Computers in Railways XII: Computer System Design and Operation in Railways and Other Transit Systems [1 ed.] 1845644689, 9781845644680

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COMPUTERS IN RAILWAYS XII

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WITeLibrary Home of the Transactions of the Wessex Institute. Papers presented at COMPRAIL 2010 are archived in the WIT elibrary in volume 114 of WIT Transactions on The Built Environment (ISSN 1743-3509). The WIT electronic-library provides the international scientific community with immediate and permanent access to individual papers presented at WIT conferences. http://library.witpress.com

TWELFTH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEM DESIGN AND OPERATION IN RAILWAYS AND OTHER TRANSIT SYSTEMS

COMPRAIL XII CONFERENCE CHAIRMEN B. Ning Beijing Jiaotong University, China C.A. Brebbia Wessex Institute of Technology, UK

CONFERENCE CO-CHAIRMEN

NATIONAL PROGRAMME COMMITTEE

C. Roberts University of Birmingham, UK A.F. Rumsey Delcan Corporation, Canada G. Sciutto Università degli Studi di Genova, Italy N. Tomii Chiba Institute of Technology, Japan

J. Guo Southwest Jiaotong University, China Y. Ji Tsinghua University, China L. Jia Beijing Jiaotong University, China M. Li Chinese Academy of Sciences, China T. Tao Beijing Jiaotong University, China

INTERNATIONAL SCIENTIFIC ADVISORY COMMITTEE E. Arias J.M. Mera A. Radtke R. Takagi P. Tzieropoulos

Organised by Beijing Jiaotong University, China Wessex Institute of Technology, UK Sponsored by WIT Transactions on the Built Environment

WIT Transactions Transactions Editor Carlos Brebbia Wessex Institute of Technology Ashurst Lodge, Ashurst Southampton SO40 7AA, UK Email: [email protected]

Editorial Board B Abersek University of Maribor, Slovenia Y N Abousleiman University of Oklahoma, USA

P L Aguilar University of Extremadura, Spain K S Al Jabri Sultan Qaboos University, Oman E Alarcon Universidad Politecnica de Madrid, Spain

A Aldama IMTA, Mexico C Alessandri Universita di Ferrara, Italy D Almorza Gomar University of Cadiz, Spain

B Alzahabi Kettering University, USA J A C Ambrosio IDMEC, Portugal A M Amer Cairo University, Egypt S A Anagnostopoulos University of Patras, Greece

M Andretta Montecatini, Italy E Angelino A.R.P.A. Lombardia, Italy H Antes Technische Universitat Braunschweig, Germany

M A Atherton South Bank University, UK A G Atkins University of Reading, UK D Aubry Ecole Centrale de Paris, France H Azegami Toyohashi University of Technology, Japan

A F M Azevedo University of Porto, Portugal J Baish Bucknell University, USA J M Baldasano Universitat Politecnica de Catalunya, Spain J G Bartzis Institute of Nuclear Technology, Greece A Bejan Duke University, USA M P Bekakos Democritus University of Thrace, Greece

G Belingardi Politecnico di Torino, Italy R Belmans Katholieke Universiteit Leuven, Belgium

C D Bertram The University of New South Wales, Australia

D E Beskos University of Patras, Greece S K Bhattacharyya Indian Institute of Technology, India

E Blums Latvian Academy of Sciences, Latvia J Boarder Cartref Consulting Systems, UK B Bobee Institut National de la Recherche Scientifique, Canada

H Boileau ESIGEC, France J J Bommer Imperial College London, UK M Bonnet Ecole Polytechnique, France C A Borrego University of Aveiro, Portugal A R Bretones University of Granada, Spain J A Bryant University of Exeter, UK F-G Buchholz Universitat Gesanthochschule Paderborn, Germany

M B Bush The University of Western Australia, Australia

F Butera Politecnico di Milano, Italy J Byrne University of Portsmouth, UK W Cantwell Liverpool University, UK D J Cartwright Bucknell University, USA P G Carydis National Technical University of Athens, Greece

J J Casares Long Universidad de Santiago de Compostela, Spain

M A Celia Princeton University, USA A Chakrabarti Indian Institute of Science, India

A H-D Cheng University of Mississippi, USA

J Chilton University of Lincoln, UK C-L Chiu University of Pittsburgh, USA H Choi Kangnung National University, Korea A Cieslak Technical University of Lodz, Poland

S Clement Transport System Centre, Australia M W Collins Brunel University, UK J J Connor Massachusetts Institute of Technology, USA

M C Constantinou State University of New York at Buffalo, USA

D E Cormack University of Toronto, Canada M Costantino Royal Bank of Scotland, UK D F Cutler Royal Botanic Gardens, UK W Czyczula Krakow University of Technology, Poland

M da Conceicao Cunha University of Coimbra, Portugal

L Dávid Károly Róbert College, Hungary A Davies University of Hertfordshire, UK M Davis Temple University, USA A B de Almeida Instituto Superior Tecnico, Portugal

E R de Arantes e Oliveira Instituto Superior Tecnico, Portugal L De Biase University of Milan, Italy R de Borst Delft University of Technology, Netherlands G De Mey University of Ghent, Belgium A De Montis Universita di Cagliari, Italy A De Naeyer Universiteit Ghent, Belgium W P De Wilde Vrije Universiteit Brussel, Belgium L Debnath University of Texas-Pan American, USA N J Dedios Mimbela Universidad de Cordoba, Spain G Degrande Katholieke Universiteit Leuven, Belgium S del Giudice University of Udine, Italy G Deplano Universita di Cagliari, Italy I Doltsinis University of Stuttgart, Germany M Domaszewski Universite de Technologie de Belfort-Montbeliard, France J Dominguez University of Seville, Spain K Dorow Pacific Northwest National Laboratory, USA W Dover University College London, UK

C Dowlen South Bank University, UK J P du Plessis University of Stellenbosch, South Africa

R Duffell University of Hertfordshire, UK A Ebel University of Cologne, Germany E E Edoutos Democritus University of Thrace, Greece

