Urban Mobility Research in India: UMI Research Symposium 2022 (Lecture Notes in Civil Engineering, 361) 981993446X, 9789819934461

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Lecture Notes in Civil Engineering

Ashish Verma M. L. Chotani   Editors

Urban Mobility Research in India UMI Research Symposium 2022

Lecture Notes in Civil Engineering Volume 361

Series Editors Marco di Prisco, Politecnico di Milano, Milano, Italy Sheng-Hong Chen, School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, China Ioannis Vayas, Institute of Steel Structures, National Technical University of Athens, Athens, Greece Sanjay Kumar Shukla, School of Engineering, Edith Cowan University, Joondalup, WA, Australia Anuj Sharma, Iowa State University, Ames, IA, USA Nagesh Kumar, Department of Civil Engineering, Indian Institute of Science Bangalore, Bengaluru, Karnataka, India Chien Ming Wang, School of Civil Engineering, The University of Queensland, Brisbane, QLD, Australia

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Ashish Verma · M. L. Chotani Editors

Urban Mobility Research in India UMI Research Symposium 2022

Editors Ashish Verma Department of Civil Engineering Indian Institute of Science (IISc) Bangalore, Karnataka, India

M. L. Chotani RDO/Consultant Institute of Urban Transport Delhi, India

ISSN 2366-2557 ISSN 2366-2565 (electronic) Lecture Notes in Civil Engineering ISBN 978-981-99-3446-1 ISBN 978-981-99-3447-8 (eBook) https://doi.org/10.1007/978-981-99-3447-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The Institute of Urban Transport India (IUT) was established in May 1997 as a professional body under the purview of the Ministry of Urban Development Government of India (MOUD) as a premier professional non-profit-making organization and registered under the Societies Registration Act. The objective of the Institute is to promote, encourage and coordinate the state of the art of urban transport including planning, development, operation, education, research and management. The annual Urban Mobility India (UMI) Conference and Expo is a flagship event held under the aegis of the Ministry of Housing and Urban Affairs, Government of India. The event is inaugurated by Hon’ble Union Minister of Housing and Urban Affairs. The genesis of UMI is from the National Urban Transport Policy of the Government of India, 2006 (NUTP), which lays a very strong emphasis on building capabilities at the State and city level to address the problems associated with urban transport and undertake the task of developing sustainable urban transport systems. The Research symposium, which forms a key part of the conference, provides a platform to highlight the current research carried out by academia and research institutes in urban transport, especially by young researchers pursuing post-graduation or Ph.D. programs. The Institute of Urban Transport (IUT) invited IISc Sustainable Transportation Lab (IST Lab) to organize the 13th Research Symposium of 15th Urban Mobility India Conference & Expo 2022. This book presents selected proceedings from 13th Research Symposium of 15th Urban Mobility India Conference & Expo 2022. The central idea of the conference was mainly focussed on “Azadi@75 Sustainable AtmaNirbhar Urban Mobility”, which is not only an interdisciplinary area in general but also one of the important challenging areas from transportation systems engineering. A total of 71 abstracts were received from different parts of the nation covering the range of themes such as Smart City & Smart Mobility, Sustainable Transportation Planning & Policy, Public Transport & Non- Motorized Transport (NMT), Road Safety for Vulnerable Road Users (VRUs) and Urban Transport Infrastructure Design for All, Sustainable Mobility & Land Use (LU), Sustainable Urban Freight, Electric Urban Mobility, and Urban Transport Governance. A double-blind review process, which is followed by most of the peer-reviewed journals, was adopted throughout and it was made sure that v

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Preface

all the papers were reviewed by at least two reviewers. Subsequently, 37 papers were provisionally selected under the condition that the review comments are addressed satisfactorily. These papers were presented in 8 sessions with 4 to 5 papers being presented in each of the sessions. There was one session each on electric and clean urban mobility; sustainable transport planning; urban transport planning; smart city, smart mobility and urban freight; traffic engineering; safety and emissions and two sessions on public transport and NMT and this book presents a compilation of those quality research papers. We acknowledge the support extended by the Institute of Urban Transport India (IUT) in managing the conference. We are also thankful to Mr. Furqan A. Bhat, Ms. Hemanthini AR, Mr. Aitichya Chandra and Ms. Almas Siddiqui and other research members of IISc Sustainable Transportation Lab (IST Lab) for their support throughout the process. Bangalore, India Delhi, India

Ashish Verma M. L. Chotani

Contents

Choice Modelling-Based Policy Evaluation for Gender-Inclusive Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ubaid Illahi, Gayathri Harihara Subramanian, and Ashish Verma

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Smart Data for Performance Monitoring of City-Bus Services—A Case Study of Ahmedabad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sarah Alexander, Shalini Sinha, and Khelan Modi

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A Critical Review of India’s Urban Governance Reforms and Its Impact on Transport Sector: Case Studies of Bangalore and Jaipur . . . . Ashish Verma, Sanjay Gupta, Mahim Khan, Monika Singh, Greg Marsden, Louise Reardon, Morgan Campbell, and Gayathri Harihara Subramanian

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Assessing the Disparity in Connectivity of Multiple Unit Trains in the National Capital Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aditya Manish Pitale, Shubhajit Sadhukhan, and Manoranjan Parida

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Review of Transportation Relevant UN SDG Targets and their Association with Sustainable Transport Indicators . . . . . . . . . . . . . . . . . . . . Rohit Singh Nitwal, Almas Siddiqui, and Ashish Verma

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Impact of Parking Pricing and Regulations on User Behavior . . . . . . . . . . 111 Minal Shetty, Shalini Sinha, and Jayita Chakraborty Comprehensive Framework for Adoption of Electric Vehicles: A Case Study of Jaipur City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Mahima Soni and Sanjay Gupta Assessing Electric Vehicle (EV) Readiness of an Indian City: A Case Study of Lucknow, Uttar Pradesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Piyush Saxena

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Contents

An Empirical Investigation into Electric Vehicle Adoption in Urban Freight—A Case Study of Delhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Saloni Gupta and Sanjay Gupta Travel Behaviour of Women in Delhi-Pre and During-Covid Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Monika Singh and Sanjay Gupta Investigating the Effects of Individual and City Tier Characteristics on Motorized Two-Wheeler Usage Behaviour: A Multilevel Modelling Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Aitichya Chandra, Hemanthini Allirani, and Ashish Verma Planning for Equitable Accessibility to Public Facilities: Case Study of Faridabad, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Shivani Khurana and Karan Barpete Women Safety in Public Transport—A Case of Ahmedabad . . . . . . . . . . . 267 R. Lakshmi and Nitika Bhakuni Assessment of Utilization of the Foot Over Bridges in Delhi . . . . . . . . . . . . 283 Akshaya Paul and Sharif Qamar Comprehensive Analysis of Post-COVID-19 Changes in Behavior and Perception of Public Transit Users in the Urban Region of a Medium-Sized City of India- Noida/Greater Noida Region (Delhi NCR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 D. Sai Kiran Varma, Shalini Rankavat, and Anuj Bhardwaj Analysing Factors Influencing Usage of Metro Services in Bengaluru, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 Meghna Verma, Ann Das, and Sneha Rikhi Investigating the Attributes Influencing Pedestrian Behaviour of Commuters for Enhancing Accessibility of Metro Stations: A Case Study of Delhi, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Mahima Kanojia, Shubhajit Sadhukhan, and Namia Islam Delay Analysis of Motorized Three-Wheelers at Roundabouts in Urban Indian Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Subhada Nayak, Mahabir Panda, and Prasanta Kumar Bhuyan Shapley Additive Explanation Method for Assessing Motorized Two-Wheeler Level of Service at Signalized Intersections . . . . . . . . . . . . . . 381 Manisha Biswal and Prasanta Kumar Bhuyan Performance Analysis of Signalized Intersections from Truck Drivers’ Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Sujeet Sahoo, Chaganti Sudha, and Prasanta Kumar Bhuyan

Contents

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Detecting Social Groups Using Low Mounted Camera in Mass Religious Gatherings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Nipun Choubey, P. Sobhana Karthika, Gangadhar Reddy, and Ashish Verma Spatial Distribution Pattern of Pedestrian Road Accidents in a South Asian City: A Case Study of Chennai . . . . . . . . . . . . . . . . . . . . . . 417 B. Karthikeyan and Karan Barpete Investigation of Transport Pollutant Emissions and Their Associated Health Impacts in North Indian Region . . . . . . . . . . . . . . . . . . . 443 Siddharth Jain and Shalini Rankavat

About the Editors

Prof. Dr. Ashish Verma is the Convenor of “IISc Sustainable Transportation Lab. (IST Lab.)”. He is Ph.D. from IIT Bombay and is currently serving as a Professor of Transportation Systems Engineering at the Department of Civil Engineering, Indian Institute of Science (IISc), Bangalore, India. Further, he was a Visiting Professor at ITMO University, Saint Petersburg, Russia during 2016 and a Visiting Fellow at Queensland University of Technology (QUT), Brisbane, Australia during 2019. Before joining IISc, he served in IIT Guwahati and Mumbai Metropolitan Region Development Authority (MMRDA). His research interests are in sustainable transportation planning & policy, integrated public transport planning and management, transport & Quality of Life (QoL), transport & climate change, modelling and optimization of transportation systems, travel behaviour analysis and modelling, pedestrian and crowd flow modelling, driver behaviour and road safety, intelligent transportation system (ITS), traffic control & management, etc. He has authored more than 250 research publications, including more than 95 journal publications in the area of sustainable transportation. He has also authored a book on “Public Transport Planning and Management in Developing Countries” published by CRC Press and another book on : “Integrated Public Transportation System—Planning and Modelling”, published by VDM Publishing House Ltd. He has also been Co-Editor of Conference Proceedings of WCTR-2016, 5th CTRG, RATE-2018 and 6th CTRG (currently under processing), published by leading publishers, Elsevier and Springer. He is the Former Editor of journal “Transport Policy”, Elsevier; Associate Editor of “Urban Rail Transit”, Springer and “Journal of Modern Mobility Systems (JMMS)”, Mason Publishing. He is the Founding and Immediate Past President of the society Transportation Research Group of India (TRG). He is presently serving as Steering & Scientific Committee Member of World Conference on Transport Research Society (WCTRS) based at University of Leeds, UK. He has also been recently elected as Hony. Secretary of Institute of Urban Transport (India). Mr. M. L. Chotani has a master’s degree in city and Regional Planning from School of Planning, Guru Nanak Dev University, Amritsar (1975) and a Post Graduate

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About the Editors

Diploma in Urban Survey and Human Settlement Analysis from International Institute of Aerospace and Earth Science (ITC) Enuschede the Netherlands (1987). He was superannuated from the post of Additional Chief Planner in August 2009 from Town and Country Planning Organization, Ministry of Housing and Urban Affairs, Govt. of India. He is a Fellow Member of Institute of Town Planner (India) and Life Member of Institute of Urban Transport (India). Currently, he is working as Consultant and RDO in IUT. He has more than 48 years of experience in Urban Planning, Urban Transport, Appraisal Evaluation and Monitoring of Urban Projects, Capacity building and teaching. He has vast experience in preparing Master Plan, Regional Plans, District Plans, Comprehensive Mobility Plan Toolkit and Evaluation of Urban Transport Projects. He was an associate in the study on E- Rickshaw in Indian Cities, City Wide Integrated Multimodal Transport Toolkit and Bus Sector study in India. He has been involved in Capacity Building Programme for officials from Metro Rail Companies and Sustainable Urban Transport Project of UN and World Bank for state government officials working in Transport Sector. Currently, he is associated with the studies on Impact of Metro Projects and Innovative financing. He has been involved in the annual flagship event on Urban Mobility India Conference cum Expo. being organized by the Ministry of Housing and Urban Affairs as well as preparation of conference proceedings thereof since the last 10 years. He is a member of editorial board of the Research Journal of IUT.

Choice Modelling-Based Policy Evaluation for Gender-Inclusive Mobility Ubaid Illahi, Gayathri Harihara Subramanian, and Ashish Verma

Abstract Gender-based differences in mode-choice behaviour have been significantly studied in developed countries; however, it remains underexplored in developing countries. Moreover, the use of unsustainable modes of transport has been increasing leading to further burdening the Climate Change issues. It calls for policy interventions from the transportation professionals and concerned stakeholders that would shift the mode share towards greener and sustainable modes of transport. Therefore, this study utilizes a discrete choice modelling approach to achieve the following objectives: (a) to develop a mode choice model, (b) to explore, identify, and test the impact of transport policy bundles on the modal split across men and women and (c) to test and analyse the impact of identified policy bundles across the gender-income groups. A Multinomial Logit (MNL) choice model was developed based on the utility maximization choice theory and the parameters were estimated. Nineteen policy instruments focusing on improving women ridership in public transportation were identified and tested on modal share through the developed MNL choice model. From the policy analysis done separately for male and female and across four income groups, it was inferred that certain policies demonstrated that low and lower-middle females are more likely to shift towards public transportation and NMT modes; however, the overall shift could be undesirable also if it does not target all the modes. This study would be useful to policymakers who would like to test the impact of a policy instrument or policy bundle across different gender-income groups with a good level of confidence. Keywords Gender inclusive mobility · Equity · Smart mobility · Policy evaluation · Mode choice modelling

U. Illahi · G. H. Subramanian (B) · A. Verma Department of Civil Engineering, Indian Institute of Science, Bangalore 560094, India e-mail: [email protected] A. Verma e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_1

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U. Illahi et al.

1 Introduction It has been broadly agreed by researchers across the globe that men and women have different transportation needs due to the difference in their daily activity-travel patterns and the associated factors (Adlakha & Parra, 2020; Carver & Veitch, 2020; Dunckel-Graglia, 2013). The workplace location, available modes of transport, costs incurred, accompanying individuals and many such factors influence the mode choice in one way or the other. Gender-based differences in mode-choice behaviour have been significantly studied in developed countries; however, it remains underexplored in developing countries (Adeel et al., 2017). Moreover, the use of unsustainable modes of transport has been increasing leading to further burdening the Climate Change issues (Parry et al., 2008). It is important to understand and highlight such issues and simultaneously seek solutions that are based on a scientific approach. It calls for policy interventions from the transportation professionals and concerned stakeholders that would shift the mode share towards greener and sustainable modes of transport. This research paper utilizes a discrete choice modelling approach to achieve the following objectives: (1) To develop a mode choice model using the RP-SP survey data set. (2) To explore, identify and test the impact of transport policy bundles (consisting of policy instruments) on the modal split across men and women. (3) To test and analyse the impact of identified policy bundles across the genderincome groups. The following sections discuss the study area, variables considered, MNL model developed, policy evaluation and results.

2 Data, Variables and Study Area A revealed and stated preference (RP-SP) survey in 2020 was conducted for Bengaluru city. The data consists of a mixture of revealed and stated preferences with four stated preference scenarios. A total of 7190 data points capturing the information corresponding to nineteen variables (refer to Table 1) were observed. The data was collected across the Bengaluru Metropolitan Region, Karnataka. The variables can be divided into three categories: (1) Decision-maker, (2) Mode, (3) Attributes of alternatives. The decision-maker, in this case, is the ‘individual’ making the mode choice. Mode is a dependent variable and consists of seven alternatives within a choice set. The available alternatives within the choice set consist of ‘car’, ‘bus’, ‘auto’, ‘two-wheeler’, ‘cycle’, ‘walk’ and ‘metro’. The third category corresponds to the attributes of these seven available modes, consisting of the ‘travel time’ and ‘travel cost’ variables. It is to be noted that car, two-wheeler, auto and metro each have both travel time as well as travel cost variables associated with them. For

Choice Modelling-Based Policy Evaluation for Gender-Inclusive Mobility

3

Table 1 Variables that were used to capture the data of revealed and stated preference (RP-SP) survey Variable category

Notation

No.

Variable

Decision-maker

x11

1

Age (16 to 58 years)

x12

2

Gender (Male = 0, Female = 1)

x13

3

Income (Low, Lower-mid, Upper-mid and High)

Mode (dependent)

CALT

4

Alternative (mode) chosen from the choice set

Attributes of alternatives

xCTT

5

Travel Time using Car

xBTT

6

Travel Time using Bus (PT)

xWKB

7

Walking Time to Bus (PT)

xWTB

8

Waiting Time (PT)

xITB

9

Interchange Time (PT)

xTWTT

10

Travel Time using Two-Wheeler

xATT

11

Travel Time using Auto

xCYT

12

Travel Time using Bicycle (NMT)

xWT

13

Walking Time (NMT)

xMTT

14

Travel time using Metro (PT)

xCTC

15

Travel Cost using Car

xBTC

16

Travel Cost using Bus (PT)

xTWTC

17

Travel Cost using Two-Wheeler

xATC

18

Travel Cost using Auto

xMTC

19

Travel Cost using Metro (PT)

Note PT–Public Transport; NMT–Non-Motorized Transport

bus, in addition to these two variables, there are three more variables quantifying ‘walking time’, ‘waiting time’ and ‘interchange time’. Cycle and walk modes have only travel time variables associated with them and it is assumed that no costs are incurred in using these two modes. The maintenance cost associated with bicycle repair has not been considered in this research.

3 Research Methodology The research methodology followed in this part of the project has been divided into eight major steps as shown in Fig. 1. The process of data segregation into genderincome groups has been presented in Fig. 2. It is noteworthy that nineteen policy instruments were identified that were put in thirteen policy bundles (explained in detail in Sect. 4), which were then compared with the Business-As-Usual (BAU) scenario. While the steps in Fig. 1 are self-explanatory and simple to comprehend, it is

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vital to provide the mathematical formulation and estimates of the model parameters of the MNL choice model, corresponding to the fifth step. These are presented in the subsequent subsection.

Fig. 1 Flow chart showing the steps followed in the research in a hierarchical manner

Choice Modelling-Based Policy Evaluation for Gender-Inclusive Mobility

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Fig. 2 Segregation process of data across gender-income groups

3.1 Mathematical Formulation and Estimated Parameters of the Model The MNL choice model that has been employed in this paper is based on the utility maximization choice theory. The utility of an individual i choosing the mode m is given by the following expression (Eq. 1): Uim = Vim + εim

(1)

where εim = error term; Vim = deterministic utility Here, we are concerned with the deterministic utility. The following equations (Eqs. 2–8) have been used to compute the deterministic utilities for an individual i using a particular mode denoted in the subscript, for example, Vi,CAR is the utility of an individual i using car and so on. Vi,CAR = αCA + βTT ∗ xCTT + βTC ∗ xCTC + β1,CA ∗ x11 + β2,CA ∗ x12 + β3,CA ∗ x13

(2) Vi,TW = αTW + βTT ∗ xTWTT + βTC ∗ xTWTC + β1,TW ∗ x11 + β2,TW ∗ x12 + β3,TW ∗ x13

(3) Vi,AUTO = αAU + βTT ∗ xATT + βTC ∗ xATC + β1,AU ∗ x11 + β2,AU ∗ x12 + β3,AU ∗ x13

(4) Vi,CYCLE = αCY + βTT ∗ xCYT + β1,CY ∗ x11 + β2,CY ∗ x12 + β3,CY ∗ x13

(5)

Vi,WALK = αWA + βTT ∗ xWT + β1,WA ∗ x11 + β2,WA ∗ x12 + β3,WA ∗ x13

(6)

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U. Illahi et al.

Vi,BUS = αBU + βTT ∗ xBTT + βTC ∗ xBTC + β1,BU ∗ x11 + β2,BU ∗ x12 + β3,BU ∗ x13 + βWK ∗ xWKB + βWT ∗ xWTB + βIT ∗ xITB

(7)

Vi,METRO = αME + βTT ∗ xMTT + βTC ∗ xMTC + β1,ME ∗ x11 + β2,ME ∗ x12 + β3,ME ∗ x13

(8) where αCA = alternate specific constant corresponding to the car; βTT = generic parameter corresponding to travel time; βTC = generic parameter corresponding to travel cost; β1,CA = parameter that defines the direction and importance of the effect of age on the utility of car; similarly, β2,CA and β3,CA corresponds to gender and income, respectively; βWK , βWT and βIT are the parameters associated with only bus and correspond to the walking time, waiting time, and inter time, respectively. Once the utilities are obtained, the next step is to compute probabilities. The probability of an individual i choosing the mode m is given by the following expression (Eq. 9): Pim = Σ

e Vim

q∈Cm e

Viq

(9)

where Vim = utility of individual i choosing the mode m; Viq = utility of individual i choosing mode q such that it lies within the choice set Cm , including mode m. The probabilities that an individual i chooses a particular mode from the given choice set are given by the following equations (refer to Eqs. 10–16): e Vi,CAR

Pi,CAR = Pi,TW =

e Vi,CAR + e Vi,TW + e Vi,AUTO + e Vi,CYCLE + e Vi,WALK + e Vi,BUS + e Vi,METRO e Vi,TW e Vi,CAR

Pi,AUTO =

Pi,BUS =

+

e Vi,AUTO

+

e Vi,CAR + e Vi,TW + e Vi,AUTO

Pi,CYCLE = Pi,WALK =

+

e Vi,TW

e Vi,CYCLE

+ e Vi,WALK + e Vi,BUS + e Vi,METRO

(10)

(11)

e Vi,AUTO (12) + e Vi,CYCLE + e Vi,WALK + e Vi,BUS + e Vi,METRO

e Vi,AUTO

e Vi,CYCLE (13) + + e Vi,WALK + e Vi,BUS + e Vi,METRO

e Vi,CAR + e Vi,TW + e Vi,AUTO

e Vi,WALK (14) + e Vi,CYCLE + e Vi,WALK + e Vi,BUS + e Vi,METRO

e Vi,CAR

+

e Vi,TW

+

e Vi,CYCLE

e Vi,BUS e Vi,CAR + e Vi,TW + e Vi,AUTO + e Vi,CYCLE + e Vi,WALK + e Vi,BUS + e Vi,METRO

(15)

Choice Modelling-Based Policy Evaluation for Gender-Inclusive Mobility

Pi,METRO =

e VCAR + e VTW + e VAUTO

e VMETRO + e VCYCLE + e VWALK + e VBUS + e VMETRO

7

(16)

The next step is to compute the probability of a mode m, which is obtained by the following expression (Eq. 17): Pm =

n Σ

Pim

(17)

i=1

Finally, to obtain the modal split, the probabilities of all seven modes were computed by the following equations (refer to Eqs. 18–24): PCAR =

n Σ

Pi,CAR

(18)

Pi,TW

(19)

Pi,AUTO

(20)

Pi,CYCLE

(21)

Pi,WALK

(22)

Pi,BUS

(23)

Pi,METRO

(24)

i=1

PTW =

n Σ i=1

PAUTO =

n Σ i=1

PCYCLE =

n Σ i=1

PWALK =

n Σ i=1

PBUS =

n Σ i=1

PMETRO =

n Σ i=1

Since the utility values are relative (not absolute), in the MNL choice model, it is important to fix a reference category. In this study, ‘car’ was taken as the reference category. The model was coded in ‘RStudio’, an Integrated Development Environment for programming language ‘R’. The estimated parameters for the MNL choice model are presented in Table 2.

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Table 2 Estimated parameters of the MNL choice model Parameter

Estimate

t-ratio

Parameter

Estimate

t-ratio

αCA

0 (fixed)

–

β2,CA

0

–

αTW

6.508

7.656

β2,TW

−3.710

−8.032

αAU

−11.983

−0.094

β2,AU

12.315

0.097

αCY

5.187

5.219

β2,CY

−1.656

−3.645

αWA

6.885

7.570

β2,WA

−0.579

−1.578

αBU

7.848

9.525

β2,BU

−1.339

−4.244

αME

7.233

9.028

β2,ME

−1.453

−4.692

βTT

−0.020

−12.858

β3,CA

0

–

βTC

−0.012

−5.588

β3,TW

−0.063

−6.194

β1,CA

0

–

β3,AU

−0.151

−5.470

β1,TW

−0.080

−4.105

β3,CY

−0.075

−6.003

β1,AU

0.086

3.085

β3,WA

−0.071

−6.524

β1,CY

−0.043

−1.798

β3,BU

−0.078

−8.275

β1,WA

−0.052

−2.469

β3,ME

−0.061

−6.635

β1,BU

−0.059

−3.372

βWK

−0.020

−0.874

β1,ME

−0.063

−3.680

βWT

−0.021

−0.980

βIT

−0.176

−2.141

Null log-likelihood = −4173.977

Final log-likelihood = −2427.871

Rho-square = 0.4183

Adjusted Rho-square = 0.4114

Akaike information criterion (AIC) = 4913.74

Bayesian information criterion (BIC) = 5078.20

Note t-ratios >1.96 (in absolute value) means that the coefficient is statistically significant for 95% confidence level, Similarly, a threshold of 1.645 is used for 90% confidence

4 Policy Description 4.1 Policy Instruments In this study, nineteen policy instruments were identified which were then used to test the impact on modal share through the developed MNL choice model. The testing was done in the form of policy bundles that consist of one or more policy instruments. The selection of these policy instruments was done based on the literature review. In order to test a policy bundle in the developed MNL choice model, the foremost step is to understand which variables get affected and in what way. Fifteen variables (numbered from 5 to 19 in Table 1) were used to test the policy bundles. These consist of ‘travel-time’ and ‘travel-cost’ variables for all the available modes. However, not all variables can get affected in a similar way. Some policy instruments directly affect the variable(s) while others affect the variable(s) in an indirect way. If the

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variable gets directly affected (positively or negatively), then the testing process is quite straightforward. However, to test the impact of those policy instruments that do not influence the variable directly, it becomes essential to make suitable assumptions. Table 3 summarizes the policies considered, their category, variables that can possibly be affected due to implementation of those policies, and assumptions made for testing those policies.

4.2 Policy Bundles In order to check the sensitivity of the developed MNL choice model, nineteen identified policy instruments were tested. The testing was done in the form of bundles which were evaluated into two groups as shown in Fig. 3. It is noteworthy that Group 1 consisted of seven individual policy instruments (called Bundle 1 to Bundle 7) while Group 2 consisted of six policy bundles (called Bundle 8 to Bundle 13) comprising of two or more policy instruments. Since Bundle 10 to Bundle 13 were more exhaustive, they were classified into four categories: (1) Planning, (2) Economic, (3) Regulatory and (4) Information Technology. The representation of these four policy bundles (B10–B13) is shown in Fig. 4. Bundle 10 is a combination of the following twelve Planning and Regulatory policy instruments: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Increasing network coverage of PT Increasing frequency of PT Encouraging park and ride Defining car-restricted zones Encouraging carpooling and HOV Lanes Adopting the hybrid work patterns Improving cycling and walking infrastructure Densifying along the transport corridors Enforcing congestion pricing Imposing polluter pays Providing dedicated (exclusive) bus lanes Implementing vehicle-free zones.

Bundle 11 is a combination of the following eleven Economic and Regulatory policy instruments. 1. 2. 3. 4. 5. 6.

Encouraging park and ride Subsidizing PT modes Defining car-restricted zones Increasing the fuel cost Adopting the hybrid work patterns Densifying along the transport corridors

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Table 3 Summary of policies and assumptions Policy and description

Category

Variables affected

Assumption

Planning Increasing network coverage of public transport

‘walking time’ to These two variables were tested at bus and ‘inter-time’ 10% reduction between two successive buses along a route

Increasing frequency of public transport

Planning

‘waiting time’ for These two variables were tested at the bus to arrive at a 10% reduction bus stop and ‘inter-time’ between two successive buses

Encouraging park and ride

Planning

‘travel cost’ • overall cost of travel will get variables of bus and reduced • ‘travel cost’ variables of bus and metro metro are reduced by 10% each

Subsidizing PT modes

Economic

‘travel cost’ • ‘travel cost’ variables of bus and variables of bus and metro are reduced by 10% each metro

Defining car restricted zones

Regulatory

‘travel-time’ and ‘travel-cost’ variables of private modes

• 10% increase in the ‘travel-time’ and ‘travel-cost’ variables of car and two-wheeler, while simultaneously reducing the ‘travel-time’ of the bus by 10%

Encouraging carpooling and HOV lanes

Planning

‘travel time’ variables of all modes

• 5% decrease in the ‘travel time’ and ‘travel cost’ variables across all modes except NMT modes and metro

Increasing the fuel Economic cost

‘travel cost’ • 10% increase in the ‘travel cost’ variables of bus, car, variables of bus, car, two-wheeler two-wheeler and and auto auto

Adopting the hybrid work patterns

‘travel time’ and • half of the workforce will be ‘travel cost’ enforced to work from home while variables across all the remaining half will work from modes except NMT offices, which could be swapped modes and metro every month or so • 5% reduction in the ‘travel time’ and ‘travel cost’ variables across all modes except NMT modes and metro

Regulation

Improving cycling Planning and walking infrastructure

Travel time variables of walk and cycle

• 10% reduction in ‘travel time’ variables of walk and cycle modes each (continued)

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Table 3 (continued) Policy and description

Category

Variables affected

Assumption

Densifying along the transport corridors

Regulation, economic

‘travel cost’ variables of car, two-wheeler and auto

• 10% increase in the ‘travel cost’ variables associated with car, two-wheeler and auto mode

Zero fares on PT for low and lower-middle income women

Economic

‘travel cost’ of bus and metro

• ‘travel cost’ of bus and metro to zero for low- and lower-mid-income women

Discounted fares on PT for women

Economic

‘travel cost’ • ‘travel cost’ variables associated variables of bus and with bus and metro are reduced by metro 10% for women across all four economic groups

Introducing integrated platforms

Information technology

‘waiting time to bus’ • 5% decrease in ‘waiting time to and ‘inter bus time’ bus’ and ‘inter bus time’

Imposing polluter pays

Regulation, economic

‘travel cost’ variables of car and two-wheeler

• 10% increase in the ‘travel cost’ variables associated with car and two-wheeler

Providing dedicated (exclusive) bus lanes

Planning

‘travel time’ variable of bus and car

• 10% reduction in the ‘travel time’ variable of bus while simultaneously increasing the ‘travel time’ variable of car by 10%

Improving the real-time information of PT system

Information technology

‘waiting time to bus’ and ‘inter bus time’ variables

• 5% reduction in waiting time to bus’ and ‘inter bus time’ variables

Implementing Regulation vehicle-free zones

‘travel time’ and ‘travel cost’ of private modes

• 5% increase in ‘travel time’ and ‘travel cost’ of private modes

Improving surveillance, design and safety measures

‘travel time’ and ‘travel cost’ of all modes

• 5% increase in ‘travel time’ and ‘travel cost’ variables of private modes • 5% decrease in ‘travel time’ and ‘travel cost’ variables of public transport and NMT modes

7. 8. 9. 10. 11.

Planning, information technology

Enforcing congestion pricing Zero fares on PT for Low and Lower-middle Income Women Discounted fares on PT for Women Imposing polluter pays Implementing vehicle-free zones.

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Fig. 3 Grouping methodology for testing and evaluating thirteen policy bundles

Policy Bundle 10

Policy Bundle 11

Planning (P) and Regulatory (R) instruments

Economic (E) and Regulatory (R) instruments

B10

B11

Policy Bundle 13

Policy Bundle 12 Planning (P), Economic (E) and Regulatory(R) instruments

Planning (P), Economic (E), Regulatory (R) and Info-Tech (IT) instruments

B13 B12 Fig. 4 Categorization of policy bundles (B10–B13)

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Bundle 12 is a combination of the following sixteen Planning, Economic, and Regulatory policy instruments. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

Increasing network coverage of PT Increasing frequency of PT Encouraging park and ride Subsidizing PT modes Defining car-restricted zones Encouraging carpooling and HOV Lanes Increasing the fuel cost Adopting the hybrid work patterns Improving cycling and walking infrastructure Densifying along the transport corridors Enforcing congestion pricing Zero fares on PT for Low and Lower-middle Income Women Discounted fares on PT for Women Imposing polluter pays Providing dedicated (exclusive) bus lanes Implementing vehicle-free zones.

Finally, Bundle 13 comprises all the nineteen policy instruments mentioned corresponding to Planning, Economic, Regulatory and Information Technology. All the policy bundles were tested on the developed MNL choice model individually which was followed by comparing the modal split across all these policy bundles with Business-As-Usual (BAU) scenario. The comparative analysis of the policy bundles with BAU was done on males and females separately. In addition to the overall impact of policy bundles on the modal share, the analysis was also done across four income groups for both males as well as females. The gender-income grouping was done as presented in Fig. 2. The four income groups that were used in this study are as follows: (1) Low (Income ≤ |7500), (2) Lower-mid (|7500 < Income ≤ |25,000), (3) Upper-mid (|25,000 < Income ≤ |45,000), and High (Income > |45,000).

5 Results and Policy Evaluation To demonstrate the effect of policies on the modal split, various policy instruments were tested on the developed MNL choice model. The testing was done using thirteen policy bundles (B1–B13), as described in Sect. 4. The evaluation of these policy bundles was done in two groups. Group 1 consists of seven policy bundles (B1–B7) while the remaining policy bundles (B8–B13) were placed in Group 2. There are two reasons for evaluating the policy bundles in groups: (1) evaluating all the policy bundles simultaneously may cause interpretability issues, and (2) to understand if there is a difference in handling the policies within the model and possibly concluding which is a better approach and why. This has been explained in the subsequent

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Sects. 5.1 and 5.2, respectively. Additionally, this study also explored how do the selected policy bundles impact different income groups. To answer that, the analysis was also carried out on four (Low-, Lower-mid, Upper-mid and High-) income groups for both genders, which is explained in Sect. 5.3.

5.1 Gender-Based Evaluation of Group 1 Policy Bundles Since Group 1 (B1–B7) consists of only individual policy instruments, its evaluation provided a glimpse of how the modal split varies by these policies. The comparison of modal split results corresponding to Group 1 (across males and females) with the business-as-usual (BAU) scenario is presented in Fig. 5. For better understanding, the deviations of the mode share in both males and females from BAU have been presented in Figs. 6 and 7. Some key findings that were observed by analysing the results of B1–B7 are presented in Table 4. The application of B1 to the developed choice model results in an increase in the bus mode which is slightly higher for females (+1.669%) than males (+1.617). It is also important to note that it has resulted in a decrease in all the other modes, the maximum being that of metro. Overall, the mode share of PT modes has increased, which is desirable. As far as B2 is concerned, it has resulted in an increase in the mode share of NMT modes, which is seen more in case of females (+1.526)

Fig. 5 Gender-segregated modal split comparison of Group 1 policy bundles with BAU scenario

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Fig. 6 Gender-segregated percent change in modal split of Group 1 across all modes

compared to males (+0.895). Furthermore, an interesting observation from the results is that walking increased more for females (+1.352), whereas in case of cycling, the increase was seen more in males (+0.264). Both economic policy instruments, B3 and B4 resulted in an increased mode share (+1.248% and +0.098%) of overall PT for females. The interesting observation was that despite an equal reduction of the fares of metro and bus, females preferred the metro over the bus. B5 and B6 resulted in an increased mode share of PT for both genders, particularly that of the bus. The increase in the mode share of the bus was seen more for females (+0.403%) compared to males (+0.352%). The analysis of the results obtained from B7 shows an increase (desirable) in the mode share of walk and bus for both genders. Interestingly, the increase in the mode share of walking for females was observed to be more (+0.499%) while the increase in the mode share of the bus was seen more in males (+0.882%). The evaluation of a few policy bundles comprising of individual policy instruments, as discussed above, leads to some interesting points. While all the policy instruments are aimed at improving the mode share of sustainable (PT and NMT) modes, the overall results do not seem to be desirable. In some cases, the mode share of PT increases but at the cost of losing NMT mode share or vice-versa (refer to Figs. 6 and 7). The reason is that the policy instrument focuses on a particular mode. How could this issue be addressed? It can be handled by developing policy bundles comprising of a combination of various policy instruments. However, the

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Fig. 7 Gender-segregated percent change of Group 1 in modal split across aggregated modes

policy bundle should be formulated in a manner that targets different modes considering the diversity of gender as well as income groups. In order to demonstrate how policy bundles can be formulated and tested in the MNL choice model, different combinations of policy instruments were formulated and tested in this study. This has been explained in the subsequent sub-section.

5.2 Gender-Based Evaluation of Group 2 Policy Bundles The modal split results obtained by testing the policy bundles (B8–B13) on the developed MNL choice model were compared with the BAU scenario. The analysis was done on segregated groups of males and females. Figure 8 presents the comparison of modal split across B8–B13 policy bundles with the BAU scenario for males and females. In order to highlight the deviation (increase or decrease) of the modal split compared with the BAU scenario, the difference between the respective modal share

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Table 4 Key highlights from the analysis of results observed Bundle

Desired shift in mode

Percent shift in males (%)

Percent shift in females (%)

B1: Providing dedicated (exclusive) bus lanes

Bus

+1.62

+1.67

B2: Improving cycling and walking infrastructure

NMT

+0.9

+1.57

Walk

+0.63

+1.36

Cycle

+0.26

+0.17

B3. Zero fares on PT for Low and Lower-middle Income Women

PT

0.00

+1.25

B4. Discounted fares on PT for Women

PT

0.00

+0.10

B5. Introducing integrated platforms B6. Improving the real-time information of PT system

PT

+0.10

+0.12

B7. Improving surveillance, design & safety measures

NMT

+0.34

+0.55

B8

PT

+0.00

+1.28 −0.87

B9

B13

Car

−0.07

NMT

−0.03

+1.01

PT

+0.10

+0.23

Car

−2.06

−2.64

Auto

+0.00

−1.88

Walk

+1.34

+1.51 −0.687

Two-wheeler

−6.16

Cycle

+1.10

+0.67

Bus**

+3.30

+1.58

Metro

+2.49

+1.45

Notes Bold represents the better (desirable) result compared to gender counterpart * Since the policy bundles B10–B13 showed a similar trend across all the modes, only the results from best-performing bundles i.e. B13 are presented here ** Despite the increase in bus mode being seen more in males, the mode share of the bus (in B13) for females is 35.775% which is more than that of males (30.963%)

in a particular policy bundle and BAU is taken. It is represented in Fig. 9. Additionally, seven modes have been aggregated into four groups: Private, Auto,1 NMT, and PT. ‘Private’ is an aggregation of ‘car’ and ‘two-wheeler’ modes, NMT is an aggregation of ‘walk’ and ‘cycle’ modes, and PT comprises of ‘bus’ and ‘metro’ modes. The deviations in the modal split (for males and females) in these four groups across the policy bundles (B8–B13) with reference to the BAU scenario have been presented in Fig. 10. 1

Disclaimer: In the RP-SP survey conducted, no males were found to adopt ‘auto’ as their primary mode of travel. This, however, might not be the situation considering the whole population.

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Fig. 8 Gender-segregated modal split comparison of Group 2 policy bundles with BAU scenario

Some key findings that were observed by analysing the results of B8, B9 and the best among the remaining four policy bundles (B13) are presented in Table 4. B8, which is a combination of two policy instruments is gender (female)-specific. The first policy instrument in B8 is specifically related to Low-income and Lower-mid income women while the second policy instrument is focused on all the income groups of women. This is the reason that no change in the mode share corresponding to males is observed in the results. Looking at the females, B8 has resulted in an increase in the overall PT mode share (+1.283), which is desirable. However, some other observations are equally important to understand. Despite the increase in the overall PT mode share for females, the mode share of bus has decreased. B8 has also resulted in the modal shift from all the other modes, the maximum being from NMT mode (−0.850), which is not desirable. B9, which is a combination of three policy instruments resulted in an increase of the mode share of PT for both genders. The increase was observed to be greater for females (+0.230) than males (+0.102). Looking at the impact of B9 on NMT mode share, contrary to males (−0.034), there is a decent shift towards walking and cycling modes in case of females (+1.014). It was observed that compared to the BAU scenario, policy bundles B10–B13 resulted in a decrease in the mode share of ‘car’, ‘two-wheeler’ and ‘auto’ modes while as the mode share of ‘walk’, ‘cycle’, ‘bus’ and ‘metro’ increased. It means that the observations made in the policy bundles are desirable. Among all the policy

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Fig. 9 Gender-segregated percent change in modal split of Group 2 across all modes

bundles, B13 which is a combination of Planning, Regulatory, Economic, and Information Technology policy instruments performed the best, both in terms of males as well as females. With reference to BAU, the reduction in the modal share of ‘car’ in B13 is more for females (−2.644%) compared to males (−2.059%). On the other hand, the decrease in the modal share of ‘two-wheeler’ is seen more in males (−6.164%) compared to females (−0.687%). However, it should also be realized that the modal share of males using ‘two-wheeler’ in the BAU scenario is much higher (10.365%) than the females (1.162%). Similarly, the modal share of ‘auto’ for females also reduced (−1.881%) in Bundle 4 with respect to the BAU scenario. The modal share of ‘cycle’ in Bundle 4 increased more for males (+1.102%) compared to females (+0.668%). Contrarily, ‘walk’ mode is seen to have increased more in females (+1.508%) than males (+1.336%). Moreover, it can be observed that ‘walk’ mode has a greater share for women across all the policy bundles. In other words, females are more likely to shift to walking mode while males prefer cycling. As far as ‘bus’ and ‘metro’ are considered, the increase in the modal share in B13 is seen more in males (+3.298% and +2.486%, respectively) compared to females (+1.583% and +1.454%, respectively). However, the modal share of ‘bus’ for females is 35.775% in Bundle 4 which is greater than that of the males (30.963%). The results obtained from the MNL choice model show that the policy bundles have desirable impacts on the modal split. By desirable, we mean reducing the modal share of car and two-wheeler while simultaneously increasing it for NMT (walk and

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Fig. 10 Gender-segregated percent change in modal split of Group 2 across aggregated modes

cycle) modes and public transport (bus and metro) modes. Studies have shown that transportation systems need to address the issues of equity as well as sustainability. Implementing a policy or a group of policies is, however, quite challenging and requires exhaustive resources in terms of manpower, costs associated, and so on. The importance of this research study is that it demonstrated the impact of transport policies can be tested beforehand with a good level of confidence. The current research study also presented how various policy instruments can be tested in the choice model to incline transportation systems towards equity and sustainability. It also demonstrated how women-specific policies or policies targeting a particular income group can be tested within the choice model. The analysis in terms of segregated gender-income groups provides an in-depth understanding of how a particular policy influences different income groups in males and females. For example, from the results, it can be seen that the modal share of cars for low-income groups among males and females is already quite low. In that case, policymakers should focus on

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improving other modes. The sensitivity to a policy instrument or policy bundle signifies whether the policies are inclining transportation systems towards equity and to what extent. This study will aid policymakers in understanding if the policy is doing justice or injustice across income-gender groups and if that is not the case then they can reframe and re-test the policies. Therefore, this can be useful to policymakers and concerned transportation stakeholders because sometimes certain groups need to be targeted to achieve equity in the system.

5.3 Modal Split Evaluation Across Income-Gender Groups The results of modal split across the income-gender groups are also analysed to draw some useful insights. It was seen that low-income as well as lower-mid-income females have a significantly higher mode share of PT (bus and metro) and walk mode, compared to males. The analysis of the results shows that compared to females, the two-wheeler is a preferred mode for males across lower, lower-mid and even uppermid income groups. The mode share of car is observed to be higher for females across upper-mid and high income groups. Surprisingly, a good proportion of upper-income males have been found to use the metro. The analysis of the modal split across income-gender groups signifies that affordability is possibly one of the major factors that attract individuals to use PT and NMT modes. At the same time, it also calls for the attention that there is always a risk of people shifting to unsustainable modes if their income levels improve. In other words, to attract more users to PT and NMT modes, policymakers need to think beyond affordability. Looking at the mode share of two-wheeler results that are inclined more towards males, the possible reasons could be associated with ease in parking and lower-cost of fuel consumption, while the possible deterrents among females could be two-wheelers being more prone to accidents and exposure to pollution. A generalized interpretation that seems to be fitting well is that while commuting, females are more concerned about safety and comfort which attracts them to use a car, provided there are no economic constraints. Contrarily, males are more concerned about the reliability of the mode. Since the metro has a dedicated right-of-way that does not get affected by the volume of traffic on roads or time of the day. It could be a possible reason why a significant proportion of high-income males are using the metro. Therefore, to address the issues of inequity and unsustainability in the transportation systems, the challenge to policymakers is to influence the choice of the users by identifying the deterrents followed by formulating policies that could be generalized or income/gender-specific. In doing so, we may expect more of an optimized and desirable modal split as demonstrated through this study.

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6 Conclusion and Policy Implications This paper aimed at developing a choice model, identifying and testing various policies, and analysing their impact on the modal split across segregated genderincome groups. To achieve these objectives, a choice model was developed using the Multinomial Logit (MNL) method. Moreover, nineteen policy instruments were identified that were divided into thirteen policy bundles. To understand the sensitivity of the developed choice model towards different policies and the impact on modal split thereof, the policy bundles were evaluated into two different groups and then compared with the BAU scenario. Group 1 (B1–B7) consists of individual policy instruments while Group 2 (B8–B13) consists of a combination of two or more of the Planning, Economic, Regulatory, and Information Technology instruments. The results showed that while the policy instruments may have desired results on the mode it targets, they may lead to undesirable results across other modes. This shortcoming could be addressed by formulating policy bundles as demonstrated in this study. If the policy bundles are formulated in the way that they target all the modes in a proper way, the overall impact on the modal split is in the desired direction across all modes. It was seen that Bundle 13 which is a combination of all the nineteen policy instruments proved to be the best for both genders in terms of reducing the mode share of ‘car’ and ‘two-wheeler’ while increasing the mode share of ‘NMT (walk and cycle)’ and ‘PT (bus and metro)’ modes. The final takeaways from this research study can be summed up as follows: • The policies in B1–B7 demonstrated that females are more likely to shift towards public transportation and NMT modes; however, the overall shift could be undesirable also if it does not target all the modes. • B10–B13 demonstrated the benefit of using a combination of policy instruments. Using these policy bundles, policymakers can target all the modes and improve the overall modal split. • Among the NMT modes, females are more likely to walk while males are more likely to cycle. • Considering the equal cost subsidies in public transportation, females are more likely to use the metro over the bus. • Among private modes, the two-wheeler is a preferred mode for males while females prefer car. • Among the public modes, the mode share of the bus is more for females compared to males. • Low-income as well as lower-mid-income females have a significantly higher mode share of PT (bus and metro) and walk mode. • Two-wheeler is a preferred mode for males across lower, lower-mid and even upper-mid income groups. • The mode share of car is observed to be higher for females across upper-mid and high-income groups.

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Through this study, it was demonstrated that it is vital to check the impact of policies (designed for a specific group) across all modes for all the gender-income groups. This is an important inference to address the issues of inequity in transportation systems. This research study would be useful to policymakers who would like to test the impact of a policy instrument or policy bundle across different genderincome groups with a good level of confidence. This would aid in providing flexibility to reframe the policy before investing valuable resources in the execution process. Moreover, this research study would be beneficial for policymakers to target a specific gender or specific issue or a combination of issues to make the modal split equitable. The major limitation is that the RP-SP data set used in this research study did not capture any males using ‘auto’ as a primary mode of travel from their origin to destination. We acknowledge that this may not be the case considering the population of the study area. Moreover, the variables corresponding to ‘metro’ mode like access (walking) time, inter-time and waiting time were not captured in the utilized data set. These issues could be addressed in future research by obtaining and utilizing a more exhaustive data set. Finally, it is recommended that future studies should also include emerging modes like ‘electric vehicles’ and test the relevant policies in the choice model.

References Adeel, M., Yeh, A. G. O., & Zhang, F. (2017). Gender inequality in mobility and mode choice in Pakistan. Transportation, 44(6), 1519–1534. https://doi.org/10.1007/s11116-016-9712-8 Adlakha, D., & Parra, D. C. (2020). Mind the gap: Gender differences in walkability, transportation and physical activity in urban India. Journal of Transport and Health, 18(August 2019), 100875. https://doi.org/10.1016/j.jth.2020.100875 Carver, A., & Veitch, J. (2020). Perceptions and patronage of public transport–are women different from men? Journal of Transport and Health, 19(September), 100955. https://doi.org/10.1016/ j.jth.2020.100955 Dunckel-Graglia, A. (2013). Women-only transportation: How “Pink” public transportation changes public perception of women’s mobility. Journal of Public Transportation, 16(2), 85–105.https:/ /doi.org/10.5038/2375-0901.16.2.5 Mejia-Dorantes, L. (2018). An example of working women in Mexico City: How can their vision reshape transport policy? Transportation Research Part A: Policy and Practice, 116(June 2017), 97–111. https://doi.org/10.1016/j.tra.2018.05.022 Parry, J., Mitchell, K., Atkins, S., Wikinson, J., Parker, J., Waugh, J., Potter, S., et al. (2008). Climate Change and Sustainable Transport-the challenge for transport professionals (No. HJP/STP//120808). The Institution of Highways and Transportation-Transport Policy Board. United Kingdom. https://www.ciht.org.uk/news/climate-change-and-sustainable-transport-thechallenge-for-transport-professionals/ Phani Kumar, P., Ravi Sekhar, C., & Parida, M. (2018). Residential dissonance in TOD neighborhoods. Journal of Transport Geography, 72(April), 166–177.https://doi.org/10.1016/j.jtrangeo. 2018.09.005

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Verma, A., & Hemanthini, A. R. (2021). Church street first–impact assessment of pedestrianizing an urban street in terms of quality of life. Indian Institute of Science Sustainable Transportation Lab (IST Lab). Verma, A., Harsha, V., & Hemanthini, A. R. (2018). Sustainable Transport Measures for Livable Bengaluru. Indo-Norway Project-CLIMATRANS. https://drive.google.com/file/d/1bVjxZGIA ACqM46mtUQjp5xUowuByuXOZ/view?usp=sharing

Smart Data for Performance Monitoring of City-Bus Services—A Case Study of Ahmedabad Sarah Alexander, Shalini Sinha, and Khelan Modi

Abstract Indian cities are experiencing a fall in public transport ridership due to deteriorating quality of services as expressed by passengers. There has been a growing awareness on improving public transport delivery and also to monitor its performance. Although transit agencies have developed various service performance indicators/measures, the typical key performance indicators used in India are focused on the operators’ perspectives, with limited emphasis on passenger and larger societal concerns. ASRTU and CIRT publications convey that many cities have installed or are in the process of installing ITS to improve performance of public transport. These applications generate huge datasets which could be leveraged for monitoring services from the user’s perspective which could aid in quality improvement decisions. The main objective of this paper is to understand how user-focused performance assessment of city bus services could be undertaken using ITS applications data. For this, Ahmedabad was taken as the case city and user-focused service assessment was undertaken using sample smart datasets. Keywords Smart data · ITS · Big data · AVL · AFC · APC · Reliability · City bus services

1 Introduction Indian cities have been experiencing an accelerated growth in the number of vehicles as compared to that of the population. However, the physical infrastructure has not kept pace with the increased demand, leading to increased congestion problems and S. Alexander (B) CEPT University, Ahmedabad, India e-mail: [email protected] S. Sinha · K. Modi Centre of Excellence in Urban Transport, CEPT University, Ahmedabad, India e-mail: [email protected] K. Modi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_2

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accidents. Daily commuters are majorly dependent on private mode of transport with public transport mode share being only 18.1% for work trips. In 2015, there were 35 million daily trips being made (excluding personal trips) using two-wheeler mode of transport. Public transport has not been able to cater to the growing travel demand and hence the ridership on public transport has been decreasing (Singh, 2016). The major reason for this is the poor service quality which includes deteriorating quality of infrastructure and services as expressed by passengers. This discourages the users from travelling by public transport, resulting in bus operators incurring huge losses, which in turn prevents them from procuring new vehicles and technology due to lack of funds. Service quality of public transport can be measured from three different perspectives—operator, user, and society (TCRP 88). The operating agency/operator is more interested in running its service efficiently so as to minimize the operating costs. User perspectives are not the primary concern. Two major focus areas from passenger’s viewpoint are ‘service availability’ which includes accessibility, and ‘service delivery’ which includes the comfort and convenience of service (refer Fig. 1). From the societal perspective, the main goal is to reduce congestion, pollution, accidents and to improve living standards.

Fig. 1 Classification of user-focused indicators (Reproduced from Sinha et al. (2019))

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Fig. 2 Typical KPIs used in India (BMTC, 2019; CIRT, 2018)

Over the past decade, there has been a policy impetus on improving public transport service levels. It is becoming increasingly important to monitor the service levels from a holistic perspective on a regular basis. Literature shows a large set of performance indicators/measures identified for evaluating and monitoring services (Attanucci et al., 1981; TRB, 2000, 2013). However, the key performance indicators being used by PT agencies in India are mainly focused on physical and financial indicators and are more directed towards operator’s perspectives (MORTH, 2017). The physical performance indicators are focused on depots, schedules added and curtailed, the effective km achieved per day along with fuel productivity and financial performance indicators are focused on cost per km and earnings per km (refer Fig. 2). Here, performance monitoring is done at an aggregate level and not at route level. As a result, a complete picture cannot be perceived from all three perspectives. MoHUA also lays out a framework to assess the performance of public transport system through Service Level Benchmarks. It includes indicators like number of buses available, availability of fleet, service coverage with respect to area of the city, average passenger waiting time, comfort in terms of crowding level, extent of non-fare revenue, staff-bus ratio and the operating ratio (MoHUA, 2012). While these indicators are used for strategic studies, most of the PT agencies are comparatively inclined to physical and financial performance indicators. In recent times, many Indian cities have also installed or are in the process of installing ITS to improve performance of public transport (ASRTU, 2017). The application of new technology has helped generate numerous datasets, which if processed, can aid service improvement decisions.

2 Bus Operations Data Sources Transit operators collect data for various reasons like scheduling the services, funding, resource allocation, route planning, etc. There are various techniques for collecting data that is used by transit operators, namely, ride checks, point checks,

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boarding counts, farebox readings and revenue count, transfer counts, passenger survey and data from ITS1 in public transport, also being referred to as ‘smart data’ (Attanucci et al., 1981). Apart from smart data, the other techniques are tedious and are mostly collected one-time whereas smart data can be collected periodically without much effort and can be used to do the same analysis. The primary sources of smart data include the following: . APC (Automatic Passenger Count): It uses optical imaging and sensors to detect passengers boarding and alighting along with the stop location. It is used for analyzing running time difference, punctuality, and passenger activity as it can track vehicle location (TRB, 2000). However, five types of errors can be found namely, malfunction of hardware, error of miscounting passengers, failure to identify correct stop, error in data segmentation, i.e., start of new trip and incorrect route and driver information. It is used for route level analysis mainly. . AVL (Automated Passenger Count): AVL is designed to provide real-time location and schedule deviation for operational control and security. Other data sources include sensors, video cameras, street imagery (Zannat & Choudhury, 2019). . AFC (Automated Fare Collection) (TRB, 1997). The 3 major data sources of smart data can be analyzed in the following manner: . Smart card data (AFC): It collects different trip records like boarding time, location of stop, stop number depending on the type of smart card, i.e., whether it is an entry-only card, or a multimodal card used during travel (Zannat & Choudhury, 2019). . AVL devices provide actual running times that can be related to ridership data to calculate reliability (Currie et al., 2012). The AVL dataset contains trip ID, vehicle ID, date, scheduled departure, and arrival time, stop number and actual departure and arrival time at stop level. AVL data can be used to analyze holding and dwell time at stops, drivers’ compliance, schedule adherence, headway regularity and travel time (Ólafsdottir, 2012). . The APC dataset contains actual arrival and departure time of passengers, number of boarding and alighting passengers, and passenger load for each stop along that route. It can be used to analyze passenger loads (Ólafsdottir, 2012).

3 Performance Assessment of Bus Services Public transport performance needs to be measured (TRB, 2003) for reporting the data to a regulatory body, for improving their services and to gauge whether its goals are being met and for communicating the results to the public to help them in understanding how transit provides a valuable service. As discussed earlier, there are three different perspectives defined by TCRP report 88. NCHRP Research Results Digest 361 has also highlighted the key performance 1

Intelligent Transport System.

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indicators and the same has been categorized under the three perspectives as seen in Fig. 3. The key categories that fall under operators’ perspective are: . Measures on ridership focus on the number of users using a service. Indicators include total ridership, or ridership by service type, passenger trips, passenger km, ratio of ridership growth to population growth and user’s per capita (DOT, 2014). . Measures on internal cost and efficiency focus on effective utilization of resources. Indicators include passengers per vehicle mile, passengers per vehicle hour, total operating cost per passenger, operating expense per vehicle revenue mile and fuel economy (miles per gallon) (DOT, 2014). . Measures on asset management focus on the maintenance of the physical components of the agency. Indicators include age of fleet by vehicle type, percent of vehicle useful life remaining, number of mechanical failures and distance between vehicle failures (DOT, 2014). The key categories that fall under user’s perspective are: . Measures on availability: This comprises of service availability measures which include total service hours provided versus total hours needed to meet transit demand, average days per week that transit service is available (DOT, 2014). . Measures on quality: This comprises on service quality indicators like speed, safety, reliability, and comfort (DOT, 2014). Finally, under society’s perspective, these measures focus on the economic and environmental impact on communities by the public transport system. Indicators include percent of non-single-occupant vehicle commuters, number of auto vehicle trips reduced, energy savings and percentage of fleet vehicles transitioned to clean or alternative fuel (DOT, 2014). For measuring these indicators, Florida Department of Transportation uses a database which includes components like a database analysis system, GIS and GPS (DOT, 2014).

Fig. 3 KPI defined by NCHRP RRD 361

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3.1 Performance Assessment from Users’ Perspective Various dimensions useful from the user’s perspective have been studied by researchers and these include: . Accessibility and availability Accessibility was studied from the point of urban area and land use as a measure that can be used to calculate distance among different areas and to analyze the convenience to reach destination from origin with respect to the transportation system (Sun et al., 2018). The indicators include accessibility to bus stops and stations, walking distance to stations the average transfer distance, and the ones that fall under availability are service coverage, service denials, frequency, stop spacing, etc. . Reliability From a user’s perspective, unreliable services amount to extra waiting time for passengers to reach the destination at a particular time. The general rule of thumb is that passengers perceive wait time as twice of in-vehicle time. The indicators include wait time consistency, travel time variability using dwell time, perception of wait time, on time performance and headway regularity. Reliability measures can be applied at various levels like system level, route level, trip level and stop level (TRB, 2020). Dwell time is defined as the time spent by a vehicle at a stop or at a station for passenger movements, including the time required to open and close the doors. However, the time spent at a stop for other reasons like waiting for a traffic signal, time spent due to bus bunching are considered as delay and not as dwell time (TRB, 2013). Passenger boarding and alighting, fare payment methods, vehicle type and size and in-vehicle circulation affect dwell time. Dwell time variability is influenced by irregular headways, crowding inside vehicles, variation in passenger demands, driver interaction with passengers and boarding and alighting of differently abled (TRB, 2013). According to TCQSM, the dwell time at each stop can be computed using Td − Pa ta + Pb tb + toc where t d is the average dwell time, Pa is the number of alighting passengers, t a is the service time taken by alighting passengers, Pb is the number of boarding passengers, t b is the service time taken by alighting passengers, t oc is the time taken for opening and closing the door (Ólafsdottir, 2012). If the on-time performance is less than 70%, then the service is likely to be perceived as highly unreliable. The vehicle bunching effect can be measured through headway adherence. It is calculated using coefficient of variation of headways which is the standard deviation of headways (actual headways—scheduled headways), divided by the average (mean) headway. Since this is a statistical method, it is concluded that if the value is greater than 0.74, then most vehicles are bunched. In the case of excess wait time, TCQSM explains that if the service is reliable with sufficient capacity, then average wait time is half the

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average headway. Excess wait can be calculated as the difference between actual wait time (AWT) and scheduled wait time (SWT). AWT will depend on the frequency and regularity of actual bus arrival and SWT can be shown as half the headway between buses. . Comfort It is the physical comfort perceived through the design or the measures taken to create ambient conditions. When passengers have to stand, it becomes difficult for them to effectively use the time. Crowding also slows down the operations as it takes more time to board and alight. It is also interlinked with reliability as uneven headways result in uneven passengers loads due to late arrivals of transit services usually accommodating regular passengers as well as those who arrived early for the following vehicle (TRB, 2020). Even drive quality influences the perception of comfort as sudden acceleration and braking makes the journey feel uncomfortable. Some indicators include load factor in terms of crowding, ride comfort indicated using speed, vehicle noise, acceleration and braking, cleanliness, seat comfort and driver behavior. TCQSM highlights that if the seated load is 150% and above, which is the condition where one-third or more than one-third of passengers are standing, perceived travel times go higher than 1.4 times of actual travel time for seated passengers and more than 2.25 times of actual travel time for standees. This causes high discomfort in boarding and alighting (TRB, 2013). . Safety It includes actual safety from crime or accidents, which results in a feeling of security. Placing stops near well-lit areas with emergency phones and surveillance cameras are some of the ways to improve security. Indicators include number of vehicle accidents per specified distance or time, passenger injuries or fatalities per specified number of boardings and number of complaints registered on safety inside buses and on bus stops by type and severity.

4 Direction of the Research There have been limited efforts to identify the factors that really matter to public transport users, and these efforts include measuring satisfaction using a Likert scale (typically from highly unsatisfied to highly satisfied). Studies by Eboli and Mazzulla, TRB, and Cham have looked at these indicators critically for assessing the influence on the performance of public transport from a user’s perspective. Satisfaction-related studies and understanding performance used majorly data collected through surveys which cannot be conducted frequently. Hence, there is a dire need to follow a system that can help in monitoring performance whenever required without taking much effort. This need can be resolved by using smart data, where the same analysis (done using traditional more cumbersome methods) can be conducted using the raw data produced whenever required by ITS.

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Fig. 4 Indicators highlighted under various dimensions falling within user’s perspective

The following figure shows various indicators that fall under these user-focused dimensions, and the datasets required to analyze them. It can be seen that the indicators that fall under reliability can be majorly studied using ITS applications as they are quantitative and can be used for monitoring regularly unlike surveys which cannot be collected often. On-time performance and other reliability indicators are very critical as users want good services. Hence, reliability indicators will be selected for the purpose of this research (see Fig. 4).

5 Aim of the Research The aim of the study is to outline a framework for monitoring performance of city bus services from user perspective using data sources from ITS application. The objectives are: . To understand various performance evaluation and monitoring processes adopted by transport agencies. . To identify indicators focused on operator, user, and larger societal perspectives. . To review to what extent user-specific indicators are being measured to monitor performance. . To study the various performance measures that can be evaluated and monitored using smart data. The research was undertaken in three stages: the first stage involved literature review to identify performance indicators from users, operator and societal perspective. The second stage included collecting data on selected indicators for Ahmedabad. Data sources and the methodology used for monitoring performance by AJL and AMTS were also collected. The third stage involved analyzing the performance of selected routes using the indicators and assessing the gap by identifying the indicators used by authorities so as to provide recommendations and way forward.

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The research has taken the case of Ahmedabad and looked at sample routes for both city buses and BRTS. Route analysis in terms of user-focused measures has been undertaken using smart data. Indicators are selected keeping in mind the type of smart datasets available.

6 Approach As already discussed, the traditional way of monitoring performance is timeconsuming and performed occasionally, and adding to this, measuring reliability is through operational and financial indicators and is analyzed at a system level. Although reliability indicators like OTP, headway regularity, runtime difference can be considered under the perspective of an operator, it also has a strong impact on the user’s choice of route and mode. Hence it is important to understand reliability from a user’s perspective using smart data to monitor performance as service is provided for them, and also to assess the gap in monitoring by identifying whether the indicators used by agencies are same as to how literature describes. This would help in understanding how concurrent the results are.

7 Profile of the City—Ahmedabad Ahmedabad, the seventh largest metropolis in India, has been taken as the case city. The city has a total area of 483 km2 (2021) with a population of 55.8 lakhs and an average density of 120 pph in 2011. The main authorities governing the city and its development are Ahmedabad Municipal Corporation (AMC) and Ahmedabad Urban Development Authority (AUDA). The city offers two public transport systems to meet the transport needs of the city, namely, AMTS,2 which is the city bus service and BRTS.3 The city is also implementing a metro system. AMTS started its service in 1947 with a fleet size of 112 buses and is maintained by AMC. The length of a bus route is an average of 17 km, ranging between 5 and 57 kms with an average bus stop spacing of 410 m. AMTS has not been able to meet the growing demand and has been operating very old buses, which has increased their operational expenditure and reduced efficiency (GIDB, 2012). Currently, it has a fleet size of 684 plying on a network length of 792 km with an average daily ridership of 5 lakhs (see Fig. 5).

2 3

Ahmedabad Municipal Transport Service. Bus Rapid Transport System.

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Fig. 5 AMTS and BRTS route network

BRTS is operated by Ahmedabad Janmarg Limited (AJL), a subsidiary of AMC. It was inaugurated in October 2009 with a network length of 60 km in phase 1 (NIUA, 2016). Currently, it has a fleet size of 260 plying on a network length of 97 km with an average daily ridership of 1.6 lakhs. The ITS is implemented in both systems and all buses are equipped with GPS to capture real-time information. They have a separate control room to manage the system operations based on real-time data collected.

8 Detailed Methodology To analyze the performance assessment of routes from users’ perspective, sample AMTS and BRTS routes were selected. Information on existing performance evaluation approach undertaken by AMTS and BRTS was also collected. For sample routes, smart data available in the form of AVL, AFC and Incidence Management System (IMS) was requested from the transit agencies. Sample route selection was undertaken using the criteria of route coverage and ridership. Routes covering different parts of the city were selected to ensure representativeness. Data that is extracted for the selected routes from the ITS was for two days i.e., 20th and 21st of December 2021 for AMTS and for eight days i.e., from 7th of March 2022 to 14th of March 2022 for BRTS. Ahmedabad does not collect APC-related data and hence load factor at vehicle level cannot be computed. AMTS routes 150 and 72 and BRTS routes 1 and 11 were hence selected (see Fig. 6) and the following are its characteristics (refer Table 1). If the service is not on-time, then customers will experience increased wait times and bus bunching occurs which results in uneven passenger loads. The first bus would have more passengers than the second bus, which reduces the comfort of passengers. Higher waiting times are also observed when bus bunching does not occur. Reliability is also linked with run-time consistency as any variability would result in irregular service being provided, which again increases the travel time, including time spent

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Fig. 6 Selected AMTS and BRTS routes

inside the vehicle and at stops. OTP signifies whether it is operating on time but it does not give an idea on the effect on passengers until it is linked with other indicators like headway regularity and customer wait times. These indicators are extremely important for high-frequency routes as users do not consult timetables for them to reach the stop and so any variation in the service provided, would increase customer Table 1 Characteristics of the selected route Route

OD of route

No. of bus stops

Route length

150

Sarkhej Gam–Chinubhai Nagar

69

26

72

Sahyadri Bunglows–Godrej Garden City

66

1

Maninagar–Ghuma Gam

11

Headway

Buses on route

No of trips

Ridership

6

18

127

6900

22.5

8

9

100

6100

34

19.5

5

13

321

31632

Odhav Ring Road to 25 LD Eng College

14.7

15

14

131

19459

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Table 2 Method selected for analyzing the selected indicators Selected indicator

Definition

Method to measure and author

On-time performance

Measures how well actual % of trips (Various transit arrival and departure times operators) are adhering to scheduled arrival and departure times

Runtime difference and consistency

Runtime difference is the difference in actual and scheduled runtimes and runtime consistency is the distribution of actual runtimes over a period of time along a particular route

Why it is selected AMTS and AJL use this method, and it is easily relatable with other indicators

Average runtime difference in AMTS and AJL mins = average scheduled use similar runtime—average scheduled calculation runtime (Cham, 2006) Cv = σ/mean of actual run time (Liu & Sinha, 2007)

Can be easily related with other indicators. Helps to understand the consistency of service

Headway regularity

Headway is the time taken between 2 vehicles at the same point along a route

Cv = σ/mean of actual run time (Cham, 2006)

Easier to compare with wait time

Bus Trajectory (linked to headway regularity)

Trajectory shows the movement of vehicle along a route to identify points where bus bunching occurs

Graph is plotted using cumulative distance over cumulative time for a particular line direction. Each coloured line represents a bus that appears (TRB, 2006)

Helpful for getting a sense of how the route operates It can help to understand the phenomena of bus bunching

Customer wait time

Time taken by users waiting to board the bus along a route

EWT = AWT-SWT Used by = ½ of actual headway − ½ Transport for London (TfL) of scheduled headway, where, Liu and Sinha (2007), TRB (2006)

wait times. The Table 2 explains the method chosen for analyzing the indicators under reliability.

9 Results and Discussion In the case of AJL, fine is imposed using per km rate. Priorities are defined from the contract document by defining ranges that fall within the range of 25–100 as low priority, 100–500 as medium priority and above 500 as high priority. It is observed that AJL monitors on-time performance at the start and end of the trip, particularly during peak periods as defined by the authority. They only monitor late departures

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from origin terminal and late arrivals at the destination terminal. On analyzing the trip report, 95% of trips along with early trips go unmonitored, which is an issue in the case of transfer for passengers. Apart from that, they monitor any deviation in schedule and missed stops. This is monitored in real-time from their control room and the penalties are based on the system km. However, for imposing penalties, they do not monitor whether the bus reaches the stop on time. Lateness is defined using the time band falling in the range of −5 to +10. All the indicators falling under a user’s perspective are observed to have low penalties. No indicators are measured at stop level. In the case of AMTS, fine is imposed per violation per bus per day. Priorities are defined from the contract document by defining ranges that fall within the range of Rs 100 to Rs 250 as low priority, 250–500 as medium priority and above 500 as high priority. It is observed that AMTS monitors only adherence at the start of the trip with a maximum permissible early departure of 5 min. According to this threshold, 58% of the trips go unmonitored. It monitors whether the trips are getting completed but the arrival at last stop is not monitored along with whether the headways are maintained at each stop. It does not monitor whether the wait times are within the permissible limits. Although most of the indicators are given a low-priority penalty, AMTS emphasizes on reducing missed stops, which is monitored in real time. On analyzing the trip report, it is observed that only 30% of the trips are completed, the remaining is marked as missed trips, which go unmonitored as there are no indicators evaluating the missed trips (see Table 3). Consolidated performance results of the selected routes based on the indicators is shown in Table 4. The figure shows the results obtained by analyzing trips based on operator’s KPI and the method followed in literature. The figure only shows the results of peak hour trips. Performance based on the indicators selected for the study are as follows: . On time performance AMTS and AJL monitor on-time performance by calculating the percentage of total completed trips by the scheduled trips to penalize operators on their missed trips. However, this does not give an understanding on the effect on passengers. On-time performance is studied first using the time window used by both operators to assess performance, which is −5 to +10 min and is calculated at stop-level. To gain an understanding on the variation of performance during a day, peak periods are defined, comprising of 2 periods, i.e., one in the morning from 7:30 am to 10:30 am and the other in the evening from 4:30 am to 7:30 pm, which corresponds to the office and school hours of the public. It can be seen that except route 1, all routes have OTP less than 70% indicating highly unreliable service. The time window that AMTS/ AJL follows is an issue as the headway for the routes is 10 min and the time band is 15 min. This time-band is an issue for all high frequency routes which have a headway less than or equal to 10 min. This is an issue as if one bus departs 5 min later than the scheduled time, say 6:05 and second bus departs 5 min earlier from the schedule time, say 6:15, then according to AMTS/AJL these buses are on time, but both buses are departing at the same time causing a bus bunching effect. From

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Table 3 KPIs by AMTS and AJL Indicator

Category

AJL

AMTS

Comfort–Ride quality Service

Speeds are only monitored (low 1. Drive above speed priority) limits (medium priority) 2. Speed violation incidents (low priority)

Comfort

Infrastructure

Unclean vehicles at the start of first trip in the morning, malfunctioning passenger door, loose/ missing passenger door (low priority)

1. Unclean buses at the start of first trip in the morning (medium priority) 2. Loose handrails, roof grabs/rails (low priority)

Reliability–Runtime difference

Schedule/ service

–

–

Reliability–Schedule adherence

Schedule/ service

1. Missed stops (low priority) 2. Stoppage at points not designated (low priority) 3. Arrival for a shift ≯10 min late and delay ≯ 20 min beyond at the end of the shift (low priority)

1. Non-completion of trips (low priority) 2. Non-stoppage at points (high priority) 3. Starting trip 5 min early to scheduled time (low)

Reliability–Dwell time

Schedule/ service

Stopping at bus station for longer than authorized by authority (low priority)

–

Reliability–Route level

Schedule/ service

–

1. Deviating from route issued by AMTS (medium) 2. Non-availability of buses for any shift (medium)

Reliability

Infrastructure

–

1. Equipment not working or kept off (medium) 2. Non-selection of trip in DDU* (low priority)

Safety

Service

–

Loss/ tampering with recordings in the complaint book (medium priority)

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Table 4 Consolidated results on comparing with operator’s KPI and KPI from literature study Selected indicator

AJL

AMTS

As per literature for routes 72, 150, 1, 11

Schedule adherence/ On time performance

Only monitors missed stops, stoppage of buses at designated points and delay for start of trip ≯ 10 min, delay for end of trip ≯ 20 min Time band used: −5 to +10 min If buses arrive within this time band, then the service is considered to be on time

Only monitors missed stops, stoppage of buses at designated points and starting trip 5 min early to scheduled time Time band used: −5 to +10 min If buses arrive within this time band, then the service is considered to be on time

Time band used is an issue as the headway for the routes is 10 min and the time band is for 15 min Time band – On time 72

150

1

11

AJL/AMTS −5 to 10 min

17%

6%

72%

24%

TfL −2.5 to 5 min

6%

3%

49%

14%

Early arrivals of more than 10 min

82%

88%

22%

64%

Late arrivals of more than 10 min

6%

4%

15%

1%

Arrival at last Not stop–Early measured versus Late

Not measured

Runtime difference and consistency

Not measured

Not measured

Runtime 2.7 consistency–Coeff. of variation (all trips)

1.9

0.5

0.2

Customer wait time

Not measured

Not measured

Customer wait 45% time—Unacceptable wait time trips (peak)

66%

29%

35%

Headway regularity

Not measured

Not measured

Headway regularityCoeff. Of variation (peak trips)

1.25

2.44

1.49

0.96

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literature it is observed that Transport for London (TfL) follows a −2.5 min to + 5 min time band to assess on-time performance. Based on this, it is observed that all four routes provide unreliable service (refer Table 4). . Arrival at last stops The actual and scheduled arrival times of buses at last stop are analyzed to understand the arrival and for this, different time ranges were taken with threshold of 5 and 10 min and is done in terms of early arrivals and late arrivals. AMTS and AJL only consider late arrivals as an issue from operational point of view. Early arrivals are an issue to passengers who need to transfer. It is observed that all routes except route 1 have more than 60% early arrival trips. As a result, there is a need to check and plan the schedule more carefully. It is observed that the least number of late arrivals were observed for route number 11 with value being 1% only, while this route had least number of late departures. In the case of AMTS, the highest number of early arrivals was seen for route 150 with the value of 88% and the same route was observed to have the highest number of early departures considering the TfL time band of 2.5 min to +5 min. Thus, we see a strong relation between early/late arrivals at last stop and early/late departures from origin when we considered the time window of −2.5 min to +5 min for calculating schedule adherence. However, this strong relation is not observed in the time band used by AJL and AMTS, which makes it irrelevant (see Table 5). . Runtime Consistency This indicator checks whether there is any difference in the run time but from a user’s perspective by checking how consistently the service is running. This helps users understand the average travel time taken from boarding the vehicle to the final stop so that they can plan their schedule accordingly. The coefficient of variation indicates that the higher the value of coefficient, the lower is the consistency of run time a route has. This makes the service highly unreliable. This indirectly influences the mode of travel that users opt for ultimately. Hence, run time consistency should Table 5 Arrival at last stop for route 72 and 150, route 1 and 11 Time Range (in mins)

Routes Early

Late

0–5 (%)

5–10

>10 (%)

4.06

5.58%

82.23

150

2.95

2.11%

88.19

1

50.60

27.8%

21.5

11

21.90

14.2%

63.9

72

72

1.02

0.51%

150

1.27

0.84%

1

58.30

26.9%

11

98.80

–

6.09 3.80 14.8 1.2

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be in operators’ interest also to keep the existing users and increase patronage over time. Trips were analyzed during both peak and off-peak periods and it is evident that the most consistent run time is found for route 11 and route 72 has lower consistency in run time, which can be explained through results of on-time performance. (refer Table 4). . Headway regularity Headway regularity was studied using coefficient of variation of headways at the stop level. Stops were selected based on major bus stops, terminals and that the distance between any two selected stops should not be more than 3 km. Firstly, the scheduled headway for all 4 routes, routes 150, 72, 1 and 11 are noted from the timetable and they are 6, 8, 5 and 15 min respectively. Then the average scheduled headway is calculated from AVL data by calculating the difference of scheduled departure at the selected stops. It is observed that this average headway is much higher than the headway given in the timetable in the case of AMTS. Then, the standard deviation and coefficient of variation of the actual headways is calculated using AVL data. Highest variation is observed for route 150 as most of the stops do not get service at regular headways. The average headway for the BRTS routes is observed to be lesser than that of the headway mentioned in the timetable, resulting in higher inconsistencies in headway. The results are consolidated and shown to route level although analysis is done at stop level (refer Table 4). If the coefficient of variation is more than 0.7, then the phenomena of bus bunching will occur. It is observed that all the routes have the coefficient, resulting in bus bunching to occur (TRB, 2013). Although users get buses before the scheduled headway, allowing for lesser wait time, buses appear to be bunched at certain points, resulting in uneven passenger loads and thereby reducing the comfort of users which increases the perceived in-vehicle time. This also leads to higher waiting times when bus bunching is not present. This also reduces the comfort of passengers. Maintaining headway regularity is necessary to avoid variations in crowding. Headway regularity must be measured to prevent bus bunching and as a result reduce waiting times of users and is very important for high frequency routes. To understand headway regularity better, the bus bunching effect is studied, and how buses move along the route and to understand how delays affect the bus operations, bus trajectories are plotted for morning peak hour and evening peak hour for all four routes against scheduled departure and actual departure, to understand the difference in bus bunching in actual scenario. The trajectories are plotted for 1 day, i.e., 21st December 2021 for AMTS routes and 7th of March 2022 for BRTS routes. Routes 1 and 11 experience bus bunching at some points due to delays, congestion, etc., marked using black boxes and due to early and late departures (marked using red boxes), which can be justified using the on-time performance indicator. Particularly in route 1, it can be observed that there is higher wait time for passengers when bus bunching is not present, which is marked using violet arrows as seen in Fig. 7. Using time band followed by AMTS/AJL, it can be seen that 72% of the trips are on time, but the trajectories show that there is a lot of bus bunching that occurs at stops for route 1. If we consider, the TfL time window, only 49% of the trips are on time which

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Fig. 7 a Bus Trajectory plot for route 1, b Bus Trajectory plot for route 11, c Bus Trajectory plot for route 72 and 150

further justifies why bus bunching occurs in the bus trajectory plot. Hence, we can understand that the time band taken must be carefully selected, and that AMTS and AJL must revise the time bands that they follow. . Customer wait time The customer wait time is again calculated at stop levels and the same stops are selected for study. The scheduled wait time and actual wait times are calculated as half of headways. The values obtained for additional wait time are distributed in a 3bin format with thresholds of 8 and 12 min, as mentioned in SLB (MoHUA, 2012). It was observed that in comparison with headway regularity, for high frequency BRTS routes like route 1, as buses arrive within 5 min (mean observed headway is 4 min), users only had to wait within normal limits. However, in the case of high frequency AMTS routes, as the mean observed headway for both routes are 52 min, users have to wait for more than 12 min which is unacceptable. The results are consolidated and shown to route level although analysis is done at stop level (refer Table 4).

