M., Awbi, H.: Predicting the air quality, thermal comfort and draught risk for a virtual classroom with desk-type personalized ventilation systems. Buildings 8(2), 35 (2018) 15. Conceição, E., Vicente, V., Lúcio, M.: Airflow inside school building office compartments with moderate environments. HVAC&R Res. 14(2), 195–207 (2008) 16. Gau, N., Niu, J., Zang, H.: Coupling CFD and human body thermoregulation model for the assessment of personalized ventilation. HVAC&R Res. 12, 497–518 (2006)

Automated Vehicles’ Effects on Urban Traffic Flow Parameters Andrea Gemma1

, Ernesto Cipriani1 , Umberto Crisalli2(B) and Livia Mannini1

,

1 Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy 2 Department of Enterprise Engineering, Tor Vergata University of Rome, 00133 Rome, Italy

[email protected]

Abstract. Transport models able to take into account both Automated Vehicles (AVs) and Conventional Vehicles (CVs) are increasingly required to support decision-makers in the transition to the future mobility. Studies on AVs mainly investigate motorways and extra-urban networks by using a traffic microsimulator approach, but very few studies investigate the more complex case of urban areas in which this approach is a very challenging problem for real (large) networks. In this last case, this study aims to analyze mixed traffic flows in presence of different AVs shares by using a microsimulation approach to estimate traffic flow parameters for a better definition of link flows and performances in presence of mixed traffic flows made of AVs and CVs. They can be used for a better implementation of more reliable meso-simulation assignment models taking into account the presence of mixed AVs and CVs traffic flows for strategic planning of large and complex networks The application to the road network of Rome (Italy) allowed to demonstrate the goodness of the proposed approach. Keywords: Automated vehicles · Microsimulation · Traffic flow · Capacity

1 Introduction Automated Vehicles (AVs) are on the way of being the reality in the future mobility scenarios in our cities. They will surely represent the definitive opportunity to increase road safety and, if connected to the powertrain electrification, to reduce air pollution and health risks, although congestion will be a negative effect to consider. In order to support decision-makers in the transition to the future, transport models able to take into account both Automated Vehicles (AVs) and Conventional Vehicles (CVs) are becoming a trend topic in the transportation research. Major research efforts in this field currently regard AV-ready road infrastructure and authorities, but also AV-ready transport models, for which the effects on capacity of a mixed flow of AVs and CVs and the traffic regulations in presence of AVs have to be properly considered. Concerning automation, the Society of Automotive Engineers [15] defines 6 levels according to different lateral and longitudinal control, i.e., steering wheel control © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_49

594

A. Gemma et al.

and acceleration and deceleration phases. Specifically, level 0 represents the traditional human driving, where no system is active for the vehicle, and the driver is continuously responsible for both driving as far as lateral control (CVs); while level 5 represents the complete automation, i.e., the human component is no longer required, and the vehicle is able to handle all situations by taking total control of driving from origin to destination with all vehicle occupants as passengers. Aiming at developing more advanced transport models to consider AVs and CVs in a mixed traffic flow, this paper presents the results of the calibration of more advanced cost function able to capture the generalized cost associated to a road graph in presence of a mixed flow of AVs and CVs. It implies the set-up of cost function parameters for different link types (primary or secondary roads) and approaching different intersection (e.g. signalized) according to the interaction of AVs and CVs. The results are carried out by using a micro-simulation approach able to reproduce mixed traffic flows made of automated vehicles (AVs) and conventional ones (CVs) by varying the different supply and demand features both at link and node level according to different driving behaviors. The obtained results are presented through some representative case examples developed on the basis of the road network of Rome. They are part of a wider system of models under development aiming at supporting the feasibility studies on the effects of the introduction of AVs in the city of Rome. The paper is structured as follows. Section 2 reports a concise state-of-the-art; Sect. 3 describes the used modelling framework; Sect. 4 presents the results of the application to a real-size network; Sect. 5 summarizes conclusions and future research perspectives.

2 State of Art Studies on AVs mainly investigate motorways and extra-urban networks that are usually modelled using single or limited sequence of links within a microsimulator. Few studies investigate urban areas as they are characterized by larger and more complex networks, which is a very challenging problem by using a microsimulation approach. Focusing on the urban case, in recent years the macro/meso-simulation approach has been proposed [4, 5, 8, 10, 11, 12] even if the problem of defining the right capacity and performances of a mixed flow of AVs and CVs is still a problem under investigation. This approach is easy-to-apply to large and complex networks aiming at assessing impacts of AVs introduction for the definition of future mobility scenarios in our cities at strategic planning level. On the other hand, the literature in this field that proposes the microsimulation approach focuses on the formulations of traffic operational capacity in mixed traffic [6], the evaluation of the effect of varying the percentage of autonomous vehicles in the overall vehicle fleet mix on transportation network performance [16], the proposal of car-following models that consider platoons of mixed (AVs and CVs) vehicles [9, 18] and for different SAE (Society of Automotive Engineers, 2018) levels [14]. Aiming towards the coordination of activities and research collaboration in Europe and over on AVs deployment, the CoEXist [7] European project can be cited. It aimed at preparing the transition phase during which AVs and CVs will co-exist on cities’ roads including also the technological development of traffic simulation tools in the view of a microsimulation approach.

Automated Vehicles’ Effects on Urban Traffic Flow Parameters

595

Concerning microsimulation software to support the analysis of the introduction of autonomous vehicles in the traffic flow, Vissim™ (PTV-AG, 2020), Aimsun™ [2] and SUMO™ [3] can be cited. Specifically, Aimsun™ is more suitable for a less complex traffic model, while Vissim™ is more suitable for a more complex traffic model. SUMO™ simulator, because is an open-source simulator, stands out for its flexibility in simulating all traffic components and behaviors, including AVs. Further details on these traffic flow simulators with the connected and autonomous vehicles can be found in Vrbani´c et al. [17]. Within this framework, this paper proposes the analysis of mixed traffic flows in presence of different AVs shares by using a microsimulation approach to estimate traffic flow parameters and performances. They can be used for a better definition of link flows and performances (e.g. capacity and link travel times) in presence of mixed traffic flows made of AVs and CVs. Such parameters can be used for a better implementation of more reliable meso-simulation assignment models taking into account the presence of mixed AVs and CVs traffic flows for strategic planning of large and complex networks or they can be used for detailed micro-simulation studies focusing on specific links or nodes to solve operative problems.

3 Modelling Traffic flow parameters play a key role in modelling network performances to be used in the demand-supply interaction (assignment). In presence of mixed traffic flows made of AVs and CVs, the assignment model requires to be: (i) multiclass compliant, as it explicitly considers the demand and supply components related to AVs and CVs; (ii) easy-to-use, as it is designed for large scale applications and solution robustness; (iii) computationally fast, because it could be potentially usable within a network design procedure. Further details on such a model can be deepened on Crisalli and Polimeni [8], while for the scope of this paper only the supply side features are recalled. In particular, the network model should be able to explicitly consider the supply facilities for different vehicle types (AVs and CVs), as well as those shared by them. For this reason, the graph explicitly includes links representing AVs dedicated lanes (i.e. links used only by AVs), links representing conventional links (i.e. links shared by AVs and CVs) as well as links connecting the above two types are also considered to model merging zones at intersections. In order to apply easy-to-use and computationally fast simulation models for the calculation of link flows and performances for large and complex (real) urban networks in presence of mixed AVs and CVs traffic flows, a user equilibrium assignment model based on the use of an adjusted-BPR cost function for the generic link i can be used [8]. Such a function allows to take into account both run time for link traversing and delay time at intersection as follows:     R2i Li fi δ + 1+γ (1) ti (fi ) V0i 2CNi SNi where L i is the length of link i, V 0i is the “free-flow” speed on link i, f i is the flow of link i, (obtained by summing up flows for each vehicle type) and, considering the final node

596

A. Gemma et al.

N of link i, Ri is the red time, C Ni is the cycle time, S Ni is the saturation flow (function of green factor Gi /C Ni and capacity S sat,i ) and γ and δ are function parameters. The values of V 0i , Ri , C Ni , S Ni and of the parameters γ and δ are differently specified according to link types (e.g. main or side streets) and allowed vehicles (AVs or CVs). In this context, this study aims to analyze mixed traffic flows in presence of different AVs shares by using a microsimulation approach to estimate parameters of (1), that is the free-flow speed V 0i and the capacity S sat,i . It implies changes in parameters of: (i) car-following, (ii) lane changing and (iii) gap-acceptance models, in order to capture the effects of the platooned CVs and AVs mixed flow, which influence the average speed in space and the density and therefore the traffic flow fundamental diagram, from which V 0i and S sat,i can be carried out. The used car following model is that proposed by Wiedemann [19] successively adjusted in the well-known Wiedemann99, where four driving modes (free driving, approaching, following or braking) are computed determining six thresholds [1]: AX: the desired distance between two stationary vehicles AX = L + CC0

(2)

where L is the length of lead vehicle. BX: the minimum following distance considered by drivers as a safe distance BX = AX + CC1 · v

(3)

where v is the slower vehicle speed. CLDV: short distance points where drivers perceive that their speeds are higher than leader speed CLDV =

CC6 (x − L)2 − CC4 17,000

(4)

where Δx is the space headway (front bumper – front bumper). SDX: the maximum following distance SDX = BX + CC2

(5)

SDV: long distance points where drivers perceive speed differences when they are approaching slower vehicles SDV = −

x − SDX − CC4 CC3

(6)

OPDV: short distance points where drivers perceive that they are travelling at a lower speed than the leader OPDV = −

CC6 (x − L)2 − δ · CC5 17,000

(7)

where δ is a dummy variable, set equal to 1 if the speed is higher than CC5 and 0 otherwise.

Automated Vehicles’ Effects on Urban Traffic Flow Parameters

597

Therefore, the Wiedemann’s model can be applied by setting the following parameters: – CC0 (Standstill Distance) has a different value depending on the type of vehicle represented by the leader; it can have a lower value if the leader is an AVs and a higher value when the leader is a traditional vehicle. – CC1 (Headway Time, time distribution of speed-dependent part of desired safety distance) the stochastic part of parameter is eliminated for AVs. – CC2 (Following Variation) representing the maximum variation of the safe distance. This parameter is decreased for AVs in order to eliminate a longitudinal oscillation during the tracking phase: since there is no uncertainty about the speed adopted by the leader vehicle and due to the continuous communication between vehicles this oscillation will not be present. – CC3 (Time to Decelerate to Enter Following) is the time before reaching the safety distance when a vehicle starts decelerating while perceiving a slower vehicle ahead. – CC4 (Negative Following Threshold) and CC5 (Positive Following Threshold) are significantly reduced since the AVs can accelerate and decelerate using the vehicle’s full potential. – CC6 (Speed Dependency of Oscillation) is set equal to zero, during the queuing process the speed and acceleration used by the follower will be based exclusively on speed differences and not on distances, since AVs obviously lack visual perception. – CC7 (Acceleration during Oscillation) is the actual acceleration during the oscillation process. – CC8 (Desired Acceleration from standstill) is the desired acceleration when starting from standstill condition. – CC9 (Desired Acceleration at 80 km/h) the desired acceleration at the speed of 80 km/h. The simulation of CVs and AVs mixed traffic flows requires to modify the above parameters for AVs to consider a different driving mode w.r.t. CVs, that is: – “cautious” (C), the vehicle strictly observes the road circulation rules and always adopts a prudential behavior, which imply large gaps among vehicles; – “normal” (N), the vehicle reproduces the human driver behavior but an extension of the capability to measure distances and speeds of the nearby vehicles within the range its sensors is enabled; – “all-knowing” (A), the vehicle has complete predictive capabilities, which also include cooperative behavior. It leads mainly to smaller gaps for all maneuvers and situations. Moreover, due to the communication between vehicles, the AVs can use the maximum acceleration while the traditional ones use the desired acceleration as well as this communication allows AVs to proceed in platoons (i.e., vehicle move on the network as a convoy of vehicles proceeding at the same speed, acceleration or deceleration and having the same temporal and spatial distance between them). Platooning allows to reduce the temporal and spatial distance among vehicles, which reflects in an increase of capacity. Such an increase depends on the length of platoons

598

A. Gemma et al.

(i.e. of the number of vehicles forming the platoon) and the literature reports different studies investigating this particular aspect and the difficult to define the correct length and the optimal number of platooned vehicles on links and intersections [7], e.g. varying from 2 to 8 vehicles on a basis of a gap of 0.2 s. Platooning will have consequences on lane changing and gap acceptance modelling. In particular, the main difference between AVs and CVs concerns the gap acceptance model, namely the gap choice, headway distribution and queued vehicles; this model is differently specified both for conflict areas and right-of-way. Specifically, in the case of AVs a unique critical gap can be considered; it replaces the set of values usually used for CVs to simulate different human driving behaviors. The follow up time can be modified through CC0 (standstill distance) and CC8 (starting acceleration from standstill) parameters, and according to the Weidmann99 formulation it also reflects on the car-following model, in which CC0, CC1 and CC8 influence the intersection capacity. Finally, a further adaptation of the lane changing model to simulate AVs regards the Safety Distance Reduction Factor, which must be decreased due to the AVs driving ability. Such a decrease should be greater in the case of communication among vehicles and should be different according to the road type (e.g., primary or secondary road) and to the above AVs driving modes (e.g., Cautious, Normal, All-knowing). Table 1 summarizes the strategy of modifying the parameters of the Wiedemann model to consider AVs driving behavior as suggested in the results of the CoEXist European Project [7].

4 Application The case study is focused on the south-east part of the road network of Rome (Italy), whose traffic is also due to the presence of the Tor Vergata University and its Hospital, representing the major demand attractor in this area. The road network under investigation (see Fig. 1) includes both urban streets and motorways usually charactered by severe congestion in the workday peak hours. In order to apply the modelling approach described in Sect. 2, the Vissim™ [13] microsimulator has been used. It has been calibrated on the basis of a traffic dataset collected in 2019 (pre-covid period), which refers to the workday morning hours (6 a.m. – 9 a.m.) when traffic peaks usually occur. The validation of the model is shown in Fig. 2, where the detected and simulated flows are compared. The goodness of validation is testified by the R2 value, that reaches 0.97. Moreover, the maximum GEH, starting from the value of 17 (before calibration), reaches the value of 4.4 (after) in the calibrated model. Aiming at estimating parameters of (1) in presence of mixed traffic flows including AVs, parameters of the Wiedemann99 model used by Vissim™ have been changed as reported in Table 2. Simulation scenarios are based on the replacement of the traditional CVs of the above traffic dataset (2019) with AVs in steps of +25% up to the complete substitution, that in the different scenarios artificially simulate the presence of all-CV vehicles (0% AVs) and all-AV ones (100% AVs), passing through the mixed cases of 25%, 50% and 75% of AVs.

Automated Vehicles’ Effects on Urban Traffic Flow Parameters

599

Table 1. AVs driving behavior - strategy of modifying the Wiedemann model parameters Driving behavior

(C) Cautious

(N) Normal

(A) All-knowing

Safety distance + reduction factor

−

−

Minimum headway (front-rear)

+

=

=

Max − deceleration for cooperative breaking

−

=

Lane changing

Lane change necessary to follow a defined route (it is not overtaking because of higher own desired speed) Current vehicle

Next vehicle

Current vehicle

Next vehicle

Current vehicle

Next vehicle

Maximum deceleration

−

−

=

−

=

+

−1 m/s per distance

−

−

=

=

=

−

Accepted deceleration

−

−

=

=

=

+

“+” recommends a parameter higher w.r.t. CVs; “−” suggests a parameter smaller w.r.t. CVs; “=” recommends the same parameter adopted for CVs

Results of the simulated scenarios in terms of capacity of a single lane link are shown in Fig. 3. They refer to different percentages of AVs in the mixed traffic flow and to different driving behaviors under the sole effect of longitudinal conditioning among vehicles, which is typical of urban roads. As we can see from Fig. 3, the reference link capacity on the considered road stretch (i.e. AVs 0%) is about 1800 veh/h/ln and is obviously the same whatever the driving behavior is. Referring to the C driving behavior, the introduction of increasing AVs percentages of vehicles in the mixed traffic flows induces a reduction of link capacity of about the 28% (from 1796 to 1294 veh/h/ln) due to the safe-centric behavior of AVs which characterizes the C driving mode. The other driving modes (N and A) allow to obtain the real benefits of automated connected and cooperative vehicles, as we can see from the comparison of link capacity values reported in Fig. 3: the greater the AVs percentage the greater the increase in capacity. In fact, passing from AVs 0% to AVs 100% we observe a capacity increase of the 21.4% (from 1796 to 2180 veh/h/ln) for the N driving mode, which arrives at the 24.6% (from 1796 to 2237 veh/h/ln) in the case of A driving mode.