G K Egan Monash University, Australia K M Elawadly Alexandria University, Egypt K-H Elmer Universitat Hannover, Germany D Elms University of Canterbury, New Zealand M E M El-Sayed Kettering University, USA D M Elsom Oxford Brookes University, UK A El-Zafrany Cranfield University, UK F Erdogan Lehigh University, USA F P Escrig University of Seville, Spain D J Evans Nottingham Trent University, UK J W Everett Rowan University, USA M Faghri University of Rhode Island, USA R A Falconer Cardiff University, UK M N Fardis University of Patras, Greece P Fedelinski Silesian Technical University, Poland

H J S Fernando Arizona State University, USA

S Finger Carnegie Mellon University, USA J I Frankel University of Tennessee, USA D M Fraser University of Cape Town, South Africa

M J Fritzler University of Calgary, Canada U Gabbert Otto-von-Guericke Universitat Magdeburg, Germany

G Gambolati Universita di Padova, Italy C J Gantes National Technical University of Athens, Greece

L Gaul Universitat Stuttgart, Germany A Genco University of Palermo, Italy N Georgantzis Universitat Jaume I, Spain P Giudici Universita di Pavia, Italy F Gomez Universidad Politecnica de Valencia, Spain

R Gomez Martin University of Granada, Spain

D Goulias University of Maryland, USA K G Goulias Pennsylvania State University, USA

F Grandori Politecnico di Milano, Italy W E Grant Texas A & M University, USA

S Grilli University of Rhode Island, USA R H J Grimshaw Loughborough University, UK

D Gross Technische Hochschule Darmstadt, Germany

R Grundmann Technische Universitat Dresden, Germany

D L Karabalis University of Patras, Greece M Karlsson Linkoping University, Sweden T Katayama Doshisha University, Japan K L Katsifarakis Aristotle University of Thessaloniki, Greece

J T Katsikadelis National Technical University of Athens, Greece

A Gualtierotti IDHEAP, Switzerland R C Gupta National University of Singapore,

E Kausel Massachusetts Institute of

Singapore J M Hale University of Newcastle, UK K Hameyer Katholieke Universiteit Leuven, Belgium C Hanke Danish Technical University, Denmark K Hayami National Institute of Informatics, Japan Y Hayashi Nagoya University, Japan L Haydock Newage International Limited, UK A H Hendrickx Free University of Brussels, Belgium C Herman John Hopkins University, USA S Heslop University of Bristol, UK I Hideaki Nagoya University, Japan D A Hills University of Oxford, UK W F Huebner Southwest Research Institute, USA J A C Humphrey Bucknell University, USA M Y Hussaini Florida State University, USA W Hutchinson Edith Cowan University, Australia T H Hyde University of Nottingham, UK M Iguchi Science University of Tokyo, Japan D B Ingham University of Leeds, UK L Int Panis VITO Expertisecentrum IMS, Belgium N Ishikawa National Defence Academy, Japan J Jaafar UiTm, Malaysia W Jager Technical University of Dresden, Germany Y Jaluria Rutgers University, USA C M Jefferson University of the West of England, UK P R Johnston Griffith University, Australia D R H Jones University of Cambridge, UK N Jones University of Liverpool, UK D Kaliampakos National Technical University of Athens, Greece N Kamiya Nagoya University, Japan

H Kawashima The University of Tokyo,

Technology, USA Japan

B A Kazimee Washington State University, USA

S Kim University of Wisconsin-Madison, USA D Kirkland Nicholas Grimshaw & Partners Ltd, UK

E Kita Nagoya University, Japan A S Kobayashi University of Washington, USA

T Kobayashi University of Tokyo, Japan D Koga Saga University, Japan S Kotake University of Tokyo, Japan A N Kounadis National Technical University of Athens, Greece

W B Kratzig Ruhr Universitat Bochum, Germany

T Krauthammer Penn State University, USA C-H Lai University of Greenwich, UK M Langseth Norwegian University of Science and Technology, Norway

B S Larsen Technical University of Denmark, Denmark

F Lattarulo Politecnico di Bari, Italy A Lebedev Moscow State University, Russia L J Leon University of Montreal, Canada D Lewis Mississippi State University, USA S lghobashi University of California Irvine, USA

K-C Lin University of New Brunswick, Canada

A A Liolios Democritus University of Thrace, Greece

S Lomov Katholieke Universiteit Leuven, Belgium

J W S Longhurst University of the West of England, UK

G Loo The University of Auckland, New Zealand

J Lourenco Universidade do Minho, Portugal J E Luco University of California at San Diego, USA

H Lui State Seismological Bureau Harbin, China

C J Lumsden University of Toronto, Canada L Lundqvist Division of Transport and Location Analysis, Sweden T Lyons Murdoch University, Australia Y-W Mai University of Sydney, Australia M Majowiecki University of Bologna, Italy D Malerba Università degli Studi di Bari, Italy G Manara University of Pisa, Italy B N Mandal Indian Statistical Institute, India Ü Mander University of Tartu, Estonia H A Mang Technische Universitat Wien, Austria G D Manolis Aristotle University of Thessaloniki, Greece W J Mansur COPPE/UFRJ, Brazil N Marchettini University of Siena, Italy J D M Marsh Griffith University, Australia J F Martin-Duque Universidad Complutense, Spain T Matsui Nagoya University, Japan G Mattrisch DaimlerChrysler AG, Germany F M Mazzolani University of Naples “Federico II”, Italy K McManis University of New Orleans, USA A C Mendes Universidade de Beira Interior, Portugal R A Meric Research Institute for Basic Sciences, Turkey J Mikielewicz Polish Academy of Sciences, Poland N Milic-Frayling Microsoft Research Ltd, UK R A W Mines University of Liverpool, UK C A Mitchell University of Sydney, Australia K Miura Kajima Corporation, Japan A Miyamoto Yamaguchi University, Japan T Miyoshi Kobe University, Japan G Molinari University of Genoa, Italy T B Moodie University of Alberta, Canada D B Murray Trinity College Dublin, Ireland G Nakhaeizadeh DaimlerChrysler AG, Germany M B Neace Mercer University, USA

D Necsulescu University of Ottawa, Canada F Neumann University of Vienna, Austria S-I Nishida Saga University, Japan H Nisitani Kyushu Sangyo University, Japan B Notaros University of Massachusetts, USA P O’Donoghue University College Dublin, Ireland