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10 Conclusion of the Study It is necessary to compare the performance as per the indicators chosen by the operator with the indicators selected for this study. AMTS only monitors early departure from the origin terminal, however, late departures are also an issue as this is closely related to how late the bus arrives at the last stop. The threshold for early and late departures must be decided based on the headway of the route. The operators only impose fine when buses depart 5 min early, i.e., only 42% of the trips are penalized as seen earlier. The remaining trips, even if it is late go unmonitored which is an issue. When we compare on-time performance using AMTS criteria, it is observed to have more trips that are on-time when compared to using TfL criteria. This is again an issue considering the headway. In the case of AJL, they only monitor late departure from origin terminal and late arrival at destination terminal, which is an issue as 95% of the trips go unmonitored considering that the threshold selected is 5 min late at the start of the trip and 20 min late at the end of the trip. Buses that depart early are unmonitored and buses that arrive late within the range of 20 min go unmonitored along with the trips that arrive early at the last stop. On analyzing the arrival of buses at the last stop for both AMTS and BRTS, it can be observed that almost 80% of the buses arrive more than 10 min earlier than the scheduled time. This results in making the service highly unreliable as majority of the trips go unmonitored. Both AJL and AMTS monitor skipped stops but not the time of actual departure with respect to the scheduled time which is effective in understanding whether there is irregularity in headways and on how long passengers must wait at the stop for the next bus to arrive. They also do not monitor how consistently the service is being provided which is an important factor of reliability. From this, we can understand that the indicators used by AMTS and AJL must be revised as they are looking at only the financial and operational factors from an operator’s perspective. It is important to monitor runtime consistency, on-time performance at trip level and stop level, headway regularity so that the time spent waiting at stops by users can be reduced. It would ensure services are available regularly allowing for minimal deviation from the scheduled times. The paper shows how datasets can be leveraged for providing a detailed understanding of service performance to take targeted actions.

11 Recommendations The recommendations that can be provided to AMTS and BRTS operators for the assessment of public transport using smart data are as follows. . Time window for measuring on-time performance should be narrowed down to a time band of −2.5 min to + 5 min as it has stronger relationship with other indicators. . Headway regularity along with customer wait time should be measured at stop level, using the same methodology in the study to understand the effect on users.

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. Run time consistency should be measured for all the routes as reaching destination on time and maintaining average travel times are major concerns of a passenger. . Terminal supervisions must be enforced to ensure vehicle leave on time as we understood the relation between bus bunching and on-time performance from bus trajectory plot. . Strategies like prioritizing traffic signal for all buses would ensure buses adhere to schedule if buses started on time as delays were also observed due to congestion from the bus trajectory plot. . More schedules can be provided, and existing schedules must be checked to reduce the variability in the service provided.

12 Way Forward This document can be used as a guiding document for operators to analyze large datasets pertaining to smart data to monitor performance and as a result implement effective strategies that would improve the overall service that would encourage more users to use public transport. Other user-specific indicators like crowding and ride quality can be considered for study using APC and other datasets available. Operators can apply the same methodology on all the routes to further suggest/implement strategies in the system. A tool can be developed where smart data is easily available and can be computed without much effort for the user-specific indicators to analyze the system at route level, stop level and even vehicle level. Acknowledgements The authors would like to express their sincere gratitude to Centre of Excellence- Urban Transport (CoE-UT), CEPT Research and Development Foundation (CRDF) and CEPT University along with AMTS, AJL and NEC for supporting this research.

References ASRTU. (2017). Best practice catalogue. Retrieved from Association of State Road Transport Undertakings: https://www.asrtu.org/wp-content/uploads/2017/10/Best-Practice.pdf Attanucci, J., Burns, I., & Wilson, N. (1981, August). Bus transit monitoring manual-Vol. 1: Data collection program design. Retrieved from https://rosap.ntl.bts.gov BMTC. (2019). Retrieved from https://mybmtc.karnataka.gov.in/info-1/Perfomance+Indicator/en Cats, O., & Gkioulou, Z. (2014, December 4). Modeling the impacts of public transport reliability and travel information on passengers’ waiting-time uncertainty. Retrieved from ScienceDirect: https://www.sciencedirect.com/science/article/pii/S2192437620300807 CIRT. (2018). State Transport Undertakings-Profile and Performance. Currie, G., Douglas, N., & Kearns, I. (2012). An Assessment of Alternative Bus Reliability Indicators. Dix, J. (2018, August 13). What is smart data? How does it help? Retrieved from NetScout: https://www.netscout.com/blog/what-smart-data-how-does-it-help#:~:text=Smart% 20data%20is%20data%20from,time%20the%20data%20is%20considered.

Smart Data for Performance Monitoring of City-Bus Services …

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DOT, F. (2014). Best practices in evaluating transit performance. Retrieved from https://www. fdot.gov/docs/default-source/transit/Pages/BestPracticesinEvaluatingTransitPerformanceFinal Report.pdf GIDB. (2012). Bus rapid transit system, Ahmedabad. Retrieved from GIDB: https://www.gidb.org/ pdf/bRTSchapter46.pdf Hansen, W. (1959). How accessibility shapes land use. Journal of the American Institute of Planners, 73–76. MoHUA. (2012). Service Level Benchmarks for Urban Transport at a Glance. MORTH. (2017). Review of the performance of State Transport Undertakings. NIUA. (2016). Urban transport initiatives in India: Best practices in PPP. Retrieved from carbonn: https://carbonn.org/uploads/tx_carbonndata/File1_AHMEDABAD%20BRTS.pdf Ólafsdottir, Á. (2012). Bus Service Performance Analysis-Case Study: Bus Line 1 in Stockholm, Sweden. Stockholm. Páez, A., Scott, D., & Morency, C. (2012). Measuring accessibility: Positive and normative implementations of various accessibility indicators. Journal of Transport Geography, 141–153. Singh, J. (2016, December 14). Retrieved from Intelligent Transport: https://www.intelligenttran sport.com/transport-articles/21458/city-public-transportation-india/#:~:text=Buses%20are% 20the%20most%20popular,to%20the%20growing%20travel%20demand Sinha, S., Swamy, H. S., & Modi, K. (2019). User Perceptions of Public Transport Service Quality. Elsevier B.V. Sun, C., Chen, X., & Zhang, H. M. (2018). An Evaluation Method of Urban Public Transport Facilities Resource Supply Based on Accessibility. TRB. (1997). TCRP report 29 closing the knowledge gap for transit maintenance employees: A systems approach. Retrieved from Onlinepubs: https://onlinepubs.trb.org/onlinepubs/tcrp/tcrp_ rpt_29.pdf TRB. (2000). TCRP synthesis 34 data analysis for bus planning and monitoring. Retrieved from Onlinepubs: https://onlinepubs.trb.org/onlinepubs/tcrp/tsyn34.pdf TRB. (2003). TCRP report 88-a guidebook for developing a transit performance-measurement system. Washington D.C. TRB. (2013). TCRP report 165 transit capacity and quality of service manual. Retrieved from https://onlinepubs.trb.org/onlinepubs/tcrp/tcrp_rpt_165fm.pdf TRB. (2020). TCRP report 215 minutes matter: a bus transit service reliability guidebook. TRB. Zannat, K. E., & Choudhury, C. F. (2019). Public transport planning: A systematic review on current state of art and future research directions. https://doi.org/10.1007/s41745-019-00125-9

A Critical Review of India’s Urban Governance Reforms and Its Impact on Transport Sector: Case Studies of Bangalore and Jaipur Ashish Verma, Sanjay Gupta, Mahim Khan, Monika Singh, Greg Marsden, Louise Reardon, Morgan Campbell, and Gayathri Harihara Subramanian

Abstract Transport is central to the development of urban areas because it directly affects the economic efficiency of the cities and the well-being of inhabitants. In the context of rapid urbanization processes, increasing travel demand, growing congestion, negative environmental impacts, the large size of investments, and the impacts of transport on daily human life, it is essential to formulate policies and strategies that enable the sustainable development of the transport sector in the cities. The redesign of the urban mobility governance system has played a pivotal role in seeking to promote more equitable, desirable, economically efficient, and environmentally sustainable cities in India. Recently, the Government of India implemented the Smart Cities Mission, to address sustainable development challenges in parts of 100 cities. This paper focuses on the implementation of the Smart City Mission to fulfil a threefold purpose (a) to examine the various governance reform initiatives implemented over the past few years to determine their impact on long-term infrastructure development projects and to identify those that could not be implemented (b) to give a detailed review of Smart Cities Mission and (c) to build a stakeholder map by conducting workshops with stakeholders, to understand the relationships between local actors, public officials, Non-Governmental Organizations, and institutions involved in sustainable transport infrastructure initiatives in Bangalore and Jaipur, and their connection to the new Smart City Mission initiative and delivery. Keywords Urban governance · Smart city mission · Institutional arrangement · Stakeholder mapping · Sustainable development A. Verma · M. Khan · G. H. Subramanian (B) Department of Civil Engineering, Indian Institute of Science, Bangalore, India e-mail: [email protected] S. Gupta · M. Singh Department of Transport Planning, School of Planning and Architecture, New Delhi, India G. Marsden · M. Campbell Institute for Transport Studies, University of Leeds, Leeds, UK L. Reardon Institute of Local Government Studies, University of Birmingham, Birmingham, UK © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_3

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1 Introduction India has had one of the world’s fastest-growing economies since the last two decades. Such growth also results in adverse environmental, social, and financial impacts derived from modernization and rapid urbanization processes. As a result, several transport policies and initiatives have emerged to improve the quality of life of Indian citizens. Whereas cities have not traditionally been very strong units of governance in India, the pressure from rapid urbanization has contributed to significant endeavours to reform the urban governance in India. Urban governance reforms took shape in 1992 by enacting the 73rd and the 74th Constitutional Amendment Acts (CAA). This recognized municipal public administrations as a formal part of a three-tier governing system, along with the Union Government and the State Governments, the strengthening of urban decentralization, and the rise of the Urban Local Bodies (ULB), the National Transport Policy, the Jawaharlal Nehru National Urban Renewal Mission (JNNURM), and the Smart Cities Mission (SCM) in 2015. The resulting transformations in policies, institutions, and administrations have been reflecting failures in coordination and integration among stakeholders into decision-making processes related to transport delivery and investment to meet the needs of citizens (Nallathiga, 2005). In 2015, the Government of India launched the urban renewal and modernization program known as “Smart Cities Mission” (SCM). This program aimed to harness technological innovations to address the challenges of urbanization in Indian cities. Through this mission, the Government of India intended to correct coordination and integration failures of the past urban transport policies. The cities that are a part of the Smart City Projects had the support of an official in the form of Special Purpose Vehicle (SPV) from the Ministry of Urban Development of India and an independent agent when they were going to develop their bids, projects, and proposals. The SPV was incorporated to ensure operational independence and autonomy in decision-making in the cities. Each city was required to create an SPV to plan, appraise, approve, release funds, implement, manage, operate, monitor, and evaluate the projects (Ministry of Urban Development, 2015). The idea of “smart cities” has been addressed in urban planning studies in India and worldwide. Researchers have explored how technology is adopted in smart cities to create environmentally sustainable urban governance models. Harrison and Donnelly (2011) argued that the concept of smart cities is not a current invention. In 1990, social movements advocated for alternative urban planning approaches in Oregon, United States, and proposed initiatives to create smart cities grounded on communication and information technologies. While across the globe, the word smart is critically engaging as a global term, in the Indian context, it is still considered as “providing basic or standard”. The configuration of governance reforms to attain sustainable and efficient urban transport through the SCM program has been analysed by researchers. Reardon

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et al. (2018) addressed the SCM program through the lens of a multilevel governance approach arguing that this program contributed to urban governance’s emergence with hierarchical links between central, state, and municipal institutions. This phenomenon goes against the decentralization regime enacted by the 74th Constitutional Amendment Act in India and impacts political participation and local projects for achieving sustainable urban development. Although all three tiers of governance are responsible for improving urban transport, substantial decision-making authority and financial capabilities are vested in the Central Government and State Government. However, states remain reluctant in devolving power to city jurisdictions, which has resulted in a fiscal deficit, a lack of coordination and integration among stakeholders, and a fragmented decision-making landscape that has brought problems to urban transport for many years (Kamal Batcha, 2013; Mukherjee & Gupta, 2018). If, as the Indian Government argues, poor coordination and limited capacity and accountability for programme delivery are at the heart of slow progress in urban transport reform, it is of significant interest to explore whether the Smart Cities Mission resolves these issues and/or creates new ones. In addition to coordination, engagement of key stakeholders and the public has been seen to be a weakness in India. Hoelscher (2016) reviewed the evolution of the SCM agenda in India and argued that its success is uncertain because of the lack of participation of poor and vulnerable men and women of urban territories. Ghosh and Arora (2019) interpreted participation within the SCM projects. They asserted that even though the SCM program strives to include citizens in the imagination of smart cities, there are obstacles to their involvement. Therefore, the objectives of this paper are threefold: (a) to examine the various governance reform initiatives implemented over the past few years to determine their impact on long-term infrastructure development projects and to identify those that could not be implemented (b) to give a detailed review of Smart Cities Mission and (c) to build a stakeholder map by conducting workshops with stakeholders, to understand the relationships between local actors, public officials, Non-Governmental Organizations, and institutions involved in sustainable transport infrastructure initiatives in Bangalore and Jaipur, and their connection to the new Smart City Mission initiative and delivery.

2 Review of Major National Urban Policy Reforms in India The demand for transportation in the cities of India has grown significantly due to the increase in population (Singh, 2005) resulting in migration from rural areas to small and large cities. The availability of motorized transport increases the income of households, commerce, and industries, generating a great demand for transport and vice versa, leading to congestion and delays. Problems related to urban transport in India have been addressed through numerous reforms. These reforms have been implemented from the nineties until the present day.

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In 1992, a significant step was taken in India to empower the ULBs, for the first time, by enacting the 74th CAA and strengthening urban decentralization. Amendments to the Constitution were made to hold the ULBs accountable for the urban planning and development of their cities, which had been the responsibility of the state government. However, it is argued that, while the constitution amendment laid down a roadmap for decentralization and greater devolution of power at the state and local levels, the implementation is slow to take place. The ULBs continue to be primarily hamstrung, both financially and functionally (Vaidya, 2009). Before 2005, ULBs used their taxes as the primary source of capital and therefore, public transport was not their priority. Between 2005 and 2006, two significant measures were taken to achieve inclusive and sustainable mobility: The National Urban Transport Policy (NUTP) and Jawaharlal Nehru National Urban Renewal Mission (JNNURM). These policies represented a shift in paradigms of sustainable urban development. The Government of India approved the NUTP in April 2006, which primarily focuses on the mobility of people. The aim was to overcome the current level of congestion and balance the mobility disparities between the different social groups by making the public transportation system more accessible, inexpensive, and efficient. The Unified Metropolitan Transport Authority (UMTA), an Indian urban transport planning agency, was seen to be crucial in achieving this aim. The Ministry of Urban Development (MoUD) mandated the establishment of UMTA to access bus funding under the JNNURM. However, only 15 cities have UMTA, which is relatively insignificant (Kochi Public Transport Day, 2018). For those that are enacted, the realities are somewhat different to the expectations. In Jaipur, the establishment of UMTA remains confined to papers. Nonetheless, in Bangalore, the transport department is hampering its creation with the fear that UMTA may reduce its powers like issuing permits to buses, bus route rationalization, or registration of new vehicles (Times of India, 2019). Faced with rising urbanization and a growing backlog in infrastructure investments, the JNNURM was established in 2005 with the goal of transforming cities into “engines of economic growth” by incentivizing urban reforms at the state and local levels through the provision of grants to accelerate infrastructure development in major cities. The main objective of the urban governance reforms developed by JNNURM was to improve ULB initiatives according to the 74th CAA, thereby enhancing their fiscal competence. Furthermore, JNNURM fostered urban structure development by ensuring quality service delivery and accountability by providing additional fundings. However, 25 state and union territories were found to have used less than 80% of the allocated budget from JNNURM funds which again shows the reluctance of the state government to adopt the prescribed reforms and lack of technical capacity at the local level to proactively identify, plan and execute projects (Nandi & Gamkhar, 2013; Kamath & Zachariah, 2015). The integration and coordination between urban transport systems, authorities, and decentralized decision-making processes that the 74th CAA inaugurated were seen as a problem for implementing mobility projects, but not as a solution. Policies and initiatives to make transportation in Indian cities efficient, sustainable, and

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enjoyable for citizens have not been successfully resolved with the NUTP, JNNURM, and UMTA. As a result, the reforms in urban governance regarding public transport in Indian cities failed to meet the mobility needs of citizens. In this context, the SCM program emerged. The Government of Indian mandates that each city has an SPV with a full-time CEO to present bids and compete for a sustainable infrastructure project related to transport. Furthermore, the SPV has nominees of the Central Government, State Government, and ULB on its board. The funds provided by the Government of India in the Smart Cities Mission to the SPV will be in the form of a tied grant and held in a separate Grant Fund. The existence of the SPV reflects the competitive, integrative, and coordinative essence of the SCM program in India. The focus of SCM is to create sustainable and efficient urban governances in Indian cities considering technologies of information and communication that can help enhance public services, such as electricity supply, affordable housing, solid waste management, health, education, mobility, water supply, safety and security, and public transport (Ministry of Housing & Urban Affairs, 2015). The SCM scheme is based on competition for grants to develop infrastructure and development projects and give shape to smart cities in India, which the SPV supports. The SPV authorizes, plans, releases funds, enforces, manages, runs, tracks, and evaluates smart cities’ development initiatives. This section provided a critical review of the transport reforms developed in India from the nineties to the present millennium. The next section will address the stakeholder mapping methodology that was used to help structure the analysis of the effects of the urban governance reforms in the Indian cities of Bangalore and Jaipur.

3 Methodology Bangalore and Jaipur are chosen as the case study sites due to their strong identities as a technological and heritage-important city, respectively. Bangalore, the capital city of Karnataka, a state in South India, is known as the Silicon Valley of India because of its position as the nation’s leading IT exporter. The city experiences the worst traffic congestion due to more focus on road-based infrastructure and lack of dedicated traffic management cells which make data-driven decisions. The city has good connectivity through public transport modes such as Bengaluru Metropolitan Transport Corporation (BMTC) and Bengaluru Metro Rail Corporation Limited (BMRCL), which are parastatal agencies. Jaipur, being the capital of Rajasthan, a state in North-west India, is known as the Pink city of India. It is listed as one of the world heritage sites by UNESCO. The city, which was designed to be a commercial capital, has kept its local commercial, artisanal, and cooperative traditions alive to this day. The city has experienced exponential population growth, a sharp rise in vehicle ownership, and an increase in various allied activities, resulting in a slew of traffic and transportation issues (Agarwal & Swamy, 2011). The city has good connectivity through public modes such as Jaipur City Transport Services Limited (JCTSL) and Jaipur Metro Rail Corporation (JMRC)

52 Table 1 General socio-demographic and transport characteristics

A. Verma et al.

Details

Bangalore

Jaipur

Population (number of persons)

1,27,64,935a

40,07,505b

Area (in sq. km)

8005c

467d

Altitude (in m)

920e

431f

Road network length (in Km)

6000g

2500h

Bus fleet size (in counts)

6501i

400j

a

https://worldpopulationreview.com/world-cities/bangalore-pop ulation b https://en.wikipedia.org/wiki/list_of_cities_and_towns_in_raj asthan c Comprehensive Traffic and Transport Study for Bangalore Metropolitan Region, June 2010 d http://jaipurmc.org/presentation/aboutmcjaipur/cityprofile.aspx e http://www.bangaloreindia.org.uk/travel-tips/location.html f https://en.wikipedia.org/wiki/jaipur g Mehta (2019) h Mehta (2019) i https://en.wikipedia.org/wiki/bangalore_metropolitan_transp ort_corporation j Mehta (2019)

run by the state government. Considering the similarities and differences between Bangalore and Jaipur, both cities were chosen to analyse the complexity of implementing SCM projects. Although the position of Bangalore is more critical within the country, it was not awarded funding within the SCM program until the third round of the process of competition for bids and grants, unlike Jaipur, which was awarded in the first round itself. This fact poses questions about the relative importance of the SCM to the city, given other opportunities. A few general socio-demographic and transport characteristics are summarized in Table 1. Mode share values of Bangalore and Jaipur are summarized in Table 2. Workshops with stakeholders were undertaken in 2018 to discover the current state of affairs of governance structure and mechanism and relevant development concerning SCM in Bangalore and Jaipur. The purposes of these workshops were to set a common platform for all stakeholders to discuss issues and concerns in connection to urban governance reforms and SCM program; identify critical stakeholders whose inputs would be significant and seek answers for some essential questions around the configuration of SPV in those cities, and the effect of this governance reform on implementation of the SCM. Subsequently, a stakeholder mapping exercise was conducted among the social agents related to urban transport in Bangalore and Jaipur to understand the interactions between them and the government agencies. The maps show the interactions between stakeholders through lines and arrows and reflect their positions in the SCM and the urban transport sector’s decision-making processes. A stakeholder workshop was organized at IISc, Bangalore, on the 5th of September 2018. Organizations, directly or indirectly related to SCM, were identified and invited

A Critical Review of India’s Urban Governance Reforms and Its Impact … Table 2 Mode share of Bangalore and Jaipur

Travel mode

53

Percentage %

Mode share of Bangalore (Source: CTTS report, 2010) Walk

34

Bicycle

4.5

Taxi

0.5

Auto

4.6

Maxi Cab

0.5

Two-Wheeler

21.4

Car/van

4.5

PT

30

Mode share of Jaipur (Source: Comprehensive Mobility Plan, Jaipur city, 2018) Walk

26

Bicycle

6

Car and Taxi

17

Two-Wheeler

27

Auto Rickshaw

6

Metro

0

City Bus

18

for the workshop. The workshop was attended by 31 participants from 19 organizations in Bangalore, such as Bruhat Bengaluru Mahanagara Palike (BBMP), Directorate of Urban Land Transport (DULT), Karnataka State Road Transport Corporation (KSRTC), iDeck (Smart City Consultant), among others. In Jaipur, the workshop was conducted on the 14th of September 2018 at HCM-RIPA Campus in Jaipur. It was attended by 32 participants from various organizations, such as Jaipur Development Authority (JDA), Jaipur Municipal Corporation (JMC), Jaipur Smart City Limited (JSCL), Town Planning Department, Ministry of Road Transport and Highways (MoRTH GoI), Rajasthan Road Safety, among others.

4 Case City of Bangalore Bangalore city has witnessed tremendous economic development, industrialization, and urbanization in the last decades due to the information technologies boom. The Bangalore Urban Region, the Bangalore Rural District, and the Ramanagar District integrate the Metropolitan Region of Bangalore (BMR) in India. Public transport city services within Bangalore are majorly catered to by two agencies: the BMTC, a government-operated agency that provides bus transport facilities to the citizens,

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and the BMRCL, an SPV established by the governments of India and Karnataka to provide metro rail services.

4.1 Urban Governance Reforms in Bangalore Formed in 2007, the municipal corporation of Bangalore, namely Bruhat Bengaluru Mahanagara Palike (BBMP), was the amalgamation of the Two Municipal Council of Kengeri (100 wards of the erstwhile Bangalore Mahanagara Palike (BMP) or Bangalore City Corporation) and Seven City Municipal Councils (such as Dasarahalli, Rajarajeshwari Nagar, Krishnarajapuram, Bommanahalli, Byatarayanapura, Mahadevapura, and Yelahanka), and 100 villages around Bangalore. Over the past few decades, Bangalore has experienced accelerated promotion of parastatal agencies responsible for service delivery and infrastructure development, including Bangalore Development Authority (BDA), Bangalore Water Supply & Sewerage Board (BWSSB), Bangalore Metropolitan Region Development Authority (BMRDA), Bangalore Electricity Supply Company (BESCOM), and Bangalore Metropolitan Transport Corporation (BMTC). Under the Chief Minister SM Khrisna, the Bangalore Task Force (BATF) was launched in 1999. Accompanied by NGOs, it worked with important agencies; for example, BMP, BESCOM, BMTC, Bangalore Police, BWWSB, BDA, and BSN or Bangalore Telecom. BATF was responsible for developing the infrastructure of Bangalore, raising additional resources from citizens to ensure efficient service delivery by building the capacity of agencies. By the end of 2010, the contributions of BATF to sustainable governance reforms in Indian cities, and especially in Bangalore, were uncertain. Then, JNNRUM was introduced and implemented. The Government of India released funds under JNNURM to a state-level nodal agency, known as Karnataka Urban Infrastructure Development Finance Corporation (KUIDFC), which would grant or loan to the implementing agency (Urban Development Department, 2016). The Master Plan was prepared by the BDA in consultation with other stakeholders and was further modified based on Janaagraha (NGO) suggestions. However, out of the 39 infrastructure and governance projects, only 25 were completed. The lack of planning and capacity at the municipality led to the failure of this mission renewal in Bangalore (Hindustan Times, 2020). In 2007, the State Government of Karnataka established the Directorate of Urban Land Transport (DULT) to ensure the integration and coordination of land use planning and transport infrastructure in urban regions. Due to the rapid growth of the city, the Bangalore Metropolitan Land Transport Authority (BMLTA) was also established in the same year. In 2015, the SCM was introduced. The SPV, Bengaluru Smart Cities Limited (BSCL), was established as a part of BBMP in 2018 for a five-year term. Since Bangalore qualified for the smart cities challenge in its third attempt, there have been many revisions in the smart city proposal.

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4.2 Urban Transport Stakeholder Mapping The Bangalore stakeholders map is presented in Fig. 1. This map illustrates the connections between the main stakeholders situated in the decentralized urban governance levels in Bangalore. In the first level, corresponding to the Government of India, the Ministry of Housing and Urban Governance, the Ministry of Finance, and the National Highway Authority of India play a central role. At the state and local level, equivalent to the Government of Karnataka, several stakeholders appear, such as the Directorate of Town & Country Planning (DTCP), the Urban Development Department, KUIDFC, UMTA, iDeck, BSCL, which is an SPV, BMRDA, BBMP, BESCOM, DULT, The Finance Department, Transport Department, KSRTC, Bangalore Traffic Police, and BMTC. Stakeholders linked to the Ministry of Finance and the Ministry of Housing and Urban Affairs are firmly connected. Meanwhile, stakeholders such as BMTC, the Transport Department, and the National Highway Authority of India are related. KSRTC does not have a significant relationship with other transport, financial, and urban authorities of the national, state, and local level. The arrows in Fig. 1 indicate the direction of dependence. For example, the arrow pointing from KUIDFC to BSCL denotes that BSCL is dependent on KUIDFC for decision making or financial dependence. Double-sided arrows indicate interdependence. For example, the double-sided arrow between Finance Department and Bangalore Traffic Police denotes that they are mutually dependent, either fiscally or in the decision making process. The overlapping of functions and responsibilities is a significant issue in Bangalore. At the state level, the KUIDFC and the DTCP have been established with similar responsibilities, such as preparation of master plans, local area planning, circulation planning, and zonal regulations. As per the recommendations of the National Urban Transport Policy (NUTP, 2014) regarding the setting up of unified Urban Metropolitan Transport Authorities (UMTAs) in million-plus cities, BMLTA (Bangalore Metropolitan Land Transport Authority) was created at DULT in 2007. The primary responsibility of DULT was to coordinate all land transport matters, supervise implementation of all transportation projects and evaluate and recommend transportation and infrastructure projects for bilateral central support. Other responsibilities were to serve as an empowered committee for all urban transportation projects, make decisions regarding integrated urban transport and land use planning, and foster the development of the projects. Nevertheless, proper channelization of authority to DULT/BMLTA and the lack of integration between associate organizations have resulted in the ineffective implementation of BMLTA. The SPV is expected to address the absence of integration by bringing various organizations under one umbrella hence improving the delivery process. Some of the salient urban transport projects initiated as part of the Smart Cities Mission in Bangalore are as follows: i. Major transport interchange renewal was planned in the city. ii. At a pan-city scale, integrated ticketing initiatives were implemented to varying degrees.

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Fig. 1 Stakeholder Mapping in Bangalore

iii. The urban public realm improvements were being conducted under the “TenderSURE” badge, and “Smart Roads” were rebranded under the Smart Cities program iv. A real-time bus information app is already in place through Bengaluru Metropolitan Transport Corporation. BBMP earlier executed the urban core redevelopment projects through TenderSURE, and the same has been taken up under the smart cities scheme. This change is expected to address the delay issues in releasing funds to the executing agencies, thereby ensuring their proactive involvement. Also, TenderSURE projects hold the executing agencies accountable for the project for a stipulated period, guaranteeing implementation quality. The scheme guidelines prescribe three monitoring committees, one at the national level (The apex committee), one at the state (high-powered steering committee), and one at the local level (smart city advisory forum). The technical experts are proposed to be a part of the city-level advisory forum. There has not been significant involvement of technical experts in the SCM for Bangalore so far. The citizen engagement in the SCM was mainly observed during the initial proposal stages. The participation of the public primarily was to inform them rather than to consult them.

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Similarly, the revisions of the proposals and the reasons for the modifications were not presented to the public. SCM evaluation for transport-related initiatives in Bangalore has not been strictly defined in the guidelines of this urban governance program. The city scorecard prescribed in the guidelines is intended to obtain a relative ranking of the participating cities. No benchmarking concerning mobility indicators or liveability index has been conducted to evaluate smart cities. The participants also iterated the need for such benchmarking to measure resource utilization and the efficiency of the proposed governance reforms. One of the suggestions was to evaluate the reduction in expenditure of organizations on projects over time, before and after specific projects. In 2018, when the workshop with stakeholders was undertaken in Bangalore, the underlying fundamental was that the SCM scheme would achieve a substantial reduction in such expenditures. Likewise, considering the organizational issues that were indicated before, one assessment criteria could be the level of coordination and collaboration among stakeholder agencies. Several points on the ground reality of the SPV structure and what stakeholders think about the SPV are deliberated in the workshop. The consultants (iDeck) who were initially tasked with implementing SCM projects were not involved in the proposal finalization and preparation phases, which Jannagraha handled. This made them unclear of what improvements were made and why. Although the primary responsibility of DULT is to coordinate all land transport matters, oversee the implementation of the projects, and appraise and recommend potential projects that can be taken up, the SPV led to drift in power from DULT which led to non-involvement of DULT at any level of the decision-making process. Also, the smart city proposals are not required to go through the municipal council, indicating a shift in the transfer of power away from the political and democratic system. The stakeholders suggested that frequent interactions between the Centre and the ULBs can be advantageous, especially for improved channelization of funds. It was mandated in the SCM guidelines that the CEO of the SPVs should not be related to any government body. Instead, it should be from the private sector; however, it was considered an advantage. For example, the stakeholders are convinced with the appointment of the Commissioner of the ULD (BBMP) as the CEO of Bangalore SPV as they think that the CEO would have a broader picture of the city. In addition, it was suggested that the integration of the public transport organizations like KSRTC and BMRCL could significantly improve the projects undertaken for the SCM under SPV.

5 Case City of Jaipur Renowned for its rich heritage, Jaipur is situated in Rajasthan State and is also known for being the “pink city” in India. The SCM project in Jaipur was implemented at the cost of 318.73 million USD. The project was fundamentally focused on retrofitting and redeveloping an area of 706 acres or either side of the Walled City between the Badi Chopad and Chhoti Chopad. As a smart city, Jaipur aspires to be a city recognized by its cultural heritage, tourism, and innovative and inclusive solutions

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to enhance the quality of life of all citizens. This aspiration is based on a history of successive urban governance reforms.

5.1 Urban Governance Reforms in Jaipur Urban reforms in Rajasthan started with the 74th CAA. The Government of Rajasthan initiated the Rajasthan Urban Infrastructure Development Project (RUIDP) with the assistance of the ADB in 1999. Capacity building of Jaipur Nagar Nigam (JNN), which is the municipal corporation of Jaipur, to deliver services, including equipment and materials, training to officers, and e-governance schemes, was one of the essential project components. Simultaneously, an action plan for the urban renewal of Jaipur was prepared by the government and implemented through various agencies, such as the Jaipur Development Authority, the JNN, the Rajasthan Housing Board, and the Tourism Department. Jaipur Action Agenda reviews the progress made on the projects identified for the implementation and is looked after by the Jaipur Action Agenda Group (JAAG). After the 74th CAA and the Rajasthan Municipalities Act, the Jaipur Municipal Council emerged. The JDA Act has enacted after creating this council in 1982. The significant departments involved in the functioning and delivery of services and infrastructure were vested under this Act. The Rajasthan Housing Board (RHB) was also created at the same time. Presently, JDA is looking to plan and implement the city development plans and infrastructure within the area of JDA considered, including the JNN area. Although the JNN area is quite afar from the Walled City, its actions, mainly planning, operation, and maintenance of selected infrastructure, are limited to this area and its immediate periphery. Line departments, such as the Public Health Engineering Department (PHED), still involve in delivering services and urban management (Department of Art, Literature and Culture, 2013). There are a number of agencies responsible for the direction of the city of Jaipur apart from the municipal corporations, development authorities, and departments. Examples of these institutions are the JNN, JDA, PHED, PWD (Public Works Department), RHB, RSRTC (Rajasthan State Road Transport Corporation), Forest Department, Tourism Department, and Archaeology Department. ADB-funded project is also involved in providing necessary infrastructure, urban development, and heritage conservation (Rao & Reddy, 2018). As mentioned earlier, the 74th CAA provided the basis for administrative decentralization and the transfer of responsibilities between municipal, state, and nationallevel government institutions in decision-making matters. Accordingly, the Rajasthan State government has amended the municipal law by bringing conformity with the constitutional provisions related to decentralization. This fact had implications for the decision-making processes regarding urban transport within the SCM projects developed within Jaipur. Then, Jaipur was selected in the first round of the SCM competition, and, as a result, the SPV named Smart City Limited was formed in 2016. To implement infrastructural projects faster, this SPV was set up as a parallel

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company with a team of experts. However, the accomplishment of the SPV could be assessed when the public delivery system is implemented. This fact has an incidence in the decision-making processes that involve urban transport stakeholders in Jaipur. The relationships among these agents are reflected in the Jaipur stakeholders map in the following section.

5.2 Urban Transport Stakeholder Mapping Overlapping functions and responsibilities is a significant issue in Jaipur in terms of stipulated time to develop each project. For example, JDA and PDA are in charge of road construction in the city. If a particular stretch of road is constructed by one body, encroachments along it is another’s function, tree plantations along the sides, and street lighting are yet other agency functions. There is no governing body in the transport department to regulate land use and related-by-laws along the city roads (Sharma, 2017). Seven departments are primarily responsible for developing the transportation component of Jaipur. These departments prepare individual plans, and their lack of interdepartmental coordination leads to questioning their efficiency. Also, due to the lack of a common urban transport management law, several government agencies influence the development of the transportation facilities. The Jaipur stakeholders map is displayed in Fig. 2. This map shows the relationships among the main stakeholders located in the decentralized urban governance levels in Jaipur. Stakeholders related to the Government of India, such as the Ministry of Housing of Urban Affairs, the Ministry of Finance, and the National Highways Authority of India, appear at the centre-level of the map. They are linked to the Government of Rajasthan at the state and local levels. Connections between the Ministry of Urban Development, the Ministry of Local Self-Government, and the Department of Transportation are explicitly shown. These institutions are related in myriad ways to other local stakeholders such as the SPV known as JMRC and RUIDP, the JDA, RUDSICO (Rajasthan Urban Drinking Water Sewerage & Infrastructure), JNN, which is a ULB, the Collectorate, the Regional Transport Office, the Jaipur Traffic Board and the Jaipur Traffic Police. Some of the salient urban transport projects initiated as part of the Smart Cities Mission in Jaipur are as follows: i. Bike share schemes are being discussed and tendered through a PPP model involving the Mission. ii. Multilevel parking infrastructure was a vital feature of the developing project work in Jaipur at the edge of the Walled City. iii. Intelligent traffic management systems were also being installed, and there was much discussion of the potential of such schemes to deliver benefits to users. iv. Jaipur had a strong focus around the UNESCO world heritage site of the Walled City, where the arguments for area-based development seem clear. Hence, there was a strong alignment of interests around the heritage area of the Walled City.

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Fig. 2 Stakeholder Mapping in Jaipur

The stakeholders in the workshop highlighted the issues at different stages of planning, implementation, and enforcement. It was found that despite the formation of the Special Purpose Vehicle (SPV) for the Jaipur Smart City Proposal, no expert has been involved for the same. The reason, as was discussed, is probably the fact that the project is politically driven rather than demand-driven. No cohesion in decision is observed, and there is mismanagement of resources due to lack of integration of organizations. Some projects were delayed for many years due to the lack of inter-departmental cohesion. The project selection is not compulsorily based on an integrated approach, and the priority is given based on expenditure to be incurred. Various issues have been raised, like absence of any public consultation at any stage of project planning, switching from reactive mode to pro-active mode for any citylevel project. It was suggested to involve academic institutions as a part of project consultation and contribution of youth professionals as a part of public consultation. There is a lack of an evaluation process to gauge the success of an implemented project. The safety factor in its entirety has not been made part of the Smart City proposal, which needs to be taken care. It was emphasized that all states should have an umbrella agency like UMTA to promote the participation of all stakeholders from various organizations for integrated governance, which will help the Smart Cities Mission succeed. There are issues with sufficient funding for these organizations. It was proposed that at least 25% of the funds collected in the form of penalties and challans by the Rajasthan state can be extended to those organizations. Also, the data collection approach needs to be improved, and education and capacity building of stakeholders from all levels (district to rural) are essential. It was mentioned that the modal share of Non-Motorised Transport (NMT) has declined from the past few years; therefore, more innovative

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projects to attract people to shift to NMT and public transport modes should be brought out.

6 Conclusion and Policy Implication This paper focused on the SCM program, its SPV mechanism, and the coordination and integration issues between urban transport stakeholders in Bangalore and Jaipur to fulfil a threefold purpose. First, this paper aimed to assess the effect of various governance reform initiatives implemented in recent years on long-term infrastructure development projects, as well as to identify those that could not be implemented Second, a detailed review of Smart Cities Mission is done and the initiatives that could not be executed are discussed. Third, a stakeholder map is built by conducting workshops with stakeholders, which helped defining the relationships between local actors, public officials, Non-Governmental Organizations, and institutions involved in sustainable transport infrastructure initiatives in Bangalore and Jaipur and understand their coordination level within the SCM projects. The methodological strategy that sustained this research was based on stakeholder workshops and maps in Bangalore and Jaipur. This strategy allows a better understanding of the SCM program from a comparative approach and an accurate assessment of its capacity to enhance the daily life of Indian citizens in public transportation matters. Bangalore and Jaipur present differences and similarities regarding cultural identity, society, and imaginary as smart cities. While Bangalore is a modern and industrialized city, Jaipur is a historical place currently considered a world cultural heritage due to its Walled City and cultural richness. Concerning the SCM, SVP, and interactions among stakeholders around transport and mobility issues, both cities expressed a convergence: stakeholders in Bangalore and Jaipur highlighted problems such as overlapping functions, delays in the configuration and presentation of projects and bids for competition, and the lack of coordination and integration with government institutions and officials. The participants suggested to necessitate a continuous public involvement in the development projects. They also iterated the need for benchmarking to measure resource utilization and the efficiency of the proposed governance reforms. One assessment criterion could be the level of coordination and collaboration achieved among the stakeholder agencies. Even though the SCM program intended to provide solutions to coordination and integration difficulties between stakeholders and institutions, given the decentralized administration inaugurated with the 73rd and 74th CAA, it failed to offer better sustainable development in Indian cities. The contributions to the mobility need of citizens in the SCM program are uncertain. Thus, the need for integrated urban metropolitan transport authorities or alternative systems capable of taking action on urban mobility on a city-wide scale, including maximizing the advantages of emerging technology and capable of attracting the talent required to drive the development of Indian cities, remains an urgent need to examine.

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References Agarwal, S., & Swami, B. L. (2011). Comprehensive approach for the development of traffic noise prediction model for Jaipur city. Environmental Monitoring and Assessment, 172(1), 113–120. Bangalore Metropolitan Transport Corporation. (2021). Wikipedia-The free encyclopedia. https:// en.wikipedia.org/wiki/Bangalore_Metropolitan_Transport_Corporation Bangalore Population. (2021). World Population Review. https://worldpopulationreview.com/ world-cities/bangalore-population Bengaluru City Guide Offering Information on Travel and Tourism in Bangalore. (2021). The garden city of India. Bangalore Guide. http://www.bangaloreindia.org.uk/travel-tips/location.html Comprehensive Traffic and Transport Study (CTTS report) for Bangalore Metropolitan Region. (2010). Wilbur Smith Associates. City Profile. (2021). Jaipur Municipal Corporation Greater, Jaipur. http://jaipurmc.org/presentation/ aboutmcjaipur/cityprofile.aspx Department of Art, Literature and Culture, Rajasthan. (2013). City Development Plan, Jaipur. Ghosh, B., & Arora, S. (2019). Smart as Democratically Transformative? An Analysis of ‘Smart City’ Sociotechnical Imaginary in India. https://opendocs.ids.ac.uk/opendocs/handle/20.500. 12413/14670 Harrison, C., & Donnelly, I. A. (2011). A theory of smart cities. In 55th annual meeting of the international society for the systems sciences 2011. Hoelscher, K. (2016). The evolution of the smart cities agenda in India. International Area Studies Review. https://doi.org/10.1177/2233865916632089 Jaipur. (2021). Wikipedia-The free encyclopedia. https://en.wikipedia.org/wiki/Jaipur Kamal Batcha, S. F. B. (2013). Understanding the governance of reforms to urban transport in developing cities. Ph.D. Thesis, University of Leeds. Kamath, L., & Zachariah, Y. (2015). Impact of JNNURM and UIDSSMT/IHSDP Programmes on Infrastructure and Governance Outcomes in Cities/Towns in India. TISS Working Paper No.7. https://www.tiss.edu/uploads/files/TISS_Working_Paper-7-Lalitha_Kamath.pdf Kochi Public Transport Day. (2018). Successes and Failures of UMTAs in India–How Kochi Can Perfect Integration of Public Transport. http://www.kochipublictransportday.org/blog-details/ 2/Successes_and_Failures_of_UMTAs_in_India_How_Kochi_can_Perfect_Integration_of_P ublic_Transport List of Cities and Towns in Rajasthan. (2021). Wikipedia-The free encyclopedia. https://en.wikipe dia.org/wiki/List_of_cities_and_towns_in_Rajasthan Mehta, S. (2019). Assessment of Urban Mobility in Jaipur using the Sustainable Urban Transport Index (SUTI), Jaipur Development Authority, Jaipur, India. Ministry of Urban Development, Government of India. (2015). Smart City Mission Statement and Guidelines (Vol. 3). https://doi.org/10.7256/2313-0539.2014.3.12545 Ministry of Housing and Urban Affairs, Government of India. (2015). Annual Resport. 3Annual_ Report_2014_15_MHUPA_English.pdf (mohua.gov.in) Mukherjee, S., & Gupta, S. (2018). Critical appraisal of urban transport funding pattern in an emerging economy: Case study-India. In Transportation research Procedia, world conference on transport research. Nallathiga, R. (2005). Institutional innovations of Urban Governance: Some examples of Indian cities. Urban India, 25(2), 1–28. Nandi, S., & Gamkhar, S. (2013). Urban challenges in India: A review of recent policy measures. Habitat International, 39, 55–61. https://doi.org/10.1016/j.habitatint.2012.10.001 NUTP. (2014). National urban transport policy. Ministry of Urban Development, Government of India (Vol. 2(1), pp. 1–39). www.iutindia.org Poor Planning Led to Failure of JNNURM-I: Govt. Panel-Delhi-Hindustan Times. (2020). Retrieved 9 August, 2020, from https://www.hindustantimes.com/delhi/poor-planning-led-to-failure-ofjnnurm-i-govt-panel/story-G2RPgNRXTkblRjcxekltxO.html

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Rao, & Reddy. (2018). Comprehensive Mobility Plan-Urban Transport Sector Assessment Report for Jaipur City. Jaipur Metro Rail Corporation Ltd. http://transport.rajasthan.gov.in/content/dam/ transport/metro/Project/DPRPhaseII/CMPExecutiveSummaryFinal.pdf Reardon, L., Marsden, G., Campbell, M., Gupta, S., Verma, A. (2018). Assessing the Applicability of Multilevel Governance in the India Context: An Analysis of the Smart Cities Mission Programme. https://www.ippapublicpolicy.org/file/paper/5cfe4800247e8.pdf Sharma, R. (2017). Restructuring road governance–a case of Jaipur, Rajasthan 8(1), 504–10. Singh, S. (2005). Review of urban transportation in India. Journal of Public Transportation. https:/ /doi.org/10.5038/2375-0901.8.1.5 Times of India. (2019). Transport Dept Putting Brakes on Formation of UMTA? | Bengaluru News. https://timesofindia.indiatimes.com/city/bengaluru/transport-dept-putting-brakes-on-for mation-of-umta/articleshow/69180817.cms Urban Development Department. (2016). Infrastructure and Governance and Basic Services for Urban Poor under JNNURM. http://uddkar.gov.in/en/JNNURM Vaidya, C. (2009). Urban issues. Reforms and forward in India. Department of Economic Affairs, Ministry of Finance, Govt. of India.

Assessing the Disparity in Connectivity of Multiple Unit Trains in the National Capital Region Aditya Manish Pitale, Shubhajit Sadhukhan, and Manoranjan Parida

Abstract Cities being the epicentre of economic activities should have better connectivity amongst themselves to provide additional development opportunities to the less developed cities. This study looks after assessing the existing network of multiple units (MUs) and determining whether any connectivity disparity exists within the sub-districts of National Capital Region (NCR). The study evaluates the disparity using four connectivity measures and Gini index to estimate the level of inequality in the NCR. The results show a huge disparity in the connectivity of different sub-districts to the existing network of MU trains. Baghpat sub-district was found to have relatively better connectivity to all other NCR parts, while Muzaffarnagar was the least connected sub-district. The value of the Gini index also portrayed the existing inequality in connectivity of sub-districts through MUs and the exclusion of a few sub-districts of NCR. The study identified the absence of connectivity in the southern and western part of the NCR which needs to be improved to encourage development in those areas. This study can be useful for the transit authorities of NCR to ensure measures for developing an equitable MU network for daily commuters. Keywords Transportation disparity · Connectivity · National capital region · Multiple units

A. M. Pitale · S. Sadhukhan (B) · M. Parida Centre for Transportation Systems (CTRANS), Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India e-mail: [email protected] A. M. Pitale e-mail: [email protected] M. Parida e-mail: [email protected] M. Parida CSIR Central Road Research Institute, New Delhi 110025, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_4

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1 Introduction The development of a nation is highly linked to its economic activities. Metropolitan cities in the country act as epicentres of these economic activities and generate numerous employment opportunities, leading to population inflow. The availability of several alternatives for employment and a better standard of living in metropolitan cities attract people from urban and non-urban areas. This urbanization leads to higher cost of living because of the burden on existing infrastructure facilities as they must facilitate a population larger than what they were initially planned for. Since living in the metropolitan cities has become overpriced, the opportunities available to people due to industrial activities lead to urban sprawl. These urban sprawls are observed to extend beyond the limits of metropolitan cities and even to the other cities that act as regional centres of the metropolitan region. People, therefore, reside in these regional centres and travel for work to the metropolitan city. The rising level of accommodation in regional centres leads to development of land use which is restricted to residential activities. The primacy of the metropolitan city curbs the development of industrial activities in its region due to which the role of regional centres gets limited to dormitories or sleeping quarters (Mumbai Metropolitan Region Development Authority, 2016). Connectivity of a regional centre to different parts of the region also plays a major role in its development. A regional centre not only attracts commuters to reside but also encourages industries due to resource availability at a comparatively lower cost. However, such opportunities to attract development will be available for a regional centre provided it has better connectivity with the metropolitan city. Thus, the development of a regional centre depends on its connectivity with other parts of the region. Monitoring the connectivity between regional centres is crucial to understand the current situation and develop necessary measures to promote equitable development (Pitale et al., 2021). In recent times, the development in most of the metropolitan regions of India can be observed to be disproportionate. The primacy of metropolitan cities is rising due to which there is a need to examine the connectivity between different cities of a region and determine the locations that need to improve connectivity and encourage equitable distribution of development opportunities. In order to determine the disparity in connectivity of different regional centres with the metropolitan city, if any, this study looks after assessing the existing network of multiple units (MUs) that connects Delhi with other regional centres of the National Capital Region (NCR). The MUs run as local trains and carry daily commuter traffic between Delhi and other cities in the NCR. This study uses the existing network of MUs along with the population and geographic area information of all the subdistricts present in the NCR. The study evaluates the connectivity by performing spatial analysis for three types of connectivity: presence of station, concentration of stations to population density, and geographical area. This study also estimates the Gini index for the above connectivity measures to determine the inequality of existing MU train network across the NCR. The output of this study will identify the

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regional centres that need better connectivity of MUs and provide more development opportunities to promote equitable development of the NCR.

2 Literature Review Equity in case of transportation services is mostly defined from the users’ aspect (Cascetta et al., 2020), whether all the users receive equal opportunities irrespective of their gender, age, income, location or neighbourhood. The equity in transport for a particular area is mainly assessed by looking after the accessibility to essential facilities and services using a particular mode of transport (Church et al., 2000; Li et al., 2018; Lucas, 2018; Lucas & Musso, 2014; Lucas & Porter, 2016; Macário, 2014; Vasconcellos, 2001). If an area has lower accessibility through transport, it is considered to have lower opportunities or difficulties to connect to essential facilities such as health centres, employment opportunities, and daily needs (Lucas et al., 2016). According to Litman, 2002, the equity in transportation can be defined using two different ways: one emphasizing equal distribution and the other on justified distribution, also known as horizontal and vertical equity (Delbosc & Currie, 2011; Le Grand, 1984). The vertical equity looks after the distribution of opportunities to access transportation services by considering the different group of people based on their needs, income, or other factors. On the contrary, the horizontal equity focuses treating all the groups of people equally and distribute the opportunities equally irrespective of their varied characteristics. The horizontal equity provides equal benefits of transportation services to all people, including but not limited to cost of travel, time of travel, and accessibility. Thus, as per horizontal equity, people of all groups shall receive equal resources, bear equal costs, and get same facilities (Currie, 2004; Fransen et al., 2015; Legrain et al., 2016; Litman, 2002; Manaugh et al., 2015). Assessing the vertical equity for a particular area is difficult as it requires detailed information related to the resident’s needs and characteristics. With respect to assess the horizontal equity of transportation services at regional level, few studies have been conducted in different parts of the world. Kim & Sultana, 2015 examined the benefits that cities of South Korea will receive from the Korean High-Speed Rail using disparity of accessibility as an indicator. Another study reviewed the regional transport plans of eight metropolitan planning organizations in California to determine the extent to which their results likely reflect the projected patterns of equity (Karner, 2016). The effects on the network coverage, accessibility, connectivity, and disparity among prefecture level cities in China was analysed by Zhang et al. (2020) to look after the variation due to the development of HSR. Luo and Zhao (2021) evaluated the improvement of HSR construction on intercity accessibility along with the changes in the spatial patterns and spatial inequality. Sometimes, the disparity in the economic development is also assessed due to the availability or introduction of transportation services. Chen et al. (2020) evaluated the impact of transportation accessibility on regional economic disparity in Greater Bay Area. To understand the joint impact of transportation service and different policies of

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megalopolis, Yang et al. (2020) examined the effect of formation of megalopolis and HSR infrastructure in China. Thus, from the literature it can be stated that assessing the disparity in the connectivity of transportation service is considered as an essential element for economic development of a region. However, the studies on equity of transportation services in metropolitan regions of India are very limited and there is a rising need to perform such studies to ensure equitable development across the region.

3 Description of Study Area The NCR has been created under the National Capital Region Planning Board Act, 1985 of Government of India, based on authorization of the Parliament by four States/ Union Territories (National Capital Region Planning Board, 2021). It is a unique metropolitan region having inter-state regional planning along with the national capital Delhi at its core. The NCR covers the complete national capital territory (NCT)-Delhi along with fourteen districts of Haryana state, eight districts of Uttar Pradesh state and two districts of Rajasthan state (see Fig. 1). The total area covered by this region is about 55,083 km2 . (National Capital Region Planning Board, 2017). MU railway network which acts as a suburban rail service and the interstate buses are currently used by passengers for intercity travel within the NCR. The suburban rail service is operated by the Northern Railway for the National Capital Region and covers Delhi, along with the adjoining districts of Faridabad, Ghaziabad, and other adjoining places in Haryana and Uttar Pradesh as mentioned in Table 1 (Total Train Info, 2022). According to Census 2011, the population of NCR was 5.81 crores (Ministry of Home Affairs, 2011). It is expected to grow to around 7 crores by 2031 and to about 11 crores by 2041. The region will be highly urbanized in the coming decades with urban populations of about 57% by 2031 and about 67% by 2041. To avoid the concentration of urbanization in the core area, it is important to ensure better connectivity and provide equity in accessibility across the region.

4 Methodology To assess the distribution of the existing network of MUs in NCR, this study has applied the following steps: Collection of required information—In this step, the information required to conduct this study was identified and collected. The population under each subdistrict was obtained from the latest census data (Ministry of Home Affairs, 2011). Information related to the existing network of MUs and the location of their stations were also extracted to understand their connectivity across the NCR. The information

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Fig. 1 National Capital Region (Source Author, adopted from Geofabrik GmbH (2021))

available through online platforms were used to determine the existing network of MUs and their station across the NCR (Total Train Info, 2022). Calculating the connectivity across the region—After collecting the required information, the connectivity of all the sub-districts was calculated. The existing network of MUs and the values calculated under different connectivity measures

70 Table 1 Districts and Sub-districts in NCR

A. M. Pitale et al.

Sr. No

State/UT

District

Sub-district

1

Delhi

Delhi

Delhi

2

Haryana

Bhiwani

Bawani Khera

3

Bhiwani

4

Siwani

5

Loharu

6

Charkhi Dadri

Charkhi Dadri

7

Faridabad

Faridabad

8

Palwal

Hathin Palwal

9 10

Gurgaon

11

Pataudi Gurgaon Ferozepur Jhirka

12 13

Nuh

Nuh

14

Jhajjar

Bahadurgarh

15

Jhajjar

16

Kosli

17

Jind

Narwana

18

Jind

19

Safidon Karnal

20

Karnal Assandh

21 Mahendragarh

22 23

Mahendragarh Narnaul

24

Panipat

Panipat

25

Rewari

Rewari Bawal

26 27

Rohtak

28

Maham Rohtak

29

Sonipat

Gohana

30

Ganaur

31

Sonipat

32

Rajasthan

Alwar

Behror

33

Mandawar

34

Tijara

35

Kishangarh Bas

36

Ramgarh

37

Alwar (continued)

Assessing the Disparity in Connectivity of Multiple Unit Trains … Table 1 (continued)

Sr. No

State/UT

District

71

Sub-district

38

Bansur

39

Thanagazi

40

Rajgarh Lachhmangarh

41 Bharatpur

42

Kaman

43

Nagar

44

Deeg

45

Nadbai

46

Kumher

47

Bharatpur

48

Weir

49

Bayana

50

Rupbas

51 52

Uttar Pradesh

Baghpat

Baghpat

Bulandshahr

Bulandshahr Anupshahr

53 54 55

Gautam Buddha Nagar

56

Dadri Gautam Buddha Nagar Jewar

57

Ghaziabad

Ghaziabad

58

Hapur

Hapur

59 60

Garhmukteshwar Meerut

61

Mawana

62 63

Meerut Muzaffarnagar

64

Muzaffarnagar Budhana

65 66

Sardhana

Jansath Muzaffarnagar + Shamli

Kairana

were used further for spatial analysis using ArcGIS. As mentioned earlier, the connectivity of each sub-district was determined based on four measures. The first measure focused on connectivity by calculating the number of MU stations available in a sub-district (n i ). The remaining measures looked after the connectivity based on the population, area, and population density served per station in each sub-district using the Eqs. 1–3 mentioned below:

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αi =

pi ni

(1)

βi =

ai ni

(2)

γi =

ρi ni

(3)

where, n i = number of stations in sub-district i pi = population of sub-district i ai = administrative area of sub-district i ρi = population density of sub-district i αi = population served per station in sub-district i βi = administrative area served per station in sub-district i γi = population density served per station in sub-district i. The values obtained under different measures assisted in identifying the locations of sub-districts having better as well as weak connectivity of existing network of MUs. These calculated connectivity measures were further used to assess the variation in the connectivity of sub-districts of NCR. Assessing the disparity—In order to assess the disparity in connectivity, the Gini index is calculated. The Gini index was initially used to analyse the regional disparity in terms of income among the provinces of China (Li & Xu, 2008). Kim and Sultana (2015) assessed the impact of high-speed rail extensions on accessibility and spatial equity changes to examine the disparity in accessibility in South Korea from 2004 to 2018. The Gini index was also used to review the spatial and temporal heterogeneity of the impact of high-speed railway on urban economy in China (Huang & Xu, 2021). In this study, the Gini index is used to measure the statistical dispersion in the connectivity measures across the sub-districts of the NCR. The Gini index is initially calculated to investigate the disparity in population distribution, population density, and MU railway stations. Further, the disparity in the population, population density, and administrative area served per station in each sub-district was also assessed using the Gini index approach. The Gini index lies between the values of 0 and 1 with 0 representing perfect equality and 1 representing perfect inequality. The higher the Gini index, the greater disparity is considered, which means that there exists a greater inequality in the connectivity of MUs in the sub-districts of the NCR. This study applied the approach mentioned by Li et al. (2018) to calculate the Gini index as mentioned in Eq. 4: Σn G(S1 , S2 , . . . Sn ) =

I Σn II I j=1 Si − S j Σn 2n × i=1 Si

i=1

where, Si = Value of the connectivity measure for sub-district i

(4)

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n = number of sub-districts in the NCR. The Gini index values obtained for different connectivity measures determined the spatial disparity in the connectivity of the existing network of MU across the sub-districts of NCR.

5 Results and Discussion Based on the information related to number of stations located in each sub-district, it was observed that there were 38 sub-districts in the NCR without any access to MUs. This indicates that more than half of the sub-districts present in the NCR are not connected by the existing network of MU. As shown in Fig. 2a, most of the sub-districts without any MU stations are located in the southern and western part of the NCR. Thus, the connectivity of these sub-district with Delhi and other parts of the NCR is affected. On the other hand, the sub-districts located in the eastern and northern part of the NCR have comparatively better connectivity. Delhi being the metropolitan city and capital, had the highest number of stations (33) followed by Baghpat (14), Ghaziabad (12), and Bulandshahr (12). The distribution of the stations across the NCR as shown in Table 2 indicates a disparity in access to the MUs. Looking at the population distribution, most of the population of the NCR can be seen concentrated in the sub-districts located in the core of the metropolitan region,

Fig. 2 a Number of stations in each sub-district, b Population of each sub-district

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Table 2 Distribution of the number of stations in the sub-districts of NCR Number of stations in a sub-district

Number of sub-districts

Cumulative number of stations

Cumulative number of sub-districts

0

38

0

38

2

3

6

41

3

7

27

48

4

6

51

54

5

1

56

55

6

3

74

58

7

2

88

60

8

2

104

62

12

2

128

64

14

1

142

65

33

1

175

66

near Delhi. This indicates that there exists an urban sprawl in the nearby regional centres. After Delhi, the population was observed to be higher in the sub-districts of Ghaziabad, Bulandshahr, and Meerut while it was lower in Siwani, Pataudi, and Bawani Khera. It can be mentioned from the population distribution that high population is mainly observed in the northern and eastern part of the NCR. The sub-districts with lower population are located in the western part of the NCR (see Fig. 2b) showing a disparity in the population distribution across the metropolitan region. Similarly, the spatial distribution of population density also observed a concentration on the eastern and north-eastern part of the NCR. The population density was found to be highest for Delhi, followed by Ghaziabad, Meerut, and Faridabad. Figure 3 shows that these highly dense sub-districts are located in the central part of the NCR. Similar to population distribution results, the density in the southern and western part of the NCR was also found low. The least population density was observed in the sub-districts of Siwani, Bawani Khera, and Loharu located in the western part of the NCR. The connectivity values measured for population served per station indicated that the sub-district of Muzaffarnagar had the highest population of more than 7,00,000 behind every station. There was one station available in Delhi and Meerut for a population close to 5,00,000. Excluding the sub-districts without any access to existing MU network, the least population per station was observed for Pataudi (40,004) followed by Ganaur (68,963) and Jind (85,172). Thus, the population distribution per station in the NCR as shown in Fig. 4a is unequal and the variation is so high that the highest value is more than fifteen times the lowest population per station in the NCR. Talking about the area served by each station in the NCR, it looked after the ratio of the administrative area to the number of stations present in the sub-district. It

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Fig. 3 Population density of each sub-district

was observed that among the sub-districts connected by MUs, the stations located in Delhi had the lowest area to be served per station, indicating a significant number of stations. Ghaziabad, Pataudi, and Baghpat were also found with less area to be covered for each station indicating better connectivity in the sub-district. On the contrary, the ratio of stations with the administrative area was maximum for the

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Fig. 4 a Population per station in sub-districts, b Administrative area per station in sub-districts

sub-districts of Jansath, Muzaffarnagar, and Jhajjar. Figure 4b shows that the subdistricts located in the northern part of the NCR had enough stations present in their administrative area compared to the other parts. Similarly, assessing the distribution of population density per station in the NCR identified that the sub-districts of Muzaffarnagar, Meerut, and Faridabad had the maximum population density behind each station. The concentration of stations per unit population density in a sub-district was higher in Baghpat, Karnal, and Jind as seen in Fig. 5. Thus, the concentration of stations in the sub-districts of the NCR showcases that the sub-district of Baghpat had relatively better connectivity to Delhi and other parts of the NCR compared to other sub-districts. The sub-district of Muzaffarnagar was identified to have poor connectivity of MUs compared to the other sub-districts of the NCR which is an alarming situation. The disparity in the connectivity of the MUs across the NCR can be observed from the above measures of connectivity and it therefore calls for necessary policy interventions to improve the overall connectivity. The estimated results in terms of Gini index after following the approach explained in the methodology section are reported further. All the computed Gini indexes were ranging between 0.7 and 0.75 showing a very high disparity in the connectivity of the existing MU trains across the sub-districts of NCR. The stations in the sub-districts of NCR were found to be the most unequally distributed measure of connectivity with a Gini index of 0.75. Such high values of the Gini indexes showcase the need

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Fig. 5 Population density per station in sub-districts

to emphasize on planning of a regional transit system in the NCR to provide equitable connectivity to all the sub-districts. The higher inequity of all the measures of connectivity directs towards taking necessary measures to improve the connectivity of the sub-districts that do not have access to MU trains (see Table 3).

78 Table 3 Gini index of all measures of connectivity

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Measure of connectivity

Gini Index

Number of stations in sub-district

0.75

Population served per station

0.73

Administrative area served per station

0.70

Population density served per station

0.72

6 Conclusions and Recommendations Encouraging equitable transportation services across the metropolitan regions has been widely emphasized in different countries. Providing equal opportunities to use a transportation service to all the users in all parts of the region is essential to promote equitable development. This study assessed the disparity in the connectivity of MU trains in the sub-districts of the NCR by spatially identifying the variations. The Gini index was also estimated to determine the inequality level in different measures of connectivity across the sub-districts. The results show that more than half of the sub-districts present in the NCR are not connected by the MU trains. Lack of connectivity of these sub-districts leads to missed chances of these areas for development along with employment opportunities and access to several other facilities. The distribution of stations across the NCR also observed inequality in serving the population, administrative area and population density. Muzaffarnagar, Delhi, and Meerut had to cater a large population per station while the number was less for Pataudi, Ganaur, and Jind sub-districts. The subdistricts of Delhi, Ghaziabad, Pataudi, and Baghpat had ample stations to cater their administrative areas, while the ratio of station to administrative area was less in the sub-districts of Jansath, Muzaffarnagar, and Jhajjar. The sub-district of Baghpat was observed with better connectivity measures indicating easy access of the sub-district from all other parts of the NCR. Muzaffarnagar performed poor indicating a need to improve its connectivity to all parts of the NCR. The Gini indexes of 0.73, 0.70, and 0.72 for the measure of connectivity considering population, administrative area, and population density show that there exists a high disparity in the service of MU trains in the NCR. Thus, it can be concluded from this study that a huge disparity exists in the connectivity of MU trains across the sub-districts of NCR which should be a concern. The sub-districts having less population have better access to stations in the northern and eastern part of the NCR compared to the sub-districts with similar populations in the southern and western parts. A huge disparity in connecting the sub-districts in southern and western parts of the NCR can be one of the causes of its less development and urbanization. Suppose the number of stations found in each sub-district is analysed along with their population density. In that case, it can be stated that the sub-districts without any stations also have less population density. In order to reduce the disparity among the sub-districts and improve the overall connectivity within the region, it is essential to connect the western and southern

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part of the region by MU network. A dedicated corridor or extension of existing rail network to these parts can ensure better connectivity across the different parts of the region and will assist in reducing the disparity in connectivity to MU. Development of a transit hub in the western and eastern parts of the region can also assist in encouraging economic development of these areas. Disparity exists in provision of MU train service in the NCR, and it needs to be carefully addressed as connectivity plays a crucial role in the economic development of that area. This study can be an initiation to assess the equity of transportation services in the metropolitan regions of India and identify the areas with gap. The outcome of this study shall be considered by the development authorities of the NCR to develop a network of MU trains that will reduce the inequality in connectivity. The transit authority of the region shall plan their regional network after performing such studies so that different parts of the region in need of better connectivity can be identified and necessary measures can be taken. Extending the connectivity to the identified areas will benefit other sub-districts and help decentralize Delhi and lead to equitable development across the NCR. Connecting the southern and western part of NCR with Delhi will provide development opportunities in that part and encourage balanced development. Acknowledgements The authors would like to acknowledge all the respondents who actively participated in this study and gave their valuable inputs. This research is supported by the Prime Minister’s Research Fellowship (PMRF) awarded to Mr. Aditya Manish Pitale from the Government of India.

References Cascetta, E., Carteni, A., Henke, I., & Pagliara, F. (2020). Economic growth, transport accessibility and regional equity impacts of high-speed railways in Italy: Ten years ex post evaluation and future perspectives. Transportation Research Part A-Policy and Practice, 139, 412–428. https:/ /doi.org/10.1016/j.tra.2020.07.008 Chen, Z., Li, Y., & Wang, P. (2020). Transportation accessibility and regional growth in the Greater Bay Area of China. Transportation Research Part D: Transport and Environment, 86(August). https://doi.org/10.1016/j.trd.2020.102453 Church, A., Frost, M., Sullivan, K., Bruno, M., & Lucas, K. (2000). Transport and social exclusion in London. Transport Policy, 7, 195–205. https://doi.org/10.1016/j.tra.2020.07.008 Currie, G. (2004). Gap analysis of public transport needs: Measuring spatial distribution of public transport needs and identifying gaps in the quality of public transport provision. Transportation Research Record, 1895, 137–146. https://doi.org/10.3141/1895-18 Delbosc, A., & Currie, G. (2011). Using Lorenz curves to assess public transport equity. Journal of Transport Geography, 19(6), 1252–1259. https://doi.org/10.1016/j.jtrangeo.2011.02.008 Fransen, K., Neutens, T., Farber, S., De Maeyer, P., Deruyter, G., & Witlox, F. (2015). Identifying public transport gaps using time-dependent accessibility levels. Journal of Transport Geography, 48, 176–187. https://doi.org/10.1016/j.jtrangeo.2015.09.008 Geofabrik GmbH. (2021). Open Street Map of India. Retrieved from https://download.geofabrik. de/asia/india.html

80

A. M. Pitale et al.

Huang, Y., & Xu, W. (Ato). (2021). Spatial and temporal heterogeneity of the impact of high-speed railway on urban economy: Empirical study of Chinese cities. Journal of Transport Geography, 91(January). https://doi.org/10.1016/j.jtrangeo.2021.102972 Karner, A. (2016). Planning for transportation equity in small regions: Towards meaningful performance assessment. Transport Policy, 52, 46–54. https://doi.org/10.1016/j.tranpol.2016. 07.004 Kim, H., & Sultana, S. (2015). The impacts of high-speed rail extensions on accessibility and spatial equity changes in South Korea from 2004 to 2018. Journal of Transport Geography, 45(2015), 48–61. https://doi.org/10.1016/j.jtrangeo.2015.04.007 Le Grand, J. (1984). Equity as an economic objective. Journal of Applied Philosophy, 1(1), 39–51. https://doi.org/10.1111/j.1468-5930.1984.tb00185.x Legrain, A., Buliung, R., & El-Geneidy, A. M. (2016). Travelling fair: Targeting equitable transit by understanding job location, sectorial concentration, and transit use among low-wage workers. Journal of Transport Geography, 53, 1–11.https://doi.org/10.1016/j.jtrangeo.2016.04.001 Li, S., & Xu, Z. (2008). The Trend of Regional Income Disparity in the People’s Republic of China. Asian Development Bank Institute, (85). Retrieved from http://www.adbi.org/discussionpaper/ 2008/01/25/2468.regional.income.disparity.prc/ Li, Z., Ren, S., Hu, N., Liu, Y., Qin, Z., Goh, R. S. M., Veeravalli, B., et al. (2018). Equality of public transit connectivity: The influence of mass rapid transit services on individual buildings for Singapore. Transportmetrica B, 0566, 1–20.https://doi.org/10.1080/21680566.2018.1449682 Litman, T. (2002). Evaluating transportation equity: Guidance for incorporating distributional impacts in transportation planning. World transport policy & practice (Vol. 8). Retrieved from http://ecoplan.org/wtpp/wt_index.htm Lucas, K. (2018). Transport poverty and inequalities. European Transport Research Review, 10–17. Lucas, K., Mattioli, G., Verlinghieri, E., & Guzman, A. (2016). Transport poverty and its adverse social consequences. Proceedings of the Institution of Civil Engineers: Transport, 169(6), 353– 365. https://doi.org/10.1680/jtran.15.00073 Lucas, K., & Musso, A. (2014). Policies for social inclusion in transportation: An introduction to the special issue. Case Studies on Transport Policy, 2(2), 37–40. https://doi.org/10.1016/j.cstp. 2014.06.002 Lucas, K., & Porter, G. (2016). Mobilities and livelihoods in urban development contexts: Introduction. Journal of Transport Geography, 55, 129–131. https://doi.org/10.1016/j.jtrangeo.2016. 07.007 Luo, H., & Zhao, S. (2021). Impacts of high-speed rail on the inequality of intercity accessibility: A case study of Liaoning Province, China. Journal of Transport Geography, 90(November 2020), 102920. https://doi.org/10.1016/j.jtrangeo.2020.102920 Macário, R. (2014). Virtual special issue on social exclusion. Case Studies on Transport Policy, 2, 3. https://doi.org/10.1016/S2213-624X(14)00062-5 Manaugh, K., Badami, M. G., & El-Geneidy, A. M. (2015). Integrating social equity into urban transportation planning: A critical evaluation of equity objectives and measures in transportation plans in north america. Transport Policy, 37, 167–176.https://doi.org/10.1016/j.tranpol.2014. 09.013 Ministry of Home Affairs. (2011). Basic Population Figures of India, States, Districts and SubDistrict, 2011. Government of India. Retrieved from https://censusindia.gov.in/census.website/ data/population-finder Mumbai Metropolitan Region Development Authority. (2016). Mumbai Metropolitan Regional Plan 2016–2036. Mumbai. National Capital Region Planning Board. (2017). NCR Constituent Areas. Retrieved April 3, 2022, from https://ncrpb.nic.in/ncrconstituent.html National Capital Region Planning Board. (2021). Draft Regional Plan-2041 National Capital Region. Delhi. Retrieved from https://ncrpb.nic.in/pdf_files/DraftRegionalPlan-2041_English. pdf

Assessing the Disparity in Connectivity of Multiple Unit Trains …

81

Pitale, A. M., Sadhukhan, S., & Parida, M. (2021). Investigating the need of regional transport systems for sustainable decentralized development of metropolitan regions in India. In International conference on sustainable urban mobility (SUMo 2020). Total Train Info. (2022). Delhi EMU Trains. Retrieved March 5, 2022, from http://www.totaltrai ninfo.com/delhi-emu.php Vasconcellos, E. A. (2001). Urban transport environment and equity: The case for developing countries. Urban transport environment and equity (1st ed.). https://doi.org/10.4324/978131 5071756 Yang, Z., Li, C., Jiao, J., Liu, W., & Zhang, F. (2020). On the joint impact of high-speed rail and megalopolis policy on regional economic growth in China. Transport Policy, 99(June), 20–30. https://doi.org/10.1016/j.tranpol.2020.08.007 Zhang, F., Yang, Z., Jiao, J., Liu, W., & Wu, W. (2020). The effects of high-speed rail development on regional equity in China. Transportation Research Part a: Policy and Practice, 141(October), 180–202. https://doi.org/10.1016/j.tra.2020.09.013

Review of Transportation Relevant UN SDG Targets and their Association with Sustainable Transport Indicators Rohit Singh Nitwal , Almas Siddiqui , and Ashish Verma

Abstract With the growing trend of incorporating UN (United Nation) Sustainable Development Goals (SDGs) in different fields of research, it has been observed that some SDGs have direct and indirect impacts on the transportation systems engineering and planning of cities. It has been evident in past studies that the transportation sector contributes to more than 70% of carbon dioxide emissions. In the Indian context, since the time of independence, several Five-Year Plans (FYPs) and transport development strategies have incorporated the targets which help in achieving sustainable development, but there has been no direct linkage of such targets with any policy instrument to date. Even though sustainable transportation strategies are mentioned in National Urban Policy Framework (NUPF) 2020 of India, incorporation of SDGs in the document is missing. There is an urgent need to study the impact of transportation on SDGs and their association with policy interventions that can lead to effective implementation of SDGs at all levels of governance. The study majorly deals with identifying UN SDGs and associated targets with direct and indirect relevance to transportation. Several UN SDG targets can be linked to sustainable transport indicators, which can further be incorporated into a policy framework. It is essential to incorporate the SDGs related to transportation in the master plans and comprehensive mobility plans to frame robust and comprehensive policy interventions for sustainable development. This paper identifies suitability of 201 sustainable transportation indicators using SMART criteria framework, reviews the Indian transport policies to understand to what extent the SDGs have been incorporated into their strategies. These indicators can be used in future studies for monitoring progress to achieve SDG targets through a comprehensive sustainable urban transport index and policy framework. Rohit Singh Nitwal and Almas Siddiqui have contributed equally as first authors. R. S. Nitwal (B) · A. Siddiqui · A. Verma Department of Civil Engineering, IISc, Bangalore, India e-mail: [email protected] A. Siddiqui e-mail: [email protected] A. Verma e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_5

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Keywords Sustainable development goals · Urban transport planning · Policy instrument · Sustainable transportation indicators

1 Introduction Transportation systems engineering impacts the urban structure in many ways. It can have a positive or negative impact on the environment which should be addressed for sustainable development of cities. Different places can be accessed through different transportation modes which save energy and time for humans to be more productive for other works, and goods and services can be even provided to inaccessible areas, and disaster-prone areas are some of the positive impacts of the transport sector on society. The negative impacts can be considered as vehicular exhaust emissions leading to air pollution, traffic congestion due to improper planning and management, etc. Due to the negative impacts of the transport sector on the environment and society, it is of utmost importance to tackle the externality which can be done by implementing sustainable development strategies. As per Brundtland Report, sustainable transportation is defined as “satisfying current transport and mobility needs without compromising the ability of future generations to meet these needs” (World Commission on Environment & Development, 1987). Traditional development strategies have degraded the resources to a huge extent and if continued will severely impact the ecosystem. The shift to sustainable development has been identified as a global priority. Thus, the United Nations (UN) in September 2015 prepared a set of 17 Sustainable Development Goals (SDGs) to be achieved by all the UN member states by 2030. To achieve a better and more sustainable future for all, these 17 goals were measured with 169 targets. The 2030 Agenda for sustainable development tackles global challenges such as poverty, climate change, inequality, peace, and environmental degradation. Figure 1 shows the icons used for the 17 SDGs of the 2030 Agenda. Earlier the UN developed eight international development goals at the Millennium Summit in 2000 to be achieved before 2015. These goals were named as Millennium Development Goals (MDGs). MDGs had only 8 goals and 21 targets. Some of the themes included were poverty, gender equality, health, and education. SDGs are built upon these MDGs with more inclusive and detailed approach. MDGs did not include any transport-related targets. Over time, the importance of the transport sector in achieving these goals was recognized. Consequently, certain goals in SDGs address transport-related targets. Substantial importance is given to sustainable transport which is evident as the United Nations Secretary-General formed a High-Level Advisory Group on Sustainable Transport (HLAG-ST). This group was formed to provide recommendations to tackle transport-related problems such as congestion, pollution, etc. The HLAG-ST produced a report in 2015 highlighting the link between transport and SDGs as transport acts as an enabler in achieving the SDG targets (UN-Habitat et al., 2015). In 2016, HLAG-ST published a Global Sustainable Transport Outlook report “Mobilizing Sustainable Transport for Development”

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Fig. 1 SDGs of the 2030 Agenda. Source United Nations (2015)

to emphasize how transport provides tangible support to the UN 2030 Agenda. This report provided recommendations in the domains of policy development, financing, and technological innovation (High-level Advisory Group on Sustainable Transport, 2016). The first UN Global Sustainable Transport Conference, 2016 highlighted the importance of sustainable transport in achieving the UN SDGs and suggested to incorporate transport policy and global partnerships. Government officials and business delegates from various countries participated in the conference. The second UN Global Sustainable Transport Conference, held in October 2021, called for a global attempt to promote sustainable transport worldwide by discussing six thematic sessions based on transport poverty, regional development, sustainable cities, etc. They recommended policies and actions focusing on all modes of transport, prioritization of private initiatives and financial investments, integrated transport systems, collaborations among countries, adoption of ambitious GHG emissions targets, and non-motorized modes in urban areas. The objectives of this paper are to highlight the direct and indirect relevance of UN SDG targets with transportation; to provide an overview of Sustainable Transport Indicators (STIs) and identify their suitability using a SMART criteria-based approach; to find the suitable STIs for direct-transport relevant SDG targets; and to review major Indian transport policies since the time of independence and their approach towards sustainable development after the commencement of SDGs in 2015. So, a review of Indian Transport Policies has been added in Sect. 5 which gives a brief idea about the inclusion of sustainable development in transport policies since the time of independence. This study can be carried forward to propose STIs for Indian Transport Policies through a comprehensive policy framework. It

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is essential to incorporate the SDGs related to transportation in the master plans and comprehensive mobility plans to frame robust and comprehensive policy interventions for sustainable development. These indicators can be used in monitoring progress to achieve these goals through future urban transport policy frameworks.