600

A. Gemma et al.

Fig. 1. The study area After 5500

4500

5000

simulated (veh/h)

simulated (veh/h)

Before 5000

4000 3500 3000 2500 2000 2000

y = 0.7112x + 774.32 R² = 0.5424 2500

3000

3500

4000

4500

5000

5500

4500 4000 3500 3000 2500 2000 2000

y = 0.9896x - 3.233 R² = 0.9657 2500

counted (veh/h)

3000

3500

4000

4500

5000

5500

counted (veh/h)

Fig. 2. Model validation

The positive effects on capacity also reflect in terms of traffic flow performances. Such performances can be measured through speed and density as can be seen in the density-flow (Fig. 4) and speed-flow (Fig. 5) curves, which describe the case of AVs100% in the N driving mode compared with the current state (AVs 0%). It can be seen the general improvement of the traffic performances obtained in the AVs100% scenario, which is evidenced both by the link capacity increasing of about 400 veh/h/ln, and by the fact that the cloud of points in the density-flow curve extends along the stable branch without ending in the unstable branch. Thus, despite the link is under congestion in the current state (AVs0%), in the case of AVs100% this is clearly mitigated and the outflow performances improve. Such an improvement is even more evident by

Automated Vehicles’ Effects on Urban Traffic Flow Parameters

601

Table 2. AVs parameters adopted in the Wiedemann99 model Car following

Lane change necessary to follow a defined route (it is not overtaking because of higher own desired speed)

CC0

1.5 m

CC1

1.6 s

CC2

0

Maximum deceleration

CC3

−11

CC4

−0.1

CC5

0.1

Lane changing

CC6

0

Safety distance reduction factor

0.7 + EABD*

CC7

0.1 m/s2

Minimum headway (front-rear)

0.65 m

CC8

3.25 m/s2

Max deceleration for cooperative breaking −4.5 m/s2

CC9

1.35 m/s2 Cooperative lane changing

Current vehicle Next vehicle −3.75 m/s2

−3.2 m/s2

−1 m/s per distance

180 m

180 m

Accepted deceleration

−1.5 m/s2

−1.0 m/s2

On

* EABD - Enforce Absolute Breaking Distance

2500 C - Cautious

capacity (veh/h/ln)

2250

2237

N - Normal A - All-knowing

2180

2147

2138 1976

2000

1912

1887

1883

1796 1750

1678 1547

1500

1430 1294

1250

1000 0%

25%

50%

75%

100%

AVs %

Fig. 3. Application results – link capacity

analyzing the speed-flow diagram in which the AVs100% cloud of points is located at much higher speeds than in the AVs0%, reaching maximum values of about 130 km/h, unlike the maximum values of the AVs0% that are around 100 km/h. In addition, the AVs0% dispersion of points is such as to create a concentration in correspondence of low speed values, while the concentration of AVs100% points is created in correspondence of speeds ranging between 120 and 90 km/h. Similar effects and related curves obviously have been obtained for the other driving modes.

602

A. Gemma et al. 80 AVs 0%

70

density [veh/km/ln]

AVs 100% 60 50 40 30 20 10 0 0

500

1000

1500

2000

2500

flow [veh/h/ln]

Fig. 4. Application results – density-flow curve (AVs 100% N driving mode) 140 AVs 0%

120

AVs 100%

speed [km/h]

100 80 60 40 20 0 0

500

1000

1500

2000

2500

flow [veh/h/ln]

Fig. 5. Application results – speed-flow curve (AVs 100% N driving mode)

Overall, the maximization of the benefits of introducing AVs besides in the platooning enabled by automated connected and cooperative vehicles, which is discussed below. Different platooning size have been simulated (2,4,6,8 vehicles) and results of the platoon of 8 vehicles exhibit an increased capacity of about 22% and better outflow performances as shown by the density-flow (Fig. 6) and speed-flow (Fig. 7) curves.

5 Conclusions This paper investigated traffic flow parameters to be set in volume-delay cost functions used in transport modelling when a mixed traffic flow made of AVs and CVs is considered. They allow to implement more reliable meso-simulation assignment models to support

Automated Vehicles’ Effects on Urban Traffic Flow Parameters

603

80 AVs 100% - not platooned

70

density [veh/km/ln]

AVs 100% - platoon 8 60 50 40 30 20 10 0 0

500

1000

1500

2000

2500

3000

flow [veh/h/ln]

Fig. 6. Application results – platooning effects on density (AVs 100% N driving mode)

140 120

speed [km/h]

100 80 AVs 100% - not platooned 60 AVs 100% - platoon 8 40 20 0 0

500

1000

1500

2000

2500

3000

flow [veh/h/ln]

Fig. 7. Application results – platooning effects on speed (AVs 100% N driving mode)

decision-makers to approach the transition to the future mobility on large and complex networks, when the presence of mixed AVs and CVs traffic flows will need to be managed. This paper proposed to use a microsimulation approach to obtain parameters needed to set-up BPR cost function parameters for different link types (primary or secondary roads) and approaching different intersection (e.g. signalized), and considering different AVs driving modes according to the interaction of AVs and CVs. Results of an application to the road network of Rome (Italy) allowed us to show the goodness of the proposed approach. In fact, the simulation and the analysis of a link representing a primary road allowed us to assess the benefits of introducing AVs in terms of traffic performances that in the better case (all AVs and vehicle platoons of 8 vehicles) is about the 30% in speed and 50% in capacity w.r.t. the current state (all CVs). The implementation of the results of this study are part of a wider system of models under development to support

604

A. Gemma et al.

feasibility studies on the effects of the introduction of AVs in the city of Rome; they represent part of the future development of this research.

References 1. Aghabayk, K., Sarvi, M., Young, W., Kautzsch, L.: A novel methodology for evolutionary calibration of VISSIM by multi-threading. In: Australasian Transport Research Forum 2013 Proceedings, pp. 1–15 (2013) 2. Aimsun Next: Connected and Autonomous Vehicles. Available at https://www.aimsun.com/ connected-and-autonomous-vehicles/. Last accessed 2022/06/08 3. Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: SUMO—simulation of urban mobility an overview. Paper presented at the 3rd International Conference on Advances in System Simulation, Barcelona, Spain, 23–29 Oct 2011 4. Bekiaris-Liberis, N., Roncoli, C., Papageorgiou, M.: Highway traffic state estimation with mixed connected and conventional vehicles. IEEE Trans. Intell. Transp. Syst. 17, 3484–3497 (2016) 5. Cantarella, G.E., Di Febbraro, A.: Transportation systems with autonomous vehicles: models and algorithms for equilibrium assignment. Transp. Res. Procedia 27, 349–356 (2017) 6. Chen, D., Ahn, S., Chitturi, M., Noyce, D.A.: Towards vehicle automation: roadway capacity formulation for traffic mixed with regular and automated vehicles. Transp. Res. Part B: Methodol. 100, 196–221 (2017) 7. CoEXist: AV-Ready Transport Models and Road Infrastructure for the Coexistence of Automated and Conventional Vehicles. European Research Project, Horizon 2020-EU.3.4., https:// www.h2020-coexist.eu. Last accessed 2021/01/14 8. Crisalli, U., Polimeni, A.: A meso-simulation approach for the estimation of traffic flows in presence of automated vehicles. Transp. Res. Procedia 47, 481–488 (2020) 9. Gong, S., Du, L.: Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles. Transp. Res. Part B 116, 25–61 (2018) 10. Levin, M.W., Boyles, S.D.: Effects of autonomous vehicle ownership on trip, mode, and route choice. Transp. Res. Rec. 2493, 29–38 (2015) 11. Levin, M.W., Kockelman, K.M., Boyles, S.D., Li, T.: A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application. Comput. Environ. Urban Syst. 64, 373–383 (2017) 12. Nuzzolo, A., Crisalli, U., Polimeni, A.: Sharing mobility: lane accommodation in urban road networks with automated vehicles. In: Proceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, USA (2018) 13. PTV-AG: VISSIM 2020 Manual (2020) 14. Sagir, F., Ukkusuri, S.V.: Mobility impacts of autonomous vehicle systems. In: Proceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, USA (2018) 15. Society of Automotive Engineers: Automated driving levels of driving automation are defined in new sae international standard j3016. https://www.sae.org/misc/pdfs/automated_driving. pdf. Last accessed 2022/06/07 16. Stanek, D., Milam, R., Huang, E., Wang, Y.: Measuring autonomous vehicle impacts on congested networks using simulation. In: Proceedings of Transportation Research Board, 97th Annual Meeting, Washington (2017) ˇ 17. Vrbani´c, F., Cakija, D., Kuši´c, K., Ivanjko, E.: Traffic flow simulators with connected and autonomous vehicles: a short review. In: Petrovi´c, M., Novaˇcko, L. (eds.) Transformation of Transportation. E, pp. 15–30. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-664 64-0_2

Automated Vehicles’ Effects on Urban Traffic Flow Parameters

605

18. Wen-Xing, Z., Li-Dong, Z.: A new car-following model for autonomous vehicles flow with mean expected velocity field. Phys. A: Stat. Mech. Its Appl. 492, 2154–2165 (2018) 19. Wiedemann, R.: Simulation Des Straßenverkehrsflusses. Instituts fur Verkehrswesen der Universitat Karlsruhe, Heft 8, Karlsruhe, Germany (1974)

The Impact of CNG on Buses Fleet Decarbonization: A Case Study João Paulo Fontoura Oliveira1 , Tânia Fontes1(B) , and Teresa Galvão1,2 1 INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Rua

Dr. Roberto Frias, 4200-465 Porto, Portugal {joao.p.fontoura,tania.d.fontes,teresa.galvao}@inesctec.pt 2 Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal

Abstract. By 2050, and in the context of decarbonization and carbon neutrality, many companies worldwide are looking for low-carbon alternatives. Transport companies are probably the most challenging due to the continuing growth in global demand and the high dependency on fossil fuels. Some alternatives are emerging to replace conventional diesel vehicles and thus reduce greenhouse gas emissions and air pollutants. One of these alternatives is the adoption of compressed natural gas (CNG). In this paper, we provide a detailed study of the current emissions from the largest bus fleet company in the metropolitan area of Oporto. For this analysis, we used a top-down and a bottom-up methodology based on EMEP/EEA guidebook to compute the CO2 and air pollution (CO, NMVOC, PM2.5 , and NOx ) emissions from the fleet. Fuel consumption, energy consumption, vehicle slaughter, electric bus incorporation, and the investments made were taken into consideration in the analyses. From the case study, the overall reduction in CO2 emission was just 6.3%, and the emission factors (air pollutants) from CNG-powered buses and diesel-powered buses are closer and closer. For confirming these results and question the effectiveness of the fleet transitions from diesel to CNG vehicles, we analysed two scenarios. The obtained results reveal the potential and effectiveness of electric buses and other fuel alternatives to reduce CO2 and air pollution. Keywords: Compressed natural gas (CNG) · Diesel · Public transport · Urban buses · Road emissions · CO2 · Pollution

1 Introduction Monitoring air pollution is extremely important for the health and well-being of the population. Every year outdoor air pollution causes about 4.2 million deaths [1]. Pollutants such as particulate matter (PM), carbon monoxide (CO), nitrogen oxides (NOx ), and non-methane volatile organic compounds (NMVOC) need to be tracked frequently. In addition, greenhouse gases (GEE) such as carbon dioxide (CO2 ) accumulate in the atmosphere they absorb the reflected infrared radiation, causing global temperatures to rise. This increase is the cause of more frequent, more intense, and adverse meteorological events such as hurricanes, floods, downpours, and winter storms [2]. It may also © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_50

The Impact of CNG on Buses Fleet Decarbonization: A Case Study

607

cause harm to other living organisms, such as animals and food crops. For these reasons, CO2 was recently named a leading pollutant by the IPCC [2]. For facing the concerns of climate change, many countries have signed the Paris Agreement to keep global warming below 2 °C. This ambitious goal requires economies around the globe to decarbonise large parts of the global energy system. The agreement also aims to strengthen countries’ capacity to deal with the impacts of climate change and support them in their efforts. In line with this goal, the European Union (EU) created the European Green Deal instrument which set outs a roadmap for preparing a long-term strategy to achieve carbon neutrality for economies [3]. The transport sector is one of the most challenging to achieve zero pollutant emissions and carbon neutrality. According to the International Energy Agency [4], CO2 emissions from the transport sector increased by less than 0.5% in 2019 (compared to 1.9% annually since 2000) and transport is responsible for 24% of direct CO2 emissions from fuel combustion. Road vehicles (cars, trucks, buses, and two- and three-wheelers) account for almost three-quarters of transport-related CO2 emissions [4]. In the EU, transport emissions increased by 0.8% in 2019 (excluding shipping) [4]. These are the slowest rates since 2014. On the other hand, projections indicate that GEE emissions from transport will decrease relatively little by 2030 compared to current levels [4]. Therefore, all transport sub-sectors need to be much more ambitious to achieve EU carbon neutrality by 2050. Furthermore, it is extremely important to track current emissions and have tools to identify the best alternatives to reduce future emissions. As opposite to hybrid technology and electric cars, the use of natural gas in conventional engines has technically matured since 2010 [5]. As result, the use of natural gas in road transport, especially in heavy-duty vehicles and buses, has been seen one of the most suitable alternatives to reduce emissions [6]. For this reason, many public transport operators are retrofitting their fleets and replacing diesel buses with compressed natural gas (CNG) buses. Therefore, it is important to track vehicle emissions so that transport operators can invest in better solutions to reduce urban pollution and achieve carbon neutrality. The main objective of this study is to analyse whether the economic investment in fleet conversion of a public transport company has been effective in reducing CO2 emissions and air pollutants. Data were collected from a public transport company operating in a medium-sized European city. The paper is structured as follows. First, a literature review is given. Then, an overview of the context of the study is provided, and the methodology used is defined. Finally, the results are presented and discussed, and the main conclusions of the paper are outlined.

2 Literature Review Several authors pointed out that the performances achieved by CNG buses are promising and could confirm the environmental interest, and lead to lower CO2 emissions if the new CNG advanced engine technology is adopted [7, 8]. As result, the number of CNGpowered vehicles is increasing. According to the Natural Gas Vehicle Statistics, at the end of 2019, the natural gas-powered vehicles reached 28,540,819 units and 33,383

608

J. P. F. Oliveira et al.

natural gas fuelling stations worldwide [9]. In Europe, the natural gas-powered vehicles reached 2,062,621 units and 5194 natural gas fuelling stations [9]. Lowel [10] concluded that the use of new diesel buses is the better choice as they can achieve higher annual NOx , CO, and CO2 reduction, compared to standard CNG bus technology. Alternatively, according to Nylund and Koponen [11], the most effective way to cut GEE emissions is to switch from fossil fuels to efficient biofuels. Merkisz et al. [12] presented the results of emissions under real traffic conditions on a regular bus route. The study concluded that the use of different types of fuels leads to a reduction of air pollutants and thus to improvement in the quality of life in the city. However, the lower efficiency of the CNG engine work cycle mat slightly increases CO2 emissions, which are also related to fuel consumption. Other studies have examined the impact of converting heavy-duty vehicle fleets. Rose et al. [5] analysed real-time operational data obtained from a municipal organization. It was found that replacing a diesel engine with a CNG engine for heavy-duty refuse vehicles does not result in any net energy savings. However, CNG-powered refuse collection vehicles lead to significant GEE savings compared to diesel-powered refuse collection vehicles, but only if a new CNG engine is used. Additionally, Galbieri et al. [13] concluded that the use of CNG in the bus fleets can have a positive impact on CO2 and PM emission mitigation but depending on how many conventional diesel vehicles are replaced. Hallquist et al. [14] also presented the results from a real-world exhaust gas diffusion method. The study concluded that CNG buses are more advantageous regarding emissions of PM than diesel buses. However, in accelerating mode, CNG buses emit more particles by number compared to diesel-fuelled buses. Given this, the best solution is not identified. Tracking the vehicles’ emissions is essential to properly determine the alternative to reduce CO2 and air pollutants.

3 Material and Methods 3.1 Data The impact of the bus fleet on emissions was analysed for a bus company operating in a medium-sized European city, Oporto. Oporto is the second-largest city in Portugal, and one of the Iberian Peninsula’s major urban areas. The company transport almost 76 million passengers yearly in a metropolitan region with around 1.7 million people (in 2021) and has an area of approximately 2395 km2 . The road network has an extension of 494 km, split from 72 lines. The occupation rate is about 13.2%, and the average travelling speed is near 15.8 km h−1 . The fleet characteristics such as vehicle model, fuel type, vehicle age, and the annual mileage per vehicle type were collected from public reports of the company [15]. Details about the slaughter and investments in the vehicles were also collected. The period of study ranges from 2012 to 2020. The focus of the study will be just the bus fleet. 3.2 Emissions Estimative To estimate the emissions, we use the EEA/EMEP guidebook which was first published in 1992 and provides concise guides on how to compile air pollutant emissions inventory

The Impact of CNG on Buses Fleet Decarbonization: A Case Study

609

from both anthropogenic and natural emission sources [16]. In our study, we used the last edition released in 2019. Furthermore, this guidebook provides us three different approaches to calculate the respective emission factor of each pollutant (based on data available): Tier 1 uses fuel as the activity indicator, in combination with average fuelspecific emission factors; Tier 2 considers the fuel used by different vehicle categories and their emission standards; and Tier 3 considers a combination of firm technical data and activity data (detailed methodology). As the data available was not possible to use the same approach to all emission factor, it was defined two different methodologies: a top-down and a bottom-up approaches. The top-down approach was used for calculating the average CO2 emission (based on the energy source consumed) and the bottom-up was used for calculating the average air pollutant emissions. Figure 1 presents an overview of the followed methods.

Fig. 1. Top-down and bottom-up methodologies.