R O O’Neill Oak Ridge National Laboratory, USA

M Ohkusu Kyushu University, Japan G Oliveto Universitá di Catania, Italy R Olsen Camp Dresser & McKee Inc., USA E Oñate Universitat Politecnica de Catalunya, Spain

K Onishi Ibaraki University, Japan P H Oosthuizen Queens University, Canada E L Ortiz Imperial College London, UK E Outa Waseda University, Japan A S Papageorgiou Rensselaer Polytechnic Institute, USA

J Park Seoul National University, Korea G Passerini Universita delle Marche, Italy B C Patten University of Georgia, USA G Pelosi University of Florence, Italy G G Penelis Aristotle University of Thessaloniki, Greece

W Perrie Bedford Institute of Oceanography, Canada

R Pietrabissa Politecnico di Milano, Italy H Pina Instituto Superior Tecnico, Portugal M F Platzer Naval Postgraduate School, USA D Poljak University of Split, Croatia V Popov Wessex Institute of Technology, UK H Power University of Nottingham, UK D Prandle Proudman Oceanographic Laboratory, UK

M Predeleanu University Paris VI, France M R I Purvis University of Portsmouth, UK I S Putra Institute of Technology Bandung, Indonesia

Y A Pykh Russian Academy of Sciences, Russia

F Rachidi EMC Group, Switzerland M Rahman Dalhousie University, Canada K R Rajagopal Texas A & M University, USA T Rang Tallinn Technical University, Estonia J Rao Case Western Reserve University, USA

A M Reinhorn State University of New York at Buffalo, USA A D Rey McGill University, Canada D N Riahi University of Illinois at UrbanaChampaign, USA B Ribas Spanish National Centre for Environmental Health, Spain K Richter Graz University of Technology, Austria S Rinaldi Politecnico di Milano, Italy F Robuste Universitat Politecnica de Catalunya, Spain J Roddick Flinders University, Australia A C Rodrigues Universidade Nova de Lisboa, Portugal F Rodrigues Poly Institute of Porto, Portugal C W Roeder University of Washington, USA J M Roesset Texas A & M University, USA W Roetzel Universitaet der Bundeswehr Hamburg, Germany V Roje University of Split, Croatia R Rosset Laboratoire d’Aerologie, France J L Rubio Centro de Investigaciones sobre Desertificacion, Spain T J Rudolphi Iowa State University, USA S Russenchuck Magnet Group, Switzerland H Ryssel Fraunhofer Institut Integrierte Schaltungen, Germany S G Saad American University in Cairo, Egypt M Saiidi University of Nevada-Reno, USA R San Jose Technical University of Madrid, Spain F J Sanchez-Sesma Instituto Mexicano del Petroleo, Mexico B Sarler Nova Gorica Polytechnic, Slovenia S A Savidis Technische Universitat Berlin, Germany A Savini Universita de Pavia, Italy G Schmid Ruhr-Universitat Bochum, Germany R Schmidt RWTH Aachen, Germany B Scholtes Universitaet of Kassel, Germany W Schreiber University of Alabama, USA A P S Selvadurai McGill University, Canada J J Sendra University of Seville, Spain J J Sharp Memorial University of Newfoundland, Canada Q Shen Massachusetts Institute of Technology, USA X Shixiong Fudan University, China

G C Sih Lehigh University, USA L C Simoes University of Coimbra, Portugal A C Singhal Arizona State University, USA P Skerget University of Maribor, Slovenia J Sladek Slovak Academy of Sciences, Slovakia

V Sladek Slovak Academy of Sciences, Slovakia

A C M Sousa University of New Brunswick, Canada

H Sozer Illinois Institute of Technology, USA D B Spalding CHAM, UK P D Spanos Rice University, USA T Speck Albert-Ludwigs-Universitaet Freiburg, Germany

C C Spyrakos National Technical University of Athens, Greece

I V Stangeeva St Petersburg University, Russia

J Stasiek Technical University of Gdansk, Poland

G E Swaters University of Alberta, Canada S Syngellakis University of Southampton, UK J Szmyd University of Mining and Metallurgy, Poland

S T Tadano Hokkaido University, Japan H Takemiya Okayama University, Japan I Takewaki Kyoto University, Japan C-L Tan Carleton University, Canada M Tanaka Shinshu University, Japan E Taniguchi Kyoto University, Japan S Tanimura Aichi University of Technology, Japan

J L Tassoulas University of Texas at Austin, USA

M A P Taylor University of South Australia, Australia

A Terranova Politecnico di Milano, Italy A G Tijhuis Technische Universiteit Eindhoven, Netherlands

T Tirabassi Institute FISBAT-CNR, Italy S Tkachenko Otto-von-Guericke-University, Germany

N Tosaka Nihon University, Japan T Tran-Cong University of Southern Queensland, Australia

R Tremblay Ecole Polytechnique, Canada I Tsukrov University of New Hampshire, USA

R Turra CINECA Interuniversity Computing Centre, Italy

S G Tushinski Moscow State University, Russia

J-L Uso Universitat Jaume I, Spain E Van den Bulck Katholieke Universiteit Leuven, Belgium D Van den Poel Ghent University, Belgium R van der Heijden Radboud University, Netherlands R van Duin Delft University of Technology, Netherlands P Vas University of Aberdeen, UK W S Venturini University of Sao Paulo, Brazil R Verhoeven Ghent University, Belgium A Viguri Universitat Jaume I, Spain Y Villacampa Esteve Universidad de Alicante, Spain F F V Vincent University of Bath, UK S Walker Imperial College, UK G Walters University of Exeter, UK B Weiss University of Vienna, Austria

H Westphal University of Magdeburg, Germany

J R Whiteman Brunel University, UK Z-Y Yan Peking University, China S Yanniotis Agricultural University of Athens, Greece

A Yeh University of Hong Kong, China J Yoon Old Dominion University, USA K Yoshizato Hiroshima University, Japan T X Yu Hong Kong University of Science & Technology, Hong Kong