2 Overview of Relevance of UN SDG Targets with Transportation There is not a specific SDG for sustainable transport, but on examining the 17 SDGs and the 169 targets, we can see that sustainable transport is linked to some of the targets. This relevance is either mentioned as direct or indirect. Table 1 shows the direct and indirect relevance of transport to 17 SDGs and 169 targets. This is done in a comprehensive manner which is not yet seen in literature so far. ITDP (2015) identified seven SDGs (goals 2, 3, 7, 9, 11, 12, and 13) and fifteen targets (targets 2.3, 2.a, 3.6, 3.9, 7.3, 7.a, 9.1, 9.4, 9.a, 11.2, 11.6, 11.7, 11.a, 12.c, and 13.2) relevant to sustainable transport without classifying these targets as direct/indirect transport related targets (ITDP, 2015). Whereas UN-Habitat et al. (2015) identified targets of eight SDGs (goals 2, 3, 6, 7, 9, 11, 12, and 13) with direct or indirect relevancy to transportation (UN-Habitat et al., 2015). UN SDG targets 3.6, 7.3, 9.1, 11.2, and 12.c were identified as direct transport targets, and 2.3, 3.9, 6.1, 11.6, 12.3, and 13.1 as indirect transport targets. Gudmundsson and Regmi (2017) addressed six SDGs (goals 3, 7, 9, 11, 12, and 13) and seven targets (targets 3.6, 7.3, 9.1, 11.2, 11.6, 12.c, and 13.2) as relevant to transportation (Gudmundsson & Regmi, 2017). UN Department of Economic and Social Affairs linked various SDGs with different topics, such as goals 3, 9, 11, 13, and 17 were identified for “sustainable urban mobility”, goals 3, 9, and 11 were identified for “responsible urban cyclist” and “better roads for all” (United Nations, 2021). All these studies have identified the direct/indirect relevance of a few SDGs and their targets with sustainable transport. This review paper describes a comprehensive list of transport-relevant SDGs and targets, including all the other unaddressed SDG targets relevant to transportation as transport actions are instrumental in achieving those targets. This list will help policymakers in identifying suitable sustainable transport indicators for these targets, which will further lead to better monitoring of transport projects and plans. The criteria to decide whether a UN SDG Target is directly or indirectly related to transport was to identify if the changes in the transportation systems planning and engineering help in achieving it or not. If this criterion is satisfied, then the next criterion was to check whether these changes impact the target achievement directly or indirectly. Direct transport-related targets address dimensions of transport explicitly whereas indirect transport-related targets address other domains where transportation plays an important role. Using the criteria, 169

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Table 1 UN SDGs and the direct and indirect relevance of their targets to transportation SDGs*

Targets*

Relevance to transport Direct

1 2 3

1.4

*****

1.a

*****

2.3 3.6

***** *****

3.9 4

5

4.2

***** *****

4.3

*****

4.5

*****

–

–

6

6.1

*****

7

7.3

*****

8

8.4 8.5

*****

8.9

*****

8.10

***** *****

9.1

*****

9.4

*****

9.a 10 11

*****

9.b

*****

–

–

11.1

*****

11.2

*****

11.3

*****

–

11.5

*****

11.6

*****

11.7

12

–

*****

8.a 9

Indirect

*****

11.a

*****

11.b

*****

12.1 12.2

***** *****

12.5

*****

12.6

*****

12.7

*****

12.a

***** (continued)

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R. S. Nitwal et al.

Table 1 (continued) SDGs*

Targets*

Relevance to transport Direct

12.b 12.c 13

13.2 14.1

15

–

16

17

***** *****

13.1

14

Indirect

***** ***** ***** –

–

16.2

*****

16.5

*****

16.6

*****

16.7

*****

17.1 17.3

***** *****

17.4

*****

17.7

*****

17.9

*****

17.11

*****

17.16

*****

17.17

*****

17.19

*****

* Statements of these goals and targets can be referred from UN SDG official website Source Authors

UN SDG Targets were identified into 27 and 24 directly and indirectly transportrelated targets, respectively. Table 1 represents UN SDGs, targets, and the relevance of these targets to transport. Direct transport-related targets such as 1.4, 4.2, 4.3, 4.5, 6.1, 11.1, 11.2, and 11.7 address the accessibility such as access to basic services (1.4, 11.1), education centers (4.2, 4.3, 4.5), safe drinking water (6.1), public spaces (11.7), and safe, affordable, accessible, sustainable transport for all (11.2). Targets 9.1, 9.4, and 17.17 include the development and upgradation of transport infrastructure. Integrated transport policy and planning have been focused in the targets 9.b, 11.3, 11.a, 11.b, 12.7, and 13.2. Targets 7.3 and 12.c mention improvement in energy efficiency and rationalization of inefficient fossil fuel subsidies, respectively. Targets 16.6 and 16.7 highlight the importance of effective institutions and inclusive decision-making at all levels, respectively. Target 3.6 addresses the reduction in deaths and injuries from road traffic accidents, and targets 1.a and 12.2 refer to natural resources and their mobilization. Indirect transport-related targets 3.9, 14.1, and 11.6 address reduction in air, water, soil, and marine pollution and environmental impact of cities. Target 2.3 addresses

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increasing agricultural productivity, which involves logistics and market accessibility for farmers and consumers. Target 8.5 aims at improving productive employment which is affected by travel time in congested areas. Sustainable patterns of consumption and production are mentioned in targets 8.4, 12.1, and 12.a which is linked to freight transportation. Targets 8.9 and 12.b aim at promoting sustainable tourism, which impacts on the demand for transportation systems through employment generation. Target 11.5 aims at reducing direct economic losses due to damaged infrastructure and disruptions to basic services during disasters. Target 13.1 aims at strengthening the resilience to climate-related hazards through adaptive transport infrastructure and services. Target 17.1 aims at improving the domestic capacity through tax and revenue collection which can be obtained from transportation sector such as toll taxes, road, and vehicular taxes. Target 17.9 highlights the importance of the international support for resource mobilization and capacity building to strengthen national plans which involve transportation systems as one of the important inputs affecting these plans. Targets 8.10 mentions strengthening the capacity of financial institutions by improving their financial and geographic accessibility in different areas of the cities for different sections of the society. Target 17.4 aims at attaining long-term debt sustainability through coordinated policies for export of goods and services that involve transportation. Targets 9.a, 17.7, and 17.16 emphasize sustainable infrastructure development through technological support and global partnership. Target 17.11 aims to increase global exports involving transportation systems for transfer of goods and services.

3 Literature Review of Sustainable Transport Indicators Over time, there has been an increase in the use of Sustainable Transport Indicators (STIs) and indices for rating and evaluating the effectiveness of transportation policies, programmes, and systems (Mahdinia et al., 2018; Nathan & Reddy, 2011). Indicators are measured to assess progress towards goals and objectives. STIs are often categorized based on three pillars of sustainability (economical, social, and environmental) and various subdivisions/themes such as accessibility, air quality, GHG emissions, infrastructure, mobility, etc. Castillo and Pitfield (2010) proposed a framework called ELASTIC. This framework identified STIs considering two sub-goals of maximizing both indicators’ relevance to sustainable transport and quality. In their study, a set of 15 STIs was finalized out of a total of 233 STIs. Haghshenas and Vaziri (2012) compared 100 global cities by forming an index which was termed as Overall Sustainable Transport Composite Index. They identified a total of 9 indicators out of 326 indicators collected from past studies. These nine indicators were categorized into three dimensions of sustainability, namely economical, environmental, and social. With the use of the Sustainable Urban Transport Index, Gudmundsson and Regmi (2017) examined the sustainability of the transportation system in four cities throughout Asia–Pacific. SUTI was

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created from 10 STIs which were shortlisted from 426 STIs using ELASTIC framework. Mahdinia et al. (2018) developed the transportation sustainability index based on 89 indicators to determine transportation sustainability for 50 U.S. states. These 89 indicators were classified into three dimensions of sustainability (economical, social, and environmental) and nine themes (air pollution and greenhouse gases emission, energy, land use, environmental efficiency of vehicles, safety, accessibility, diversity, expenditure, and benefit of transportation systems users, expenditure, and revenue of transportation systems operators). Illahi and Mir (2022) formed a methodology to develop a Sustainable Transportation Attainment Index (ISTA ) and evaluated transport sustainability. They categorized 116 STIs into 10 themes based on three sustainability pillars. This index was prepared for 26 Indian states and National Capital Territory (NCT) of Delhi. In total, 74 STIs were identified for Asian countries by UNCRD and CAI-Asia (2011). The study assessed the data availability of the STIs for the 20 Environmentally Sustainable Transport (EST) goals of the Bangkok 2020 Declaration. These EST can also be taken as transportation subdivisions/themes. Nathan and Reddy (2011) identified 54 STIs from past literature and categorized these indicators under three dimensions of urban sustainability (economic efficiency, social well-being, and ecological acceptability). They used multi-view black box (MVBB) methodology to obtain suitable STIs for Mumbai city. Zheng et al. (2013) developed a tool to prepare a suitable sustainable transport index. The tool named Transportation Index for Sustainable Places (TISP) was obtained from 22 STIs grouped under 19 themes (no indicators were defined for 3 themes) and three sustainability dimensions. Using methods of Rough Sets Theory (RST) and Two-Stage Principal Component Analysis (TSPCA), 19 STIs were aggregated by Shiau et al. (2015) to assess the transport sustainability of Taiwan from 1993 to 2010. Indicators were classified into four dimensions, i.e., Economy, Environment, Society, and Energy. Themes/subdivisions were not taken into consideration. Zope et al. (2019) used benchmarking as a method to evaluate and monitor urban transportation sustainability. They formed an index to measure the performance of selected Indian cities’ passenger transport systems and provided benchmarks for future improvements. The index is formed using 8 STIs categorized into three sustainable transport dimensions (economic, environmental, and social) and eight transportation themes. Identifying suitable STIs from an initial list of a large pool of indicators using appropriate criteria is common in literature (Castillo & Pitfield, 2010; Nathan & Reddy, 2011; Haghshenas & Vaziri, 2012; Chakhtoura & Pojani, 2016; Sdoukopoulos & Pitsiava-Latinopoulou, 2017). In these studies, indicators from several past studies were extracted to obtain a potential list that can be further linked to the transport-related UN SDG Targets. Table 2 lists the STIs, the scale of the study, number of dimensions and themes for sustainable transport from 8 studies. To select suitable indicators from a large pool of indicators, a widely used approach known as the “SMART” (Specific, Measurable, Attainable, Relevant, and Timely) criteria has been used in this study. It is used to identify goals and objectives for better results in a project, however the method can also be applied for indicator

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Table 2 List of 8 studies with scale, number of sustainable transport dimensions, themes, and indicators S. No.

Authors (Year) Scale

# Sustainable Transport Dimensions/Pillars

# Transportation Themes/ Subdivisions

# Sustainable Transport Indicators

1

Nathan and Reddy (2011)

Urban

3

0

54

2

UNCRD and CAI-Asia (2011)

National

0

19

74

3

Haghshenas and Vaziri (2012)

Urban

3

0

9

4

Zheng et al. (2013)

National

3

19

22

5

Shiau et al. (2015)

National

4

0

19

6

Mahdinia et al. National (2018)

3

9

89

7

Zope et al. (2019)

Urban

3

8

8

8

Illahi and Mir (2022)

National

3

10

116

Total indicators = 391 Source Authors

Table 3 “SMART” Criteria for shortlisting indicators Specific

Measurable

Attainable

Relevant

Timely

Indicator should be specific and clear in achieving the objective of the study i.e., to identify the appropriate STI for indicator framework

Indicator should have well defined formula for measurement

Indicator should be feasible to attain after checking monetary constraint

Indicator should provide information to policy makers for implementing action plans and policies

Data for indicator must be available and collected within stipulated time

Source Authors

selection (Broughton & Hampshire, 1997). Table 3 defines the SMART criteria used to shortlist the potential indicators from the non-identical list of indicators which was referred and modified from the study conducted by Gudmundsson et al. (2016).

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4 Identifying Suitable Sustainable Transport Indicators (STIs) A total of 391 indicators were extracted from past studies. Firstly, the identical indicators were removed from the list, and 255 STIs were obtained, then the SMART criteria were applied. Secondly, the indicators which satisfy all the five criteria were shortlisted in the potential list of indicators. The indicators were given binary weights (either 0 or 1) for each criterion, and an indicator was chosen if all the five criteria were met in the SMART criteria approach. Thirdly, a potential list of 201 STIs was obtained from 391 STIs (as shown in Appendix 1). Finally, this list of STIs was linked to the UN SDG targets directly relevant to transportation (as shown in Appendix 2) to make the SDG targets operational and have associated indicators for each target. This work was accomplished by segregating the STIs under different keywords like access, fatalities, action plans and policies, conventional vehicle capacity, aircrafts, employment, emissions, energy, expenditure, funds/revenues, GDP, GHG emissions, income, trips, vehicle performance, etc., based on the information conveyed by the STIs. These keywords were matched with the scope of the identified 27 directrelevant SDG targets and a comprehensive list of STIs associated with these targets was obtained. For example, 11.2 targets comprised of the safety, and accessibility of the transportation systems, so the number of unsafe driving cases registered, and % of population with access to transit, etc., were identified STIs with keywords fatalities, and access, respectively. This list can be further compressed based on the data availability of these indicators at the national or at local body frameworks. National policies can identify the most important and relevant indicators from this list and incorporate them for further improvement at sub-levels of governance. State and local area policies can utilize this list to improve their database, monitoring systems in implementing sustainable transportation strategies. Experts and practitioners can discuss further about this potential list and finalize a list of indicators to be used in their policies with certain benchmarks.

5 Review of Indian Transport Policy In the post-independence era, the Five-Year Plans (FYPs) were in place from 1951 till 2012 which focused on economic development in different domains like energy, transportation, power, etc. The railway transportation was focused more in the initial FYPs and then the plans were made for improving and increasing the road infrastructure in India. Several transport projects and policies were introduced to improve the urban and rural transportation systems. Some of these policies include Metro Railways (Construction of Works), 1978 Act and Amendment Act in 1982 for development of metro railways in metropolitan cities; The Air (Prevention and Control of Pollution) Act, 1981 and The Environment (Protection) Act, 1986 to reduce pollution and improve the environment; and The Motor Vehicles Act, 1988 to reduce accidents,

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penalize violators of traffic rules, and provide permits to control vehicles. In the twenty-first century, the Integrated Transport Policy, 2001 and National Auto Fuel Policy, 2003 were introduced by Planning Commission (now known as NITI AYOG) and Ministry of Petroleum and Natural Gas, respectively, to improve the efficiency of transportation systems in the liberalized Indian economy. In 2005, the Ministry of Urban Development introduced the Jawaharlal Nehru Urban Renewal Mission (JNNURM) to empower the Urban Local Bodies (ULBs) to upgrade their infrastructure and prepare Comprehensive Mobility Plans (CMPs) and Comprehensive Development Plans (CDPs). Further, the National Urban Transport Policy (NUTP) was formed in 2006 under the Ministry of Urban Development (MoUD) advocating the establishment of the Unified Metropolitan Transport Authority (UMTA) in Indian cities with million plus population. National Road Safety Policy, 2010 identified poor people as the vulnerable population group who are the victims of road accidents in India. National Electric Mobility Mission Plan (NEMMP), 2013 focused on early adopters of Electric Vehicles (EVs). NUTP 2014 authored by Institute of Urban Transport (IUT) and issued by Ministry of Urban Development, Government of India (MoUD) mentions sustainable transportation strategies. Smart Cities Mission (SCM) was launched in June, 2015 and the UN SDGs were agreed to be adopted for Agenda 2030 in September 2015. As per Udas-Mankikar and Driver (2021), SDGs 1, 2, 3, 6, 8, 9, 10, 11, 13, 14, 15, and 16 were identified as potential goals to be fulfilled through blue-green infrastructure plan in the SCM. SDG 11 is the goal that directly links with the SCM and the transportation sector. Green Urban Mobility Scheme, 2017 promotes NMTs, EVs, and hybrid vehicles to reduce vehicular emissions drastically. On the same line, the National E-Mobility Programme launched by the Ministry of Energy in 2018 focuses on subsidizing the EV industry. National Policy on Transit-Oriented Development, 2017 by Ministry of Housing and Urban Affairs (MoHUA) focuses on the integration of land-use and transportation by prioritizing NMTs with less reliability on private vehicles. Metro Rail Policy 2017 identified the need for framework to implement metro rail projects (Verma et al., 2021). These policies seem to be directly linked with SDGs. It has also been observed that CMPs and CDPs do not address the UN SDGs and there is a lack of guidelines to follow for specific targets related to urban transport planning and management. Land use policies and acquisition methods add on to the problem associated with integrated land-use transport planning. National Urban Policy Framework (NUPF) 2020 emphasizes sustainable transportation without any incorporation of SDG targets. This review of the Indian transport policies and schemes clearly shows lack of incorporation of STIs and relevant SDG targets which would improve tracking and monitoring of the planning process. Planning and governing tools need to be devised for specific transport-related UN SDG targets for efficient urban transport governance. A robust guideline with policy framework is needed to improve the functioning and reliability of the CMPs and transport policies.

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6 Conclusion Sustainable Development Goals have been instrumental in shifting the focus of traditional development strategies to more sustainable approaches. These shifts in the last 12 years have led to identification of several domains for research and development which were not focused on before the introduction of UN SDGs. It is evident from research and practice that these domains have been monitored now in a better way. The targets associated with SDGs are linked to several indicators just to initiate the shift to sustainability for achieving Agenda 2030. This study provides a potential list of 201 STIs from 391. Associated STIs were found for the 27 SDG targets directly related to transportation for which data availability and feasibility can be checked to find their relevance with the Indian Transport Policies. This would help the policy makers to review their policies and create specific action plans for achieving SDG 2030 Agenda. Different countries have identified their own ways of achieving these SDG targets through missions, schemes, plans, and policies. Indian transport policies have been reviewed to understand the level of incorporation of SDGs in their guidelines. For indirectly relevant UN SDG targets, similar approach can be considered. This work can be further extended to provide a comprehensive policy framework with specific STIs under different themes for specific SDG target. These indicators can be shortlisted further based on their relevance with the agendas and schemes set by government at different levels, and on the data availability. Further reduction in number of STIs lies with the Urban Local Bodies (ULBs) and policy makers. This list is comprehensive with respect to the literature reviewed for the STIs and new indicators can also be added by decision makers based on the continuum of changes in development processes. The urban local bodies and researchers can use this list for further analysis instead of replicating any indicator framework. This extension can help the policymakers to incorporate accountability and transparency in dealing with different departments for integrated land-use and transport planning. Several short-term and long-term mini goals can be identified for monitoring the efficiency of existing policies and an integrated policy framework can be created to achieve the 2030 Agenda.

Appendix 1: Potential List of 201 Sustainable Transport Indicators (STIs)

S. no.

Selected STIs

1

% of population with access to transit

2

% of school children using private transport

3

Average travel time to work (continued)

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(continued) S. no.

Selected STIs

4

Number of public transport vehicles and stations permitting full universal access for users in wheelchairs and parents with prams

5

Sufficient human and financial resources devoted to Environmentally Sustainable Transport (EST) at national level, regional level and at local level

6

Number of unresolved environmental cases pending in court road and rail transport

7

Number of existing transport Nationally Appropriate Mitigation Actions (NAMAs)

8

Number of transport Global Environment Facility (GEF) projects approved for the country

9

Number of cities in the country having formally developed integrated land usetransport plan

10

Number of cities monitoring noise levels

11

Number of cities or areas utilizing road tolls

12

Number of cities participating in a Car- Free Day programme

13

Number of cities that conduct roadway spot checks on vehicle emissions

14

Number of cities utilizing electronic fare cards on their public transport system

15

Number of cities with a control centre to manage traffic incidents and manage public transport fleets

16

Number of cities with active parking management programmes

17

Number of cities with bus systems using pre-board fare verification and stations designed for at-level fast boarding

18

Number of cities with dedicated cycleways

19

Number of cities with Non-Motorised Transport (NMT) specifically highlighted in the city’s integrated transport master plans

20

Number of cities with policies in place to prohibit smoking in public places, including public transport systems

21

Number of cities with shared bicycle programmes and number of shared bikes per programme

22

Number of cities with trunk bus corridors operating on dedicated busway lanes in the median of the roadway (Bus Rapid Transit)

23

Number of policies and/or programs that promote telecommuting

24

Number of policies developed encouraging Information and Communications Technologies (ICT) as a substitute for travel (e.g. WFH)

25

Number of public transport projects achieving transit-oriented development (TOD) around station

26

Number of Public–Private Partnerships (PPPs) implemented in transport projects

27

Number of units developed in purpose-built mixed-use projects

28

Number of adopted fatality reduction targets

29

Total number of passenger trips by scheduled flight operations per area

30

Total number of passenger trips by scheduled flight operations per capita

31

Annual freight shipment by rail per capita (continued)

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(continued) S. no.

Selected STIs

32

Annual public transportation property damage by public transportation incidents per annual unlinked passenger trips by transit

33

Average speed of transport (category wise)

34

Bus fare per kilometer

35

Vehicle kilometers traveled per person at the metropolitan and national levels

36

Mode share of freight transport (truck, rail, barge, minivan, and non-motorized) at the metropolitan and national levels

37

Mode share of inter-city transport (private motorized vehicles, bus, rail, and boat) at the metropolitan and national levels

38

Mode share of passenger transport (walking, bicycles, car driver, car passenger, motorcycle driver, motorcycle passenger, motorized three-wheelers, non-motorized three-wheelers, buses, minibuses, and urban rail) at the metropolitan and national levels

39

Mode share of high-quality inter-city bus services

40

Mode share of high-speed inter-city rail services

41

Number of kilometres of dedicated, median busways (Bus Rapid Transit)

42

Number of kilometres of high-speed inter-city rail

43

Number of kilometres of MRT

44

Number of public transport stations with tactile paving tiles for the sight impaired

45

Number of public transport stations and vehicles using real-time information display

46

Number of public transport vehicles per city with Automatic Vehicle Location tracking technology

47

Passenger-Kilometers (PKM) traveled by transit/Vehicle-Kilometers (VKM) traveled by transit

48

% of SRTUs occupancy ratio

49

PKM traveled by private modes/VKM traveled by private modes

50

Proportion of service disability compliant for % of bus fleet

51

Proportion of service disability compliant for % of railway stations

52

Public parking space per 1000 vehicle (category wise)

53

SRTUs passenger kilometres performed

54

Ton-Kilometers (TKM) traveled by truck /VKM traveled by truck

55

Total number of registered vehicles per capita

56

Total number of vehicles per capita

57

Annual air pollution emissions by transportation per total energy used by transportation

58

% employees of organised sector using private transport

59

Annual public transportation payroll per number of paid employees

60

SRTUs staff productivity measured in km per staff per day

61

SRTUs staff/bus ratio (continued)

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(continued) S. no.

Selected STIs

62

Annual energy consumption of major petroleum products by transportation per Annual VKT

63

Annual renewable energy potential per Annual VKT

64

Annual Compressed Natural Gas (CNG) consumption per total transportation energy consumption

65

Annual electricity used per total energy consumption by transportation

66

Transport energy efficiency (monetary)

67

Transport energy efficiency (utility)

68

Earnings per PKT for buses in public transport system

69

Earnings per PKT for rail in public transport systems

70

Expenses per PKT for buses in public transport system

71

Expenses per PKT for rail in public transport systems

72

% of SRTUs fleet utilization

73

Annual transportation revenues per transportation expenditures

74

% of transportation expenditure from federal funding

75

Annual SRTUs expenditure per GSDP per capita

76

Annual public transportation expenditures per public transportation funds

77

Annual public transportation revenues per public transportation expenditures

78

Annual traffic fatalities per total number of licensed drivers

79

Annual fatalities in railways per total length of railway track

80

Annual public transportation fatalities per total number of buses

81

Annual Reported Road Traffic Fatalities (RRTFs) per capita

82

Annual Reported Serious Injuries (RSIs) in road traffic per capita

83

Annual RRTFs per Annual VKT

84

Annual RRTFs per total number of drivers with valid license

85

Annual RSIs in road traffic per Annual VKT

86

Annual RSIs in road traffic per total number of drivers with valid license

87

Annual number of bus passenger fatalities per total number of buses

88

Annual vulnerable traffic fatalities include pedestrian and bicyclist per total traffic fatalities

89

Number of fatalities caused by transportation sector per capita

90

Number of unsafe driving cases registered (Signal jump, drunk and drive, without license, etc.) per year

91

Number of traffic fatalities/VKM traveled by private modes

92

Number of Traffic fatality related to freight transport

93

Annual accrual allocation of funds under CRF schemes per GSDP per capita

94

Annual accrual release of funds under CRF schemes per GSDP per capita (continued)

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(continued) S. no.

Selected STIs

95

Funds allocated under economic importance and inter-state connectivity per GSDP per capita

96

Funds released under economic importance and inter-state connectivity per GSDP per capita

97

% of state GDP spent on fuel

98

% of state GDP spent on transportation

99

Annual SRTUs revenue per GSDP per capita

100

Annual public transportation fund per GDP per capita

101

Annual transportation expenditures per GDP per capita

102

Freight transport intensity (ratio of total freight moved to GDP)

103

GDP growth per VMT growth

104

Revenue from the rate of taxes levied on diesel

105

Revenue from the rate of taxes levied on petrol

106

Annual GHG emissions by freight transport (truck, rail, barge, minivan, and non-motorized) per total vehicle kilometres travelled (VKT)

107

Annual GHG emissions by inter-city transport (private motorized vehicles, bus, rail, and boat) per total vehicle kilometres travelled (VKT)

108

Annual GHG emissions by passenger transport (walking, bicycles, car driver, car passenger, motorcycle driver, motorcycle passenger, motorized three-wheelers, non-motorized three-wheelers, buses, minibuses, and urban rail) per total vehicle kilometres travelled (VKT)

109

Annual GHG emissions by transportation per capita

110

Annual air pollution emissions by transportation per capita

111

Annual greenhouse gases by transportation per total energy used by transportation

112

Annual on-road air pollution emission per total annual VKT

113

Annual on-road greenhouse gases per total annual VKT

114

Air Quality Index

115

Number of applications for greenhouse gas emission reduction credits (for transport projects)

116

% of household income spent on transportation

117

Average income of population using transit relative to average state income

118

Proportion of HH expenditure on transportation among income groups

119

Population Density (persons per sq. km) per capita GDP

120

Amount of increase in property value along corridors of quality public transport projects

121

Land consumption for transportation infrastructure (private, public) per capita

122

Land urbanized per population growth

123

Number of inland dry ports (inter-modal road or rail connectivity to port)

124

Parking costs (reflect true land cost or market value) (continued)

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(continued) S. no.

Selected STIs

125

Population per square kilometer along major public transport corridors

126

Employment per square kilometer along major public transport corridors

127

% length of railway routes electrified per total length of railway routes

128

% of exclusive and controlled right-of-way motor bus transit route per total motor bus transit route length

129

% of road length double or more lane in total

130

% of road length having footpath

131

% of road length having street lighting

132

% of total motor bus transit route length per total roads length

133

Fund allocation for repair and maintenance of NHs per total length of NHs

134

Total length of National Highways (NHs) per capita

135

Total length of NHs per Annual VKT

136

Total length of NHs per area

137

Total length of other PWD roads per Annual VKT

138

Total length of other PWD roads per area

139

Total length of other PWD roads per capita

140

Total length of railway roads per total length of running railway track

141

Total length of roads per Annual VKT

142

Total length of roads per area

143

Total length of roads per capita

144

Total length of rural roads per Annual VKT

145

Total length of rural roads per area

146

Total length of rural roads per capita

147

Total length of SHs per Annual VKT

148

Total length of SHs per area

149

Total length of SHs per capita

150

Total length of urban roads per Annual VKT

151

Total length of urban roads per area

152

Total length of urban roads per capita

153

Total motor bus route length per area

154

Total number of fuel filling stations per total length of roads

155

Pedestrian mode share

156

Bicycle mode share

157

% of children walking to school

158

% of commuting to work via NMT modes

159

Number of kilometers of cycleways

160

Number of cities surveyed or audited for a “walkability” score (continued)

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(continued) S. no.

Selected STIs

161

Proportion of mobile population using non-motorized transport (walking or cycling)

162

Market share (or number) of alternative fuels for road transport, including renewably-generated electricity, natural gas, and sustainably managed and cultivated biofuels that do not compete with food crops

163

Market share (or number) of electric vehicles, hybrid vehicles, and fuel cell vehicles

164

Number of alternative fuel station per number of alternative fuel vehicle

165

% of vehicles with alternative fuels per total number of vehicles

166

Vehicle share with renewable fuels for 3 wheelers

167

Vehicle share with renewable fuels 3 Cars/jeeps/taxis

168

Vehicle share with renewable fuels for Buses

169

Average time spent in traffic

170

Existence of NMT component in road and traffic design guidelines

171

Annual inter-state work trips per capita

172

Annual work trips (transit, walk, bicycle, motorcycle, taxicab, carpooled, etc.) except drive alone per total annual work trips

173

Average freight trip distance regionally and nationally

174

Average passenger trip length in capital and/or key cities

175

Domestic tourist visits per area

176

Congestion Index

177

Peak Hour Journey Speed (kmph)

178

% of annual non motorize work trips per total annual work trips

179

% of annual work trips by public transportation per total annual work trips

180

Transport monetary efficiency (category wise)

181

% biofuels allowed/mandated

182

Annual motor fuel used by transportation per capita

183

Annual motor fuel used by transportation per total annual VKT

184

Annual motor fuel used in public transportation per annual unlinked passenger trips by public transportation

185

Annual motor fuel used in public transportation per capita

186

Annual motor fuel used in public transportation per motor fuel used in private transportation

187

Annual motor fuel used in public transportation per total motor fuel used by transportation

188

Annual total cost spend for gasoline price including taxes per capita

189

Annual total cost spend for gasoline price including taxes per total annual VKT

190

Fossil fuel consumption by transport system

191

Freight train kilometres performed per capita

192

Fuel efficiency levels of passenger and freight fleets

193

Fuel price of subsidy (continued)

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(continued) S. no.

Selected STIs

194

Current fuel quality standards

195

Possible timeline for attainment of EURO IV (or equivalent) fuel quality standard

196

% consumption of diesel fuel in the transportation sector

197

% of Annual consumption of diesel by private transportation modes

198

% of Annual consumption of petrol by public transportation modes including shared taxis and three-wheelers

199

% of CNG filling stations to petrol filling stations

200

% of consumption of petrol by private transportation modes including cars and two-wheelers

201

SRTUs vehicle productivity measured in km per bus per day

Appendix 2: Potential List of Suitable STIs for UN SDG Targets Directly Relevant to Transportation Identified 27 Number of Suitable sustainable transport indicators (STIs) for each target direct-relevant sustainable SDG targets transport indicators (STIs) 1.4 and 11.1

11

% of population with access to transit; % of school children using private transport; Number of public transport vehicles and stations permitting full universal access for users in wheelchairs and parents with prams; Number of kilometres of footpaths that have been upgraded to be fully accessible to persons in wheelchairs; Proportion of HH expenditure on transportation among income groups; % of household income spent on transportation; % of population with access to transit; Average income of population using transit relative to average state income; Parking costs (reflect true land cost or market value); Number of cities with active parking management programmes; Public parking space per 1000 vehicle (category wise)

1.a

6

Annual freight shipment by rail per capita; Average speed of transport (category wise); Mode share of freight transport (truck, rail, barge, minivan, and non-motorized) at the metropolitan and national levels; Average freight trip distance regionally and nationally; Freight train kilometres performed per capita; Freight transport intensity (ratio of total freight moved to GDP)

3.6

15

Annual Reported Road Traffic Fatalities (RRTFs) per capita; Annual Reported Serious Injuries (RSIs) in road traffic per capita; Annual RRTFs per Annual VKT; Annual RRTFs per total number of drivers with valid license; Annual RSIs in road traffic per Annual VKT; Annual RSIs in road traffic per total number of drivers with valid license; Number of adopted fatality reduction targets; Number of cities with a control centre to manage traffic incidents and manage public transport fleets; Annual number of bus passenger fatalities per total number of buses; Annual public transportation fatalities per total number of buses; Annual traffic fatalities per total number of licensed drivers; Annual vulnerable traffic fatalities include pedestrian and bicyclist per total traffic fatalities; Number of traffic fatalities/VKM travelled by private modes; Number of Traffic fatality related to freight transport; Number of unsafe driving cases registered (Signal jump, drunk and drive, without license, etc.) per year (continued)

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(continued) Identified 27 Number of Suitable sustainable transport indicators (STIs) for each target direct-relevant sustainable SDG targets transport indicators (STIs) 4.2

2

% of population with access to transit; % of school children using private transport

4.3

2

% of population with access to transit; % of school children using private transport

4.5

3

% of population with access to transit; % of school children using private transport; Number of public transport vehicles and stations permitting full universal access for users in wheelchairs and parents with prams

6.1

0

7.3

12

Fuel efficiency levels of passenger and freight fleets; Annual Compressed Natural Gas (CNG) consumption per total transportation energy consumption; Annual electricity used per total energy consumption by transportation; Annual energy consumption of major petroleum products by transportation per Annual VKT; Annual renewable energy potential per Annual VKT; Transport energy efficiency (monetary); Transport energy efficiency (utility); Annual air pollution emissions by transportation per total energy used by transportation; Number of cities that conduct roadway spot checks on vehicle emissions; Average speed of transport (category wise); Number of kilometres of dedicated, median busways (Bus Rapid Transit); Number of kilometres of high-speed inter-city rail

9.1

40

Number of public transport vehicles and stations permitting full universal access for users in wheelchairs and parents with prams; Adoption of fatality reduction targets; Number of cities monitoring noise levels; Number of cities utilizing electronic fare cards on their public transport system; Number of cities with active parking management programmes; Number of cities with bus systems using pre-board fare verification and stations designed for at-level fast boarding; Number of cities with dedicated cycleways; Number of kilometres of dedicated, median busways (Bus Rapid Transit); Number of kilometres of high-speed inter-city rail; Land consumption for transportation infrastructure (private, public) per capita; % of road length double or more lane in total; % of road length having footpath; % of road length having street lighting; Number of inland dry ports (inter-modal road or rail connectivity to port); Number of kilometers of cycleways; Number of kilometres of dedicated, median busways (Bus Rapid Transit); Number of kilometres of high-speed inter-city rail; Number of kilometres of MRT; Number of public transport projects achieving transit-oriented development (TOD) around station; Number of public transport stations and vehicles using real-time information display; Number of public transport stations with tactile paving tiles for the sight impaired; Total length of National Highways (NHs) per capita; Total length of NHs per Annual VKT; Total length of NHs per area; Total length of other PWD roads per Annual VKT; Total length of other PWD roads per area; Total length of other PWD roads per capita; Total length of railway roads per total length of running railway track; Total length of roads per Annual VKT; Total length of roads per area; Total length of roads per capita; Total length of rural roads per Annual VKT; Total length of rural roads per area; Total length of rural roads per capita; Total length of SHs per Annual VKT; Total length of SHs per area; Total length of SHs per capita; Total length of urban roads per Annual VKT; Total length of urban roads per area; Total length of urban roads per capita; Total number of fuel filling stations per total length of roads (continued)

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(continued) Identified 27 Number of Suitable sustainable transport indicators (STIs) for each target direct-relevant sustainable SDG targets transport indicators (STIs) 9.4

40

Number of adopted fatality reduction targets; Number of cities utilizing electronic fare cards on their public transport system; Number of Public–Private Partnerships (PPPs) implemented in transport projects; Annual total cost spend for gasoline price including taxes per total annual VKT; Annual transportation expenditures per GDP per capita; Congestion Index; Air Quality Index; Earnings per PKT for buses in public transport system; Earnings per PKT for rail in public transport systems; Fossil fuel consumption by transport system; Fund allocation for repair and maintenance of NHs per total length of NHs; Funds allocated under economic importance and inter-state connectivity per GSDP per capita; Funds released under economic importance and inter-state connectivity per GSDP per capita; Land consumption for transportation infrastructure (private, public) per capita; Land urbanized per population growth; Market share (or number) of alternative fuels for road transport, including renewably-generated electricity, natural gas, and sustainably managed and cultivated biofuels that do not compete with food crops; Market share (or number) of electric vehicles, hybrid vehicles, and fuel cell vehicles; Mode share of freight transport (truck, rail, barge, minivan, and non-motorized) at the metropolitan and national levels; Mode of high-quality inter-city bus services Mode share of high-speed inter-city rail services; Mode share of inter-city transport (private motorized vehicles, bus, rail, and boat) at the metropolitan and national levels; Mode share of passenger transport (walking, bicycles, car driver, car passenger, motorcycle driver, motorcycle passenger, motorized three-wheelers, non-motorized three-wheelers, buses, minibuses, and urban rail) at the metropolitan and national levels; Number of alternative fuel station per number of alternative fuel vehicle; Number of applications for greenhouse gas emission reduction credits (for transport projects); Number of existing transport Nationally Appropriate Mitigation Actions (NAMAs); Number of policies developed encouraging Information and Communications Technologies (ICT) as a substitute for travel (e.g. WFH); Number of public transport projects achieving transit-oriented development (TOD) around station; Number of transport Global Environment Facility (GEF) projects approved for the country; Number of units developed in purpose- built mixed-use projects; Number of transport Global Environment Facility (GEF) projects approved for the country; Number of units developed in purpose- built mixed-use projects; Possible timeline for attainment of EURO IV (or equivalent) fuel quality standard; Proportion of service disability compliant for % of bus fleet; Proportion of service disability compliant for % of railway stations; Public parking space per 1000 vehicle (category wise); SRTUs staff/ bus ratio; SRTUs vehicle productivity measured in km per bus per day; Sufficient human and financial resources devoted to Environmentally Sustainable Transport (EST) at national level, regional level and at local level; Total motor bus route length per area; Total number of fuel filling stations per total length of roads

9.b

1

Number of public transport vehicles per city with Automatic Vehicle Location tracking technology

11.2

71

% of population with access to transit; % of school children using private transport; % of Annual consumption of petrol by public transportation modes including shared taxis and three-wheelers; Average travel time to work; Number of adopted fatality reduction targets; Number of cities monitoring noise levels; Number of cities that conduct roadway spot checks on vehicle emissions; Number of cities utilizing electronic fare cards on their public transport system; Number of cities with a control centre to manage traffic incidents and manage public transport fleets; Number of cities with active parking management programmes; Public parking space per 1000 vehicle (category wise); Number of cities with bus systems using pre-board fare verification and stations designed for at-level fast boarding; Number of cities with dedicated cycleways; Number of public transport projects achieving transit-oriented development (TOD) around station; (continued)

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(continued) Identified 27 Number of Suitable sustainable transport indicators (STIs) for each target direct-relevant sustainable SDG targets transport indicators (STIs) Number of kilometres of dedicated, median busways (Bus Rapid Transit); Number of kilometres of high-speed inter-city rail; Annual Reported Road Traffic Fatalities (RRTFs) per capita; Annual Reported Serious Injuries (RSIs) in road traffic per capita; Annual RRTFs per Annual VKT; Annual RRTFs per total number of drivers with valid license; Annual RSIs in road traffic per Annual VKT; Annual RSIs in road traffic per total number of drivers with valid license; Number of adopted fatality reduction targets; Number of cities with a control centre to manage traffic incidents and manage public transport fleets; Annual number of bus passenger fatalities per total number of buses; Annual public transportation fatalities per total number of buses; Annual traffic fatalities per total number of licensed drivers; Annual vulnerable traffic fatalities include pedestrian and bicyclist per total traffic fatalities; Number of traffic fatalities/VKM traveled by private modes; Number of Traffic fatality related to freight transport; Number of unsafe driving cases registered (Signal jump, drunk and drive, without license, etc.) per year; Annual fatalities in railways per total length of railway track; % of annual work trips by public transportation per total annual work trips; Land consumption for transportation infrastructure (private, public) per capita; Number of cities with policies in place to prohibit smoking in public places, including public transport systems; Number of public transport stations and vehicles using real-time information display; Number of public transport stations with tactile paving tiles for the sight impaired; Number of public transport vehicles and stations permitting full universal access for users in wheelchairs and parents with prams; Number of public transport vehicles per city with Automatic Vehicle Location tracking technology Number of Public–Private Partnerships (PPPs) implemented in transport projects; Population per square kilometer along major public transport corridors; Mode share of freight transport (truck, rail, barge, minivan, and non-motorized) at the metropolitan and national levels; Mode share of high-quality inter-city bus services; Mode share of high-speed inter-city rail services; Mode share of inter-city transport (private motorized vehicles, bus, rail, and boat) at the metropolitan and national levels; Mode share of passenger transport (walking, bicycles, car driver, car passenger, motorcycle driver, motorcycle passenger, motorized three-wheelers, non-motorized three-wheelers, buses, minibuses, and urban rail) at the metropolitan and national levels; % of annual non motorize work trips per total annual work trips; % of annual work trips by public transportation per total annual work trips; Annual inter-state work trips per capita; Annual public transportation property damage by public transportation incidents per annual unlinked passenger trips by transit; Annual work trips (transit, walk, bicycle, motorcycle, taxicab, carpooled, etc.) except drive alone per total annual work trips; Average freight trip distance regionally and nationally; Average passenger trip length in capital and/or key cities; Congestion Index; Domestic tourist visits per area; Peak Hour Journey Speed (kmph); Total number of passenger trips by scheduled flight operations per area; Total number of passenger trips by scheduled flight operations per capita; % of SRTUs fleet utilization; % of SRTUs occupancy ratio; % of state GDP spent on fuel; % of state GDP spent on transportation; % of total motor bus transit route length per total roads length; Air Quality Index; Annual air pollution emissions by transportation per capita; Annual GHG emissions by freight transport (truck, rail, barge, minivan, and non-motorized) per total vehicle kilometres travelled (VKT); Annual GHG emissions by inter-city transport (private motorized vehicles, bus, rail, and boat) per total vehicle kilometres travelled (VKT); Annual GHG emissions by passenger transport (walking, bicycles, car driver, car passenger, motorcycle driver, motorcycle passenger, motorized three-wheelers, non-motorized three-wheelers, buses, minibuses, and urban rail) per total vehicle kilometres travelled (VKT); Annual greenhouse gases by transportation per total energy used by transportation; Annual on-road air pollution emission per total annual VKT; Annual on-road greenhouse gases per total annual VKT; Number of applications for greenhouse gas emission reduction credits (for transport projects); (continued)

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(continued) Identified 27 Number of Suitable sustainable transport indicators (STIs) for each target direct-relevant sustainable SDG targets transport indicators (STIs) 11.3

17

Number of cities in the country having formally developed integrated land use- transport plan; Number of cities monitoring noise levels; Number of cities or areas utilizing road tolls; Number of cities participating in a Car- Free Day programme; Number of cities utilizing electronic fare cards on their public transport system; Number of cities with a control centre to manage traffic incidents and manage public transport fleets; Number of cities with active parking management programmes; Number of cities with bus systems using pre-board fare verification and stations designed for at-level fast boarding; Number of cities with non-motorised transport (NMT) specifically highlighted in the city’s integrated transport master plans; Number of public transport projects achieving transit-oriented development (TOD) around station; Total number of passenger trips by scheduled flight operations per area; Total number of passenger trips by scheduled flight operations per capita; Number of cities that conduct roadway spot checks on vehicle emissions; Number of cities with dedicated cycleways; Number of cities with shared bicycle programmes and number of shared bikes per programme; Number of kilometres of dedicated, median busways (Bus Rapid Transit); Amount of increase in property value along corridors of quality public transport projects

11.7

2

11.a

13

Number of public transport projects achieving transit-oriented development (TOD) around station; Sufficient human and financial resources devoted to Environmentally Sustainable Transport (EST) at national level, regional level and at local level; % of SRTUs occupancy ratio; Annual freight shipment by rail per capita; Mode share of high-quality inter-city bus services; Mode share of high-speed inter-city rail services; Mode share of inter-city transport (private motorized vehicles, bus, rail, and boat) at the metropolitan and national levels; Mode share of passenger transport (walking, bicycles, car driver, car passenger, motorcycle driver, motorcycle passenger, motorized three-wheelers, non-motorized three-wheelers, buses, minibuses, and urban rail) at the metropolitan and national levels; Amount of increase in property value along corridors of quality public transport projects; Number of applications for greenhouse gas emission reduction credits (for transport projects); Number of Public–Private Partnerships (PPPs) implemented in transport projects; Number of transport Global Environment Facility (GEF) projects approved for the country; Number of units developed in purpose- built mixed-use projects

11.b

13

Number of cities in the country having formally developed integrated land usetransport plan; Number of cities that conduct roadway spot checks on vehicle emissions; Number of cities utilizing electronic fare cards on their public transport system; Number of cities with a control centre to manage traffic incidents and manage public transport fleets; Number of cities with active parking management programmes; Number of cities with bus systems using pre-board fare verification and stations designed for at-level fast boarding; Number of policies and/or programs that promote telecommuting; Number of policies developed encouraging Information and Communications Technologies (ICT) as a substitute for travel (e.g. WFH); % of SRTUs occupancy ratio; Annual public transportation property damage by public transportation incidents per annual unlinked passenger trips by transit; Average speed of transport (category wise); Number of kilometres of dedicated, median busways (Bus Rapid Transit); Number of kilometres of high-speed inter-city rail

12.2

6

% of population with access to transit; Number of cities with policies in place to prohibit smoking in public places, including public transport systems;

Sufficient human and financial resources devoted to Environmentally Sustainable Transport (EST) at national level, regional level and at local level; Transport energy efficiency (monetary); Transport energy efficiency (utility); Transport monetary efficiency (category wise); Fuel efficiency levels of passenger and freight fleets; Fund allocation for repair and maintenance of NHs per total length of NHs; (continued)

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(continued) Identified 27 Number of Suitable sustainable transport indicators (STIs) for each target direct-relevant sustainable SDG targets transport indicators (STIs) 12.6

3

Number of applications for greenhouse gas emission reduction credits (for transport projects); Number of Public–Private Partnerships (PPPs) implemented in transport projects; Number of transport Global Environment Facility (GEF) projects approved for the country

12.7

7

Number of cities in the country having formally developed integrated land usetransport plan; Number of existing transport Nationally Appropriate Mitigation Actions (NAMAs); Number of cities with policies in place to prohibit smoking in public places, including public transport systems; Number of transport Global Environment Facility (GEF) projects approved for the country; Number of units developed in purpose-built mixed-use projects; Number of Public–Private Partnerships (PPPs) implemented in transport projects; Sufficient human and financial resources devoted to Environmentally Sustainable Transport (EST) at national level, regional level and at local level

12.c

20

Fossil fuel consumption by transport system; Fuel price of subsidy; % biofuels allowed/ mandated; % consumption of diesel fuel in the transportation sector; % of state GDP spent on fuel; % of vehicles with alternative fuels per total number of vehicles; Annual motor fuel used by transportation per capita; Annual motor fuel used by transportation per total annual VKT; Annual motor fuel used in public transportation per annual unlinked passenger trips by public transportation; Annual motor fuel used in public transportation per capita; Annual motor fuel used in public transportation per motor fuel used in private transportation; Annual motor fuel used in public transportation per total motor fuel used by transportation; Current fuel quality standards; Fuel efficiency levels of passenger and freight fleets; Number of alternative fuel station per number of alternative fuel vehicle; Possible timeline for attainment of EURO IV (or equivalent) fuel quality standard; Total number of fuel filling stations per total length of roads; Vehicle share with renewable fuels 3 Cars/jeeps/taxis; Vehicle share with renewable fuels for 3 wheelers; Vehicle share with renewable fuels for Buses;

13.2

17

Number of cities that conduct roadway spot checks on vehicle emissions; Possible timeline for attainment of EURO IV (or equivalent) fuel quality standard; Total number of fuel filling stations per total length of roads; Number of transport Global Environment Facility (GEF) projects approved for the country; Number of units developed in purpose- built mixed-use projects; Number of Public–Private Partnerships (PPPs) implemented in transport projects; Sufficient human and financial resources devoted to Environmentally Sustainable Transport (EST) at national level, regional level and at local level; Fuel efficiency levels of passenger and freight fleets; Annual Compressed Natural Gas (CNG) consumption per total transportation energy consumption; Annual electricity used per total energy consumption by transportation; Annual energy consumption of major petroleum products by transportation per Annual VKT; Annual renewable energy potential per Annual VKT; Transport energy efficiency (monetary); Transport energy efficiency (utility); Annual air pollution emissions by transportation per total energy used by transportation; Number of kilometres of dedicated, median busways (Bus Rapid Transit); Number of kilometres of high-speed inter-city rail

16.6

4

Number of unresolved environmental cases pending in court road and rail transport; Number of cities in the country having formally developed integrated land usetransport plan; Sufficient human and financial resources devoted to Environmentally Sustainable Transport (EST) at national level, regional level and at local level; Number of Public–Private Partnerships (PPPs) implemented in transport projects

16.7

3

Existence of NMT component in road and traffic design guidelines; Sufficient human and financial resources devoted to Environmentally Sustainable Transport (EST) at national level, regional level and at local level; Number of Public–Private Partnerships (PPPs) implemented in transport projects (continued)

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(continued) Identified 27 Number of Suitable sustainable transport indicators (STIs) for each target direct-relevant sustainable SDG targets transport indicators (STIs) 17.3

18

Annual accrual allocation of funds under CRF schemes per GSDP per capita; Annual accrual release of funds under CRF schemes per GSDP per capita; Annual public transportation revenues per public transportation expenditures; Annual SRTUs revenue per GSDP per capita; Annual transportation revenues per transportation expenditures; Funds allocated under economic importance and inter-state connectivity per GSDP per capita; Funds released under economic importance and inter-state connectivity per GSDP per capita; Revenue from the rate of taxes levied on diesel; Revenue from the rate of taxes levied on petrol; % of transportation expenditure from federal funding; Annual public transportation expenditures per public transportation funds; Annual SRTUs expenditure per GSDP per capita; Annual transportation expenditures per GDP per capita; Earnings per PKT for buses in public transport system; Earnings per PKT for rail in public transport systems; Expenses per PKT for buses in public transport system; Expenses per PKT for rail in public transport systems; Proportion of HH expenditure on transportation among income groups

17.17

2

Number of Public–Private Partnerships (PPPs) implemented in transport projects, % of transportation expenditure from federal funding

17.19

105

% length of railway routes electrified per total length of railway routes; % of household income spent on transportation; % of SRTUs fleet utilization; % of transportation expenditure from federal funding; % of vehicles with alternative fuels per total number of vehicles; Air Quality Index; Annual accrual allocation of funds under CRF schemes per GSDP per capita; Annual air pollution emissions by transportation per capita; Annual air pollution emissions by transportation per total energy used by transportation; Annual GHG emissions by transportation per capita; Annual greenhouse gases by transportation per total energy used by transportation; Annual on-road air pollution emission per total annual VKT; Annual on-road greenhouse gases per total annual VKT; Annual public transportation expenditures per public transportation funds; Annual public transportation fatalities per total number of buses; Annual public transportation fund per GDP per capita; Annual public transportation revenues per public transportation expenditures; Annual renewable energy potential per Annual VKT; Annual SRTUs expenditure per GSDP per capita; Annual SRTUs revenue per GSDP per capita; Annual total cost spend for gasoline price including taxes per capita; Annual total cost spend for gasoline price including taxes per total annual VKT; Annual transportation expenditures per GDP per capita; Annual transportation revenues per transportation expenditures; Annual work trips (transit, walk, bicycle, motorcycle, taxicab, carpooled, etc.) except drive alone per total annual work trips; Average freight trip distance regionally and nationally; Average passenger trip length in capital and/or key cities; Average speed of transport (category wise); Average time spent in traffic; Average travel time to work; Bicycle mode share; Bus fare per kilometer; Congestion Index; Current fuel quality standards; Domestic tourist visits per area; Earnings per PKT for buses in public transport system; Earnings per PKT for rail in public transport systems; Employment per square kilometer along major public transport corridors; Existence of NMT component in road and traffic design guidelines; Fossil fuel consumption by transport system; Freight train kilometres performed per capita; Freight transport intensity (ratio of total freight moved to GDP); Fuel efficiency levels of passenger and freight fleets; Fuel price of subsidy; Fund allocation for repair and maintenance of NHs per total length of NHs; Funds allocated under economic importance and inter-state connectivity per GSDP per capita; Funds released under economic importance and inter-state connectivity per GSDP per capita; Land consumption for transportation infrastructure (private, public) per capita; Land urbanized per population growth; Market share (or number) of alternative fuels for road transport, including renewably-generated electricity, natural gas, and sustainably managed and cultivated biofuels that do not compete with food crops; (continued)

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(continued) Identified 27 Number of Suitable sustainable transport indicators (STIs) for each target direct-relevant sustainable SDG targets transport indicators (STIs) Market share (or number) of electric vehicles, hybrid vehicles, and fuel cell vehicles; Mode share of freight transport (truck, rail, barge, minivan, and non-motorized) at the metropolitan and national levels; Mode share of high-quality inter-city bus services; Mode share of high-speed inter-city rail services; Mode share of inter-city transport (private motorized vehicles, bus, rail, and boat) at the metropolitan and national levels; Mode share of passenger transport (walking, bicycles, car driver, car passenger, motorcycle driver, motorcycle passenger, motorized three-wheelers, non-motorized three-wheelers, buses, minibuses, and urban rail) at the metropolitan and national levels; Number of adopted fatality reduction targets; Number of alternative fuel station per number of alternative fuel vehicle; Number of applications for greenhouse gas emission reduction credits (for transport projects); Number of cities in the country having formally developed integrated land use- transport plan; Number of cities monitoring noise levels; Number of cities participating in a Car- Free Day programme; Number of cities surveyed or audited for a “walkability” score; Number of cities that conduct roadway spot checks on vehicle emissions; Number of cities utilizing electronic fare cards on their public transport system; Number of cities with a control centre to manage traffic incidents and manage public transport fleets; Number of cities with active parking management programmes; Number of cities with bus systems using pre-board fare verification and stations designed for at-level fast boarding; Number of cities with dedicated cycleways; Number of cities with Non-Motorised Transport (NMT) specifically highlighted in the city’s integrated transport master plans; Number of cities with policies in place to prohibit smoking in public places, including public transport systems; Number of cities with shared bicycle programmes and number of shared bikes per programme; Number of existing transport Nationally Appropriate Mitigation Actions (NAMAs); Number of fatalities caused by transportation sector per capita; Number of inland dry ports (inter-modal road or rail connectivity to port); Number of policies and/or programs that promote telecommuting; Number of policies developed encouraging Information and Communications Technologies (ICT) as a substitute for travel (e.g. WFH); Number of public transport projects achieving transit-oriented development (TOD) around station; Number of public transport stations and vehicles using real-time information display; Number of public transport stations with tactile paving tiles for the sight impaired; Number of public transport vehicles and stations permitting full universal access for users in wheelchairs and parents with prams; Number of public transport vehicles per city with Automatic Vehicle Location tracking technology; Number of Public–Private Partnerships (PPPs) implemented in transport projects; Number of transport Global Environment Facility (GEF) projects approved for the country; Number of units developed in purpose- built mixed-use projects; Number of unresolved environmental cases pending in court road and rail transport; Population Density (persons per sq. km) per capita GDP; Population per square kilometer along major public transport corridors; Possible timeline for attainment of EURO IV (or equivalent) fuel quality standard; Proportion of HH expenditure on transportation among income groups; Proportion of mobile population using non-motorised transport (walking or cycling); Proportion of service disability compliant for % of bus fleet; Proportion of service disability compliant for % of railway stations; Public parking space per 1000 vehicle (category wise); Revenue from the rate of taxes levied on diesel; Revenue from the rate of taxes levied on petrol; SRTUs passenger kilometres performed; SRTUs staff productivity measured in km per staff per day; SRTUs staff/bus ratio; SRTUs vehicle productivity measured in km per bus per day; Sufficient human and financial resources devoted to Environmentally Sustainable Transport (EST) at national level, regional level and at local level; Total number of vehicles per capita; Transport energy efficiency (monetary); Transport energy efficiency (utility); Transport monetary efficiency (category wise); Vehicle kilometers travelled per person at the metropolitan and national levels

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References Broughton, B., & Hampshire, J. (1997). Bridging the gap: A guide to monitoring and evaluating development projects. ACFOA. Castillo, H., & Pitfield, D. E. (2010). ELASTIC–a methodological framework for identifying and selecting sustainable transport indicators. Transportation Research Part D: Transport and Environment, 15, 179–188. https://doi.org/10.1016/j.trd.2009.09.002 Chakhtoura, C., & Pojani, D. (2016). Indicator-based evaluation of sustainable transport plans: A framework for Paris and other large cities. Transport Policy, 50, 15–28. https://doi.org/10.1016/ j.tranpol.2016.05.014 Gudmundsson, H., Hall, R. P., Marsden, G., & Zietsman, J. (2016). Indicators. In Sustainable transportation (pp. XIII, 304). Springer Gudmundsson, H., & Regmi, M. B. (2017). Transport and communications bulletin for Asia and the Pacific. Developing the sustainable urban transport index. Haghshenas, H., & Vaziri, M. (2012). Urban sustainable transportation indicators for global comparison. Ecological Indicators, 15, 115–121. https://doi.org/10.1016/j.ecolind.2011.09.010 High-Level Advisory Group on Sustainable Transport. (2016). Mobilizing sustainable transport for development. Illahi, U., & Mir, M. S. (2022). An indicator-based integrated methodology for evaluating sustainability in transportation systems using multivariate statistics and fuzzy logic. Journal of Science and Technology Policy Management, 13, 43–72. https://doi.org/10.1108/JSTPM-12-2019-0116 ITDP. (2015). The Role of Transport in the Sustainable Development Goals. Retrieved September 20, 2022, from https://www.itdp.org/2015/05/26/the-role-of-transport-in-the-sustainable-dev elopment-goals/ Mahdinia, I., Habibian, M., Hatamzadeh, Y., & Gudmundsson, H. (2018). An indicator-based algorithm to measure transportation sustainability: A case study of the U.S. states. Ecological Indicators, 89, 738–754. https://doi.org/10.1016/j.ecolind.2017.12.019 Nathan, H. S. K., & Reddy, B. S. (2011). Urban Transport Sustainability Indicators-Application of Multi-view Black-box (MVBB) framework Urban Transport Sustainability IndicatorsApplication of Multi-view Black-box (MVBB) framework Acknowledgements. Sdoukopoulos, A., & Pitsiava-Latinopoulou, M. (2017). Assessing urban mobility sustainability through a system of indicators: The case of Greek cities. In WIT transactions on ecology and the environment (pp. 617–631). WIT Press. Shiau, T. A., Huang, M. W., & Lin, W. Y. (2015). Developing an indicator system for measuring Taiwan’s transport sustainability. International Journal of Sustainable Transportation, 9, 81–92. https://doi.org/10.1080/15568318.2012.738775 Udas-Mankikar, S., & Driver, B. (2021). Occasional paper blue-green infrastructure: An opportunity for indian cities. UN-Habitat, UNEP, & SLoCaT. (2015). Analysis of the transport relevance of each of the 17 SDGs. UNCRD & CAI-Asia. (2011). Data and indicators for environmentally sustainable transport under the Bankok 2020 declaration. In The 6th regional environmentally sustainable transport (EST) Forum in Asia (p. 28) United Nations. (2015). Sustainable development. Retrieved September 20, 2022, from https://sdgs. un.org/goals United Nations. (2021). Second UN global sustainable transport conference. Retrieved September 20, 2022, from https://sdgs.un.org/partnerships/action-networks/2nd-transport-conference

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Verma, A., Harsha, V., & Subramanian, G. H. (2021). Evolution of urban transportation policies in India: A review and analysis. Transportation in Developing Economies, 7, 1–15. https://doi. org/10.1007/s40890-021-00136-1 World Commission on Environment and Development. (1987). Our common future. Zheng, J., Garrick, N. W., Atkinson-Palombo, C., et al. (2013). Guidelines on developing performance metrics for evaluating transportation sustainability. Research in Transportation Business & Management, 7, 4–13.https://doi.org/10.1016/j.rtbm.2013.02.001 Zope, R., Vasudevan, N., Arkatkar, S. S., & Joshi, G. (2019). Benchmarking: A tool for evaluation and monitoring sustainability of urban transport system in metropolitan cities of India. Sustainable Cities and Society, 45, 48–58. https://doi.org/10.1016/j.scs.2018.11.011

Impact of Parking Pricing and Regulations on User Behavior Minal Shetty, Shalini Sinha, and Jayita Chakraborty

Abstract Parking has the potential to increase or reduce traffic congestion. One parking policy technique that can be utilized to maintain controlled parking, cut down on car use, and achieve desired modal split is parking pricing and regulations. Regulating parking is important to maintain the balance between the supply and demand management. Therefore, it is of utmost importance for the city authorities to understand people’s attitudes toward parking, regulations, and their awareness before formulating any policy for better long-term results. Parking duration varies based on the trip purpose, mode choice, and parking availability. In this study, using Cox regression, an attempt has been made to estimate the duration of parking by modeling different variables that would impact the decision of parking on-street or off-street. The study area was delineated based on the high parking demand in the city, for which a random sampling of 300 drivers was carried out for seven days at different hours of the day, both on-street and off-street. Results indicated that most on-street parking happens for a shorter duration while off-street parking is for longer duration. The socio-economic characteristics, trip purpose, and frequency significantly impact the parking duration. The study also highlighted the changing parking pattern due to existing enforcement in the city and its impact on their day-to-day routine before and after the pandemic scenario. In conclusion, strict parking enforcement affects the behavior of parking of short-term and long-term parkers and is a strong predictor of user attitude and behavior. Keywords User behavior · Parking · Regulations · Decisions

M. Shetty (B) · S. Sinha · J. Chakraborty Center of Excellence in Urban Transport, CRDF, CEPT University, Ahmedabad, India e-mail: [email protected] S. Sinha e-mail: [email protected] J. Chakraborty e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_6

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1 Introduction Cities have started to realize the importance of parking management to cope with the increasing vehicular demand and that it must be appropriately incorporated to support sustainable mobility. It has become of utmost importance to change the perspective of vehicle owners regarding how they look at parking on public roads. Therefore, efforts should be made by mobility experts to keep private vehicles off-street as the sole purpose of the streets should be to prioritize people rather than parking. Parking policy has been an essential instrument for urban mobility management. One of the significant policy dilemmas that urban planners face is short-term versus long-term parking. The dilemma can also be originated based on the parking location, primarily due to the influence of duration on a person’s parking decision of on-street versus off-street parking. Therefore, before implementing any policy, it is crucial to understand the parking behavior of the commuters, the kind of regulations to be enforced, and the intensity of enforcement. Charging for parking is often an effective strategy for bettering parking management and reducing traffic congestion in urban areas (Barter, 2011). A good parking policy may have many positive implications for sustainable transport, and a poor parking policy can have the opposite effect (Simi´cevi´c, 2013). One example that would justify this statement is the example of Bangkok, where the policy to increase the number of streets to accommodate more vehicles instead worsened traffic congestion in these cities, leading to speed reduction. Therefore, understanding the user and their attitude toward parking plays a vital role in the decision-making process before implementing any policy. As a result, an attempt has been made in this study to comprehend user behavior and assess the driver’s attitude when parking in Ahmedabad.

2 Study Methodology Figure 1 represents the approach of the study. This research paper has focused on. 1. Reviewing and identifying different parking regulations, interventions, and their role in parking management. 2. Modeling the factors influencing the duration of parking for the case city of Ahmedabad. 3. Identifying and assessing the factors influencing parking durations that may be considered during policy formulation.

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Fig. 1 Study methodology

3 Literature Review Psychologists and academics have proposed several theories based on their study of human behavior. Studying behavior is quite challenging and practically very important. It is critical and equally difficult to understand human behavior as the way they would respond to different problems would comparatively be very different. Behavior may be studied at many levels and in various methods. The concept further explains that a person’s conduct may be described as how they interact with other people, society, objects, or situations. Studies show that implementing parking strategies influences choices made by an individual. For example, Habitual parking—A regular user will tend to park at a similar place every time.

3.1 Theory of Planned Behavior The theory of planned behavior is one of the most used models in the literature to investigate pro-environmental behavior. The Theory of Planned Behavior (Ajzen, 1991) assumes that the best prediction of behavior is given by asking people if they intend to behave differently. According to Ajzen, following are some of the determinants that explain behavioral intention. 1. The attitude (behavioral tendency or personal attitude toward a particular behavior). For example, Awareness about the no parking zone and yet continue to park there. 2. The subjective norm (opinions of others about the behavior).

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Fig. 2 Explaining the concept of planned behaviour. Source The theory of planned behavior, Ajzen (1991)

For example, If enforcement is made stricter, preference of people related to parking. Since the objective of this study is to assess the existing scenario and the people’s mindset toward parking and factors that influence the parking decision, the above determinants were used and the questionnaires for the survey were prepared accordingly. The actual behavior that generates feedback regarding the behavior’s expectations is shown as feedback lines in Fig. 2. According to the theory of planned behavior, attitude together with behavioral intention, can be used directly to predict behavioral achievement.

3.2 Impact of Parking Regulations on User Behavior In many case studies, the study was first implemented on a smaller scale like the university level by delineating zones and implementing regulations and charges on these zones based on parking frequency, duration, peak hours, and non-peak hours (Shoup, 2005). It was observed that when enforcement got stricter, people preferred the following: a. b. c. d.

Park in the neighborhood street and walk. Shift to another mode. Shift to off-street parking or any adjacent location. Pay and park.

Various examples across different cities have seen the difference in city mobility and parking based on policy implementation. Table 1 summarizes the policies adopted by various cities and the impacts observed.

3.3 Analytical Method The statistical methods for analysing a “time to event result variable,” A time to event variable shows how long it will be until a participant experiences an interesting

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Table 1 Policies adopted by different cities International cities City

Measures adopted

Impact observed

Amsterdam

The goal was to reduce congestion and emission and promote an alternative mode of transport. To support the mobility plan of the city and to prioritize people over vehicles following measures were taken On-street: (sudden increase in price throughout the city) 1. Drivers to pay on an hourly basis for parking on-street 2. Permits for people staying in the nearby residential area to continue to park on street. This was applicable in certain zones only 3. Discounted parking price in shopping zones 4. Paid parking policy already existed and continued to be the same Off-street: (created clusters) 1. Cluster 1 was located around the city center in a smaller capacity 2. Clusters 2 and 3 around business parks with larger capacities Private operators took care of these, and rates were higher than on-street parking

1. Traffic was reduced by 2.5% throughout the day and 3% during peak hours 2. Parking was reduced by 9% 3. The parking supply was reduced but did not make a significant difference 4. Parking pricing affected the price of demand 5. Congestion reduced 6. Source of revenue generation

Copenhagen The goal was to reduce emissions, promote public space reclamation and promote an alternative mode of transport. The city was divided into three zones based on colors indicating different prices. There is free parking for cyclists, vehicles with a disabled badge, electric cars, and free parking during weekends On-street: 1. Cycling tracks, bicycle parking, active streets, and daylighting measures are installed Off-street: 1. Converting the on-street space to off-street to support alternative transport modes 2. Avoid the need for minimum parking requirements 3. Combining with shared parking after evening for residential users with a permit

1. Reduction in on-street parking supply 2. Shared spaces reduce the number of vehicles being used 3. Safer and active streets 4. Switch the alternative mode of transport 5. Pushing visitors to use public transport

(continued)

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Table 1 (continued) International cities City

Measures adopted

Impact observed

London

The goal was to reduce congestion, promote public space reclamation and promote an alternative mode of transport CO2 -based parking fees are based on the vehicle engine type. Each authority will decide the intensity of enforcement On-street: 1. Peak, off-peak parking to manage to park if on-street occupancy increases to more than 80% 2. The maximum parking duration is 4 h 3. No unregulated zone Off-street: 1. Residential parking permit to park off-street only (Zhan et al. 2004)

1. Due to the street’s time limit, people prefer parking off-street irrespective of parking prices 2. Policy intervention is different for commercial vehicle when compared to residential permits 3. Controlled parking zone to discourage long-term parking 4. Permit only valid for the designated district

Hong Kong

Minimum parking requirements and 1. Encourage shared public parking in traditional demand-driving approach to conjunction with market-oriented parking is now changing to technique of parking management market-driven parking supply 2. Pushing to use public transport On-street: 3. Reduced spillover since additional parking goes to priced parking 1. Time limit 2 h if demand is high else, uniform pricing in most streets Off-street: 1. Public Housing Authority holds a huge parking share. The government has developed multi-story parking in certain areas 2. Vacant parking lots leases to private operators

Tokyo

1. Parking system based on market 1. Illegal parking was reduced demand with commercial 2. Off-street appears irrelevant due to market-priced parking in most parts low parking requirements 2. The policy excludes on-street parking 3. Restrictive use of car 3. Proof of secured parking at night before registering a car (parking proof) On-street: 1. Time limit 60 min uniform pricing in most streets Off-street: 1. A small multi-story facility in each ward is provided (continued)

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Table 1 (continued) International cities City

Measures adopted

Impact observed

Pune

1. Car parking fees are four times those of a two-wheeler 2. Prices vary depending on the time of day 3. Rates in Parking Zone A are Rs 71, while rates in Parking Zone D are Rs 30 4. Off-street parking is less expensive than on-street parking

1. Congested lanes getting decongested 2. Illegal parking was observed to be reducing 3. Supporting as a source of revenue for the city Note This was at implementation stage and long-term result is yet to be observed. The above impacts are based on various studies and discussions

Bangalore

1. Only allow permitted vehicle parking certificate holders to park in specified lots for short periods (hourly duration) 2. Parking in the center of the city will be costlier than parking in the periphery as the land value is different 3. An increase in tariff for the number of hours parked will be a cumulative scale; the fee will increase with every rise in hours parked 4. The demarcation between parking and pedestrian facilities using temporary and permanent structures 5. Easy access to transport facilities in all parts of the city, including metro stations, transport hubs, bus bays, and truck terminals 6. Creating awareness in the entire city

Note: Bangalore’s Parking Policy 2.0 came effectively from 2021 as per studies and therefore it is soon to talk about the impacts due to its implementation. The city authorities are in the process of understanding and analyzing the pros and cons of the policy (Directorate of Urban Transport, 2008) The reason to take this case study is to understand the issues and the measure adopted to resolve the problem

Indian cities

event. Time to event variables have distinctive characteristics (Chan, 2016). First, the distributions of times to events are frequently skewed and are always positive. There are different methods to assess the survival and hazard function. Lack of complete information at leads to incompleteness to follow up on the situation. In some cases, the study ends, or a person withdraws from the study before the event, hence the true survival time (also known as failure time) is unknown. The individuals’ survival time is longer than their most recent follow-up period, and this period is referred as censored times. Based on censoring type and follow-up information available the approach method is selected. Several popular methods used for this kind of analysis are: (i) Kaplan–Meier approach (ii) Cox proportional hazard model

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3.4 Summary From the literature review, it can be observed that it is crucial to regulate parking to meet the increasing demand and at the same time to use it as a push measure to support sustainable transport. One of the possible interventions to solve the parking demand issue, is by making the enforcement stricter. For a better parking management, the planning authorities along local bodies will have to rework the structuring of the framework at the city level. Several studies suggest that driver’s decision changes based on the location, duration, awareness, and attitude toward the parking. Socio-economic characteristics also impact the nature of parking. Parking behavior is very complex to understand; therefore, it is crucial to capture information, and thus using the revealed preference method would be ideal. For this purpose, the theory of planned behavior can be taken forward as a part of this study to understand the attitude of the drivers at the time of parking the car. From the various case studies identified, parking pricing strategies, and regulating the zone have significantly impacted the change in the driver’s behavior. Parking characteristic is different; therefore, pricing strategies should be formulated accordingly. By providing free parking for cycling and incentivizing, parking can be used as a push measure to promote sustainable transport (Marsden, 2006). Successful policy measures and regulations can control irregular parking on the streets. For statistical analysis of the study, the different variables may be tested with regard to the duration of parking; given that the participant has lived up to a specific point in time. The duration model can be used to analyse the hazard rate, which is the risk of failure (i.e., the risk or probability of experiencing the event of interest). Cox proportional hazards regression analysis, which links several risk factors to survival time, is one of the most often used regression techniques for survival outcomes. For study and analysis, the duration variable is the dependent variable, and the rest is the independent variable.