In the top-down methodology, the CO2 emissions were derived from true statistical energy consumption, which is generally known from statistical sources [16]. However, the necessity to allocate emissions to different vehicle categories and technologies cannot be covered only just with statistical consumption, and this is not provided separately for each vehicle class. Taking this into consideration, the calculation for the CO2 emission factor was simplified to use the Tier 1 approach. The top-down methodology uses the annual energy source consumed per fuel type (diesel, natural gas, and electricity) and the annual mileage travelled by energy source. With the respective emission factor, we calculated the average emission by energy source (in kg km−1 ). From electric buses was calculated a corresponding average emission per kilometre, assuming an average of 275 g of CO2 per kWh from the electricity grid [17]. The bottom-up methodology uses as a base the characteristics of fleet distribution, particularly the vehicle model, fuel type, and vehicle age. Therefore, the fleet distribution

610

J. P. F. Oliveira et al.

was categorized and quantified by respective emission standards (Euro I, Euro II, Euro III, Euro IV, Euro V, and EEV) [18]. Based on that, we select the Tier 2 method. This decision was based on the data available in the company reports. Data such as the way the vehicle is driven, the speed, acceleration, and its load are not publicly available, making it impossible to use the Tier 3 option. Although the emission estimation covers many types of exhaust emissions, in this study we take into consideration just the emissions from some air pollutants: CO, NOx , NMVOC, and PM. As PM mass emissions in vehicle exhaust mainly fall in the PM2.5 size range, all PM mass emission factors are assumed to correspond to PM2.5 . Based on the fuel used by the vehicle category and its emission standards (the vehicle technology), default standard emission factors (EF) were selected. The average pollutant emission was estimated by multiplying the standard EF by the annual mileage per vehicle type. Emissions were not estimated for electric buses in the bottom-up methodology, due to the nature of their engine.

4 Results 4.1 Investments, Vehicle Fleet Characterization, and Demand By 2012, the company has committed to reducing emissions from its fleet. The strategy has been focused on: (i) diversification of the energy used in the fleet, increasing the number of buses powered by natural gas and electric buses; and (ii) the renewal of the fleet, replacing old buses with new ones (more efficient and less pollutant). For reducing the pollutant emissions from vehicles, the company implemented multiple actions that changed the fleet structure of the company over time. The efforts of the investments started in 2018 when the company invested 47.4 Me in new infrastructure and new buses. Old diesel buses powered scrapped in 2015, were replaced by CNG-powered vehicles (EEV) in 2018. Later, in 2019 and 2020, the old CNG-powered buses (Euro II) and the old diesel-powered buses (Euro VI) were scrapped and replaced by new CNG-powered buses (EEV) and electric buses. The investment evolution made from 2012 to 2020 is detailed in Fig. 2. In 2020, the bus fleet has 425 vehicles, 76% powered by CNG (natural gas standard (N = 295) and articulated (N = 29)), 20% powered by diesel (diesel standard (N = 43), articulated (N = 20), double-decker (N = 15) and minibuses (N = 8)), and 3.5% powered by electricity (electric standard (N = 15)). The CNG models used were MAN NL 233 (1ª Series), MAN NL 310 (2ª Series), MAN NL 310 (3ª Series), MAN NL A22 (4ª Series), MAN A69 GNC LE (5ª Series), and MAN LCG (1ª Series), while the dieselpowered vehicles were MAN NL 263, Mercedes Citaro (6ª Series), Mercedes Citaro (5ª Series), Mercedes (4ª Series), Volvo B9 Articulated, MAN Lion City, and Volkswagen Mini. The Caetano Bus E. City Gold is the electric bus model. An overall analysis of the level of service shows that the number of transported passengers and the mileage travel decreased tills 2015. After that, the values slighter increase until 2019. In 2020, the number of transported passengers is still constant with average daily traffic of about 195 thousand passengers. However, the mileage travel decreased by about 64.5% (compared to 2019). Figure 3 outlines the evolution from 2012 to 2020 observed by vehicle energy type.

The Impact of CNG on Buses Fleet Decarbonization: A Case Study

611

Fig. 2. Evolution of the bus fleet highlighting the slaughters (O) and investments (I) made from 2012 to 2020.

Fig. 3. Evolution of the mileage travel (a) and the transported passenger (b) between 2012 and 2020.

4.2 CO2 Emissions and Energy Consumption The overall values of CO2 emissions, as well as the efficiency per kilometre trav-elled, were computed for the vehicle fleet. The obtained results were compared with the energy consumption. Figure 4 shows the results achieved.

612

J. P. F. Oliveira et al.

The results demonstrate that the 47.4 Me of investments done between 2018 to 2020 did not have a high impact on CO2 emissions (Fig. 4.a.ii). After a strong fleet renewal, the CO2 total emissions decreased in 2020 by just only 6.3% (compared with 2012). The CO2 emission factor of CNG-powered vehicles is very close to diesel-powered vehicles (Fig. 4.a.i). Only in 2012 and 2020 CNG-powered vehicles was more advantageous than diesel-powered vehicles, reaching a difference of 14.9%. Using electricity, the corresponding CO2 emission factor was about three times lower. The emission factor from CNG-powered vehicles increased from 2012 to 2019. However, after scraping 27.2% of older vehicles in 2020 the emission factor from CNG-powered vehicles decreased. The emission factor of diesel-powered vehicles in 2020 was higher than the 2012 levels. Even using a considerable number of new CNG buses, the energy consumption was still higher than diesel in these years (Fig. 4.b.i and b.ii). The maximum gap between CNG and diesel energy consumption was achieved in 2017 (37.7%), while the minimum difference was achieved in 2012 (5.0%). In 2018, electric buses started to operate in the fleet. The energy consumption of those vehicles was about five times lower than CNG vehicles.

Fig. 4. CO2 emissions (a) and energy consumption (b) from 2012 to 2020.

The Impact of CNG on Buses Fleet Decarbonization: A Case Study

613

4.3 Pollutant Emissions and Scenarios We estimated the emission factor (g km−1 ) and the total emissions (tons) for the vehicle fleet of the transport company. Emissions were computed for CO, NMVOC, NOx and PM2.5 . The results achieved are shows on Fig. 5. After a set of investments in the vehicle fleet, the overall emissions decreased from 2012 to 2020: 45.7% of CO, 64.9% of NMVOC, 59.0% of NOx and 60.8% of PM2.5 . While for some pollutants such as CO and NOx , the emission decreased (mostly because of new CNG-powered vehicles), in the case of PM2.5 this is related to scrapped dieselpowered buses. A comparison of the emission factors between different vehicle types shows that CNG-powered buses have much higher CO and NOx emissions than diesel buses. This trend has decreased over the years. For CO, this difference ranges from 301.8% in 2016 to 89.0% in 2020, while for NOx , ranges from 478.9% in 2016 to 66.7% in 2020. Therefore, the investments made in the vehicle fleet achieve a real improvement in the fleet. Overall, the investments in the vehicle fleet contribute to improving pollutant emissions. However, from 2019 to 2020, the PM2.5 emission factors from diesel increased to levels close to what was observed before 2015. A slight increase is also observed for CO and NOx . The only pollutant analysed that constant decreases the emission factor is NMVOC. This is directly related to the scrapping of diesel buses. In 2014, 52 Euro II and 5 Euro V diesel-powered buses were scrapped (corresponding to 26.1% of the diesel fleet), but in 2020, 4 Euro III and 52 Euro VI diesel-powered buses were scrapped (corresponding to 39.4% of the diesel fleet). Concerning NMVOC and PM2.5 , the emission factors from diesel-powered buses are higher than the emission factors of CNG-powered buses. PM2.5 has in 2020 an emission factor 88.0% lower using CNG than diesel. Regarding NMVOC, the emission factors using CNG, and diesel were almost equal between 2015 and 2017, but in 2020 the emission factor reached a difference of 64.4%. For analysing the impact of vehicle retrofit on pollutant emissions we defined two distinct scenarios. Such scenarios were created to establish two distinct and hypothetical fleet evolution after 2018. Based on that, was possible analyse two different options that could be implemented by the company, and check if the strategy to invest mostly in new CNG buses was the best option at that time. For both cases, the number of total vehicles in the fleet as a baseline was maintained constant. Below is the detailed description of each scenario: • Scenario 1: if from 2018 to 2020 the company had invested just in new diesel buses instead of investing in new CNG buses (e.g., do not buy 223 CNG buses (EEV) but buy the same 223 units of the Mercedes Citaro 6ª Series model (Euro VI)). The strategy of scrapping the old diesel buses (Euro VI) is maintained to keep the number of total vehicles constant. In this scenario, in 2020, the new fleet composition would be 23.8% of CNG buses (N = 101), 72.7% of diesel buses (N = 309), and 3.5% of electric buses (N = 15). • Scenario 2: if from 2018 to 2020 the company had started scrapping all the CNG buses from the fleet and had invested just in new diesel buses. The only CNG bus model that maintains was the articulated MAN LCG 1ª Series with 29 units. This model is

614

J. P. F. Oliveira et al.

very specific and is not possible to replace. The strategy of scrapping the old diesel buses (Euro VI) would change, and the company did not scrap any diesel buses. With this, the total number of vehicles would keep constant. In this scenario, in 2020, the new fleet composition would be 6.9% of CNG buses (N = 29), 89.6% of diesel buses (N = 381), and 3.5% of electric buses (N = 15). In addition to the case study results, the results of each scenario reveal a different perspective that could have been implemented by the company. Below are the results of each scenario: • Scenario 1: CO emissions would be reduced to 68.3%, NMVOC emissions would be reduced to 27.2%, NOx emissions would be reduced to 66.3%, and PM2.5 emissions would be reduced to 63.8%, compared to 2012. Figure 6 shows the results achieved. • Scenario 2: CO emissions would be reduced to 72.6%, NMVOC emissions would be reduced to 7.3%, NOx emissions would be reduced to 78.9%, and PM2.5 emissions would be reduced to 66.4%, compared to 2012. Figure 7 shows the results achieved.

5 Discussion Public transportation systems provide mobility and access to many activities, and are a vital system for cities. Due to continued high demand, the shift to a low carbon public transportation system is challenging, especially in big cities. The challenge is to improve mobility while at the same time reducing GEE emissions and air pollution. It has become ever more evident that fossil fuels are an energy source that will be less important in the future due to limitations in reserves and the pacts and agreements done to reduce CO2 emissions. To prepare for the future, all European cities are looking for new alternatives to reduce emissions from public transport. The combustion process results in a high quantity of heat, steam, CO2 , and byproducts from incomplete fuel oxidation, called exhaust emissions (e.g., CO, NMVOC, and PM) that are directly related to the amount of fossil fuel consumed. Emissions of the remaining pollutants are strongly related to the amount and type of fuel used. These emissions are also affected by the way the vehicle is driven, the speed, acceleration, and vehicle load. Diesel engines have been in commercial use for over a century, used in commercial trucks, trains, ships, and stationary power sources. Because of this, diesel engines have become a mature and reliable technology. The new diesel engines can achieve high performance with high efficiency. However, diesel engines burn a mixture of fuel and air, therefore the exhaust emissions are mainly CO, PM, and NOx . Nowadays electric power has emerged as a real option for the transportation sector, due to technological developments, the high interest in renewable energy, and the potential reduction of transportation’s impact on climate change and greenhouse gas emissions [11]. Therefore, electric motors have much higher efficiency than otto or diesel motors [19]. An important advantage of electric vehicles is that they do not emit any pollutants directly. However, due to the limitations of diesel and electric-powered vehicles, companies have been looking for alternatives.

The Impact of CNG on Buses Fleet Decarbonization: A Case Study

615

Fig. 5. Emission factors (i) and total emission (ii) for (a) CO, (b) NMVOC, (c) NOx , and (d) PM2.5, recorded by the transport company from 2012 to 2020 (baseline situation).

616

J. P. F. Oliveira et al.

Fig. 6. Emission factors (i) and total emission (ii) for (a) CO, (b) NMVOC, (c) NOx , and (d) PM2.5, recorded by the transport company from 2012 to 2020 (considering scenario 1).

The Impact of CNG on Buses Fleet Decarbonization: A Case Study

617

Fig. 7. Emission factors (i) and total emission (ii) for (a) CO, (b) NMVOC, (c) NOx , and (d) PM2.5, recorded by the transport company from 2012 to 2020 (considering scenario 2).

618

J. P. F. Oliveira et al.

Last years, CNG-powered vehicles have emerged as an alternative for bus companies. CNG vehicles have environmental advantages, emitting substantially less NOx and PM than all conventional engines available today (assuming a comparable power and weight). Also, CNG has the highest energy/carbon ratio of any fossil fuel, and thus produces less CO2 per unit of energy. According to the U.S. Energy Information Administration [20], the amount of CO2 released per BTU (British Thermal Unit) of heat, when CNG is burned, is about 28% less than diesel. However, the CNG-powered engine efficiency is considerably lower than diesel engines [21]. Therefore, the amount of CO2 reduction can be much lower than expected and, as shown in the case study assessed, equivalent to a diesel-powered vehicle fleet (Fig. 4.a.i). CNG is composed basically of methane (CH4 ) and may also contain heavier gaseous hydrocarbons such as ethane (C2 H6 ), propane (C3 H8 ), and butane (C4 H10 ) [20]. As it is a gas, there is always the possibility of leaking, and if this happens, releasing one kilogram of CH4 is equivalent to a direct release of 84 kg of CO2 into the atmosphere.

6 Conclusions In this study, we assessed the impact of the renewal of the bus fleet on GEE and pollutant emissions by analysing a medium European city, Oporto, in Portugal. Data were collected from the largest public transport operator in Oporto and slaughters and investments were analysed from 2012 to 2020. Based on EMEP/EEA guidebook, emissions were computed using two distinct methodologies: top-down to calculate the average CO2 emission; and bottom-up to calculate the average air pollutant emissions. To analyse alternative solutions, two distinct scenarios were investigated. Considering the analyses of each scenario and the analyses of the case study, the impact of CNG on buses fleet has pros and cons: • Pros: In a general perspective all air pollutant levels declined. Related to the management of PM2.5 concentration values [22], and this is the case where the company is operating, we can conclude that the investments in CNG-powered were efficient and appropriate, as the levels of PM2.5 emissions declined. • Cons: during the 8 years analysed were invested 47.4 Me and scraped 235 vehicles, ~60% of the total vehicles observed. However, an overall reduction in CO2 emission was just only 6.3%. Moreover, the energy consumption per kilometre travelled per year is much higher using CNG-powered vehicles than diesel-powered vehicles. • From both scenarios, we can conclude that is possible to reduce pollutant emissions just with diesel vehicle renovation, which requires much fewer investments than CNGpowered vehicles. Therefore, we can conclude that CNG-powered vehicles are an option that is technologically well developed. However, the low efficiency of CNG engines leads companies to consider other alternatives to reduce CO2 emissions and air pollution. The bus fleet decarbonization through CNG buses needs more public discussion and attention. Based on the data available, pure electric buses can be a good option. The reasons are: (i) demonstrated high efficiency of electric motors, (ii) very low corresponding CO2 emissions per km, and (iii) zero pollutant emissions. Moreover, the emission factors from

The Impact of CNG on Buses Fleet Decarbonization: A Case Study

619

CNG-powered buses and diesel-powered buses are closer and closer, which can justify the bet on the energy diversification of the fleet. In a general view, it is necessary to invest more in electricity and other fuel alternatives to reduce CO2 and air pollution. The results of the present study can be used as input to the strategic decision-making process for future transport energy policy. Acknowledgement. This work is financed by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within the project e-LOG (EXPL/ECI-TRA/0679/2021). Tânia Fontes also thanks FCT for the Post-Doctoral scholarship SFRH/BPD/109426/2015.

References 1. World Health Organization (WHO) Homepage: Air pollution section. https://www.who.int/ health-topics/air-pollution#tab=tab_1. Last accessed 2021/12/18 2. Climate Change 2021 Homepage: The Physical Science Basis. https://www.ipcc.ch/report/ ar6/wg1/. Last accessed 2021/12/18 3. European Union Homepage: European Green Deal Section. https://ec.europa.eu/info/strategy/ priorities-2019-2024/european-green-deal_en. Last accessed 2021/12/18 4. IEA Homepage: Tracking Transport Report. Retrieved from https://www.iea.org/. Last accessed 2021/12/22 5. Rose, L., Hussain, M., Ahmed, S., Malek, K., Costanzo, R., Kjeang, E.: A comparative life cycle assessment of diesel and compressed natural gas-powered refuse collection vehicles in a Canadian city. Energy Policy 52, 453–461 (2013) 6. Engerer, H., Horn, M.: Natural gas vehicles: an option for Europe. Energy Policy 38, 1017– 1029 (2010) 7. Plassat, P., Coroller, G.: Comparative study on exhaust emissions from diesel and CNG powered urban buses. In: DEER 2003 Conference. French Agency of Environment and Energy Management (ADEME) Air & Transport Division (2003) 8. Pulkrabek, W.W.: Engineering Fundamentals of the Internal Combustion Engine. Prentice Hall (1997) 9. NGV (Natural Gas Vehicle) Database: NGV Global Statistics Homepage. Retrieved from https://www.iangv.org/current-ngv-stats/. Last accessed 2021/12/22 10. Lowell, D.: Clean Diesel Versus CNG Buses: Cost, Air Quality, and Climate Impacts 11. Nylund, N.-O., Koponen, K.: Fuel and technology alternatives for buses. VTT Technol. 46 (2012) 12. Merkisz, J., Fuc, P., Lijewski, P., Pielecha, J.: Actual emissions from urban buses powered with diesel and gas engines. In: TRA (ed.), 6th Transport Research Arena 3070–3078 (2016) 13. Galbieri, R., Brito, T., Mouette, D., Costa, H., dos Santos, E., Fagá, M.: Bus Fleet Emissions: New Strategies for Mitigation by Adopting Natural Gas. Part of Springer Nature (2017) 14. Hallquist, A.M., Jerksjo, M., Fallgren, H., Westerlund, J., Sjodin, A.: Particle and gaseous emissions from individual diesel and CNG buses. Atmos. Chem. Phys. Discuss 13, 5337–5350 (2013) 15. STCP Homepage: Institutional Section. Retrieved from https://www.stcp.pt/pt/institucional/ governo-societario/relatorio-e-contas/. Last accessed 2022/01/21 16. EEA: EMEP/EEA air pollutant emission inventory guidebook. Guidebook from European Monitoring and Evaluation Program (EMEP) and European Environment Agency (EEA) (2019). https://www.eea.europa.eu/