M Zador Technical University of Budapest, Hungary

K Zakrzewski Politechnika Lodzka, Poland M Zamir University of Western Ontario, Canada

R Zarnic University of Ljubljana, Slovenia G Zharkova Institute of Theoretical and Applied Mechanics, Russia

N Zhong Maebashi Institute of Technology, Japan

H G Zimmermann Siemens AG, Germany

COMPUTERS IN RAILWAYS XII COMPUTER SYSTEM DESIGN AND OPERATION AND OTHER TRANSIT SYSTEMS

Editors

B. Ning Beijing Jiaotong University, China C.A. Brebbia Wessex Institute of Technology, UK

IN

RAILWAYS

B. Ning Beijing Jiaotong University, China C.A. Brebbia Wessex Institute of Technology, UK

Published by WIT Press Ashurst Lodge, Ashurst, Southampton, SO40 7AA, UK Tel: 44 (0) 238 029 3223; Fax: 44 (0) 238 029 2853 E-Mail: [email protected] http://www.witpress.com For USA, Canada and Mexico Computational Mechanics Inc 25 Bridge Street, Billerica, MA 01821, USA Tel: 978 667 5841; Fax: 978 667 7582 E-Mail: [email protected] http://www.witpress.com British Library Cataloguing-in-Publication Data A Catalogue record for this book is available from the British Library ISBN: 978-1-84564-468-0 ISSN: 1746-4498 (print) ISSN: 1743-3509 (on-line) The texts of the papers in this volume were set individually by the authors or under their supervision. Only minor corrections to the text may have been carried out by the publisher. No responsibility is assumed by the Publisher, the Editors and Authors for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. The Publisher does not necessarily endorse the ideas held, or views expressed by the Editors or Authors of the material contained in its publications. © WIT Press 2010 Printed in Great Britain by MPG Books Group, Bodmin and King’s Lynn. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the Publisher.

Preface

The International Conference on System Design and Operation in Railways and other Transit Systems (COMPRAIL) has become the most successful conference in its field since it started in 1987. This book contains papers accepted for presentation at the 12th meeting in the series, held in Beijing, China in 2010. The book reflects the new achievements and applications of computer based technologies in management, design and operation of passenger and freight transit systems. Rail transport has many advantages over other systems in terms of capacity, punctuality, being weather resistant, savings in fuel and land, and fairly low pollution. It is a low-carbon emission transport mode and ought to be the backbone of any regional and city comprehensive travel system. Safety is one of the central topics of rail systems, together with efficiency. Computer based technologies have always played an important role in the safety and efficiency of transit systems. Many countries have recently become interested in using high speed railways, resulting in up to now, more than 10,000 km of high speed track in the world. By 2020, the total length of high speed railways will reach 18,000 km in China alone. These topics are discussed in this book and it is expected that they will become even more important in future COMPRAIL meetings. The above are just some of the themes presented in this volume, which contains a substantial number of sections covering topics such as: Advanced train control; Traffic control and safety of high-speed railways in Asia; Computer techniques; Planning; Maglev and high speed railways; Metro and other transit systems; Energy supply and consumption; Dynamics and wheel/rail interface; Operations quality; Monitoring and maintenance; Safety and security; Timetable planning.

The Editors are grateful to all the authors for their excellent papers as well as to the members of the International Scientific Advisory Committee who participated in the review process. They all contributed to the success of the Conference and the publication of this book. Their help will ensure the continued success of COMPRAIL. The Editors Beijing Jiaotong University, China, 2010

Contents Section 1: Advanced train control Design, development, application, safety assessment and simulation of the railway signaling system B. Ning, T. Tang, C. Gao & J. Xun...................................................................... 3 Research on the simulation of an Automatic Train over speed Protection driver-machine interface based on Model Driven Architecture B. Y. Guo, W. Du & Y. J. Mao ........................................................................... 13 A framework for modeling train control systems based on agent and cellular automata J. Xun, B. Ning & T. Tang ................................................................................. 23 A new train GPS positioning algorithm in satellite incomplete condition based on optimization and the digital track map X. Jia, D. Chen & H. Wang ............................................................................... 35 Simulation of a high-speed train control system based on High Level Architecture and its credibility analysis Wei ShangGuan, J.-Q. Chen, B. Li, L.-N. Guo, M. Li & L.-Y. Chen ................. 45 Research on a hybrid map matching algorithm for Global Navigation Satellite System based train positioning J. Liu, B. Cai, T. Tang, J. Wang & Wei ShangGuan.......................................... 59 Automated system testing of an automatic train protection system B. Friman & T. Andreiouk................................................................................. 71 Design and implementation of a distributed railway signalling simulator X. Hei, W. Ma, L. Wang & N. Ouyang............................................................... 81

Train tracking problem using a hybrid system model Y. Wang, R. Luo, F. Cao & B. Ning................................................................... 89 Latent energy savings due to the innovative use of advisory speeds to avoid occupation conflicts F. Mehta, C. Rößiger & M. Montigel ................................................................ 99

Section 2: Traffic control and safety of high-speed railways in Asia Special session organised by N. Tomii How the punctuality of the Shinkansen has been achieved N. Tomii ........................................................................................................... 111 Linkage of a conventional line dispatch system with the Shinkansen dispatch system Y. Yoshino ........................................................................................................ 121 Train scheduling of Shinkansen and relationship to reliable train operation S. Sone & Y. Zhongping................................................................................... 133 Rescue operations on dedicated high speed railway lines R. Takagi.......................................................................................................... 141 Track measurement by Kyushu Shinkansen cars in commercial service H. Moritaka & T. Matsumoto .......................................................................... 147 Development of a high-speed overhead contact line measurement device for the Kyushu Shinkansen N. Kinoshita, Y. Himeno & R. Igata ................................................................ 155 The analysis of train reliability for the Taiwan High Speed Rail J.-C. Jong, T.-H. Lin, C.-K. Lee & H.-L. Hu ................................................... 169

Section 3: Communications Development of a railway signaling device based on mixed digital and analog signals using digital signal processors R. Ishikawa, D. Koshino, H. Mochizuki, S. Takahashi, H. Nakamura, S. Nishida & M. Sano............................................................... 183 A multi scalable model based on a connexity graph representation L. Gély, G. Dessagne, P. Pesneau & F. Vanderbeck....................................... 193