4 Data Collection Ahmedabad is the seventh largest city in India with a polycentric urban pattern on the eastern bank of the river Sabarmati. According to the census, the city’s population is growing at a decadal pace, rising at 22.31%. Currently, the city has vehicular ownership of nearly 33 lakhs as per study done by (the Center of Excellence in Urban Transport, CEPT University) for the year 2021 and has been growing annually by 15%. In 2011, Ahmedabad had approximately 46 cars per 1000 population which has now increased to 91 cars per 1000 population. Figure 3 indicates that 90% of the vehicles registered are 2-wheelers and 4-wheelers in Ahmedabad. This exponential growth in the number of vehicles (Fig. 4) on the road would adversely impact the environment leading to haphazard parking on the streets of the city.

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4000000 3500000 3000000 2500000 2000000 1500000 1000000 500000 0

250000 200000 150000 100000 50000 0

Total number of vehicles

300000

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Total number of 2-wheelers

Fig. 3 Composition of vehicles—2011. Source Integrated Mobility Plan for Greater Ahmedabad region, Horizon year 2031

Yearly vehicle registration for Ahmedabad 2W

4W

Grand Total

Fig. 4 Increase in vehicle ownership. Source The Center of Excellence in Urban Transport and CEPT University

The Ahmedabad parking policy was formulated in the year 2021 and it identifies the parking issues in the city. The key issues highlighted were the haphazard on-street parking on major roads and under-utilized off-street parking. The policy mentions proposal for five new multilevel off-street parking. It also details the step-by-step process of preparing a parking plan for the city of Ahmedabad. The policy also outlines the pricing strategies to respond to parking demand in the city. A state-level committee approved the parking policy in October (Trivedi, 2021).

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In 2018, a drive against illegal parking took place within the city by Ahmedabad Municipal Corporation along with the support of Ahmedabad Traffic Department. Over 50,000 m2 area was freed from encroachment (refer Table 2 for areas covered during the 2018 parking drive). It penalized illegal parking of vehicles on roads, issuing notices to the commercial owners for violating parking regulations, and removing encroachment on the public road. Authorized notifications from the traffic department were issued to those who park their vehicles on public highways, notably in “No-Parking Zones”. Parking was not permitted at intersections. If a car was seen parked within 50 m of any junction in the city, the relevant Police Officer on duty was subject to take additional disciplinary action. As per discussions with the stakeholders and the authorities, there is already a parking supply of 32,031 vehicle spaces in the city at different locations and 3,878 proposed (AMC, 2021). During the time of the study, it was observed that off-street parking was underutilized. So, the major issue is to optimize the usage of designated parking spaces while minimizing on-street parking. Currently, in Ahmedabad there are eight towing stations in the city and 67 trucks for towing the vehicle under a private contract, and 12 trucks under the government. After the implementation of drive in the year 2018 it was reported that in 2019 maximum number of vehicles were towed for violating parking regulations in the city (refer Fig. 5). The drop observed in 2020 is due to the covid pandemic. Even after the strict enforcement, people’s behavior does not seem to have changed in the long run. The number of challans issued has been decreased post-2019. This laxity in enforcement has led to increase in on-street parking. The decrease in echallan in 2020 is due to pandemic scenario. The increase in number of fines collected in 2021 exceed the fine collected at the time of the drive may be due to change penalty charges as per Motor Vehicle Act (refer Fig. 6). Even though there is a drop due to the Covid situation, the penalties collected are maximum in 2021. This means that there has been an increase in the charges for vehicles (refer Table 3). The drive took place in the year 2018 and the impact of the drive can be observed in 2019, with the number of e-challans generated being the highest and off which the majority is 2-wheelers (refer Fig. 7). In 2020 the pandemic took place therefore the decrease, and it can be Table 2 Area freed of encroachment against illegal parking in 2018 parking drive Zone

Areas

Road length (km)

East

Viratnagar, Nikol, Gomtipur, Odhav, Vastral, Amraiwadi

27

North

Sardarnagar, Krishnagar, Saijpur, Naroda, Kubernagar

39

Central

Khadia, Dariapur, Shahpur, Jamalpur, Shahibaug

58.5

South

Maninagar, Khokhra, Behrampura, Danilimbda

33

West

Navrangpura, Paldi, Naranpura, Vadaj, Sabarmati

105

North West

Sarkhej, Vejalpur, Gota, Bodakdev, Jodhpur, Thaltej

19.5

Total network freed from encroachment

282

Source (TNN 2018) https://timesofindia.indiatimes.com/city/ahmedabad/282km-of-space-freedof-encroachment-on-city-roads/articlesshow/65275002.cms

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Number of vehicles

250000 200000 150000 100000 50000 0 2018

2019

2020

2021

2022

Year

Amount in ₹

Fig. 5 Vehicles towed between 2018 and 2022. Source Traffic Police Department, Ahmedabad

800000000 700000000 600000000 500000000 400000000 300000000 200000000 100000000 0 2018

2019

2020

2021

2022

Year

Fig. 6 Total fines collected. Source Traffic Police Department, Ahmedabad

observed that there has been an increase in the e-challans post-pandemic, which also indicates the lax in the enforcement.

4.1 Observation The drive was not envisioned for an extended period; therefore, encroachments have come up again, and people have started to park on the footpaths and roads. The drive brought immediate results, and if continued, it is likely to obtain long time results as

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Table 3 Towing charges for 2018 and 2022 Vehicle type

Clamping (|)

Towing charge (|)

No parking fine (|)

2018

2018

2018 and 2022

2022

2022

2-wheeler

250

200

250

300

500

4-wheeler

300

300

500

600

500

Number

Source Traffic Police Department, Ahmedabad

1400000 1200000 1000000 800000 600000 400000 200000 0 2018

2019

2020

2021

Year 2-wheeler, E-Challan

2022 (March)

4-wheeler, E-Challan

Fig. 7 E-challan generated between 2018 and 2022. Source Traffic Police Department, Ahmedabad

there was a change in the behavior of people observed with the strict enforcement. Currently, the drive made people cautious about how they park on the roads. Thus, a need to change the approach and understand the user’s behavior and find reasons why they continue to park their vehicles on the streets designated as no-parking zone. However, on-street parking has now increased on the roads and added to the congestion.

5 Survey Area Prahlad Nagar is located southwest of Ahmedabad and is amongst the areas with high parking demand as per Ahmedabad’s parking policy. The stretch delineated has commercial street front. The purpose of the survey was to study the behavior of people parking on-street and off-street in Prahlad Nagar, Ahmedabad. It was an attempt to study people’s behavior concerning the enforcement in the area. This will explain the existing parking scenario in Prahlad Nagar and people’s behavioral change after the 2018 drive.

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Fig. 8 Survey location. Source Author, Site visit, Mar’22

This study aims to capture the public’s perception of parking regulations and pricing in the city of Ahmedabad. A stretch of Prahlad Nagar with high parking demand was selected for the survey. The survey was conducted in March 2022 for seven days at different times, i.e., morning (8:00 a.m. to 12:00 p.m.), afternoon (12:00 p.m. to 4:00 p.m.), and evening (4:00 p.m. to 8:00 p.m.) The survey was conducted through the circulation of hard copies and face-to-face discussions with the respondent. The survey was done across three stretches, i.e., main road, side street, and off the street as can be seen in Fig. 8.

5.1 Basic Observations from the Site 1. Approximately 1000 vehicles are parked in the delineated area of which nearly 766 are bikes and 234 are cars. 2. The 4-wheelers are usually clamped. 3. The location already has a free AMC parking lot which is underutilized. 4. There is new multi-level parking being constructed by AMC. 5. Many vehicles are being parked in the no-parking zone. 6. Main street parking is less during the morning hours when compared to the afternoon or evening. 7. On-side street parking is maximum during the evenings mainly due to the commercial activities on the road and side street. Figure 9 indicates the reduction in towing of 2-wheelers in the year 2018 for Prahlad Nagar. This itself indicates that the enforcement was eased out in the location

M. Shetty et al.

Number of vehicles

124 160 140 120 100 80 60 40 20 0

Year

Fig. 9 Reduction in towing of 2-wheelers. Source Ahmedabad traffic police, N division

Off-Street 39%

Main Road 41%

Side Street 20% Fig. 10 Distribution of samples

thereby leading to people to park randomly on the streets. For the purpose of the survey, the samples were distributed in 3 stretches (refer Fig. 10).

5.2 Sample Size Determination Based on the theory of planned behavior, the questionnaire for the survey was prepared for the desired outcomes. The survey aimed to assess the attitudes and awareness of people about parking regulations. The questionnaire was divided into four sections. Some of the responses were recorded in Yes or No format on a Likert Scale score of 1 = Strongly Disagree and 5 = Strongly Agree and perspective was

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included to understand the existing situation on parking. The survey conducted was amongst the 2-wheeler and 4-wheeler users of the study area parking either illegally on the main road or side street and the off-street parking of the study area. The responses collected will be analyzed in the following ways. 1. Correlation Matrix—To check the association of variables selected for research with each other. 2. Hazard-based duration—To analyze the threshold of parking in the study area during different day hours. For deciding the sample size in the delineated area, the parking volume was estimated 1000 vehicles per day for the entire stretch. Based on the below given equation (i) the sampling size was calculated using the following equation (Watson, 2001). )

( n=

P(1−P) A2 + P(1−P) 2 N Z

R

(1)

where: n N P A Z R

Sample size required Number of people in the population Estimated variance in population Precision desired Confidence level Estimated response rate.

Based on this a sample size of 290 samples was arrived at. For analysis the cox regression was used to establish the relationship between parking survival and several explanatory prognostic variables (Walters, 2012). It estimates the hazard or risk of parking for an individual, given their different variables. This model is done using the SPSS program. The final model from the analysis will produce an equation for the hazard as a function of various explanatory factors. The coefficients for each explanatory variable must be examined while interpreting the Cox model. A positive regression coefficient for an explanatory variable indicates a higher hazard and hence a worse prognosis. On the other hand, a negative regression coefficient indicates that patients with higher values have a better prognosis. For this study, based on the parking details collected the event was a known event and therefore it was necessary to analyse the variables dependent on the duration to find the threshold.

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6 Sample Description A survey of 310 motorists was carried out, amongst which only 291 samples were in usable form from the main survey as the rest of the survey were incomplete. Amongst which 175 samples were for on-street parking and 116 were for off-street parking. The responses received from the survey are shown in Table 4.

7 Data Analysis As discussed previously the method used for analysis was Cox regression and for testing the relationship between the variables correlation matrix was done with respect to which further analysis was done.

7.1 Testing the Correlation Between the Variables for On-Street Parking Interpreting the below given Fig. 11 the correlation is insignificant, and the two variables are not linearly related if the value is approaching 0 and p > 0.05. The intensity of the color depicts the relationship of the variables. Closer to +1 or −1, the two variables are closely associated. For example, the variable pay and park at AMC plots are closely associated with users’ awareness about the no parking zone, the type of user (if they visit daily, occasionally, or visitor), and trip purpose (market, recreation). As the value = 0, the variables have no relation. As the value gets close to −0.01 the variables are related inversely. For example, searching for parking is inversely related to parking type (main road, side street). The below graph (Fig. 12) is semiparametric based on the proportional hazard approach because EXP(βX) is used as the functional form of covariate influence. From the survey of 175 respondents, it can be observed that as the duration increases, the risk of parking on the street reduces. It can also be observed that maximum parking is happening between 10 and 30 min, with an average duration of 20.8 min and a standard deviation of 14.75 min. As the duration of time increases, the parking on the street reduces. In other words, the tendency to park on the street is pre-dominantly happening for a shorter duration. After understanding the correlation between the variables, it is important to determine the significant factors that affect parking duration. A Cox proportional hazard model was run using these variables and is discussed further in the study. From this, it would give an understanding of the characteristics of these people, having a significant impact on the duration of parking happening on the streets. A positive hazard refers to a shorter parking duration, and a negative hazard refers to a longer parking duration.

Impact of Parking Pricing and Regulations on User Behavior Table 4 Socio-economic and parking characteristics of sample collected

Variables

On-street (%) n = 175

127 Off-street (%) n = 116

Gender Female

45%

35%

Male

55%

65%

Age group 18–24

22%

19%

25–59

77%

81%

60 and above

2%

–

55%

84%

Occupation Salaried Self employed

15%

4%

Others

30%

12%

Vehicle type 2-wheeler

41%

42%

4-wheeler

59%

58%

Income INR (|) 10,000–50,000

53%

70%

50,000–100,000

26%

18%

>100,000

21%

12%

Trip purpose Business

5%

11%

Market

42%

7%

Recreation

50%

27%

Work

1%

56%

Others

1%

–

Parking time of the day Morning

34%

75%

Afternoon

21%

7%

Evening

45%

18%

User type Daily

23%

55%

Weekly

15%

5%

Visitor

61%

40%

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Variables

Parking type Gender Age group Occupation Vehicle type Highest education Income Trip purpose Parking time of day User Type Awareness of no parking zone Awareness of nearby parking location Fined before Search for parking Pay & Park at AMC plots Park Off-Street Use another mode Park somewhere else Change time of travel

Parking type

1.00 -0.14 0.07 0.08 0.05 0.17 0.13 -0.05 0.02 -0.11 0.08 0.00 0.05 -0.47 -0.07 -0.11 0.15 0.13 -0.47

Gender

1.00 -0.11 0.16 -0.19 -0.13 -0.15 -0.01 0.00 -0.01 -0.06 -0.15 -0.08 0.03 -0.09 -0.02 0.03 -0.04 0.05

Age group

1.00 -0.58 0.39 0.40 0.24 -0.13 0.02 -0.05 0.10 -0.08 -0.06 0.11 0.10 0.04 -0.11 0.14 -0.05

Occupati on

1.00 -0.15 -0.24 0.07 0.11 0.06 -0.08 -0.07 -0.06 -0.03 -0.14 -0.09 0.00 0.03 -0.08 -0.10

Vehicle type

1.00 0.45 0.56 -0.14 0.00 -0.15 -0.01 -0.16 -0.18 0.20 0.38 -0.04 -0.30 0.28 -0.02

Highest educatio n

1.00 0.26 -0.10 -0.05 0.14 0.34 -0.01 -0.05 0.11 0.39 -0.08 -0.21 0.14 -0.21

Income

1.00 -0.04 -0.01 -0.06 0.16 -0.04 -0.39 0.17 0.17 -0.06 -0.22 0.25 -0.18

Trip purpose

1.00 0.01 0.33 0.15 0.46 -0.01 -0.20 -0.06 -0.17 -0.16 -0.06 -0.14

Parking time of day

1.00 -0.08 -0.03 -0.06 0.09 -0.02 -0.17 -0.04 -0.04 -0.16 0.00

User Type

Awarene ss of no parking zone

1.00 0.51 0.46 0.11 0.19 0.14 -0.28 -0.30 -0.03 -0.08

1.00 0.55 -0.06 0.04 0.19 -0.15 -0.41 0.22 -0.25

Awarene ss of nearby parking location

1.00 -0.18 -0.18 -0.03 -0.34 -0.33 0.07 -0.29

Fined before

1.00 -0.16 -0.13 0.00 0.04 -0.23 0.04

Search for parking

1.00 0.13 0.14 -0.01 0.05 0.50

Pay & Park at AMC plots

1.00 -0.01 -0.42 0.36 0.06

Park OffStreet

1.00 0.16 0.03 0.16

Use another mode

1.00 -0.34 0.23

Park somewh ere else

1.00 0.06

Change time of travel

1

Fig. 11 Co-relation between variables for on-street parking

Fig. 12 Hazard function at a mean of covariates for on-street parking

The base category for each variable is mentioned in the above-given table. For example, For the occupation group, the reference category is others which means all the other categories are compared to the groups containing people under selfemployed, business, and other occupations. Similar comparisons are made in the other cases. From the Table 5 it can be interpreted that the frequency of people visiting the place is observed to be daily or weekly for various purposes. Along with daily users, there is a probability that people who visit the place weekly and are aware of fines may park for a shorter duration. If the trip purpose is market, then people will park on the main road for a shorter duration when compared to people coming for work. People with 2-wheeler who come to the area frequently are more likely to be parking for a shorter duration when compared to visitors or weekly users who use 4-wheelers. Duration of on-street parking is high for motorists with higher income between INR 50,000 and 100,000. Since the regression coefficient is positive for the main road,

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people prefer parking there instead of on the side street for shorter duration. There is a strong disagreement in the attitude of people with regards to the change in travel time due to the shorter duration of parking. Long-term parkers do not prefer to pay and park or change their travel time. People who have been fined park for shorter duration when compared to people who have not experienced fines. The possibility of people parking somewhere else if enforcement is made stricter is much higher in people’s opinion rather than changing time. The hazard rate for people parking off street is high so if the enforcement is made stricter then there is a strong possibility that people will opt for a shorter duration or will change parking location but will not prefer to change their travel time.

7.2 Testing the Correlation Between the Variables for Off-Street Parking From the Fig. 13, it can be observed that the variable parking full is closely associated to the variable search for parking. As the value = 0 there is no relation between the variables. As the value gets close to −0.01 the variables are inversely related. For example, user type (daily, occasional, visitor) is inversely related to trip purpose (market, recreation, work, business, others). From the survey of 116 respondents of off-street parking, (refer Fig. 14) it can be observed that as the duration or parking increases, the risk of off-street parking decreases. It can also be observed that maximum parking happens for more than 60 min with an average duration of parking being 346.2 min and a standard deviation of 292.97 min. As the duration of time increases the parking on the street reduces. In other words, the tendency of parking off-street is mainly observed to be happening for a longer duration. After understanding the correlation between the variables, it is essential to determine the significant factors that affect the time of parking. A Cox proportional hazard model was run using these variables and is discussed further in the study. From this it would give an understanding of the characteristics of these people, having a significant impact on the duration of parking for off-street parking (refer Table 6). From the above table, it can be analyzed that the people parking off the street have a negative co-efficient for the daily user, so it can be stated that they park off street for a longer duration. People parking off-street are aware of the towing of the vehicles, and since they park for a longer duration, their preferred choice of parking is offstreet parking. For trip purposes, it can be interpreted that off-street parking for the market, business, and recreational purpose is happening for a shorter duration. The coefficient for the income ranging between 10,000–50,000 and 50,000–100,000 is negative, which can closely be associated to park off-street. People with trip purposes other than work park for a shorter duration on the street. The off-street parking is free,

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Table 5 Outputs for socio-economic and user characteristics for on-street parking Variable

Regression coefficient (b)

Standard error SE(b)

P-value

Hazard ratio EXP(βX)

95% CI for hazard ratio Lower

Upper

Occupation Salaried = 1 Other = 0

0.626

0.276

0.023

1.870

1.089

3.210

Vehicle type 2-wheeler = 1 Other = 0

0.502

0.306

0.101

1.652

0.907

3.010

Income 50,000–100,000 =1 Other = 0

0.938 1.045

0.333 0.293

0.005 0.000

2.556 2.843

1.332 1.600

4.905 5.049

User type Daily = 1 Other = 0

0.780 0.654

0.341 0.326

0.022 0.045

2.182 1.924

1.119 1.015

4.256 3.647

User type Weekly = 1 Other = 0

0.675

0.363

0.063

1.964

0.965

3.998

Fined Yes = 1 No = 0

0.486

0.266

0.067

1.626

0.966

2.738

Parking location Main road = 1 Other = 0

0.899

0.323

0.005

2.458

1.304

4.633

Trip purpose Work = 1 Other = 0

2.027

0.792

0.011

7.592

1.607

35.867

Trip purpose Market = 1 Other = 0

0.921

0.305

0.003

2.513

1.381

4.571

−0.662

0.271

0.015

0.516

0.303

0.878

1.123 4.270

0.310 1.134

0.000 0.000

3.073 7.526

1.674 7.745

5.641 66.571

Change travel −0.579 time −10.28 Strongly disagree −1.129 =1 Other = 0

0.371 0.309 0.315

0.119 0.001 0.000

0.561 0.358 0.323

0.271 0.195 0.174

1.160 0.656 0.600

Parking location Main road = 1 Other = 0

0.394

0.062

2.082

0.962

4.503

Trip purpose Recreation = 1 Other = 0 Park off-street Agree = 1 Other = 0

0.733

(continued)

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Table 5 (continued) Variable

Regression coefficient (b)

Standard error SE(b)

P-value

Hazard ratio EXP(βX)

95% CI for hazard ratio Lower

Upper

Pay & Park Strongly disagree =1 Other = 0

−1.740

0.599

0.004

0.175

0.054

0.568

Use another mode Strongly disagree =1 Other = 0

−2.993

0.633

0.000

0.050

0.015

0.173

0.627

0.315

0.047

1.871

1.010

3.468

Park somewhere else Agree = 1 Other = 0

Fig. 13 Correlation between variables for off-street parking

but commuters still do not prefer to park in allotted space because of accessibility distance to different activity nodes.

7.3 Findings For on-street parking, people parking on main road are regular parkers parking for shorter duration and are aware of fines. People parking on-street for longer duration are non-regular parkers. Users from salaried and middle-income groups using 2wheeler tend to park for a shorter duration on the main road. Also, the main reason they visit is for market and recreation due to which people end up parking on the main road since they park for a shorter duration. These people parking on the main

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Fig. 14 Hazard function at a mean of covariates for off-street parking

roads have also not been fined and therefore continue to park there. People visiting for a shorter duration do not favor paying and parking or parking off-street as it will increase the cost and time. These people will not prefer to pay and park or change their travel time at any cost. However, if enforcement is made stricter their next preferred location would be on the side street or off street. For off-street parking, people are mostly daily users having to park for longer duration. These people are also aware of fines, so they are more sensitive toward parking on the main road. Most people parking off-street have also been fined and therefore change in parking behavior has been observed. Also, in their opinion, if they do not find parking, they will park in the next available parking location as they are aware of towing and fines. Table 6 Outputs for socio-economic and user characteristics for off-street parking Variable

Regression coefficient (b)

Standard error SE(b)

P-value

Awareness of vehicle towing Agree = 1 Other = 0

−0.344 −0.465

0.265 0.265

0.194 0.079

0.709 0.628

0.421 0.374

1.19 1.056

0.851 2.255 1.174

0.439 0.577 0.423

0.052 0.000 0.005

2.342 9.537 3.235

0.991 3.080 1.412

5.53 29.525 7.409

Trip purpose Market = 1 Other = 0

Hazard ratio EXP(βX)

95% CI for hazard ratio Lower

Upper

(continued)

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Table 6 (continued) Variable

Regression coefficient (b)

Standard error SE(b)

P-value

Hazard ratio EXP(βX)

95% CI for hazard ratio Lower

Upper

Trip purpose Business = 1 Other = 0

1.622 2.858 1.868

0.448 0.514 0.437

0.000 0.000 0.000

5.065 17.435 6.476

2.104 3.372 2.752

12.19 47.704 15.240

Trip purpose Recreation = 1 Other = 0

3.006 4.242

0.400 0.536

0.000 0.000

20.199 69.549

9.226 24.333

44.22 198.786

Income −1.342 10,000–50,000 = 1 Other = 0

0.377

0.000

0.261

0.125

0.547

−1.661 −0.678

0.450 0.275

0.000 0.014

0.190 0.507

0.079 0.296

0.45 0.871

1.400

0.630

0.026

4.054

1.178

Change travel −1.670 time Strongly disagree =1 Other = 0

0.532

0.002

0.188

0.066

0.534

0.584

0.257

0.023

1.794

1.084

2.967

Pay and Park Agree = 1 Other = 0

2.725 −1.912

0.844 0.565

0.001 0.001

15.253 0.148

2.918 0.049

79.741 0.447

User type Daily = 1 Other = 0

−4.142

0.737

0.000

0.016

0.004

0.067

Occupation Salaried = 1 Other = 0

−0.532

0.311

0.087

0.587

0.319

1.080

Parking type Unpaid = 1 Paid = 0

−1.196

0.379

0.002

0.303

0.144

0.636

0.903

0.493

0.067

2.466

0.938

6.483

Income 50,000–100,000 =1 Other = 0 Pay and Park Strongly disagree =1 Other = 0

Park somewhere else Neutral = 1 Other = 0

Been fined Yes = 1 No = 0

13.94

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8 Conclusion This study intended to focus on user perspective at the time of parking and collect the users’ perception if enforcement is made stricter. Irregularity in parking causes an increase in congestion thereby lowering the speed of the vehicles on roads. The factors that influence the decision of the driver at the time of parking were studied and analyzed. The study also evaluated various policies and interventions proposed by different cities for better understanding. From these studies, it can be interpreted that in cities in India and across the world, parking is one of the concerns that need to be given importance for better streets. The drive in 2018 was short-term initiative post pandemic the enforcement has gone down. While people are aware of the enforcement, the irregularity in parking has increased due to slackness in enforcement. For this study parking was viewed from a different lens i.e., in terms of regulations and parking duration for the stretch of Prahlad Nagar in Ahmedabad. The results indicated that most of the on-street parking happens for a shorter duration on the main street. Therefore, there are continuous movements of vehicles being parked and leaving the spot during the morning and the majority in the evening. Most of these users parking are aware of the no-parking zones but are parking daily since the duration is shorter and enforcement is slack. Therefore, trip purpose and frequency of use have a significant impact on parking duration. Most of the vehicles being parked are 2-wheelers for a shorter duration and therefore not in favour of paying and parking. Users from higher income group have been fined for on-street parking. However, these people are willing to risk the fine and are insensitive toward it. Hence would continue to park close to the destination. The analysis also indicates that for on-street parking with the help of signboards, no parking and delineating zones are of no significance unless the enforcement is made stricter. For off-street parking, the results indicate that these people are aware of the fines and therefore more sensitive toward parking on the main road. Although some people are parking for a shorter duration on the off-street most people are long-term parkers and parking on daily basis. Therefore, this group of people even though enforcement is made stricter will not change their travel time or pay and park. Since they are aware of the fines, they will avoid parking on the main roads and park in the next available location. Thus, the information on alternative parking available and strict enforcement plays an important role in the decision-making process of the user. This shows that strict parking enforcement has an impact on the behavior of parking. There were certain limitations to the study as the area delineated was a particular stretch based on high parking demand. The analysis method used was cox regression and there were certain unexplained covariates that need to be investigated for which advanced technique would be used. Acknowledgements We would like to show our gratitude to the Center of Excellence in Urban Tranport, CRDF, CEPT University for funding this research. We thank our reviewers who provided their insights and expertise that greatly assisted in improving this manuscript. We are immensely

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grateful to the people from traffic police department, panelists, and fellow colleagues from CEPT University who have supported in this process of research.

References Ahmedabad Municipal Corporation. (n.d.). Parking Policy 2021. Ahmedabad. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. Barter, P. (2011). Parking requirements in some major Asian cities. Chan, F. C. (2016). The basics of survival analysis. Center of Excellence in Urban Transport—CEPT University and Ahmedabad Urban Development Authority (AUDA). (2013). Integrated Mobility Plan for Greater Ahmedabad region. Ahmedabad. Directorate of Urban Transport, Bangalore. (2008). Policy paper for parking in the Bangalore Metropolitan Region. Marsden, G. R. (2006). The evidence base for parking policies—A review. Transport Policy, 13(6), 447–457. Shoup, D. (2005). The high cost of free parking. Simi´cevi´c, J. (2013). The effect of parking charges and time limit to car usage and parking behaviour. Trivedi, S. (2021). Gujarat approves new AMC parking policy. Walters, S. (2012). What is a Cox model?. Watson, J. (2001). How to determine a sample size: Tipsheet #60. Penn State Cooperative Extension. Zhan, G., & Shuai, R. (n.d.). From minimum to maximum: Impact of the London parking reform on residential parking supply from 2004 to 2010?. 282km of Space Freed of Encroachment on Ahmedabad Roads. (2018, August 5). TNN News. https://timesofindia.indiatimes.com/city/ahmedabad/282km-of-space-freed-of-enc roachment-on-city-roads/articleshow/65275002.cms

Comprehensive Framework for Adoption of Electric Vehicles: A Case Study of Jaipur City Mahima Soni and Sanjay Gupta

Abstract With the rapid urbanisation, demand for mobility in India is likely to witness an expeditious increase, leading to increased air pollution and energy consumption. For catering to this demand in a sustainable way, there has been a growing thrust by the GoI in recent past to switch from ICE vehicles to electric vehicles (EVs). Although, various policy efforts have been made by both central and state government for incentivising EV adoption but the growth in EV registrations are not as anticipated, except for a few states like Delhi. One of the main reasons for this lacklustre EV uptake is the absence of understanding key barriers of stakeholders involved in the process of EV adoption. The present paper is an attempt to analyse those key factors for the city of Jaipur through primary surveys, stakeholders consultation and a behaviour model. Behavioural factors are analysed through the application of SEM, which reveals that attitude, and perceived control behaviour are the key influencing variables for EV adoption in Jaipur. Also, technical, infrastructural, and financial factors are analysed using Analytical Hierarchy Process, which reveals that high upfront cost, low driving range, and inadequate charging infrastructure are major barriers in EV adoption. Based on various factors identified through surveys and stakeholder consultations, as well as the current poor status of EV adoption in the city, the paper has outlined a planning framework for efficient EV adoption in the future. Keywords Electric vehicles (EVs) · Internal combustion engine (ICE) vehicles · Sustainable · Analytical hierarchy process (AHP) · Structural equation modelling (SEM) · EV adoption behaviour · Extended theory of planned behaviour

M. Soni (B) · S. Gupta School of Planning and Architecture, New Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_7

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1 Introduction The Indian transport sector is responsible for 13.5% of India’s energy-related CO2 emissions, with road transport accounting for 90% of the sector’s total final energy consumption followed by rail and domestic aviation (https://climateactiontracker. org/documents/832/CAT_2020-12-09_Report_DecarbonisingIndianTransportSe ctor_Dec2020.pdf). Majority of the vehicles on the road operate on fuels like petrol, diesel and CNG, burning of which contributes to the rise in carbon emissions. It further results in poor air quality and environmental imbalance in urban areas. The prospect of rapid global temperature increase is also another aspect that has created a need for reduction in the consumption of fossil fuels and its associated emissions. In this context, the Government of India has taken various initiatives which include National Electric Mobility Mission Plan 2020 in 2013, to lower its carbon footprint while providing reliable, affordable and efficient mobility through sales of 6–7 million EVs by 2020. In 2016, the Ministry of Power proposed a target for India to achieve 100% E-mobility by 2030 (Sarkar & Nigam, 2017). Although, these targets were unrealistic in nature as the supporting policies and infrastructure initiatives were felt to be missing. After realising this scenario, schemes like FAME India (Faster Adoption and Manufacturing of Electric vehicles) under the NEMMP 2020 were launched, encouraging the purchase of EVs and Hybrid vehicles through demand creation, technology platforms, charging infrastructure and fiscal incentives was launched in 2015 (Government of India, 2015). Further, MoP revised their target in 2018 to achieve 30% E-mobility by 2030, and formulated guidelines and standards of charging infrastructure for EVs specifying their location, tariff, phase-wise roll out, implementation framework, etc. It was further amended in 2020 wherein, minimum one charging station needs to be present in a grid of 3 × 3 km in cities and at every 25 km on highways on both sides (Government of India, 2019a). In 2019, the Phase II of FAME India scheme was also notified, where electrification of private (2W, 4W), public (Buses) and shared (3W and Rickshaws) transport modes were being promoted through demand incentives of approximately Rs. 8,596 Crores (Government of India, 2019b). Ministry of Heavy Industry also sanctioned 520 EV charging stations under FAME India Phase I, followed by 2,877 EV charging stations in 68 cities across 25 States/UT’s and 1,576 EV charging stations along 9 Expressways and 16 Highways under FAME India Phase II (PIB Delhi, 2022). Apart from all these initiatives, there are many states that have been proactively promoting EV adoption subsidies on new EV (2W, 3W and 4W) purchase, tax and toll exemptions, introducing E-buses as pilot projects in metropolitan cities, setting up a network of EV infrastructure at public places and bus stops, promoting EV manufacturers to set up industries with land and tax subsidies, and formulating a state EV policy which further defines the vision for catering the increasing mobility demand in a sustainable manner. Over 14 states and 1 UT (i.e., Delhi) have their own notified state EV policy. A comparative study of all these policies was done on the basis of five parameters i.e., regulatory targets (new EV registrations targeted),

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demand incentives (road and motor tax exemptions, purchase subsidy), infrastructure incentives (capital subsidy for EV charging stations, electrical duty exemptions, provision of EV zones), manufacturing Incentive (capital interest subsidy, exemption from stamp duty/SGST, etc.), complimentary incentives (development of skill centres, recycling ecosystem and R&D). It was found that Karnataka, Telangana, Tamil Nadu, Andhra Pradesh, UP and Punjab are the states with best EV policies, but according to the EV density (EV registrations per 1000 population) of all these states, Delhi was on the top followed by Karnataka and UP. Even though Delhi was lacking manufacturing incentives in its state policy, it managed to be on the top because it gave various fiscal incentives like higher purchase subsidies to EV buyers, road tax/parking fee exemptions, and many other incentives which promoted early EV adoption in the state.

2 Literature Review There has been a great amount of research done in the past decade to understand the barriers in EV adoption through various theories, methods and techniques in both Indian and global context. Theories adopted in most of these research included theory of planned behaviour (TPB), extended TPB, theory of reasoned action, etc. These theories are then applied in a specific context through various analytical techniques like structural equation modelling (SEM), meta-analysis from reference papers, analytical hierarchy process (AHP), push–pull and mooring framework, preferred reporting items for systematic reviews and meta-analysis (PRISMA), multi-criteria decision making model, spatial econometric modelling, linear regression, mediation analysis, agent-based modelling, confirmatory factor analysis, etc., to analyse the significance of the barriers identified in the process of EV adoption. Most common barriers found in these studies included charging time, capital expenditure cost, awareness among buyers, behavioural characteristics, charging infrastructure, environmental concern, attitude, driving range, standardised chargers, monetary benefits, operational cost, resale, safety, speed, etc. Based on a detailed literature review, it was identified that these barriers can be categorised into four broad factors, i.e., technical, infrastructure, financial, and behavioural factors that influence EV adoption. After segregating them into their respective factors, a few issues were identified which were the most researched barriers and were found most suitable in context of Jaipur through stakeholder consultation and expert surveys. Table 1 shows the list of all the shortlisted barriers in EV adoption with their literature references. During the literature review, it was also found that majority of the research is focused on either a single factor or a combination of two factors, but none of the research papers were focusing on an overall perspective, taking into account all the barriers that are suitable with respect to the study context. Shalender and Sharma (2021) and Khurana et al. (2020) examined the behavioural factors that affects the consumer’s adoption of an EV through the application of SEM and found that Attitude

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Table 1 Factors and issues affecting EV adoption Factors

Barriers/issues

Literature reference

Technical

Limited range of EV’s/range anxiety

Kumar and Alok (2019) and stakeholder interaction

Long charging time

Mukherjee and Ryan (2019)

Limited EV models

Stakeholder interaction

Infrastructural

Financial

Behavioural

Inadequate charging infrastructure Bhattacharyya and Thakre (2020) Lack of maintenance and repair workshops

Stakeholder interaction

Lack of standardization of chargers

Bhattacharyya and Thakre (2020)

Higher upfront cost High cost of fast charging at PCS

Kumar et al. (2021) and stakeholder interaction

Lack of financing (credit) options

Stakeholder interaction

Additional cost of charging infrastructure at home

Bhattacharyya and Thakre (2020)

Lack of subsidies on electricity tariff

Stakeholder interaction

Attitude

Adnan et al. (2017), Shalender and Sharma (2021)

Moral norm Perceived behavioural control Subjective norm Environment concern

Source Adapted from various literature and stakeholder consultation

strongly influences the behaviour intention of an individual for the adoption of electric cars in India. Bhattacharyya and Thakre (2020) identified that higher upfront price of EVs and availability of charging stations were the most influential factors to define consumer preference for EV adoption. Bhat et al. (2022) examined the intention to adopt EVs among the potential vehicle buyers of India and the factors influencing their decision-making process. It was found that environmental and technological enthusiasm, social image and influence, perceived benefits, performance expectancy and facilitating conditions have positive impact on consumers’ intention to adopt EVs. Goel et al. (2021) studied barriers in EV adoption from various perspectives such as technical, infrastructure, policy and market in Indian context. It was also observed that abundant literature was present on the technological and infrastructural factors acting as a barrier in EV adoption but only a few focused on modelling of consumer behaviour, which indirectly influence the EV adoption intention. Therefore, this research is undertaken in order to provide a holistic view, by focusing on all type of barriers including technical, financial, infrastructural and behavioural factors, which further impacts EV adoption in case of Jaipur city.

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The objective of this paper is to provide an overview of policies promoting Emobility in Indian cities and to identify the key barriers in the process of EV adoption from the perspective of various stakeholders through primary surveys, stakeholder consultation and user behaviour modelling. It also provides a policy framework addressing all the identified issues and observations from the analysis, in order to accelerate the paradigm shift from ICE to electric vehicles in Jaipur city.

3 Research Methodology The research methodology adopted for the present study comprises of following stages: Stage 1 A baseline stakeholder analysis was carried out in Jaipur to understand the factors affecting EV adoption. In this context, 15 charging point operator surveys, 14 EV dealer surveys, 100 EV user surveys and 150 ICE vehicle user surveys respectively were conducted. A representative sample has been surveyed for EV dealer and EV charging point operator whereas convenient sampling method has been applied for EV user (75% response rate and 75 responses) and ICE user (93% response rate and 139 responses) surveys. These surveys were conducted at EV charging stations, EV dealer showrooms, commercial areas of old city, parking area in malls, hotels, residential buildings, etc. Apart from this, household surveys were also conducted. It was done through both online and offline mode. Stage 2 The factors obtained from various stakeholder surveys and literature review were then grouped into three broad categories, i.e., Technical, Infrastructure and Financial factors and an AHP was conducted for prioritising the issues in EV adoption for Jaipur city. For conducting AHP, numerous stakeholders have been identified and surveyed including 9 EV experts (from CUTS International, WRI, Bask Research Foundation, RMI India, Shakti Sustainable Energy Foundation, Professors from Malviya National Institute of Technology, Jaipur), 7 Government officials of Jaipur (from Jaipur Vidyut Vitaran Nigam Limited, Rajasthan State Transport department, Jaipur Development Authority, Jaipur Nagar Nigam, Regional Transport Office, Jaipur Traffic Police), 5 EV dealers, 6 Charging point operator, 24 ICE vehicle users and 15 EV users. The questionnaire for AHP was designed such that it helps in pairwise comparison of all the issues and factors on a 9-point Likert scale. However, a 9-point Likert scale was chosen to get more elaborate results on the priority issues. Stage 3 Along with Technical, Infrastructure and Financial factors, few behavioural factors were also adopted from the Extended Theory of Planned Behaviour (TPB) which

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were then assessed using a multivariate statistical technique called Structural Equation Modelling (SEM). SEM analysis was conducted on the basis of responses from 139 ICE vehicle users. The questionnaire for analysing the EV adoption behaviour of ICE vehicle user were designed in a way where the latent variables like attitude, environmental concern, moral norm, perceived control behaviour, subjective norm and adoption intention were measured through a number of observed variables on a 5 point Likert scale.

4 Case Study Profile and Data Analysis Jaipur, also known as pink city was selected for the purpose of this study. It is the state capital of Rajasthan, with a population of over 30.46 lakh (Census, 2011) and also one of the fastest growing cities with rapid development in trade, manufacturing and services. The transport system of the city includes public transport like city buses and metro, para transit transport like autos, e-rickshaw, cycle rickshaws, etc., and private modes like two-wheelers, cars, etc. Among 17.77 lakh total registered vehicles in Jaipur, only 34,515 vehicles (1.9%) were electric vehicles (RTO Jaipur, May 22). It includes two-wheelers, car, three-wheeler goods vehicle, E-rickshaw and E-rickshaw with carts. The lower rate of EV adoption in Jaipur was the major reason for which this research has been undertaken. The city of Jaipur needs a comprehensive policy framework for better EV penetration that addresses the issues faced by various stakeholders.

4.1 Stakeholder Analysis EV Dealers Characteristics For the purpose of this survey, nine E-2W dealers, three E-3W dealers and two Erickshaw dealers in the city were surveyed. It included dealers like Ampere Electric Vehicles, TATA motors, MG motors, PURE Electric Vehicles, Deltic, Mahindra, Okaya green, Ather space, Hero Electric, Hop Electric Mobility, etc. Table 2 shows the general and operational specifications for the EVs available in the market in Jaipur. As we can observe from the above table, there were only a few EV models available in each vehicle segment, which is lower as compared to the number of models available in ICE vehicle market. Also, EVs have relatively higher upfront costs as compared to fuel-based vehicles. The major barriers of ICE vehicle users to shift to EV’s in the opinion of the EV dealers were lack of vehicle diversity (limited EV models), lower driving range of EV’s, high upfront cost of EV’s as compared to ICE vehicles, longer charging time and lack of repair and maintenance shops for EV’s.

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Table 2 General and operational specifications of EVs available in the market, Jaipur General specifications Vehicle segment

No. of models

Battery capacity (kW)

Price (in Lakh)

Charging type

2 wheeler

6

2.2–2.9

0.7–1.5

3-plug point

E-rickshaw

4

7.5–8.5

3.0–3.5

3-plug point

4 wheeler

3

30–40

12–17

2

50–70

23–25

CCS, CHAdeMO, Type 2 AC

Average charging time (h)

Average charging cost/charge (Rs.)

Operational details Vehicle segment

Average mileage (km)

2 wheeler

90

6

43

E-rickshaw

110

7.5

90

4 wheeler

240

11

375

460

8.5

325

Source Primary survey, Feb 2022

EV Charging Point Operator Characteristics There are a total of 23 EV charging stations with plug-in charging facility and no battery swapping facility in the city. The charging stations are all located within the premise of a hotel, institute, parking lot of malls, EV showrooms, etc. There were no stations present in public spaces like community centres, hospital, marketplaces or offices, etc., with an individual plot. Majority of the stations were only for E-4W, as fast charging is only available in that vehicle segment. There were no EV models in Jaipur which have the provision of battery swapping. It was also found that 17 (22%) wards out of the total 77 wards were being served through the existing EV charging infrastructure whereas 60 (78%) wards were left unserved and needed new charging infrastructure facilities resulting in an estimated 20% population of Jaipur currently being served through the existing 23 EV charging stations. The major barriers of ICE vehicle user to shift to EVs in the opinion of Charging point operators were lack of vehicle diversity (limited EV models) lower driving range of EVs, lack of financing options for EVs and high upfront cost of EVs as compared to ICE vehicles. EV User Characteristics For the purpose of this study, 75 EV users responded to the surveys conducted at charging stations. Among all the respondents E-rickshaw users surveyed were 48%, followed by 36% electric two-wheeler users and 16% four-wheeler users, respectively. The frequency of charging the EVs by the users and E-rickshaw operators

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Fig. 1 Frequency of charging the EV’s—EV users

were also recorded and analysed. Figure 1 shows that majority of E-rickshaw and E-2W users charge their EVs thrice a week, whereas, majority of E-4W users charge their EVs twice a week. The average daily utilisation of E-2W was 12 km, 35 km in case of E-rickshaw and 42 km in case of E-4W respectively as per primary surveys. 64% of the EV users have additional EV charging infrastructure at their home, while only 22% of respondents prefer public charging over private charging, as the access distance for majority of the EV users was more than 10 km. After analysing the average charging time, achieved driving range and cost/km, it was found that on an average an E-2W is charged for 5.5 h, which covers 78 km costing Rs. 0.5/km. Whereas, an E-4W is charged for 9.5 h, which covers 220 km costing about Rs. 1.2/km. Thus, it can be stated that the average cost/km of an EV is one-third to that of conventional fuel vehicle. Also, an E-rickshaw is charged for approximately 7 h and covers 95 km in that charge, costing the user about 2 Rs/ km. The primary motivation of EV users to shift from ICE vehicles included lower operating cost (45%), subsidies on purchase of EVs (28%), environment concern (12%), increasing prices of fuel (9%) and cutting-edge technology (6%) respectively. The major barriers for EV users were lack of access to public charging infrastructure, long charging time of EV’s, high tariff for fast charging and lack of EV repair and maintenance workshops. ICE Vehicle User/Non-EV User Characteristics For the purpose of this study, 139 ICE vehicle users responded to the primary surveys comprising 23% auto users, 32% two-wheeler users and 45% four-wheeler users respectively. The average daily utilisation of 2W was 25 km, 39 km for autos and 45 km for 4W respectively. The reported average cost of operation was Rs. 2.6/km for 2W, Rs. 4.7/km for autos and Rs. 5.6/km for 4 wheeler, respectively. Some of the major reasons expressed for low vehicle utilisation of EVs includes range anxiety/ fear of running out of charge and absence of robust charging infrastructure network.

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On the other hand some of the major barriers of ICE vehicle users to shift to EV’s identified were inadequate public charging infrastructure in the city and in highways, lack of vehicle diversity (limited EV models), lower driving range of EVs and high upfront cost of EV’s (as compared to ICE vehicles).

4.2 Prioritising Issues in EV Adoption Using AHP AHP is a multi-criteria decision making tool used in this research for prioritising the factors and constructs in affecting EV adoption according to various stakeholders for the city of Jaipur. AHP was selected as it allows the researcher to estimate the inconsistency index which is important for ensuring that the decision is consistent as well as unbiased (Adhikari et al., 2020). The constructs/issues are identified through literature and stakeholder interaction (as discussed in the methodology) are grouped in three broad factors/criterias i.e. Technical, Infrastructural and Financial factors affecting EV adoption. Further, AHP was used to estimate the importance of these issues as well as factors and rank them according to their criteria weights. Six groups of stakeholders were identified for conducting AHP and prioritising the issues in EV adoption for the case study of Jaipur including EV user, ICE Vehicle user, EV Dealer, EV Charging point operator, Government officials and EV Experts. A total of 66 responses were taken on a 9-point Likert scale on the above issues and factors in a pairwise manner. The steps followed for conducting the AHP are as follows: Step 1: To formulate a goal, which was to rank the identified issues and criterias affecting EV adoption in the order of their importance. Step 2: Identification and categorization of the issues into criteria. Three broad criterias were identified, i.e., technical issues, infrastructure issues and economic or financial issues The structure of AHP was as shown in Fig. 2. Step 3: Estimating weights for each criteria of issues and specific issues within each criteria using Eq. 4.1 Aw = λmax × w A w λmax

(4.1)

comparison/priority matrix, eigenvector (priority weight), maximum eigenvalue.

Step 4: Calculation of Consistency Index using Eq. 4.2: CI = (λmax − n)/(n − 1)

(4.2)

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Fig. 2 Structure of AHP for EV adoption

CI

consistency index (0 value denotes perfectly consistent judgment among all respondents). λmax max Eigenvalue, approximately calculated with the following equation:

λmax = average [(Aw)1/ w1, (Aw)2/ w2 . . . ]

(4.3)

Step 5: Calculation of Consistency Ratio using Eq. 4.4. CR = CI/RI

(4.4)

CR Consistency Ratio. CI Consistency Index, and RI Random Index ( for n = 11 issues, RI = 1.51). CR ≤ 0.1 denotes an acceptable range, where the data is considered to be significant. After conducting all the above steps and obtaining an acceptable consistency ratio, ranking for issues in each criterias and among the criterias themselves were done. The results of the same have been described in detail in Table 3. It was observed that among infrastructure issues, inadequate charging infrastructure was the most significant barrier in EV adoption. Whereas, among financial issues, higher upfront cost of EVs is the most important issue. Among technical issues, lower driving range of EV is the major concern in the process of EV adoption. Therefore, this study confirms the finding of Adhikari et al. (2020) for the case of Nepal and

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Table 3 Ranking of criteria wise issues Ranking of infrastructure issues Stakeholders/ issues

EV user

Non EV user

Charging operator

EV dealer

Govt. official

EV expert

Ranking

Inadequate charging infrastructure

1

1

1

2

1

1

1

Lack of 3 standardisation of EV chargers

3

3

3

3

2

3

2

2

2

1

2

3

2

Non EV user

Charging operator

EV dealer

Govt. official

EV expert

Ranking

Lack of subsidies 4 on electricity tariff

4

3

4

3

3

4

High cost of fast charging at PCS

2

3

4

3

2

2

2

Higher upfront cost of EV than ICE vehicles

1

1

1

1

1

1

1

Add. cost of charging infra. at home

3

2

2

2

4

4

3

Non EV user

Charging operator

EV dealer

Govt. official

EV expert

Ranking

Range anxiety/ 2 low driving range of EV

1

2

1

1

2

1

Long charging time of EV’s

1

2

3

3

2

1

2

Limited models of EV in market

3

3

1

2

3

3

3

Stakeholders/ criteria

EV user

Non EV user

Charging operator

EV dealer

Govt. official

EV expert

Ranking

Infrastructure

3

3

3

2

1

1

2

Financial

1

2

2

3

3

3

3

Lack of repair and maintenance workshops

Ranking of financial issues Stakeholders/ issues

EV user

Ranking of technical issues Stakeholders/ issues

EV user

Ranking of criteria

(continued)

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Table 3 (continued) Ranking of infrastructure issues Stakeholders/ issues

EV user

Non EV user

Charging operator

EV dealer

Govt. official

EV expert

Ranking

Technical

2

1

1

1

2

2

1

Kumar et al. (2021) for the case of India. Also, if all three categories of issues in EV adoption were taken into account, technical issues were among the most significant barriers in EV adoption, followed by infrastructure and financial issues, as per the criteria weights and all the relevant stakeholders.

4.3 Analysing EV Adoption Intention Using SEM Extended theory of Planned behaviour is an extension of TPB, where Moral Norm and Environment concern along with Attitude, Subjective Norm and Perceived behaviour control are the five behavioural constructs that affect the Adoption Intention of an individual. It is taken up as the model for the research in order to determine the behavioural factors influencing the EV adoption intention of ICE vehicle users of Jaipur. Attitude is primarily based on behavioural beliefs and evaluation of outcomes of an individual (Fishbein & Ajzen, 1975), Subjective Norm is based on one’s normative beliefs, motivation to comply (Fishbein & Ajzen, 1975) and to behave in a socially acceptable manner (Ajzen, 1991), Perceived Control Behaviour is based on the individual’s control beliefs and the ease or difficulty with which they demonstrate a particular kind of behaviour (Ajzen, 1991), Moral Norm is based on the individual’s feeling of responsibility towards his/her action (Ajzen, 1991), and Environment Concern is based on the individual’s awareness towards the environmental issues, which is a great concern at both global and national level. Figure 3 shows the five constructs that affect EV adoption intention. The Extended Theory of Planned Behaviour was tested in the case of Jaipur city through application of SEM. The detailed methodology for conducting SEM is as follows: Step 1: Model Specification The extended theory of planned behaviour was tested through primary survey of 139 ICE vehicle users. As the latent variables can only be measured through their observed variables, a list of latent and observed variables were adopted (Khurana et al., 2020) and modified for the purpose of this study as shown in Table 4. Step 2: Data Preparation A questionnaire was developed wherein the observed variables have been rated on a 5-point Likert scale. There were no missing data as all the questions were mandatory.

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Fig. 3 Extended theory of planned behaviour. Source Shalender and Sharma (2021)

All the values for the listed observed variables came out to be significant for Standard deviation, Kurtosis and Skewness. Step 3: Estimation of SEM The data set for observed variables passed the tests of KMO and Bartlett’s test, Goodness of fit test, test for Convergent and Discriminant validity and Cronbach Alpha as per the requirement for moving on to confirmatory analysis. After the statistical tests were conducted in SPSS, confirmatory factor analysis (CFA) has been performed, with the estimation of factor loadings, Cronbach Alpha, Composite Reliability and Average variance computed as shown in Table 5. It was found that all the observed variables except SN 1 and PBC 3 were well within their threshold limits which are mentioned in the top row. Higher factor loadings show that factor loads highly on the variable and the correlation is stronger. Higher value for Cronbach alpha and Composite reliability is an indicator of internal consistency within the items of a group. Average variance extracted determines that the questions for observed variable strongly reflect the characteristics of the latent variable.

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Table 4 Latent and observed variables for SEM analysis Latent variable

Coding

Observed variable

Attitude

ATT1

I consider the adoption of EVs 5-point Likert scale favourable

ATT2

Driving an EV will be a wise decision

EC1

I take into consideration environment consequences while buying an EV

EC2

Buying an EV would help reducing air pollution and contribute to environment for saving future generation

MN1

I believe, it is my moral responsibility to adopt EV

MN2

I take into consideration environment consequences while buying an EV

PBC1

Overall cost of owning an EV would be low due to incentives

PBC2

EV’s are as safe as compared to ICE vehicles

PBC3

I will save on fuel expenses, as operational cost an EV would be lower

PBC4

The price of EV is important when I decide to adopt it

SN1

Buying an EV is a status symbol for me

SN2

I will consider the wishes of my family/friends while adopting EV

AI1

I would definitely adopt EV in the future

AI2

I would recommend the adoption of EV to others

Environment concern

Moral norm

Perceived behaviour control

Social norm

Adoption intention

Scale

Step 4: Model Evaluation and Modification There were two iterations done as reliability values of two observed variables were not within the threshold limits. It was observed from Table 6 that all the measurement values for Output 2 are well within the accepted range including the p-value, GFI, AGFI, RMSEA and PCLOSE values. The path diagram for Iteration 2 is as shown in the Fig. 4. Based on t-values obtained it can be concluded that all the factors positively influence EV adoption intention. Also, the path coefficients determine the degree

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Table 5 Reliability values S. no. Latent variable

1

Attitude

Observed variable

Factor loadings (>0.7)

Cronbach alpha (>0.7)

Composite reliability (>0.7)

Average variance extracted (>0.5)

ATT 1

0.94

0.95

0.85

0.71

ATT 2

0.96 0.97

0.96

0.86

0.68

0.72

0.83

0.82

0.98

0.87

0.77

0.79

0.80

0.65

0.94

0.82

0.78

2

Environment concern

EC 1 EC 2

0.97

3

Subjective norm

SN 1

0.00

SN 2

0.84

Moral norm

MN 1

0.99

MN 2

0.98

PBC 1

0.97

PBC 2

0.85

PBC 3

0.17

PBC 4

0.87

AI 1

0.90

AI 2

0.99

4 5

6

Perceived behaviour control

Adoption intention

Source Primary survey, Feb 2022. Analysed in IBM SPSS statistics Version 22.0

Table 6 Measurement model fitness—iteration 1 and 2

Measurement model fitness Measure

Threshold

Output 1

Output 2

(Chi-square/df) Cmin/df

0.05

0.02

0.08

CFI

>0.90

0.97

0.96

GFI

>0.95

0.67

0.98

AGFI

>0.80

0.72

0.82

SRMR

>0.09

0.12

0.12

RMSEA

0.05

0.0

0.08

Source Primary survey analysed in IBM SPSS AMOS (version 26)

of significance of a variable influencing EV adoption, wherein it can be observed that Perceived Behaviour Control, Moral Norm and Attitude are the most important variables which positively influence EV Adoption Intention and Behaviour as highlighted by Shalender and Sharma (2021) and Khurana et al. (2020). It can also be inferred that Environment concerns and Subjective norms were not strong indicators of EV adoption intention in case of Jaipur city which was a unique insight specific to the city of Jaipur unlike the findings from other Indian literature.

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Fig. 4 Path diagram—iteration 2. Source Primary Data analysed in IBM SPSS AMOS (version 26)

5 Proposed Planning and Policy Framework for Efficient EV Adoption in Jaipur A detailed review of all the state EV policies has been done in order to identify the key policy initiatives of best-case cities with higher EV penetration rate. Based on the baselines stakeholder analysis, prioritisation of issues through AHP and behavioural analysis for EV adoption intention using SEM, various policy interventions have been proposed for the city of Jaipur. Some of the key drivers identified for the proposed policy framework include notification of Rajasthan’s EV policy, regulatory incentives, provisions for direct and indirect purchase incentives, organising EV awareness programs, EV charging infrastructure incentives, complementary policy initiatives, institutional framework, upskill training and job creations. Table 7 shows the existing and proposed policy framework for efficient EV adoption in Jaipur: It can be inferred from the above table that a lot of initiatives that are required for developing an effective EV Ecosystem are missing in case of Jaipur. However, with the proposed policy changes, increased fiscal benefits, tax exemptions and subsidies, development of charging infrastructure network, upskill training, creating an institutional framework and setting up of achievable targets, Jaipur is more likely to experience a rapid growth in the number of EV registrations.

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Table 7 Planning and policy framework for EV adoption in Jaipur Policy drivers

Existing policies for Jaipur

Proposed strategies/targets/proposals

State EV policy

Rajasthan state EV policy is in drafting stage

Rajasthan EV policy to be drafted approved by the state govt

Regulatory incentives

None

1. 30% share of EV till 2030 and 50% till 2050 2. % share of new EV registrations (2W—50%; 3W—100%; 4W—20%; buses—100%; last mile delivery/ freight—100%; LCV—30%; govt. vehicles/school bus/vans—100%)

Direct purchase incentives

1. E 2W—upto Rs. 10,000 2. E auto, rickshaw, E-carriers—upto Rs. 20,000

1. Higher purchase incentives on EVs for first 5 years (2W—upto Rs. 30,000, 4W—1.5 lakh, IPT—upto Rs. 12,000; PT—upto Rs. 20 lakh/bus; and freight/delivery vehicles—upto Rs. 30,000 depending on battery capacity) 2. Loans at subsidised rate of 4% from RFC for EV

Indirect incentives

1. 75% waiving of SGST for E 4W 2. 100% registration fees waiver on 2W and 3W 3. Green license plate for EVs

1. 100% SGST reimbursement—in all vehicle segment 2. 100% registration fees waiver—in all vehicle segment 3. Other tax benefit (permit fee/tolls/green tax benefits)

Awareness programs

None

1. Advertisement of EV and its benefits by print, TV, radio and social media 2. Awareness to be created through exhibitions, EV expo, E mobility zones for tourists and information dissemination for promoting EVs

Charging infrastructure 1. Byelaws for EV 1. Assessment of required EV charging incentives charging stations infrastructure should be done 2020 by UDH, GoR 2. Location of fast and slow charging/battery swapping stations with desired guideline 2. EOI for 75 charging and standards (at residential buildings, stations in Jaipur parking lots, commercial area, mall, 3. Amendments in business and work places, community model building centres, bus stands, taxi stands) byelaws 2016 3. 25% capital subsidy upto Rs. 5,00,00 per station for charging equipment/machinery for first 100 PCS/battery swapping stations, proposed safety standards/ guidelines (continued)

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Table 7 (continued) Policy drivers

Existing policies for Jaipur

Proposed strategies/targets/proposals

Complementary policies

None

1. Subsidized electricity tariff for PCS @ Rs. 4.5/unit of electricity 2. Promote electricity generation through solar power and V2G technology 3. Electrical duty—100% exemption 4. Proposals—repair and maintenance workshops

Institutional framework None

1. State Level Committee to be formed to monitor progress, address major impediments, implementation and make amendments to policy 2. Creation of an umbrella (non-lapsable) “State EV Fund” to be funded through add. taxes, cess, fees, etc., on polluting vehicles 3. EV cell in ULB for planning and implementing incentives and infrastructure

Upskill training and job None creation

1. Setting up research/training institutes, EV testing centres, quality control labs, promoting start-ups based on E-mobility 2. Develop skill enhancement centres for vocational courses on EV ecosystem

Conclusions The present study found that there were various policy initiatives taken at the central level, but some of the states that have notified their own State EV policies were performing better than others. It was also found that Delhi and Karnataka were one of the best-performing states in terms of EV registered per 1000 population due to various policy interventions like higher EV purchase subsidies, tax exemptions, low electricity tariff, etc. It was also observed that there was an absence of Rajasthan state EV policy and there were only a few initiatives taken for promotion of EVs in Jaipur. The stakeholder analysis of EV users revealed that 64% of EV users in Jaipur are currently charging their EVs at home, which was due to the inadequate public charging infrastructure present in the city. The market analysis also showed that the EV market is smaller as compared to the ICE vehicle market and that too with a high upfront cost of EVs. Responses elicited from multiple stakeholders through AHP, revealed that issues such as higher upfront cost, low driving range of EVs and inadequate charging infrastructure were the priority issues for all the stakeholders and are critical influencers of the EV adoption rate in city of Jaipur. Behavioural analysis through SEM showed that perceived control behaviour, moral norm and attitude are the key variables that influenced EV adoption intention of the people in Jaipur. Finally, the issues identified throughout the paper formed the basis for evolving a policy framework that will promote efficient EV adoption in Jaipur city in

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the future. The outcome of this study will also help urban and transport planners to formulate necessary strategies and promote required policy initiatives in order to boost EV growth in the city, by addressing the issues of all the stakeholders as well as understanding and influencing the consumer behaviour in favour of adopting electric vehicles in the future. Limitations This research study is limited to EV Adoption among private transport modes, i.e., two wheeler, three-wheeler and car users in urban areas of Jaipur. All the research was conducted based on news articles, government notifications, research papers, reports, online literature, limited primary surveys and secondary data collected through government authorities and other private organisations. All primary and secondary surveys have been conducted during prevailing COVID-19 pandemic with limited resources. Contribution of the Study This study contributes to building an improved understanding of issues from perspective of multiple stakeholders in EV Adoption and their integration in policy making. It also contributes in highlighting the importance of behavioural approach of various stakeholders in promoting EVs in India.

References Adhikari, M., Ghimire, L. P., Kim, Y., Aryal, P., & Khadka, S. B. (2020). Identification and analysis of barriers against electric vehicle use. Sustainability, 12, 4850. https://www.mdpi.com/20711050/12/12/4850 Adnan, N., Nordin, S. M., & Rahman, I. (2017). Adoption of PHEV/EV in Malaysia: A critical review on predicting consumer behaviour. Renewable and Sustainable Energy Reviews. https:/ /www.researchgate.net/publication/312717986_Adoption_of_PHEVEV_in_Malaysia_A_crit ical_review_on_predicting_consumer_behaviour Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://www.sciencedirect.com/science/article/pii/074959789 190020T Bhat, F., Verma, M., & Verma, A. (2022). Measuring and modelling electric vehicle adoption of Indian consumers. Transportation in Developing Economies, 8. https://www.researchgate.net/ publication/357106879_Measuring_and_Modelling_Electric_Vehicle_Adoption_of_Indian_ Consumers Bhattacharyya, S., & Thakre, S. (2020). Exploring the factors influencing electric vehicle adoption: An empirical investigation in the emerging economy context of India. Foresight. https:// www.researchgate.net/publication/346123128_Exploring_the_factors_influencing_electric_v ehicle_adoption_an_empirical_investigation_in_the_emerging_economy_context_of_India 2020 Decarbonising the Indian transport sector pathways and policies. Climate Action Tracker. New Climate Institute and Climate Analytics. https://climateactiontracker.org/documents/832/ CAT_2020-12-09_Report_DecarbonisingIndianTransportSector_Dec2020.pdf Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley. https://www.researchgate.net/publication/233897090

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Goel, S., Sharma, R., & Rathore, A. K. (2021). A review on barrier and challenges of electric vehicle in India and vehicle to grid optimisation. Transportation Engineering. https://www.researchgate.net/publication/349488028_A_Review_on_Bar rier_and_Challenges_of_Electric_Vehicle_in_India_and_Vehicle_to_Grid_Optimisation Government of India. (2015). Scheme for faster adoption and manufacturing of (hybrid &) electric vehicles in India—FAME India. Department of Heavy Industry, Ministry of Heavy Industries and Public Enterprises. https://heavyindustries.gov.in/writereaddata/UploadFile/Gazette_Noti fication_FAME_India.pdf Government of India. (2019a). Charging infrastructure for electric vehicles (EV)—The revised consolidated guidelines & standards—Reg. Ministry of Power. https://dae.gov.in/writereaddata/ scs24022022_1.pdf Government of India. (2019b). Scheme for faster adoption and manufacturing of (hybrid &) electric vehicles in India Phase II (FAME India Phase II). Department of Heavy Industry, Ministry of Heavy Industries and Public Enterprises. https://fame2.heavyindustries.gov.in/WriteReadData/ userfiles/8th%20March%202019b%20Gazette%20Notification%20FAME-II.pdf Khurana, A., Kumar, V. V. R., & Sidhpuria, M. (2020). A study on the adoption of electric vehicles in India: The mediating role of attitude. Vision: The Journal of Business Perspective, 24. https://www.researchgate.net/publication/337792075_A_Study_on_the_Adoption_of_ Electric_Vehicles_in_India_The_Mediating_Role_of_Attitude/references Kumar, R. R., & Alok, K. (2019). Adoption of electric vehicle: A literature review and prospects for sustainability. Journal of Cleaner Production. https://www.researchgate.net/publication/338 232674_Adoption_of_electric_vehicle_A_literature_review_and_prospects_for_sustainability Kumar, P. P., Narang, P., Singh, T., & Agrawal, S. (2021). Relative significance of barriers to electric vehicle adoption in India using AHP—Fuzzy TOPSIS approach. https://www.researchg ate.net/publication/350126498_Relative_Significance_of_Barriers_to_Electric_Vehicle_Adop tion_in_India_using_AHP-_Fuzzy_TOPSIS_approach/references Mukherjee, C. S., & Ryan, L. (2019). Factors influencing early battery electric vehicle adoption in Ireland. Renewable and Sustainable Energy Reviews. https://www.researchgate.net/publication/ 338084242_Factors_influencing_early_battery_electric_vehicle_adoption_in_Ireland PIB Delhi. (2022, July 19). Over 13 lakh electric vehicles in use in India; centre is taking a number of steps to promote use of electric vehicles in India. Ministry of Heavy Industries. https://pib. gov.in/PressReleaseIframePage.aspx?PRID=1842704 Sarkar, E. P., & Nigam, A. (2017, April 6). 100% electric vehicle mobility by 2030: Is India really prepared for it? The Indian Express. https://indianexpress.com/article/blogs/100-electric-veh icle-mobility-by-2030-is-india-really-prepared-for-it/ Shalender, K., & Sharma, N. (2021). Using extended theory of planned behaviour (TPB) to predict adoption intention of electric vehicles in India. Environment, Development and Sustainability. https://www.researchgate.net/publication/338784549_Using_extended_theory_ of_planned_behaviour_TPB_to_predict_adoption_intention_of_electric_vehicles_in_India

Assessing Electric Vehicle (EV) Readiness of an Indian City: A Case Study of Lucknow, Uttar Pradesh Piyush Saxena

Abstract Accelerated urbanisation due to rising population has led to increasing growth of conventional internal combustion engine (ICE) mode of transport, making it a major contributor to the global carbon emissions. Road transport accounts for ~74% of transport-related greenhouse gas (GHG) emissions (IEA, 2020). India’s nationally determined commitments could be accelerated by switching to electric vehicles. Several states have approved their specific electric vehicle (EV) policies which vary viz-a-viz policy interventions. At this stage, cities need to brace themselves for acting as a lighthouse of EV adoption. Lucknow, being the capital of the state as well as having been identified as one of the model EV city under Uttar Pradesh (UP) EV policy, has been selected as the study area for this study. As per vahan dashboard, a total of 25,77,750 vehicles were registered in Lucknow till March 2022 out of which only 34,704 were EVs. This shows low penetration of EVs, approximately 1.35% of total vehicles registered in Lucknow. A mix methodological approach has been used to assess EV readiness in terms of availability of EV infrastructure, policy interventions and penetration of EVs in the study area. A set of 15 indicators were framed based on secondary research and these indicators included criterion from planning, policy, technological and infrastructure interventions, comprising of qualitative and quantitative data. Each indicator was given equal scoring and benchmarking was done based on the best-performing city within Indian context. A Google form-based pilot survey was conducted for the stakeholders [end-users, Think Tanks, State/city government officials, Original equipment manufacturers (OEMs)] to assess the importance of factors involved in EV readiness. Keywords EV · Electric vehicle · Readiness · Lucknow · Infrastructure

Similarity Index = 7%. P. Saxena (B) The Energy and Resources Institute, New Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_8

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Abbreviations BAU BEE DHI EV EVI FAME FC FY GHG GST ICE NEMMP PCS SC SDGs UP w.r.t 2W 3W

Business as usual Bureau of Energy Efficiency Department of Heavy Industries Electric Vehicle Electric Vehicles Initiative Faster Adoption & Manufacturing of Hybrid and Electric Vehicles Fast chargers Financial year Greenhouse gas Goods and Services Tax Internal Combustion Engine National Electric Mobility Mission Plan Public charging station Slow chargers Sustainable Development Goals Uttar Pradesh With respect to Two wheeler Three wheeler

1 Introduction Increasing share of internal combustion engine (ICE) mode of transport due to rising population is one of the major contributor for increasing carbon emissions. Road transport accounts for 75% of transportation sector-related greenhouse gas (GHG) emissions (International Energy Agency, 2020). This indicates an urgent need to shift towards a more sustainable mode of transport, which could be done by increasing the pace of electrification of vehicles. A study by Soman et al. (2020) mentioned that a total of 5,30,560 electric vehicles (including 2W, 3W, e-rickshaws, cars and buses) were sold in India till the year 2020. India had pledged in the Paris Agreement in 2015 to reduce its per head emission by 30–35% by the end of the year 2030. India’s nationally determined commitments at COP-26 to reduce total projected carbon emissions by 1 billion tonnes from now to the year 2030 could be accelerated by switching to a low-carbon mode of transport.

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1.1 Indian EV Ecosystem National Electric Mobility Mission Plan 2020 (NEMMP, 2020), launched in the year 2013 aimed to achieve 6–7 million EVs by the year 2020 in India. Till year 2020, only 0.5 million EVs were sold, showcasing the low penetration of electric vehicles in India. As per Jain and Lutken (2020), share of EVs in Indian automotive market accounts to less than 0.1%. Thus, electrification of vehicles is at an initial stage in India, making it foremost important to assess the available ecosystem for EV transition. Several Indian states have developed their own EV policies based on incentives, EV value chain and infrastructure development. The pace of EV adoption is different in different states which could be justified with the fact that while 0.1 million EVs were registered in states like Karnataka and Maharashtra, only 0.028 million EVs were registered in Kerala till March 2022. Faster Adoption & Manufacturing of Hybrid & Electric Vehicle (FAME) scheme was launched in 2015 to induce faster adoption of electric vehicles, with an outlay of approximately INR 895 crores. Second phase of FAME (FAME-II) was launched in 2019, initially for a period of 3 years with total outlay of 10,000 crores. Later, FAME-II was extended till the year 2024. Union Budget 2022–23 promotes shift to use of public transport in urban areas with the main focus on special mobility zones and EV vehicles. Battery swapping policy will aid in greater penetration of e-2W and e-3W. State EV policies are important in providing supply incentive, Goods and services tax (GST) reduction as well as rebate on road tax etc. In India, efforts for EV adoption have been done by various central agencies involved such as DHI, BEE, etc. Several states have approved their specific EV policies and they vary with respect to interventions in EV technology. Some EV policies are inclined towards manufacturing while some policies are inclined towards increasing penetration for EVs. But at this stage, cities need to brace themselves for acting as a lighthouse of EV adoption. In India, EV registrations per year has increased from 2018 to 2021 by approximately 49% (Fig. 1). Similarly, EV registrations per year in Uttar Pradesh has increased from 2018 to 2021 by approximately 26%. This shows the increasing pace of electrification of vehicles in India. Uttar Pradesh is the 3rd largest beneficiary of FAME scheme and 3rd largest automobile consumer in India by number of vehicles registered. EV sales in UP were highest during FY 2021–2022. Lucknow being the capital of Uttar Pradesh as well as being a model EV city under UP EV policy has been selected as the study area for this study. It is a highly significant city as it is the centre of policy making in UP. Even though EV sales were highest in UP, there is a need to look at the current EV readiness of Lucknow to understand whether the city is ready to adopt EVs and to understand the factors responsible for slower EV adoption in the study area.

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EV registrations (per year)

EVs registered

2,50,000

1,96,111

2,00,000 1,61,314 1,50,000

1,19,652

1,32,069 66,702

1,00,000

55,795

53,210

31,259

50,000 0 2018

2019

India

Year

2020

2021

Uttar Pradesh

Fig. 1 EV registrations (per year) in India and Uttar Pradesh. Source Vahan Dashboard

2 Research Objectives 1. To project the EV growth in Lucknow by 2030 and estimate infrastructure requirements. 2. To assess the current EV readiness of Lucknow and understand the challenges in EV adoption.

3 Background 3.1 Existing Policy Ecosystem for Electric Vehicles in India India is a part of Clean Energy Ministerial (CEM’s) Electric Vehicles Initiative (EVI) along with other 13 member countries and 23 supporting organisations, with the objective to reach 30% EV share by 2030. It has been estimated that achievement of this target of 30% EV penetration in India would lead to savings of approximately 474 million tonnes of oil equivalent and 842 million tonnes of CO2 emissions over lifetime (World Economic Forum, 2019). Several reports indicate that if 50% of public transport is electrified, it would result in 9% decrease in ambient air pollution. Electrification of vehicles would reduce the dependence on fossil fuels and it would act as a clean source of transportation. EV transition would also facilitate accomplishment of multiple Sustainable Development Goals (SDGs: 3, 7, 11, 12 & 13). India is one of the top 5 automobile markets in the world. Almost 80% of transport fossil fuel need is met by imported crude oil from other countries. Globally, countries such as Germany, France, Norway have set targets for total EV transition by 2030– 40. According to a Parliamentary Report, 1,59,575 EVs were sold in India during

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Accelerated urbanisation

Increasing transport demand

Rising dependency on crude oil imports

Rising pollution levels

Fig. 2 Reasons for EV transition

FY2020-21. National Electric Mobility Plan laid the foundation of Electric mobility in India by setting a goal to achieve 6–7 million EVs by 2020. EV market in India is still in initial stages as the global share of EVs is very less in India. India’s vehicle industry is expected to reach $250–$280 billion by the year 2026 and shows a high capacity for development. There is huge potential in India to increase the EV market share through government subsidies and incentives for adopting EV technology (Fig. 2). Accelerated urbanisation due to rising population increases transport demand. This in turn increases dependency on crude oil imports. Uncertainty in crude oil prices and higher dependency on crude oil imports results in rising pollution levels. EV transition is best suited to break this cycle.