620

J. P. F. Oliveira et al.

17. APREN: Boletim Eletricidade Renovável (2019). https://www.apren.pt/contents/publications reportcarditems/02-boletim-energias-renovaveis-vf.pdf 18. Regulation No 582/2011 concerning emissions from heavy-duty vehicles. https://eur-lex.eur opa.eu/legal-content/EN/TXT/?uri=CELEX%3A02011R0582-20210101 19. Argonne National Laboratory: An extensive study on sizing, Energy consumption, and cost of advanced vehicle technology (2018). Retrieved from the U.S. Department of Energy website, https://afdc.energy.gov/fuels/electricity_research.html 20. U.S. Energy Information Administration Homepage. https://www.eia.gov/energyexplained/ natural-gas/. Last accessed 2021/12/22 21. Nanaki, E.A., Koroneos, C.J., Xydis, G.A., Rovas, D.: Comparative environmental assessment of Athens urban buses diesel, CNG and biofuel-powered. Transp. Policy 35, 311–318 (2014) 22. Ambient air quality and cleaner air for Europe. Directive 2008/50/EC of the European Parliament (2008). https://ec.europa.eu/environment/air/quality/existing_leg.htm

A Study on the Use of Autonomous Vehicles for the Interconnection of Urban Transport Interchanges Anastasia Georganti, Nikolaos Soumpasis, and Giannis Adamos(B) Traffic, Transportation and Logistics Laboratory, University of Thessaly, Pedion Areos, 38334 Volos, Greece [email protected]

Abstract. In recent years the design of a multimodal transport system has been promoted and expanded to minimize the environmental impacts that are flourishing under the effect of rapid urbanization. Multimodal transport is expected to compete with the comfort and instant accessibility that the private car offers. In this paper, the proper design of such a network is studied, which implies seamless services, in the framework of which the itineraries and schedules of the various means are coordinated with each other. A structured literature review was conducted, addressing applications of smart payment methods, as well as services related to intelligent transport systems provided in Greece and abroad, while the need to address the challenges arising from the implementation of such a system is emphasized. The city of Volos, Greece was chosen to be tested as a case study, since it is a tourist attraction throughout the year, emphasizing the need of interconnectivity. In particular, the operation of each terminal in the city is analyzed, while at the same time the interconnection between them is evaluated, with the Urban Public Transport Operator acting as a connecting link. Data were collected through an online questionnaire survey capturing the habits and willingness of 248 travelers to participate in an integrated transport system, along with interviews with stakeholders from selected sectors, i.e. public authorities, operators, businesses, academia, etc. Results showed that users are quite positive to the idea of introducing automation in public transport and consider that there will be an improvement in the situation of the road network and in their daily movements. In addition, they showed enthusiasm for the concept of multimodality, and they assessed it as a system to be quite resilient even in times of pandemic like COVID-19. Lastly, measures are proposed that will upgrade the interconnection of the terminals and consequently facilitate mobility of all passengers. Keywords: City-hubs · Public transport · Autonomous systems · Travelers · Operators

Thematic track: Urban planning and transport infrastructure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_51

Fostering the Autonomous Driving in Urban Mobility Operation (Passengers and Goods) – INTEGRA Network Sergio Güerri Ferraz(B)

and Mireia Calvo Monteagudo

ITENE Research Centre, Albert Einstein Street 1, 46980 Paterna, Valencia, Spain [email protected]

Abstract. Autonomous driving is an innovative solution for future transport but, which are the suitable scenarios to start its implementation? The INTEGRA project analyses the different scenarios where autonomous vehicles can be applied taking into consideration the accessibility and integration of all the population. This analysis is focused on the different situation in which this technology can be implemented in an urban environment for transporting both freight and passengers. Firstly, the city movements are analysed in deep from the automation point of view. In order to study freight transport is considered both business to business and business to consumer delivery. For both deliveries the parameters analysed are type of product, type of vehicle needed, infrastructure required, which user will pick up the goods and how. In turn, the passenger transport is considered as an integrated one that would benefit all the citizens, especially those who are not able to drive. After this analysis, the study recommends the suitable scenarios to start implementing autonomous driving considering the view of stakeholders involved in each scenario. In any case, and in conclusion, it must be realised that initially not all the scenarios and situations will be able to change to autonomous vehicle due to the users’ expectations and the need of interactions, i.e., probably the autonomous vehicle will be a standard operation but in specific situations a person will be needed in this transport, and both systems will need to collaborate. Keywords: Autonomous vehicle · Transport · Smart city (delivery citizens)

1 Initial Overview For years, pollution and traffic have been among the main problems facing cities. In this context, social and economic trends, changing living and consumption habits of citizens and the exponential increase in digital commerce are expected to generate significant increases in activity and flow of goods that will further stress the ecosystem in the coming years. For this reason, it is necessary to take measures in terms of mobility and, specifically in urban distribution of goods to ensure the quality of life of citizens in large cities. These problems not only have consequences for the well-being of citizens, but © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_52

Fostering the Autonomous Driving in Urban Mobility Operation

623

also generate a significant loss of competitiveness. According to the EU,1 metropolitan mobility accounts for 40% of all CO2 emissions of road transport and up to 70% of other pollutants from transport. In addition, congestion in the EU is often located in and around urban areas and costs nearly EUR 100 billion, or 1% of the EU’s GDP, annually. Freight distribution, on its part, represents among 20 and 25% of road space use, 10–20% of urban road traffic and 45% of transport related energy consumption.2 Urban freight is also responsible of a notorious part of ambient noise, vibration and congestion, therefore affecting quality life.3 Achieving a cleaner and more efficient urban logistics system requires better integration of urban freight both within the transport system, the city and the metropolitan and surrounding areas.4 Cities are changing, and so must the urban distribution of goods. As a result, last mile operations must adapt to new trends: • • • •

Customer demand: e-commerce, instant deliveries. Technology supporting the sector: clean fuel (including electric vehicles - EVs). Telematics, use of real-time data. Business models and operations: internet ordering, Omni-channel, near-sourcing, port-centric logistics. • Fragmentation of supply chains: increasing number of vans. • Increased political profile of freight transport, also due to safety issues (fatal accidents). In this context, local authorities have recently developed a growing, yet probably marginal, awareness of the crucial role urban freight measures play within the overall urban mobility system. They should aim at balancing two apparently conflicting elements: a freight distribution system effectively and efficiently responding to market demand, and a satisfactory environmental sustainability level.5 Therefore necessary to identify measures that could solve this trade-off by maximising the efficiency of the services and freight deliveries and minimising the number of trips and the derived environmental impacts.6 The EU Commission has estimated that the implementation of the comprehensive set of recommendations from the SUMP process in an area can lead to a CO2 emission reduction of between 35 and 70% by 2040,7 with projected savings in public and private 1 European Commission. Clean transport, Urban transport. https://ec.europa.eu/transport/the

mes/urban/urban_mobility_en. 2 Green Paper “Towards a new culture for urban mobility”, EC 25/09/2007. 3 Quak, H. J. (2008) Sustainability of Urban Freight Transport – Retail Distribution and Local

4 5 6 7

Regulations in Cities. ERIM, Rotterdam (ERIM Ph.D. Series Research in Management 124, TRAIL Thesis Series T2008/5). http://www.ertrac.org/uploads/documentsearch/id36/ERTRAC_Alice_Urban_Freight.pdf. Taniguchi, E., Thompson, R. 2014. City Logistics: Mapping The Future. CRC Press. Policy Department for Structural and Cohesion Policies Directorate-General for Internal Policies PE 652.211. 2020. “Sustainable and smart urban transport”. http://www.ppmc-transport.org/formulate-sustainable-urban-mobility-plans-sumps-in-pri mary-and-secondary-cities.

624

S. G. Ferraz and M. C. Monteagudo

capital and urban transport operating costs in excess of $100 trillion until 2050, and a potential reduction of about 6% of global transport CO2 emissions by 2030. With the above objective in mind, the digital transformation process is intended to drive forward technological and organisational innovations, in this context, autonomous vehicles can play a great role on this conversion into friendly, greener and smart cities. Autonomous driving is a technology that can be applied in different contexts of urban mobility, different scenarios for both passengers and goods are identified. INTEGRA8 project called “Strategic cooperation for research on technologies for autonomous and connected high-security mobility in complex environments” aims to stablish the take off the implementation of autonomous driving in cities. In this sense, INTEGRA has 4 main research areas: 1. Autonomous driving: Develop algorithms for an intelligent and safety driving taking in consideration the environment movements of other vehicles, citizens and road signals. 2. Safety and security of the passenger: Considering the sitting position and the internal security protocols. 3. Communication protocol and simulation: The technologies developed in the previous steps, will be tested in a simulation before its implementation including the vehicle to everything communication to the autonomous vehicle. 4. Security of the goods transported by autonomous vehicles: Analysis and design of the goods container avoiding goods breaking. In order to stablish the ideal scenario for the autonomous vehicle implementation, a deep analysis has been performed in the framework of the project.

2 Methodology The methodology followed during the analysis was the following: 1. Establishment of the requirements needed to consider the implementation of changing the traditional operative into a new delivery system with autonomous vehicles. 2. Identification of the different delivery operatives in urban areas. 3. Analysis of benefits and inconveniences for implementing autonomous vehicles in each scenario 4. Identify the profiles participating in the delivery 5. Interviews with the different companies and entities involved in the delivery. 6. Selection of the delivery scenarios suitable to implement autonomous vehicles.

8 INTEGRA is a consortium project financed by the Spanish Ministry of Science and Innovation

and CDTI. File: 00140188 CER-20211031.

Fostering the Autonomous Driving in Urban Mobility Operation

625

3 Scenarios Analysis The scenarios for autonomous last-mile freight delivery must be defined according to the operation of the vehicles themselves. Depending on the services they provide and to whom the final delivery is addressed: – Business to Business (B2B) From department stores to small shops. All small shops in urban areas need to be provisioned and receive products on a daily or weekly basis from their suppliers. Depending on where the initial product comes from, this type of delivery is usually carried out using different modes of transport, with the last stage being carried out by road transport, usually by truck. This involves the delivery of large volumes of goods. Delivery is usually made on the outskirts of cities to warehouses or smaller shops. These deliveries are usually scheduled, regular and familiar products. – Business to Consumer (B2C) From retail to the end consumer. This is where the shop or retailer makes the final delivery to the consumer’s home. These are usually deliveries with a high degree of capillarity, to different destinations and with small volumes in each delivery. They are made in urban areas in short times and delivery can be made with lighter vehicles. If the final consumer is not present at the time of delivery, we are dealing with a failed delivery, and if the consumer needs to return a product, we are dealing with reverse logistics. A second differentiation can be made within B2C according to the final delivery point: – Those purchases that are delivered to the end customer’s house. – Purchases that are delivered to convenience points selected by the end customer, whether physical shops or lockers, and the end user is responsible for picking it up at the previously agreed delivery point. Within these operations there are several variables to take into account: – – – – – –

Type of product to be delivered: size, fragility, geometry… Type of vehicle: MMA, stowage, number of deliveries… User who picks up the load: profile, age, functional diversity… Loading/unloading places, low emission zones, pedestrian zones, traffic density… Road closures, dead ends, congestion… Road surface: condition, paving stones, slope…

Table 1 summarizes the different operatives within last mile delivery, and the capabilities of using autonomous vehicles. Based on these operatives some specific scenarios for delivering using autonomous vehicles are considered:

626

S. G. Ferraz and M. C. Monteagudo Table 1. Last mile operation characterisation.

Type

Characteristics

Vehicle interaction

Profiles

B2B

Heavy, but known parcels Regular delivery Same origin/destination (known) Loading/unloading zones Large vehicles, trucks…

In municipalities, city centre Unloading: Delivery person or shop personnel Unloading by another robot Incorporate unloading mode in the vehicle itself to a dock

Restaurants, cafeterias (HORECA) Different type of shops: Fashion shops (clothing, footwear…), hardware store… Supermarkets Logistic companies

B2C

Usually light, unfamiliar parcels, different geometry Capillary delivery Unknown destinations, different each time No loading/unloading zones Smaller vehicles (vans) Several deliveries on the same route End customer interacts with the vehicle to make the pick-up Residential areas, city centres, isolated dwellings…

Own residence – Notice of arrival of the vehicle to the user – Confirmation that the user has found the vehicle – Opening of the vehicle to pick up the load – Collection of the correct load Locker – Vehicle opening – Selection of the right package – Opening the locker – Take the package and place it in the locker a. Robotic arm attached to the vehicle b. Another robot picks it up and places it c. Personal Commerce – Staff interacts with the vehicle and picks up the packages

Home-delivered food restaurants Delivery drivers Logistics operators E-commerce customers E-commerce companies E-commerce platform

1. Business to Business: Delivery to restaurants/supermarkets: Heavier and bulky goods Supplying restaurants or supermarkets is a suitable starting point for the implementation of autonomous vehicles. – Periodic and familiar routes – Familiar parcels – In destination, the supermarket or restaurant has employees expecting to receipt the goods. – Someone can pick up the goods from the autonomous vehicle.

Fostering the Autonomous Driving in Urban Mobility Operation

627

Inconveniences – Big vehicles – The route is not only at urban level. – Carry products from various supermarkets/restaurants 2. Delivery of parcels to companies in working areas Benefits – – – –

Small parcels Short distances between one destination and other Few traffic in valley hours. An employee will receive the parcel in the reception of the company. Inconvenience

– Each destination is different every day, although it is in the same area and the area is familiar. 3. Business to Consumer: Delivery of food to doorsteps The delivery of food to home is a possible scenario for starting to implement autonomous vehicles. The main reasons are: – – – – –

This kind of deliveries usually have short distances to get the destinations. It is usually transported one or two deliveries per trip as maximum. All the deliveries are concentrated at the same time. The parcels usually have a similar size. The final consumer is at home when the parcel arrives. The consumer can pick up the parcel from the autonomous vehicle. Inconveniences

– The parcel should maintain the temperature. – The consumer should go out from home to pick the food. 4. Business to Consumer: Convenience point. Lockers This option is considered that the autonomous vehicle is similar to a mobile locker. In this case, a large autonomous vehicle is moving around the city as a mobile point of convenience (mobile locker) between fixed points. Final consumers choose the time and place where they will pick up the parcel from a list of fixed points in the city. Benefits:

628

S. G. Ferraz and M. C. Monteagudo

– The route is always the same. – Final consumers book where and when pick the parcel from a list of fix points. – Several parcels are delivered at the same time Inconvenience – Different kind and size of parcels 5. Business to Consumer. E-commerce delivery at home This case is related to the classical last mile delivery, directly to the consumer’s house. Initially, this case has more barriers than benefits for starting the autonomous vehicle implementation, the main reasons are: – – – – –

Each route is different and have a different destination The parcels are different in size, volume and weight. Several parcels are transport in the same vehicle Final consumer cannot be at home at the moment of the delivery. Sometimes it is necessary to carry the parcel inside the house of the final consumer, due to the parcel is heavy or the consumer is not able to carry it. – Many houses don’t have elevators and the consumer should go out to pick the parcel. – If the final consumer is elderly or have a functional diversity, maybe cannot go out to pick up the parcel or interact with the autonomous vehicle. The main conclusion in the delivery scenarios is that although autonomous vehicles will benefit and improve the last mile delivery reducing congestion and pollution, traditional delivery will continue operating in the city complementing those cases which autonomous vehicles cannot operate properly. In addition, at the beginning of its implementation, a delivery person will be probably needed to support final consumers in the interaction with the autonomous vehicle. In order to validate the proposed analysis, a foresight campaign was carried out, including 30 in-depth interviews with figures responsible for the processes considered in companies with the following profiles: food delivery platforms (such as Just Eat, Glovo…); online supermarket with app and riders; logistics operators (such as SEUR, MRW…); ‘dot pack’ collection points; and restaurants with home delivery. This campaign included questions to characterise the activity of each agent interviewed, as well as the possibility of using autonomous driving in their operations, and its impact on their work. Specifically, these interviews were carried out in Madrid (70%), Seville, Barcelona, Valencia, and smaller locations nationwide (Spain). In concrete terms, in the case of logistics operators, companies were selected with operations at different geographical levels: local, inter-provincial, national, and mixed (local-inter-provincial). In turn, the profiles considered within the structure of the organisation were mainly operations manager and, to a lesser extent, customer service. The main results of the campaign were as follows:

Fostering the Autonomous Driving in Urban Mobility Operation

629

• HORECA companies consider the option of using autonomous vehicles (VA) to be very attractive, as long as the safety of the product and its transport and the correct interaction with the customer are guaranteed • Logistics operators see the use of VA as positive, but have doubts and misgivings about the system, especially as they see it as a threat to the jobs of delivery drivers. In any case, they believe this system will come sooner or later • Delivery platforms are mostly positive about the VA, and start to visualise possibilities for implementation in their business, especially in courier and press deliveries (nonimplementation in their business, especially in courier and press deliveries (nonperishable packages without thermal needs). In short, following the study carried out, the operational scenarios selected to begin the deployment of the autonomous vehicle are as follows: 1. B2C - Delivery of food to doorsteps 2. B2C - Asynchronous delivery. a. Large autonomous vehicle moving around the city as a mobile convenience point (mobile locker) b. Citizens request place and time to pick up the parcel, or c. Locker has a pre-arranged time and place and the citizen goes to pick up the parcel. 3. B2B - Parcel delivery to companies in work areas. Finally, it should be noted that a similar analysis is currently under development for passenger transport operations, the results of which will make it possible to establish scenarios for the use of autonomous vehicles in these operations.