Universal communication infrastructure for locomotives U. Lieske .......................................................................................................... 205

Section 4: Computer techniques Research on a novel train positioning method with a single image B. Guo, T. Tang & Z. Yu .................................................................................. 213 Software redundancy design for a Human-Machine Interface in railway vehicles G. Zheng & J. Chen ......................................................................................... 221 Study on the method of traction motor load simulation on railway vehicles F. Lu, S. Li, L. Xu & Z. Yang ........................................................................... 233 Formalizing train control language: automating analysis of train stations A. Svendsen, B. Møller-Pedersen, Ø. Haugen, J. Endresen & E. Carlson.................................................................................................... 245 Design and operation assessment of railway stations using passenger simulation D. Li & B. Han................................................................................................. 257 Modeling of an interoperability test bench for the on-board system of a train control system based on Colored Petri Nets L. Yuan, T. Tang, K. Li & Y. Liu...................................................................... 271

Section 5: Planning How regular is a regular-interval timetable? From theory to application P. Tzieropoulos, D. Emery & D. Tron ............................................................. 283 Port Hinterland traffic: modern planning IT methods A. Radtke.......................................................................................................... 295 Generating optimal signal positions E. A. G. Weits & D. van de Weijenberg........................................................... 307

A method for the improvement need definition of large, single-track rail network analysis and infrastructure using “Rail Traffic System Analysis” T. Kosonen ....................................................................................................... 319 Automatic location-finding of train crew using GSM technology F. Makkinga & B. Sturm.................................................................................. 327 Alignment analysis of urban railways based on passenger travel demand J. L. E. Andersen & A. Landex......................................................................... 337 Maintenance plan optimization for a train fleet K. Doganay & M. Bohlin................................................................................. 349 SAT.engine: automated planning and validation tools for modern train control systems B. Wenzel, J. Schuette & S. Jurtz ..................................................................... 359 Case studies in planning crew members J. P. Martins & E. Morgado ............................................................................ 371 Generating and optimizing strategies for the migration of the European Train Control System C. Lackhove, B. Jaeger & K. Lemmer ............................................................. 383 Synthesis of railway infrastructure J. Spönemann & E. Wendler............................................................................ 395 Dimensioning of a railway station for unknown operation O. Lindfeldt & A.-I. Lundberg ......................................................................... 407 The simulation of passengers’ time-space characteristics using ticket sales records with insufficient data J.-C. Jong & E.-F. Chang................................................................................ 419 Headway generation with ROBERTO A. D. Middelkoop............................................................................................. 431 Development and implementation of new principles and systems for train traffic control in Sweden B. Sandblad, A. W. Andersson, A. Kauppi & G. Isaksson-Lutteman.................. 441

Section 6: Maglev and high speed railways A model for the coordination between high-speed railway lines and conventional rail lines in a railway passenger transportation corridor Y. Bao .............................................................................................................. 453 Derivation of the safety requirements for control systems based on the interoperability property of the Maglev train W. Zheng, J. R. Müeller & K. Li ...................................................................... 467 Dynamic characteristics modelling and adaptability research of the balise transmission module in high speed railways H. Zhao, S. Sun & W. Li .................................................................................. 475

Section 7: Metro and other transit systems CBTC test simulation bench J. M. Mera, I. Gómez-Rey & E. Rodrigo ......................................................... 485 Development of the new CBTC system simulation and performance analysis R. Chen & J. Guo............................................................................................. 497 Efficient design of Automatic Train Operation speed profiles with on board energy storage devices M. Domínguez, A. Fernández, A. P. Cucala & J. Blanquer ............................ 509 Research on the load spectrum distribution and structure optimization of locomotive traction seats W. Wang, M. Wang & Z. Liu ........................................................................... 521 Generation of emergency scheme for urban rail transit by case-based reasoning F. Li, R. Xu & W. Zhu ...................................................................................... 529 Application and perspectives for interoperable systems in Italy and Europe R. Bozzo, R. Genova & F. Ballini .................................................................... 537

Section 8: Energy supply and consumption A method to optimise train energy consumption combining manual energy efficient driving and scheduling C. Sicre, P. Cucala, A. Fernández, J. A. Jiménez, I. Ribera & A. Serrano.................................................................................................... 549 Driving equipment with three-phase inverters and asynchronous traction motors for trolleys and trams V. Radulescu, I. Strainescu, L. Moroianu, S. Gheorghe, E. Tudor, V. Lupu, F. Bozas, A. Dascalu, G. Mitroi & D. Braslasu ................................ 561 Development, testing and implementation of the pantograph damage assessment system (PANDAS) A. Daadbin & J. Rosinski ................................................................................ 573

Section 9: Dynamics and wheel/rail interface Strategies for less motion sickness on tilting trains R. Persson & B. Kufver.................................................................................... 581 Railway vehicle and bridge interaction: some approaches and applications G. Mikheev, E. Krugovova & R. Kovalev ........................................................ 593 Certain aspects of the CEN standard for the evaluation of ride comfort for rail passengers B. Kufver, R. Persson & J. Wingren ................................................................ 605 Latest development on the simulation of rolling contact fatigue crack growth in rails L. Zhang, S. Mellings, J. Baynham & R. Adey................................................. 615

Section 10: Operations quality Disruption handling in large railway networks F. Corman, A. D’Ariano & I. A. Hansen ......................................................... 629 A multi-stage linear prediction model for the irregularity of the longitudinal level over unit railway sections H. Chang, R. Liu & Q. Li................................................................................. 641 Systematic analyses of train run deviations from the timetable T. Richter ......................................................................................................... 651

A novel peak power demand reduction strategy under a moving block signalling system Q. Gu, L. Pei, F. Cao & T. Tang ..................................................................... 663

Section 11: Monitoring and maintenance Development of an ES2-type point machine (monitoring of point machine) N. Obata, T. Ichikura, H. Narita & H. Tanaka................................................ 677 A heuristic approach to railway track maintenance scheduling L. M. Quiroga & E. Schnieder......................................................................... 687 Track test monitoring system using a multipurpose experimental train H. Matsuda, M. Takikawa, T. Nanmoku & E. Yazawa .................................... 701