3.2 Assessment of EV Policies Around the World 39% of all new cars in Norway’s Oslo are electric, making it ‘EV capital of the world’. National level schemes in India such as FAME-II are playing a crucial role in faster adoption of electric vehicles. Several states have notified EV policies to encourage switching to electric vehicles. A recent study by ORF quantified the need for 29,00,000 public charging points backed by an investment of INR 20,600 crores to achieve the target of 30% EV by 2030 nationally. More than 20 Indian states have approved EV policies with a mixture of fiscal and non-fiscal interventions. These interventions include exemptions in road tax, registration fees, etc. (Table 1).

3.3 Uttar Pradesh EV Manufacturing and Mobility Policy Uttar Pradesh EV manufacturing and Mobility Policy, 2019 provides exemption from registration fees and road tax for first 1 lakh buyers of EVs manufactured within Uttar

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Table 1 Comparative assessment of EV Policies and incentives around the world S. No

Country

Fiscal incentives

Non fiscal incentives

1

China

Government subsidies, insurance discounts, vehicle inspection fee exemption, exemption for vehicle registration fees

parking benefits, dedicated EV registration channel, dedicated license plates, no driving restriction, toll exemption, development of city charging

2

Canada

Subsidies for EV purchase

Zero emission vehicle, unrestricted access for EVs in the High occupancy Vehicle (HOV)

3

United States

Incentives for EV development, incentives for EVSE purchases

Development of city charging, vehicle inspections or emissions test exemptions, electricity rate reduction for EV charging

4

South Korea

Tax rebates for EV purchase, subsidy for installation cost of public charging infrastructure

Development of city charging, development of special charging zones

5

Japan

Japan’s incentive for New Clean Energy Vehicle Purchases

Japan 2030 fuel economy standards

6

Norway

Exemptions for purchase tax, VAT, Charging rights for people living in and an 80% reduction for apartment buildings registration tax

Pradesh. The policy duration of UP EV policy is 5 years. The major target of the policy includes induction of 1,000 e-buses in a phased manner. The policy sets an ambitious target of achieving 70% e-mobility in public transportation in 10 identified cities by the year 2024 and all the cities by 2030. It promises rolling out nearly 10 lakh EVs by 2024 and to set up nearly 2 lakh slow and fast charging, swapping stations by 2024. This policy majorly aligns towards bringing in investments for setting up manufacturing units of high density power storage of at least 5GWH capacity. It provides capital subsidies to service units on fixed capital investments. These include capital interest subsidy, stamp duty and electricity duty exemption, SGST reimbursement etc. State government will encourage EV manufacturers to establish recycling service outlets and cooperate with battery manufacturing units and scrap merchants to build regional recycling systems.

4 Methodology A mix methodological approach comprising of both primary and secondary research was utilised for this study (Fig. 3). As a first step, policies and relevant literature related to electric vehicles pertaining to national level, state level and city level interventions were studied. These documents included FAME Scheme, Uttar Pradesh

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EV policy, National Electric Mobility Mission Plan 2020, etc. Table 2 highlights the type of data used in this study. For the first objective to study the growth of EVs in Lucknow, statistical data for vehicle registrations (at national level as well as Lucknow level) was collected through vahan dashboard. Further, two scenarios were build to analyse the future growth of electric vehicles based on geometric population forecasting method. First scenario (Business as usual scenario) considered average growth of vehicle registrations of last 3 consecutive years. Second scenario (30% EV by the year 2030) considered 30% share of electric vehicles out of newly registered vehicles nationally. A comparative analysis of these scenarios was done to showcase the possible projections of EVs in Lucknow. For the second objective, a set of indicators was framed based on secondary research and review of literature to assess the EV readiness of the study area. These indicators included criterion from planning, policy, technological and infrastructural interventions, comprising of both qualitative and quantitative data. Each indicator was given equal scoring. Since the benchmarks for these indicators were not present during the time of the study, therefore benchmarks were created based on the bestperforming city with Indian context. A google survey form was floated across the stakeholders (End-users, Think Tanks, State/City Government Officials, OEMs) to assess the importance of different factors involved in electric vehicle readiness as well as to identify the potential challenges in the deployment of electric vehicles in Lucknow. Total of 24 samples were collected through this survey. Same questionnaire was floated across these stakeholders to understand the challenges and bottlenecks for EV adoption in Lucknow. Following scenarios were utilised for projecting EV growth1. Business as usual scenario—In this scenario, average growth rate was taken to be constant and projections were made based on geometric projection method. This scenario showcases the existing scenario and it was extended till the year 2030. 2. EV 30 by 30—In this scenario, India’s target of achieving 30% e-mobility has been taken into consideration. Average Compounded Annual Growth rate (CAGR) of vehicle registrations was used to forecast new registrations nationally. 30% share of EVs was considered out of the newly registered vehicles by the year 2030. Linearly the % of EVs out of new registrations was increased till the year 2030. Out of India’s total EV registrations, 27% were accounted for the state of Uttar Pradesh (as per existing situation). 12% EVs out of the share of Uttar Pradesh were projected for Lucknow. Based on EV projections in the first objective, capacity need assessment of Lucknow was conducted. Responses from google form were analysed for the second objective. Overall, this study will aid in structuring the strategies to be adopted for increasing EV readiness in terms of EV infrastructure as well as policy interventions.

164 Table 2 Types of data

P. Saxena

S. No

Data utilised

Type

1

Statistical data for vehicular registrations

Secondary data

2

EV readiness survey (Google form)

Primary data

Fig. 3 Methodology adopted under the study

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5 Results 5.1 To Project the EV Growth in Lucknow by 2030 and Estimate Infrastructure Requirements Despite of decreasing vehicle sales owing to the pandemic during the year 2019–20, electric vehicle’s growth in India has overall increased from 2019 to 2021. Figure 4 shows total vehicle registrations along with EV registrations in India, Uttar Pradesh and Lucknow. According to the vahan dashboard, only 1.35% of the total vehicular registrations till March 2022 were EVs, therefore indicating the initial level of penetration of electric vehicles in Lucknow. To assess the growth of vehicles, average growth rate of three consecutive years (2019, 2020, 2021) were taken into account. Geometric projection method was used to project the growth of EVs in Lucknow. Tables 3 and 4 indicate the growth rate (%) of vehicle registrations and EV registrations, respectively. It is clearly evident from the data that even though growth rate of total vehicle registrations has decreased, growth rate of EVs has increased (except for 2019–20 due to the pandemic). In Lucknow, EV registrations (per year) have increased from 2018 to 2021 by approx. 67% (Fig. 5).

Fig. 4 Vehicle registrations till March 2022. Source Vahan Dashboard

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Table 3 Growth rate (%) of vehicle registrations

India

UP

Lucknow

2019

−5.51

0.19

−10.11

2020

−23.69

−21.93

−28.49

2021

2.62

−28.49

2.24

Source Vahan Dashboard

Table 4 Growth rate (%) of EV registrations

India

UP

Lucknow

2018–19

22

5

−19

2019–20

−26

−44

−18

2020–21

64

113

150

Average

20.0

24.7

37.7

Source Vahan Dashboard

EV Registrations (per year) - Lucknow 8,000 6,975

EV registrations

7,000 6,000 5,000

4,188

4,000

3,413

2,789

3,000 2,000 1,000 0 2018

2019

Year

2020

2021

Fig. 5 EV registrations (per year) in Lucknow. Source Vahan Dashboard

5.1.1

Scenario:1 [Business as Usual Scenario]

For projecting EV growth for Business as usual scenario, average growth rate of past three consecutive years (2019, 2020 and 2021) was taken into consideration. EV registrations were projected using geometric progression method (Table 5).

5.1.2

Inferences

As per the projection in BAU scenario, it is evident that the target for National Electric Mobility Mission Plan (NEMMP) would be achieved by the year 2031. Similarly,

Assessing Electric Vehicle (EV) Readiness of an Indian City: A Case … Table 5 Scenario 1 [Business As Usual] projections till 2030

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EV registrations (Cumulative) Year

India

2022

12,97,367

UP 3,64,816

Lucknow 47,787

2023

15,56,840

4,54,926

65,803

2024

18,68,208

5,67,292

90,611

2025

22,41,850

7,07,413

1,24,771

2026

26,90,220

8,82,145

1,71,810

2027

32,28,264

11,00,034

2,36,583

2028

38,73,917

13,71,743

3,25,775

2029

46,48,700

17,10,563

4,48,592

2030

55,78,440

21,33,072

6,17,711

Source Analysis

the target of Uttar Pradesh EV policy of rolling out 10 lakh+ EVs would be achieved by the year 2027. This scenario projects that Lucknow will have 6 lakh+ EVs on road by the year 2030, thereby imparting that the share of EVs in Lucknow is projected to increase from the current 4% to 11% by the year 2030 (with respect to the total EV registrations in India). By the end of the year 2030, Uttar Pradesh will have 21 lakh+ EVs on road.

5.1.3

Scenario:2 [30% EV Share by 2030]

For projecting growth of EVs in this scenario, % share of EVs was increased linearly from existing share to 30% by the year 2030. Using geometric projection method. For this scenario, % share of EV was increased linearly from existing share to 30% by the year 2030. Table 6 shows the EV growth in India, Uttar Pradesh, and Lucknow, based on 1. EV registrations in India were taken to be 30% of new vehicle registrations. 2. EV registrations in Uttar Pradesh were taken to be 27% of the total EVs registered in India. This is in line with the current scenario 3. EV registrations in Lucknow were taken to be 12% of EVs registered in Uttar Pradesh. 4. Geometric Projection Method was used to project the growth of EVs. 5.1.4

Inferences

In order for India to achieve 30% EV share by the year 2030, EV registrations will likely increase by 100% by 2030 as compared to the year 2022. NEMMP targets would be achieved by the year 2026. Target of Uttar Pradesh EV policy of rolling out 10 lakh+ EVs would be achieved by mid-2025. Share of EVs in Lucknow is projected

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Table 6 Scenario 2 [30% EV share by the year 2030] 2022

New registrations (India)

EVs registrations in India

2,59,72,514

12,98,626

UP

Lucknow

3,50,629

42,075 74,595

2023

2,83,36,013

23,02,301

6,21,621

2024

3,09,14,590

34,77,891

9,39,031

1,12,684

2025

3,37,27,818

48,48,374

13,09,061

1,57,087

2026

3,67,97,049

64,39,484

17,38,661

2,08,639

2027

4,01,45,581

82,80,026

22,35,607

2,68,273

2028

4,37,98,828

1,04,02,222

28,08,600

3,37,032

2029

4,77,84,522

1,28,42,090

34,67,364

4,16,084

2030

5,21,32,913

1,56,39,874

42,22,766

5,06,732

Source Analysis

to decrease from the current 4% to 3.2% (with respect to the total EV registrations in India) (Fig. 6). It is clear from both the scenario that EV registrations would increase at a higher rate in future. This massive increase in EV registrations would require major push for EV infrastructure in Lucknow. NITI Aayog’s provisioning norms for EV charging stations mandates the following number of Charging points with vehicle segment classification (Table 7). Scenario Analysis - EV projections for Lucknow 7,00,000 6,17,711

No. of Electric Vehicles

6,00,000 4,16,084 5,06,732

5,00,000 4,00,000

4,48,592

3,37,032 2,68,273

3,00,000

3,25,775 2,08,639

2,00,000

1,57,087

2,36,583

1,12,684

1,00,000 0 2021

1,71,810

74,595 42,075 47,787

2022

1,24,771 65,803

2023

90,611

2024

2025

Scenario- 1 (Business As Usual)

2026 Year

2027

2028

2029

Scenario -2 (30% EV by 2030)

Fig. 6 EV projections in Lucknow based on two scenarios. Source Analysis

2030

2031

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Table 7 Provision norms for charging points Provision norms for charging points

4Ws

3Ws

2Ws

PV(buses)

1 SC per 3 EVs

1 SC per 2 EVs

1 SC per 2 EVs

1 FC per 10 EVs

Source NITI Aayog

As per the amendment in the MBBL- 2016 by the Ministry of Housing and Urban Affairs, at least one public charging station should be available in a grid of 3 km × 3 km. Therefore, Lucknow will need minimum 281 public charging stations. Total charging points required by 2030 as per existing provisioning norms would be 2,86,206 charging points.

5.2 To Assess the Current EV Readiness of Lucknow and Understand the Challenges in EV Adoption EV Readiness: EV Readiness is defined as the degree to which adoption of EVs is supported, as reflected in the presence of various types of policy instruments, infrastructure development, municipal investments in PEV technology and participation in relevant stakeholder coalitions (Clark-Sutton et al., 2016). Owing to the non-availability of benchmarks for EV readiness, benchmarking was done based on the best-performing Indian city for that particular indicator. Existing EV scenario of different Indian cities were also studied along with the study area to get a better understanding of existing EV situation. In case of Yes/No Indicators, full score were assigned in case of Yes and 0 was assigned in case of No. Full weightages were given to each of the selected indicators. A maximum score of 100 was equally divided across final 15 indicators. In this manner, each indicator carried score of ‘0’ as minimum and ‘6.67’ as maximum (Tables 8 and 9). The total score of Lucknow for EV readiness assessment turns out to be 51.60 (Average). The city lacks proper infrastructure for EV charging, non-fiscal privileges such as reserved EV parking lanes etc. There is no separate institution for EVs in Lucknow. Although EV charging stations are present in Lucknow, the share of PCS with respect to the national share is very less. Several studies show that less share of PCS in a city leads to low penetration of EVs in that area. The city also lacks in electrification of third-party fleets. Thus, there is an urgent need to augment the capacity of public charging stations as well as regulatory frameworks for electrification of cab fleets and government fleets.

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Table 8 Benchmarks and scoring methodology S. No

Indicators

Unit

Benchmark

Scoring methodology

1

Share of PCS in Lucknow w.r.t. PCS in India

Nos

Maximum share of PCS is Relative scoring 34% in Delhi w.r.t. maximum in Indian city

2

Ratio of EV per charging station

Nos

15

International Benchmark

3

Charging points per 1,000 EVs

Nos

1.32 in case of Delhi (Average charging points per charging station = 2.5) * CEEW- e-mobility dashboard

Relative scoring w.r.t. maximum in Indian city

4

EVs per 1,000 persons

Nos

Maximum value = 35.8 ( Delhi)

Relative scoring w.r.t. maximum in Indian city

5

Ratio of e-buses w.r.t. total bus fleet (city government)

Nos

70%

Relative scoring w.r.t. maximum in Indian city

6

Share of renewable sources of generation (State-wise)

%

National average share (2021)—24.5%

Relative scoring w.r.t. national average share of renewable source

7

Electricity tariff

Rs/ Unit

7.8

Absolute score if electricity tariff is less than national average

8

Presence of battery swap stations Yes/ No

Yes: 6.67 No: 0

9

Presence of fiscal incentives for EV adoption

Yes/ No

10

Presence of a separate institutional body for EVs

Yes/ No

Absolute scoring for these indicators: Yes = 6.67 score No = 0 score

11

Presence of e-mobility targets in City’s climate action plan

Yes/ No

12

Parking privileges—Whether the Yes/ city offers free, reduced cost, or No reserved parking for EV owners

13

Mandatory registrations for EV in DCR/bye-laws

Yes/ No

14

EVSE permitting—Whether the city has streamlined the permitting process for installing EVSE

Yes/ No

(continued)

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Table 8 (continued) S. No

Indicators

Unit

15

EV car sharing service—Whether an EV car sharing service is offered in the city

Yes/ No

Benchmark

Scoring methodology

Table 9 EV readiness scoring S. No

Indicators

Value

Scoring

1

Share of PCS in Lucknow w.r.t. PCS in India

0.63%

0.12

2

Ratio of EVs per charging station

227.5

0

3

Charging points per 1,000 EVs

0.81

4.09

4

EVs per 1,000 persons

6.526

1.22

5

Ratio of e-buses w.r.t. total bus fleet (city government)

15%

1.43

6

Share of renewable sources of generation (State-wise)

17%

4.72

7

Electricity Tariff

7.5

6.67

8

Presence of Battery Swap Stations

Yes

6.67

9

Presence of fiscal incentives for EV adoption

Yes

6.67

10

Presence of a separate institutional body for EVs

No

0

11

Presence of e-mobility targets in city’s climate action plan

Yes

6.67

12

Parking privileges- Whether the city offers free, reduced cost, or reserved parking for EV owners

No

0

13

Mandatory registrations for EV in DCR/bye-laws

Yes

6.67

14

EVSE permitting

Yes

6.67

15

EV car sharing service

No

0

Total score

51.60

Challenges and Bottlenecks for EV Adoption in Lucknow A Google form-based pilot survey was floated across various stakeholders involved in the e-mobility sector in Lucknow. The sample constituted 60% male respondents and 40% female respondents. Figure 7 indicates the age distribution of the sample. As per the survey, the average scoring of Lucknow in terms of EV readiness with respect to other Indian cities turned out to be 2.8/5, i.e. 56% (Fig. 8). The above chart indicates the current status of EV penetration in different sectors in Lucknow (Fig. 9). Although the share of e-buses in the total bus fleet is only 15%, the city possess high EV penetration in public transportation with respect to other categories. Penetration of EVs is very less in private transport which is a major challenge. Survey also indicated low penetration in commercial and government

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Fig. 7 Age classification of the respondents. Source Primary survey

Think Tank/ Research Institute 20%

City /State Governme nt 8%

STAKEHOLDERS

OEM 4%

End-users 68%

Fig. 8 Distribution of the stakeholders. Source Primary survey

EV Penetration in Different Sectors No. of Responses

20 15 10 5 0 Public Transport

Private Transport Government Fleet

Very Low

Low

Medium

Commercial Transport

High

Fig. 9 EV penetration in Lucknow. Source Primary survey

fleets which could be worked out by regulatory frameworks for the electrification of government fleet. Analysis of survey data resulted in exploring the importance of different factors responsible for EV adoption in Lucknow. 88% of the respondents categorised the

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% of responses

Role of Factors in EV adoption 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Purchase Price

Extremely Important

No of EV models

Very Important

Availability of charging infrastructure

Somewhat Important

Fiscal Incentives

Less Important

Fig. 10 Factors affecting EV adoption in Lucknow. Source Primary survey

availability of EV charging station as a very important factor for EV adoption in Lucknow (Fig. 10). Figure 11 highlights the challenges in EV adoption, based on the primary survey. Fig. 11 Challenges in EV adoption. Source Primary survey

Infrastructure Planning

Behavioural change Challenges for EV adoption in Lucknow

Competitive Pricing of EVs

Lack of awareness among citizens

Lack of Charging infrastructure

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P. Saxena

6 Conclusion The estimated EV stock in Lucknow by the year 2030 is expected to cross 5 lakh+ EVs in the BAU scenario and 6 lakh+ EVs in EV30 by 2030 scenario. Owing to this massive increase in EVs, Lucknow will require a major push for EV infrastructure. To support the growth in the number of EVs, Lucknow will require approximately 2,86,206 charging points by the year 2030, thereby inducing a need for adequate provisioning of charging infrastructure within the city. Secondary research shows that less share of public charging stations leads to low penetration of EVs in a region. Till March 2022, there were 227 EVs per public charging station, which is very high. This justifies the lack of available public charging stations. Lucknow is majorly dominated by 2W segment as it accounts for 73% of the total vehicle registrations. Light motor vehicles account for 20% whereas medium and heavy-duty vehicles constitute 3% of the total vehicle registrations till March 2022. Existing EV readiness in Lucknow turns out to be average, totalling to a score of 51.60. The city lacks public charging infrastructure, non-fiscal incentives and mandatory provisions for transition of government fleet. For this, government should increase the pace of electrification of its fleet to encourage EV transition. The pace of electrification of public transport can be increased. Electric buses are present in Lucknow but it is only 15% of the total bus fleet. Recently, 35 more e-buses have been added in July 2022. UP EV policy allows incentives for EVs manufacturers only in Uttar Pradesh. Creation of a dedicated EV cell will aid in better implementation of EV policy as it will integrate different stakeholders involved for e-mobility in one platform. As per the pilot survey, 50% of the respondents were not aware of any awareness programs for e-mobility in Lucknow, therefore consumer awareness campaigns could be designed to increase the social capacity of the city.

7 Limitations of the Study 1. This study is valid for Lucknow only. 2. This study was conducted in a short duration of 4 months. 3. Unavailability of benchmarks for EV readiness.

References Clark-Sutton, K., Siddiki, S., Carley, S., Wanner, C., Rupp, J., & Graham, J. (2016). Plug-in electric vehicle readiness: Rating cities in the United States. The Electricity Journal, 29(1), 30–40. Jain, N., & Lutken, T. (2020). Mapping U.S.-India partnerships in electric mobility. Center for Strategic and International Studies.

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NITI Aayog. (2021). Status quo analysis of various segments of electric mobility and low carbon passenger road transport in India. Delhi: GIZ. NITI Aayog et al. (2021). Handbook of electric vehicle charging infrastructure implementation Version-1. https://www.niti.gov.in/sites/default/files/2021-08/HandbookforEVChargingInf rastructureImplementation081221.pdf Ojha, A., Kalra, N., Ranjan, A., & Singh, T. (2021). Study on electric vehicles in Delhi NCR: Future prospects and possibilities. International Research Journal of Engineering and Technology, 8(9). International Energy Agency. India 2020. Available at: https://www.iea.org/reports/india-2020 Singh, V., Singh, V., & Vaibhav, S. (2021). Analysis of electric vehicle trends, development and policies in India. Case Studies on Transport Policy, 9(3), 1180–1197. Ministry of Heavy Industries (MHI). National Electric Mobility Mission Plan.(2020). Available at: https://heavyindustries.gov.in/writereaddata/Content/NEMMP2020.pdf The Energy and Resources Institute (TERI). (2022). Readiness and capacity needs assessment for electric vehicle adoption in Indian cities. New Delhi: TERI Uttar Pradesh Electric Vehicle Manufacturing and Mobility Policy. (2019). World Economic Forum. (2019). EV-Ready India Part 1: Value chain analysis of state EV policies. World Economic Forum.

An Empirical Investigation into Electric Vehicle Adoption in Urban Freight—A Case Study of Delhi Saloni Gupta and Sanjay Gupta

Abstract The rapid expansion of urban delivery vehicles in India as a result of rising freight demand will have a significant detrimental effect on the environment in the years ahead. To accomplish the objectives of rapid decarbonization, particularly from freight, there is a growing need to rely on recent technological advances in delivery systems, where electrification is critical. In the last 10 years, the Government of India has developed a lot of incentives for EVs. However, despite all these efforts, the market for EVs in India hasn’t picked up as expected, particularly in the urban freight segment. Through an empirical analysis in the city of Delhi, the present paper attempts to emphasize the preferences and constraints confronting urban freight operators in the adoption of EVs in their delivery operations. To calculate the weights of various barriers to EV adoption and potential solutions, the Analytical Hierarchy Process (AHP) was used. According to the study, EV adoption is hampered by a lack of universal charging infrastructure, short ranges, and restricted payload capacity, which prevents EVs from being widely used in the urban freight sector. Keywords Urban freight · Multi criteria decision making · Decarbonization · Electric vehicle

1 Introduction Cities housed more than half of the world’s population in 2010. At the same time, this figure surpassed 80% in developed countries such as the United States, Canada, and Europe (Holguín-Veras, 2015). Estimates suggest that urban residency is expected to increase 1.5 times by 2045 (World Bank, 2015). Cities create over 80% of global GDP, and with rising urbanization demand and the need for additional products deliveries S. Gupta (B) · S. Gupta Centre for Urban Freight Studies (CUFS), Transport Planning Department, School of Planning and Architecture, New Delhi, India e-mail: [email protected] S. Gupta e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_9

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to these areas are likely to rise (GIZ, 2010). The urban goods movement has always played an important part in city dynamics. “Almost all global trade originates passes through or ends in a metropolitan area” (VREF and RPA, 2016). The reason for this is that transportation of goods within cities is critical, as cities are economic and social hubs (GIZ, 2010). “Urban freight traffic accounts for about 10–15% of kilometres traveled” (Stefanelli et al., 2015). It employs between 2 and 5% of all city workers, and it is estimated that 3–5% of metropolitan territory is dedicated to logistical activities (Macário, 2012). Commodities leaving metropolitan areas account for 20– 25% of freight vehicle kilometres, whereas entering goods account for 40–50%. The remainder is related to internal exchange (Stefanelli et al., 2015). Furthermore, goods produced in cities account for “0.1 shipment per person per day; 1 shipment per economic activity per week; between 300 and 400 goods vehicle trips per 1,000 persons per day; and between 30 and 50 tonnes per person per year” (Dablanc, 2009). A similar situation has been observed in India like in so many other countries. Goods movement within and outside of Delhi has increased significantly for a long time. According to a temporal analysis of freight traffic, the number of goods travelling into and out of the city increased from 184,946 tonnes in 1993 to 265,807 tonnes in 1996, a 12.9% annual growth rate (Gupta, 2017). According to NITI AAYOG, the urban logistics sector is expected to grow at a CAGR of 10.5% (NITI Aayog and RMI, 2018). Although the urban freight sector is necessary for the city’s growth and economic development, it is crucial to remember that it is associated with negative externalities such as GHG emissions, congestion, noise pollution, parking challenges, and so on. Globally, transportation accounts for 14% of greenhouse gas emissions, with freight accounting for 48% of the emissions (Fried et al., 2020). According to estimates, “Urban supply chain accounts for 16% of surface freight CO2 emissions” (SLOCAT Partnership, 2018). Around 25% of urban vehicles in a typical European metropolis are freight vehicles. They account for around 20–30% of vehicle kilometers travelled and are responsible for 16–50% of carbon emissions (Rommerts, 2011; SanchezDiaz & Browne, 2018). Some research additionally indicates that “urban goods distribution is liable for 25% of urban transport-related CO2 emissions and 30–50% of other transport-related pollutants”. Similarly, freight accounts for 40% of total transport emissions in India (ALICE and ERTRAC, 2014; GIZ, 2010, 2016). Lack of knowledge and information on the urban goods movement patterns, the disintegration of stakeholders’ understanding, as well as overall vision of urban goods movement, are all factors that act as impediments to efficient city logistics. There is also a lack of an urban freight policy framework that emphasizes the evaluation of multiple stakeholders’ perspectives. It is worth noting that passenger and freight transportation accounted for approximately 59% and 41% of worldwide transport energy consumption and emissions, respectively. Despite accounting for a sizable portion of overall emissions, urban freight transportation is frequently overlooked in the planning and policymaking processes. The purpose of this study is to look at the key challenges and potential implementation strategies associated with the use of small electric freight vehicles in India. The following is how the paper is structured: The second section examines the literature on the use of electric freight

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vehicles. Section 3 describes the methods and data collection for the paper. Section 4 delves deeply into the findings, while Sect. 5 concludes with a conclusion and policy recommendation.

2 Potential of Electric Vehicles in Urban Freight The Electric Freight Vehicles (EFVs) differ from the Electric Vehicles (EVs) in several ways, including infrastructure requirements, vehicle size, and technologies. While acquiring an EV is an individual decision, adopting an EFV requires a group decision that considers various logistics and operational aspects (˙Imre et al., 2021). Even though the study’s primary goal is to identify the barriers and enabling strategies to the electrification of Light Good Vehicle (LGV)/Light Commercial Vehicles (LCV); using the EFV literature as a guide, we have included data and conclusions from EFV-related studies whenever possible. According to a Dutch study, the average cargo size of urban commercial vehicles varies between 130 and 420 kilogrammes per trip, depending on the type of commodity (van Amstel et al., 2018). Only a small portion of a delivery van’s payload is used. LFVs are undoubtedly a better solution for clean and efficient city logistics having an enormous electrification potential. “EFVs offer an efficient and promising solution to the environmental issues associated with urban freight” (van Duin et al., 2013). This idea is supported by certain small-scale pilot trials. For example, as per Giordano et al. (2018) CO2 emissions in cities where new EFVs replace old diesel vans and the energy mix is reasonably clean can be reduced by 93–98% and 85–99%, respectively. When EFVs using coal-fired power are compared to new diesel vans, CO2 emissions and air pollutants are reduced by 12–13% and 25–92%, respectively. Muñoz-Villamizar et al. (2018) estimated that when EFVs that are operated for more than three years, are found to be more economical and environmentally friendly than conventional ICE freight. These findings suggest that the economic competitiveness of EFVs improves with time, in tandem with advancements in vehicle and battery technology. A similar study conducted in the Netherlands shows that the use of EFVs generated a positive NPV after 4 years, with a total emission reduction potential estimated at 60% (van Duin et al., 2013). Particularly in the case of LCVs, as per Browne et al. (2010) use of this specific segment in urban goods distribution is important for several reasons 1. This segment is responsible for last-mile delivery of many time-critical commodities; 2. LCVs outnumber HCVs on urban roads; 3. LCVs frequently spend a much greater proportion of their total journey in urban areas than HGVs; and 4. The LCV fleet consumes a large amount of fossil fuel and thus contributes significantly to CO2 emissions. The use of EFVs can also reduce total transportation costs, for example, the use of Light electric freight vehicles (LEFVs) in the hospitality industry resulted in a reduction of total transportation costs in the range of 50–60%, and when a fresh food provider for ready-to-eat meal box used LEFVs on one of the 3 routes, a saving of 37% of transport costs was observed (van Amstel et al., 2018).

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However, a variety of challenges such as range anxiety, insufficient charging/ battery swapping facilities, limited financing options, and many others impede the increased adoption of electric vehicles in the city cargo sector (Conrad et al., 2011; Felipe et al., 2014; Goeke et al., 2015; Martins-Turner et al., 2020; Montoya et al., 2017; Pelletier et al., 2016; Quak et al., 2016b). An analysis of drivers’ experience and attitudes towards EFVs in the London area displayed that around 15% of drivers reported being constantly or frequently anxious about running out of battery power, but overall, 72% support replacing diesel-powered vehicles (EREVUE, 2017). Insufficient vehicle range has also been determined to be an obstacle to EFV adoption (van Duin et al., 2013). The Delphi assessment on hurdles to freight electric vehicles in London indicated that the primary barriers are cost and vehicle performance (Ablola et al., 2015). As per Melander et al. (2022) “Uncertainty regarding political and regulatory uncertainties, technological and infrastructure-related concerns, and operational uncertainty were identified as major impediments,” according to the report and emphasise that government incentives and specific regulatory environments, such as low-emission zones, are the most sought-after electrification drivers. According to a study conducted in Germany, the ability to access low-emission zones, the total cost of ownership (TCO), and emission reductions were discovered to be motivators of EFV adapatability. The obstacles such as high purchasing price, charging infrastructure, and range limitations were reported in the literature (Taefi et al., 2016). Less payload capacity has also been observed to be “an important issue for the adaptation of electric vehicles in urban freight” (Rizet et al., 2016). According to a user preference analysis conducted by transportation operators, the most important measure to spread the use of EFV is to develop an “extensive charging infrastructure and to enforce monetary incentives through subsidisation or tax exemption” (Lebeau et al., 2016).

3 Methodology and Data Collection The current paper attempts to highlight the preferences and issues confronting small commercial vehicle operators regarding the adoption of EVs in their freight operations through an empirical investigation. In the South Delhi area, a sample of 300 small commercial vehicle operators was counted using face-to-face interviews. The interviews were conducted near wholesale markets and warehouses to assess their general awareness of commercial electric vehicles and altitudinal responses to identify various barriers and challenges, as well as potential remedial strategies, to scale up EV adoption in the light commercial vehicle segment. The Analytical Hierarchy Process (AHP) was used to determine the weights to various barriers and challenges to EV adoption, as well as preferences for potential strategies. These 300 samples are made up of responses with consistency ratios of less than 10%. Based on the ownership and type of fuel used, the stakeholders (transport operators) have been divided into three categories, first Self-owned/Rented vehicles using

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CNG/Petrol; second vehicles owned by companies using CNG/Petrol, and third Electric Vehicles. An extensive literature review was carried out to identify the impediments, which were further classified as Technical, Economic, Infrastructural, and Social (refer Table 1). A comprehensive list of potential solution and strategies were also collated from the literature. This includes extensive EV charging infrastructure; Awareness Campaigns for logistics providers, Financing from public banks, and the provision of more subsidies (Haddadian et al., 2015; Kim et al., 2019; Ling et al., 2021). Table 1 shows the categorization of barriers related to electric vehicle adoption. Analysis of responses related to general awareness related to electric freight vehicle state that 91% of the vehicle operators were aware of E-Vehicles used for Urban Goods Movement. Of this, 71% were aware of the subsidies for EVs. However, 32% said that they are somewhat familiar with the subsidies and the rest 68% said that they had just heard about them. Table 1 Categorization of barriers related to electric vehicle adaptation Barrier classifications

Barriers

Source

Technological

Less electric range/charge

Bonges et al. (2016), Jensen et al. (2013), Quak et al. (2016a)

Less battery life

Carley et al. (2013), Haddadian et al. (2015), Pelletier et al. (2014)

Few EV models

Albertus (2010), Haddadian et al. (2015), Quak et al. (2016a), Xue et al. (2014)

Economical

Infrastructural

Social

High cost of vehicle

Adhikari et al. (2020), Cherchi (2017)

High battery replacement cost

Carley et al. (2013), Long (2012), Sovacool et al. (2009)

Limited financing options

Gan (2003), Mock et al. (2014), Wikström et al. (2016)

High price on charging

Kim et al. (2018)

Inadequate charging infrastructure

Haddadian et al. (2015), Jensen et al. (2013), Mock et al. (2014)

Absence of battery swapping facility

Ahmad et al. (2020), Yang et al. (2021)

Less repair and maintenance shops

Quak et al. (2016a), Weiller et al. (2014)

General awareness related to EV

Broadbent et al. (2017), Lutsey et al. (2015)

Anxiety related to reliability and performance

Franke et al. (2012), Quak et al. (2016a), Xue et al. (2014)

Lack of awareness regarding Carley et al. (2013), Wikström et al. (2016) emission reduction potential of EV

182 Table 2 Weights of categories of barriers

S. Gupta and S. Gupta

Barrier category Self/rented vehicles (CNG/petrol) (%)

Owned by company (CNG/petrol) (%)

Electric vehicles (%)

Infrastructural

35.8

33.9

33.4

Economical

22.5

11.6

11.6

Technological

29.5

39.0

41.2

Social

12.2

15.5

13.8

4 Results and Discussion 4.1 Weights of Categories of Barrier The stakeholder category-wise estimation of AHP results is shown in Table 2. In this case, infrastructure barriers are especially significant for self-owned/rented vehicles (35.8%). However, in the case of company-owned vehicles, technological barriers are most critical, with a weightage of 39%. Similarly, in the case of electric vehicles, which are mostly owned by companies, the technological barrier is the most critical, accounting for 41.2% of the total weightage. This is because, in the current system, all infrastructure-related facilities are provided by the businesses that own the vehicles.

4.2 Weightage Within Barrier Categories The percentage weights of the barriers within each category were then estimated using AHP analysis. Table 3 shows that in terms of technological barriers, less electrical range per charge is ranked first by all the stakeholders with a weightage of 67% for self/rental vehicles, 68% for company-owned CNG/Petrol vehicles, and 74% for electric vehicles. This suggests that the largest technological constraint is the limited range per charge. Economical barriers analysis indicates that high vehicle costs are ranked as the most important impediments in the case of Self/Rented vehicles, with a weightage of 56.6%. Whereas in the case of both company-owned vehicles and electric vehicles, higher charging costs are considered the most significant barrier, with weights of 40.9% and 51.7%, respectively. With regards to infrastructural barriers, inadequate charging infrastructure is ranked as the most important barrier by Self/Rented vehicle samples, whereas the absence of battery swapping facility is ranked as the most important barrier by company-owned CNG/Petrol vehicles and Electric vehicle samples, with weights of 52.8% and 65.5%, respectively. This is because most companies have charging stations, however, self-owned/rented vehicle operators will be required to use a public

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Table 3 Weights within category of barriers Barriers

Self/rented vehicles (CNG/petrol) (%)

Owned by company (CNG/petrol) (%)

Electric vehicles (%)

Less electric range/charge

67

68

74

Less battery life

30

18

22.2

Few EV models

3.6

14

4

High cost of vehicle

56.6

11.9

12.7

High battery replacement cost

27.5

29.7

19.3

Limited financing options

4.7

17.5

16.2

High price on charging

11.2

40.9

51.7

Inadequate charging infrastructure

77.9

14

9.5

Less repair and maintenance shops

14.3

33.3

25

Absence of battery swapping facility

7.9

52.8

65.5

Poor awareness

27

33

31

Anxiety related to reliability and performance

62

62

65

Lack of knowledge on EV’s ability to reduce emissions

11

5

4

Technological barriers

Economical barriers

Infrastructural barriers

Social barriers

changing facility. Within the social barrier category, anxiety related to reliability and performance is the most important obstacle across all the 3 categories of stakeholders.

4.3 Global Weights of Barriers The priority weight of each category was multiplied by the priority weight of the relevant barrier within each category to obtain the overall weight of the barriers. This was done separately for each of the three stakeholder groups of vehicle operators. The global weights of barriers are determined by the geometric means of individual responses across barrier categories and stakeholder categories (refer Table 4). As per the analysis of global weights presented in Table 4, less electric range/ charge (25%), inadequate charging infrastructure (17%), and absence of a battery swapping facility (14%) were identified as the top three barriers. These were followed by issues such as less battery life, and fewer repair and maintenance shops having a weightage of 8% which can be categorized as moderately impacting barriers. High cost of the vehicle, few EV models, and lack of awareness regarding the emission

184 Table 4 AHP weights of barriers for small EFV adaptation

S. Gupta and S. Gupta

Barriers of small EFV adaption

AHP weights (%)

Less electric range/charge

25

Inadequate charging infrastructure

17

Absence of battery swapping facility

14

Less battery life

8

Less repair and maintenance shops

8

Anxiety related to reliability and performance

6

High price on charging

4

General awareness related to EV

4

High battery replacement cost

4

Limited financing options

3

High cost of vehicle

3

Few EV Models

2

Lack of knowledge on EV’s ability to reduce emissions

2

reduction potential of EV, were identified as the least affecting barriers related to the widespread infiltration specifically for small EFV in the Indian freight distribution system.

4.4 Ranking of Potential Solutions and Strategies The AHP analytical technique was used to rank the potential solutions as strategies assisting in the adaption of small EFV. The local weights were computed separately for each stakeholder type, and global weights were also determined. All the stakeholders are of the opinion that extensive EV charging infrastructure is the most preferred solution strategy having a weightage of 62%. This was followed by solution strategies with include awareness campaigns for logistics providers (21%) and financing from public banks (10%). The least preferred strategy by stakeholders was the provision of more subsidies having a weightage of 7% (refer Table 5).

4.5 Total Cost of Ownership Contrary to popular belief, the high cost of the vehicle has the least impact on the global weights of barriers. To quantify the financial impact of small EFV, a comparative analysis of the total cost of ownership (TCO) of small EFV and GNG freight vehicles was performed using the methodology developed by TERI (2021) and

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Table 5 Ranking of potential solutions and strategies Solutions and strategies

Stakeholder local weights

Global weights

Self/rented vehicles (CNG/petrol) (%)

Owned by company (CNG/petrol) (%)

Electric vehicles (%)

Extensive EV charging infrastructure

56.6

66.8

62.1

62

Awareness campaigns for logistics providers

31.8

13.7

22.2

21

Financing from public banks

9.2

11

9.4

10

More subsidies

5.5

6.8

6.2

7

Kumar et al. (2020). Capital cost, operational cost, and vehicle operation details are among the input variables. Capital cost components include parameters like vehicle purchase price, discount rate, and resale value. Fuel cost (including electricity), maintenance, and staff costs are examples of operational cost components. According to secondary information, the small EFV costs 538,318 INR, while the GNG small freight vehicle costs 574,162 INR, Assuming a vehicle life of 10 years, a discount rate of 10%, and a capital recovery factor of 0.16 (TRR, 2020), the TCO/km for a small EFV is estimated to be 15.68 INR/km and 18.35 INR/km for a GNG small freight vehicle. Indicating that small EFV is more financially viable and supporting the lower ranking of “High Cost of Vehicle” in AHP analysis.

5 Conclusion and Policy Implication Electrifying urban freight is one of the potential and emergent decarbonisation strategy in the Indian context. The present study reveals that the diverse categories of small commercial vehicles in Delhi are fairly consistent in their preference for EV adoption criteria with the majority of stakeholders are of the opinion that Less Electric Range/Charge followed by inadequate charging infrastructure is the major barrier that hinders that widespread adaption of EV in urban freight. Stakeholder response studies carried out reveal that policy imperatives in terms of strengthening EV charging/battery swapping infrastructure for freight vehicles and ensuring technological improvements in enhancing electric range per charge are desirable strategies to scale up EV adoption in amongst small commercial vehicle operators. The Delhi government has set a goal of deploying 18,000 Charging/battery swapping points by 2024. However, there are still several challenges that need to be addressed. The first one is the High capital cost of setting up a charging/battery swapping station, where there is a need to develop and test various PPP schemes to meet the target of setting up extensive charging infrastructure. Secondly, there is an issue with multiple types of charging connectors. CCS (Combined Charging

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System), CHAdeMO, Bharat DC-001, and AC-001 connectors should all be installed in a single charging station, allowing consumers to choose between fast and slow charging based on their needs and timing. The charging stations should include both slow and fast chargers. Superchargers can also be installed in high-demand areas to reduce charging time. The third consideration is the location of EV charging/battery swapping stations. The charging station’s location or design should be such that it is easily visible, accessible, saves time, and reduces the charging queue. To that end, the location should be based on the area’s overall demand, with features such as ample parking space, accessibility, ease of setup, a convenient waiting area, and so on, so that consumers can charge their EVs at the most convenient time. To locate the charging infrastructure in a way that meets the needs of all stakeholders, more investigation is needed. There is also a need to develop standards and guidelines for the design and placement of charging/battery swapping stations. Additionally, here is a need to ensure land availability for charging infrastructure meant for freight vehicles at appropriate locations in local level layout plans by the concerned urban local bodies, along with a focus on wide spread use of electricity generated via renewable sources. It might also be beneficial to conduct sensitization programmes for small commercial vehicles fleet operators regarding the utility of EVs and various incentives available in the government programs to encourage EV adoption in the freight segment.

References Ablola, M., et al. (2015). Delphi study of the drivers and barriers of electric vehicles in London. In 5th IET Hybrid and Electric Vehicles Conference (HEVC 2014). London: IET. https://doi.org/ 10.1049/cp.2014.0953 Adhikari, et al. (2020). Identification and analysis of barriers against electric vehicle use. Energy Sustainability, 12(12). https://doi.org/10.3390/su12124850 Ahmad, F., et al. (2020). Battery swapping station for electric vehicles: Opportunities and challenges. IET Smart Grid, 3(3), 280–286. https://doi.org/10.1049/iet-stg.2019.0059 Albertus, E. J. (2010). Batteries for electric and hybrid-electric vehicles. Annual Review of Chemical and Biomolecular Engineering, 1. https://doi.org/10.1146/annurev-chembioeng-073009100942 ALICE and ERTRAC. (2014). Urban freight research roadmap. ERTRAC. van Amstel, W. P., et al. (2018). City logistics: Light and electric. Amsterdam: Urban Technology Research Programme—Faculty of Technology, Amsterdam University of Applied Sciences. Retrieved June 27, 2022, from https://www.hva.nl/binaries/content/assets/subsites/kc-techniek/ publicaties/lefv-logic.english.pdf Bonges, H. A., et al. (2016). Addressing electric vehicle (EV) sales and range anxiety through parking layout, policy and regulation. Transportation Research Part a: Policy and Practice, 83, 63–73. https://doi.org/10.1016/j.tra.2015.09.011 Broadbent, G. H., et al. (2017). Electric vehicle adoption: An analysis of best practice and pitfalls for policy making from experiences of Europe and the US. Geography Compass, 12(2). https:/ /doi.org/10.1111/gec3.12358 Browne, M., et al. (2010). Light goods vehicles in urban areas. In R. G. Eiichi Tanguchi (Ed.), The Sixth International Conference on City Logistics Published in Procedia Social and Behavioral Sciences (Vol. 2, pp. 5911–5919). Elsevier. https://doi.org/10.1016/j.sbspro.2010.04.006

An Empirical Investigation into Electric Vehicle Adoption in Urban …

187

Carley, S., et al. (2013). Intent to purchase a plug-in electric vehicle: A survey of early impressions in large US cites. Transportation Research Part d: Transport and Environment, 18, 39–45. https:/ /doi.org/10.1016/j.trd.2012.09.007 Cherchi, E. (2017). A stated choice experiment to measure the effect of informational and normative conformity in the preference for electric vehicles. Transportation Research Part A: Policy and Practice, 100, 88–104. https://doi.org/10.1016/j.tra.2017.04.009 Conrad, R. G., et al. (2011). Industrial engineering research conference. In T. D. Aken (Ed.). Retrieved July 27, 2022, from http://web.cecs.pdx.edu/~maf/Conference_Proceedings/2011_ The_Recharging_Vehicle_Routing_Problem.pdf Dablanc, L. (2009). Freight transport for development toolkit: Urban freight—freight transport, a key for the new urban economy. ppiaf. van Duin, J. H. R., et al. (2013). Towards e(lectric)-urban freight: First promising steps in the electric vehicle revolution. European Transport\Trasporti Europei (54), 9–28. Retrieved July 27, 2022, from https://www.openstarts.units.it/bitstream/10077/8875/1/ET_2013_54_9%20van% 20Duin%20et%20al.pdf EREVUE. (2017). Drivers’ experience and attitudes towards EFVs. Imperial College of London. London: FREVUE. Retrieved July 27, 2022, from https://frevue.eu/wp-content/uploads/2017/ 09/Drivers%E2%80%99-experience-and-attitudes-towards-EFVs-version-2-1.pdf Felipe, Á., et al. (2014). A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges. Transportation Research Part e: Logistics and Transportation Review, 72, 111–128. https://doi.org/10.1016/j.tre.2014.09.003 Franke, T., et al. (2012). Experiencing range in an electric vehicle: Understanding psychological barriers. Applied Psychology, 61(3), 368–391. https://doi.org/10.1111/j.1464-0597.2011. 00474.x Fried, T., et al. (2020, September 15). Integrated transport: The road to zero-emission cities goes through freight. The City Fix. https://thecityfix.com/blog/the-road-to-zero-emission-citiesgoes-through-freight/ Gan, L. (2003). Globalization of the automobile industry in China: Dynamics and barriers in greening of the road transportation. Energy Policy, 31(6), 537–551. https://doi.org/10.1016/ S0301-4215(02)00097-6 Giordano, A., et al. (2018). Environmental and economic comparison of diesel and battery electric delivery vans to inform city logistics fleet replacement strategies. (P. P.-S. Patrick Jochem, Ed.) Transportation Research Part D: Transport and Environment, 64, 216–229. https://doi.org/10. 1016/j.trd.2017.10.003 GIZ. (2010). Urban freight in developing countries. GIZ. GIZ. (2016). Urban freight and logistics: The state of practices in India. GIZ. Goeke, D., et al. (2015). Routing a mixed fleet of electric and conventional vehicles. European Journal of Operational Research, 245(1), 81–99. https://doi.org/10.1016/j.ejor.2015.01.049 Gupta, D. S. (2017). Role of NMT in distribution of goods in the metropolitan city of Delhi (pp. 978– 984). Elsevier. Haddadian, G., et al. (2015). Accelerating the global adoption of electric vehicles: Barriers and drivers. The Electricity Journal, 28, 53–68. https://doi.org/10.1016/j.tej.2015.11.011 Holguín-Veras, J. (2015). Freight demand management and its role in the quest for sustainability. ˙Imre, S., ¸ et al. (2021). Understanding barriers and enablers of electric vehicles in urban freight transport: Addressing stakeholder needs in Turkey. (D. R. Dr. Stanislaw Iwan, Ed.) Sustainable Cities and Society, 68. https://doi.org/10.1016/j.scs.2021.102794 Jensen, A. F., et al. (2013). On the stability of preferences and attitudes before and after experiencing an electric vehicle. Transportation Research Part D: Transport and Environment, 25, 24–32. https://doi.org/10.1016/j.trd.2013.07.006 Kim, M.-K., et al. (2018). Perceived value and adoption intention for electric vehicles in Korea: Moderating effects of environmental traits and government supports. Energy, 159, 799–809. https://doi.org/10.1016/j.energy.2018.06.064

188

S. Gupta and S. Gupta

Kim, E., et al. (2019). Key drivers behind the adoption of electric vehicle in Korea: An analysis of the revealed preferences. Sustainability, 11(23). https://doi.org/10.3390/su11236854 Kumar, P., et al. (2020). Total cost of ownership analysis of the impact of vehicle usage on the economic viability of electric vehicles in India. Journal of Transport Research Board, 2674(11), 563–572. https://doi.org/10.1177/0361198120947089 Lebeau, P., et al. (2016). Exploring the choice of battery electric vehicles in city logistics: A conjointbased choice analysis. Transportation Research Part e: Logistics and Transportation Review, 91, 245–258. https://doi.org/10.1016/j.tre.2016.04.004 Ling, Z., et al. (2021). Determining the factors that influence electric vehicle adoption: A stated preference survey study in Beijing, China. Sustainability, 13. https://doi.org/10.3390/su1321 11719 Long, O. E. (2012). Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions. (A. M. Atle Midttun, Ed.) Energy Policy, 48, 717–729. https://doi. org/10.1016/j.enpol.2012.06.009 Lutsey, N., et al. (2015, July). Assessment of leading electric vehicle promotion activities in United States cities. White Paper. ICCT. Retrieved April 8, 2022, from https://theicct.org/sites/default/ files/publications/ICCT_EV-promotion-US-cities_20150729.pdf Macário, R. D. (2012). Innovation policy as a driver for the development of urban logistics. ITF. Martins-Turner, K., et al. (2020). Electrification of urban freight transport. (E. S. Yasar, Ed.) Procedia Computer Science, 170, 757–763. https://doi.org/10.1016/j.procs.2020.03.159 Melander, L., et al. (2022). Drivers for and barriers to electric freight vehicle adoption in Stolkhome. Transportation Research Part D: Transport and Environment, 108. https://doi.org/10.1016/j.trd. 2022.103317 Mock, P., et al. (2014, May). Driving electrification: A global comparison of fiscal incentive policy. ICCT. Retrieved April 3, 2022, from https://theicct.org/sites/default/files/publications/ICCT_ EV-fiscal-incentives_20140506.pdf Montoya, A., et al. (2017). The electric vehicle routing problem with nonlinear charging function. (W. S. Chen, Ed.) Transportation Research Part B: Methodological, 103, 87–110. https://doi. org/10.1016/j.trb.2017.02.004 Muñoz-Villamizar, A., et al. (2018). Short- and mid-term evaluation of the use of electric vehicles in urban freight transport collaborative networks: A case study. International Journal of Logistics Research and Applications, 22(3), 229–252. https://doi.org/10.1080/13675567.2018.1513467 NITI Aayog and RMI. (2018). Goods on the move: Efficiency & sustainability in Indian logistics. New Delhi: NITI Aayog. Pelletier, S., et al. (2014). Battery electric vehicles for goods distribution: A survey of vehicle technology, market penetration, incentives and practices. Retrieved August 5, 2021, from https:/ /www.cirrelt.ca/documentstravail/cirrelt-2014-43.pdf Pelletier, S., et al. (2016). 50th anniversary invited article—Goods distribution with electric vehicles: Review and research perspectives. Transportation Science, 50(1), 3–22. https://doi.org/10.1287/ trsc.2015.0646 Quak, H., et al. (2016a). Possibilities and barriers for using electric-powered vehicles in city logistics practice. In R. G. Eiichi Taniguchi (Ed.), 10th International Conference on City Logistics (Vol. 12, pp. 157–169). Tenerife: Transportation Research Procedia. https://doi.org/10.1016/j.trpro. 2016.02.055 Quak, H., et al. (2016b). Zero emission city logistics: Current practices in freight electromobility and feasibility in the near future. (L. R. Zofka, Ed.) Transportation Research Procedia- Special Issue Transport Research Arena TRA 2016, 14, 1506–1515. https://doi.org/10.1016/j.trpro.2016. 05.115 Rizet, C., et al. (2016). The constraints of vehicle range and congestion for the use of electric vehicles for urban freight in France. In R. G. Eiichi Taniguchi (Ed.), Tenth International Conference on City Logistics (Vol. 12, pp. 500–507). Tenerife: Transportation Research Procedia. https://doi. org/10.1016/j.trpro.2016.02.005 Rommerts, M. (2011). EU transport policy white paper 2011. EU.

An Empirical Investigation into Electric Vehicle Adoption in Urban …

189

Sanchez-Diaz, I., & Browne, M. (2018). Accommodating urban freight in city planning. European Transport Research Review, 10(55). https://doi.org/10.1186/s12544-018-0327-3 SLOCAT Partnership. (2018). Transport and climate change global status report. GIZ. Sovacool, B. K., et al. (2009). Beyond batteries: An examination of the benefits and barriers to plug-in hybrid electric vehicles (PHEVs) and a vehicle-to-grid (V2G) transition. Energy Policy, 37(3), 1095–1103. https://doi.org/10.1016/j.enpol.2008.10.005 Stefanelli, T., et al. (2015). CIVITAS WIKI Policy note—Smart choices for cities making urban freight logistics more sustainable. CIVITAS. Taefi, T. T., et al. (2016). Supporting the adoption of electric vehicles in urban road freight transport— A multi-criteria analysis of policy measures in Germany. Transportation Research Part a: Policy and Practice, 19, 61–79. https://doi.org/10.1016/j.tra.2016.06.003 TERI. (2021). Decarbonizing urban freight in Surat and Bengaluru. Environmental Defence Fund. https://www.teriin.org/sites/default/files/files/suf-decarbonization-freight-report.pdf TRR. (2020). Economic viability of EVs in India. New Delhi. VREF and RPA. (2016). Why goods movement matter: Strategies for moving goods in metropolitan areas. VREF. Weiller, C., et al. (2014). Using electric vehicles for energy services: Industry perspectives. Energy, 77, 194–200. https://doi.org/10.1016/j.energy.2014.06.066 Wikström, M., et al. (2016). Investigating barriers for plug-in electric vehicle deployment in fleets. Transportation Research Part d: Transport and Environment, 49, 59–67. https://doi.org/10.1016/ j.trd.2016.08.008 World Bank. (2015). Urban development overview. The World Bank. http://www.worldbank.org Xue, Y., et al. (2014). Understanding socio-technical barriers to sustainable mobility—Insights from demonstration program of EVs in China. Problems of Sustainable Development, 9(1), 29–36. Yang, X., et al. (2021). Deploying battery swap stations for shared electric vehicles using trajectory data. Transportation Research Part D: Transport and Environment, 97. https://doi.org/10.1016/ j.trd.2021.102943

Travel Behaviour of Women in Delhi-Pre and During-Covid Scenario Monika Singh and Sanjay Gupta

Abstract Women in developing countries face a number of constraints that limit their ability to travel between home and work like income and job constraints, domestic and family responsibilities, and labour market constraint travel patterns of working women. From gender-oriented transport issue, women face inequities in terms of intra-household allocation of transport tasks and resources. COVID -19 pandemic has also severely impacted mobility levels of urban context across the globe. Very limited research has been carried out to assess the impact of pandemic on women’s mobility levels. This paper is one such attempt to assess the mobility patterns of women before COVID-19 and during COVID-19 in case city—Delhi. It is observed from this study that mobility characteristics of trip performed by women were significantly different pre as well as throughout the COVID-19 pandemic. Factor analysis technique used for analysing the parameters having an impact on mode choice pre and during the pandemic. Major shift from public transportation to private transportation and nonmotorised modes was observed. Women prioritised pandemicrelated concerns over other concerns when selecting a mode for their commute during COVID-19 pandemic. Gender, car/vehicle ownership, occupation, distance travelled, trip purpose, and pandemic-related concerned aspects were observed as underlying factors of mode choice during COVID-19. Keywords Transport disadvantaged · Social exclusion · Women’s mobility patterns · COVID-19 · Travel behaviour

M. Singh (B) School of Planning and Architecture, New Delhi, India e-mail: [email protected] S. Gupta 4-Block-B, Indraprastha Estate, New Delhi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_10

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1 Introduction Women’s travel behaviour is a critical aspect of their mobility and is influenced by a range of factors, including cultural norms, socio-economic status, and urban infrastructure. This study examines the travel behaviour of women in Delhi, India, both pre and during the COVID-19 pandemic. Pre-pandemic, women in Delhi faced various challenges when it came to traveling, including cultural restrictions, inadequate public transportation, and safety concerns. The pandemic has resulted in significant changes in travel behaviour, with many women avoiding public transportation and other forms of travel due to health concerns and the impact of lockdowns and social distancing measures. The pandemic has also resulted in significant economic disruption, with women being particularly affected. This has resulted in a modal shift, with many women opting for alternative forms of transportation, such as walking, cycling, and private vehicles. This study highlights the importance of addressing the challenges faced by women in terms of mobility and travel and the implications of these changes for the future of urban transportation. Women’s mobility refers to their ability to move freely and safely in public spaces, and it is a critical aspect of gender equality. Despite advances in many societies, women still face numerous barriers and disadvantages when it comes to mobility in urban areas. This report aims to explore the various factors that restricts women’s mobility specifically in urban areas and the consequences that these limitations have on their lives. . Societal norms and cultural practices: In many cultures, there are deeply ingrained beliefs that limit women’s mobility and restrict their access to public spaces. These cultural norms and practices can vary from strict dress codes to expectations that women should avoid going out at night. Such restrictions can limit opportunities for education, employment, and recreation. . Lack of safe public transportation: In many cities, public transportation systems are inadequate, making it difficult for women to get around safely. Poor lighting, overcrowding, and a lack of security measures can make women vulnerable to harassment and violence, which can deter them from using public transportation. . Inadequate infrastructure: Poorly designed and maintained roads, sidewalks, and pedestrian walkways can make it difficult for women to move around comfortably and safely. This can be especially challenging for women with disabilities or for those who are elderly or pregnant. . Economic constraints: Women in low-income communities often have limited financial resources, making it difficult for them to access private transportation options. This can further restrict their mobility access to essential services, such as healthcare and education. . Harassment and violence: Women are often subjected to harassment and violence while traveling in public spaces, which can have a significant impact on their mobility. This can include physical assault, sexual harassment, and verbal abuse, and it can prevent women from using public transportation or walking in public spaces.

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The disadvantages that women face when it comes to mobility in urban areas are wide-ranging and have serious consequences for their well-being and quality of life. Addressing these barriers requires a multi-faceted approach, including policies and programs that promote gender equality, improve public transportation systems, and create safe public spaces for women. By addressing these challenges, it will be possible to create more inclusive and equitable urban environments that support the mobility of all women. It is well understood that safe, comfortable, accessible, and affordable transportation can not only help achieve women’s practical and daily needs, access to schools and commercial areas, but it will also contribute to their empowerment by improving their access to opportunities. As per SDG (2020), representation of women in work force participation would contribute to nation’s economic growth, often in the double digits. The national and global efforts in equitous development of gender have largely focused on the individual empowering of women in conformity with her life cycle needs of literacy, family welfare programme, child development programmes. Whilst empowering of women has led to improved quality of life and social mix characterised by a large middle-income group, reduced household size and increasing economic surpluses, etc., their issues of mobility, intrinsically linked with income, empowerment, and improved literacy, particularly in the context of women, have received little attention in the research efforts in the developing environments. Proper planning of public transport and mobility policies is required to ensure people’s equality of access to goods and services. Mobility of persons, is an important component of any space economy with the major focus on their safety and security, lack of this particular factor discourages many of the women commuters (Shah et al., 2017). Spread of novel coronavirus has disrupted the international travel trends and mobility-related activities across the nations. Past studies have identified that mobility patterns of people contribute directly to the outbreak of communicable diseases, especially during pandemics (Belik et al., 2011; Funk et al., 2010). Furthermore, various strict and preventive steps have been implemented or regulated by governments of various nations around the world based on their local governance, socioeconomic factor, and cultural context to limit the spread of novel coronavirus and flatten the curve of positive cases. Closing of schools, online schooling system, work from home, closing of store and restaurant, limits on public gatherings, social activities, and meetings, national lockdown, curfews, restricting public transportation and shared mobility operations to control travel and spread of COVID-19, regulating safety protocols to control the spread such as social distancing and mask-wearing, closing of airports and international borders, and so on. According to recent research conducted, highlighted the importance of work from home (i.e., limiting home-based trips to workplace) and decrease in consumption (i.e., reducing home-based trips for shopping), restricting social activities and international commute as effective policy measures (Jones et al., 2020; Yilmazkuday, 2020). These policies, however, may have an impact not just on population’s travel habits, but also on their health and well-being (Vos, 2020). Lockdowns and curfews related to the COVID-19 pandemic, according to studies, as a result of which many women working from home more frequently. According

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to surveys, more than a third (37%) of people now work full-time from home, up from 1% pre-COVID-19. Women are affected in a variety of ways by this extra time at home: on the one hand, some of them have more free time as a result of changes in commute and travel patterns; on the other hand, they have added responsibilities and commitments with their already hectic schedule. This is true for majority of women, whether or not they are caretakers, according to a Deloitte study (2020). According to the survey, 74% of respondents said the pandemic has changed their personal time and daily routine pattern, and 87% said these changes have had a negative impact on their health and lifestyle. In addition to having to make significant changes in their daily lives, number of working women are concerned about the pandemic’s short and long-term impact on their professional advancement. Almost 70% of women who reported adverse impact on their daily routines during the pandemic, believe that these changes have prevented or will prevent the disease from spreading. In today’s on-demand work environment, nearly one-third (29%) of women who believe they must constantly be available are apprehensive that if they are unable to meet these requirements, their career advancement would be impacted. According to a Forbes 2020 study that included men and women from 17 countries, pandemic has taken a disproportionate toll on women. Women reported more mental health stress and more time spent doing housework. According to the study, 48% of female respondents said they spent more time doing housework since the pandemic. Gender disparities were especially noticeable in low- and middle-income households. Women have less access to social security and a lower capacity to absorb economic shocks as compared to men, according to research. Job cuts and layoffs will disproportionately affect women as they take on more domestic responsibilities (Abdullah et al., 2020). From various research works, findings may not be directly applicable to the current pandemic because the COVID-19 pandemic is a global health crisis in comparison to previous pandemics. As a result, the current study will consider the effects of COVID19 pandemic on women’s travel behaviour. The authors conducted an empirical study in Delhi to assess the likely impacts of COVID-19 on the mobility patterns of working women. Various aspects of changes in travel behaviour prior to and during COVID19 are investigated, as well as factors affecting such changes. Focus of this is on trips made out of necessity and for basic needs, and population was made to make those trips for number of reasons.

2 Overvıew of Mobility Patterns of Women—Global Experıences The inclusion of a gender perspective in mobility analysis seeks to prevent the creation of barriers and inequalities for women. It is widely acknowledged that the travel needs of women differ from those of men, and these differences are characterised by persistent inequalities between the two gender groups (Bhatt et al., 2015; Transport for London, 2016; Woetzel & ve di˘gerleri, 2015). Women face inequities

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in intra-household allocation of transportation tasks and resources as a result of the gender-oriented transportation issue (Peters, 2006). Various studies on women’s travel behaviour highlight the shift in mode choice and, as a result, the demand placed on walking and cycling infrastructure (Shah & Raman, 2019). However, certain policies which focuses on sustainable modes of transport have significantly ignored women (GRHS, 2013; Khosla, 2009). A number of literatures on mobility of women focuses on their travel demand and concerns related to the available infrastructure (Rachmita & Siregar, 2018; Sen, 1996). In all the above-cited research efforts the focus on understanding the travel behaviour of working women specifically in context of developing countries and an emerging economy like India is sadly lacking to paucity of documentary evidence. In India, mobility research has relied heavily on household travel surveys conducted for the preparation of long-term transportation plans, which clearly identified gender disparities (Sharma & Gupta, 1998). The traditional analysis undertaken was unable to identify causal factors for the observed inequities. Activity diary studies conducted in India (Sharma & Gupta, 1998) confirmed that mobility across genders is more closely related to household labour distributions, and thus women’s mobility reflected the inadequacies, limitations, and methodological issues observed during the comparative analysis, emphasising the need for a concerted international effort to appreciate the mobility levels in developing environments (Sharma & Gupta, 1998). Various countries imposed varying degrees of restrictions during pandemics to prevent and limit virus spread. These pandemic-related restrictions could have a significant impact on people’s lifestyles and social interactions. Travel and outdoor activities, in particular, may be adversely affected (Colquhoun, 1971; Haas et al., 2020; Mogaji, 2020).

3 Method and Materials To assess women’s travel behaviour, a survey questionnaire was created using Google forms, and the snowball sample collection technique was used in the months of October 2019 and December 2020, respectively, before and during the COVID19 scenario. About 700 responses were obtained from various women spread over various metro stations and bus stops in different parts of Delhi namely ITO, Saket, Mayur Vihar, Motibagh, Shakarpur, CR Park, Shahpur Jat, Rajouri Garden, Lajpat Nagar, Janakpuri covering different part of city and different land use typology. The questionnaire was divided into three parts: (1) socioeconomic and demographic characteristics, (2) trip details prior to and during the COVID-19 pandemic, and (3) parameters influencing mode choice prior to and during the COVID-19 pandemic. A five-point Likert scale method was used to assess each section. Underlying factors influencing mode preferences were discovered using exploratory factor analysis. After conducting Exploratory Factor Analysis, factor scores were computed to determine each respondent’s relative standing on the

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extracted factors. The Bartlett method was used to analyse factor scores in this study because refined methods are more precise in studying travel behaviour (Chauhan et al., 2021; DiStefano et al., 2009).

4 Comparative Trends of Mobility Levels of Working Women During Pre-COVID-19 and During COVID-19 From the primary survey, it is clearly observed that there are inequities observed in mobility patterns across various category of working women in case city of Delhi observed post-pandemic. From Table 1 it is observed that women travelled for longer distance (Mean = 23.4 km) before pandemic for non-work-related trips as compared to during-pandemic (Mean = 16.7 km) in a day by chaining their various trips including work and other household responsibilities resulting in 29% decline in total mileage whereas, 57% decline is observed for work-related trip length. Further, the average monthly transport expenses too showed a decline from Rs. 3504 during pre-COVID-19 to Rs. 980 during the COVID-19 period resulting in 75% decrease in monthly expenses for work-related trips exhibiting a conservative approach adopted by working women during COVID-19 in minimising her mobility related travel expenditure. In case of non-work-related trips, 56% decline in expenses was observed. The mean travel times too showed a decline of 26% from 58 min in pre-COVID-19 to 43 min during COVID-19 for non-work-related trips where it is 39% decline in work-related trips. Mode wise comparative analysis of working women during the two period reveals a decline in total daily travel distance of working women by 19% in case of metro and 22% in case of bus respectively (Table 2). Further, the mean daily travel times of working women too showed a decline by 17% in case of metro and 6% in case of buses respectively. The monthly travel expenditure in case of metro showed a decline by 47% largely owing to decrease in mean daily travel distance by metro due Table 1 Overall mobility pattern of women in Delhi Variable

Daily distance travelled (in km)

Work

Pre-Covid-19

During Covid-19

Mean

Mean

28.21

12.32

Percentage change (%) over pre-Covid-19 −57.21

16.7

−28.75

Expense on transport (in Rs.)

Work

3905.65

980.00

−75.0

Non-work

3504.57

1498.85

−56.5

Daily travel time (in minutes)

Work

61.21

35.45

−38.53

Non-work

57.67

42.74

−25.8

Non-work

23.44

Source Primary survey by Authors (2020)

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to absence of metro facility as well work from home option. In the case of buses, women took advantage of the free travel scheme for women, which was launched in October 2019, as it significantly reduced their transportation costs and encouraged them to explore new opportunities. Respondent’s mode of transportation is influenced by a variety of factors. Parameters that are likely to influence mode choice during pandemic were identified. Figures 1 and 2 show the distribution of responses for various parameters prior to and during COVID-19 pandemic. During COVID-19, respondents rated infection-related factors on priority, such as COVID-19 protocols for safety measures, cleanliness, and virus contamination. Whereas, parameters that influence mode choice, such as time saving, comfort during travel and expenditure, become less important during pandemic. Initial research has shown that parameters—travel time, fare (Horowitz, 2000), comfort during travel and accessibility (Morikawa, 2003) all play an important role in mode choice behaviour under optimal circumstances. Table 3 summarises that Wilcoxon signed-rank test results, which were used to relate the parameters influencing mode selection pre and during pandemic situation. Statistical tests were performed, and it was discovered that respondents prioritised parameters—safety and security, as well as cleanliness, virus contamination, safety protocols regulated during pandemic, online recharge of travel cards, app-based cab service during the pandemic. During a pandemic, respondents prioritised comfort, cost, and travel time significantly less. This suggests that during a pandemic, people’s perceptions of infection risk become more prominent when choosing a mode of transportation. As previously stated, the primary mode of transportation is determined by the primary reason for travel. The primary reason for travel during a pandemic may be significantly different than in normal circumstances. Furthermore, due to infection concerns during a pandemic, people prioritise different modes of transportation regardless of their travel purpose. Table 3 depicts the use of exploratory factor analysis to investigate mode choice behaviour during the COVID-19 pandemic. Pre- and during COVID-19, factors related to mode choice were addressed by factor analysis (principal-axis factoring with Varimax rotation). Based on Eigenvalues specification (i.e., eigenvalues > 1), the solutions produced two factors that Table 2 Mode wise mobility pattern of women in Delhi Variable

Daily distance travelled (in km) Expense on transport (in Rs.) Daily travel time (in minutes)

Categories of variable

Pre-Covid-19

During Covid-19

Average

Average

Percentage change (%) over pre-Covid

Metro

26.3

21.2

−19.39

Bus

31.4

24.6

−21.6

Metro

2105

1125

−46.6

Bus

Free fare

Free fare

No change

Metro

58.3

68

16.6

Bus

68.5

64.3

−6.1

Source Primary survey by Authors (2020)

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Fig. 1 Factors affecting mode choice pre-COVID-19

Fig. 2 Factors affecting mode choice during COVID-19

accounted for approximately 60.71% and 71.65% of the total variance for pre and during COVID-19 scenarios, respectively. The factor loadings for pre and during COVID-19 scenarios are presented in Table 4. A cut-off value of 0.40 was used for factor loadings.

Travel Behaviour of Women in Delhi-Pre and During-Covid Scenario Table 3 Comparison of parameters affecting mode choice pre and during COVID-19

Factors COVID-19 protocols

199

Mean ranks

Z

Negative

Positive

232.26

523.19

−39.562

Virus contamination

319.71

478.54

−19.34

Socio-Economic factor

319.23

389.62

−11.16

Travel time

287.91

241.87

−7.651

Travel cost

330.71

318.21

−5.61

Public transport cleanliness

189.86

301.76

−19.97

Comfort

236.98

279.63

−14.18

Safety and security

221.32

324.81

−21.029

Cab service

289.08

412.42

−19.536

Online recharge

191.19

276.56

−11.67

Samples were adequate (KMO measure > 0.750), and the Bartlett sphericity test was proven significant (0.000). The determinants of the matrices analysed were 0.071 and 0.015, respectively, for pre and during COVID-19 scenarios. Cronbach’s alpha was acceptable for both factors 1 and 2 in both scenarios. To understand the relationship between the various factors influencing women’s mode choice during the pandemic, the Bartlett test was considered to measure the factor scores. Mann Whitney U test explains the impact of socio-demographic determinants on COVID19-related factors as seen in Table 4, Factor 1 clearly highlights the priority of women during the pandemic, whereas Factor 2 denotes items of less or no concern. It was discovered that owners of four-wheeler respondents prioritised pandemicrelated items when surveyed, but these effects were statistically proven insignificant. Working women, particularly involved in essential services required to report to work, are provided with transportation by adhering to all pandemic-related safety norms and regulations, leading them to prioritise pandemic-related factors as their major concern.

5 Discussion and Conclusions This study examined how women travelled in Delhi pre and during pandemic. It also used a survey questionnaire to evaluate differences in travel behaviour following the outbreak of pandemic and the correlation between both the situational differences and personal attributes. Shopping became a necessity for basic needs and the primary trip purpose. Shift from non-discretionary and recreational trips to discretionary trips suggests that shopping trips necessitate special consideration during pandemic due to various concerns about virus spread. To reduce certain trips, like for work or education, authorities may impose self-isolation or lockdowns. Despite the fact that daily distance travelled

200 Table 4 Analysis of the parameters influencing mode choice using principal axis factor analysis

M. Singh and S. Gupta

Parameters

Factor 1

Factor 2

Pre-COVID-19 COVID-19 protocols

0.721

Socio-Economic factors

0.767

Virus contamination

0.624

Cleanliness

0.679 0.648

Comfort Safety and security

0.809

% of variance explained

29.201

31.512

Cronbach’s alpha

0.782

0.671

During-COVID-19 COVID-19 protocols

0.867

Socio-Economic factors

0.724

Virus contamination

0.758

Cleanliness

0.789

Comfort

0.702

Safety and security

0.698

% of variance explained

47.421

24.231

Cronbach’s alpha

0.887

0.694

was significantly reduced during the pandemic, outcome of this study is that the shift from private vehicle to nonmotorised modes (i.e., walk and cycling) was not significant, which largely reflects the barriers to active transportation use and adoption in India, particularly during the pandemic. The COVID-19 pandemic had a global impact on livelihood and working conditions. It prompted advisories to advise people to avoid travelling unless it is absolutely necessary. The pandemic has changed the commuting preference of women due to the fear of COVID-19 while using public transportation and the difficulty in following social distancing. Number of trips has also decreased as more people use online work platforms. Also, during pandemics, public transport ridership decreases due to restrictions imposed by government (like suspended services) and people’s concerns about infection. Despite the fact that it is not safe from a pandemic, public transportation will continue to be a critical need in society. It was also discovered that people were extremely concerned about pandemic and risks associated with public transportation. As a result, strategies implemented for making public transport safe during pandemic, such as limiting the number of passengers to maintain social distancing in bus and train, even if this causes additional delays for some passengers. This study provides some practical insights for decision-making authorities to implement measures to control the pandemic situation and also when new pandemic like breaks out, highlighting the utility of the current research for transportation

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systems policy planning for women in particular. This study looked at changes in travel behaviour, which should help the transport industry in managing existing travel behaviour. In future, scope of the study could be explored for developing and developed nations to investigate the spread of the COVID-19 in other nations. Furthermore, this study looked at intra-city travel for investigating the impact of COVID-19 pandemic on women’s travel characteristics. Findings of this study could be helpful in framing policy recommendations for number of stakeholders associated (health professionals, mobility experts, local political authorities, and so on) in such pandemic-like situations in future, thereby restricting new cases and deaths. Social distancing and restrictions on public movement have significant and direct impact on the health. The outcome suggests guidelines and intervention related to mobility sector and how it can help mitigate the impact of COVID-19 on most vulnerable groups. Future COVID-19 pandemic mitigation policies and strategies should clearly consider various groups within women as a gender, such as old-age population, in order to provide them protection from pandemic. Furthermore, findings will help to frame strategies related to employment and finance, aimed for low-income group to “incentivize” them to reduce the trips related to work. Providing access to facilities such as commercial, employment, education, healthcare, and recreation—should be the priority for urban transportation decision makers in pandemic situation particularly for women. Accessibility, rather than mobility, should be the priority for economic and social well-being. Mobility system influences the ability to reach people, goods, and services, and urban transportation systems enable and define economic and social activities in society. Discussing the psychological outcomes of fear and forced cohabitation, in particular, is the major factor affecting women’s ability to access activities by using public transport system. Following the pandemic, policy measure should be emphasised particularly user group with mobility requirement, elderly population specially women, and other vulnerable user group like women of low-income group, the homeless, and the transport disadvantaged.