4 Conclusions In general, the implementation of the autonomous vehicle in urban operations will depend to a large extent on how well the solution is received by society and how well the user adapts to it and its conditions. In this context, there are several aspects that will significantly influence its deployment: • • • • •

Advancement of technological development Guaranteed operational security Simple and user-friendly interaction with different user typologies Economic profitability No threat to staff (delivery staff, drivers).

630

S. G. Ferraz and M. C. Monteagudo

References 1. Lempp, M., Siegfried, P.: Automotive Disruption and the Urban Mobility Revolution. Rethinking the Business Model 2030. Springer (2022) 2. Dimitrakopoulos, G., Tsakanikas, A., Panagipotopoulos, E.: Autonomous Vehicles. Technologies, Regulations and Societal Impacts. Elsevier (2021) 3. Mauer, M., Gerdes, J.C., Lenz, B.: Autonomous Driving. Technical, Legal and Social Aspects. Springer (2020) 4. Martin, G.: Sustainability Prospects for Autonomous Vehicles. Environmental, Social, and Urban. Routledge (2019) 5. Riggs, W.: Disruptive Transport. Driverless Cars, Transport Innovation and the Sustainable City of Tomorrow. Routledge (2019) 6. Maurer, M., Gerdes, J.C., Lenz, B., Winner, H. (eds.): Autonomous Driving. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-48847-8 7. Grush, B., Niles, J.: The End of Driving. Transportation Systems and Public Policy Planning for Autonomous Vehicles. Elsevier (2018) 8. Green Paper: Towards a new culture for urban mobility. EC 25/09/2007 9. Quak, H.J.: Sustainability of Urban Freight Transport – Retail Distribution and Local Regulations in Cities. ERIM, Rotterdam (ERIM Ph.D. Series Research in Management 124, TRAIL Thesis Series T2008/5) (2008). Author, F., Author, S., Author, T.: Book title, 2nd edn. Publisher, Location (1999) 10. Taniguchi, E., Thompson, R.: City Logistics: Mapping The Future. CRC Press (2014)

Emerging and Innovative Technologies in Transport: New Energy and Mobility Outlook for the Netherlands

Optimization-Based Comparison of Rebalanced Docked and Dockless Micromobility Systems Fabio Paparella(B) , Banchon Sripanha, Theo Hofman, and Mauro Salazar Eindhoven University of Techonology, PO Box 513, 5600 MB Eindhoven, The Netherlands [email protected]

Abstract. Shared micromobility systems are rapidly pervading urban environments. Usually, they are either dockless, in line with free-floating paradigms whereby vehicles can be left and picked up anywhere within the region of operation, or have docking stations with predefined parking slots. In this paper, we present an optimization-based framework to analyze and compare the advantages and disadvantages of these two different types of micromobility systems. We also include the possibility of rebalancing the system by the operator. First, we leverage graph theory to build a linear time-invariant network flow model of the two systems and use it to frame the time-optimal routing problem. Specifically, we formulate a linear program (LP) for the dockless system and a mixedinteger linear program (MILP) for the docked one whereby we jointly optimize the siting of the docking stations. Given their structure, both problems can be solved with off-the-shelf algorithms and global optimality guarantees. Second, we showcase our framework with a case study of Manhattan, NYC, whereby we quantitatively compare the performance achievable by the two micromobility paradigms. Our simulations suggest that increasing the number of stations of docked micromobility systems may decrease the average travel time up to a minimum aligned with the travel time achievable by dockless systems. Thereby, adding more stations does not significantly improve the system’s performance. Moreover, due to the slightly asymmetric travel demands, a mild rebalance of the system is enough to boost its performance. Keywords: Micromobility systems · Smart mobility Mobility-as-a-service · Personalized mobility

·

c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_53

634

1

F. Paparella et al.

Introduction

Building more sustainable urban transportation systems has been a central focus for cities with the goal of creating a seamless mobility experience while reducing traffic, noise and pollution [1]. In the act of pursuing this, the popularity of shared micromobility is growing at an incredible rate. It includes small-scale vehicles, such as bicycles, scooters, mopeds, and Segways. They can be human-powered or electric, and often cover short-distance trips [2]. As of 2019, the growth in micromobility increased by 62% in total rides taken, which is driven mostly by a 130% increase in e-scooter trips [3]. The adoption of micromobility and the remarkable growth of e-scooters do not come without a price. Many providers of the shared micromobility vehicles encounter vandalism and theft, whilst citizens experience urban clutter [4,5]. The deployment of docking stations for micromobility vehicles is a solution to tackle these problems [6]. Thereby, users have to move the vehicles from one docking station to another, eliminating the choice of parking in not appropriate spots. Whilst the proposal of deploying docking stations is encouraging in these terms, it might not be beneficial for the users. The deployment of the docking stations could have a negative impact on the average user travel time compared to a dockless micromobility system. The average time includes the vehicle travel time, the user’s walking time and the user’s waiting time. The waiting time is the time it takes to use the application to grab or park the vehicles, also known as the scan-and-go framework [7]. Travel time is an important metric, as traveling in the shortest amount of time is paramount for many use cases [8–10]. In addition, cities have different urban infrastructures and spatial-temporal distribution of demands. For these reasons, the potential exists to analyze and compare the performance between docked and dockless micromobility system in a specific area with its specific distribution of demands. In this paper, we present an optimization-based algorithm to compare the best possible achievable performance of the docked and dockless micromobility systems shown in Fig. 1.

Fig. 1. Supergraphs of the docked (left) and dockless (right) micromobility systems consisting of two networks: the walking network GW (the top green layer), and the road network GR (the bottom red layer), with the black dashed arrows representing the switching arcs. The P sign indicates the docking stations where a vehicle can be parked.

Optimization-Based Comparison of Rebalanced Docked

1.1

635

Literature Review

Our work contributes to the stream of mobility-on-demand, which we review in the following. Recently, a large amount of work has been devoted to studying the operation of Autonomous Mobility-on-Demand (AMoD) systems [11]. Different approaches can be used to characterize and control AMoD systems, from queuing-theoretical models [12,13] to simulation-based models [14,15] and multi-commodity network flow models [16,17]. The authors of [8,18] leveraged mesoscopic fluidic methods to include congestion-aware routing in mixed traffic, while in [9] they developed a pricing and tolling schemes to maximize social welfare in an intermodal AMoD framework. In our previous work [19] we performed a comparison between docked and dockless micromobility systems, but without the possibility of rebalancing by the operator. Focusing on electric AMoD, we leveraged Directed Acyclic Graphs (DAGs) to jointly optimize the number of vehicles and their optimal battery size for an AMoD system to maximize the profitability of the operator [20]. Inspired by [21], the authors of [22] used expanded network flow models to optimize the charging station siting and sizing jointly with the operation of the fleet. Related to design of micromobility vehicles and infrastructure, Nikiforiadis et al. [23] describe a mathematical model that maximizes the demands or area coverage and minimizes the unbalanced stations by determining the optimal locations for the stations. In [24], the authors determine the best locations for new micromobility stations in a small urban area. In summary, different optimization approaches are available. However, to the best of the author’s knowledge, no optimization-based fluidic model exists that is specifically tailored to compare the performance of docked and dockless micromobility systems. The goal of this paper is to introduce a mesoscopic fluidic optimization framework to analyze and compare docked and dockless micromobility systems with active rebalancing by the operator. In particular, the contribution of this paper is threefold: (i) an optimization-based fluidic model that minimizes the average user travel time, whereby for dockless systems we also enforce a selfish behavior of users parking only at their exact destination; (ii) a comparative performance analysis between the two systems that can be rebalanced by the operator; (iii) a LP formulation for the dockless and a MILP formulation for the docked system so that global optimality is guaranteed and (iv) a real-world case-study for the peninsula of Manhattan, NYC. The rest of the paper is organized as follows: In Sect. 2 we describe the mathematical formulation along with the optimization problem. Section 3 presents a real-world case-study of the peninsula of Manhattan, NYC and discusses the results. Finally, we draw the conclusions and point to future research directions in Sect. 4.

636

2

F. Paparella et al.

Methodology

In this section, we present a mesoscopic fluidic model for the optimal routing of docked and dockless micromobility systems. A fluidic model is an approximation of the corresponding stochastic queueing model, where people entering the system follow a Poisson process and where arc travel times are nondeterministic [25]. We express both the docked and the dockless system in a (mixed integer) linear fashion to leverage the advantages of global optimality guarantees and finding the optimal solution with commercial solvers. First, we introduce the mathematical notation used. With this in hand, we describe the objective function and the constraints to formulate the two optimization problems for the both docked and dockless micromobility system. Finally, we describe the methodology for comparing the performance of the two systems. 2.1

General Framework

We consider a system which consists of two transportation modes: walking and driving shared micromobility vehicles. We denote GR = (VR , AR ) as the road network and GW = (VW , AW ) as the pedestrian walking graph where (VR , AR ) and (VW , AW ) are the sets of vertices and arcs on the road and walking network, respectively. Then, we define a supergraph G = (V, A), as the union of both networks and a set of switching arcs, denoted by AS ⊆ VW × VR ∪ VR × VW , that connect the networks to allow users to switch modes (see black dashed lines in Fig. 1). Formally, G is composed of the set of vertices V = VW ∪ VR and arcs A = AW ∪ AR ∪ AS . We define a transportation request as rm = (om , dm , αm ), where αm ∈ R+ is the amount of users traveling from origin om ∈ V to destination dm ∈ V per unit time. We denote a set of M travel requests by R := {rm }m∈M with M := {1, ..., M } be the set of requests. We denote xm ij as the user flow induced by the m-th OD pair on link (i, j). Subsequently, we let xvij be the vehicle flow on link (i, j) ∈ AR . The summation of all users flow is  xuij = xm ∀(i, j) ∈ A. ij (1) m∈M |A|

Let tij : R+ → R+ be the constant time a vehicle takes to traverse link (i, j). A few comments are in order. We consider a time invariant transportation demand. This assumption can be done if the requests change slowly compared to the average travel time, that usually holds in dense urban environments [26]. We also allow fractional flows of both vehicles and users. The lost accuracy is negligible for the mesoscopic perspective of our study, as shown in [9]. We neglect congestion because the subject of this study is a micro-mobility system. Then, we add the following assumptions for the modeling of the micromobility systems with respect to the ones made in [25]: (i) We exclude users who use shared micromobility vehicles for recreational activities and ride-sharing; (ii) The operator is allowed to rebalance the system; (iii) The average travel time per

Optimization-Based Comparison of Rebalanced Docked

637

arc is known in advance; (iv) Users in the dockless micromobility system have a selfish behavior, meaning they park the vehicle only when they reach their destination. 2.2

Docked Micromobility System

In this paper, the goal is to minimize the total travel time experienced by users. We first formulate the planning problem for the docked system as follows:  tij · xuij min (2a) (i,j)∈A

s.t.

∀(i, j) ∈ AR

(2b)

∀(i, j) ∈ AW ∪ AS .

(2c)

xuij = xvij

xvij = 0

The linear objective (2a) is composed of the free-flow travel time tij and the user flow xuij . (2b) enforces that the user flow on the road graph is equal to the vehicle flow, while constraint (2c) keeps the vehicle flow only on the road network. The users’ flow generated by demand m is:   xm xm ij + 1j=om · αm = jk + 1j=dm · αm (i,j)∈A (j,k)∈A (2d) ∀m ∈ M, ∀j ∈ V, where the indicator function 1i=j is equal to 1 when i = j and 0 otherwise. Constraint (2d) is the flow conservation and demand compliance as in a multicommodity transportation setting. The operator is allowed to rebalance vehicles from a node to another, thus the vehicles’ balance is expressed as follows:   xvij + 1j · βjin = xvjk + 1j · βjout ∀j ∈ VR , (2e) (i,j)∈AR

(j,k)∈AR

where βjin ≥ 0 and βjout ≥ 0 are continuous variables (analogous relaxation explained in Sect. 2.1) that indicate the amounts of vehicles per unit time that the operator injects in or takes out of node j. Moreover, the operator cannot increase or decrease the total number of vehicles in the system:   βiin = βiout ≤ βmax,tot , (2f) i∈VR

i∈VR

where βmax,tot is the maximum amount of vehicles the operator is capable of rebalancing per unit time. In addition, the operator is allowed to rebalance vehicles exclusively between docking stations: βiin ≤ βmax · bi

∀i ∈ VR ,

(2g)

638

F. Paparella et al.

where βmax is the maximum amount of vehicles the operator is capable of rebalancing per unit time per station. We ensure that the fleet size is bounded by a maximum amount of vehicles nf,max ,  tvij · xvij ≤ nf,max . (2h) (i,j)∈AR

xuij ≥ 0 xvij ≥ 0

∀(i, j) ∈ A

(2i)

∀(i, j) ∈ AR ,

(2j)

Constraints (2i)–(2j) restrict the flows to non-negative values. xuij ≤ nc,max · bi

∀(i, j) ∈ AS ∀i ∈ VW

(2k)

xuji ≤ nc,max · bi

∀(j, i) ∈ AS ∀i ∈ VW

(2l)



bi ≤ ns,max .

(2m)

i∈VW

Binary variable bi depicts a docking station when bi = 1 at node i ∈ VW and 0 otherwise. Furthermore, constraints (2k)–(2l) ensure a capacity limit nc,max to each available docking station at node bi = 1, and constraints (2m) limit the maximum amount of stations to ns,max . Note that we jointly optimize the siting of the docking stations, but because of the binary variable bi , the optimization problem becomes a mixed integer LP. Summarizing, given a set of transportation requests R, the optimal users flow that minimizes the total user travel time for a docked system and the siting of the stations is given by the solution of Problem (2). 2.3

Dockless Micromobility System

This section presents an optimization model for dockless micromobility systems. Compared to Sect. 2.2, vehicles can be picked-up and dropped-off everywhere on the road network. Additionally, we include the assumption that users have selfish behavior and they will drop-off the vehicle only when they reach their destination. To enforce this behavior, we expand the supergraph G as shown in Fig. 2. We put an additional destination network GD = (VD , 0), where the set of arcs is null because it is not possible to walk after reaching it. We define a new set of switching arcs AS as the union of three sets of directed switching arcs AS ⊆ AWR ∪ ARD ∪ AWD . These sets of switching arcs represent respectively the directed arcs between networks GW ∪ GR ,GR ∪ GD , and GW ∪ GD . The destination of each demand dm ∈ VD enforces that each user cannot walk to their destination after using the vehicle, implicating that they park it only after reaching their destination. The problem is then expressed by:

Optimization-Based Comparison of Rebalanced Docked

639

Fig. 2. Revised supergraph of the dockless micromobility system consisting of three networks: the walking network (the top green layer), the road network (the bottom red layer), and the destination network (the bottom green layer), with the black dashed arrows representing the switching arcs.

min



tij · xuij

(3a)

(i,j)∈A

s.t.

(2b) − (2f ), (2h) − (2j)

xuij ≤ nc,max

∀(i, j) ∈ AWR ∪ ARD

(3b)

xuji ≤ nc,max

∀(j, i) ∈ AWR ∪ ARD .

(3c)

Equations (3b) and (3c) enforce that there is a threshold on the number of vehicles that can be parked in each spot per unit time. Summarizing, given a set of transportation requests R, the optimal users flow that minimizes the total user travel time for a dockless system, enforcing a selfish behaviors of users is given by the solution of Problem (3).

3

Results

In this section, we showcase our models on a real-world case-study for Manhattan, NYC. The road network, shown in Fig. 3, consists of 357 nodes, 1006 links, and 2500 OD pairs. The topology of the map is based on OpenStreetMaps [27]. The OD demands were supplied by technology providers authorized under the Taxicab & Livery Passenger Enhancement Programs to the NYC Taxi and Limousine Commission. The data are built using historical data of taxi rides that occurred on March 1, 2018. Although taxi rides are not micromobility, in

640

F. Paparella et al.

Fig. 3. NYC taxi and limousine network with the black dashed line indicating the used area of network for the case study.

NYC the average trip is about 2.4km, which is in line with a micromobility request. Moreover, we assume that: (i) Users take on average one minute to grab or park the vehicles; (ii) The maximum speed of the vehicles is 25km/h and (iii) The walking speed is 5km/h. We analyze and compare the results of the docked and the dockless models we described in the previous section. To have an insight on the influence of the docking stations on the system, we perform multiple simulations where a different number of stations can be supplied as an input to the model, from zero, i.e. only walking mode, to one stations per node, i.e. free floating system. We characterize the performance at each instance, the average travel time, defined as  u∗ (i,j)∈A tij · xij  , (4) tavg = m∈M αm obtained by dividing the total time by the total number of users in the network. Figure 4 shows the results of our simulations. The black line depicts tavg , expressed in minutes, while the percentage of walked distance/time are depicted as bars in the left and right figure, respectively. We observe that, as expected, tavg is decreasing when the amount of stations increases. The tavg has the largest value in the case of zero stations, where everyone is forced to walk. We note that, with a low number of stations, a small increase reflects in a higher decrease of tavg . The increase in deployment of docking stations results in an increase in micromobility users. In particular, we see how with 200 docking stations, equal to approximately 4 stations/km2 , we reach a plateau with a comparable perfor-

Optimization-Based Comparison of Rebalanced Docked

(a) Distance-based modal share

641

(b) Time-based modal share

Fig. 4. Distance-based and time-based modal share and average travel time per user for station capacity nc,max = 18vehicles/h and no rebalancing, i.e. βmax,tot = 0. For each instance, the docking stations are optimally sited.