Section 12: Safety and security Verification of quantitative requirements for GNSS-based railway applications H. Mocek, A. Filip & L. Bažant ....................................................................... 711 Modelling and design of the formal approach for generating test sequences of ETCS level 2 based on the CPN X. Zhao, Y. Zhang, W. Zheng, T. Tang & R. Mu.............................................. 723 The experimental evaluation of the EGNOS safety-of-life services for railway signalling A. Filip, L. Bažant & H. Mocek ....................................................................... 735 System safety property-oriented test sequences generating method based on model checking Y. Zhang, X. Q. Zhao, W. Zheng & T. Tang ..................................................... 747 Scenario-based modeling and verification of system requirement specification for the European Train Control System W. Tang, B. Ning, T. Xu & L. Zhao ................................................................. 759 ROSA – a computer based safety model for European railways J. Schütte & M. Geisler.................................................................................... 771

An IP network-based signal control system for automatic block signal and its functional enhancement K. Hayakawa, T. Miura, R. Ishima, H. Soutome, H. Tanuma & Y. Yoshida .................................................................................................... 783 The improvement of the safety-case process in practice: from problems and a promising approach to highly automated safety case guidance J. R. Müeller, W. Zheng & E. Schnieder.......................................................... 795 State-based risk frequency estimation of a rail traffic signal system Y. Zhang, J. Guo & L. Liu................................................................................ 805 Use of model transformation for the formal analysis of railway interlocking models T. Xu, O. M. Santos, X. Ge & J. Woodcock ..................................................... 815 A model-based framework for the safety analysis of computer-based railway signalling systems R. Niu & T. Tang ............................................................................................. 827 A scenario-based safety argumentation for CBTC safety case architecture C. Liu, X. Sha, F. Yan & T. Tang..................................................................... 839 The cost benefit analysis of level crossing safety measures R. Ben Aoun, E.-M. El Koursi & E. Lemaire ................................................... 851 Proposal of the standard-based method for communication safety enhancement in railway signalling systems H.-J. Jo, J.-G. Hwang, B.-H. Kim, K.-M. Lee & Y.-K. Kim ............................. 863

Section 13: Timetable planning A heuristic algorithm for the circulation plan of railway electrical motor units J. Miao, Y. Yu & Y. Wang ................................................................................ 877 Working out an incomplete cyclic train timetable for high-speed railways by computer D. Yang, L. Nie, Y. Tan, Z. He & Y. Zhang...................................................... 889 A novel research on the relation between the number of passengers and the braking distance of a metro L. Wang, Y. Li & X. Hei................................................................................... 901

Computation and evaluation of scheduled waiting time for railway networks A. Landex......................................................................................................... 911 Computation of a suburban night train timetable based on key performance indicators B. Schittenhelm & A. Landex ........................................................................... 923 A cooperative strategy framework of train rescheduling for portal junctions leading into bottleneck sections L. Chen, F. Schmid, B. Ning, C. Roberts & T. Tang........................................ 935 Circle rail transit line timetable scheduling using Rail TPM J. Zhibin, G. Jia & X. Ruihua .......................................................................... 945 A simulation analysis of train rescheduling strategies on Chinese passenger dedicated lines Z. He, L. Meng, H. Li & L. Nie ........................................................................ 953 An efficient MIP model for locomotive routing and scheduling M. Aronsson, P. Kreuger & J. Gjerdrum......................................................... 963 Timetable attractiveness parameters B. Schittenhelm ................................................................................................ 975

Author Index .................................................................................................. 985

Section 1 Advanced train control

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Design, development, application, safety assessment and simulation of the railway signaling system B. Ning, T. Tang, C. Gao & J. Xun The State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, P.R. China

Abstract The railway signaling system is one of the key subsystems in railway systems to ensure the train operation efficiency and safety. It is a complicated system. However, the railway signaling system is not independent in railway systems. In this paper, five parts of the railway signaling system with their features and the relationship are described in detail. Firstly, the core system of the railway signaling system is designed and developed. Re-design is carried out for the application of the core system for the specified rolling stocks and lines. The safety of the core system and the applied system needs to be assessed. Finally, a complete simulation system should be built for testing, installation, maintenance and the technique upgrading of the systems. This paper helps people to get a deep understanding about the functions, design and development, applications and simulation of railway signaling systems. Keywords: railway signaling system, system design, safety assessment, simulation.

1 Introduction The railway signaling system is the brain and nerve system of railway systems, which ensures the safety and efficiency of the train operation. However, compared with civil engineering, such as lines, bridges, tunnels, and rolling stock, the cost for a signaling system is relatively low. Generally speaking, it is less than 10% of the whole cost for a railway system. Little attention has been paid give to it, either for the main line railway systems or the underground ones. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100011

4 Computers in Railways XII With the quick development of railway systems, especially in the high-speed railways and high-density urban transit systems, the importance of the signaling system has been realized by more and more people. In order to get a better understanding of the railway signaling system, we divide it into the core system, the minimal system and the application system, according to their functions and applications. Meanwhile, design, re-design, simulation, and safety assessment of the railway signaling system in particular are also introduced. There are two typical railway control systems in the world, which have been developed into standardizations. One is the ETCS (European Train Control System) for the railway signaling system in Europe, the other is the CTCS (Chinese Train Control System) for the railway signaling system in China. In this paper, the two systems are taken as examples to show how the signaling systems are designed, developed, re-designed, assessed and simulated. The core systems of a railway signaling system are defined. According to the requirements of the application, the task of the core systems is described. The railway signaling system is a requirements-tailored product for different lines and different rolling stocks. Furthermore, the railway signaling system must be fail-safe and reliable. In the design of the core system and the re-design of an applicable system, some of the special principles must be considered. Therefore, safety assessment must be carried out for the signaling system. In addition, the simulation system has become one of the necessary tools for the design, application and maintenance of the signaling system. Much knowledge is accumulated during the whole cycle of the signaling system, while it is relatively simple from the view of the function points. With the introduction in the following sections, people will understand why the railway signaling system is important, special and high cost.