References Abdullah, M., Dias, C., Muley, D., & Shahin, Md. (2020). Exploring the impacts of COVID-19 on travel behaviour and mode preferences. Transportation Research Interdisciplinary Perspectives. Bhatt, A., Menon, R. & Khan, A. (2015). Women’s safety in public transport. WRI Indian. Belik, V., Geisel, T., & Brockmann, D. (2011). Natural human mobility patterns and spatial spread of infectious diseases. Physical Review X, 1(1), 011001. Chauhan, S. C., Kathuria, A., & Sekhar, C.R. (2021). Examining risky riding behaviour in India using Motorcycle rider behaviour questionnaire. Accident Analysis and Prevention. Colquhoun, D. (1971). Lectures on biostatistics: an introduction to statistics with applications in biology and medicine. David Colquhoun. de Haas, M., Faber, R., & Hamersma, M. (2020). How COVID-19 and the Dutch ‘intelligent lockdown’change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands. Transportation Research Interdisciplinary Perspectives, 100150.

202

M. Singh and S. Gupta

De Vos, J. (2020). The effect of COVID-19 and subsequent social distancing on travel behavior. Transportation Research Interdisciplinary Perspectives 100121. DiStefano, C., Zhu, M., & Mindrila, D. (2009). Understanding and using factor scores: Considerations for the applied researcher. Practical Assessment, Research and Evaluation, 14(1), 20. Funk, S., Salathé, M., & Jansen, V. A. (2010). Modelling the influence of human behaviour on the spread of infectious diseases: A review. Journal of the Royal Society, Interface, 7(50), 1247–1256. GRHS. (2013). Gender and Sustainable urban mobility, basım yeri bilinmiyor: UN Habitat. Horowitz, A. J. (2000). Quick response system II, Version 6 reference manual. University of Wisconsin and AJH Associates. Jones, C. J., Philippon, T., & Venkateswaran, V. (2020). Optimal mitigation policies in a pandemic: Social distancing and working from home (No. w26984). National Bureau of Economic Research. Khosla, R. (2009). Addressing gender concerns in India’s urban renewal mission. New Delhi: UNDP. Mogaji, E. (2020). Impact of COVID-19 on transportation in Lagos, Nigeria. Transportation Research Interdisciplinary Perspectives, 100154. Morikawa. (2003). Travel behavior analysis and its implication to urban transport planning for Asian cities: Case studies of Bangkok, Kuala Lumpur, Manila, and Nagoya, ICRA Project Report, Research Project Funded by International Cooperative Research Activity(ICRA). http:/ /www.easts.info/activities/icra/2001/ICRA-TravelBehaviorAnalysis.pdf Peters, D. (2006). Gender issues in transport—Applying an integrative perspective. Urban Mobility Management, 1–15. Rachmita, F., & Siregar, D. (2018). In Jakarta’s urban villages, women are moving mobility forward. Sustainable Transport, 28–29. Sen, L. (1996). The transportation needs of American women in a cross-cultural context. In Women’s travel issues- Proceedings from the second national conference (pp. 457–465). Shah, S., & Raman, A. (2019). What do women and girls want from urban mobility systems? Ola Mobility Institute. Shah, S., Viswanath, K., Vyas, S. & Gadepalli, S. (2017). Women and transport in Indian cities. ITDP India and Safetipin. Sharma, A., & Gupta, S. (1998). Women’s mobility in Indian cities. In: Balkema, Rotterdam, Freeman & Jamet (Eds.) (pp. 669–674). Transport for London. (2016). Action on equality. TFL. Woetzel, J. ve di˘gerleri. (2015). The power of parity; Advancing women’s equality in India. basım yeri bilinmiyor: Mckinsey Global Institute. Yilmazkuday, H. (2020). COVID-19 deaths and inter-county travel: Daily evidence from the US. Available at SSRN 3568838.

Investigating the Effects of Individual and City Tier Characteristics on Motorized Two-Wheeler Usage Behaviour: A Multilevel Modelling Approach Aitichya Chandra, Hemanthini Allirani, and Ashish Verma

Abstract Private transportation modes such as motorized two-wheelers and cars constitute more than 80% of the vehicle composition across most Indian cities. The primary reasons for their popularity include increased purchasing power, easy navigation capability through city traffic, low purchase and maintenance cost, and suitability to heterogeneous traffic conditions. Currently, urban mobility policies that regulate motorized two-wheelers in India are fragmented and often locally applicable. The situation is worse in second-tier Indian cities, where motorized two-wheeler regulation policies are practically invisible. There lies a need for a well-structured regulatory policy vision to achieve sustainable motorized two-wheeler usage in India. Any robust policy vision framework must address the individual motorized two-wheeler usage behaviour at a city level and consider the visible variability of motorized twowheeler usage behaviour across different tiers of cities. Hence, another important question arises, along with a need to study the effects of individual characteristics on motorized two-wheeler usage behaviour. Is there any variability in motorized twowheeler usage characteristics across different tiers of Indian cities? This study aims to investigate the effect of individual and city tier characteristics on motorized twowheeler usage behaviour through a multilevel logistic modelling approach. The city tiers are demarcated using k-means clustering on city-level variables. The multilevel model, along with exploring the relationship between dependent and independent variables at the individual (Level-1) and city level (Level-2), also quantifies the variation between different city tiers. The results are expected to be critical in framing effective urban mobility policies on sustainable and resilient motorized two-wheeler usage across Indian cities. Keywords Motorized two-wheeler usage · Multilevel modelling · K-means clustering · Sustainable urban mobility

A. Chandra · H. Allirani · A. Verma (B) Department of Civil Engineering, Indian Institute of Science Bangalore, Bangalore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_11

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1 Introduction Motorized two-wheeler is the most popular transport mode across India. The reason for its popularity is manifold, including affordability, convenience, fast, ease of navigation, and parking (Agarwal, 2006; Bansal et al., 2021). Two-wheeler constitutes the maximum share of all motorized vehicles in production (79.8%) and sales (80.8%). In 2019, nearly 83% of India’s newly registered motorized vehicles were two-wheelers (Transport Research Wing, 2021). On average, about 49.7% of the households in India own a motorized two-wheeler. The state with the highest number of households owning a motorcycle is Punjab (75.6%), followed by Rajasthan (66.4%), and Tamil Nadu (63.9%) (Shukla et al., 2019). The tremendous increase in urban population over the past three decades and the rapid urbanization of cities have resulted in increased travel demand and high motorized vehicle ownership and use. The lack of adequate transport infrastructure and public transit services to meet growing travel demand increased the dependency on motorized vehicles. Longer trip distance due to urban sprawl makes the dependency on non-motorized transport such as walking and bicycle less feasible. In contrast, high motorized vehicle usage makes non-motorized transport less safe. Nowadays, motorcycles are preferred over cars for various reasons, including cost-effectiveness and easy navigation (Agarwal, 2006; Bansal et al., 2021). Motorized two-wheelers are affordable even for low-income households since the cost of second-hand vehicles ranges between Rs. 40,000/- and Rs. 60,000/-. Similarly, the motorcycle is a convenient choice for middle-income families as well. The recent road development programs by the Government of India in rural areas, small towns, and cities have increased motorized two-wheeler ownership in small towns and cities. Additionally, the rise in women commuters is another reason for the surge in motorized two-wheeler use since it is considered a safe and convenient mode of transport. Considering different tiers of cities in India, the share of motorized two-wheeler trips is high in tier two cities (Agra, Nagpur, etc.) compared to tier one cities (Bengaluru, Chennai, etc.) (Jindel et al., 2022). Likewise, the average distance travelled by motorcycle is higher in tier-1 and tier-2 cities. As mentioned in Comprehensive Mobility Plans, in most tier-two cities, motorcycles are parked for a short duration. Thus, a difference in motorized two-wheeler usage characteristics is observed among the different tiers of cities. However, the current classification of cities is primarily based on the population composition of the respective cities. Less is explored on understanding the motorized two-wheeler usage aspects among tiers of cities, including individuals’ characteristics. In addition, motorized two-wheeler sales in India are forecasted to grow from 21.2 million in 2019 to 24.9 million in 2024. Therefore, it is necessary to study the motorcycle usage aspects to draw appropriate policy measures. Thus, the study aims to investigate the effect of individual and city tier characteristics on motorized two-wheeler usage behaviour through a multilevel modelling approach. The investigation explores the following research questions: 1. What are the different tiers of cities?

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2. Is there a variation in motorized two-wheeler usage characteristics among the different tiers of cities? 3. How much is the variation, and which variables are causing the variation?

2 Research Background Motorized two-wheeler production and sales have tremendously increased over the years, as India tops the world in production and sales, followed by China (Sinha, 2020). Several studies focus on the factors influencing the purchase of motorized two-wheelers in Indian cities. The survey conducted in the Patna region highlights that nearly 93.2% of the respondents purchase motorcycles due to necessity, followed by comfort (74.8%) and easy handling (62.1%). More than 50% stated that travelling by motorized two-wheelers saves time, and almost 30% mentioned that absence of public transport is another reason to opt for a motorcycle (Sinha, 2020). In addition to the usual reasons to shift to motorized two-wheelers, factors such as independence, status, and passion are also considered motivational factors for women to buy motorized two-wheelers (Mayee et al., 2021). Furthermore, the number of research also focuses on investigating helmet usage behaviour in connection with safety and parking behaviour. The research conducted in Mumbai to study the effect of the helmet usage law introduced in 2016 emphasized the increase in overall helmet usage by almost 20%, and females seem to follow the law more stringently than males (Marisamynathan et al., 2020). The study in Surat city investigated the preference of proposed on-street parking facilities in the Central Business District in line with the National Urban Transport Policy 2006. Despite providing designated parking spaces, 50% of illegal motorcycle parking was observed during peak demand (Patel & Dave, 2016). Further, one of the studies elucidates the influence of the city’s regional context, such as affluence, population density, and average household size, on a motorized two-wheeler and four-wheeler ownership (Anirudh et al., 2022). Also, on average, for every car owner, there are nearly four motorcycle owners in a city (Goel & Mohan, 2020). Motorized two-wheelers are primarily preferred for short travel distances, i.e., less than 5 km, and demography, built environment, and availability seem to influence motorcycle ownership. Although many researchers have explored the various aspects of motorized two-wheeler usage characteristics, there is a dearth of knowledge in understanding the individual motorcycle usage aspects combined with city-level attributes. Since such a model helps in evaluating if there is any significant change in the motorized two-wheeler usage pattern across tiers of cities. Thus, the multilevel modelling framework proposed in this study seeks to understand the variation in motorcycle usage patterns by incorporating individual and city-level data.

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3 Methodological Framework 3.1 Data Description An online survey was undertaken to investigate the role and attitudes of present motorized two-wheeler riders. A questionnaire was created and distributed via social media to evaluate the function and attitude of motorized two-wheeler users. Google forms were used to collect the samples. The two-wheeler usage, travel, parking, and socioeconomic characteristics of respondents were collected in 34 questions. A pilot study was conducted at the lab or department level to improve the questionnaire. Survey participation was kept voluntary and anonymous to promote unbiased responses and a greater response rate. Of the 528 replies in the final sample set acquired through convenient sampling, 399 participants from more than 110 Indian cities were recognized as regular motorized two-wheeler users. As per Cochran’s sample size formulation, the minimum surveys required at 95% confidence interval and 5% precision is 385. Therefore, the analysis, observations, and conclusions are based on those 399 responses. Table 1 provides a descriptive summary of the responses. As per the report by EMBARQ India, in most Indian cities, the share of male and female twowheeler is around 70% and 30%, respectively. The proportion of male and female two-wheeler users from the samples is 23 and 77%, which nearly reflects the actual scenario. A plot showing the spread of the 110 city locations on the Indian map is provided in Fig. 1. Further, city-level attributes such as female-to-male gender ratio, literacy rate, and population density were acquired from open-source data based on the census 2011 for all 110 cities. More details on the data can be found in Jindel et al. (2022).

3.2 Demarcating City Tiers Using K-Means Clustering Four K-means clustering is one of the most widely used machine learning techniques to group observations into k groups. The idea is to cluster observations into groups such that observations within the same cluster are as similar as possible (in terms of the required variables), whereas observations from different clusters are as dissimilar as possible (in terms of the required variables) (Kassambara, 2017). Each cluster is represented by the cluster centroid defined by the mean of observations assigned to the cluster. The most standard algorithm to perform k-means clustering aims to minimize the total within-cluster sum of squares (WSS) defined as below (Chandra et al., 2020). WSS =

k Σ Σ k=1 xi ∈Ck

(xi − μk )2

(1)

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Table 1 Summary of variables Description of variables

Proportion (%)

Description of variables

Proportion (%)

Socio-economic characteristics

Motorized two-wheeler usage characteristics

Gender

Helmet usage

Female*b

23.3

Always

65.9

Male

76.7

Sometimes or never*b

34.1

PUC check

Age 18–22 years*b

16.3

At least twice a year

38.1

23–28 years

41.6

Less than twice a year*b

61.9

29–35 years

16.8

Share with family

36–45 years

7.5

Above 45 years

17.8

Income

Yes

78.7

No*b

21.3

Travel characteristics

Less than Rs. 30,000*b

19.5

Travel distance

Rs. 30,000–50,000

19.8

Less than 2 km*b

11.0

Rs. 50,000–100,000

27.6

2–6 km

33.6

More than Rs. 100,000

33.1

6–10 km

22.6

More than 10 km

32.8

Occupation Student*b

33.8

Travel time

Paid employee

48.9

Less than 15 min*b

18.3

Self-employed

8.0

15–30 min

31.8

Others

9.3

30–45 min

29.6

More than 45 min

20.3

Education Higher secondary*b

3.5

Co-passenger

Undergraduate

30.1

Yes

31.8

Postgraduate

53.4

No*b

68.2

Above postgraduate

11.5

Parking characteristics

Others

1.5

Marital status

Parking type Off-street parking*b

39.8 60.2

Unmarried*b

59.1

On-street parking

Married

40.9

Parking hour

Motorized two-wheeler usage characteristics

Less than 1 h

47.1

Years of usage

Otherwise*b

52.9

Less than 2 years*b

18.2

Parking cost

2–5 years

20.6

No parking charge*b

55.9

5–10 years

24.1

Less than Rs. 10

14.3

More than 10 years

37.3

Rs. 10–30

26.1

Frequency of usage

More than Rs. 30

3.8 (continued)

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Table 1 (continued) Description of variables

Proportion (%)

Everyday

62.9

Otherwise*b

37.1

Description of variables

Proportion (%)

Annual maintenance cost Less than Rs. 5,000*b

60.4

Rs. 5,000–6,000

23.8

Rs. 6,000–8,000

10.0

More than Rs. 8,000 *b Variables

5.8

are considered as a base in the models

Fig. 1 Spread of respondent’s cities across India

Here xi is the observation belonging to the cluster Ck and μk is the cluster centroid. The optimal number of clusters is found by plotting the WSS in the Y -axis and the number of clusters k in the X -axis. The location of a bend/knee/elbow in the plot represents the appropriate number of clusters (Chandra et al., 2020; Kassambara, 2017). For this study, individuals need to be assigned city tiers based on the cities

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they belong to. The city tier for each city is obtained by performing k-means clustering on the 110 cities. The required variables were population density, literacy rate, and gender ratio.

3.3 Multilevel Modelling Framework The intended problem statement leads to a nested data structure i.e., individuals nested in city tiers (clusters). Figure 2 represents the nested data structure available for the present study. At Level-1, we have individuals with Level-1 predictor variables. At Level-2, we have city tiers with Level-2 predictor variables. Such data structure violates an important assumption in linear models, i.e., independence of residuals. In a nested structure, individuals within the same cluster are more likely to perform similarly than individuals in different clusters. A direct analogy to our study implies that a specific motorized two-wheeler usage behaviour might vary between different tiers (clusters) of cities. Multilevel modelling aims to disentangle the within-cluster effects from betweencluster effects (Pani et al., 2019). There are two implications of a multilevel modelling framework. First, the outcome variables (motorized two-wheeler usage behaviour) are allowed to vary between clusters (city tiers) to differentiate between the average outcome in the overall sample (later referred to as fixed intercept) and the variation of outcome from one specific cluster to another (later referred as random intercept variance). Second, the effects of lower-level variables on the outcome are also allowed to vary between clusters to differentiate between the average effects of lower-level variables in the overall sample (later referred to as the fixed slope) and the variation of this effect from one specific cluster to another (later referred as random slope variance). A step-by-step explanation of the modelling framework is given below (Sommet & Morselli, 2021). Step 1: Preparing the Data Data preparation involves centering the data. Level-2 predictor variables can only be grand-mean centered. The Level-1 predictor can either follow grand-mean centered or cluster-mean centered. Grand-mean centering implies subtracting the general mean of the predictor variable from each value within the variable. The resulting fixed slope effects then correspond to the average general effect. In the present context, fixed slope effects from the grand-mean centered Level-1 variable will explain the general between-individual impact of that variable, regardless of the city tier. Clustermean centering implies subtracting the cluster-specific mean of the predictor variable from each value within the variable. The resulting fixed slope effects then correspond to the cluster-specific effect. In the present context, fixed slope effects from the cluster-mean centered Level-1 variable will explain the within-city tier effect of that variable. The type of centering influences model estimation and results; hence, it should be chosen carefully. Grand-mean centering is applicable when the modeller is interested

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Fig. 2 Nested data structure showing Level 1 and Level 2

in finding the effect of the level-2 predictor or the between-individual effect of the level predictor. Cluster-mean centering is preferred when analysis aims to find the within-cluster effect of a level-1 variable. In our case, cluster-mean centering is used as the aim is to test the hypothesis that motorized two-wheeler usage variables for individuals also depend on the type of city tier. Step 2: Estimating the Empty Model After data preparation, the next step is to understand the extent to which the odds of outcome variables vary from one cluster to another. This is done through an empty model with no predictors, also known as the unconditional mean model given in Eq. (2). Logit(odds) = B00 + u 0 j

(2)

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Here Logit(odds) is the log-odds that the individual i from city tier j shows a particular usage behaviour. B00 is the fixed intercept, and u 0 j is the level-2 residual signifying the deviance of the cluster-specific intercept from the fixed intercept. Based on the coefficient estimates from Eq. (1), one can find the Intraclass Correlation Coefficient (ICC) given in Eq. (3).   var u 0 j ICC =    2 var u 0 j + π3

(3)

  Here var u 0 j is the random intercept variance. ICC quantifies the extent to which log-odds of outcome vary between city tiers and ranges from 0 to 1. A higher value of ICC signifies more variance in outcome variables between city tiers. Step 3: Estimating the Intermediate Model After estimating the extent to which odds of the outcome vary between city tiers, the next step is to quantify the extent to which the effect of Level-1 variables varies from one city tier to another. To achieve this, two models, (a) constrained intermediate model (CIM) and (b) augmented intermediate model (AIM), need to be run, followed by comparing both through a likelihood ratio test. The constrained intermediate model contains all Level-1 variables and all Level-2 variables. The structural form of CIM is given in Eq. (4). Logit(odds) = B00 + B10 X i j + B01 C j + u 0 j

(4)

Here X i j is the Level-1 predictor variable, C j is the Level-2 predictor variable, B10 is the fixed slope of X i j (overall effect of Level-1 variable), and B10 is the fixed slope of X j (overall effect of Level-2 variable). The augmented intermediate model is an extension of the constrained intermediate model by introducing the residual term associated with the Level-1 variable, hence, estimating the random slope variance. The structural form of AIM is shown in Eq. (5).   Logit(odds) = B00 + B10 + u 1 j X i j + B01 C j + u 0 j

(5)

Here u 1 j is the Level-1 residual signifying the deviance of the cluster-specific slope from the fixed slope. To determine whether considering city tier-based variation of the effect of Level-1 variables improves the model, one has to show that AIM fits better than the CIM. The degree of fit can be checked based on the Likelihood ratio (LR) test. The LR is calculated using Eq. (6). LRχ 2 (1) = Deviance(CIM) − Deviance(AIM)

(6)

Deviance(CIM) is the deviance of the constrained intermediate model and Deviance(AIM) is the deviance of the augmented intermediate model. The term (1) signifies the number of additional parameters being estimated in the AIM as

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compared to CIM. A smaller value of deviance represents better fit. If LRχ 2 (1) is significant, it will indicate the effects of the Level-1 variable between city tiers and vice-versa. However, even if it turns out that the deviance of AIM is significantly lower than the deviance of CIM, it does not necessarily imply the absence of between cluster variation in Level-1 variable effects. Step 4: Estimating the Final Model If results from the last step suggest that deviance of AIM is significantly lower than deviance of CIM, the final model can incorporate the interactions between Level-1 and Level-2 variables. The equation for the final model is given in Eq. (7).   Logit(odds) = B00 + B10 + u 1 j X i j + B01 C j + B11 X i j C j + u 0 j

(7)

Here B11 is the coefficient estimate associated with the cross-level interaction between Level-1 and Level-2 variables.

4 Results and Discussion The plot showing WSS and k is given in Figure 3, which shows that the bend/elbow occurs at k = 5. Hence, the optimal number of clusters i.e., the number of city tiers is taken as 5. At present, the Government of India demarcates Indian cities into six tiers. Hence, the tier demarcation achieved by this paper might not be identical to the demarcation proposed by the Government of India. However, the difference might not be significant as the change in the number of tiers is not huge.

Within Cluster Sum of Squares

1.60E+009 1.40E+009 1.20E+009 1.00E+009 8.00E+008 6.00E+008 4.00E+008 2.00E+008 0.00E+000

0

2

4

6

8

10

12

k Fig. 3 WSS versus k plot

14

16

18

20

22

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The cluster centroids for Tier 1 with respect to the three city level variables, i.e., Gender Ratio (per 1000 male), Literacy Rate (%), and Population Density (per sq. km) are 967, 87.12, and 16,822, respectively. Corresponding cluster centroids for Tier 2 are 905, 86.77, and 27,710. For Tier 3, the centroids are 959, 85.17, and 2784. In the case of Tier 4, centroids are 910, 85.03, and 10,663. Finally, for Tier 5, the cluster centroids are 966, 88.09, and 6611. Tier 1 and Tier 2 cities are highly populated cities and are in line with the Tier 1 and Tier 2 cities prescribed by the Government of India. After clustering observations into Tiers, we developed the Empty model. The parameters of the Empty Model, along with the ICC values, are given in Table 2. Table 2 hints towards existence of a small degree of ICC in three motorized twowheeler usage behaviour, Helmet Usage, Parking type, and Frequency of Usage. It suggests that only Helmet Usage, Parking type, and Frequency of Usage behaviour vary between different tiers of cities. Although the variation is not large, the presence of even a nominal level of heterogeneity among different city tiers indicates that we cannot assume uniform results from centralized policies regulating motorized twowheeler usage. There is always a small possibility that helmet, parking, and frequency of usage behaviours may vary across different types of cities even if the rules are uniform throughout India. The constrained intermediate model (CIM) is developed for all the dependent variables, and the augmented intermediate model (AIM) is only developed for variables showing non-negative ICC values, i.e., Helmet Usage, Parking type, and Frequency of Usage. The fixed slope coefficients obtained from CIM are provided in Table 3. In the case of Helmet Usage behaviour, the odds of a traveller using a Helmet regularly decrease as years of usage increase compared to new users. As maintenance costs increase, the odds of using the Helmet regularly increase by 1.74 times. The use of helmets also decreases with age but increases with income. It is visible that education and marital status do not have any association with helmet usage. Results also suggest that regular use of helmet increase with the increase in gender ratio, literacy, and population density. In the case of parking behaviour, parking hour decreases Table 2 Empty model results and ICC Dependent variables

Parameters B00

μ0 j

Log likelihood

ICC

Helmet usage

0.717***

0.388

−254.5

3.35

Parking hour

–

0

−275.9

0

Parking type

−0.411***

9.15e−08

−268.3

2.54e−15

PUC check

−0.485***

0

−265.1

0

Frequency of usage

0.539***

0.146

−263.0

0.01

Co-passenger

−0.761***

0

−249.6

0

Family share

1.306***

0

−206.7

0

***p-value ≤ 0.001

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with the increase of usage years and travel time. Parking duration increases with increased maintenance cost and age. Income and occupation do not have any significant association with parking duration. As education increases, the odds of parking for a higher duration also increase. Only two city-level variables, population density, and gender ratio, affect parking hours. With increased population density and gender representation, parking duration also increases. Parking type behaviour is majorly influenced by years of usage, travel distance, travel time, maintenance cost, and age. The gender ratio is the only city-level variable influencing the type of parking. Results reveal that the odds of on-street parking increased with increased years of usage, travel distance, and travel time. Once the travel time exceeds 45 min, the odds of on-street parking increase greatly. With the increase in maintenance costs, the propensity for off-street parking increases. Additionally, younger people (less than 35 years old) prefer off-street parking. Income, occupation, and education show no significantly large influence on parking type behaviour. As the gender ratio increases in the city, the odds of on-street parking increase. PUC check behaviour is majorly affected by travel distance, travel time, maintenance cost, occupation, marital status, and gender ratio. Regular PUC checks are preferred if travel time increases and distance decreases. The odds of regular PUC checks are also higher as maintenance cost increases. Paid employees and highly educated users also prefer frequent PUC checks compared to others. However, PUC checks reduce with an increased gender ratio. The daily frequency of usage largely depends on years of usage, travel distance, age, income, occupation, gender ratio, and literacy rate. The frequency of usage rises steeply as usage years grow. The odds of using motorized two-wheeler daily increase by four times if users have been operating motorized two-wheelers for more than ten years. As the travel distance increases, the odds of using a motorized two-wheeler daily increase steeply. For example, the odds of using a motorized two-wheeler daily increase by seven times if users travel more than 10 km. Moreover, the upper middle age group (36–45 years) prefers using motorized two-wheelers daily compared to others. Daily usage of motorized two-wheelers is not preferred among higher income and self-employed users. The tendency to use motorized two-wheelers is also higher among more educated users, specifically users having an education qualification of postgraduate or above. Daily usage further rises with the growing literacy rate and gender ratio. The odd of accompanying co-passenger is majorly associated with years of usage, age, occupation, education, literacy rate, and population density. Accompanying a co-passenger is more preferred by users who have been using motorized twowheelers for a relatively lesser period. It decreases with the increased age of travellers. Self-employed and higher educated users also show the tendency to travel alone rather than with a co-passenger. An increase in gender ratio and literacy rate also negatively influences the user’s decision to carry additional passengers. The final usage behaviour under consideration, i.e., the tendency to share motorized twowheelers with family, is primarily influenced by years of usage, travel distance, travel time, parking cost, maintenance cost, income, occupation, and education. The odds of sharing a motorized two-wheeler with family increases as years of usage increases.

Gender

Maintenance cost

Parking cost

Travel time

Travel distance

Years of usage

Intercept 0.443* 0.536*

−0.515*

−0.493

−0.307

0.114

5–10 years

More than 10 years

2–6 km

6–10 km

−0.456*

−0.255

0.197

−0.134

Male

0.636* −0.211

−0.599* −1.371**

0.553*

0.657

Rs. 6,000–8,000

More than Rs. 8,000

0.483*

−0.308

0.125

Rs. 5,000–6,000

0.053 0.495

−0.236 −0.834

0.154

More than Rs. 30 0.179

Rs. 10–30

0.237

0.095

0.104

Less than Rs. 10

0.996**

−0.086

More than 45 min

1.500***

0.533* 0.786**

0.085 −0.107

0.201

0.383

15–30 min

−1.152**

−1.364**

More than 10 km 0.577

30–45 min

−0.755** −0.761**

0.023 −0.311

−0.461

0.201

1.412**

0.487*

0.732**

0.441

0.151

0.074

0.832*

0.444

0.503*

−0.048

−1.053**

−0.805**

−0.136

−0.112

−0.111

−1.414*

−0.452*

−0.165

0.083

2–5 years

0.021

PUC check

Parking type

Parking hour

1.085*

Helmet usage

Dependent variables

−0.125

Independent variables

Table 3 Fixed slope coefficients of constrained intermediate model (CIM)

−0.639*

−0.161

0.424

0.435

0.053

0.264

−0.068

−0.264

−0.433

0.210

−0.509

0.146

0.019

−0.385 −0.152

−0.321

(continued)

−0.955**

−0.707*

−0.003

−0.394

−0.441

−0.463*

−0.436

−0.437

−0.407

−0.025 0.187

−0.912*

−0.659

−0.529

0.239

1.054**

0.999**

2.995**

Family share

0.204

0.467

0.360

−0.146

−0.105

−0.487*

0.745

Co-passenger

−0.059

0.209

0.107

1.970***

1.631***

0.835**

1.482***

1.305***

1.102**

−2.071**

Frequency of usage

Investigating the Effects of Individual and City Tier Characteristics … 215

Marital status

Education

Occupation

Income

Age

Married

0.075

−0.231

−0.373

1.013**

−0.830

−0.705

2.184

0.972

Others

−0.303

−0.276

−0.001

−0.181

1.266*

0.412

0.778

−1.236**

−0.830

0.144

−0.713

−0.109

Above postgraduate

−0.347

−0.055

1.011**

−0.697*

0.829**

−1.927

−1.031*

−0.569

−0.528

−0.424

−0.742*

−0.113

−0.753

−0.582* −0.398

−0.855

0.198

−2.216***

−2.314**

−1.463**

−0.987**

Co-passenger

−0.156

0.167

0.376

0.925*

0.027

0.501

Frequency of usage

0.769*

0.721**

0.178

0.046

0.418

−0.638

−0.167

−0.443

0.195

PUC check

0.343

−0.361* −0.311

−0.665

−0.362

Undergraduate

Postgraduate

0.553

0.384

Others

−0.022

−0.107

−0.121 −7.1e−04

0.384

−0.594*

Paid employee

Self-employed

0.054

0.693**

More than Rs. 100,000

−0.099

0.215

−0.343

−0.063

Rs. 50,000–100,000

0.214

−0.255

0.294

Rs. 30,000–50,000

−0.138 0.174

−0.656

0.051

36–45 years

Above 45 years

0.665* 1.019*

−0.169

−0.231 −0.817*

−0.695*

−0.534

23–28 years

29–35 years

Parking type

−0.505

Parking hour

Dependent variables

Helmet usage

Independent variables

Table 3 (continued)

(continued)

−0.114

0.461

0.074

1.069*

−0.409

−0.616

−0.015

−0.837**

0.217

0.281

0.648*

−0.495

0.004

−0.057

−0.470

Family share

216 A. Chandra et al.

*** p-value ≤ 0.001 ** p-value ≤ 0.05 * p-value ≤ 0.1

−1.2e−05 −242.1

−2e−05* −242.3

5e−05***

−228.4

Population density

Log likelihood

−0.01***

−235.7

−1e−05

0.001

−0.004* −0.026

0.311* −0.009

0.005**

−0.047*

PUC check

Parking type

Parking hour

Gender ratio

Helmet usage

Dependent variables

Literacy rate

Independent variables

Table 3 (continued)

−218.4

−8e−07

−0.047*

−0.009***

Frequency of usage

−222.0

2e−05*

−0.034*

0.001

Co-passenger

−178.6

1e−05

−0.008

0.002

Family share

Investigating the Effects of Individual and City Tier Characteristics … 217

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Table 4 Likelihood ratio test comparing CIM and AIM Dependent variable

Model

AIC

BIC

Log likelihood

Deviance

χ2

df

p-value

9.84

116

1

15.2

116

1

30.0

116

1

Helmet usage CIM

528.8

672.40

−228.4

456.8

AIM

750.9

1357.3

−223.4

446.9

CIM

554.3

697.97

−241.2

482.3

AIM

771.1

1377.4

−233.5

467.1

CIM

508.8

652.41

−218.4

436.8

AIM

710.7

1317.1

−203.4

406.7

Parking type Frequency of usage

However, as travel time, distance, parking, and maintenance cost increases, users tend not to share their motorized two-wheelers. Moreover, the propensity of sharing motorized two-wheeler with family is higher among females than males. Results suggest that higher educated, higher income, and paid employees, prefer sharing with family. Interestingly, no city-level variables affect the sharing behaviour of motorized two-wheeler users. Finally, the augmented intermediate model (AIM) was developed to check whether introducing the clustering effect of the Level-1 variable improves model fit. The Likelihood Ratio (LR) test is given in Table 4. It suggests that the deviance of the AIM is not significantly lower than that of the CIM, indicating that including tier-based variation in Level-1 independent variables does not significantly improve the model. Therefore, the effect of different types of city-tiers is not significant on lower-level variables. This observation is in line with the low ICC values obtained in Table 2. Based on the results, we avoided developing the overall model as it will not give any better results as compared to CIM. For brevity, no further discussion on the AIM results (as given in Tables 5 and 6) is provided separately.

5 Research Implication and Application The present study results can contribute towards policy decisions intended to regulate and promote sustainable usage of two-wheelers at national and individual levels. The primary implication from the model suggests that Helmet Usage, Parking type, and Frequency of Usage behaviour vary between different tiers of cities. Hence, any strategic decisions aimed at regulating the three mentioned behaviours may not provide consistent results across all tiers of cities. Instead, any regulations on Helmet Usage, Parking type, and Frequency of Usage need to be customized based on the type of city tier. Such customized strategies may ensure that enforcement for optimal usage of helmets and parking types is followed strictly at local levels. Similarly, policies to discourage two-wheeler usage by decreasing the frequency of usage will also serve better when customized based on city tier.

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Table 5 Fixed slope coefficients of augmented intermediate model (AIM) Independent variables

Dependent variables Helmet usage Parking type Frequency of usage 1.108*

0.311

−2.800

2–5 years

−0.294

−0.509*

1.502**

5–10 years

−0.494*

−0.491*

1.519***

More than 10 years

−0.497

−0.429

1.918**

2–6 km

−0.354

−0.937*

1.037**

6–10 km

−0.023

−0.879*

2.019**

More than 10 km

0.419

−1.354**

2.400***

15–30 min

0.302

0.637*

0.202

30–45 min

0.546*

0.998**

0.573

More than 45 min

0.161

1.586**

0.193

Less than Rs. 10

0.181

0.387

−0.393

Rs. 10–30

0.236

0.012

−0.177

More than Rs. 30

0.193

Intercept Years of usage

Travel distance

Travel time

Parking cost

−0.035

0.276

Maintenance cost Rs. 5,000–6,000

0.083

0.418*

−0.410

Rs. 6,000–8,000

0.829

0.514

0.192 0.086

More than Rs. 8,000

0.649

−0.368

Gender

Male

−0.056

−0.315

−0.252

Age

23–28 years

−0.903**

0.659*

0.278

Income

29–35 years

−0.894*

1.050

0.012

36–45 years

−0.886*

−0.300

0.834

Above 45 years

−0.061

−0.133

0.437

Rs. 30,000–50,000

0.398

0.231

−0.126

Rs. 50,000–100,000

0.046

0.196

−0.424

0.160

−0.106**

More than Rs. 100,000 0.848* Occupation

Education

Marital status Gender ratio

Paid employee

0.616*

−0.015

−0.341

Self-employed

0.626

0.138

−0.793*

Others

0.501

1.175**

1.257

Undergraduate

−0.924*

−0.774

1.484

Postgraduate

−0.610

−0.642

1.056

Above postgraduate

−0.204

0.103

2.322

Others

6.210

−1.065

1.257

Married

0.143

−0.309

−0.256

0.005**

−0.003

0.011***

Literacy rate

0.048*

−0.022

−0.072**

Population density

4e−05**

−1e−05

2e−05 (continued)

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Table 5 (continued) Independent variables

Dependent variables Helmet usage Parking type Frequency of usage

Log likelihood

−223.5

−233.5

−203.4

*** p-value ≤ 0.001 ** p-value ≤ 0.05 * p-value ≤ 0.1

Findings from the present study also contribute towards two broad policy strategies for sustainable usage of two-wheelers. These strategies are (i) the re-organization of transport infrastructure to induce push from two-wheelers and pull towards sustainable alternatives, and (ii) Regulations for ensuring safety, and maintenance standards to ensure safety, efficiency, and vehicle ownership. For example, low-income older individuals should be the target group for promoting adherence to helmet usage in densely populated cities. On the contrary, policymakers must focus on low- and middle-income males to ensure regular PUC checks. Focusing on the selected groups may lead to better adherence to safety and maintenance standards. A relationship between helmet usage and annual maintenance cost may also imply that regular helmet users are commuters that travel to work daily and, therefore, spend more on the maintenance of their vehicles for better fuel efficiency and safety. Therefore, regular commuters with higher incomes can be selected as community leaders to promote the safe and efficient usage of two-wheelers. Usage behaviour such as parking type, parking duration, sharing with family, and frequency of usage can help formulate crucial strategies for re-organization and improvement of public transportation. For example, cities with high population density and gender ratio can modify parking charges to disincentivize people from using parking facilities for long durations. It will push them to use public transit. Based on the results, people belonging to higher income groups and higher age groups can be chosen as the target population to administer the disincentive policies. Moreover, high-income individuals using two-wheelers for longer need more incentive to use public transit. Since the tendency to share two-wheeler decreases with an increase in travel time, travel distance, parking cost, and maintenance cost, providing paratransit and non-motorized transport mobility options to a well-connected public transit system might be the key to motivating individuals relying on family-owned two-wheelers to shift to public transit. While the implications of this study may help in policy and planning for sustainable and resilient usage of two-wheelers and promote the use of public transit and green mobility alternatives, it has certain limitations. The current data set cannot bring out a significant variance in usage behaviour between city-tiers and needs more introspection. However, the overall results bring out new findings that extend the existing literature on two-wheeler usage behaviour. Additionally, the need for customization of regulatory policies based on the population density, literacy, and gender ratio of the

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Table 6 Random slope coefficients of augmented intermediate model (AIM) Independent variables

Dependent variables Helmet usage Parking type Frequency of usage

Intercept Years of usage

Travel distance

Travel time

Parking cost

0.000

0.000

0.002

Less than 2 years

0.334

0.002

0.746

2–5 years

0.087

0.004

0.353

5–10 years

0.009

0.002

0.722

More than 10 years

0.078

0.002

0.129

Less than 2 km

0.165

0.236

0.004

2–6 km

0.091

0.380

0.002

6–10 km

0.236

0.114

0.007

More than 10 km

0.210

0.234

0.007

Less than 15 min

0.001

0.249

0.268

15–30 min

0.001

0.002

0.222

30–45 min

0.001

0.002

0.391

More than 45 min

0.006

0.411

0.008

No parking charge

0.188

0.296

0.199

Less than Rs. 10

0.425

0.369

0.723

Rs. 10–30

0.005

0.019

0.178

More than Rs. 30

0.535

0.527

0.232

0.002

0.000

0.001

Maintenance cost Less than Rs. 5,000

Gender Age

Income

Occupation

Rs. 5,000–6,000

0.008

0.001

0.013

Rs. 6,000–8,000

1.154

0.000

0.033

More than Rs. 8,000

0.044

0.001

0.021

Female

0.001

0.377

0.363

Male

0.003

0.120

0.148

18–22 years

0.002

0.462

0.410

23–28 years

0.006

0.052

0.488

29–35 years

0.005

1.124

0.132

36–45 years

0.013

0.235

1.303

Above 45 years

0.003

0.547

1.168

Less than Rs. 30,000

0.302

0.433

0.068

Rs. 30,000–50,000

0.244

0.392

0.145

Rs. 50,000–100,000

0.028

0.275

0.277

More than Rs. 100,000 0.184

0.507

0.154

Student

0.001

0.281

0.002

Paid employee

0.003

0.001

0.061

Self-employed

0.014

0.004

0.354

Others

0.002

0.002

0.321 (continued)

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Table 6 (continued) Independent variables

Dependent variables Helmet usage Parking type Frequency of usage

Education

Marital status

Higher secondary

0.001

0.006

4.601

Undergraduate

0.042

0.002

0.304

Postgraduate

0.087

0.001

0.327

Above postgraduate

0.616

0.005

0.478

Others

7.361

0.013

1.183

Unmarried

0.000

0.000

0.001

Married

0.001

0.000

0.009

city remains one of the key highlights of this study. Further research is encouraged to address the limitations and perform policy analysis based on the observations.

6 Conclusions The study attempts to distinguish the motorized two-wheeler usage characteristics of different tiers of Indian cities based on a multi-level approach. The data from almost 110 cities is obtained through the online survey, and the demographic attributes of the cities are collected through secondary sources. Initially, the cities are classified into five clusters using the k-means clustering technique. A small degree of ICC is observed for three motorized two-wheeler usage patterns, such as helmet usage, parking type, and frequency of usage, indicating a small possibility for variation in these three behaviours among tiers of cities. The city-level variable, gender ratio, seems to influence all the dependent variables. The results indicate that the frequency of helmet usage decreases with increased experience of motorized twowheeler usage. Females prefer to use helmets regularly than males. In addition, preference for on-street parking increases with travel time, and off-street parking facilities are desired by the younger population. Paid employees and highly educated users opt for regular PUC checks more than others. The desire to use motorcycles daily is high among the upper middle age group (36–45 years). However, the developed AIM indicated that the tier-based variation in Level-1 independent variables does not significantly improve the model. Thus, the extension of work would be to improve the model.

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References Agarwal, O. (2006). Regulation of motorized two-wheelers in India. Transportation Research Record: Journal of the Transportation Research Board, 29–36. https://doi.org/10.3141/1954-05 Anirudh, B., Mazumder, T. N., & Das, A. (2022). Examining effects of city’s size and regional context on vehicle ownership levels in the Indian context. Transportation Research Part D: Transport and Environment, 108, 103279. https://doi.org/10.1016/j.trd.2022.103279 Bansal, P., Dua, R., Krueger, R., & Graham, D. J. (2021). Fuel economy valuation and preferences of Indian two-wheeler buyers. Journal of Cleaner Production, 294, 126328. https://doi.org/10. 1016/j.jclepro.2021.126328 Chandra, A., Pani, A., & Sahu, P. K. (2020). Designing zoning systems for freight transportation planning: A GIS-based approach for automated zone design using public data sources. Transportation Research Procedia, 48, 605–619. https://doi.org/10.1016/j.trpro.2020.08.063 Goel, R., & Mohan, D. (2020). Investigating the association between population density and travel patterns in Indian cities—An analysis of 2011 census data. Cities, 100, 102656. https://doi.org/ 10.1016/j.cities.2020.102656 Jindel, J., Chandra, A., Allirani, H., & Verma, A. (2022). Studying two-wheeler usage in the context of sustainable and resilient urban mobility policies in India. Transportation Research Record: Journal of the Transportation Research Board, 2676, 424–436. https://doi.org/10.1177/036119 81221074644 Kassambara, A. (2017). Practical guide to cluster analysis in R: Unsupervised machine learning. Journal of Computational and Graphical Statistics, 187. Marisamynathan, M., Perumal, V., & Gupta, S. (2020). Modeling helmet usage behavior of motorized two-wheeler riders in developing countries. Transportation Research Procedia, 48, 3121–3131. https://doi.org/10.1016/j.trpro.2020.08.177 Mayee, A. J., Jain, A., & Joshi, N. (2021). Factors motivating buying behavior of female two wheeler users in the district of Palghar. Journal of Management Research and Analysis, 7, 154–158. https://doi.org/10.18231/j.jmra.2020.037 Pani, A., Sahu, P. K., Chandra, A., & Sarkar, A. K. (2019). Assessing the extent of modifiable areal unit problem in modelling freight (trip) generation: Relationship between zone design and model estimation results. Journal of Transport Geography, 80, 102524. https://doi.org/10.1016/ j.jtrangeo.2019.102524 Patel, M., & Dave, S. (2016). Modeling the response to paid on street parking policy for two wheelers and four wheelers on busy urban streets of CBD area—A case study of Surat city. Transportation Research Procedia, 17, 576–585. https://doi.org/10.1016/j.trpro.2016.11.112 Shukla, S., Chatwal, N. S., & Bharti, P., et al. (2019). India report 2018/2019. Sinha, R. (2020). Factors affecting the demand of two wheelers in Patna-Region. Sommet, N., & Morselli, D. (2021). Keep calm and learn multilevel linear modeling: A three-step procedure using SPSS, Stata, R, and Mplus. The International Review of Social Psychology, 34, 203–218. https://doi.org/10.5334/irsp.555 Transport Research Wing. (2021). Road transport year book (2017–2018 & 2018–2019).

Planning for Equitable Accessibility to Public Facilities: Case Study of Faridabad, India Shivani Khurana and Karan Barpete

Abstract This paper presents a case study of Faridabad, India, to investigate the role of urban planning in ensuring equitable accessibility to public facilities. The study focuses on the concept of Equitable Accessibility, which refers to providing an unbiased level of transit service to vulnerable populations, such as economically weaker sections, senior citizens and working women. The study utilizes GIS-based spatial analysis, and an analysis of street videos to measure the level of service of street infrastructure, and accessibility to public facilities, such as schools, hospitals and parks. The study also evaluates the existing urban planning policies to determine their effectiveness in promoting equitable access. The analysis reveals that degraded street infrastructure and inadequate pedestrian facilities lead to difficulties in reaching destinations and higher travel time, distances, cost and unsafety, particularly for vulnerable populations who are dependent on walking, cycling and public transportation. The study identifies specific routes that are highly inaccessible due to poor infrastructure quality and are exposed to the majority of vulnerable people. A GIS assessment model is built to calculate the equitable routes for walking and driving to different public facilities. The findings argue that urban planning can play a crucial role in promoting equity and reducing social exclusion by incorporating accessibility considerations into decision-making and ensuring that public facilities are located in areas that are accessible to all residents. Keywords Accessibility · Vulnerable group · Equitable distribution · Road network · Inclusive · Street rating · Street infrastructure · Public facility · Working women · Senior citizen · Economically weaker sections · Neighbourhood · Planning

The similarity of the paper is 1%. S. Khurana (B) · K. Barpete School of Planning and Architecture, Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_12

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1 Introduction A central goal of transportation equity is to facilitate social and economic opportunities by providing equitable levels of access to affordable and reliable transportation options based on the needs of the populations being served, particularly populations that are traditionally underserved. This population group includes individuals such as low income, minority, elderly and working women. To bring equity to the transportation sector, there is a need to observe the inequalities and disparities in society that create the unequal distribution of transportation systems. Further, the value of inequality can be assessed and analysed through a predetermined value system. These actions will assist in realigning transportation resource distribution to protect disadvantaged people (‘Transportation Equity in Practice’, 2020).

2 Need Analysis According to census data of 2011, 60% of the users travel within 5 kms of the city, and 40% of the trips are made on foot and by bicycle. The share of walking and cycling is dominant in cities, and the census data puts the share of people using personal modes to travel for work purposes at less than 14%. This signifies that people depend on public transit systems or NMT for travel purposes. As per MoHUA 2019, it was recorded that more than 50% of the funds used in smart city missions were for building roads and highway expansion. In contrast, only 7% of the fund was spent on the footpath, and NMT infrastructure, which is far too less as 50% of the investment was made for 15% of the users and 24% was invested on the rest, 85% of the users which demonstrates the level of inequality for infrastructure development projects. Understanding the mode share distribution in the municipal corporation Faridabad (Fig. 1), it can be seen from the figure below that 48% of the users travel in the city using two-wheelers and four-wheelers, and 52% of the people prefer using public transport, walking and cycling as their mode choice. The research needs to distribute the Urban street infrastructure equitably at the routes carrying this 52% of the vulnerable users (Associates and Asia, 2018).

3 Economically Weaker Section Group Urbanization is increasing much faster in India. According to the Slum Compendium of India 2015 report, the slum population has been recorded as high as 45% in some cities. The people living in slums face financial deficits and poor infrastructure services, affecting the city’s economy.

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Fig. 1 Mode share in MCF

Equity should be a defining parameter for the development of urban and transport planning in India, as private vehicles serve only a fraction of the population that does not pay the full costs of occupying the road, polluting the air, draining precious resources and causing health risks and misbalancing the ecosystem. The poor engage sustainably in urban environments sent by walking, cycling for short trips and taking public transport for long trips. They use every opportunity available to engage with local goods and services sustainably, but our policies do not involve the needs and demands of such people. The CSE’s calculations estimated the travelling cost for unskilled daily wage labourers. They spent around 8% of their income on non-AC buses, 14% on AC buses and 22% on the Delhi metro.

3.1 Working Women Women often face difficulties travelling to work. A survey of 602 metro users and 703 non-metro users conducted by MMRDA stated that only 35% of the women feel that routes towards metro stations are safe. A larger portion of women prefers travelling in a women’s coach only. This leads to less participation of women in the working class, or even if they participate, the ease of accessibility to metro stations is not there. Women also spend more of their income on travel to ensure safer travel routes. It was observed that they spend 21% more money than male users to ensure a safe commute. Such reports are in all metropolitan cities, and it is a growing concern that requires attention to make the user group safe and content.

3.2 Old Age People In India, the proportion of elderly persons is expected to rise from 8% in 2015 to 20% in 2050, posing a growing concern for planners and policymakers. Senior citizens’ challenges in accessing public amenities should be discussed and given space on

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relevant platforms. Research published in neighbourhood supports for active ageing India, 2020, surveyed 55 users in New Delhi and Chennai as these metropolitan cities were studied to understand the need of the users, which reported that access routes between home and public spaces were not designed and maintained effectively to meet the needs of older adults with mobility issues. The bus stops and risers are not built in accordance with this vulnerability. Sometimes the bus does not even stop at the designed areas. Many elderly persons find walking on footpaths with open utility holes or uneven surfaces difficult. Because of the huge volume of motorized traffic and the lack of priority given to pedestrians, they feel insecure. As a result, some elderly persons refuse to leave their homes and only travel for limited distances, creating feelings of isolation or reliance.

4 Methodology The site area of research comprises New Industrial Township, one of the oldest settlements of Faridabad, having an area of 13.25 km2 . The area under the scope of the study is 9.5 km2 lying centrally in the city. It has a curvaceous road network design of arterial and collector roads, and the local roads form a gridiron pattern. The areas around NIT, Faridabad, have grown organically to house workers in the factories of Faridabad and NCT. The areas around the railway station have a high residential density of 250 PPH (Associates and Asia, 2018). The study area has several factors responsible for providing better accessibility. It has a share of mixed-use development and residential land use, an existing metro corridor to Delhi, a proposed metro corridor to Gurugram, and a road hierarchy having 4.2 km of NH running along its length. The length of the sub-arterial road is 22 km, collector roads are 16 km, and local streets as 163 km. The major transit nodes in the study area are three metro stations, i.e. Old Faridabad metro station, Neelam Ajronda metro station and Bata Chowk metro station, at a distance of 1.5 and 1.3 km, respectively. It also has a bus stand at a central location currently unavailable and Faridabad Railway station. This site has the potential for assessment due to its network connectivity to all these transit nodes. The whole site has been divided into regular Hexagonal grids. Each side is 100 m. This shape is considered for analysis as hexagons are the closest shape to a circle and can effectively form an evenly spaced grid. The grid would help better understand the connectivity between users and land use activities. The regular hexagon, having the centroid, portrays equal distance in all six directions. The map below represents all five zones of New Industrial Township, Sector 20 A, sector 20 B and Gandhi Colony (Map 1). The road hierarchy has been overlaid on hexagonal grids. The spatial distribution of the area has been done in the hexagonal grid using the generate tessellation tool in ArcGIS Pro to better assess the equity parameters in accessibility planning. In this map, GRID ID has been provided to each grid’s centroid, which will help identify the grids and their associated attributes (Burdziej, 2019) (Fig. 2).

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Fig. 2 Zone divisions and hexagonal grid layout

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5 Data Analysis In this research, accessibility is analysed spatially using network analysis and a hexagonal grid layout. The intent is to identify the hexagonal grids having high or low accessibility concerning the most vulnerable areas. So the three broad parameters are the spatial distribution of activities, accessibility assessment and socio-demographic groups implemented in the site area. The movement of people is carried out along the transportation networks such as streets, and sidewalks. The distance and time taken to reach destinations along the network routes are essential in assessing the accessibility of the area. The following analysis is imposed on the quality of street infrastructure available on those routes, which will help identify the most vulnerable routes in reaching different land-use activities (Pereira & Karner, 2021). Spatial Distribution of Public Facilities: Accessibility to certain facilities significantly impacts the city’s economic growth. The map on the left (Map 2) highlights five public facilities: hospitals, schools/colleges, grocery shops, banks and parks. These facilities are considered essential and are accessed by people for most purposes. The grids having a high number of these activities indicate the ease of accessing them in the shortest distance and time, hence boosting the economic value of that area (Guzman et al., 2017) (Fig. 3). It was observed that the neighbourhood is rich in public facilities and has a high percentage of mixed-use planning, which gives a positive outcome for higher accessibility in the area. The map here shows diverse amenities on the collector road

Fig. 3 Location of important amenities and all major amenities superimposed on a hex grid

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(central stretch) in each zone (Nit Zone 1, 2, 3 and 5). Zone Nit 4, sector 20-A and sector 20-B have the least amenities. Socio-demographic group: To better understand the accessibility pattern, it is important to know the user groups involved in the process. For any travel journey, several socio-demographic factors play an essential role, as their needs and requirements will affect the travel pattern. This research talks about the equitable distribution of resources, especially to the most vulnerable group of people, to assess the accessibility pattern. The three most vulnerable groups affected by transportation accessibility are studied below to do justice to the research (INTALInC, 1386). Economically weaker section group: The map shows the distribution of low-income group households over the study area (Map 3). The analysis has been performed by geo-tagging the EWS household locations and spatially joining them on a hexagonal grid layout. The grids with a high number of low-income households have been highlighted to understand the spatial distribution of these vulnerabilities. The vulnerable population ranges from 0 to 10 households per grid to 653 households per grid, and they are mostly located along the railway line and old market areas. In Faridabad, persons in the Lower income group (monthly income fewer than Rs. 15,000 per person) spend the most significant percentage (nearly 14%) of their monthly income on travel. Therefore, non-auto modes are considered for equity analysis since many low-income and disadvantaged households are more likely to rely upon transit, walking, biking and micro-mobility options (Lucas et al., 2016a, 2016b) (Fig. 4). 26% of the grids have economically weaker section households, most located along the railway road and track. The commercial land use belt of sectors 20 A and B also have slum settlements. The central open space in the study area has also been identified with economically weaker section settlements. The remaining EWS settlements are near marketplaces in the neighbourhood. Senior citizens: The dataset shown below was produced based on the population census/ projection-based estimates for the 2020 World Pop population count. The source provides estimated data of the total no of people per grid cell in the form of Geotiff format at a resolution of 3 arc (approximately 100 m at the equator) with a geographic coordinate system, WGS84. The data from the source was extracted using the zonal statistic tool of ArcGIS Pro and was joined with a hexagonal grid layout to identify the populous grid cells of certain age groups in the study area. The male population ranges from 60 to 80 years old and is considered under the senior age group, plotted in hexagonal grids below (Figs. 5 and 6). The female population ranges from the age group 60 to 80 years are considered under the old age group. The data taken from world pop sources have been plotted in hexagonal grids above. Working women: The World pop data has been taken to analyse the total number of women living in each grid aged 25–60. Lack of safety in public spaces affects women’s rights and ability to participate equally in the city.

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Fig. 4 Spatial mapping of low-income households, 2020

To work, many girls and women use non-motorized transport modes, walking and bicycling. The Women population in the age group 25–60 has been considered the age group of working women (Fig. 7).

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Fig. 5 Spatial mapping of male population of 60, 70 and 80 years old

Fig. 6 Spatial mapping of female population of 60, 70 and 80 years old

6 Street Matrix Analysis The analysis was performed to assess the urban infrastructure quality of each street. Many indicators reflect the efficiency and comfort of users, safety, security and certainty, and pleasantness and attractiveness of the street. Each street was surveyed on 12 attributes: Pukka Road, Cycle Track, Continuous Footpath, Street Parking, Road crossing, Universal accessibility, Pothole free street, Drainage and sewerage system, User Information, Active frontage, Street Lights and Tree Shade. These parameters are considered essential for assessing the walkable quality of the built environment, as most people consider walking to access different opportunities in the city. These attribute together compliments the assessment of pedestrian accessibility (Curado et al., 2021).

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Fig. 7 Spatial mapping of working women population for 25, 45 and 60 years old

Arterial roads having a length of 4.2 km, is a national highway 100 m wide right of way (Figs. 8 and 9). The above images reflect the quality of the road, where the availability of the pedestrian network, street light and user information is present. The arterial road has a massive right of way which pedestrians do not use to reach their destinations. The urban infrastructure of the highway is in good condition for improved quality of mobility, but pedestrians do not use these roads because of high-speed moving vehicles. The table below shows the overall scoring for this road, where value 1 represents that the particular facility is available on the street, whereas 0 represents the unavailability of the facility on the road assessed (Fig. 10).

6.1 Sub-Arterial Roads The key map shows the existing sub-arterial road network in the study area (Fig. 11). The above images reflect the quality of sub-arterial roads, having 30 m of right of way; wherein the first image, it can be seen that there is no road crossing infrastructure, and students are crossing the busiest street without any safety measures. The second image shows the street full of potholes and the absence of a footpath. Further lack of parking space and cycle lane is there. The last image reflects the shaded pathway, and there in the scoring table below, credit one is given to road 1A under the shaded street facility (Figs. 12 and 13).

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Fig. 8 Key map showing arterial road

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Fig. 9 Images of the arterial road

Fig. 10 Street scoring for arterial road

6.2 Collector Roads The key map shows the collector road network in the study area (Figs. 14 and 15). The above images show the quality of collector roads having the right of way from 12 to 15 m. Most of these streets have pukka roads, good drainage and a sewerage network, but many do not have street lights, street parking and road crossing facilities. The first image above has an open drain line along the street, the second image has the facility of a footpath and street parking, whereas the fourth image shows the absence of a footpath and poor drainage network. The table below shows the street score of all the collector roads. Most streets score an average value of 3–5 out of 12, which is very low (Fig. 16).

6.3 Local Streets The key map below shows the local level streets network in red colour (Fig. 17). The local streets having the right of way between 6 and 9 m do not have adequate street lighting, shaded pathways for pedestrians, footpaths, road crossing, user information and other facilities. However, most streets are pukka streets and have sewerage

Planning for Equitable Accessibility to Public Facilities: Case Study …

Fig. 11 Key map showing sub-arterial road

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Fig. 12 Images of sub-arterial road

Fig. 13 Street scoring for sub-arterial road

and drainage networks. The images below reflect two opposite conditions of a neighbourhood, where the former has good quality roads with proper crossing and footpaths. In contrast, the latter does not have street lights and is full of potholes, an open drainage network and no other urban street infrastructure (Fig. 18). The table below shows the street score of all local roads where most of the streets score up to 50% of the value, which is very low (Fig. 19).

6.4 Cumulative Assessment of all Streets The cumulative assessment shows that all the streets lack cycle tracks, universal accessibility and tree shades along footpaths for pedestrians. The quality of arterial roads is relatively better than all other types of roads. Although the streets are well cemented, most have closed drainage and sewerage networks. The signages and user information are only on the arterial road and very low on other street types. The arterial road lacks active frontage, and other streets do have signs (Fig. 20).

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Fig. 14 Key map showing collector roads

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Fig. 15 Images of collector road

Fig. 16 Street scoring for collector road

The map below shows all the streets with the score chart. We can easily identify the location of streets scoring poorly or high. The city has an average score of 5–6 regarding urban street infrastructure availability. However, we can see that streets near the railway line are scoring really poor grades, one and streets in NIT 4, with planned housing complex, have the highest score between ranges of 8–9 out of 12 attributes. After importing all the amenities on the street map, we can observe the areas with more diverse activities and street scoring. Such as, streets score a value of 5, have a

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Fig. 17 Key map of local streets

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Fig. 18 Images showing local streets

diverse number of activities, Streets score a value of 2, slum settlements, streets score a value of 1, have industries in the neighbourhood and Streets score a value of 9, and have hospital facilities. So this tells us that places like slum settlements and industrial areas have really poor infrastructure, and they have a maximum number of people belonging to economically weaker section groups who access these streets on foot and by cycle. This analysis will help in prioritizing the streets that need immediate interventions (Figs. 21 and 22).

6.5 Calculating Shortest Route To reach any destination, we always find the quickest or the shortest route to access it. The analysis was performed based on a similar concept of finding the shortest distance and time required to reach a destination with minimum travel time and distance. As the site is uniformly divided into hexagonal grids, the origin of each route is considered the centroid of the hexagon and the destination is considered the closest public facility or transit node. The database from the ArcGIS network analyst was considered to calculate the best routes for each trip. The most convenient and affordable means of accessibility is considered walking time, so maps were developed assuming the speed to be 5 km/hr. (Singh, 2018).

Planning for Equitable Accessibility to Public Facilities: Case Study …

Attributes Street /Value

no

243

Length

Drainage potholes Pakka Cycle Continuous Surface Road Universal & User Active Street free Trees Total Road track Footpath Parking Crossing Accessibility Sewerage information Frontage Lights street network

KM

0/1

0/1

0/1

0/1

0/1

0/1

0/1

0/1

0/1

0/1

0/1

0/1

12

1A

1.61

1

0

1

0

0

0

1

1

0

1

0

0

5

1B

2.7

1

0

0

0

0

0

1

1

0

1

0

0

4

1C

3.3

1

0

0

1

0

0

0

1

0

0

0

0

3

1D

2.7

1

0

0

0

0

0

1

1

0

1

1

0

5

1E

1.6

1

0

0

1

0

0

1

0

0

1

0

0

4

1F

2.2

1

0

1

0

0

0

1

1

0

1

1

0

6

0.97

1

0

0

0

0

0

1

1

0

1

0

0

4

1.16

1

0

0

1

0

0

1

1

0

1

0

0

5

1J

1.7

1

0

0

0

0

0

0

1

0

1

0

0

3

1K

0.85

1

0

0

0

0

0

1

1

0

0

0

0

3

NG1

4.4

1

0

0

0

0

0

0

0

0

0

0

0

1

NG2

2.1

1

0

0

1

0

0

1

1

0

1

0

0

5

Local 1G Streets 1H

Tikona 0.69 Park

1

0

0

0

0

0

0

0

0

0

0

0

1

2A

2.6

1

0

1

1

0

0

1

1

0

1

1

0

7

2B

0.96

1

0

0

0

0

0

1

0

0

1

0

0

3

2C

3.11

1

0

0

1

0

0

1

1

0

1

1

0

6

2D

0.92

1

0

1

0

0

0

1

1

0

1

1

0

6

2

0.43

0

0

0

0

0

0

0

0

0

1

0

0

1

2E

3.5

1

0

0

1

0

0

1

1

0

1

1

0

6

2F

2.7

1

0

0

0

0

0

0

1

0

1

1

0

4

Local 2G Streets

2.4

1

0

0

0

0

0

1

1

0

1

1

0

5

2F

2.4

1

0

0

1

0

0

1

1

0

1

1

0

6

2H

2.4

1

0

0

1

0

0

1

1

0

1

1

0

6

2J

1.62

1

0

0

1

0

0

1

1

0

0

1

0

5

2K

2.3

1

0

1

1

0

0

1

1

0

1

1

0

7

2L

1.3

1

0

1

1

0

0

1

1

0

1

1

0

7

2M

2.3

1

0

1

1

0

0

1

1

0

1

1

1

8

2N

1.3

1

0

1

1

0

0

1

1

0

1

1

0

7

3A

3.4

1

0

0

1

0

0

0

1

0

1

1

0

5

3B

1.17 1

0

0

0

0

0

0

0

0

0

0

0

1

3C Local Streets 3D 3E

2.54 1

0

0

1

0

0

0

1

0

1

1

0

5

2.1

1

0

1

1

0

0

0

1

0

1

1

1

7

2.1

1

0

0

1

0

0

0

1

0

1

1

0

5

3F

2.6

1

0

0

1

0

0

0

1

0

1

1

0

5

3G

2.3

1

0

0

1

0

0

0

1

0

1

1

0

5

Fig. 19 Street scoring for local roads

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S. Khurana and K. Barpete 2.9 3H Rahul 3.8 Colony NIT 4 6.1 Block 1 NIT 4 3.2 Block 2 NIT 4 0.46 Block 3 NIT 4 2.06 Block 4 NIT 4 0.56 Block 7 NIT 4 1.12 Block 8 NIT 4 Block 0.3 81

1

0

1

1

0

0

0

1

0

1

1

0

6

0

0

0

0

0

0

0

0

0

1

0

0

1

1

0

1

1

1

0

1

1

1

0

1

1

9

1

0

1

1

0

0

1

1

1

0

1

1

8

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

1

1

1

0

1

1

1

0

1

1

9

1

0

0

0

0

0

0

1

0

0

0

0

2

1

0

1

1

1

1

1

1

1

1

1

1

11

0

0

0

0

0

0

0

0

0

0

0

1

1

SGM 1 0.89 1

0

0

0

0

0

1

0

0

1

0

0

3

SGM 2 0.78 1 Gandhi 2.95 Colony 1

0

0

0

0

0

1

0

0

1

0

0

3

0

0

0

0

0

0

0

0

1

0

0

2

5

1.44 1

0

0

0

0

0

0

0

0

1

0

0

2

5A

1.7

1

0

0

1

0

0

1

1

0

1

1

0

6

5B

2.09 1

0

0

1

0

0

0

1

0

1

1

0

5

5C

1.03 1

0

0

1

0

0

1

1

1

1

0

0

6

5D

2.9

1

0

0

1

0

0

1

1

0

1

0

0

5

5E

5

1

0

1

1

0

0

1

1

1

1

0

0

7

5F

2.03 1

0

0

1

0

0

0

1

0

1

0

0

4

5G

1.5

1

0

0

0

0

0

0

1

0

1

1

1

5

5H

1.2

1

0

0

1

0

0

1

1

0

1

0

1

6

5J

0.87 1

0

0

1

0

0

1

1

0

1

1

1

7

5K

2.1

1

0

0

1

0

0

0

1

0

1

0

1

5

5L

2.2

1

0

0

1

0

0

1

1

0

1

1

0

6

5N

2.2

1

0

0

1

0

0

1

1

0

1

1

0

6

1

0

0

1

0

0

1

1

0

1

1

0

6

1

0

1

0

0

0

0

1

0

1

0

0

4

1

0

0

0

0

0

0

0

0

1

0

0

2

0.78 5P NIT 5 3.24 RR FRUIT 2.27 NAGAR Old 1.69 Road

0

0

0

0

0

0

0

0

0

1

0

0

1

20A

2.53 1

0

1

1

1

0

1

1

0

0

0

0

6

20A2

2.66 0

0

0

0

0

0

0

0

0

1

0

0

1

20A3

1.7

1

0

0

0

0

0

0

0

0

1

0

0

2

Local 20B1 Streets 20B2

2.59 1

0

0

0

0

0

1

1

0

0

0

0

3

3.5

1

0

0

0

0

0

0

0

0

1

0

0

2

20B3

2.36 0

0

0

0

0

0

0

0

0

0

0

1

1

2.59 1 20B4 AC 7.01 NAGAR 1

0

0

0

0

0

0

0

0

1

0

0

2

0

0

0

0

0

0

0

0

1

0

0

2

ESI

1.85 1

0

1

1

1

0

1

1

1

1

1

0

9

NIT 1

0.9

1

0

1

0

1

0

1

1

1

0

1

1

8

NIT 2

1.7

1

0

0

0

0

0

1

1

0

0

0

1

4

Fig. 19 (continued)

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Fig. 20 Cumulative assessment of street matrix

6.6 Metro Station As we know, the origin of each route is the centroid of the hexagonal trip, and the destination is the nearest metro station to them. The following maps show the walking time and travel distance taken by each shortest route to reach a particular destination. We can observe from these maps that routes closer to metro stations located on the national highway take less time and kilometres, whereas routes from NIT 3 and parts of NIT 2 do take longer time and distances. The graph below shows the total distance travelled for each route within the study area to access these metro stations. The range of the distance lies from 0.1 km to 4 km. The average distance travelled is 2.3 km within the study area (Figs. 23 and 24). In the case of walking time required to reach the destination, the range lies from 0.79 min to 48 min which is relatively high from the concept of having 15 min walking city. The average time taken by each route to access these transit nodes is 28 min, and it is not an ideal time to access the important destination (Fig. 25).

6.7 Railway Station A similar analysis was performed to calculate the shortest route from each centroid point to the railway station near old Faridabad Chowk. Here, in the map below, we can see that the routes closer to the railway stations take less time, but there is no dense road network around the station. The areas like NIT 1 and 2 take the longest

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Fig. 21 Street score mapping

travel time and distance. Moreover, settlements near the national highway take more walking time to reach the station (Fig. 26). The graph below shows the distribution of total kilometres routes takes to reach the destination. In this case, the range lies between 0.8 kms and 4.8 kms, whereas the mean value is around 3, which is more than the metro station accessibility (Fig. 27). The range of walking time required to reach the station lies between 9.62 min and 57.76 min, and the average time taken is 35 min of walking distance. The time taken

Planning for Equitable Accessibility to Public Facilities: Case Study …

Fig. 22 Street score map with the location of public facilities

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Fig. 23 Distribution of total kilometres for the shortest route to the metro station

Fig. 24 Walking time and travel distance required to reach the metro station from point location

Fig. 25 Distribution of total walk time for the shortest route to the metro station

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Fig. 26 Walking time and travel distance required to reach the railway station from point location

Fig. 27 Distribution of Total travel distance for the shortest route to the railway station

to reach the railway station is very high if we compare it with the concept of 15 min walking the city (Fig. 28).

7 Linear Regression Method To support the research using a statistical method, linear regression analysis was performed, which enables the understanding of relationships among multiple factors. The dependent variable is taken as an economically weaker section group where the model shows the r square value as 0.549 (moderately significant), meaning that

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Fig. 28 Distribution of total walk time for the shortest route to the railway station

54% of the mentioned variables impact the dependent variable, and the variables with the lower p values (sig) have the most significant impact have been highlighted (Fig. 29).

7.1 Issues with the Shortest Route to the Railway Station In the case of economically weaker sections of society, the density map of their population is overlaid with the most vulnerable routes. The routes’ vulnerable characteristics are the longest walking time to the railway station and poor street-quality infrastructure. We can identify the routes and correct them on a priority basis using the Map below. The red colour routes take 39–57 min of walking time to reach the railway station and have the least infrastructure quality having a mean score value between 1.5 and 3.3 out of 12 parameters (Fig. 30). The table below depicts hexagonal grids with economically weaker section groups and the street quality for routes leading from these grids to the railway station. This table helps in identifying the reason for making streets poor. The lack of a cycle path, improper traffic crossings, non-continuous footpaths and a lack of tree cover along footpaths were all noted as issues for low-street-quality routes (Fig. 31).

7.2 Accessibility Issues for Working Women Changing the dependent variable to the working women group and understanding the relationship between this vulnerability and the shortest distance to metro stations. The model shows the dependency of 37% of the working women class variables, which may not be high but is a significant count to understand the relationship. The table below shows the attributes like active frontage along the metro routes, drainage network, footpaths, user information and streetlights having a p-value closer to 0 and hence have the most significant impact on the dependent variable (Fig. 32). The table below shows the routes taking more than 15 min to walk to the metro station with the quality of infrastructure on these routes. Some routes originating

Planning for Equitable Accessibility to Public Facilities: Case Study …

Fig. 29 Linear regression calculation for EWS as dependent variable

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Fig. 30 EWS population mapping with the shortest route to the railway station

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Fig. 31 Prioritization of Street Quality Index_EWS population routes to the railway station

from Grid Id N-6 and N7 are taking less walking time but still scoring a poor grade in terms of street infrastructure assessment. The factors responsible for poor quality can be studied from the above table, which scores a value closer to zero. The map below shows the green routes taking 33–48 min to walk to the metro station, with the quality of infrastructure scoring 2.1–4.2 out of 12 points on these routes (Figs. 33 and 34).

7.3 Accessibility Issues for Old Age People When the analysis takes the dependent variable as the Senior Citizen population, the model shows a dependency of 37% on the quality of streets variable (Fig. 35). In the table below, readers can see a hexagonal grid with a high density of older people, elements of poor street infrastructure and the total score of the mean value of the street quality index. This table helps in determining why a street is impoverished. Similarly, if a street is in poor condition, the table will explain why it is such (whether it can be the longest walking time or an infrastructure issue). From the table above, we can identify the issues such as the routes originating from Grid Id O-4 does not have a cycle track, poor drainage network, poor footpaths, no pedestrian crossings, less signage and absence of tree shade along footpaths. None of the streets has features contributing to universal accessibility (Figs. 36 and 37).

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Fig. 32 Linear regression calculation for working women as dependent variable

The map above shows the senior citizen population distribution, overlaid with the shortest distance travelled to the nearest metro station. As a result, the green route represents the most vulnerable route as it takes the most walking time and has the poorest infrastructure quality. This analysis determines which segments of the street should be repaired first. Prioritization would be given to hexagonal grids with the highest density of older people and the most vulnerable streets.

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Fig. 33 Prioritization of Street Quality Index_Working Women population routes to metro station

8 Results The statistical model selected for street quality on shortest route analysis shows a moderate proportion of its dependent variable. At the same time, the statistical significance of the model is supported by the changes in independent variables correlating with the shift in the dependent variables. In this model, the independent variables are the shortest route of railway stations, metro stations, IPT stand and amenities with different street quality aspects. There is a significant relationship between the quality of the routes impacting the dependant variables selected as EWS category people, working women and senior citizens. The major issues with working women and the old age population were associated with poor accessibility to the nearest metro stations. The street infrastructure getting the minimum score value is active frontage on streets, drainage networks on metro routes, footpath, user information and street light. With the EWS group, there was poor accessibility identified on railway station routes and IPT nodes.

8.1 Prototype Model for ArcGIS (Using Model Builder) The model was prepared to automate the vigorous steps involved in the methodology of a two-click system. This prototype successfully runs an area of 130 square kilometres (maximum points 5000). The tool was made to calculate the shortest distance

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Fig. 34 Women age group 25–60 mapping with the shortest distance to the metro station

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Fig. 35 Linear regression calculation for working women as the dependent variable

required with minimum walking time to different public facilities. The network analysis that calculates the shortest distance is automatically extracted from the ArcGIS database. These tools can be used in other neighbourhoods or at the city level. The quality of each street can be analysed with Street Quality Index and overlaid on each hexagonal grid. There were several steps involved in building this model. The mandatory things required to run this model successfully are the study area shape file and the location of all public facilities on which the shortest distance is calculated. Step: 1 Model for preparing hexagonal grids The concept of the hexagonal grid was used to divide the whole site into evenly spaced grids to understand the connectivity between different users and land-use

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Fig. 36 Prioritization of Street Quality Index_Old age population routes to the metro station

activities using generate tessellation command. The length of the hexagonal edge is 100 m and is made over the site boundary (Fig. 14). After that, the population data extracted from the world pop website was organized in these grids using a zonal statistics tool. All three vulnerabilities were added using the spatial join element. To run this successfully, the user only has to add study boundary and population data (Optional) (Fig. 38). Step: 2 Model for the shortest route to the public facility The model here employs ArcGIS’ network analysis to build the shortest route to various public facilities, with the origin of each route being the centroid of a hexagonal grid and the destination of each route being the public facility such as a park, school, grocery shops, hospitals and banks (Fig. 39). Similarly, for the three transit nodes used in the research, which are railway station, metro station and IPT nodes, accessibility using the closest facility layer was prepared (Fig. 40). This tool requires the input of the destination shape file, which can be any public facility or transit node. Using the tool ’Make closest facility analysis’ and adding the destination location, the shortest route layer will have all the attributes of walking time of distance travelled. The model successfully run-on a city-level analysis, where the urbanizable area of Faridabad was selected, and the shortest route was calculated for the nearest railway station and metro station. There are 12 metro stations and three railway stations in Faridabad. The map below shows the walking distance required to reach the stations (Fig. 41).