(a) Distance-based modal share

(b) Time-based modal share

Fig. 5. Distance-based and time-based modal share and average travel time per user with no rebalancing by the operator, i.e. βmax,tot = 0, and ns,max = 200 stations.

mance w.r.t. a dockless system. However, the modal share of the micromobility mode never reaches 100%, even when the maximum amount of docking stations is reached. The reason is the imbalance of the vehicles with respect to the demands. The presence of the docking stations is correlated with the availability of the micromobility vehicles in a network, but in absence of rebalancing, the performance of the system does not improve significantly. In Fig. 5, we simulate again a scenario with 200 docking stations with increasing station capacity (vehicles per unit time that can pass through that station). Then, also in this case, due to the absence of external rebalancing, even with a substantial increase in the capacity of each station, the usage does not increase significantly. We also reach a threshold up to which no improvement of the performance of the system is obtained. Dut to this, rebalancing by the operator is crucial for the performance of a system, especially in cases with highly asymmetric demands. To conclude, Fig. 6 shows the improvement in performance with a varying level of

642

F. Paparella et al.

(a) Distance-based modal share

(b) Time-based modal share

Fig. 6. Distance-based and time-based modal share and average travel time per user with rebalancing by the operator, ns,max = 200 stations with capacity nc,max = 30 vehicles per unit time.

rebalancing βmax to overcome the asymmetry of demands. In this way, a larger portion of users does not experience first/last mile walking to pick-up/drop-off their vehicles.

4

Conclusions

This paper proposed an optimization framework to compare the performance achievable by docked and dockless micromobility systems. Owing to the (mixedinteger) convex structure of the optimization problems we formulated, we could rapidly compute the globally optimal routing (and station siting for docked systems) solution with off-the-shelf algorithms. We presented a case-study for Manhattan and we showed that the average travel time of docked systems may be decreased by increasing the number of stations, until reaching a plateau in line with the optimal performance of dockless systems, indicating that adding too many docking stations and/or increasing their capacity can improve the performance up to a certain point. Finally, due to the travel demands that are slightly asymmetric, a mild rebalance of the system is sufficient to boost its performance. In a future paper we will include the costs to deploy a docking station based on the location, that will make the solution more realistic. Moreover, this paper can be further extended by including intermodality, accounting for charging schedules, and by jointly optimizing the individual vehicles. Acknowledgments. We thank Dr. I. New for proofreading this paper. This publication is part of the project NEON with project number 17628 of the research program Crossover which is (partly) financed by the Dutch Research Council (NWO).

Optimization-Based Comparison of Rebalanced Docked

643

References 1. Simlett, J., M¨ uller, T.: Micromobility: moving cities into a sustainable future. EY (2020). Available online at: www.assets.ey.com/content/dam/ey-sites/ey-com/ en gl/topics/automotive-and-transportation/automotive-transportation-pdfs/eymicromobility-moving-cities-into-a-sustainable-future.pdf 2. Bennett, C., Ackerman, E., Fan, B., Bigham, J., Carrington, P., Fox, S.: Accessibility and the crowded sidewalk: micromobility’s impact on public space (2020). Available online at: www.cs.cmu.edu/∼jbigham/pubs/pdfs/2021/crowded-sidewalk.pdf 3. Bloomberg: U.s. scooter ridership surged in 2019. Now what? Available online at (2020) www.bloomberg.com/news/articles/2020-08-27/how-big-was2019-s-scooter-boom-and-what-s-next 4. Julka, H.: Why vandalism is the biggest threat to success of e-scooter startups. Medium (2019) Available online at: www.harsimranjulka.medium.com/whyvandalism-is-the-biggest-threat-to-success-of-e-scooter-startups-f8c1763155fb 5. McFarland, M.: Scooters are a huge problem for cities. no one knows how to solve it yet. CNN (2019). Available online at: www.edition.cnn.com/2019/08/30/tech/ scooter-management/index.html 6. Gauquelin, A.: Why docks are part of the future of micromobility (2019). www. shared-micromobility.com/ Available online at: www.shared-micromobility.com/ why-docks-are-part-of-the-future-of-micromobility/ 7. Tuncer, S., Brown, B.: E-scooters on the ground: lessons for redesigning urban micro-mobility. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, 2020 8. Wollenstein-Betech, S., Salazar, M., Houshmand, A., Pavone, M., Cassandras, C.G., Paschalidis, I.C.: Routing and rebalancing intermodal autonomous mobilityon-demand systems in mixed traffic. IEEE Trans. Intell. Transp. Syst. 2021 (in press) 9. Salazar, M., Lanzetti, N., Rossi, F., Schiffer, M., Pavone, M.: Intermodal autonomous mobility-on-demand. IEEE Trans. Intell. Transp. Syst. 21(9), 3946– 3960 (2020) 10. Bullo, F., Frazzoli, E., Pavone, M., Savla, K., Smith, S.L.: Dynamic vehicle routing for robotic systems. Proc. IEEE 99(9), 1482–1504 (2011) 11. Zardini, G., Lanzetti, N., Pavone, M., Frazzoli, E.: Analysis and control of autonomous mobility-on-demand systems. In: Annual Review of Control, Robotics, and Autonomous Systems, vol. 5 (2022) 12. Zhang, R., Pavone, M.: Control of robotic mobility-on-demand systems: a queueing-theoretical perspective. Int. J. Robot. Res. 35(1–3), 186–203 (2016) 13. Iglesias, R., Rossi, F., Zhang, R., Pavone, M.: A BCMP network approach to modeling and controlling autonomous mobility-on-demand systems. Int. J. Robot. Res. 38(2–3), 357–374 (2019) 14. Levin, M.W., Kockelman, K.M., Boyles, S.D., Li, T.: A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application. Comput., Environ. Urban Syst. 64, 373–383 (2017) 15. H¨ orl, S., Ruch, C., Becker, F., Frazzoli, E., Axhausen, K.W.: Fleet control algorithms for automated mobility: a simulation assessment for Zurich. In: Annual Meeting of the Transportation Research Board, 2018 16. Rossi, F., Zhang, R., Hindy, Y., Pavone, M.: Routing autonomous vehicles in congested transportation networks: structural properties and coordination algorithms. Auton. Robots 42(7), 1427–1442 (2018)

644

F. Paparella et al.

17. Iglesias, R., Rossi, F., Wang, K., Hallac, D., Leskovec, J., Pavone, M.: Data-driven model predictive control of autonomous mobility-on-demand systems. In: Proceedings IEEE Conference on Robotics and Automation, 2018 18. Wollenstein-Betech, S., Houshmand, A., Salazar, M., Pavone, M., Cassandras, C.G., Paschalidis, I.C.: Congestion-aware routing and rebalancing of autonomous mobility-on-demand systems in mixed traffic. In: Proceedings IEEE International Conference on Intelligent Transportation Systems, 2020 19. Paparella, F., Sripanha, B., Hofman, T., Salazar, M.: Optimization-based comparison of docked and dockless micromobility systems. In: Proceedings ITS European Congress, 2022 20. Paparella, F., Hofman, T., Salazar, M.: Joint optimization of number of vehicles, battery capacity and operations of an electric autonomous mobility-on-demand fleet. In: Proceedings IEEE Conference on Decision and Control, 2022, (in press) 21. Rossi, F., Iglesias, R., Alizadeh, M., Pavone, M.: On the interaction between autonomous mobility-on-demand systems and the power network: models and coordination algorithms. IEEE Trans. Control Netw. Syst. 7(1), 384–397 (2020) 22. Luke, J., Salazar, M., Rajagopal, R., Pavone, M.: Joint optimization of electric vehicle fleet operations and charging station siting. In: Proceedings IEEE International Conference on Intelligent Transportation Systems, 2021 (in press) 23. Nikiforiadis, A., Ayfantopoulou, G., Salanova, G., Josep, M., Boufidis, N.: Determining the optimal locations for bike-sharing stations: methodological approach and application in the city of Thessaloniki, Greece. Transp. Res. Procedia 52, 557–564 (2021) 24. Askarzadeh, T., Bridgelall, R.: Micromobility station placement optimization for a rural setting. J. Adv. Transp., pp. 1–10 (2021) 25. Pavone, M., Smith, S.L., Frazzoli, E., Rus, D.: Load balancing for mobility-ondemand systems. In: Robotics: Science and Systems, 2011 26. Neuburger, H.: The economics of heavily congested roads. Transp. Res. 5(4), 283– 293 (1971) 27. Haklay, M., Weber, P.: OpenStreetMap: user-generated street maps. IEEE Pervasive Comput. 7(4), 12–18 (2008)

Quantifying the Charging Flexibility of Electric Vehicles; An Improved Agent-Based Approach with Realistic Travel Patterns Peter Hogeveen1(B) , Vincent A. Mosmuller2 , Maarten Steinbuch1 , and Geert P. J. Verbong2 1 Department of Mechanical Engineering, Eindhoven University of Technology, P.O. Box 513,

5600 MB Eindhoven, The Netherlands [email protected] 2 Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands

Abstract. Existing modelling research that attempts to quantify how flexible charging sessions of electric vehicles are, have been constrained by either charging data or inadequate mobility data. This resulted in significant underestimations of the charging flexibility. In this article an agent-based model is developed that is able to quantify the charging flexibility more realistically. A new charging flexibility metric is defined that takes future trips and the state-of-charge of the vehicles into account. The developed approach leverages detailed activity patterns from the ALBATROSS-model by simulating vehicle utilization from a household perspective in different neighborhood types. The results show that the over 80% of the evening peak charging demand from electric vehicles can be mitigated when utilizing the charging flexibility. It also shows that about half of the charging demand can be extended by more than 40 h. These results demonstrate the great potential of electric vehicle to balance the grid and enable high degrees of renewable energy production. Keywords: Charging flexibility · Electric vehicles · Agent-based modeling

1 Introduction Electric vehicles can be game-changers or game-breakers in future decentralized and renewable energy systems. Electric vehicles have a high electricity demand and, potentially, a high level of flexibility in their charging sessions. With smart charging, electric vehicles can balance the energy system, avoiding grid investments and optimizing the use of renewable energy. Without smart charging, full adoption of electric vehicles will lead to daily low-voltage grid congestion and frequent imbalances in (renewable) electricity supply and demand. The charging flexibility of electric vehicles is a loosely defined concept that relates to the amount of freedom of a charging session to shift and extend the charging. This includes a dimension of time and a dimension of energy. In Table 1 can be seen that © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_54

646

P. Hogeveen et al.

existing scientific literature quantifies the charging flexibility with metrics based on either the plug-in time of the charging session or the next departure time of the vehicle. This research will show that such metrics result in significant underestimations of the actual charging flexibility, as it can have dynamics beyond the next departure time. The metric formulas from Table 1 also show that most metrics from previous research lack intuitive design. This reduces the applicability and the societal value of the quantification studies. Table 1. Definitions of charging flexibility metrics in literature Article

Metric description

Metric formula

[1]

The total plugged-in time minus the charging time

i = Ti i Tflex connect − Tcharge

[2]

Two metrics based on the actual charging power demand of vehicles and the potential power of the vehicles that are plugged-in while not charging

PpotentialUp (t) = PmaxCharging (t) − PdemandCharging (t) PpotentialDown (t) = PdemandCharging (t)

[3]

 The fraction of the total Pflex (t, ) = Ps s∈Sflex plug-in time spend charging    and the potential power arrive , t depart increase or decrease at each Sflex (t, ) = s ∈ S [t, t + ] ⊂ ts s  moment (measured based on charging ∧δs ≥  ∧ δsidle ≥  plugged-in vehicles)

[4]

A normalized fraction of the Iflexibility = 1 − charging time and potential plug-in time de-rived from the arrival time and departure time

[5]

A time metric representing the fraction of the charging time over the total available time. An energy metric representing to what extent charging demand is shifted

[6]

Bcap (TSOC −ISOC ) 1 ∗ t −T EVchrgp pout pin

−tbau Tflex = tcoordinated tdepart −tbau

Eflex =

EbeyondBauconsumed EbeyondBau Max

The Time-shift-Flexibility is Flexshift,k,P = t tk k ∈  k,p the fraction of the total plug-in time over the necessary charging time at a given charging power

Readers are referred to the corresponding articles for an elaborate explanation of the metrics and their formula

Quantifying the Charging Flexibility of Electric Vehicles

647

Quantification of the charging flexibility has gained significant research interest over the past years. This is partially due to the ongoing transition towards sustainable energy and electrified mobility. However, existing research fails to capture the complete picture of the charging flexibility. Mainly as a result of the limited availability of realistic and detailed data on travel behavior. This article attempts to solve that issue by developing an agent-based model with an improved metric of the charging flexibility. This metric requires detailed and realistic travel behavior which is provided by ALBATROSS (more in Sect. 3.2). The model is applied to a case study of two Dutch neighborhoods and the improved charging flexibility metric is compared to one that is similar to those of previous research. The next section is a literature review on how previous studies modeled and quantified the charging flexibility. Section 3 elaborates on the proposed approach. The results of the case study are presented in Sect. 4 and, subsequently, discussed in Sect. 5. The findings of this research are concluded in Sect. 6.

2 Literature Review Current research attempts to quantify the charging flexibility with models based on either real-world charging data or mobility data. Unfortunately, realistically modeling mobility behavior poses a variety of problems that are generally outside the research scope. Firstly, mobility behavior is highly complex and irregular which quickly leads to unrealistic simplifications. Secondly, sufficiently detailed mobility data is often unavailable. Mainly because of complex dependencies of mobility on weather, holidays, family composition, multi-day dynamics, geography, culture, and external circumstances such as COVID19. Finally, vehicles are used in a household context, rendering individual mobility data inadequate to study charging flexibility. These issues are evident in the research papers that model the charging flexibility based on mobility behavior. For example, Clement-Nynz et al. [7] analyzed flexibility with simplified travel profiles. The authors state that more detailed travel would greatly improve the accuracy of their analysis. Similarly, Sadeghianpourhamami et al. [5] defined statistical distributions of travel behavior for typical user groups that are used to assess the charging flexibility in terms of plug-in times and charging times. Accordingly, the charging flexibility was utilized by a variety of smart charging frameworks. In [2], Guthoff et al. used German travel data of individuals and Markov-Chain Monte Carlo simulations to simulate charging behavior. The charging load curve (based on charging upon arrival) compared to a ‘grid contact curve’ (the charging power times the number of fully charged electric vehicles still plugged in) as their measure of flexibility. The charging load curve represents positive flexibility (can be shifted forward), and the grid contact curve represents negative flexibility (can be shifted backward). To bypass the issues of modeling mobility behavior, researchers turn to quantifying the charging flexibility based on charging data. Charging data is significantly easier to obtain, e.g. via charge point operators, however, it is segmented in both accessibility (public versus private) and location (home versus work). These segmentations result in a partial perspective of the charging demand. The main disadvantage of charging data with regard to the charging flexibility, is that it only shows plug-in periods. Completely

648

P. Hogeveen et al.

missing ‘not plugged-in’ idle time. Specifically with public charging data it is problematic to extract the charging flexibility of the vehicles, re vehicle owners are incentivized to move their vehicle when it is fully charged. Lastly, current charging data is based on the behavior of early adopters which are not representative for the whole population. Example studies that explore the charging flexibility with charging data are [4, 7– 11]. All these studies massively underestimate the charging flexibility in the view of the authors of this paper. For example, Gerritsma et al. used public charging data to assess the charging flexibility [1]. Their results indicate that the majority of charging sessions have less than 13 h of flexibility. They do conclude that more flexibility can be expected in neighborhoods with home charging, as there are significantly less plug-in constraints. In [12], Beltramo et al. also model flexibility based on Dutch public charging data. In their study, the impact of charging electric vehicles on the national electricity grid is explored. Develder et al. combined Dutch public charging data with home charging data from Belgium [3]. The charging flexibility was quantified by the charging power that a demand response system can choose to either consume or shift to a later moment. Additionally, they assessed the potential of this flexibility to be utilized in renewable energy systems. Besides the studies mentioned above which directly explore the charging flexibility, there are numerous articles on topics such as smart charging, vehicle-to-grid and sector coupling, that indirectly assume certain levels of charging flexibility from electric vehicles each month [13–18]. The vast majority of these articles do so either unknowingly or without verifying whether their solutions fit the charging flexibility that electric vehicles can provide. This indicates the relevance to provide more realistic quantifications of the charging flexibility.

3 Methodology This section elaborates on the agent-based model that is developed to simulate mobility behavior and quantify the charging flexibility. Central to the approach is the design of people-agents with realistic activity schedules from which vehicle utilization and the charging demand follow. Since not all details of the model can be explained in this article, the authors also refer to the model itself for anyone interested in more details. The model can be run and downloaded from the Anylogic Cloud.1 3.1 Agent-Based Model Overview Agent-based modeling is a bottom-up modeling approach where emergent system behavior results from actions and interactions of individual agents [19]. In this case, the agentbased model consists of four agent types: adults, vehicles, households and activities. Their heterogeneity and input parameters (e.g. household composition, number of vehicles, driver licenses, employment details, income levels, battery capacity) are defined by a ‘population database’. Model users can adjust this database to create any size and 1 https://cloud.anylogic.com/model/eac62782-60f7-4ec1-bf34-80733e2d4017?mode=SET

TINGS.

Quantifying the Charging Flexibility of Electric Vehicles

649

type of neighborhood or city. Another input database provides the multi-day activity schedules for each adult agent within the created population. In this ‘activity database’, each row corresponds to one activity of a specific adult. It contains information about the activity location, start time, travel time, and modality, and more. In Appendix A examples snippets of both databases are shown (Fig. 1).