2 Definition of railway control systems and their core systems The railway system can generally be divided into three parts shown in Fig. 1.

lines, bridges and tunnels

Figure 1:

Rolling Stock

The railway system.

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One part is the infrastructure, which includes the lines, bridges, and tunnels. It is called the fixed part of railway Fsystem. The second part is the rolling stock, which is called as the movement part of railway system. The third part is the signaling system, which is called the brain and the nerve system of railway system. As shown in Fig. 1, trains run on the lines controlled by the signaling system in any railway systems. Therefore, the signaling system ensures trains to operate safely and efficiently. The roles and functions of the signaling system in railway systems are clearly stated in Fig. 1. It is obvious the signaling system is the brain and the nerve system of railway system. Without signaling system, railway systems cannot operate efficiently and safely. It also can be seen that the signaling system has close relationship with rolling stocks and infrastructures. The configuration of the signaling system is given in Fig. 2. Usually, there are four parts included in the signaling system: (1) On-board control system, (2) Station control system and wayside system, (3) Central control system, (4) Communication network including mobile telecommunication. The core systems of the signaling system are consisted by the above four parts in Fig. 2. The interlocking system and RBC (Radio Block Control) belong to the station control systems. The on-board control system, control center and the communication system are also one of the core systems for signaling system. In more details, the vital computer for interlocking system, on-board system and RBC system, and the basic software for the four parts are also belong to the core part of the signaling system. In the paper, the core systems are the foundation of the signaling system, and are called as the basic models of the signaling system. Up to now, the functions of the signaling system in railway system, and the relationship between the core systems and the signaling system are explained. When ETCS or CTCS is analyzed, the four parts, or the core systems can be seen easily. In the ETCS, there are Euro-cab, Euro-radio (GSM-R) and Eurointerlocking, etc. In the CTCS, there are Chinese on-board system (Universal cab signaling), GSM-R and Chinese interlocking systems (four kinds of interlocking systems), etc. as in Ning et al. [3].

Figure 2:

Configuration of railway signaling.

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3 Design and development of the core systems Interlocking system is one of the core systems for the signaling system. An interlocking system for a typical station layout is firstly designed and developed to ensure a right route establishment. In the interlocking system, the basic interlocking logic relationship among the routes, switches, and signaling must be strictly ensured for the typical station layout. Usually, strict algorithms are used in the core systems to guarantee that conflicting routes can never been established in the same system. After an interlocking system is designed, it must be tested thoroughly. Based on the station layout, a complete test set should be built. Possibly, a simulation system for the interlocking system is designed to test its logic functions. The test will ensure the correctness and the safety of the system. Before an interlocking system is designed, the specifications of the system requirements and the system functions should be finalized. The specifications are the basic files of the design and the test. Of course, the typical station layout must be defined to ensure the functions of the interlocking system to be complete. As the core system, ATP system (one of the on-board system), RBC system, the central control system, and the communication network connecting the core systems should be designed and developed. The processes are the same as the design and development of the interlocking systems. The classification and process can be found in the files of ETCS and CTCS in Ning et al. [3]. In order to design and develop the core system, the prototype for the core system should be designed and developed. Design and development of the prototypes for the core system of the signaling system must obey the design principles of the software engineering. It is divided into the three levels. The first level is the system management level to operate the whole system management including the safety requirement in the vital computer. The second level is to deal with the logic requirements of the systems, i.e. the function rules. The third level is the application level to match function requirements and an application database. Fig. 3 shows the relation of the three levels. The core systems of a signaling system should include six units based on the four parts. They are the central control unit, the station control unit, the RBC control unit, the on-board control unit, the communication network unit and the wayside unit with radio unit. The six units consist into a minimal signaling

Figure 3:

Configuration of the software system for a core system.

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Figure 4:

7

Configuration of a minimal signaling system.

system shown in Fig. 4. The minimal signaling system is the foundation of an application signaling system, and meets all the function requirements of a signaling system. Usually, only the prototype of the minimal signaling system can be found in laboratory.

4 Re-design of application of the core system for the specified lines After development of the core systems for the signaling system, there we have the prototype of the minimal signaling system. Before the application of real signaling system, redesign must be carried out for an applied line based on the core systems and the minimal signaling system. The main task of the redesign is to match the database of a real line and the minimal signaling system with the core systems. The redesign turns the minimal signaling system into a real application signaling system. It needs experts with good skills, while the importance of the step is often ignored. The designers need to know both the core systems and all the requirements of the application line. That is why the signaling system is called as requirements-tailed system, and it costs. During the redesign, the database for the line and rolling stock must be established. For example, some parameters such as curves and slopes of the line, the parameters for rolling stock, traction features and braking features of rolling stock are needed for the on-board system (ATP or ATO). An interlocking system needs the data for the layout of each station along the lines. There is different number of routes for different station. At each station, the number of switches and the positions of switches are different. For the central control system, all the data from the lines and the requirements are needed to general a train plan and a WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

8 Computers in Railways XII train operation graph. At the same time, the disposition of communication network units and wayside units such as position of design for radio units and balises must be carried out. In order to simplify the redesign without reducing the correctness, a computer-aid design (CAD) tool is developed. Different CAD tools are designed for the different units, such as Interlocking CAD tool and RBC CAD tool etc in Mitchell [5]. After the redesign of an application signaling system is finished, the whole system is test to verify the functions and safety. To test the signaling system, a simulation system and environments should be built. Test set and test dictionary for a line should been accumulated and established to ensure the test.