Planning for Equitable Accessibility to Public Facilities: Case Study …

Fig. 37 Old age population mapping with the shortest distance to the metro station

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Fig. 38 Model for preparing hexagon grid

Fig. 39 Model for the shortest route to public facility

Fig. 40 Model for the shortest route to the transit node

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Fig. 41 Heat map of walking time required to reach metro and railway stations

The figure above shows the maximum distance travelled to reach the nearest station is 7.6 km, and the mean distance is 2.8 km (Fig. 42). The figure having total walking time on the X-axis and a number of grids on the Y-axis shows that the maximum time travelled to reach the nearest station is 90 min, and the meantime is 34 min. Which is higher than the 15 min walking city concept (Figs. 43 and 44).

Fig. 42 Distribution of total distance required to reach transit nodes

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Fig. 43 Distribution of total walk time required to reach transit nodes

Fig. 44 Routes accessible within 15 min of walking time to reach the stations

The above map shows the highlighted routes accessible within 15 min of walking to the nearest stations. This density map shows that the grids under 15 min of accessibility have a mixed density range of 350–480 PPH. The map below shows the driving time required to reach the stations. We can observe that areas near the western periphery take the longest driving time within the city to reach the nearest stations.

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Fig. 45 Overall driving time to transit stations and 7 min driving time coverage

In contrast, the rest of the urbanized area can be accessible within 15 min of driving time (Fig. 45). The map on the right shows the route that will be accessed within 7 min of driving time to the nearest stations. As the average time required in the selected site area is 7 min, this analysis was performed to identify the areas falling beyond the average travel time requirement. The maps are overlaid with the density maps, and we can see several areas on the western side of the National Highway with high density that cannot be accessed within 7 min of driving time to stations. The density map can also reflect the proportion of more vulnerable people in these areas (Fig. 46). The maximum driving time to reach the nearest station is 22 min, and the meantime is 7 min (Fig. 18). So, this tool allows us to identify the driving time and walking time required to access the metro stations and railway stations within a defined area. This tool can be used in any city or neighbourhood globally.

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Fig. 46 Distribution of total driving time required on the shortest route to the nearest station

9 Conclusion The community currently lies in a zone of low accessibility and high vulnerability. The foremost step should be taken to reduce the number of people falling into the vulnerable category, followed by making constructive efforts to increase accessibility in the community (Lucas et al., 2016a, 2016b). Efforts should be made to increase accessibility in the community and to reduce the impacts of inaccessibility on vulnerable communities by recognizing the importance of slower modes and shorter trips, as well as the impact of land use considerations like density and mix (Fig. 47).

Fig. 47 Propose strategy to improve the accessibility in the city

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To achieve high accessibility and low vulnerability in a city, several recommendations can be worked upon: 1. Equity should be considered an essential framework for a city-wide plan for the deployment of integrated and affordable public transport services to all settlements, especially to vulnerable communities 2. There should be a settlement-wise plan for the improvement in accessibility and transportation connectivity of users. Area-level infrastructure should be assessed, and measures should be adopted for a minimum level of accessibility towards services. 3. TOD policy should be implemented: Compact high-density, mixed land-use and mixed-income development within a 400–500 m radius of transit nodes like metro stations or railway stations are needed for better accessibility. 4. The data-driven actions for targeted improvements in all settlements will help upgrade vulnerable people’s conditions. 5. The concept of shared street design should also be used by frame guidelines for improving street and access infrastructure in planned and unplanned low-income settlements. 6. Special incentives should be given to the vulnerable communities in terms of minimum fare charges and fixed transit routes to accommodate these people. (Litman, 2010)

References Associates, L. E. A., & Asia, S. (2018) Faridabad smart city limited project management consultancy to design, develop, manage and implement smart city project smart city mission detailed project report for city bus services In Faridabad Wadia Techno–Engineering Services Limited, India Avi. Bleˇci´c, I., Congiu, T., Fancello, G., & Andrea Trunfio, G. (2020). Planning and design support tools for walkability: A guide for urban analysts. Sustainability (Switzerland), 12(11). https://doi.org/ 10.3390/su12114405 Burdziej, J. (2019). Using hexagonal grids and network analysis for spatial accessibility assessment in urban environments—A case study of public amenities in Toru´n. Miscellanea Geographica, 23(2), 99–110. https://doi.org/10.2478/mgrsd-2018-0037 Curado, M., Rodriguez, R., Jimenez, M., Tortosa, L., & Vicent, J. F. (2021). A new methodology to study street accessibility: A case study of avila (Spain)’. ISPRS International Journal of Geo-Information, 10(7). https://doi.org/10.3390/ijgi10070491 Guzman, L. A., Oviedo, D., & Rivera, C. (2017). Assessing equity in transport accessibility to work and study: The Bogotá region. Journal of Transport Geography, 58, 236–246. https://doi.org/ 10.1016/j.jtrangeo.2016.12.016 INTALInC. (1386). Transport and mobiltiies: Meetings the needs of vulnerable populations in developing cities (p. 32, 117). Litman, T. (2010). Land use impacts on transport (January 2008) (p. 65). https://doi.org/10.1007/ 978-3-642-54876-5 Litman, T. (2012) Evaluating accessibility for transport planning evaluating accessibility for transportation planning. www.vtpi.org [email protected] 250-508-5150 (January 2008). www.vtp i.org

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Lucas, K., Mattioli, G., Verlinghieri, E., & Guzman, A. (2016a). Transport poverty and its adverse social consequences. Proceedings of the Institution of Civil Engineers: Transport, 169(6), 353– 365. https://doi.org/10.1680/jtran.15.00073 Lucas, K., van Wee, B., & Maat, K. (2016b). A method to evaluate equitable accessibility: Combining ethical theories and accessibility-based approaches. Transportation, 43(3), 473–490. https://doi.org/10.1007/s11116-015-9585-2 Pereira, R. H. M., & Karner, A. (2021). Transportation equity. International Encyclopedia of Transportation, 1, 271–277. https://doi.org/10.1016/b978-0-08-102671-7.10053-3 Singh, S. (2018). Walkability of transit-oriented development: Evaluating the pedestrian situation of Faridabad metro stations walkability of transit-oriented development: Evaluating the pedestrian situation of Faridabad Metro Stations (July) (pp. 5–6). https://doi.org/10.13140/RG.2.2.25618. 76489 ‘Transportation Equity in Practice’. (2020).

Women Safety in Public Transport—A Case of Ahmedabad R. Lakshmi and Nitika Bhakuni

Abstract Women are subjected to physical aggression, sexual harassment, and other sorts of unpleasant behaviour while travelling on public transport. This is not only morally repugnant, but it also has economic and societal consequences, as well as worsening other forms of inequality. Since women’s safety on public transportation is a worldwide issue, this study focuses on the issue in India, considering Ahmedabad as a case study. This study intends to assess the safety of women on public buses in Ahmedabad, specifically the Amdavad Municipal Transport Service (AMTS) and Bus Rapid Transit System (BRTS). The study further explores factors that influence women’s perception of safety. To gain a better understanding of the issue, 482 surveys were conducted at various locations across the city to capture the perceptions of safety, bus usage, and other miscellaneous aspects from women of different occupational backgrounds and locations in Ahmedabad. The acquired primary data on women’s safety perception was analysed using relevant research tools such as Importance Satisfaction Analysis and Factor Analysis. These results imply that travel experience and the condition of physical infrastructure can influence the overall safety perception of women. It is also found that occupation, location, and the type of bus service (AMTS/BRTS) also lead to a change in the perception of safety. The study further investigated the mismatch between the women’s expectations and the local authorities’ (public transport and police) initiatives to provide a safer environment. Keywords Women safety · Public transport

R. Lakshmi (B) Faculty of Planning, CEPT University, Ahmedabad, India e-mail: [email protected] N. Bhakuni Centre of Excellence in Urban Transport, CEPT University, Ahmedabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_13

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1 Introduction The majority of working women in cities rely on public transportation. As a result, most commuters face harassment from other passengers and bus drivers, which can take the form of verbal, physical, or psychological abuse. Cases like these are underreported, which paints a grim image of public transport discouraging women from travelling via bus. The annual crime data only includes information on the various sorts of harassment directed at women however, it has no mention of the place of occurrence of these incidences. 60–80% of sexual abuse cases are not reported to the police due to fear of further victimization by the criminal justice system and society (Bandagi, 2021). This points out the importance of women’s safety and security programs in the country. Women’s difficulties in public places have resurfaced in India following the horrific incidence of a brutal rape in December 2012. It elevated this issue, which had previously been isolated to feminist and queer movements, to the public conversation and sparked action from civil society. As a result, government agencies at all levels have taken up initiatives to improve public transit safety (Sonal Shah, 2017). Women’s access to public locations is limited due to their fear of being attacked. As a result, understanding women’s perceptions of safety is critical for their comfort when travelling in public settings. The objectives of the study are to identify the factors that contribute to the perception of safety against harassment in public transport, assess women’s perception of safety in public transport and identify issues faced by them, and compare differences between safety perception and initiatives on the ground, to provide insights to the authorities involved in planning for safety infrastructure.

2 Literature Study In many Indian cities, women are the primary consumers of public transit. Women’s travel patterns are different, as they incorporate several destinations within a single trip. Women make shorter and more trips, which often require them to change, divert, and break their journeys to pick up children, run errands, shop, or take on other family obligations (Marianne Vanderschuren, 2016). The travel pattern of women is referred to as “mobility of care” (Sonal Shah, 2017). Women are harassed not just when travelling by public transportation but also while waiting for the bus at the bus stop and on the way to the stop. Women are commonly seen altering their travel patterns and behaviors to avoid or respond to harassment. Stepping out of the vehicle at earlier stops, standing at a stop with other people, waiting with a group of ladies, carrying a big bag to avoid being touched, and so on are examples of this (Anand & Tiwari, 2007). There are a variety of factors that can make a woman feel unsafe or secure while waiting or travelling on public transport. This can be broadly classified into social & personal factors, and factors related to spatial location, transport & infrastructure.

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Social and Personal Factor: A woman’s personal attributes, such as her age, occupation, and frequency of travel, can all influence her overall sense of safety and security (IDR, 2021; Shilpa phadke, 2015). Age is a factor that influences travel habits, which most mobility studies overlook. The majority of women assaulted on public transportation are ladies aged 15–25 (Verma, 2020). Women in this age category are more exposed to having to take public transport for any reason, whether educational, recreational, or employment. When it comes to the frequency of travel, women who take public transportation on a regular basis are frequently harassed when walking to and from transit stations or while travelling on public transport (Anvita Anand, 2006). Women of lower income are generally more inclined to use non-motorized vehicles or public transport (Mahadevia & Advani, 2016) Social factors, such as the presence of people at a public transit stop, may have varying effects on various women (Anvita Anand, 2006). Women’s safety perceptions are also influenced by their perceptions of their family and their experiences using public transportation. Lack of awareness of their rights and lack of awareness of how to report an issue, lack of awareness of security measures available on public transport, etc. may further influence women’s perception of safety (Jagori, 2010). Awareness can be both social and personal. When it comes to social awareness, awareness among the family, awareness among the officials, and transport staff also help to improve and influence women’s safety perception (Sakhi, 2011). Spatial location, transport, and infrastructure: When it comes to spatial location, isolated places of bus stops and lack of street lights influence women’s mobility (Valan, 2020). Bus stops are one area where women feel safe waiting, and the design makes them even safer. Openness and visibility from everywhere to the bus stop and from the bus stop are very important (Loukaitou-Sideris, 2009). Overcrowding on the bus is also a problem because passengers are constantly at risk of being touched, shoved, or misbehaving in some way. According to the studies, waiting times are regarded to be more difficult than walking times (Vande Walle, 2006).Waiting times are perceived to be nearly 3 times longer than they are and 2.6 times longer than in-vehicle time by PT users (Chowdary, 2020). This is due to the perception that waiting time is unproductive. As an active step to ease their worry, many women use their phones to interact with friends and family during that time or act confident to avoid unwanted attention (Chowdary, 2020). Solutions based on technology, such as the installation of CCTV, panic buttons, help lines, and public announcements of bus arrival times, among other things, make women feel safer and more at ease (Ko et al., 2019). Through the literature review, it is understood that there are certain factors that influence women’s perception of safety. Age, occupation, economic status, geography, societal norms, and other factors all influence how women perceive safety and security. Apart from this, physical elements also aid women’s perception of safety. All these factors differ from woman to woman. What feels safe to one person may not be the same to another woman. Poor quality public infrastructure, a lack of a sensitive planning approach that provides for gender-friendly spaces, and visible security measures that can ensure a secure environment for women are among the primary challenges that have served as regular reminders of the state of women’s safety.

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3 Methodology Ahmedabad is taken as the case city. Ahmedabad operates two different kinds of busbased public transport services one is the Ahmedabad Municipal Transport Service (AMTS) and the other being Bus Rapid Transport System (BRTS). Quantitative and qualitative data were gathered to examine women’s perceptions of safety and to identify gaps in the existing measures taken by the authorities to provide safer services. A user perception survey was conducted at 10 different locations in Ahmedabad, where 5 locations are from east Ahmedabad and 5 from west Ahmedabad. The locations were selected based on factors such as public transport accessibility and locations, where crimes are likely to be high (in consultation with the police department), institutional areas and public spaces, ensuring the geographical coverage of these locations in Ahmedabad. A random sampling technique was employed for sample selection, and a questionnaire comprising four question sets was considered for the survey. Wherein, set 1 comprised of questions related to personal attributes like age, occupation, and income, followed by sets 2, and 3, which contain statements intended to evaluate each respondent’s perception of safety on public buses. Set 4 consists of questions related to satisfaction and statements regarding infrastructure, safety at bus stops, and security measures, respectively. The sample included women public transport users (frequently or occasionally). Non-users were not considered for the survey. The survey was conducted both online and offline (at a predetermined location). The majority of the survey was conducted offline, which aided in learning more about the location. Meanwhile, while conducting the survey, attempts were made to record some of the women’s experiences. A total of 500 surveys were conducted, which consists of 50 surveys per location, out of which 482 clean samples were considered for analysis. Apart from user surveys initiatives undertaken by local authorities were analysed via detailed semi-structured interviews with the stakeholders. The officials interviewed included Deputy Transport Manager (AMTS), General Manager (BRTS), and officials from Police Department to better understand the initiatives taken up by the respective authority towards women’s safety and including standard operating procedures and challenges faced. Apart from them, frontline transit staff was also interviewed to gain insights into their level of sensitivity towards women’s safety and challenges faced on the ground (7 conductors from AMTS, 6 BRTS, and 5 AMTS drivers). The study analysed the responses of women travelling on public transport via behavioural analysis. Important factors that influence the behaviour of women on public transport and their satisfaction with the current availability of respective factors were analysed using importance satisfaction analysis. This method was preferred because it helped to understand which factors are important for women and also their level of satisfaction with the current system. Lastly, the categorization of factors influencing the behaviour of women on public transport was obtained via factor analysis, which helped to reduce the total number of

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variables to a smaller set of factors. The flexibility provided by factor analysis makes it a great tool for exploration and interpretation. The survey consists of different questions which have similar underlying factors. Hence, the factor analysis helped to understand the underlying factors in various variables. Qualitative analysis was conducted to understand the initiatives undertaken by the authorities and gap analysis helped in identifying the issues in the provisioning of safety initiatives. The following sections briefly summarise the data analysis and findings.

4 Data Analysis 4.1 Behavioral Analysis SPSS software is used for cross-tabulation necessary for the identified analysis. About 35% of the women were between the ages of 15 and 25, and 52% were between the ages of 25 and 50. Women between the ages of 50 and 65 accounted for 13% of the survey, while women over 65 accounted for 1%. The survey consisted of 41% of AMTS users and 59% of BRTS users. The perception among the women might be different and sometimes attributes such as location, age, and occupation also have an impact on their safety perception. As a result, the composition of samples based on occupation, the kind of public transport used, and location are mentioned in Tables 1 and 2 When asked about their perception of safety on public transportation, 41% of women agreed that they felt safe while travelling on public transport. However, for women, irrespective of their profession and location, 59% of them agreed that public transport is not a safe option for their travel. 84% of the respondents agreed that they or their loved ones faced harassment while travelling on public transport. The Table 1 Survey based on occupation Working/Non-working

Women surveyed (%)

Working

238 (49%)

Managerial level professionals (teachers, doctors, IT professionals, banking sector)

79 (14%)

Mid-level (clerical and administration)

89 (18%)

Vendor/business

52 (11%)

Worker (LIG)

18 (6%)

Non-working

99 (21%)

Housewife

80 (17%)

Retired

19 (4%)

Student

145 (30%)

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Table 2 Location wise distribution of samples West Ahmedabad

East Ahmedabad

AMTS

BRTS

AMTS

BRTS

Working

58 (24%)

68 (28%)

94 (39%)

43 (18%)

Non-working

10 (4%)

19 (8%)

19 (8%)

19 (8%)

Students

39 (16%)

48 (20%)

26 (11%)

39 (16%)

experience and perception of one’s journey can be passed on to another through word of mouth. One’s reference group’s experiences could change one’s own perception to some extent, especially when the reference group is of known people (Meghna Verma, 2019). 76% of the women responded that they did not report a harassment incident. Most women did not want to get involved with the police or the court, which is one of the reasons for the low rate of incident reporting. Worst of all, 9% of women changed their travel time and stopped using public transportation as a result of such instances. The society is vastly different in the eastern and western parts of Ahmedabad (Mahadevia et al., 2014). In east Ahmedabad, as the area is more male dominated while doing the survey, women also mentioned that they are only allowed to travel up to a certain hour at night and, in some cases, accompanied by a male member of their household. From the observation and in institutions, resulting in more young women travelling, contributing to the significant presence of women on public transport. Women in the west follow fewer safety strategies such as avoiding certain clothing, being hyper-aware of their surroundings, planning routes in advance, etc., compared to women in the east. The result indicated a significant influence of location on the safety perception of women, which is mentioned in Table 3. No major difference was found between the safety perception of working and non-working women. ‘Fear of not being safe’ was the same for all women. The only Table 3 Safety strategies adopted by women Safety strategies

% Working women

% Non-working women

% Students

Location in the city

East

West

East

West

East

West

Avoid certain clothing

30%

24%

40%

38%

34%

33%

Text/Call someone while travelling

12%

10%

14%

20%

18%

20%

Plan a route in advance

17%

15%

19%

17%

14%

18%

Be hyper-aware of your surrounding

20%

18%

24%

19%

27%

20%

Carry pepper spray/pin in bags

1%

1%

2%

3%

2%

3%

Take a longer route home

0%

0%

0%

1%

1%

0%

None

20%

32%

0%

2%

4%

6%

Total

100%

100%

100%

100%

100%

100%

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difference is that as compared to women who are non-working and students, working women reported the confidence to react or report harassment incidences.

4.2 Women’s Perception of Safety Importance Satisfaction survey was conducted to gain a better understanding of women’s perceptions. 15 parameters were identified through the literature study, and each question was measured on a Likert scale. The factors considered for the survey and the results of the analysis are presented in Table 4. The graph indicated in Fig. 1 is the importance satisfaction analysis in general. The responses were plotted in a graph where the X-axis is the mean value of importance Table 4 Factors considered for ISA Code

Factors

Importance factor mean

Satisfaction factor mean

Q1

Presence of streetlights

4.34

2.56

Q2

Other women’s presence at the 4.47 stop

2.82

Q3

Design of a bus stop/station

4.31

2.90

Q4

Presence of CCTV cameras in bus stop

4.04

1.66

Q5

Presence of vehicle information

4.05

2.28

Q6

Police visibility in the neighborhood

4.55

2.28

Q7

Availability of women’s helpline number

3.30

1.73

Q8

Regularity of bus timing

4.17

2.60

Q9

Presence of CCTV security system inside the public transport

4.07

1.53

Q10

Presence of women travelers in 4.48 the Public Transport

4.16

Q11

Presence of female staff as (bus driver and conductor) in public transport

4.58

2.24

Q12

Awareness about the panic button and its working

4.61

2.33

Q13

Online surveillance monitoring of public transport

4.01

1.93

Q14

Availability of reserved seats for women

4.48

4.33

Q15

Crowding on the bus

3.69

3.58

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Fig. 1 Important satisfaction analysis (ISA)

and the Y-axis is the mean value of satisfaction. This has 4 quadrants where quadrant 1, is for high importance and high satisfaction, quadrant 2 is for low importance and high satisfaction, quadrant 3 is for low importance and low satisfaction; quadrant 4 is for high importance and low satisfaction. A similar analysis was also carried out to understand the differences in the following groups: . BRTS and AMTS users, . Working, non-working women and students, . Ahmedabad East and West (Location). The overall analysis suggested that the availability of helpline numbers is less important irrespective of bus service usage, occupation status, and location in the city. Apart from that, all user groups are satisfied with the crowding on the bus. There are a few perspective differences among the users, which are explained further. Considering the AMTS and BRTS user perspectives, there is a difference in the satisfaction level of both. BRTS users are satisfied with the design of the bus stop and the CCTV surveillance, monitoring of the bus, and regularity of bus timing, as well as the availability of information. These provisions are currently available in BRTS services but completely missing in the AMTS service. AMTS users are not satisfied with the current service and consider these parameters important for their safety while commuting. When looking into the perspective of working women, one major change noticed is the presence of vehicle information. This might be because they are regular travellers and might already know the bus timing. According to the non-working women, they are not regular commuters and they prefer to travel during off-peak hours and gave priority to comfort. Hence, for them, the regularity of bus timing is not as important

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as the comfort of travelling. Similarly, students and non-working women also feel that CCTV cameras are not important to them. Similarly, results were obtained from women in the east of Ahmedabad. Further discussions revealed that as per their perception, these cameras can help solve crime after it has been committed, the footage can only act as evidence of the crime that happened. However, they had doubts related to the working of cameras and live monitoring which can contribute towards a reduction in crime. In addition, women pointed out that, along with the installation of CCTV, appropriate on-field surveillance with quicker and easier responses by the police department to situations and complaints may aid in reducing harassment issues, which will, in turn, result in a better perception of safety while travelling. Along with the provision of CCTV cameras, more “eyes on the street” as natural surveillance through mixed-land use and the presence of hawkers and a diverse gender/age population, along with women’s presence in the surroundings, also aids women in feeling safe when using public transportation. Women also expressed that they would feel safer if the transit agency staff was women.

4.3 Factors Influencing Women’s Safety As a part of the study 21 variables were identified from the literature which influenced the safety perception of women. These variables were analysed further using factor analysis to determine if a large number of observable variables are linked to a smaller number of unobservable variables. To determine the adequacy of the sample Kaiser–Meyer–Olkin (KMO) test for performed. Results indicated that sampling adequacy was 0.718 (higher than 0.5) which is rated as middling, and Bartlett’s test indicates significance (p-value = 0.000). This indicates that the sample was sufficient to undertake factor analysis, which is presented in Table 5. According to the factor analysis, a sense of safety is very important when travelling through public places, and the components such as pretending to be confident and always being aware of one’s surroundings, among others, indicate the level of anxiety that women experience while travelling or waiting for public transportation. They are apprehensive, which is evident from their behavior of always being connected through their phones to family or friends while waiting or travelling on a bus, always vigilant to avoid unwanted attention and the feeling of being safe in crowded stops or vehicles. Women’s freedom of movement is limited both during the day and at night due to fear and worry. It is understood that women expect better facilities in terms of lighting and the timing of the bus, etc. Even though security provisions in public locations play an important role in women’s safety perceptions, the opinions of friends and family also have a large effect on their impression of safety. Women’s perceptions of safety are influenced not only by their own personal experiences but also by the experiences and worries of their family and friends. The first factor indicates the availability of information and infrastructure, the factor loaded high, which means that women’s safety depends on the information available to them. This helps them to plan their trips, and they can reach the bus

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Table 5 Factor analysis results Variables

Factor Factor Factor 3 Factor Factor 5 1 2 4

Presence of streetlights

0.834

Design of a bus stop/station

0.664

Presence of vehicle information

0.744

Availability of women’s helpline number

0.323

Regularity of bus timing

0.721

Presence of CCTV security system inside the public transport

0.478

Online surveillance monitoring of public transport

0.368

Presence of CCTV cameras in bus stop

0.329

Awareness about the panic button and its working

0.611

Presence of women travelers in the public transport

0.817

Presence of Female staff as (bus driver and conductor) in public transport

0.761

Another women’s presence at the stop

0.881

Police visibility in the neighborhood

0.653

Availability of reserved seats for women

0.763

Overcrowding on the bus

−0.211

My family thinks public transport is safe

−0.649

My family is not worried about me using public transport at night

−0.615

I feel safer waiting in a crowded station/stop

0.708

I am usually aware/alert of my surrounding when waiting at the station/stop

0.622

I always connected through phone to my family or friends while waiting or travelling bus

0.720

I usually pretend to be confident while waiting at the station/stop to avoid unwanted attention

0.766

stop by following the bus timing. Design and the location of the bus stops and the presence of streetlights are also loaded high, this indicates its significance in women’s perception of safety. The second factor indicates the woman’s perception (feeling of safety). Women prefer the presence of other women while travelling on public transport. Overcrowding in the bus has a negative loading factor, which means that women are not comfortable travelling in an overcrowded bus. The reason is overcrowding creates an opportunity for the culprit to misbehave and favours their escape easily taking

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advantage of a crowded situation. The availability of women’s reserved seat loading factors is also high, which means that these features can make women feel safer. The third factor indicates technology. The factor loading is very low, but not negative. Thus, it can be understood that technology gives women a sense of safety to an extent, but not as much as the presence of other women can contribute to the feeling of safety. In general, women have a preconceived notion that most of the cameras installed in public places are not working. So not just installing different technological safety measures make a woman feel safe, but the awareness factor is also high, which indicates that awareness of how and when to use this technology is also very important. The fourth factor indicates the women’s attitude, where the factors load high on pretending to be confident. The main point to be noted is that they act confident rather than being confident, to avoid attention, as they feel that people will take advantage if they find a woman or girl who is not confident or scared. The fifth factor indicates social perception. The negative factor loading indicates that family members do not consider public transport safe for women members and are worried about them using public transport at night. Hence, from the literature and through behavioural analysis, ISA, and factor analysis, there are a few factors that emerge prominent from the safety perception. These can be categorised into 5 key factors, which are very important for improving women’s safety perception. They are, 1. Infrastructure and information availability: Women have obstacles when taking public transportation due to inadequate infrastructure development and a lack of secure spaces. The lack of adequate street lighting and information (such as transit route maps, timings, and schedules) about the routes and frequency of buses on certain routes (including any changes) also poses a barrier for women who commute. 2. Incorporation of technology: Proper surveillance, panic buttons also bus tracking and monitoring system systems give women a sense of security and safety when using public transportation. 3. Locational factors: Location of bus stops along deserted; lonely stretches were only part of the problem that was highlighted by female passengers. This becomes a bigger issue for women commuters, especially after dark. This directly impacts their movement at this time of the day. 4. Awareness: Lack of knowledge of reporting procedures and existing laws about women’s safety and equal rights to movement and space in the city makes women feel unsafe and less confident while travelling on public transport. 5. Gender mainstreaming: Male domination in public transport as fellow travellers and in the workforce as drivers or conductors also makes women feel insecure while travelling on public transport. These 5 categories help one to identify the areas where interventions or a need for improvement of existing measures are required to help women feel safer while using public transport.

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5 Gap Analysis Analysis was undertaken to identify gaps that exist on the ground in terms of safety measures adopted by agencies as compared to the five factors identified above. The availability of real-time information on the route, schedule is lacking in AMTS stops. Though static information on bus numbers and schedules is available at all stops. In terms of buses, no major issues are reported by the women as all AMTS buses have a real-time passenger information system inside the buses. Studies show that providing prior information to the users regarding the bus routes and timing can influence the perception of safety, which is also reported by women during the surveys. Issues were also observed from the design perspective of the bus shelters itself, where poor or faulty lighting was observed in many locations, especially in the east part of the city. On the other hand, BRTS users seem to be satisfied with the real-time information provided at the bus stops, including good lighting, providing a better safety perception for women in the system. From the perspective of CCTV cameras, it has been observed that all BRTS stops and buses have cameras installed on the other hand in AMTS has also provided cameras on 150 buses. The ISA results point out that while the cameras may give women a sense of protection, they offer little to improve or facilitate their access to a specific area of a city. The measures for availing help from the police are restricted to the helpline number. However, other studies (Shah, 2017) show alternative options such as panic buttons are important for women. Even though these technology options are available the use and information on their availability has not reached the users especially women who are naïve about the presence of these systems. Overall, there is a gap in terms of the availability of alternative technology options to contact police, along with the integration of these systems at the stop level coupled with a lack of awareness in the women of these options. Through the literature study and the analysis of the survey results, it is understood that women consider safety over comfort (Shilpa phadke, 2015). Through the analysis, it is observed that deserted places, underutilized places, etc., and maledominated locations are not favorable places for the provision of bus stops. This is majorly applicable to AMTS bus stops, especially in east Ahmedabad. Many respondents expressed concern about the narrow and poorly lit alleys and the low presence of women during the night on buses and bus stops. On the other hand, such issues were not reported by the BRTS users. During a discussion with the AMTS authorities, it was mentioned they are aware of these issues and working towards improvement from the perspective of bus stop location after the augmentation of the fleet. One of the key aspects that women stated was the lack of knowledge of reporting procedures to be followed. As per the discussions with higher officials from AMTS and BRTS, it was reported that proper procedures for registering a complaint were in place. If the complaint is against a passenger on public transportation, the driver or conductor is required to summon the police. However, while interviewing the drivers and conductors, it was found that they were not clear on the procedure to be followed to report this issue to the police authorities. The sensitising training to deal with

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women’s safety issues, along with reporting incidences to the police authorities, was missing in AMTS. One of the major challenges that is faced by the police department is, women are not coming forward to report the issues. The police authority is in charge of the safe city project under the Nirbhaya scheme. One of the current efforts under the Safe City Project is the She Teams. This team patrol only happens during peak hours such as the morning 9–11 and evening 4–8. However, the night patrol by the She Teams is missing. Besides that, the ‘She Team’ helpline number is not published anywhere in public places, especially at bus stops and inside the bus. ‘She Team’ also makes frequent visits to semi-public spaces such as schools, universities, and offices, where it conducts self-defence mechanisms. However, an awareness programme regarding the reporting procedure is missing. With respect to gender mainstreaming in public transit agencies, AMTS is reported to have only 2 conductors, even though they want to hire more women in their operations. They are struggling to fill these posts as women are not willing to work as drivers and conductors. Similarly, BRTS has 4 women drivers out of a total of 800 and 12 women security as ground staff. The women staff are allocated to the busiest station from 10 a.m. to 8 p.m. However, no women staff are available between 8 and 11 p.m. The staff composition of AMTS and BRTS indicates that women are underrepresented in the organization. Lack of female staff can also lead to a lack of understanding of women’s needs when developing safety measures. Interviews with transit agencies and primary surveys further indicated that AMTS bus stops are poorly maintained. Information availability at the stops is missing, and there are no security measures at the stops.

6 Conclusion Through surveys and an analysis of safety perception, this paper attempted to pinpoint the key factors that influence women’s perceptions of safety. These include the availability of infrastructure and information, the use of technology, locational factors, security, awareness, and gender mainstreaming. A similar study conducted on women safety in public transport at Bhopal (Bhatt et al., n.d.) has concluded that there are four main areas in which all problems can be classified, which include Infrastructure and Vehicles, Enforcement/Grievance System, Institutional Capacity and Service Planning and Operations. A study conducted in Indore also pointed out some of the similar key factors that influence women’s safety perception which include, security, misconduct behaviour, infrastructure convenience and technical assistance (Mayank Choudhary et al., 2018). Similarly, the study conducted by organizations like Jagori, Akshara and Sakhi (Akshara, 2016; Jagori, 2010; Sakhi, 2011) also highlighted the similar crucial elements that affect women’s perceptions of safety when using public transportation. All these studies come up with similar key areas that strongly need improvement to make it safer for women to travel by public transportation.

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The study itself points out gaps from the perspective of transit infrastructure availability, especially in eastern Ahmedabad. It is also observed that overall perception of safety improves with better-designed and maintained systems, as in the case of BRTS in Ahmedabad. Gender mainstreaming in transit agencies is required, along with gender sensitization of all staff. Transit authorities need to improve their facilities to attract more women in the public transport field. One of the important aspects to be looked into is the simplification of reporting mechanisms by the authorities and providing various means for reaching out for help. Awareness campaigns would be helpful in the process. Display ads informing people about the different types of abuse and the laws that govern them will inspire women to seek assistance, as well as bystanders to help. Behaviour training and gender sensitization of bus conductors and drivers and depot managers with regular follow-ups should be considered. From the study, it is evident that working women and students are the main users of public transport. They are using public transport because it is the most affordable mode of transportation and it is well connected across the city. Safe, comfortable, and convenient transport not only contributes to fulfilling women’s practical needs but also contributes to their strategic empowerment by facilitating access to social and economic opportunities.

References Bhatt, A. (n.d.). Women’s safety in public transport–A pilot initiative in Bhopal. WRI. Retrieved from https://wrirosscities.org/research/publication/womens-safety-public-transport-pilot-initia tive-bhopal Akshara, C. (2016). Empowering women’s mobility. Akshara. Anand, A., & Tiwari, G. (2007). A gendered perspective of the shelter—transport—livelihood link: The case of poor women in Delhi. Transport Reviews, 26(1), 63–80. https://doi.org/10.1080/014 41640500175615 Anvita Anand, G. T. (2006). A gendered perspective of the shelter–transport–livelihood link: The case of poor women in Delhi. Delhi.https://doi.org/10.1080/01441640500175615 Bandagi, A. (2021). We need a gender-sensitive public transport system. idr. https://idronline.org/ why-india-needs-a-gender-sensitive-public-transport-system/ Bank, A. D. (2015). Policy brief: A safe public transportation environment for women and girls. ADB. https://genderinsite.net/sites/default/files/safe-public-transport-women-girls.pdf Chowdary, S. (2020, May). Examining women’s perception of safety during waiting times at public transport terminals. https://www.researchgate.net/publication/341430205_Examining_ women%27s_perception_of_safety_during_waiting_times_at_public_transport_terminals IDR, I. D. (2021). We need a gender-sensitive public transport system. https://www.youthkiawaaz. com/2021/02/we-need-a-gender-sensitive-public-transport-system/ Institute, E. (2013). Importance-satisfaction analysis. ETC Institute. Jagori. (2010). A Baseline survey on women’s safety of the nine districts of Delhi: 2010. http:// www.safedelhi.in/public-transport.html Ko, J., Lee, S., & Byun, M. (2019). Exploring factors associated with commute mode choice: An application of city-level general social survey data. Transport Policy, 75. https://doi.org/10.1016/ j.tranpol.2018.12.007

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Loukaitou-Sideris, A. (2009). How to ease women’s fear of transportation environments case studies and best practices. Mineta International Institute for Surface Transportation Policy Studies. https://transweb.sjsu.edu/sites/default/files/2611-women-transportation.pdf Mahadevia, D., & Advani, D. (2016). Gender differentials in travel pattern - The case of a mid-sized city, Rajkot. Transportation Research Part D: Transport and Environment. https://doi.org/10. 1016/j.trd.2016.01.00 Mahadevia, Desai, & Vyas. (2014). City profile: Ahmedabad. Centre for Urban Equity (CUE). https://www.academia.edu/24733900/City_Profile_Ahmedabad Marianne Vanderschuren, H. A. (2016). Safe and sound. International research on womens personal safety in public transport. Mayank Choudhary, S. D. (2018). Women safety in public transport. Retrieved from https://www. ijamtes.org/gallery/26.%20may%20%20ijmte%20-%20491.pdf Meghna Verma, N. R. (2019). Young women’s perception of safety in public buses: A study of two Indian cities (Ahmedabad and Bangalore). WCTR. MHA. (2019). She raksha Quarterly Newsletter Jan-March 2019. Sakhi. (2011). Are cities in Kerela safe for Women? Research findings of tge study conducted in Thiruvanathapuram and Kozhikode cities. SAKHI Women’s Resource Centre. Shah, V. (2017). Women and transport in Indian cities. https://www.itdp.in/wp-content/uploads/ 2018/01/181202_Women-and-Transport-in-Indian-Cities.pdf Shilpa Phadke, S. R. (2015). Why loiter?: Women and risk on Mumbai streets. Sonal Shah, K. V. (2017). Women and transport in Indian cities. ITDP and Safetipin. Valan, M. L. (2020). Victimology of sexual harassment on public transportation: Evidence from India. SAGE. https://journals.sagepub.com/doi/pdf/10.1177/2516606920927303 Vande Walle, S. (2006). Space and time related determinants of public transport use in trip chains. https://ideas.repec.org/a/eee/transa/v40y2006i2p151-162.html Verma, M. (2020). Young Women’s perception of safety in public buses: A Study of Two Indian Cities (Ahmedabad and Bangalore). https://www.researchgate.net/publication/344979832_ Young_Women’s_Perception_of_Safety_in_Public_Buses_A_Study_of_Two_Indian_Cities_ Ahmedabad_and_Bangalore/citation/download

Assessment of Utilization of the Foot Over Bridges in Delhi Akshaya Paul and Sharif Qamar

Abstract There has been an increase in the urban population coupled with economic opportunities which have motorized and privatized travel patterns. Private motorized vehicles are given priority right of way, which has made pedestrians the most vulnerable set of road users. The policies governing walkability in Indian cities do not recommend grade-separated foot over bridges (FOBs) because the shortest direct route at crossings must be given to pedestrians. The current study looks at mobility parameters and the sociological perspective of pedestrians for utilization of FOBs in Delhi and suggests improvements for conducive pedestrian movement. It is found that in Delhi, 23% of people walk to work as compared to 3% of work trips by cars/vans/jeeps and 13% in scooters/motorcycles/mopeds yet infrastructure does not favour pedestrians. The four indicators selected were comfort, accessibility, security, and connectivity. Using the indicators, the locations studied were FOBs at Azadpur Chowk, IIT Gate and ITO. The ITO FOB scored the maximum but lacks accessibility for all users. From the survey, it was observed pedestrians crossing twice a day prefer crossing at grade and the younger population between 21 and 30 years were found to use the FOBs more compared to other age groups. For easy access, escalators are a must for pedestrians irrespective of their gender. Based on observations at the site and primary survey, improvements for mid-block crossings at IIT Gate and ITO and intersection crossing at Azadpur Chowk were recommended to propose better accessible and user-friendly pedestrian crossing facilities. Keywords Foot over bridges (FOB) · Pedestrians · Delhi · Walkability · Sustainable transport

A. Paul (B) · S. Qamar The Energy and Resources Institute (TERI), New Delhi, India e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_14

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Abbreviations FOB MPD 2041 PKM RoW UTTIPEC

Foot over bridge Master Plan of Delhi, 2041 Passenger kilometre Right of way Unified Traffic and Transportation Infrastructure (Planning & Engineering) Centre

1 Introduction 1.1 Background One of the key challenges in urban centres in India is to meet the increasing mobility demand of the population and simultaneously reduce local air pollution and emissions (Maheshwari et al., 2020). But it is often perceived that mobility demands are met by private vehicles reflecting a car-centric paradigm. In 2002, 79.2 million vehicle kilometres were travelled in Delhi and nearly doubled to 150.6 million vehicle kilometres by 2010 (Mishra & Goyal, 2014). In 2019, Delhi had 11.4 million registered vehicles, the highest among the million-plus cities, this has led to increasing levels of emissions and ambient air pollution. The modal share of pedestrians has declined but walking remains the dominant form of transportation in many cities (Soliz & PérezLópez, 2022). On comparing the CO2 emissions by different modes, a diesel car emits 188.6 gm/passenger-km (PKM), a BRTS (AC bus) 36.9 gm/PKM, a 2-wheeler 36.5 gm/PKM, a metro in Delhi 19.7 gm/PKM, and walking and cycling 0 gm/PKM (TERI, 2012). Thus, walking and cycling need to be aggressively promoted for short travels for the decarbonization of the road transport sector.

1.2 Pedestrian Crossing Infrastructure—FOBs Safety, continuity, and comfort are the key principles in planning pedestrian infrastructure for a safe walking experience. The authorities have built pedestrian crossing infrastructures such as foot over bridges (FOBs) to eliminate pedestrian flow in carriageways (Soliz & Pérez-López, 2022). In the past decade, grade-separated pedestrian infrastructure1 such as the FOBs has sprung up in urban areas, despite being largely unused by pedestrians. A study by Arellana et al. (2022) in Colombia, observed the preferred paths were to use direct crossings with no detours. FOBs are 1

A bridge that eliminates crossing conflicts at intersections by vertical separation of roadways in space.

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inconvenient for pedestrians because of the increased walking length and the high effort and time required for climbing up and down the stairs (Rahman et al., 2022). It is also inaccessible for the elderly and persons with special abilities, access to persons in wheelchairs is impossible in the absence of lifts or ramps, and women pedestrians often perceive FOBs as unsafe (Massingue & Oviedo, 2021). This study focuses on FOBs and related usage in Delhi, which is an infrastructure with the potential to exclude or cause inconvenience to those depending on active transportation modes.

1.3 Study Area: Delhi The National Capital Territory of Delhi (NCT) has an area of 1483 km2 . and a total road length of 33,198 km (Accident Research Cell, Delhi Police, 2020). There were 6.41 lakh new vehicles added over the 118.4 lakh registered vehicles for the year 2019; compounding at a yearly growth of 5.72% (Accident Research Cell, Delhi Police, 2020). The cost of traffic congestion on roads in Delhi costs Rs. 60,000 crores annually (Joseph et al., 2015) because of the idling of vehicles, productivity loss, air pollution, and road crashes. The most vulnerable road users are pedestrians; 42% of the fatalities involved pedestrians in 2021 (Government of NCT of Delhi, 2022). However, 23% of people walk to work as compared to 3% of work trips by cars/vans/jeeps and 13% by scooters/motorcycles/mopeds (TERI, 2018) yet most roads are not conducive for pedestrian movement due to unequal road space distribution (MPD 2041). The roads are designed to provide uninterrupted flow to the traffic by blocking entire carriageways, higher speed limits for vehicles, etc. The apathy towards pedestrians can be seen in inadequate infrastructure and the lack of maintenance of the present infrastructure. But approximately 90 FOBs have been installed at various places in Delhi by 2020–21 and there was ongoing work on 5 FOBs (Economic Survey of Delhi 2021–22).

1.4 Aim of the Study The current study looks into FOBs in Delhi with respect to pedestrian mobility parameters. Mobility parameters such as accessibility—low physical effort by all, safety, comfort, quality, environmental effect, and location have been looked into in the study. The purpose of the study is to assess the infrastructure and utilization of the FOBs. It also evaluates the contribution of FOBs in aiding urban mobility based on a scoring developed by the authors. The structure of the study first looks into the literature on walkability, pedestrian infrastructure, and policies governing FOBs in India. Next, looks at the present situation of three selected FOBs in Delhi and gives absolute scores to each based on indicators and sub-indicators through a primary survey and interaction with pedestrians. The sociological aspects of the user and non-user pedestrians of FOB were captured through a self-administrated

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questionnaire. The goal is to propose more accessible and user-friendly pedestrian crossing facilities for the study area. The paper also provides ground-level challenges and solutions before planning and implementing FOBs in Delhi.

2 Literature Review Active transport, i.e. cycling and walking, is an important step toward improving environmental quality in terms of local air quality and climate change mitigation and in contributing to public and private health (Kim & Hall, 2022). Active transport will also aid in achieving energy security by reducing fuel imports for India by reducing the usage of motorized transport. Various measures for active transport are vital for a more sustainable, more equitable, and healthier mobility paradigm (Tuominen et al., 2022).

2.1 Pedestrian Public Spaces Walking is fundamental to urban life and plays an integral role in providing sustainable mobility in cities. On the contrary, mobility paradigms emphasize on speed and efficiency of motorized transport as the main indicators (Tuominen et al., 2022). The urban streets originally planned as public spaces for all have been progressively claimed by motorized traffic encroaching on the pedestrian spaces (Campisi et al., 2022). However, most Indian cities have pedestrian trips which account for a quarter to a third of all trips yet 27% of the private motor vehicle trips occupy 75% of the right of way (RoW) (ITDP & MoHUA, 2019). Walking encourages social interaction and supports policy objectives aligned with public health (Tuominen et al., 2022). It enables individuals to intimately interact with their environment, such interactions can influence their life satisfaction and well-being (Chan & Li, 2022).

2.2 Measuring Walkability Walkability can be defined as the characteristics of the built environment and land use that support a pedestrian-friendly environment in urban areas. Quantifying walkability will address the comprehensive needs of the pedestrian and support inclusive interventions (Moura et al., 2017). There is adequate evidence of the relation between the built environment and walking behaviour; therefore, the focus should be to identify and assess the built environment attributes that contribute to walkability. Infrastructure has a direct impact on pedestrians’ satisfaction, a poor-quality pedestrian infrastructure can decrease walking trips.

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The aim of pedestrian infrastructure should be to create accessibility for all users (Campisi et al., 2022), not disregard the more disadvantaged. Since FOBs are grade-separated, facilities such as escalators, lifts, or ramps aid in reducing the time and effort required to climb the stairs. The static features such as adequate width, presence of street furniture and shade, and quality of the ground surface outline comfort of pedestrians while usage of pedestrian infrastructure (Gao et al., 2022). The environmental features that influence walkability are street connectivity, dwelling density, land use mix related to the distribution of amenities (Gao et al., 2022), availability of footpaths, and attributes of the route such as mixed traffic intersections (Jafari et al., 2022).

2.3 Gender Perspective Physical activity patterns vary by gender, compared to men, women commonly report barriers such as unsafe neighbourhood conditions, risks to safety, low walkability, unsupportive pedestrian infrastructure, poor built environment, and financial constraints among others (Adlakha & Parra, 2020). Transportation plays a key factor that allows women to participate in the workforce and access social opportunities. In Indian cities, 31% of females access their workplaces on foot as compared to 28% of males (Tiwari & Nishant, 2018). Within a distance of less than 1 km, 84% of females were pedestrians compared to 65% of male pedestrians. As the distance increases the mode share of pedestrians decreases, at a distance of 6–10 km, only 22% of males were pedestrians while a higher share, i.e. 34% of females were still pedestrians (Tiwari & Nishant, 2018). With the difference in the number of female pedestrians and the travel pattern associated, it is vital to look at pedestrian infrastructure through a gender lens. Also, women bear disproportionate impacts from poorly built environments, pedestrian infrastructure, or public transport compared to men. Other personal characteristics such as age, walking in a group, educational level, etc., also attribute to pedestrian crossing behaviour (Arellana et al., 2022).

2.4 Foot Over Bridges Most transport network development activity tends to prioritize motorized vehicle movement with grade-separated crossings for pedestrians to avoid conflicts. The major advantage of FOBs is ensuring safety by reducing conflict points, especially along high and high-speed motorized movements. However, pedestrian infrastructure should provide seamless movement to pedestrians including vulnerable users such as persons with special abilities, caregivers with prams, children, and the elderly.

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FOBs increase walking distance and time, encroach into footpath space, and cost up to 20 times more than at-grade signalized crossings (National Association of City Transportation Officials). FOB is often unutilized because of its poor quality, increased length of the crossing, and inaccessibility for vulnerable users which makes pedestrians cross at grade (Rahman et al., 2022). FOBs that are safe, comfortable, and continuous are missing in cities, which has led to many road fatalities. Globally, road traffic injuries are the leading cause of death for people aged between 5 and 29 years (WHO, 2018) and these are preventable. In India in 2020, traffic accidents accounted for 39.9% of the major causes of accidental deaths (National Crime Records Bureau, 2021).

2.4.1

Current Status of the FOBs in Key Indian Cities

In Hyderabad, Greater Hyderabad Municipal Corporation (GHMC) has made budgetary allocations for the construction and improvements of subways and FOBs from 2019 to 2020. In 2021, simultaneous work on 36 FOBs by various departments including GHMC and Hyderabad Metropolitan Development Authority was undertaken (Vadlamudi, 2021). In 2010, Pune proposed to build three FOBs, namely Mrutyunjay Mandir, Karve Road FOB, Sutar bus stand, Karve Road FOB and Paud Road and Karve Road FOB (Parisar, 2010). Despite huge spending on FOB, the dilapidated condition of FOB was revealed in 2021 in a structural audit, which recommended the dismantling instead of reinstallation at another location. Even though the Pune pedestrian policy aims to provide consistent and high-quality infrastructure to pedestrians with equitable allocation of road space when it comes to an intersection crossing Pune Municipal Corporation planned to build 200 more pedestrian bridges (Parisar, 2010). Across cities in India, high budget allocations have been made over the years for the construction of FOBs without any pedestrian audits conducted. Even though 23% of people in urban areas rely on walking for work trips (TERI, 2018), FOBs are being constructed even though it increases inconvenience for pedestrians.

2.4.2

Guidelines Related to FOBs

Many policies have given pedestrians the right to walk yet the implementation is contrary. The Sustainable Development Goals2 (SDG) 3.6 has a goal of halving the number of global deaths and injuries from road traffic accidents by 2030 and SDG 11.2 aims to provide access to safe, affordable, accessible, and sustainable transport systems for all, particularly by expanding public transport by 2030. The Unified Traffic and Transportation Infrastructure (Planning & Engineering) Centre (UTTIPEC) Guidelines, 2009, mention shortest possible direct route to cross 2

Sustainable Development Goals are a collection of 17 interlinked global goals set by the United Nations General Assembly to achieve a more sustainable future for all by 2030.

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the street must be given to pedestrians, therefore, preference is at-grade crossing. The guidelines also state the construction of FOB should be an exception and built only when at-grade crossings are infeasible. Since there is ambiguity in the construction of FOBs and study that needs to be taken up before planning FOBs; many FOBs are constructed irrespectively and eventually unutilized by pedestrians. Clause no. 6.7.2 of Indian Roads Congress (IRC) 103: 2012 states pedestrian crossings should be provided at every 150 m distance depending on the surrounding land use and pedestrian footfall. The Urban Street Design Guideline Pune, 2016, prescribes crossings to be either signalized crossings at junctions with refuge islands or mid-block crossings supported by traffic calming measures. The priority at intersections should be for pedestrians with the most natural and convenient path. The guidelines recognize the non-usage of grade-separated crossings such as FOB and subways because of the inconvenience to pedestrians. It mentions the avoidance of FOB at intersections under all circumstances; however, allows the construction in very exceptional cases as a last resort. The final assessment of grade-separated crossings must be based on scientific surveys and applicable standards. The Master Plan of Delhi 2041 recognizes the problems in infrastructure for walking faced by citizens especially persons with disabilities, the elderly, and children however without statutory backing it fails to implement the envisioned mobility plans. MPD 2041 further states pedestrians should remain at grade with comfortable and safe access and minimum detours. Grade-separated infrastructure should be avoided to prevent unnecessary detours; only in unavoidable circumstances such as the presence of highways in peripheral zones of urban areas, grade-separated infrastructure is allowed. Such pedestrian crossings should be universally accessible and frequent with at least 4 crossing structures per km.

3 Data and Methodology The three FOBs selected are geographically spread across Delhi and are on different road types. The Azadpur Chowk FOB is on the Ring Road, IIT Gate FOB is near the Outer Ring Road, and ITO FOB is on IP Marg (Table 1). The barricading on the roads under the FOBs was different hence the behaviour of pedestrians while crossing roads was studied. Through reviewed literature, the significant indicators identified were comfort, accessibility, security, and connectivity (Arellana et al, 2022; Gao et al, 2022; Jafari et al, 2022). To assess the current infrastructure of the selected FOBs with regard to mobility parameters fifteen sub-indicators were selected. Each sub-indicator was measured and scored, based on equal weights, through on-site observations of the existing infrastructure at the three FOBs. The score for each FOB was converted to a percentage to compare the FOBs (Fig. 1).

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Table 1 Comparison of selected FOBs Criterion

Azadpur Chowk FOB

IIT Gate FOB

ITO FOB

Geographical spread

North Delhi

South Delhi

Central Delhi

Road name

Ring road

Outer ring road

IP Marg

Barricading

Open at-grade crossing

Barricading on the median with a gap in between

Complete barricading on the median

Access to FOB

Ramp and escalators

Stairs and lifts

Stairs and escalators

Nearest public transit

Metro and public bus stop

Public bus stop

Public bus stop

Fig. 1 Heat map of fatal crashes in Delhi 2020–21. The heat map shows the fatal crashes and their locations. The areas in red represent locations with a high incidence of fatal crashes. Source Government of NCT of Delhi (2022)

3.1 Azadpur Chowk FOB The Azadpur Chowk FOB has three sections spread over the Ring Road. One of the sections is connected to the Azadpur metro station and another to the Azadpur bus terminal providing direct connectivity. The FOB provides accessibility with escalators and ramps on either side of the road; however, both the escalators do not work. The entrances of the FOB are obstructed by the street vendors present on the adjoining footpaths of the road network (Fig. 12). It was one of the high-risk areas

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Fig. 2 The Azadpur Chowk FOB Source: Google Maps

with fatal crashes within 250 m radius and had a high number of pedestrian fatalities in 2020–2021 (Government of NCT of Delhi, 2022) (Fig. 2).

3.2 IIT Gate FOB The FOB at IIT Gate is present at Hauz Khas over a six-lane Sri Aurobindo Marg Road which has a speed limit of 50 kmph, near the Outer Ring Road. The FOB has stairs and lifts on both sides for all users. The stair has an unobstructed entrance; however, the entrance of the lift is obstructed by a tree. The FOB is accessible by a footpath, which has tactile paving but the height of the footpath is 0.60 m and abruptly ends at the entrance of the FOB. The FOB does not have an active frontage as the dead walls face the street and the frontage of the FOB is obstructed by the tree canopy. The Outer Ring Road has been identified as a high-risk corridor with 4 fatalities per km (Government of NCT of Delhi, 2022) (Fig. 3).

3.3 ITO FOB The ITO FOB is present on the Indraprastha Marg with institutional land use dominant around the FOB. The public bus stops are present at the foot of the FOB and the ITO metro station is 300 m away. Public Works Department has appointed a guard through a contractor to be present on the FOB. The FOB has a stair with risers of 0.05 m,

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Fig. 3 The IIT Gate FOB Source: Google Maps

a cycle ramp of 0.60 m in width, and escalators on both sides which however were non-functional over the period of the survey (Fig. 4). The utilization of the FOBs by the pedestrians was measured in three 10-min time intervals by manually counting the pedestrians crossing using the FOB and crossing at grade, categorized as users and non-users, respectively. In three such intervals, taken at peak hours during the weekdays, the average users and non-users were calculated to find the percentage of usage at the selected locations. At Azadpur

Fig. 4 The ITO FOB Source: Google Maps

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Chowk the observations were taken from 8:50 to 9:20 am, at IIT Gate from 9:00 to 9:30 am, and at ITO from 17:00 to 17:30 pm. The sociological aspects of pedestrians through personal characteristics such as age, educational background, gender, and behaviour in a group were studied by conducting primary surveys for a sample of 20 at each of the FOBs through a questionnaire. The questionnaire3 also had a stated preference survey to understand pedestrian preferences for security and accessibility measures, volume and speed of vehicles, and the width of the road in determining crossing behaviour. The sample of the pedestrian survey reflects the percentage of users and non-users of the FOBs. Various case studies of different pedestrian infrastructures were looked at to determine the suitable alternate pedestrian crossing infrastructure for each site according to the street characteristics.

4 Observations 4.1 Comparison and Scoring of the Three Selected FOBs by the Authors 4.1.1

Indicator: Comfort

The shade infrastructure provided at two of the FOBs ITO and Azadpur Chowk are maintained poorly with broken roofs (Fig. 5) and at IIT Gate the shade was completely provided and maintained (Fig. 6). The riser of the step provides a comfortable experience while climbing the stairs; with an increase in the riser, pedestrians get tired. According to the IRC 103: 2012, the riser of a step should not exceed 0.15 m. At ITO the riser was 0.05 m (Fig. 7) and was comfortable. The riser at IIT Gate was 0.17 m and at Azadpur Chowk as the escalators do not work, it acts as the stairs with a riser of 0.20 m, creating discomfort for pedestrians. According to UTTIPEC Guidelines, 2010 there must be resting places and seating provided at a minimum of two locations along the bridge as it will increase the usability while providing comfort. None of the locations has resting/seating places provided. Overall, on the comfort indicator, the FOB at Azadpur Chowk scores the least and maximum at ITO, it provides comfort for users except for resting/seating places.

3

For questionnaire contact authors.

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Fig. 5 Broken roofing at Azadpur Chowk FOB and ITO FOB Fig. 6 Shade and surface of IIT Gate FOB

Fig. 7 The 0.05-m riser of the stair at ITO FOB

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Fig. 8 Non- functional escalators at Azadpur Chowk and ITO FOB

4.1.2

Indicator: Accessibility

The accessibility parameters rate FOBs on providing universal access to all users. FOBs should have a combination of the infrastructure of either staircase, ramp, escalator, or lift for universal accessibility. The escalators’ availability can be the most significant factor for the usage of FOB (Arellana et al., 2022); however, cannot be the only measure. At Azadpur Chowk and ITO, the escalators provided were not working (Fig. 8) as observed during the site visits and reported during the survey. The lifts are provided only at IIT Gate (Fig. 9) and were observed to be in working condition. However, through the survey users stated recent breakdowns of the lifts. The ramps were present only at Azadpur Chowk. The tactile paving/tiles were not being provided at any of the locations. The least accessible was ITO FOB, however, the rest of the locations have bare minimum accessibility as the existing infrastructure was not maintained.

4.1.3

Indicator: Security

The walking security for users of the FOB will increase its usability (Gao et al., 2022). The lighting is provided at all locations but at Azadpur Chowk the lights at one of the entrances were missing (Fig. 10). There was a security guard at ITO FOB (Fig. 11). The presence of street vendors helps in place-making (Neethi et al., 2021). The street vendors were present along the road stretch only at Azadpur Chowk (Fig. 12). With proper lighting and a security guard present, ITO FOB was the most secure, then Azadpur Chowk FOB, and the least safe was IIT Gate FOB.

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Fig. 9 Lift at IIT Gate FOB

4.1.4

Indicator: Connectivity

The FOB should be connected with other related pedestrian facilities to provide seamless travel (Battarra & Mazzeo, 2022). There were no public amenities such as public toilets or drinking water facilities around any of the three FOBs. There was signage only on one side of the carriageway at IIT Gate FOB and was missing at the rest. At Azadpur Chowk, the street vendors block most of the entrance access to the FOB. All three FOBs were near public transit stations. The least was scored at Azadpur Chowk then ITO. IIT Gate is the most connected. For the overall scoring4 (Table 2 and annexure), ITO FOB has scored the maximum with 38.5% but does not provide universal accessibility at all. IIT Gate FOB scores 35.4%; however, it fails in providing accessibility or comfort to pedestrians. The least is scored by Azadpur Chowk FOB with 32.8%, as it lacks comfort, accessibility, and connectivity.

4

For details on scoring used in the study refer annexure.

Assessment of Utilization of the Foot Over Bridges in Delhi Fig. 10 No lights at one of the stretches at Azadpur Chowk FOB

Fig. 11 Guard at ITO FOB

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Fig. 12 Street vendors blocking the entrance of Azadpur Chowk FOB

4.2 Utilization of FOBs At Azadpur Chowk the road has an at-grade signalized crossing which was utilized by 2/3 of the pedestrians; hence, the low, i.e., 33.2% utilization of the FOB. However, it does not have a separate signal for pedestrians. Few pedestrians also cross the carriageway at random spots even with a 0.60-m-high median. At IIT Gate, the road at grade is closed by barricades; however, with a small gap spotted, pedestrians prefer crossing at grade. At ITO, the road at-grade is completely closed by barricades on the median; therefore, the pedestrians use the FOB hence the 100% utilization (Table 3).

4.3 Characteristics of Users and Non-users of FOBs 4.3.1

Non-users of FOB

The carriageway at ITO is completely closed; there were no non-users. Therefore, the difference in behaviour could not be captured. It was observed that most of the nonusers were pedestrians who crossed the road twice a day at both sites. At Azadpur Chowk, out of the non-users, 64% of pedestrians crossed twice a day. At IIT Gate, a similar behaviour was observed. 64% of the non-users crossed twice a day and only 5% of pedestrians who crossed twice used the FOB. It was evident that pedestrians crossing the road more than once a day would prefer at-grade crossings (Fig. 13).

4.3.2

Age of Pedestrians

64% of non-users of FOB were between 21 and 40 years of age at Azadpur Chowk. 57.1% of pedestrians who utilized the FOB were between 21and 30 years old and

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Table 2 Scoring of each sub-indicator of three FOBs S. No

Azadpur Chowk

IIT Gate

ITO

Comfort 1

Shade

75

100

75

2

The riser of the stair

0

25

100

3

Resting/Seating places on FOB

0

0

0

Total score (x)

75/300

125/300

175/300

Average score (x/3) (out of 100)

25.00

41.67

58.33

Accessibility 4

Escalators

50

0

50

5

Lift

0

100

0

6

Ramps

75

0

0

7

Tactile paving/Tiles

0

0

0

Total score (x)

125

100

50

Average score (x/4) (out of 100)

31.25

25

12.5

Security 8

Lighting on the FOB

50

75

75

9

Security guards

0

0

75

10

Presence of street vendors

75

25

0

Total score (x)

125/300

100/300

150/300

Average score (x/3) (out of 100)

41.67

33.33

50.00

Connectivity 11

Public amenities

0

0

0

12

Signage about FOB

0

25

0

13

Nearest public transit stop within 500 m

100

100

100

Total score (x)

100/300

125/300

100/300

Average score (x/3) (out of 100)

33.33

41.67

33.33

Total (out of 400)

131.25

141.67

154.17

32.8%

35.4%

38.5%

Table 3 Utilization by pedestrians of FOBs Azadpur Chowk

IIT Gate

User

Non-user

User

Non-user

ITO User

Non-user

95

154

6

5

84

0

80

148

5

5

103

0

42

134

4

7

91

0

Average

72

145

5

6

93

0

Percentage of usage

33.2%

46.9%

100.0%

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Fig. 13 Usage patterns in a day at Azadpur Chowk and IIT Gate FOBs

Usage of Azadpur Chowk FOB (in %) 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0%

Non-user of FOB User of FOB

Not often

Once

Twice

Usage of IIT Gate FOB (in%) 40.0% 35.0% 30.0% 25.0% 20.0%

Non-user of FOB

15.0%

User of FOB

10.0% 5.0% 0.0% Not often

Once

Twice

with increasing age the number of users decreased because and the effort required to climb the non-functional escalator. At IIT Gate, 45.4% of the non-users were in the age bracket of 21–30 years. Through the survey, it was witnessed that as the pedestrians’ age increases, they tend to use safer crossings such as FOB even though the FOB is very high and tiring, in contrast to observations at Azadpur Chowk (Fig. 14).

4.3.3

Educational Background of Pedestrian

At Azadpur Chowk, it was observed that the higher educational level of a pedestrian did not determine in choosing a FOB as 28.5% of pedestrians were diploma holders or graduates and 100% of them were non-users of FOB. At the IIT Gate crossing, 72.7% of non-users have a diploma, graduation or post-graduation and they chose the at-grade crossing which is more convenient than FOB (Fig. 15).

4.3.4

Behaviour of Pedestrians in a Group

The non-users exhibited tendencies of riskier behaviour when in a group at IIT Gate and Azadpur Chowk while crossing at grade which gives them more visibility to

Assessment of Utilization of the Foot Over Bridges in Delhi Fig. 14 Usage pattern based on pedestrians’ age at Azadpur Chowk and IIT Gate FOBs

301

Usage based on Pedestrians' age at Azadpur Chowk FOB 25.0% 20.0% 15.0% Non-User of FOB User of FOB

10.0% 5.0% 0.0% 21-30

31-40

41-50

51-60

Usage based on Pedestrians' age at IIT Gate FOB 30% 25% 20% Non-User of FOB

15%

User of FOB

10% 5% 0% 21-30

Fig. 15 Usage pattern based on the educational level of pedestrians at Azadpur Chowk and IIT Gate FOBs

31-40

41-50

51-60

Educational level of pedestrians at Azadpur Chowk FOB 30.0% 25.0% 20.0% 15.0%

Non-User of FOB

10.0%

User of FOB

5.0% 0.0% No formal education

Up to 12th

Diploma/ Graduation

Post-Graduation

Educational level of pedestrians at Azadpur Chowk FOB 30.0% 25.0% 20.0% 15.0%

Non-User of FOB

10.0%

User of FOB

5.0% 0.0% No formal education

Up to 12th

Diploma/ Graduation

Post-Graduation

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Fig. 16 Group characteristics of pedestrians at Azadpur Chowk and IIT Gate

Group Characteristics at Azadpur Chowk FOB 50.0% 40.0% 30.0% Non-user of FOB 20.0%

User of FOB

10.0% 0.0% Not in a group

In a group

Group characteristics at IIT Gate FOB 50.0% 40.0% 30.0% Non-user of FOB 20.0%

User of FOB

10.0% 0.0% Not in a group

In a group

the incoming traffic. At IIT Gate 81.8% of the time, a person who crossed a grade was in a group and at Azadpur Chowk it was 71.4%. At both locations, a group of pedestrians crossing chose riskier behaviour at the pedestrian crossing as they chose the at-grade crossing over FOB (Fig. 16).

4.4 Perspective of Pedestrians Based on Gender 4.4.1

On Security Measures

In the stated preference survey at Azadpur Chowk, 54.5% of the female respondents considered lighting on the FOB as a security measure whereas 50% males of considered a security guard on the FOB as the most important security measure. The second preference of females is security guards and for males was CCTV cameras. It was observed that males do not consider the lighting on the FOB as important in comparison to females. At IIT Gate, 65% of the male and female pedestrians demand a security guard at FOB alike because of the nuisance caused by drunkards. The second preference was given to lighting on the FOB by 44.4% and 63.6% of males and females, respectively. At ITO, 60% of females consider lighting and 50% of males consider security guards on FOB as the first preference for security

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measures. Secondly, security guards were considered important by female pedestrians and lighting by male pedestrians. The third preference of both pedestrians was CCTV cameras. At three locations, females considered lighting and security guards as important for a perceived sense of safety while males considered security guards as an important factor. The installation of CCTV cameras was not considered important by either gender as it is perceived to be helpful after an incident has occurred.

4.4.2

On Accessibility

At Azadpur Chowk and ITO, the male and female pedestrians alike prefer escalators in working conditions as it is the shortest path. The female pedestrians at all three locations chose ramps next as it is accessible to all and lastly lifts as the confined space is perceived to be unsafe. At IIT Gate, the first preference of male pedestrians was lifts, followed by escalators and then ramps. At all three locations, escalators were considered the most important. Secondly, the ramps were considered more accessible than lifts for female pedestrians whereas ramps were the last choice of male pedestrians.

5 Alternate Pedestrian Crossing With the low utilization rates at IIT Gate and Azadpur Chowk FOBs, it would be feasible to provide more frequent and safer at-grade crossings for pedestrians. The footpaths should be continuous with pedestrian crossings and should have 3 zones— frontage zone, pedestrian zone, and furniture zone (ITDP & MoHUA, 2019). The elements that need to be incorporated for universal design include tactile paving, signages, separate street lights at the height of pedestrians, and public amenities at all three locations. At Azdapur Chowk the street vendors need to be allocated a place so that they do not encroach on the pedestrian zone, which should be a clear 1.8 m (IRC:103). At IIT Gate, the frontage of the footpaths are dead walls, which could be made vibrant, especially near commercial land use. The footpath ends abruptly at the FOB which should be continuous and the bus stop should be in the furniture zone so that it doesn’t encroach on the footpath. The footpaths at ITO also have a dead wall frontage of the institutions in the vicinity similar measures at IIT Gate could be adopted. All utility boxes should be placed in the furniture zone. The footpaths should be at the same level at property entrances at ITO with warning tiles to assist the visually challenged.

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5.1 Pedestrian Crossings 5.1.1

Mid-Block Crossing at IIT Gate and ITO

A marked tabletop pedestrian crossing at the same level as the footpath should be provided at an interval of at least every 200 m. The purpose is to reduce vehicle speeds and emphasize the presence of pedestrian crossings. To ensure safety, these crossings should be signalized. The width should be as wide as the adjacent footpath and the surface material different for easier recognition by both driver and pedestrian. The refuge islands are essential in a mid-block crossing because it offers pedestrians time to rest and reorient themselves. At both places, median fences are installed; therefore, breaks should be provided at frequent intervals for mid-block crossing with refuge islands. Alternatively, positioning a subway midway below street level so that pedestrians descend only halfway instead of a full stairway and an elevation of the carriageway would be necessary stretching over a length along with a gradient. The subway should be properly lit ensuring security and providing staircases and ramps for accessibility by all.

5.1.2

Intersection Crossing at Azadpur Chowk

A raised intersection crossing at the level of the footpath with auditory pedestrian signals should be provided at Azadpur Chowk. The vehicles would slow down at the ramped crossing with different surface materials. If the crossing is at the level of the road, each corner of the footpath must be ramped for pedestrians. Wherever slip roads or turn pockets are present, raised tabletop crossings must be present between the footpath and pedestrian refuge (IRC:103). Pedestrian refuge islands must be provided at medians. The refuge should be large enough to handle observed pedestrian volumes. The Azadpur Chowk crossing should have exclusive signals with sufficient time for all pedestrians, especially vulnerable pedestrians to safely cross the road. Recommendations . All three FOBs need to maintain the infrastructure such as escalators and lifts for accessibility, shade roofing for thermal comfort, lighting on the FOB for security, and visible signages to inform the pedestrians about FOB crossing. . The riser of stairs needs to be below 15 cm. . At the three FOBs resting/seating places need to be installed. . Universal design including tactile paving and auditory signages, separate street lights at the height of pedestrians, and public amenities should be installed at all three locations.

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. Accessibility improvement can be achieved with wide table-top at-grade crossings which have different surface materials, signals, and proper refuge islands for pedestrians. . The footpaths should be continuous with pedestrian crossings and have 3 zones, i.e. frontage zone, pedestrian zone with clear 1.8-m width, and furniture zone. . Proper allocation of space for street vendors in the furniture zone. . A shallow pedestrian underpass midway below street level so that pedestrians descend only halfway with an elevation of the carriageway. . FOBs could be designed to facilitate crossings for cyclists with a cycle ramp such as at ITO FOB. . The infrastructure improvement for motorized transport such as the enforcement of the speed limits, sufficient traffic-calming measures, etc. should be enforced.

6 Conclusions The poor maintenance quality and detours involved in the pedestrian infrastructure of the three FOBs make them undesirable. To increase utilization of FOBs, it should be well integrated with other pedestrian infrastructure and coupled with public amenities. To meet the net zero emission targets, it is important to retain the modal mix of pedestrians in Indian cities but the dominant RoW for motor vehicles has increased the risk at pedestrian crossings. The emphasis should be on designing safe and comfortable streets that make walking convenient for all users and activities. The streamlining of FOBs in road infrastructure development is a concern, especially with the low utilization levels. From a long-term perspective, shallow pedestrian underpasses, even though require high costs, with few steps for the comfort of the pedestrians could be suggested. Limitations of the study The specially abled persons’ perceptions could not be captured because, during site visits and surveys, they were not found on site using either the FOB or pedestrian crossings. Dynamic indicators such as the noise of the traffic should also be incorporated into future studies.

Annexure See Table 4.

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Table 4 Sub–Indicator scoring Likert range

1 (0)

2 (25)

3 (50)

4 (75)

5 (100)

Comfort 1

Shade on FOB

No roof shading on the entire stretch

Roof shading present in some stretches

Roof shading Broken roof is not shading effective

Completely maintained roof shading

2

0.15 m riser of the stair

>0.20 m riser

0.15–0.2 m riser

0.15 m riser

10 resting/ seating place

Accessibility 4

Escalators

Not present Present on either side

Present on both sides

Present but not Both sides working working escalators

5

Lift

Not present Present on either side

Present on both sides

Present but not Both sides working working lifts

6

Ramps

Not Present Present on either side

Present on both sides

Present but not Present and comfortable comfortable slope slope

7

Tactile paving/tiles

Tactile Tactile paving/ Present and paving/tiles tiles present continuous not present path on FOB

Continuous path on FOB and stairs

Integrated with footpath

Present and work at all stretches

Street lights at pedestrian level

Security 8

Lighting on the FOB

Streetlights are not present

Street lights Present at present but not stretches on working FOB

9

Security Guards

No security Security guards guards in nearby land use

Security Guard present guard present at the at FOB designated time

Guard present throughout the day at FOB

10

Presence of street vendors around FOB

Land use prevents street vendors

Present at intervals of time

Present throughout the day in designated places

No street vendors around FOB

Present but encroaches on pedestrian space

Connectivity (continued)

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Table 4 (continued) Likert range

1 (0)

2 (25)

4 (75)

5 (100)

11

Public amenities

No public amenities present

Drinking water Drinking amenity water/toilets present present

3 (50)

Present amenities are not accessible

Accessible amenities for all

12

Signage about FOB

No signage

Signage on either side of RoW

Signage on both sides of RoW

Signage Visible present but not signage on visible both sides of RoW

13

Nearest public transit stop

>1 km walking distance to nearest transit stop

500–1000 m walking distance

100–500 m walking distance

0.7 values indicate good reliability and consistent data (Hair et al., 2019).

3.2 Regression Analysis Multiple linear regression analysis is employed to study the relationship among factors obtained from factor analysis. It is a linear relationship between two or more independent variables (X1 , X2 … Xn ) and the dependent variable (Y). β’s are the regression coefficients of independent variables. The positive coefficient indicates that the independent variable has a statistically positive influence on the dependent variables. In contrast, the negative value of β’s indicates the adverse impact on the dependent variable. The mathematical expression of the multiple linear regression (MLR) equation is as follows:

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Y = α + β1 X1 + β2 X2 + β3 X3 + . . . + βi Xi + ε where Y is the dependent variable; βi refers to the regression coefficients of ith independent variable; Xi refers to the ith independent variable; ε is the error term.

4 Study Context and Survey Database 4.1 Study Context Bengaluru, the capital city of state Karnataka, India, with a population of over 13 million, is one of the fastest growing metropolises with an economic growth rate of about 8.5% (BMRCL DPR., 2019). The city being the heart of modern India contributes about 35.9% Gross State Domestic Product (GSDP) (BMRCL DPR., 2019). With the rapid urbanization, the traffic studies (BMRCL, DPR Phase 2-B., 2019) indicate that the road network has exceeded its capacity in the city and no further growth can be permitted. Therefore, to solve this issue, Bengaluru must be oriented towards public transport which supports economic growth and promotes good quality of life. Metro systems are superior to other modes for their high carrying capacity, fast and safe travel. It is also a people and environmentally friendly transport mode. Bengaluru Metro, christened as ‘Namma Metro’ (Our Metro), has been in operation since 2010. Therefore, metro in Bengaluru city is only a recent development and does not serve a large area and hence has low ridership. It currently operates on a 56 km network length, out of which the east–west line is 25.6 km long, starting from Baiyappanahalli in the east and terminating at Kengeri terminal in the west and north–south line is 30.4 km long commencing at Nagasandra in the north and terminating at Silk Institute in the south. Currently, there are 51 metro stations and 380,000 daily metro riders (BMRC website). Being the key mass rapid transit system (MRTS) in the city, it is, therefore, important to understand its service quality and factors influencing the ridership or usage of metro from the perspective of passengers.

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4.2 Survey Design and Data Collection A survey is conducted with the Metro travelers in Bengaluru to identify the factors influencing the usage of Metro ridership. For the purpose of this survey, a structured questionnaire with 8 attributes such as ‘service availability’, ‘passenger ease’, ‘passenger information’, ‘amenities’, ‘safety and security’, ‘seamless connectivity’, ‘environmental impact’ and ‘overall service quality’ (Anjali et al., 2020; Parida et al., 2020) having 46 indicators with a five-point Likert scale of 5 for ‘absolutely agree’ to 1 for ‘absolutely disagree’ is designed to assess the satisfaction level of commuters and repeat usage by them. The questionnaire also consists of questions related to the socio-economic and travel characteristics of the passengers, such as age, gender, qualification, occupation, income, vehicle ownership, travel frequency and travel time. The questions in the survey and the format was given utmost importance in order to minimize the respondent’s resistance, ease of understanding; thereby, taking less time in responding without omitting the required quality and quantity of data. A Face to Face (FTF) and an online survey were conducted. 700 metro users’ responses were obtained. The FTF survey was carried out along the metro lines of Bengaluru and at certain road stretches in Yeshwanthpur, MSR, and New BEL road from 24 June to 18 July 2022. Prior to the survey, the respondents are informed regarding the project and study purpose. Stratified random sampling is carried out to ensure heterogeneity and obtain an unbiased response from the Metro users.