Fig. 1. Agent-based mobility model structure

At the startup of a simulation, household agents and their corresponding adult agents are created based on the population database. The adult agents create a travel schedule by adding activity agents, based on the activity database, to an activity population. During a simulation run, statecharts and time-out events within each adult- and vehicleagent account for actually performing the mobility and charging behavior. Adults go on trips with several modalities. This behavior is dictated within the activity database. When a trip’s modality is “Car”, the adult occupies the vehicle-agent that belongs to his household. In some cases, two adults share one electric vehicle in the household. In other cases, two adults have two vehicles, or one adult has one vehicle. 3.2 Case Study and ALBATROSS Activity Data A case study can be performed with the model by implementing the population database and the activity database of the case study. The first one describes the size of the urban area all socio-demographics. The second one provides all mobility behavior. In this article, two different case studies are performed: a Dutch city center neighborhood (Eindhoven) and a Dutch rural neighborhood (Sterksel). The databases for this are provided by ALBATROSS. ALBATROSS is an activity-based travel demand model developed at the Eindhoven University of Technology [20–22]. It generates realistic activity and travel patterns for individuals of each household in a specified (Dutch) region. ALBATROSS is algorithmically trained on large datasets of detailed Dutch mobility behavior from a household perspective. To generate mobility profiles ALBATROSS uses sociodemographics from Statistics Netherlands (CBS), local accessibility of public transport modes and geographical mapping of visiting and employment areas (see Appendix C).

650

P. Hogeveen et al.

ALBATROSS takes a variety of mobility dynamics into account that are crucial to realistic modeling of vehicle utilization. Among these are: • • • •

employment details and work locations, availability of vehicles/driver’s licenses for determining modalities, local public transport availability, and family composition and trips.

3.3 Charging Behavior Adult-agents follow their activity schedule and occasionally go on trips with their (electric) vehicles. Upon arrival home, the state-of-charge of the vehicle is calculated based on the trip distance, the battery capacity, and energy economy of the vehicle. A decision to charge the vehicle is made with the charging heuristics from Table 2. The charging heuristics and other input distributions, such the charging speed, initial state of charge and battery capacities, can be adjusted according to the case study. The values applied in this article can be found in Appendix D. Table 2. Charging heuristics kWh under maximum capacity [kWh]

Probability of charging (%)

>40

55

54

−1.142

Household situation - Living alone

−1.176

*

0.622 0.059

Household situation - Living with partner, no children

0.501

*

0.293 0.087

Household situation - Living with partner and children

0.112

Household situation - Other

0.563

0.256 0.661

(continued)

692

V. Caiati and S. Rasouli Table 4. (continued)

Attribute

Estimate

Std. p-value error

Trip context Trip purpose - Education/work

−0.465

Trip purpose - Others

0.465

Trip accompany - Alone

0.535

Trip accompany - Not alone

−0.535

Time pressure - Yes

0.170

Time pressure - No

−0.170

***

0.150 0.002

***

0.137 0.000 0.130 0.193

P&R attributes Distance between P&R and origin - minutes

−0.005

**

0.002 0.023

Parking Price in P&R - Free

0.134

0.106 0.205

Parking Price in P&R - 1euro/day; 10 cents/hr

−0.034

0.091 0.706

Parking Price in P&R - 1.5 euro/day; 20 cents/hr

−0.059

0.123 0.632

Parking Price in P&R - 2 euro/day; 30 cents/hr

−0.041

Incentives - None

0.010

0.028 0.729

Incentives - every 10 P&R ride: e2 voucher

−0.010

0.111 0.930

Incentives - every 10 P&R ride: e3 voucher

−0.083

0.108 0.444

Incentives - every 10 P&R ride: e4 voucher

0.083

Real time info num of available parking spots: Yes −0.079

0.065 0.227

Real time info num of available parking spots: No 0.079 Status Quo (Private Car) Travel Time (TT) - minutes

−0.005

Parking search time - minutes

−0.003

Walking time from origin to the parking place minutes

−0.004

Walking time from parking place to the destination- minutes

−0.056

Parking cost at destination – euros

−0.002

0.005 0.601

Park type at dest. - On street parking (no permit needed)

0.033

0.074 0.653

Park type at dest. - On street parking (with permit) −0.946 Park type at dest. - Own/shared garage/reserved parking space/dedicated parking space

0.913

Car size - Small/mid-size

−0.126

0.009 0.576 **

0.001 0.025 0.002 0.086

***

0.010 0.000

***

0.101 0.000

**

0.059 0.032 (continued)

A Stated Adaptation Approach to Assess Mode Change Behavior

693

Table 4. (continued) Attribute

Estimate

Std. p-value error

Car size - Large/SUV

0.126

Car age - New (less than 4 years)

−0.127

Car age - Older (more than 4 years)

0.127

Goodness of fit indexes

Loglikelihood −1.529.69 (estimated)

**

0.059 0.032

Loglikelihood −1725.69 (base) R-sqrd

0.1136

R2Adj

0.1024

In Munich, monetary incentives in the FI scenario would increase the choice probability for P&R and PT over the Base by 3.8%, while are not effective for increasing choice probability for PV (0.5% decrease compared to base scenario). This would suggest that the simulated incentives are not sufficient. As for Lublin, under the FI scenario, on average the probability of choosing P&R and bus increases over the Base by 8.8% A smaller increase of about 2.2% is observed in the choice probability of P&R and BS. In Munich, ISD results to be the most promising scenario in reducing the probability for choosing own car while increasing the choice probability for remaining alternatives. The simulated combined improvements at the level of mobility services provision (i.e., two connecting modes) have the effects of improving the choice probability over the Base and FI scenario by 1.4% and 1.8%, respectively. In Lublin, the simulated combined improvements in ISD would reduce the probability of choosing own car by 5.3% compared to the Base. More precisely, the increase in choice probability of conventional bus is equal to 4.6%, while the increase is 0.7% for BS. Table 5. Scenario analysis for Munich and Lublin Scenarios Base (%)

FI (%)

ISD (%)

P&R and PT P&R and PV SQ

15.70 12.30 72

19.50 11.80 68.70

25.60 13.60 60.80

P&R and Bus P&R and BS SQ

20.30 8.50 71.20

29.10 10.70 60.30

24.90 9.20 65.90

694

V. Caiati and S. Rasouli

5 Conclusions This paper has presented a stated adaptation approach to assess car drivers modal shift in presence of P&R facilities and certain connecting transport modes in two European cities. A MNL has been estimated to investigate the effect of service attributes and trip and individual characteristics on the P&R and mode choice. Comparing Munich and Lublin reveals some intriguing insights. In Munich when people travel for work and education and are under time pressure, they are more willing to use P&R. In Lublin the opposite conditions holds: when the trip purpose is work or education and characterized by time pressure, respondents are less likely to choose P&R. This might be linked to the type of transport modes offered in P&R. In Munich PT includes modes that do not have delay (e.g. tram, train) or pick up van which that provides the luxury of picking up people right inside the P&R facility. In Lublin however, one of the connecting modes is conventional bus with possible delay and access and egress time needed. The other mode provided in Lublin was BS, which also requires certain familiarity and willingness to cycle. Also, the speed of bike obviously cannot compete with the private car and this difference is pronounced when travel distance is long. The scenario analysis provided interesting insights for both cities. In Lublin, one would recommend city authorities and transport managers to first focus on budget allocation for financial incentives. Possible policy leverages should be directed towards a reduction of passengers’ costs when using P&R and connecting mobility services and provision of monetary incentives as a premium for frequent P&R usage. Although at a first instance this monetary policy action would appear expensive, the social and economic benefit will be higher in the longer term: more sustainable modes of transport such as bus and BS would be used by current car drivers, thereby reducing traffic congestion in city center and in other highly affected areas. As a second policy leverage, transport planners and policy makers can focus on improving the quality of the mobility services offered. More precisely, to encourage current car drivers to switch to more sustainable transport modes, as shared bike and bus, urban planning strategies may consider improving bike infrastructure or increase priority lines for bus. Compared to Lublin, the analysis in Munich suggests prioritizing improve in the service quality of the connecting modes. More precisely, the development of an integrated tariff system, improvement in current timetables and a more efficient and reliable transport connections may encourage car drivers to abandon their cars in favor of P&R when travelling to congested city areas. Lastly, policies based only on financial incentives may still influence improving the success of P&R: we recommend policy makers to pay more attention in designing the monetary measures aimed at encouraging the usage of PV. The presented study shows some limitations. An MNL model is estimated to analyze the collected data. However, we are aware that this model is not able to treat uncertainties, but we believe that it could provide a good base for discussion and further studies. Therefore, further research is needed for more comprehensive and multifaceted investigation of mode change behavior in presence of P&R, including the use of latent class and mixed logit models and the incorporation of individual attitudes and perceptions. It is our intention to report on such studies in the future.

A Stated Adaptation Approach to Assess Mode Change Behavior

695

Acknowledgements. The authors are grateful to EIT Urban Mobility for funding this research, which is part of the project AI-TRAWELL.

References 1. Parkhurst, G., Meek, S.: The effectiveness of park-and-ride as a policy measure for more sustainable mobility. Transp. Sustain. 5, 185–211 (2014) 2. Dijk, M., de Haes, J., Montalvo, C.: Park-and-ride motivations and air quality norms in Europe. J. Transp. Geogr. 30, 149–160 (2013) 3. Macioszek, E., Kurek, A.: The use of a park and ride system a case study based on the City of Cracow (Poland). Energies (Basel) 13 (2020) 4. Ortega, J., Tóth, J., Péter, T., Moslem, S.: An integrated model of park-and-ride facilities for sustainable urban mobility. Sustainability (Switzerland) 12 (2020) 5. Bos, R., Temme, R.: A roadmap towards sustainable mobility in Breda. Transp. Res. Procedia 4, 103–115 (2014) 6. Tennøy, A., Hanssen, J.U., Øksenholt, K.V.: Developing a tool for assessing park-and-ride facilities in a sustainable mobility perspective. Urban, Plan. Transp. Res. 8, 1–23 (2020) 7. Haque, A.M., Brakewood, C., Rezaei, S., Khojandi, A.: A literature review on park-and-rides. J. Transp. Land Use 14, 1039–1060 (2021) 8. Cornejo, L., Perez, S., Hernandez, S.: An Approach to Comprehensively Evaluate Potential Park and Ride Facilities (2014) 9. Niles, J.S. Pogodzinski, J. M.: Bus Transit Operational Efficiency Resulting from Passenger Bus Transit Operational Efficiency Resulting from Passenger Boardings at Park-and-Ride Facilities Boardings at Park-and-Ride Facilities (2016) 10. Fan, W., Jiang, X., Erdogan, S.: Land-Use Policy for Transit Station Areas: Park-and-Ride Versus Transit-Oriented Development Science Foundation of China. View project PRESTO: Plan for Regional Sustainability Tomorrow View project (2016) 11. Cao, J., Duncan, M.: Associations among distance, quality, and safety when walking from a park-and-ride facility to the transit station in the twin cities. J. Plan. Educ. Res. 39, 496–507 (2019) 12. Karamychev, V., van Reeven, P.: Park-and-ride: good for the city, good for the region? Reg. Sci. Urban Econ. 41, 455–464 (2011) 13. Pang, H., Khani, A.: Modeling park-and-ride location choice of heterogeneous commuters. Transportation 45(1), 71–87 (2016). https://doi.org/10.1007/s11116-016-9723-5 14. Webb, A., Khani, A.: Park-and-ride choice behavior in a multimodal network with overlapping routes. Transp. Res. Rec. 2674, 150–160 (2020) 15. Louviere, J.J., Hensher, D.A., Swait, J.D.: Stated choice methods: analysis and application. Stated Choice Methods. Cambridge University Press (2010) 16. Feneri, A.M., Rasouli, S., Timmermans, H.J.P.: Issues in the design and application of stated adaptation surveys to examine behavioural change: the example of Mobility-as-a-service. Transport in Human Scale Cities, NECTAR Series on Transportation and Communications Networks Research, 96–108 (2021)

Estimating Availability Effects in Travel Mode Choice Among E-bikes and Other Sustainable Mobility Services: Results of a Stated Portfolio Choice Experiment Xueting Ren(B) , Soora Rasouli, Harry J. P. Timmermans, and Astrid Kemperman Urban Planning and Transportation Group, Eindhoven University of Technology, Eindhoven, The Netherlands [email protected]

Abstract. Electric bikes are considered an important sustainable alternative to private cars. This transportation mode competes with other new mobility modes, such as Shared Mobility and Mobility as a Service (MaaS). Because these services may not always be available, people may face a variety of choice options in different cities/regions. Despite their relevance, most studies of e-bike mode choice do not consider these availability effects, which may bias estimated acceptance rates and market shares of e-bikes. This paper reports the formulation and estimation results of a discrete portfolio choice model incorporating the availability effects of other sustainable mobility services to explore individuals’ willingness to buy pedelecs and speed pedelecs. We designed a stated portfolio choice experiment considering varying choice set composition, where Shared Mobility or/and MaaS may not be available. An error component logit model was formulated to analyze the availability effects. The knowledge we gained regarding the willingness to buy e-bikes in the presence or absence of motorized shared mobility options has major practical implications as some pilot studies have evidenced decreasing use of active modes once motorized shared mobility becomes conveniently available. Keywords: Availability effects · E-bike · Shared mobility · Mobility as a service (MaaS) · Pedelec · Speed pedelec

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_57

Role of Service Uncertainty in Decision to Use Demand Responsive Transport Services, a Stated Adaptation Choice Experiment Shangqi Li(B) , Soora Rasouli, and Harry J. P. Timmermans Urban Planning and Transportation Group, Eindhoven University of Technology, Eindhoven, The Netherlands [email protected]

Abstract. Demand responsive transport (DRT) although existed for decades, has recently become more attractive due to availability of real time (demand and supply) data and advanced matching algorithms. DRT is advantageous in reducing traffic and space occupancy if each service is simultaneously used by multiple travelers. Despite its benefits, travelers’ willingness to adopt this transport service is essential for such a service to have a meaningful impact on the living environment. Apart from service characteristics such as travel cost, travel time, waiting time and convenience, the uncertainty involved in the service delivery can be an additional factor for travelers no to be eager in using such a service. In this study, a web-based stated adaptation experiment is designed to understand the travelers’ choice of DRT in different contexts. Stated adaptation choice experiment first collect travel history and then expose respondents to two DRT options designed on the basis on the reported trip characteristics. A regret- rejoice based model is estimated to identify the relationship between the features of DRT service (including uncertain characteristics) and people’s adaptation behavior. Keywords: Demand responsive transport · Stated adaptation experiment · Uncertainty

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_58

Active and Non-motorized Travel: Walking and Cycling Infrastructure

How is the Redesign of Public Space for Active Mobility and Healthy Neighborhoods Perceived and Accepted? Experiences from a Temporary Real-World Experiment in Berlin Katharina Goetting1(B) and Julia Jarass2 1 Institute for Advanced Sustainability Studies E. V., Potsdam, Germany

[email protected] 2 German Aerospace Center (DLR)—Institute of Transport Research, Berlin, Germany

[email protected]

Abstract. For developing healthy and environmentally friendly cities an innovative redesign of urban infrastructure is necessary. However, changes to the current infrastructure are not always adopted and accepted immediately. Therefore, it is crucial to understand why people accept or refuse the transformation of public space towards active mobility. Taking the example of Berlin, a four-week realworld experiment (RWE) was conducted in summer 2021 when a street was transformed to a car-free square. Parklets, which are wooden platforms on parking spaces, made this alternative use of space visible for residents and enabled them to experience the infrastructural change in their daily lives. However, these temporary experiments and infrastructural changes in general are controversial among residents. After the intervention, we measured residents’ acceptability as attitude and intention to react (protest etc.) within a household survey (N = 155). Using regression analyses, we examined the influence of socio-demographics and psychological variables (perceived fairness, affect and place attachment) on acceptability. The survey shows that almost as many participants favor the redesign as oppose it. Moreover, we found that on the attitudinal level, acceptability is influenced by perceived fairness, affect, place attachment, gender, and age. Whereas for behavioral acceptability, only perceived fairness plays a significant role. This demonstrates that the transport transition is strongly influenced by the idea of fairness. If the benefits are clearly recognizable for different population groups and the distribution of space feels fair, changes to the built environment are more easily accepted. Keywords: Acceptability of transformation · Active mobility · Real-world experiment · Fairness · Transport transition · Redesign of public space

Katharina Goetting and Julia Jarass these authors contributed equally to this work. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. G. Nathanail et al. (Eds.): CSUM 2022, 2023. https://doi.org/10.1007/978-3-031-23721-8_59

702

K. Goetting and J. Jarass

1 Introduction Katharina Goetting and Julia Jarass these authors contributed equally to this work. Transforming the transport sector also means fundamentally rethinking the distribution of public space. Especially in cities, public space is a scarce resource that can be used in different ways. In Germany, cars currently take up a lot of space in public spaces, considerably more than public transport vehicles, bicycles, or pedestrians [1]. However, on average, cars are parked for 23 h a day and often dominate the street scene - whether driving or standing. This leaves less space for other purposes of public life. It is therefore important to analyze how public space can be designed in such a way that it benefits as many people as possible and can promote active mobility. Lots of negative influences of the motorized individual transport have been started to be tackled and receive attention, such as noise, air pollution or traffic accidents. Space consumption and the necessity of the redistribution of public space, however, has only recently started to be thought of in politics and administrations. There is a growing number of cities that (temporarily) implement changes of the built environment to test how streets can be redistributed to active mobility. The variety of measures spans from car-free summer streets (e.g. cool streets, Vienna) and temporary interventions (e.g. pop-up plaza and parklets, Berlin) to permanent car-free inner-city plans (e.g. Oslo, Madrid, Helsinki). Some of these measures aim at improving the quality of life in the neighborhood during summer or on the weekends and others try to create partially carfree cities and have a greater impact on the transport infrastructure. Regardless if the approach is on a smaller scale or on a comprehensive spatial level, there is the fear that people do not like a fundamental change and redistribution of streets. Therefore, it is necessary to understand how the redesign of streets is perceived and accepted by the population. Using the example of a real-world experiment where a street was transformed into a pedestrian zone which was open for cycling for one month we are going to analyze how residents accept this change of public space. More specifically, our research question is to what extent socio-demographic and psychological factors influence the attitudinal and behavioral acceptability of the transformation of public space. The paper is structured as follows. First, the theoretical background is presented. Then, we outline the case study and the methods together with a first description of the data. We describe the results of the regression analysis and discuss the findings. Limitations are outlined and a conclusion is drawn.