4.3 Descriptive Analysis The survey sample consists of metro users in Bengaluru, of which 56.38% are male and the rest 43.8% are female. The data unfolds that 54.14% of travelers are between the age group of 18 to 28 years. Nearly 27.77% of metro users are between 29 and 38 years and 10.24% are between 39 and 48 years. The remaining 7.85% are elderly passengers. The data highlights that most passengers (66.7%) own at least one vehicle shown in Table 1. The majority of households, about 37.24%, own a two-wheeler. Four-wheelers are owned by 12.13%, whereas 13.26% own both 2-wheelers and 4-wheelers. Only 1.83% of the population owns a bicycle. Considering the occupation with travel frequency, Table 2 depicts that 32.68% of total private sector employees use metro, only 6.59% of IT and 3.51% of non-IT sectors commute daily in Metro. Among the 30% students population using metro, 5.610% use metro daily and about 7.29% travel rarely. Among the 8.56% of selfemployed, only 1.96% of them commute by metro daily. Out of 5.61% of government employees using metro, only 0.56% of them commute daily. Thus a very small percentage of total respondents travel by metro on a daily basis. To further understand this, data on vehicle ownership by the respondents was looked upon.

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Table 1 Vehicular ownership

Bicycle

1.83%

2-wheeeler (2W)

37.24%

4-wheeler (4W)

12.13% 0.42%

2W, Bicycle

13.26%

2W, 4w

0.28%

4W, Bicycle

1.55%

2W,4W, Bicycle No vehicles

33.3%

Table 2 Travel frequency of Bengaluru Metro users occupation wise Occupation Student Private sector Government Business SelfHomemaker Retired versus (%) employee employee employed (%) (%) Non- IT travel (%) (%) (%) Sector IT frequency Sector (%) (%) Daily

5.61

3.51

6.59

0.56

0.70

1.96

0.00

0.00

Thrice a week

3.37

1.54

1.68

0.84

0.98

1.12

1.40

0.14

Twice a week

2.81

1.12

1.68

0.42

1.40

0.42

1.82

0.56

Once a week

6.59

2.24

2.53

0.56

1.68

2.53

2.95

0.84

Once a month

4.35

1.54

5.19

1.40

3.37

0.98

2.38

0.14

Rarely

7.29

1.40

3.65

1.82

1.82

1.54

2.24

0.56

Total share 30.01

11.36

21.32

5.61

9.96

8.56

10.80

2.24

Table 3 shows the availability of personal vehicles can influence the frequency of travelers towards the metro station. From the analysis, passengers who access the metro station by foot have the highest percentage, about 48%. The data also depicts that a higher rate of passengers transit to metro through public modes (i.e., by foot48%, 25.30% by auto rickshaws, 8.74% by bus). Therefore, the use of public transit systems led road users to choose sustainable modes. The passengers using their owned private vehicles as the connective mode to metro station are fewer comparatively (10.35% by 2W, 3.91% by 4W). It is also noted that people owning car do not commute by metro daily. This fewer rate in the modal shift of individuals owning vehicles can also be due to the lack of parking facilities at the Metro station. This requires the attention of concerned authorities. Therefore, the study highlights that only 20% of metro users commute daily, 25.53% of commuters travel once a week, 13.8% and 12.88% travelling twice and thrice a week and 13.34% travelling once a month and 15.18% travelling rarely.

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Table 3 Travel frequency with a connective mode of travel to metro station in Bengaluru Mode versus travel Foot (%) Cycle (%) 2W (%) 3W (%) 4W (%) Cab (%) Bus (%) frequency Daily

0

1.84

1.15

0.00

0.46

1.61

Thrice a week

3.68

0.23

0.92

3.91

1.38

1.38

1.38

Twice a week

6.21

0.46

1.84

3.22

0.46

0.46

1.15

Once a week

7.82

0.23

4.14

9.43

0.92

1.15

1.84

Once a month

6.67

0.00

0.69

3.68

0.46

0.00

1.84

Rarely

8.74

0.00

0.92

3.91

0.69

0.00

0.92

48.06

0.92

10.35

25.30

3.91

3.45

8.74

Total share

14.94

5 Factor Analysis and Model Development This section focuses on determining the factor that influences the usage of metro services in Bengaluru, using Exploratory Factor Analysis (EFA) by applying Principal Component Analysis (PCA) with Varimax rotation on 46 indicators using IBM SPSS- 26.

5.1 Test of Suitability of Factor Analysis (FA) The KMO measures 0.96, therefore the sample size is sufficient and the data has good sampling adequacy. The Bartlett test of sphericity is 0.000, therefore significant and indicates that the data set is suitable for FA (Hair et al., 2019) The factor loadings are restricted to 0.5.

5.2 Determination of Factors The survey data was analysed employing EFA in PCA for an absolute value of 0.5. The number of factors to be extracted was fixed to 8 from the literature (de Ona et al., 2018; Parida et al., 2020). During the iteration process, few of the indicators were dropped due to poor factor loadings or cross-loading. Finally, the PCA was determined by extracting 7 factors. The factors were loaded better than the previous trials, with the least missing information with a total variance of 66.52%. The Cronbach alpha value for two factors was above 0.9, four factors with values between (0.7 and 0.85) and one factor with 0.68. Hence the reliability of factors explaining the variables was satisfactory, as depicted in Table 4. Eventually, the extracted 7 factors from PCA are named based on the trait of the variables loaded in each factor in Table 4, i.e., Passenger Ease (PE), Overall Service Quality Satisfaction and Loyalty Intention

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(SQLI), Smooth transition (ST), Operation and Safety (OS), Anxiety (A), Amenities (AMT), Service Available (SA).

5.3 Regression Model Development A regression model developed using SPSS software with FA is shown in Table 4 with the SQLI factor as the dependent variable and the remaining 6 factors as independent variables. The analysis of variance (ANOVA) of the developed model is summarized in Table 5. The summated scale is estimated by taking the mean of all the items or variables that loads in the same factor. For instance, in this study, the mean of all the variables in the ‘SA’ factor is combined together to get the summated scale and the same is repeated for all the factors extracted. This process is done for simplicity and to reduce the complexity of the model. Table 6 depicts the coefficients of the independent variable (factors extracted) for the model generated with R-square value of 0.588 were significant (p-value < 0.05). The resultant regression model as obtained from the analysis is as follows: YSQLI = 0.625 + 0.234 ∗ PE + 0.106 ∗ ST + 0.243 ∗ MOS − 0.072 ∗ A + 0.184 ∗ AMT + 0.172 ∗ SA + 0.385 The regression model depicts that the ‘Metro operation and safety’ along with ‘passenger ease’ to be the most dominating factor as the coefficient (Table 6) is the highest amongst the other attributes. ‘Amenities’ and ‘service available’ are the subsequent attributes that positively influence overall satisfaction towards the service quality satisfaction and loyalty intention. The ‘smooth transition’ attribute has the lowest coefficient, therefore less dominant compared to the other attributes impacting SQLI. However, the results depict that the attribute ‘anxiety’ has a negative impact on SQLI amongst metro users.

6 Discussion and Conclusion The following insights are drawn from the regression model: • In the present study, Metro Operations and Safety is considered to be the most dominating factor. Reliable and clear travel information, ease of access to smart card facilities, safe environment from theft and robbery inside Metro with the provision of support systems like the use of handrails is perceived to increase the commuter’s trust and increase the satisfaction level of passengers. This relationship has been proven to be consistently dominating in past studies as well (De Oña et al., 2018; Parida et al., 2020; Suman, 2017).

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Table 4 Extracted factors from principal component analysis Factors Passenger Ease (PE)

Overall Service Quality Satisfaction and Loyalty Intention (SQLI)

Smooth transition (ST)

Metro Operations and Safety (MOS)

Service quality indicators

Factor loadings

Cronbach alpha 0.920

Staff is informative and helpful

0.782

Attitude of security personnel

0.744

Ease of purchasing tickets/cards from the counter

0.740

Ease of ticket verification by the validators

0.740

Comfort level inside the metro station

0.731

Ease of Metro line interchange within the metro station

0.651

Metro helps me to reach my destination on time

0.606

Performance of Lifts and escalators inside the station

0.577

Metro services are time-saving

0.796

Willingness to make most of the trips by metro if the entire city has a metro network

0.742

Metro is safe to commute

0.736

Recommend others to use metro as well

0.715

Affordability of metro travel

0.708

Convenience to use metro

0.626

Satisfaction with metro trips

0.610

Prior alerts on the destination changes through websites, mobile apps and television

0.661

Landscape outside the metro station (parks, gardens, etc.)

0.653

Convenience for commuting in metro for physically challenged

0.628

The mobile networks coverage inside the metro

0.554

Ease of access to reach metro stations

0.523

Level of service of metro is good

0.502

Clarity in travel-related information

0.736

Updated, accurate and reliable information in the metro station

0.711

Safe environment from theft and robbery inside the metro

0.600

0.910

0.840

0.839

(continued)

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Table 4 (continued) Factors

Anxiety (A)

Amenities (AMT)

Service available (SA)

Service quality indicators

Factor loadings

Accessibility of Metro smart card facilities

0.563

Convenience to use handrails/grab handles inside the metro

0.539

Fear of slipping, falling and accidents at metro doors and escalators

0.898

Fear of harassment in metro

0.881

Fear of aggression (violence) inside metro stations while boarding a metro due to crowd

0.859

Availability of wheelchairs and other support systems in Metro

0.657

Proper parking facilities in metro stations

0.638

Proper lighting facilities available in the metro stations

0.574

Network connectivity of the metro across the city is good

0.634

Convenience in the scheduled timings of metro

0.579

Waiting time on the platform

0.539

Cronbach alpha

0.899

0.729

0.681

Kaiser–Meyer–Olkin Measure = 0.956 Bartlett’s Test of Sphericity (p-value

Table 5 Model summary ANOVAa Model 1

Sum of Squares

df

Mean Square

F

Sig

Regression

149.117

6

24.853

167.762

0.000b

Residual

104.590

706

0.148

Total

253.707

712

a Dependent

variable: Mean_QSLI b Predictors: (Constant), Mean of Service available, Mean_Anxiety, Mean of Amenities, Mean of Operation and safety, Mean of Smooth transition, Mean of Passenger Ease (1)

• Along with the factor of Metro Operation and Safety, the factor Passenger Ease is found to be an equally influential factor. The attitude of the staff personnel, ease of purchase of tickets from the metro counter, and the comfort level inside the metro with effective metro line interchanges to destination make the metro travel comfortable and positively influence the satisfaction level of passengers. Similar results were reported in studies done by De Oña et al. (2018), Parida et al. (2020), Suman (2017).

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Table 6 Regression model coefficients Coefficients

Model

Factors nameB 1

(Constant)

t-test

Sig.

Std. Error 0.625

0.126

4.963

0.000

F1

Mean of Passenger 0.234 Ease

0.036

6.454

0.000

F2

Mean of Smooth transition

0.106

0.033

3.259

0.001

F3

Mean of Metro Operation and Safety

0.243

0.037

6.624

0.000

F4

Mean Anxiety

−0.072

0.016

−4.625

0.000

F5

Mean of Amenities 0.184

0.035

5.331

0.000

F6

Mean of Service available

0.029

5.939

0.000

a Dependent

0.172

Variable: Mean_SQLI

• In the present study, it was found that service availability and amenities positively influence commuter’s satisfaction with respect to service quality and also encourages them to give a positive word of mouth to other. Similar findings were noted by Yanık et al. (2017), Parida et al. (2020). Thus, if the metro services are planned, scheduled and rendered effectively with a good metro-rail network, then it can reduce the travel and waiting time. The facilities provided in the station significantly impact the modal share percentage of metro users. For instance, the provision of efficient parking facilities can increase the modal shift towards metro. • The coefficient of the smooth transition factor is pretty low (0.106). Although it may not be a key influencing factor in improving the satisfaction and loyalty intention of travelers, however, prior alerts, a good landscape, disabled-friendly infrastructure, good connectivity to the destination can build a positive attitude towards the usage of metro services. • The novelty of this study was found in the factor ‘anxiety’, which was extracted separately, unlike in the studies done in the past where the items of this factor come under ’safety and security’ (de Oña et al., 2018; Parida et al., 2021). Anxiety has a negative coefficient, shows a negative impact on overall service quality satisfaction and loyalty intention (SQLI). Therefore, satisfaction reduces if fear is infused while traveling and in turn the commuter will not just be reluctant to go for repeat use of the services but will not recommend others also to use Metro services. Such commuters may give negative word of mouth to others. Hence, utmost importance must be given to the factor ’Anxiety’ and the authorities like Bengaluru Metro Rail Corporation Limited (BMRCL) need to pay attention in addressing the passenger’s concerns related to fear of slipping or falling at metro doors and escalators, fear of harassment in metro, and fear of aggression (violence)

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inside metro stations while boarding a metro due to crowd. Implementing open spaces, more security personnel and user-friendly infrastructures while planning can defuse such fears and anxieties that will, in turn, increase the ridership of Metro. Besides the factors extracted, in the descriptive analysis in Sect. 4.3, it is inferred that passengers’ owning a private vehicle mainly a four-wheeler are less inclined to metro transit. Therefore, the authorities must take effective measures in all the ongoing construction activities (yellow, pink and blue lines) and future (orange) Metro lines to provide proper parking facilities that can attract all personal vehicle users (cars, two-wheelers) and in turn increase the percentage of modal shift from private to metro transit. Providing better last-mile connectivity with easy and safe access to the commuters who walk or cycle to metro station will also result in more usage of metro services. The model developed with the factors influencing the usage of metro services can bridge the service gaps of Bengaluru Metro to attract commuters. Further, this model can be used in the future also for carrying out service quality assessments of other metro systems in India.

7 Limitations and Scope of Future Research The developed model is structured to assess the satisfaction of metro users towards the overall service quality satisfaction and intention towards the continuous usage of metro services in Bengaluru city. The sample size is restricted to 700 only, a study with larger data can give a more robust model for the measure of satisfaction and loyalty intention towards metro services. The gender perception on the satisfaction level of service quality for metro transits are not studied, this can be a future scope of work.

References Anjali, S. (2020). Measuring commuters perception on service quality Using SERVQUAL in Delhi Metro. Academia. https://www.academia.edu/30353691/Measuring_Commuters_Percep tion_on_Service_Quality_Using_SERVQUAL_in_Delhi_Metro Anatole, T. (2016). Landscape and travel: An introduction. Studies in the history of Gardens and Designed Landscapes. Taylor and Francis. https://doi.org/10.1080/14601176.2016.1152105 Aydin, N. (2017). A fuzzy based multi-dimensional and multi-period service quality evaluation online for rail transit systems. Transport Policy, 55, 87–98. Bengaluru Metro Rail Corporation. (2019). Detailed Project Report for Airport Metro Line, Phase 2B. https://doi.org/10.1016/j.tranpol.2017.02.001 Chakrabarti, S., & Giuliano, G. (2015). Does service reliability determine transit patronage? Insights from the Los Angeles metro bus system. Transport Policy, 42, 12–20.

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Deb, S., Ahmed, M. A. (2022). Service quality estimation and improvement plan of bus Service: A perception and expectation based analysis. Case Studies on Transport Policy,10(3), 1775–1789. https://doi.org/10.1016/j.cstp.2022.07.008 de Díez-Mesa, F., de Ona, R., & Ona, J. (2018). Bayesian networks and structural equation modelling to develop service quality models: Metro of Seville case study. Transportation Research Part A: Policy Practice, 118, 1–13. https://doi.org/10.1016/j.tra.2018.08.012.DMRC.(2019).DMRCREPORT-2018-2019.pdf Eboli and Mazzulla. (2011). A methodology for evaluating transit service quality based on subjective and objective measures from the passenger’s point of view. Transport Policy, 18(1), 172–181. https://doi.org/10.1016/j.tranpol.2010.07.007 Elms, P.C. (1997). Defining and measuring service availability for complex transportation networks. Journal of Advanced Transportation, 32(1), 75–88. Fathima, A. (2022). Air pollution levels in Bengaluru exceed WHO standards: Green peace report Read more at: http://timesofindia.indiatimes.com/articleshow/89181488.cms?utm_source=con tentofinterest&utm_medium Hair, J. F., Black, C. W., Babin , J. B., & Anderson, E. R. (2019). Multivariate data analysised. (8.ed). Cengage Learning. Kubota, H., & Joewono, B.T. (2007). User satisfaction with paratransit in competition with motorization in Indonesia: Anticipation of future implications. Transportation, Springer, 34(3), 337–354. https://doi.org/10.1007/s11116-007-9119-7 Li, L., Bai, Y., Song, Z., Chen, A., & Wu, B. (2018). Public transportation competitiveness analysis based on current passenger loyalty. Transportation Research Part A Policy Practice, 113, 213– 226. Lierop, D. V., & Geneidy, A. E. (2016). Enjoying loyalty: The relationship between service quality, customer satisfaction, and behavioral intentions in public transit. Research in Transportation Economics, 59(2016), 50–59. Liou, J., Chao-Che Hsu, b., Chen, Y. (2014). Improving transportation service quality based on information fusion. Transportation Research, 67, 225–239. https://doi.org/10.1016/j.tra.2014. 07.007 Noriel, C., & Janna, M. (2020).The perception of service quality among paratransit users in Metro Manila using structural equations modelling (SEM) approach. Journal of Research in Transportation Economics, Elsevier. Okamura, T., Kaneko, Y., Nakamura, F., & Wang, R. (2013). Passengers’ attitudes to the service items of jeepneys in Metro manila by different lifestyles. Journal of the Eastern Asia Society for Transportation Studies, 10, 1384–1395. https://www.jstage.jst.go.jp/article/easts/10/0/10_ 1384/_pdf Parida, M. Jyoti, M., Jogendra, K, N., et al. (2020). Interrelationships among service quality factors of metro rail transit system: An integrated Bayesian networks and PLS-SEM approach. Transportation Research, Elsevier, 320–336. https://doi.org/10.1016/j.tra.2020.08.014 Parida, M. Jyoti, M., & Jogendra, K. N. (2021). Establishing service quality interrelations for metro rail transit: Does gender really matter? Transportation Research, Elsevier, 97.https://doi.org/10. 1016/j.trd.2021.102888 Rahman, F., Das, T., Hadiuzzaman, M., & Hossain, S. (2016) Perceived service quality of paratransit in developing countries: A structural equation approach. Transportation Research Part A: Policy and Practice, 93, 23–38. Ralf Peter Schafer. (2021). TomTom traffic index: Global traffic congestion up as Bengaluru takes crown of world’s most traffic congested city. https://archive.autofutures.tv/2020/01/29/tom tom-traffic-index-global-traffic-congestion-up-as-bengaluru-takes-crown-of-worlds-most-tra ffic-congested-city/ Sumaedi S., et al. (2012). The empirical study of public transport passengers’ behavioral intentions. International Journal of Traffic and Transportation Engineering, 2(1), 83–97. http://ijtte.com/ uploads/2012-03-20/d4c8811d-88a5-92b1p8.pdf

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Suman, P. (2017). Service quality attributes affecting the metro passengers of Kolkata, West Bengal, India. International Journal of Current Research, 9(5), 50035–50042 Verma, M., Verma, A., Ajith, P., & Sneha, S. (2014). Urban bus transport service quality and sustainable development: Understanding the service gaps. Indian Journal of Transport Management, 98–112. Verma, M., Manoj, M., & Verma, A. (2017). Analysis of aspiration for owning a car among youths in a city of a developing country, India. Transporation in Development Economics, 3, 7. https:/ /doi.org/10.1007/s40890-017-0037-x Verma, M., Rahulb, T. M., Vinayakc, P., & Verma, A. (2018). Influence of childhood and adulthood attitudinal perceptions on bicycle usage in the Bangalore city. Journal of Transport Geography. https://doi.org/10.1016/j.jtrangeo.2018.08.016 Verma, M., Rodejab, N., Manoj, M., & Verma, A. (2019). Young women’s perception of safety in public buses: A study of two Indian cities (Ahmedabad and Bangalore). Transportation Research Procedia, 48(2020), 3254–3263. Yanık, S., Aktas, E., & Topcu, Y. I. (2017). Traveler satisfaction in rapid rail systems: The case of Istanbul metro. International Journal of Sustainability Transportation, 11(9), 642–658. https:// doi.org/10.1080/15568318.2017.1301602

Investigating the Attributes Influencing Pedestrian Behaviour of Commuters for Enhancing Accessibility of Metro Stations: A Case Study of Delhi, India Mahima Kanojia, Shubhajit Sadhukhan, and Namia Islam

Abstract In recent years, the metro rail system has experienced significant growth in major metropolitan areas and this growth is anticipated to continue in India. Land use, accessibility, travel behaviour, walkability, and density are all favourably impacted by the metro. Contrarily, if metro access areas are left unplanned, it could result in clogged streets, poor accessibility, and disorganized urban changes in the vicinity of transit hubs. For regulated urban development, better accessibility, greater use of public transportation, and place-making, metro station areas must be improved. This creates a pressing necessity to assess the current transportation infrastructures and urban built form within the area of influence of metro stations. In order to improve sustainable urban mobility, the current study focuses on analysing the attributes which influence metro commuters to walk in station access areas. Accordingly, Saket and Vishwavidyalaya are chosen as two candidate stations on the Delhi Metro Yellow line. The study’s objective is to evaluate metro access areas and pinpoint the attributes that need to be improved so that all commuters can easily walk and traverse through the city. Importance-Satisfaction Analysis (ISA) is the methodological approach used. A primary survey was carried out using a well-structured questionnaire of 340 participants comprising metro commuters. The results showed that some areas need immediate interventions, including physical infrastructure, public amenities, safety and security, and general ambience. The study will help planners and policymakers learn about the problems with first mile connectivity and create safe and inclusive access area plans.

M. Kanojia · S. Sadhukhan (B) · N. Islam Department of Architecture and Planning, Indian Institute of Technology Roorkee, Roorkee 247667, India e-mail: [email protected] M. Kanojia e-mail: [email protected] N. Islam e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_17

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Keywords Accessibility planning · Metro station access area · Pedestrian infrastructure · Walkability · Importance-satisfaction analysis · Commuter satisfaction

1 Introduction In Indian cities, the number of vehicles is rapidly increasing. India had a threefold growth in the number of cities between 1961 and 2011, a fivefold increase in population, and a 200-fold increase in the number of vehicles (MoRTH, 2021). Delhi had the highest percentage of people who drove vehicles among any Indian city. In large urban systems, such as those seen in megacities and metropolitan areas, a significant portion of commuters were found to use traditional public transportation, with most of these individuals walking the final and last mile distances (HUDCO, 2015). In India, the share of urban trips handled by public transportation is directly related to the size of the city. This means that in larger cities, the average share of commuting by public transportation will be higher. As a result, a strong correlation can be seen between urbanization and increased demand for public transportation in India, as depicted in Fig. 1. A good public transportation system allows commuters to take multiple trips and cover larger distances in a day, thus increasing the load of this system. An integrated and multi-dimensional approach needs to be prepared and implemented to make the metro system more efficient and utilize its full potential. Some of the issues

Fig. 1 Existing modal split in Indian cities as a % of total trips. Source MoUT, GoI (2010)

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with respect to station accessibility planning include lack of pedestrian facilities, no provision of cycle lanes, lack of seamless multi-modal integration, poor traffic management, inconspicuous passenger information system, unnoticeable wayfinding signages, inadequate public bicycle sharing systems, encroachment by vending activities, presence of stray animals, lack of cleanliness, poor air quality, and poorly planned stations with lack of efficient accessibility from adjoining roads where regular buses operate. This leads to traffic congestion and overcrowded station areas which are unable to receive estimated ridership. Therefore, a need arises to address these problems at the planning stage itself. It may not be possible to redesign the existing stations but certain corrective measures can increase overall commuter satisfaction. A complete trip encompasses three components between origin and destination, namely first-mile connectivity, public transit, and last-mile connectivity. Thus, station area planning must be done in such a way that all three components are addressed holistically. The present study focuses on understanding the pedestrian behaviour of commuters to improve the overall accessibility of metro station areas.

1.1 Need of the Study Indian cities are primarily navigated by foot, bicycle, or cycle rickshaw. Despite the fact that Delhi is regarded as being car-friendly, non-motorized transport accounts for 41% of all trips, with walking accounting for 35% of the modal share (MoUD, 2016a). Well-functioning public transport is crucial in bringing efficiency to the overall performance of the city’s transport system and improving the quality of life for the rapidly rising urban population. Non-motorized transportation planning should be an integral component of station access area development to ensure improved accessibility to metro stations, eventually increasing public transport ridership. However, in India, several metro rail systems are being developed with metro station areas being largely unplanned and having inadequate infrastructure. This is a result of a lack of focus areas, public funding, and inter-departmental coordination among various transport and implementing agencies involved in station area development. Beyond infrastructure improvements, non-motorized transportation in India needs to be made more attractive and environmentally friendly. Institutional transformation, the involvement of the corporate sector, cross-agency cooperation, an investment priority structure, financial mechanisms, technology advancement, and most crucially, a cultural shift are all required. The need arises to study the various attributes which the commuters perceive to be important in enhancing the overall accessibility of metro station areas. The study focuses on addressing the issues of first and last mile connectivity to achieve ease of walking through spatial-economic planning.

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1.2 Objectives (a) To identify attributes influencing walkability in station access area through a field survey; (b) To assess the existing condition of pedestrian infrastructure and facilities within the study area in terms of users’ satisfaction; (c) To evaluate the performance of pedestrian infrastructure for the two selected stations of the Delhi Metro Rail Transit System; (d) To investigate the importance of attributes in enhancing the overall accessibility of metro stations with similar land use and urban conditions in Indian cities.

1.3 Scope of the Study The scope of this study includes a commuting mode of non-motorized transportation, i.e. pedestrian infrastructure and facilities. The study will encompass metro stations on the Yellow line of Delhi Metro with a single line passing through them. Delineation of the station access area includes a buffer zone of approximately 500 m around the selected metro stations.

2 Literature Review In order to understand the nature of the travel characteristics of the daily commuter at the Delhi Metro; trip length, vehicle ownership, location of the metro station (classified as administrative units within Delhi, and neighbouring cities), population density around the metro station, etc. serve as key indicators to understand access-egress of the metro users (Goel & Tiwari, 2016). Station area design consisting of pedestrian or cyclist or parking facilities, connectivity via feeder buses or other modes, and platform accessibility plays another crucial role in order to enhance the passenger’s comfort and convenience (Saygaonkar et al., 2016). For commuters accessing the metro station on foot (1 km radius from start or destination); understanding the condition of footpaths and associated pedestrian facilities would be taken up for consideration (Mandal, 2020). Policy plays a critical role in enhancing the access environment of a metro station (Gupta et al., 2019). Robust policy also needs to take the economic and equity implications of the introduction of a metro system into consideration. Land is the foundation for any developmental intervention, and land cover change pattern in the context of the development of MRTS shall serve as critical indicators for planning metro expansions in the long run (Ahmad et al., 2015). Transit Oriented Development (TOD) could encourage people to live and consume near transit station areas by walking and cycling rather than using motorized options, giving way to more sustainable mobility in the process of suburbanization (Pengjun & Shengxiao, 2018).

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A scientific approach to prioritize an important set of determinants influencing commuters’ decision to use a bicycle as an access or egress mode to metro rail may assist in understanding user perception of bicycle-metro integration better (Panchal et al., 2020). Additionally, Shared Automated Vehicles may be an attractive way to experience first-mile last-mile (FMLM) connections to existing and future transit systems, replacing walk-to-transit, drive-to-transit, or even drive-only trips (Huang et al., 2021). The physical imposition of the metro edifice on Delhi’s landscape has led to the creation of a new cultural geography. The spaces created within the metro itself (on trains and in stations) and the practices associated with those new spaces, the spatial imaginaries experienced by individual riders are some key factors that will play an important role in metro expansion eventually. This will ensure high metro ridership to consider it economically feasible. The literature review suggests some crucial research gaps which need to be addressed through further research in the vastly explored area of accessibility within the influence of metro station areas. Most of the authors have selected the same interchange stations possibly due to the availability of data. However, the present study will encompass metro stations on the Yellow line of the Delhi Metro with a single line passing through them, with high development potential and footfall (DDA, 2021). A study area focused on a buffer zone of 500 m will allow a clearer understanding of the people’s perspective regarding attributes related to walking and detailed aspects of area-based development and local access area planning (MoUD, 2016b). The study, therefore, ensures public participation in the planning process. The results achieved will help bridge these research gaps.

3 Methodology Importance-Satisfaction Analysis (ISA) or Importance-Performance Analysis (IPA) is a powerful and efficient tool that helps to assess the satisfaction of users based on two aspects, namely the importance and performance of attributes from the users’ perspective (Pitale et al., 2023). Overall satisfaction is recorded after plotting the importance and performance of individual attributes. The main idea is to prioritize different attributes according to responses received from the target audience that is metro commuters with respect to this study. The attributes are plotted in different quadrants based on their relative importance and performance. Each quadrant provides different management strategies to identify areas that require improvement in order to achieve a high satisfaction rate from the users. The four zones in an IPA matrix (Fig. 2) are as follows: . Quadrant A (Concentrate here)—This zone represents that attributes are important but are performing poorly. It indicates that these areas need constructive measures to improve their performance and produce maximum results.

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Fig. 2 Importance-performance matrix. Source Martilla and James (1977, p. 78)

. Quadrant B (Keep up the good work)—This zone represents that attributes are important and are performing well. It indicates that these areas require continued investment and attention for them to keep performing well. . Quadrant C (Possible overkill)—This zone represents that attributes are not considered to be of much importance but are performing well. It indicates a high user satisfaction rate. . Quadrant D (Low priority)—This zone represents that attributes are not performing well but are considered to be less important by users. Hence, these areas can be focused on later on after the management and implementation of other important aspects. IPA tool uses four stages for a systematic analysis (Deacon & Du Rand, 2013) These include: . Stage 1—Descriptive analysis of attributes/indicators identified for measuring . Stage 2—Separation of importance and performance attributes/indicators . Stage 3—Calculation of mean for importance and performance of each attribute/ indicator and plotting them on the IPA matrix . Stage 4—Analysis of IPA matrix to identify attributes/indicators which need improvement in order to achieve overall satisfaction of the users. Figure 3 shows the methodology framework followed for the purpose of this study.

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Fig. 3 Methodology framework for the study

4 Study Area The National Capital Territory of Delhi has been the most important administrative centre with a strategic location for trade and commerce. The rationale behind choosing Delhi is its highly efficient public transport system and a variety of public transportation alternatives. Delhi is the abode of one of the most efficient Mass Rapid Transit System in India, Delhi Metro Rail Network (as shown in Fig. 4). Delhi fulfils the intent of getting analysis based on land usage practices because its vast Metro rail network integrates various city regions with diverse land use patterns. The multi-modal integration is strong with a plethora of feeder modes available to access metro stations. However, there is a lack of focus on the infrastructure and facilities required for the smooth functioning of these feeder services. This results in commuters being dependent on motorized modes for first and last mile connectivity. The traffic congestion caused by motorized modes of transportation and inefficient traffic signals creates a significant barrier to a free and safe pedestrian environment. Lack of adequate pedestrian infrastructure is further deteriorated by the poor condition of sidewalks and the encroachment of existing footpaths. This has reduced the effectiveness of pedestrian facilities in the overall transport infrastructure in Delhi leading to poor air quality.

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Fig. 4 Delhi metro rail network. Source DMRC

Often, it is observed in many studies that stations with a single line passing through them get neglected even though they have high development potential. For instance, Rajiv Chowk, Kashmere Gate, INA, etc. stations receive much more attention due to the availability of data and high ridership (DMRC, 2022). Saket and Vishwavidyalaya metro station areas (Fig. 5i and iii) have been chosen for this study. Both of these stations have a single Yellow line passing through them (Fig. 5i). The two station areas lie within the administrative boundary of the NCT of Delhi; have a variety of land use distribution; high ridership and development potential; and have distinct quality and identity. Vishwavidyalaya is a predominantly institutional area due to Delhi University. Saket is a predominantly residential area with unique characteristics of having planned and unplanned areas, along with the upcoming mixed-use and commercial streets like Champa Gali, near the Garden of Five Senses.

4.1 Station Area Profile See Figs. 6, 7, 8 and Table 1.

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Fig. 5 (Clockwise): (i) Delhi Metro yellow line stations with land use map and 500 m buffer station areas; (ii) Vishwavidyalaya station area; and (iii) Saket station area. Source MPD; Google map 2041

Fig. 6 (From left to right): (i) Battery-operated cycle facility at Saket metro station; (ii) Gramin Sewa at Saket metro station; and (iii) E-rickshaws at Vishwavidyalaya metro station. Source Authors

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Fig. 7 Plan showing existing road amenities and street hierarchy at Saket station. Source Authors

Fig. 8 Plan showing existing road amenities and street hierarchy at Vishwavidyalaya station. Source Authors

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Table 1 Shared travel mode services available in the influence area of the selected metro stations (DMRC, 2022) S. No

Attribute

Saket metro station

Vishwavidyalaya metro station

1

Travel facilities

Electric bikes—Yulu bikes (Gate No. 2 circulating area) Electric bikes—Yulu bikes (Gate No. 1 circulating area) App-based cab booking—Uber (Exit AFC Gate - South)

Public cycle sharing—Delhi Cycle Pvt. Ltd. (Near Gate No. 3) Electric bikes—Yulu bikes (Near Gate No. 3) E-Rickshaw service—Treasure Wase Pvt. Ltd. (Near Gate No. 3) E-Scooters—Allt Pvt. Ltd. (Near Gate No. 4)

2

Cycle sharing— Service Availability

Pedal cycle facility—No Battery-Operated cycle facility—Yes

Pedal cycle facility—Yes Battery-operated cycle facility—Yes

3

Cycle sharing—Fare (Timings - Round the clock)

For pedal cycles—N/A For battery-operated cycles—Rs. 10/- for 10 min

For pedal cycles—Rs. 05/- for 30 min For Battery-Operated Cycles—Rs. 10/- for 10 min

4

E-Rickshaw Services

No

No

5

E-Scooter services (Timings - Round the clock)

No

Yes Number of E-scooters—6 Fare—Rs. 01/- per min; Rs. 50/for one hour; Rs. 120/- for one day

6

Cab aggregator services

Yes Fare—As per Uber’s tariff rates Timings—From starting of metro services until its closure

Yes Fare—As per Uber’s tariff rates Timings—From starting of metro services until its closure

7

Auto-Rickshaw E-booth

No

No

4.2 Issues Related to Accessibility of Delhi Metro Stations Reconnaissance survey was conducted on possible critical stretches within each station area to understand the condition of existing pedestrian facilities. This will help in identifying attributes influencing walkability and subsequently prioritizing the streets for improvement and phasing of implementation procedures (UTTIPEC, 2010). This section comprises the identified issues of station area planning which were considered while designing the survey questionnaire.

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Fig. 9 (From left to right): (i) Discontinuity of pathways hampered by electric sub-stations; (ii) Encroached pedestrian pathways by parked vehicles and absence of tactile paving; and (iii) Encroached pedestrian pathways by houses, street vendors and vehicles. Source Authors

Fig. 10 (From left to right): (i) Encroached footpaths creating conflicts among vehicular and pedestrian users; (ii) Lack of paved footpaths leading to more dust and pollution; and (iii) Lack of cleanliness, high level of footpaths with broken ramps which are located faraway. Source Authors

4.2.1

Saket Station Access Area

See Figs. 9 and 10.

4.2.2

Vishwavidyalaya Station Access Area

See Figs. 11 and 12.

5 Data Collection A user satisfaction survey was conducted with a sample size of 340 commuters who have used the considered facilities at the two candidate stations, Saket and Vishwavidyalaya on the Yellow line. It was done partly online and partly as a physical survey in February 2022. The respondents were asked about their demographic

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Fig. 11 (From left to right): (i) Presence of stray animals creating a mess causing a public health concern; ii) Encroached pedestrian pathways further disrupted by manhole over tactile paving; and (iii) Pedestrian pathways encroached by transformers, vehicle parking, and street vendors. Source Authors

Fig. 12 (From left to right): (i) Encroached pedestrian pathway by street vendor; (ii) Footpath with many obstructions; and (iii) Encroached pedestrian pathway by a street vendor and informal establishments. Source Authors

profile, travel behaviour, and metro station accessibility details. The survey was conducted to determine the metro commuters’ perception of importance and satisfaction towards the identified attributes derived from the field survey. The definitions used to describe these attributes were tested with a few laymen before they were placed on the respondents to ensure that it is easily understandable by all. The survey questionnaire was well-structured into six sections based on the mode choices of commuters. The people who opted for walking to cover the last mile distance in the selected stations only were allowed to move further in the online survey form, while the rest of them were redirected to the feedback section before submitting. It contained a mix of questions in the multiple choice and short answer type options. It was based on a 5-point Likert-type scale. It was made available in both English and Hindi languages. It took almost 5–7 min to get each individual response form.

6 Data Analysis This section deals with the study findings from a sample size of 340 respondents regarding the importance and satisfaction of various attributes of pedestrian facilities and NMT facilities by commuters to access metro stations of Saket and Vishwavidyalaya on the Delhi Metro Yellow line. It is to be noted that the respondents were

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asked clearly to answer the questions considering an average day in their lifestyle before the COVID-19 pandemic. This section comprises ten parts. The first three parts examine the demographic, travel behaviour, and accessibility data of the respondents collected from the survey. Parts four to ten examine mean values of importance and satisfaction of attributes related to walking to describe the results of data and plot them on the Importance-Performance Analysis (IPA) matrix.

6.1 Demographic Data of the Respondents Based on the primary survey conducted, the findings from Fig. 13i show that 87% (296) of respondents have stayed in Delhi for more than 4 years, 9% (30) have stayed in Delhi from 1 to 4 years, and only 4% (14) have stayed in Delhi for less than 1 year. This suggests that the respondents are aware of and familiar with the various modes of transportation in the city. Figure 13ii shows that 61% (207) of respondents are male commuters whereas female commuters form only 39% (133) of the total respondents. Figure 14i depicts that the study is dominated by the 18–35 age group which makes up 60% of the entire sample size, which is 204 respondents, followed by the 35–60 age group which forms 35% of the sample, consisting of 119 respondents. The smallest portion is of the age group of more than 60 years which makes 5% and contains 17 respondents. Figure 14ii shows that 52% (177) of the respondents are graduates, followed by 33% (112) of post-graduates, and a meagre 10% (34) of candidates who have qualified 12th standard. The smallest portion of 5% (17) is formed by candidates who are Ph.D. scholars or higher in their educational qualification.

Fig. 13 (From left to right): (i) Duration of Stay in Delhi; (ii) Gender-wise Distribution of Commuters

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Fig. 14 (From left to right): (i) Age-wise distribution; (ii) Educational qualification of commuters

Figure 15 shows that a major chunk of the sample works in a government office or has a private job, which are 36% (122) and 34% (116), respectively. A major portion of 20% (68) is formed by students alone. Apart from these three occupations, the rest of the people consist of 4% (14) retired from their jobs; 3% (10) are currently self-employed or business owners; 2% (7) are housewives; and only 1% (3) are unemployed.

Fig. 15 Occupation-wise distribution of commuters

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6.2 Travel Behaviour Data of the Respondents Figure 16 shows that 54% (184) of commuters travel on all weekdays (Monday to Friday), 26% (88) of commuters travel daily (all 7 days in a week), 10% (34) of commuters travel on alternative days in a week, 6% (20) of commuters travel only during the weekends (Saturday and Sunday) and 4% (14) of commuters travel once a week. Figure 17 shows that 65% (221) of commuters travel for work, 16% (54) of commuters travel for educational purpose, 11% (37) of commuters travel for recreational fun, 4% (14) of commuters travel for household/healthcare purposes, and 4% (14) of commuters travel for shopping. Figure 18 shows that 26% (88) of commuters travel for a total duration of 30– 45 min, 26% (88) of commuters travel for 45 min–1 h, 24% (82) of commuters travel

Fig. 16 Trip-frequency-wise distribution of commuters

Fig. 17 Trip-purpose-wise distribution of commuters

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for 15–30 min, 16% (55) of commuters travel for more than 1 h, and 8% (27) of commuters travel for less than 15 min in their entire journey. Figure 19 shows that a majority of commuters forming 67% (228) of the sample do not own a cycle. On the other hand, 52% (178) of commuters own a 4-wheeler and 51% (173) of commuters own a 2-wheeler. Only 26% (89) of commuters own a cycle which is also not used quite often.

Fig. 18 Trip-duration-wise distribution of commuters

Fig. 19 Vehicle ownership of commuters

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6.3 Accessibility Data of the Respondents Figure 20 shows that the study is dominated by 80% (274) of commuters who travel for 15–30 min from their origin to reach the nearest metro station, followed by 10% (33) of commuters who travel for 5–15 min to cover the first mile distance, and another 10% (33) of commuters who travel for less than 5 min in their first mile. No commuter has to travel for more than 30 min to reach the station which suggests that the transit nodes on the metro line are well-planned. Figure 21 shows that the study is dominated by 59% (200) of commuters who walk for first mile connectivity, followed by 14% (47) of commuters using autorickshaws, 13% (44) using e-rickshaws, 4% (14) using public bus, 4% (14) using 4-wheeler, and 4% (14) using 2-wheeler. No commuter in the survey uses shared cycle, taxi/cabs, and cycle rickshaws as a mode of transport. Figure 22 shows that the study is dominated by 50% (170) of commuters who travel for 5–15 min from the metro station to reach their destination, followed by 18% (60) of commuters who travel for less than 5 min to cover the last mile distance, and 16% (55) of commuters who travel for 15–30 min in their last mile. Here, a considerable portion of 16% (55) of commuters travel for more than 30 min to reach their desired destination. Figure 23 shows that the study is dominated by 75% (256) of commuters who walk for last mile connectivity, followed by 14% (47) of commuters using autorickshaws, 10% (34) using e-rickshaws, and 1% (3) using public bus/DMRC feeder bus. No commuter in the survey uses shared cycle, taxi/cabs, and cycle rickshaws as a mode of transport for last mile connectivity. This highlights the need to improve pedestrian infrastructure to further enhance accessibility.

Fig. 20 First mile connectivity—Time duration

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Fig. 21 First mile connectivity—Mode share

Fig. 22 Last mile connectivity—Time duration

6.4 Analysis of Importance Attributes The mean (M) and standard deviation (SD) have been calculated and presented in Table 2 to assess each of the importance attributes and sub-attributes of walking as per the responses recorded from the primary survey conducted.

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Fig. 23 Last mile connectivity—Mode share

6.5 Analysis of Satisfaction Attributes The mean (M) and standard deviation (SD) have been calculated and presented in Table 3 to assess each of the satisfaction attributes and sub-attributes of walking as per the responses recorded from the primary survey conducted.

6.6 Means for Importance and Satisfaction Importance and satisfaction mean for the main attributes of walking can be observed in Table 4. Mean for importance range from 3.95 to 2.43 and mean for satisfaction range from 2.93 to 2.27. The Table below suggests that Safety and Security showed the highest importance rate whereas Built Environment showed the lowest importance rate. In addition to this, both Physical Infrastructure and Built Environment showed the highest satisfaction rates having mean values quite close to each other whereas Comfortable Environment showed the lowest satisfaction rate. Mean for the main attributes of importance and satisfaction can be observed in Fig. 24. It can be seen that the mean for attributes of importance is predominantly higher than satisfaction, except for built environment, which suggests its low priority by the commuters. The maximum difference between importance and satisfaction is observed in the mean of comfortable environment whereas the least difference is in the mean of public amenities. The overall importance and satisfaction values show unsatisfactory results suggesting that commuters are dissatisfied with the existing pedestrian facilities.

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Table 2 Descriptive analysis of importance attributes of walking S.No

Attributes (Walking)

1

2

3

4

5

n (%)

n (%)

n (%)

n (%)

n (%)

Mean

SD

1

Infrastructure

3.66

1.519

1.1

Wide footpaths

14 (7)

14 (7)

52 (26)

40 (20)

80 (40)

3.79

1.381

1.2

Continuity of footpaths

20 (10)

26 (13)

46 (23)

34 (17)

74 (37)

3.58

1.518

1.3

Good surface quality of footpaths

20 (10)

20 (10)

26 (13)

54 (27)

80 (40)

3.77

1.490

1.4

Universal design and accessibility

34 (17)

26 (13)

20 (10)

46 (23)

74 (37)

3.50

1.684

2

Public amenities

3.22

1.469

2.1

Street furniture

60 (30)

40 (20)

54 (27)

40 (20)

6 (3)

2.46

1.336

2.2

Toilet facilities

34 (17)

12 (6)

54 (27)

66 (33)

34 (17)

3.27

1.448

2.3

Food and beverage shops

74 (37)

40 (20)

60 (30)

20 (10)

6 (3)

2.22

1.271

2.4

Encroachment-free footpaths

34 (17)

20 (10)

40 (20)

40 (20)

66 (33)

3.42

1.629

2.5

Footpaths free from stray animals

34 (17)

0 (0) 40 (20)

12 (6)

114(57)

3.86

1.688

2.6

Proximity to the nearest metro station

20 (10)

0 (0) 40 (20)

26 (13)

114(57)

4.07

1.443

3

Safety and security

3.95

1.538

3.1

Adequate street lighting

14 (7)

14 (7)

12 (6)

26 (13)

134(67)

4.26

1.402

3.2

Wayfinding/Signages

26 (13)

28 (14)

26 (13)

34 (17)

86 (43)

3.63

1.641

3.3

Crossing facilities on roads

20 (10)

6 (3) 20 (10)

40 (20)

114(57)

4.11

1.448

3.4

Slow speed of motorized vehicles

20 (10)

26 (13)

26 (13)

28 (14)

100(50)

3.81

1.587

3.5

Street surveillance

26 (13)

14 (7)

14 (7)

34 (17)

112(56)

3.96

1.612

4

Comfortable environment

3.59

1.461

4.1

Shaded pathways

14 (7)

54 (27)

46 (23)

40 (20)

46 (23)

3.25

1.418

4.2

Cleanliness

14 (7)

14 (7)

40 (20)

32 (16)

100(50)

3.95

1.418 (continued)

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Table 2 (continued) S.No

Attributes (Walking)

1

2

3

4

5

n (%)

n (%)

n (%)

n (%)

n (%)

Mean

SD

4.3

Good air quality

14 (7)

20 (10)

46 (23)

26 (13)

94 (47)

3.83

1.458

4.4

Less noise

20 (10)

26 (13)

60 (30)

40 (20)

54 (27)

3.41

1.433

4.5

Good ambience

34 (17)

6 (3) 46 (23)

48 (24)

66 (33)

3.53

1.577

5

Built environment

2.43

1.268

5.1

Building frontage and aesthetics

74 (37)

60 (30)

46 (23)

20 (10)

0 (0)

2.06

1.116

5.2

Building height

60 (30)

46 (23)

66 (33)

20 (10)

8 (4)

2.35

1.259

5.3

Building setback

46 (23)

20 (10)

68 (34)

46 (23)

20 (10)

2.87

1.429

Note 1-Least important; 2-Somewhat important; 3-Moderately important; 4-Very important; 5Extremely important

6.7 Importance-Satisfaction Analysis This section deals with locating attributes of walking in the Importance-Performance/ Satisfaction Matrix or simply the IPA matrix. To identify the position of each attribute in the IPA matrix, mean for importance and satisfaction attributes and sub-attributes were calculated and plotted on the IPA matrix. Table 5 summarizes the values of mean and standard deviation for walking. In general, the IPA matrix has quadrants that depict the relative importance and satisfaction of attributes based on their positioning. The quadrants are Quadrant A (Concentrate here), Quadrant B (Keep up the good work), Quadrant C (Low priority), and Quadrant D (Possible overkill).

6.8 Locating Each Attribute of Walking in the IPA Matrix Figure 25 shows that out of a total of 23 attributes for walking, a majority of 16 attributes lie in Quadrant A which suggests that many variables require immediate attention because they are rated as highly important but show less satisfaction rate. 2 attributes including universal design and accessibility and proximity to nearest metro station lie in Quadrant B suggesting that the metro stations are well planned and designed by DMRC. In order for them to keep performing well, this institution must continue to invest in station area design. 4 attributes lie in Quadrant C which is

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Table 3 Descriptive analysis of satisfaction attributes of walking S.No

Attributes (Walking)

1

2

3

4

5

n (%)

n (%)

n (%)

n (%)

n (%)

1

Infrastructure

1.1

Wide footpaths

26 (13)

68 (34)

46 (23)

40 (20)

20 (10)

1.2

Continuity of footpaths

40 (20)

46 (23)

54 (27)

1.3

Good surface quality of footpaths

26 (13)

68 (34)

1.4

Universal design and accessibility

34 (17)

Mean

SD

2.93

1.396

2.80

1.332

54 (27)

6 (3) 2.70

1.289

46 (23)

40 (20)

20 (10)

2.80

1.332

20 (10)

40 (20)

40 (20)

66 (33)

3.42

1.629

2

Public amenities

2.73

1.241

2.1

Street furniture

26 (13)

88 (44)

54 (27)

26 (13)

6 (3) 2.49

1.090

2.2

Toilet facilities

60 (30)

40 (20)

60 (30)

34 (17)

6 (3) 2.43

1.306

2.3

Food and beverage shops

26 (13)

20 (10)

108(54)

40 (20)

6 (3) 2.90

1.078

2.4

Encroachment-free footpaths

46 (23)

68 (34)

46 (23)

26 (13)

14 (7)

2.47

1.318

2.5

Footpaths free from stray animals

54 (27)

80 (40)

40 (20)

6 (3) 20 (10)

2.29

1.326

2.6

Proximity to the nearest metro station

20 (10)

6 (3) 26 (13)

88 (44)

60 (30)

3.81

1.329

3

Safety and security

2.62

1.272

3.1

Adequate street lighting

34 (17)

46 (23)

54 (27)

46 (23)

20 (10)

2.86

1.379

3.2

Wayfinding/Signages

34 (17)

60 (30)

40 (20)

46 (23)

20 (10)

2.79

1.399

3.3

Crossing facilities on roads

14 (7)

60 (30)

66 (33)

46 (23)

14 (7)

2.93

1.165

3.4

Slow speed of motorized vehicles

60 (30)

74 (37)

46 (23)

20 (10)

0 (0) 2.13

1.068

3.5

Street surveillance

60 (30)

46 (23)

60 (30)

20 (10)

14 (7)

1.352

2.41

4

Comfortable environment

2.27

1.147

4.1

Shaded pathways

54 (27)

40 (20)

66 (33)

34 (17)

6 (3) 2.49

1.280

4.2

Cleanliness

46 (23)

68 (34)

60 (30)

20 (10)

6 (3) 2.36

1.157 (continued)

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Table 3 (continued) S.No

Attributes (Walking)

1

2

3

4

5

n (%)

n (%)

n (%)

n (%)

n (%)

Mean

SD

4.3

Good air quality

80 (40)

74 (37)

40 (20)

6 (3) 0 (0) 1.86

0.936

4.4

Less noise

60 (30)

68 (34)

46 (23)

26 (13)

0 (0) 2.19

1.126

4.5

Good ambience

54 (27)

40 (20)

74 (37)

26 (13)

6 (3) 2.45

1.239

5

Built environment

2.92

0.852

5.1

Building frontage and aesthetics

14 (7)

40 (20)

112(56)

34 (17)

0 (0) 2.83

0.881

5.2

Building height

6 (3) 26 (13)

120(60)

48 (24)

0 (0) 3.05

0.781

5.3

Building setback

14 (7)

112(56)

40 (20)

0 (0) 2.89

0.893

34 (17)

Note 1-Very Dissatisfied; 2-Dissatisfied; 3- Neutral; 4-Satisfied; 5-Very Satisfied Table 4 Mean and ranking of main attributes of walking S.No

Attributes (Walking)

Importance Mean

Satisfaction Ranking

Mean

Ranking

1

Infrastructure

3.66

2

2.93

1

2

Public amenities

3.22

4

2.73

2

3

Safety and security

3.95

1

2.62

3

4

Comfortable environment

3.59

3

2.27

4

5

Built environment

2.43

5

2.92

1

Fig. 24 Means for importance and satisfaction—Attributes (Walking)

Mean/Importance

Mean/Satisfaction

Infrastructure 4 3 2 Built Environment

1

Public Amenities

0

Comfortable Environment

Safety and Security

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Table 5 Location of each attribute of walking in IPA matrix Code

Attributes (Walking)

Importance

Satisfaction

Mean

Mean

SD

Quadrant

SD

WFP

Wide footpaths

3.79

1.381

2.80

1.332

Quadrant-A

CFP

Continuity of footpaths 3.58

1.518

2.70

1.289

Quadrant-A

SRF

Good surface quality of 3.77 footpaths

1.490

2.80

1.332

Quadrant-A

UNI

Universal design and accessibility

1.684

3.42

1.629

Quadrant-B

3.50

FUR

Street furniture

2.46

1.336

2.49

1.090

Quadrant-D

TOI

Toilet facilities

3.27

1.448

2.43

1.306

Quadrant-A

FBS

Food and Beverage shops

2.22

1.271

2.90

1.078

Quadrant-D

EFF

Encroachment-free footpaths

3.42

1.629

2.47

1.318

Quadrant-A

ANI

Footpaths free from stray animals

3.86

1.688

2.29

1.326

Quadrant-A

PRX

Proximity to the nearest metro station

4.07

1.443

3.81

1.329

Quadrant-B

STR

Adequate street lighting

4.26

1.402

2.86

1.379

Quadrant-A

WAY

Wayfinding/Signages

3.63

1.641

2.79

1.399

Quadrant-A

CRS

Crossing facilities on roads

4.11

1.448

2.93

1.165

Quadrant-A

SPD

Slow speed of motorized vehicles

3.81

1.587

2.13

1.068

Quadrant-A

SUR

Street surveillance

3.96

1.612

2.41

1.352

Quadrant-A

SHA

Shaded pathways

3.25

1.418

2.49

1.280

Quadrant-A

CLN

Cleanliness

3.95

1.418

2.36

1.157

Quadrant-A

AQI

Good air quality

3.83

1.458

1.86

0.936

Quadrant-A

NSE

Less noise

3.41

1.433

2.19

1.126

Quadrant-A

AMB

Good ambience

3.53

1.577

2.45

1.239

Quadrant-A

FRN

Building frontage and aesthetics

2.06

1.116

2.83

0.881

Quadrant-D

HGT

Building height

2.35

1.259

3.05

0.781

Quadrant-C

SET

Building setback

2.87

1.429

2.89

0.893

Quadrant-D

a low-priority zone suggesting that these factors are considered to be less important by the commuters but also have low satisfaction rates. Therefore, the provision of quality street furniture, food, and beverage shops, building frontage, and building setbacks need to be addressed but after concentrating on attributes of much higher importance. Building height is the only attribute that lies in Quadrant D illustrating

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Fig. 25 Importance-satisfaction analysis for each attribute of walking

that people in Delhi are complying with the building height norms provided as per the bye-laws, TOD policy, and AAI.

6.9 Locating Main Attributes of Walking in IPA Matrix IPA matrix of main attributes for walking shown in Fig. 26 illustrates that out of a total of 5 main attributes, 4 attributes lie in Quadrant A and only 1 attribute lies in Quadrant C. This implies that attributes including physical infrastructure, public amenities, safety and security, and a comfortable environment need more concentration because these attributes have a high importance rate but less satisfaction rate. Conversely, the built environment attribute lies in the low priority zone implying that it has less importance rate as well as less satisfaction rate. Table 6 shows the location of the main attributes of walking in the IPA matrix.

6.9.1

Station Area Inventory Data

The above analysis aids in the identification of major attributes on the basis of which the two selected metro stations on the Delhi Metro Yellow line, Saket and Vishwavidyalaya were evaluated for a further detailed survey. In succession to this, data was collected based on a reconnaissance survey by the author for both the station areas within a buffer zone of 500 m described as intense development zones as per Delhi TOD Policy. Consecutively, data was compiled to analyse the ease of walking on

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Fig. 26 Importance-satisfaction analysis for main attributes of walking

Table 6 Location of main attributes of walking in IPA matrix Code

Attributes (Walking)

Importance

Satisfaction

Mean

SD

Mean

SD

INF

Infrastructure

3.66

1.519

2.93

1.396

Quadrant Quadrant-A

PAM

Public amenities

3.22

1.469

2.73

1.241

Quadrant-A

SSE

Safety and security

3.95

1.538

2.62

1.272

Quadrant-A

CEN

Comfortable environment

3.59

1.461

2.27

1.147

Quadrant-A

BEN

Built environment

2.43

1.268

2.92

0.852

Quadrant-D

critical stretches and access routes within each station area for a better and improved interpretation of results. Based on these results, several design proposals, network modelling, corrective measures, and planning interventions were recommended for the two station areas. But these lie beyond the scope of this study. Therefore, a summary of inventory analysis can be seen in Table 7 based on the attributes analysed in this study. Each of these main attributes comprises of a set of sub-attributes including good quality and wide footpaths/sidewalks, continuity of footpaths, surface quality of footpaths, encroachment-free footpaths, footpaths free from stray animals, station design and accessibility, number and locations of station entry/exit points, stairways, escalators and elevators, street lighting, street furniture, toilet facilities, foot-over bridges and subways, crossing facilities, vending activities, street surveillance, cleanliness, existing and future land use, road ambience, surrounding built environment, etc.

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7 Conclusion The Importance-Satisfaction Analysis is found to be a simple yet helpful tool for identifying the strengths and weaknesses of station access areas. The findings suggested that the majority of metro commuters prefer to walk to cover the first and last mile connectivity as a part of their entire trip. Hence, it necessitates identifying areas of intervention regarding pedestrian infrastructure and facilities to enhance the accessibility of metro station areas. The Importance-Performance Analysis revealed that all attributes of walking except the built environment are considered to be extremely important to commuters, but all of them show a low satisfaction rate. This suggests that most of the commuters prefer walking to cover the first mile distance for accessing metro stations but do not find it safe or comfortable in doing so. These areas require rigorous improvement immediately for improved satisfaction. It is observed that there are no differences in overall importance and satisfaction pertaining to gender and station area. In terms of the overall performance of specific station areas, it was found that the Vishwavidyalaya station area performs way better as compared to the Saket station area in all aspects. This is due to well-laid-out road infrastructure with adequate width, continuous sidewalks, and encroachment management strategies. The existing demand for using sidewalks comes from the high footfall of students at Delhi University. However, a crucial inference is that the provision of pedestrian facilities should not depend on the demand, but it should be the other way around, i.e. demand must be generated to urge commuters to walk by creating an enabling environment within station access areas. It was also observed that existing pedestrian infrastructure can be improved by eliminating barriers like irregular manhole covers, broken paving, electric substations and transistors. It is thus significant to carry out holistic planning at the design stage itself such as planning of bulb-outs, green buffer areas, drainage Table 7 Metro station area inventory analysis

S. No

Station name

Saket

Vishwavidyalaya

General attributes 1

Platform accessibility

Satisfied

Good

2

Access routes facilities

Poor

Satisfied

3

Feeder connectivity

Satisfied

Good

4

Parking facilities

Satisfied

Satisfied

5

City connectivity

Good

Good

Satisfied

Good

Pedestrian attributes 7

Physical infrastructure

8

Public amenities

Satisfied

Satisfied

9

Safety and security

Poor

Good

10

Comfort and ambience

Poor

Good

11

Built environment

Satisfied

Good

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systems, crossing facilities, subways and foot-over bridges, and height of kerbs; and well-integrated components such as locations of trees, streetlights, traffic signals and signages, manholes, sitting spaces, vending areas, parking facilities, dustbins, electric transistors, road intersections, so on, and so forth. The study enables planners and policymakers to learn about the issues in first mile connectivity and prepare future plans of action. This research forms a foundation for future studies regarding station access area improvement and development. A way forward is to propose detailed physical design enhancement measures for the selected stations and for other metro stations with similar urban conditions, in order to make them more safe, inclusive, and accessible via walking.

References Ahmad, S., Avtar, R., Sethi, M., & Surjan, A. (2015). Delhi’s land cover change in post transit era. Cities, 50, 111–118. Deacon, A., & Du Rand, G. E. (2013). An importance-performance assessment of delegates’ satisfaction with the catering component of courses offered by continuing education at the University of Pretoria. Journal of Consumer Sciences, 41. DDA. (2021). Transit oriented development policy in Delhi. Delhi Development Authority: New Delhi. DMRC (2022). DMRC Ridership details. Delhi Metro Rail Corporation official website. https:// www.delhimetrorail.com/ Goel, R., & Tiwari, G. (2016). Access–egress and other travel characteristics of metro users in Delhi and its satellite cities. IATSS Research. 29(2), 164–172. Gupta, A., Bivina, G. R., & Parida, M. (2019). How far people can walk to access metro? A study of access trip characteristics of Delhi metro users. In Proceedings of the Eastern Asia Society for Transportation Studies, Vol. 12. Huang, Y., Kockelman, K. M., Garikapati, V., Zhu, L., & Young, S. (2021). Use of shared automated vehicles for first-mile last-mile service: Micro-simulation of rail-transit connections in Austin, Texas. Transportation Research Record, 2675(2), 135–149. HUDCO. (2015). Research report on Improving Public Transport System Addressing the Missing Link. HUDCO: New Delhi. Mandal, A. (2020). Walkability and Connectivity of Delhi Metro stations at Peri-Urban areas. Research paper. International Journal of Scientific & Engineering Research, 11(8), 329–336. Martilla, J., & James, J. (1977). Importance-performance analysis. Journal of Marketing, 41(1), 77–79. Master Plan of Delhi. (2041). Publsihed by DDA. MoUD. (2016a). Report of the High powered committee on Decongesting Traffic in Delhi. Ministry of Urban Development, GoI, New Delhi. MoUD. (2016b). NMT Guidance Document. Ministry of Urban Development, GoI: New Delhi. MoRTH. (2021). Road Transport Year Book 2017–18 & 2018–19. Transport Research Wing, Ministry of Road Transport & Highways, GoI: New Delhi. Panchal, J., Majumdar, B. B., Ram, V. V., & Basu, D. (2020). Analysis of user perception towards a key set of attributes related to Bicycle-Metro integration: A case study of Hyderabad, India. Transportation Research Procedia, 48, 3532–3544. Pengjun, Z. & Shengxiao, L. (2018). Suburbanization, land use of TOD and lifestyle mobility in the suburbs: An examination of passengers’ choice to live, shop and entertain in the metro station areas of Beijing. Journal of Transport and Land Use, 11(1)

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Saygaonkar, P., Swami, M., & Parida, M. (2016). Station area design approach for enhancing multimodal urban transport system, New Delhi. Transportation Research Procedia, 17, 16–31. UTTIPEC. (2010). Street Design Guidelines. UTTIPEC, Delhi Development Authority: New Delhi.

Delay Analysis of Motorized Three-Wheelers at Roundabouts in Urban Indian Context Subhada Nayak, Mahabir Panda, and Prasanta Kumar Bhuyan

Abstract The heterogeneity of traffic is the key factor affecting speed, travel time and convenience of road users. Motorized three-wheelers being prominent Paratransit options, provides mobility to millions of people, best feasible for providing door-to-door service in metropolitan regions and facilitate flexible passenger transportation. It does not necessarily follow fixed routes and schedules, as their performance and operational characteristics differ from cars, but remains largely unorganized. In traffic streams at different roadway infrastructures, presence of other class vehicles creates significant impediments to Motorized three-wheeler’s progress. The three-wheeler delay considered in this study is the time taken to travel from entry point to exit point during normal traffic flow conditions minus time taken during free flow conditions. This study develops the delay models with an average delay of threewheelers at roundabouts as the dependent variable and several independent variables selected after going through statistical checks. The variables considered in the delay modelling are circulating flow, entry flow, roundabout diameter, weaving length, entry width, and length of non-weaving section, circulating lanes, roadside commercial activity, percentage of motorized three-wheelers, and percentage of heavy motorized vehicles. Multilinear regression and stepwise regression analysis is carried out to fit and validate the proposed delay models. Keywords Average delay of three-wheeler · Roundabout · Motorised three-wheeler · Stepwise regression analysis

1 Introduction Increased population in urban areas increases demand for a significant proportion of modal trips. Since the start of rapidly growing urbanization over many parts of the world, the planning and design of transportation networks in urban areas have given priority to meet the requirements in the day-to-day life of any individual. The rise in S. Nayak (B) · M. Panda · P. K. Bhuyan National Institute of Technology, Rourkela, Odisha 769008, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Verma and M. L. Chotani (eds.), Urban Mobility Research in India, Lecture Notes in Civil Engineering 361, https://doi.org/10.1007/978-981-99-3447-8_18

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vehicular movements increases the volume of motorized/non-motorized vehicles and pedestrians, thus certainly causing problems of accidents and congestion in urban areas. With the rapid urbanization in developing countries, their urban and suburban parts have seen tremendous growth in population density, travel demand, and automobile usage. As nearly 80 percent of the travel demand is expected to be met by the road-based transportation system, the effective management of road traffic on the existing unplanned road infrastructures has emerged as the greatest challenge for road planners, engineers, policymakers, and researchers. As a result, the need for a public mode of transportation (three-wheelers) is gradually growing to accommodate both personal and professional needs. On the other hand, managing road networks has proven to be very difficult due to a lack of resources, increased congestion, and a crowded operating environment for the roadways. To assist the present purpose, this study has inspected important quantitative parameters (traffic operational parameters, road geometric parameters, builtenvironmental parameters), which affect drivers’ satisfaction levels in scenarios of diverse traffic flow. Simultaneously, a standard questionnaire was designed which includes various qualitative service attributes (related to road geometry, traffic operation, safety, comfort, maintenance, and aesthetics), that will help to examine the qualitative measures from the driver’s point of view. The attributes included in the questionnaire reflect the Auto-rickshaw user’s requirement for various road facilities. The systematic evaluation of roundabout performance from users’ viewpoints is supported by a number of approaches. However, these methodologies are not referred to in developing countries, where the traffic flow and road geometric conditions are considerably different with poor lane discipline. Data collected from different types of roundabouts were analyzed to examine the significance of each parameter for the Three-wheeler delay model. Statistical techniques give less complexity in terms of interpretation of output, which in turn simplifies the mathematical models for their use by transportation professionals. Therefore, this study has proposed the application of Statistical techniques (Multi-linear regression and step-wise regression) in the development of the 3WDelay model.

2 Literature Review Despite the fact that both public and private transportation services offer a quick and dependable means of transportation, there are some gaps that they are still unable to fill, including late-night and early-morning travel, travel up to the destination, travel in rural areas and small cities, and more (Sriraman et al., 2006). These considerations made Para-transit, or informal public transportation, a crucial part of people’s daily lives. Three Wheelers are the most common and most popular para-transit mode. The demand for three-wheelers has surely increased due to a variety of factors, including a high demand for travel, an expanding population, an increase in the number of

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workplaces, an increase in the number of trips each trip, and a lack of parking spaces (Hook & Fabian, 2009). A roundabout is a classic example of an unsignalized juncture where the mainstream and minor traffic streams flow around the central island (Akçelik, 2008). Also, the road traffic flows almost continuously in one direction around a central island. Depending upon whether the country follows the left- or right-hand rule, it allows the transmission of vehicle traffic in a clockwise or anticlockwise direction (Macioszek & Kurek, 2020). Streamlining of the traffic flow at entry is obtained by channelization so as to reduce the severity and number of conflict points at the intersection. Channelization is done by geometric design which enables tangent entry of approaching traffic to merge with the circulating flow at desirable speed (Trinh et al., 2020). Measurements of delay by test car were determined using a linearly referenced GPS. Probe vehicles were inserted with GPS receivers, differential correction units, and laptops for data storage. The receivers in each vehicle have recorded location, time, distance, and speed in one-second increments, which made it possible to trace vehicle trajectories and to measure field delay accurately. The delay data has been examined using vehicles outfitted with GPS technology at the signalized intersection (Akçelik, 2014). This technique includes algorithms to analyze acceleration profiles and speed profiles to identify critical control delay points, such as deceleration, accelerating ending points and onset points (Chodur, 2005). This process permitted the analysis of large data sets and provided consistent results. However, the approach experienced some difficulty in handling closely spaced intersections and over-capacity conditions. A method for calculating intersection delay patterns using measured travel times is also examined. The researchers calculated the delay for any car approaching the intersection from the delay patterns in order to provide the user with time-dependent intersection delay information (Ghosh-dastidar et al., 2006). An empirical model was developed to capture the queue discharge behavior at signalized intersections (Anusha et al., 2016). The model was put into practice by the authors, who then compared it to other delay models that were already in use. The study’s findings demonstrated that the proposed model might improve upon the shortcomings of the earlier models and estimate delay more precisely and accurately. A study has been conducted to quantify the control delays at the approaches of two intersections in Bozeman city (Zhong et al., 2005). The levels of service for the lane groups of studied intersections are estimated using calculated control delay (Guler & Menendez, 2016). Using a weighted average method, LOS for the entire intersection approach was found out. For intersections functioning under conditions of diverse traffic flow, an improvised delay model has been presented (HCM, 2010; Saha et al., 2017). For developing nations where traffic is highly diverse, with essentially no lanes and inadequate discipline, the current model’s homogeneous traffic assumptions lead to inaccurate conclusions. The current model was built on homogenous traffic conditions, which produced inaccurate findings for emerging nations where the traffic is highly diverse with virtually no lanes and inadequate discipline. The authors found that the difference between the observed delay and estimated delay to be less than 5%.

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A plot was made between the measured queue lengths versus time. Using Simpson’s 1/3rd rule, the total delay in a cycle and average delay per vehicle were estimated accurately (Flannery et al., 2005).

3 Methodology There hasn’t been much research done on how road traffic operates when there is mixed traffic flow and different roundabout geometries. The complexity of traffic behavior in India has expanded due to the country’s stable road network and rapidly increasing traffic. While the traffic flow considered in this study is significantly heterogeneous, the models created to analyse traffic behavior made the assumption that traffic is homogeneous. Thus, this paper suggests a method for calculating motorized three-wheeler delays at roundabouts based on entry and circulating flows, taking into account the variability of traffic flow in the Indian context. Three-wheeler mobility is significantly hindered by the presence of other vehicles since the performance and operating characteristics of these vehicles differ from those of other vehicles in the traffic stream at a signalized intersection. The amount of vehicles in the intersection at any given time, the type of vehicles, and their acceleration capabilities all affect how long it takes for them to pass through the intersection and how much delay they produce. In the recent past roundabouts have been operated as intersection alternatives due to the introduction of offside priority rule. Before implementation of the rule, roundabouts operated under weaving section principle. The principle of new priority rule is that priority has been given to circulating traffic and in that situation the minor stream traffic has to find suitable gap to merge into the major stream of traffic flow. The operational performance is measured in terms of number of vehicles incoming toward the roundabout. Hence estimation of capacity is in the form of entry capacity rather than weaving capacity. Since drivers of minor stream enter into the roundabouts when suitable gaps are available to merge into the major stream; delay primarily depends on the circulating flow and accepted gaps. The delay depends upon the circulating traffic which is known to be entry/circulating flow relationships that basically depend upon interaction among drivers and roundabout geometrics. Due to substantial variations in behavior of the user and prevailing requirement of different local situations; arrangement, performance efficiency and nature of road traffic is diverse in nature with various parts of India. It has been observed that behavior and traffic composition along road infrastructure is different in various parts of India. Hence, various locations have been selected for the selection of any site and variables for study purpose. Considering the different factors, 11 roundabouts were selected across the country for this study. Roundabouts are located in different cities in the country such as Cuttack, Bhubaneswar, Rourkela, Nashik, Ranchi, Nagpur, Trivandrum and Kanpur. These cities are situated under five different states of India such as Odisha, Maharashtra, Jharkhand, Kerala and Uttar Pradesh. Data collection is taking place when traffic is not affected by any kind of environmental influence. The traffic flow such

Delay Analysis of Motorized Three-Wheelers at Roundabouts in Urban … Table 1 Input Parameters with data collection process

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Parameter

Name

Traffic

Entry flow (Veh/h) Extracted from video

Geometric

Build environmental

Remark

Circulating flow (Veh/h)

Extracted from video

Roundabout diameter (m)

From site

Weaving length (m)

From site

Entry width (m)

From site

Width of non-weaving section

From site

Roadside commercial activity

Visual assessment

No. of circulating lanes

Visual assessment

Percentage of motorized two-wheeler

Visual assessment

Percentage heavy motor vehicles

Visual assessment

as entry flow, circulating flow is collected by videography in peak hours and partial geometric data is collected by using tapes measurement. In video recording data collection, the video cameras are placed at such a frame that entire stretch of entry flow, weaving sections, circulating flow shall be cover for all the legs of roundabout. The traffic flow data is collected either morning peak hour or evening peak hour on the weekdays. Delay also estimated using video recording. Data collection process of different input variables is shown as below in Table 1. Circulating flow, entry flow, roundabout diameter, weaving length, entry width, length of non-weaving section, road side commercial activity, no of circulating lanes, percentage of motorized twowheelers, percentage of Heavy motor vehicles are these parameters are considered as the independent parameters. Average three-wheeler delay is considered as a dependent parameter. The relation between two parameters is shown by the Spearmen correlation coefficient. The correlation test was done to all the parameters.

3.1 Spearman Correlation Analysis It is clear that circulating flow and roadside commercial activities has the highest correlation with average delay with coefficient value 0.7 and 0.65, respectively,

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followed by weaving width, entry flow, entry width, diameter of Central Island and then weaving length respectively. If the value of the coefficient is more than 0.9 it shows the multi-collinearity between two parameters. In the above table, none of parameter is highly linearly correlated. The two parameters have significant correlation. If the value of coefficient is more than 0.9 it shows the multi-collinearity between two parameters. In the above table, none of the parameter is highly linearly correlated. The two parameters have significant correlation, if the value of correlation coefficient is not less than 0.3. Hence from the above table, it is clear that all independent parameters are significantly related with the average three-wheeler delay and they can be used as a model development.

3.2 Development of Statistical Models Multilinear Regression Analysis When the number of independent variables is two or more than two, then this linear regression is called multilinear regression. When only one independent variable and none dependent variable are there then being called as linear regression. If independent variable is more than one, then this regression is called as multivariate regression. From the statistical approach, multilinear regression has linear approach with the model in the relation of one dependent variable and more independent variable. Regression has three main purpose of use, firstly how much effect on dependent variable due to independent variable. Secondly, it used effect of change in value of independent variable on change in dependent variable and lastly, it is used to predict the future value or trend of dependent variable. Multilinear regression equation in general form is given below. yi = β0 + β1 x1 + β2 x2 + β3 x3 + β4 x4 + · · · + β p x p + ε

(1)

where, yi x ∊ β0 , β1 , … βp p i

ith sample point of dependent variable ith sample point of p number independent variable error term are the regression parameters is the number of independent variables and is the sample number which varies from 1 to n.

Regression analysis finds the relationship between the independent parameters and dependent parameters. Coefficient of determination (R2 ) determines the how well the model fits for data sets used. It is one of the outputs of regression analysis which shows proportion of variance in dependent variable predictable by independent variables. On the other hand, it shows how efficient the model reproduces the observed output values. The range of R2 is in between 0 and 1. The value of R2 closer to 1 means closer to goodness of fit in the model for given data sets. In the test regression,

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all independent parameters are used. 70% of data is taken from data set to develop the regression model and 30% rest has remained for validation of the model. There is random selection of 70% data sets. The Analysis is carried out in SPSS software. The results are shown in Tables 2, 3 and 4. The R2 obtained is 0.731 which means 73.1% of variance in dependent variable can be explained by the independent variables. According to the coefficient table, the coefficient or factor analogues to entry flow are zero. Hence it states that the entry flow parameter doesn’t have any contribution toward predicting the average delay of three-wheelers. Circulating flow has significant in this model as both the p-value and t-statistic value are satisfying the condition of significant test. The p-value should be