2 Theoretical Background – Influences on Acceptability Acceptability or acceptance is defined and operationalized in many different ways depending on the discipline and the context [2, 3]. In the recent study we refer to the definition of Busse and Siebert [2] who define acceptability as a complex scientific construct which “can be assigned to a particular degree (from opposition and rejection to high acceptability and engagement) and can be made on a certain level, including attitude, action, or utilization” (p. 243).

How is the Redesign of Public Space for Active Mobility

703

We focus on attitudinal and behavioral acceptability (as intention to react or protest) and compare the relevant factors for each dimension. As potential psychological predictors we chose perceived distributional fairness, affect, and place attachment. Additionally, we examined how car use, gender, and age influence the acceptability of RWE. 2.1 Distributional Fairness Distributional fairness plays a key role in the redesign of public space. Firstly, the redesign provides access to the limited resource public space and safety for residents who are disadvantaged by the status quo mobility system such as pedestrians, cyclists, children, or older residents. For this reason, residents may value the intervention as fair. Secondly, the redesign redistributes costs and benefits, as car driving and parking becomes more uncomfortable, whereas cycling and walking become more comfortable and safer. Depending on their interests, experiences and societal position residents could feel cut off in their freedom or comfort, and therefore feel a personal injustice [4], e.g. due to the loss of parking spaces. Thus, these residents may consider the intervention as unfair. In their review, Ejelöv and Nilsson [5] summarize that perceived fairness has been proven to be one of the most relevant factors for acceptability within transport transition research. For example, Schuitema et al. [6] found a strong correlation between fairness and acceptability regarding six policy measures that either reduce car use or car ownership. Thus, respondents accepted the measures more likely if they evaluated them to be fairer. As the RWE aims at addressing spatial inequality within the neighborhood, we expected perceived fairness to have a significant influence on residents’ acceptability. 2.2 Affect Several studies have shown that affect influences acceptability strongly [7, 8]. For example, affect has a direct effect on attitude towards low emission zones and an indirect effect on acceptability measured as behavioral and attitudinal support for low emission zones [8]. Besides this, the culture of automobility is deeply rooted in industrialized societies and related to many emotions [9]. A redesign, therefore, could evoke strong emotions among the residents. Residents may become angry or annoyed because parking space is removed due to the RWE. Furthermore, people could be worried about changes in their neighborhood in general or regarding gentrification [10]. On the other hand, they could draw hope, if they see and experience little changes in their neighborhood created by real-world experiments. We, therefore, also consider affect to have a significant influence on the acceptability of the redistribution. 2.3 Place Attachment In recent literature, place attachment is discussed and examined as predictor of acceptability, especially, if changes in the familiar landscape are made [11, 12]. On the one hand, people can perceive a change in the environment as a disruption of place attachment

704

K. Goetting and J. Jarass

[13]. This is especially the case with industrial or technological changes in the natural landscape, such as wind farms [13]. On the other hand, changes in the neighborhood can also be perceived as enrichment and strengthen the place attachment [12]. Unlike technology changes, the RWE aims to change how people interact with and experience their familiar environment. Consequently, place attachment could increase through the intervention and residents may accept the intervention more likely if they have experienced a strong place attachment during the intervention. Hence, we assume that place attachment has a positive influence on residents’ acceptability. 2.4 Socio-demographics We expect that socio-demographic characteristics also play an important role for acceptability. Namely car use, gender and age are assumed to influence the acceptability of public space redesign. As the redesign has more disadvantages for car users (e.g. less parking space), we assume car use to have a significant negative influence on the acceptability of street space redistribution. The need of more public space and safe infrastructure might depend on residents’ gender related tasks such as childcare, buying groceries, caring for relatives, and caring for social interaction in general. Research shows that women have to make more complex journeys in everyday life and do them more often on foot, by bike or by public transport [14]. The redesign enables them to get around more easily and safely. Therefore, women may have a more positive opinion towards the intervention compared to male socialized residents. There is not much research about mobility patterns and needs from non-binary people. However, a mixed methods study in Israel revealed that queer people (sample includes 28 non-binary people) mainly walk, cycle, or use public transport in the city, although, they often experience violence, harassment and discrimination in public spaces [15]. In this study, improving infrastructure was one of the proposed policy measures [15]. Consequently, we expected non-binary residents to have a more positive opinion than male residents. As the design of the parklets and the offer of activities tend to be rather tailored to younger people, age could also have an influence on acceptability. Moreover, older residents are generally more critical of change. Consequently, we expect older residents to accept the intervention less likely.

3 Case Study and Methods 3.1 Real-World Experiment (RWE) With the approval of the administration of the district Tempelhof-Schöneberg for organizing an event, the Kiez erFahren project made one section of Barbarossastraße in Berlin Schöneberg car-free in August 2021. Thus, neither driving nor parking was allowed during this period. About 100 m of the street were transformed and 40 parking spaces were eliminated [16]. The area is located within the inner city of Berlin with a high density of housing and retail. Next to Barbarossastraße, there is a busy street with shops, restaurants and cafés. The street itself offers three shops, a restaurant, a pub, and once a week a distribution point for a small marketplace.

How is the Redesign of Public Space for Active Mobility

705

In order to use the vacated space for other purposes, 10 parklets were installed, which served different functions. The parklets are wooden constructions that offer space for urban gardening, swapping and giving away, sports and exercise, small events, activities, and exchange on the surface of a parking lot. Residents were invited to participate in activities during the project and to initiate their own activities (Fig. 1).

Fig. 1. Parklets in Barbarossastraße during the real-world experiment (Source Nähring 2021)

3.2 Quantitative Survey and Regression Model Before and after the RWE the DLR Institute of Transport Research and the Institute of Advanced Sustainability Studies conducted a household survey with the help of colleagues from the project Kiez erFahren. For the following analysis we address the data of the survey which was conducted after the RWE in September 2021. A total of 1700 households in the adjacent residential blocks of Barbarossastraße were asked to participate in the survey, either in a pen-and-paper or in an online survey. Since we knew from other projects that the scientific survey is sometimes used as a ‘voting format’ for or against the redesign of streets and people participate more than once, we distributed an access code to assure that only one person per household participates at a time. A total of 196 people took part in the survey (a response rate of 11%). All relevant psychological constructs are presented in Table 1. All scales were designed on a 5-point Likert scale and showed sufficient reliability (α = 0.946–0.971). For car use we created a dummy variable (private car driving vs. non-driving). As the number of non-binary residents was too small for statistical analysis, we created a dummy variable male vs. non-male respondents. In the following table the psychological predictor variables and the two dependent variables are presented.

4 Results 4.1 Descriptive Results A non-significant Little’s test [20] indicated that data is missing completely at random (χ2 (1117) = 1093.6, p = 0.886). Furthermore, logistic regression analysis for categorial variables (gender and car use) remained insignificant for all predictors. Therefore, we

706

K. Goetting and J. Jarass Table 1. Descriptives of dependent variables and predictors Author

Example item (number of M (SD) items)

Newly designed

What is your opinion 2.99 (1.73) about the temporary Summer Street Barbarossa? (Very positive to very negative; 1)

Dependent variables Attitudinal acceptability

Behavioral acceptability Newly designed following I would take part in a 3.04 (1.40) Huijts et al. [17] demonstration for/against the perpetuation of the Summer Street Barbarossa. (4) Predictors Perceived fairness

Newly designed following I think the space on the 2.99 (1.49) Klaus et al. [18] Summer Street was fairly distributed. (6)

Affect

Klaus et al. [18], abbreviated

Place attachment

Newly designed following I have many good 2.59 (1.29) Afshar et al. [19] memories of the last four weeks in the Summer Street Barbarossa. (7)

Regarding the temporary Summer Street Barbarossa, I feel angry. (7)

3.10 (1.58)

Note. Displayed are statistics of cases with listwise exclusion of missing values (N = 155) M = mean, SD = standard deviation

decided to conduct listwise deletion on the data set despite the data showed a high cumulative missing quote of 19.7% for regression model A and 17.6% for regression model B. After listwise deletion the sample included 155 participants. To detect middle effect sizes (f2 = 0.15, [21]), our sample size N = 155 had a statistical test power of 0.96 in a multiple regression analysis with six predictors (see hypotheses) at α = 0.05 [22]. Therefore, the sample size is suitable to detect middle effect sizes but no small effect sizes. Most participants had a strong opinion to the RWE. Only a few participants (5.8%) indicated a neutral opinion. Around 48% of the participants had a (rather) negative opinion, whereas 46.5% of the participants had a (rather) positive opinion about the RWE. Regarding socio-demographic characteristics we found that the age group 18– 29 years is the smallest, there is no one under 18 years in the sample. More than a third of the respondents is between 50 and 64 years old which represents the biggest

How is the Redesign of Public Space for Active Mobility

707

age group. Regarding gender, there is an overhang in female respondents in our sample (55.5%) and only two non-binary respondents. The full-time and part-time employment rate is very high (72.8%) and the share of people with a graduate degree is even higher with 78.7%. Car ownership is comparable to the inner city of Berlin: In the sample about 37.7% do not have a car and the monthly net income per household is rather high. In the inner city of Berlin 44% households have at least one car. In the following table the predictors and the two dependent variables are presented. 4.2 Regression Analysis The attitudinal dimension of acceptability showed a very strong positive correlation with perceived fairness (r = 0.893), place attachment (r = 0.893) and affect (r = 0.861; all p < 0.001). The behavioral dimension of acceptability also had a strong relationship with perceived fairness (r = 0.741), place attachment (r = 0.731) and affect (r = 0.723, all p < 0.001). Age was negatively correlated with attitudinal (r = −0.253; p = 0.002) and behavioral acceptability (−0.176; p = 0.028). The two dimensions of acceptability showed a correlation of r = 0.728 (p < 0.001). The variance inflation factors (VIF) were below the critical threshold of 10, but above the recommended threshold of 4 [23]. We therefore conducted an extended collinearity diagnostic and checked the condition indices and the regression coefficient variance–decomposition matrix. For both dependent variables two dimensions for each of the condition indices exceeded a threshold of 15 which means that there could be a problem with multicollinearity. However, we did not identify any pairs of variables with variance proportions above 90%. Consequently, we decided to keep fairness and affect in the model for now and discuss further consequences in the limitations. We firstly run a multiple regression analysis with attitudinal acceptability as dependent variables and fairness, affect, place attachment and socio-demographic variables as independent variables. Results are presented in Table 2. Perceived fairness (β = 288, p < 0.001), affect (β = 0.304, p < 0.001) and place attachment (β = 0.361, p < 0.001) emerged as the strongest predictors for attitudinal acceptability in our sample. Nonmale gender identity (β = 0.066, p = 0.004) – in reference to male gender identity -was also positively but less strongly correlated with attitudinal acceptability, whereas age (β = −0.072, p = 0.002) was negatively correlated with attitudinal acceptability. That means, that non-male and younger residents were more likely to accept the RWE in reference to male and older residents. Unexpectedly, car use was found to be nonsignificant, although it showed a positive correlation with attitudinal acceptability. In sum, the variables explained 0.924% of the variance in attitudinal acceptability. We run a second multiple regression analysis with behavioral acceptability as dependent variables and fairness, affect, place attachment and socio-demographic control variables and the variables used before as independent variables (see Table 2). For behavioral acceptability, perceived fairness only showed a strong and positive correlation (β = 0.370, p = 0.012). All other variables remained non-significant, although they showed a significant correlation with behavioral acceptability, except gender. The model explained a significant proportion of variance in behavioral acceptability (adjusted R2 = 0.586, p < 0.001) (Table 3).

708

K. Goetting and J. Jarass Table 2. Regression model A: Dependent variable: attitudinal acceptability

Model

B

SE

(Constant)

−0.177

0.197

Perceived fairness

0.335

0.073

0.288

0.05), except the private car (Sig. 0.034 < 0.05) where women feel less safe than men. Besides, as presented in Table 2, men chose to modify their pedestrian route more often than women to move away from other pedestrians due to fear of coronavirus transmission, but this differentiation is not statistically significant. Finally, age exerted a statistically significant influence (Sig. 0.00 < 0.05) on respondents’ attitude to modify their pedestrian movement, with older age classes (>55 years old) showing the highest frequency of route change due to of their higher vulnerability to the virus transmission. 3.2 ITS Evaluation, Sustainable Mobility Interventions and Covid-19 Regarding the evaluation of ITS by the citizens, the degree of their satisfaction with the operation of ITS was measured in a 5-level Likert scale (where 1: not at all satisfied, 2: somewhat satisfied, 3: neutral, 4: satisfied and 5: very satisfied). Moreover, the citizens’ perception concerning the potential contribution of ITS in addressing the impact of the

Smart and Resilient Urban Mobility During the COVID-19

827

Table 1. Impact of the pandemic on mobility behaviors Trip frequency by mode and purpose / travel safety perception Before Covid-19

During Covid-19

Wilcoxon test

Unsafety during Covid-19

Median

Median

p-value

Median

City bus

3

2

0.001

4

Car as a driver

4

4

0.87

1

Car as a passenger

3

3

0.011

N/A 4

Transport mode

Taxi

2

1

0.001

Motorcycle

1

1

0.001

1

4

0.001

1

3

4

0.001

1

Work

5

4

0.001

Education

4

1

0.001

Leisure/Entertainment

4

1

0.001

Exercise/Walking

4

4

0.001

Bicycle Pedestrian

3

Trip purpose

Shopping

3

2

0.001

Medical visit

2

2

0.001

Family care

3

3

0.001

Personal affairs

3

2

0.001

N/A

1 Significant at 1%

pandemic on urban mobility safety was also measured in a 5-level Likert scale (where 1: none, 2: low, 3: moderate, 4: high and 5: very high contribution). It is noteworthy, that 94% of the sample agree with the statement that Trikala is a smart city (agreed: 36.4%, totally agreed: 57.6%). The respondents declared satisfied (median = 4) with the CityMobil2 pilot project, the Cities-4-people Mobility Communities, and the Integrated Intelligent Transport Information System (Trikala GIS). They kept a neutral stance towards the other smart applications, possibly due to lack of information and limited use. Nevertheless, in most cases residents state that ITS could have a high contribution (median = 4) to the city’s resilience and the adaptation of urban mobility to pandemics. According to the Mann-Whitney U test and the mean rank analysis presented in Table 3, it is observed that the degree of satisfaction of the respondents towards all the ITS applications depend on their knowledge and use of the different systems, at a statistically significant level. Similarly, there is a statistically significant relationship between the perception that citizens have of the intelligence of the city of Trikala and their view on the potential of ITS to contribute to the city’s resilience. Respondents who

828

Z. Olympisiou and A. Papagiannakis Table 2. Change of pedestrian route in relation to gender and age

Demographic factor

Frequency of pedestrian route change Median

Mean rank

p-value

Man

4

208.54

0.120

Woman

3

191.11

65

4

215.81

Gender

Age 0.0011

1 Significant at 1%

perceive Trikala as a smart city considered that ITS could enhance travel safety during the pandemic. Concerning the pop-up urban interventions and active mobility measures that could help build healthy streets in the short term, as well as to promote sustainable mobility in the long term, the vast majority of residents opt for the temporary extension of bicycle lanes (77.5%). The second category of preference includes the reclaim of sidewalks space from pedestrians’ obstacles, the temporary extension of pedestrian streets, the creation of temporary traffic calming zones and the implementation of artificial intelligence in mobility systems (e.g., travel demand forecasting, Covid-19 contact tracing and temperature screening applications) (38.9%, 34.1%, 33.8% and 32.1% respectively). The third group of intervention chosen by less than 30% of the respondents are the following: enhancing the quality of bus services, promoting car-sharing with strict compliance with Covid-19 protection measures, seasonal transformation of urban arterials in car-free roads, creating parklets by converting parking spaces (29%, 23.2%, 23.5% and 19.2% respectively). It is noteworthy that most citizens believe that all of the above sustainable mobility measures could be beneficial if they were implemented permanently after the end of the pandemic (33.1% and 64.4% declared yes and probably yes). Finally, they also declared that the pandemic offers an opportunity to boost alternative mobility behavior, reduce CO2 emissions from traffic, improve air quality and tackle climate change (about 90% of the sample stated that the improvements could be important or very important). Table 4 presents the results of Chi-Square test for independence, which used to discover if there is a relationship between the citizens’ preferences of pop-up intervention that could strengthen city resilience and their gender, age and opinion about the intelligence of Trikala. It seems that there is a statistically significant difference of views between men and women regarding the creation of seasonal car-free arterial roads, the

Smart and Resilient Urban Mobility During the COVID-19

829

Table 3. Citizens’ evaluation of ITS Degree of satisfaction of ITS ITS project

Sample median

Mean rank

p-value

ITS knowledge Yes

Mean rank

p-value

ITS use

No

Yes

No

CityMobil2

4

202.84

95.53