5 Safety assessment The signaling system is a system to ensure train operation safety. Therefore, it must be self-safety in its whole life cycle. Fail-safe concept was put forward for the railway signaling system in the early 1900s. Safety assessment for the railway signaling system begins with the start of the system design. From the core system design to redesign of an application signaling system, from the prototype of the core systems to the minimal system, from manufacturing to installation, from operation to maintenance, safety assessment must be taken during the whole life cycle. This is the main reason why a signaling system is complicated and high cost. There are always two groups of persons in this area. One is to implement the signaling system. Safety assessment is done by another group to ensure the system‘s implementing to be monitored. Moreover, the second group should involve from the beginning of the system design. In other word, the whole process of the signaling system design, manufacturing, installation and operation must be monitored and assessed. Methods and principles for software engineering must be used for the files management and flow management to do safety assessments of the signaling system. For a big project of railway signaling, the third professional company is invited to do the safety assessment for the project. What is the meaning for RAMS? The RAMS means Reliability, Availability, Maintainability and Safety of the system. According to EN50126 (CENELEC 1999), the definition of RAMS can be found easily in Theeg and Vlasenko [6]. System reliability is defined as the probability that the system can perform a required function under given conditions for a given time interval. System availability is defined as the ability of a system to be in a state to perform the required function under given conditions over a given time interval, assuming that the required external sources of help is provided. System maintainability is defined as the probability that a given active maintenance action, for a system under given conditions of use, can be carried out within a stated time interval when the maintenance is performed under stated conditions and using stated procedures and resources. System safety is defined as fail-safe requirements that system cannot give dangerous output when a given fault occurs. Reliability and WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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maintainability are both probability values which lead to failure and maintenance rate respectively, related to a defined time period. The signaling system is required to be with high availability, i.e. low failure rate and high maintenance rate. System safety is the system quality requirement, and different with reliability. When the concept, as reliability and safety of railway signaling system, is discussed, there are still some of different views as in Ning et al. [2]. In order to ensure the requirements of RAMS for the signaling system to be satisfied, fault-tolerant design, fault-diagnosis and fault test are applied in the design and redesigned for signaling system development. Comparer is often used in the design of signaling system to fulfill the fail-safe requirement of the system. The comparer can be implemented both by hardware and software. In the safety assessment, a simulation system can also be used to testify if RAMS requirement of the signaling system is performed. It can be used for safety assessment of the core system, the minimal system and the application system of signaling system. Fault set and fault models of signaling system are analyzed and built.

6 Simulation systems Nowadays, it is difficult to imagine how to design and develop a signaling system based on computers without simulation system tools. Simulation systems for the core system and an application system of the railway signaling system have become an important tool for its development, application and maintenance. As far as the functions of the simulation system are concerned, there are many kinds of simulation systems for the signaling system. Some of them have been mentioned in the paper. Simulation models construction and simulation platforms selection are the first step for development of simulation systems for signaling systems as in Xun et al. [1]. There are numbers of different models and algorithms for the different applications. There are also many kinds of simulation platforms to be selected for development of simulation systems. The above two issues are not addressed in detains here since the limit of the paper contents. As development tool, a simulation system is developed for the design of core signaling systems and the minimal signaling system. By use of the simulation system, the core signaling systems are designed, and their functions, safety and reliability etc. are tested and proved. As design tool, a simulation system is developed for redesigning an application signaling system based on the minimal signaling system. The task of the simulation system is to redesign the application signaling system according to the database of the application line. By use of the simulation system, the requirements and configuration of the application system are satisfied, established, and proved. Usually, a simulation system should be established for an application signaling system. Before the application signaling system is put into operation, the task of the simulation system is to test the functions, safety and fault-tolerant features of the application signaling system and to ensure the correct connection WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

10 Computers in Railways XII among the core signaling systems based on the database of the application line. After the application signaling system is put into operation, the task of the simulation system is to monitor the operation of the application system by sharing the real-time data with the operating system. Meanwhile, as maintenance tool, the simulation system plays an important role in diagnosing a fault and maintaining the system when the fault occurs during the operation of the application system. Moreover, when some of the parts in the application system are revised or upgraded technically, the parts should be tested firstly in the simulation system before be put into the real system. It can be seen from the above description that the simulation systems have the different classifications and functions. A common databases, test sets and function models should be established and accumulated. The different simulation systems could use the same database, the same test set and the same function models. Interlocking system can be taken as an example. Interlocking function test are the same at the core system development and an application signaling system. As a design tool during the redesign for an application system, it uses the same database with the simulation system of an application signaling system. One of the difficult tasks is to establish a perfect test sets by use of accumulating.

7 Conclusion To get a better understand on the railway signaling system, in the paper, its design and development are defined as the two periods: core system design and application system design, as shown in Fig. 5. It is also introduced in details how simulation systems and safety assessment play an important role in the whole life cycle of a signaling system. The relationship between the phases and systems is

Figure 5:

The phases of the signaling system.

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explained. The key points at each phase are described. This paper gives an overall picture and the whole process of railway signaling system. It shows the importance of the railway signaling system.

Acknowledgement The research is supported by the National Science foundation: Basic theories and key technologies of train control and organization (60634010).

References [1] Xun, J., Ning, B. & Li, K., Multi-objective optimization method for the ATO system using Cellular Automata. Computers in Railways XI -Computer System Design and Operation in the Railway and Other Transit Systems. vol. 103, eds. Allan, J., Arias, E., Brebbia, C. A., Goodman, C. J., Rumsey, A. F., Sciutto, G. & Tomii, N., WIT Press: Toledo, pp. 173-182, 2008. [2] Ning, B., Tang, T., Qiu, K. & Gao, C., CBTC (Communication Based Train Control): system and development, Computers in Railways X-Computer System Design and Operation in the Railway and Other Transit Systems. vol. 103, eds. Allan, J., Brebbia, C. A., Rumsey, A. F., Sciutto, G., Sone, S. & Goodman, C. J., WIT Press: Prague, Czech Republic, pp. 413-420, 2006. [3] Ning, B., Tang, T., Qiu, K., Gao, C. & Wang, Q., “CTCS-Chinese Train Control System”, Computers in Railways IX-Computer System Design and Operation in the Railway and Other Transit Systems. vol. 103, eds. Allan, J., Brebbia, C. A., Hill, R. J., Sciutto, G., & Sone, S., WIT Press: Dresden, Germany, pp. 262-272, 2004. [4] Rail Safety and Standards Board, Engineering safety Management (the Yellow Book). [5] Mitchell, L., The Sustainable Railway Use of Advisory Systems for Energy Savings, IRSE NEWS 151, pp. 2-7, 2009. [6] Theeg, G. & Vlasenko, S., Railway Signalling and Interlocking, Eurail Press, pp. 17-21 and pp. 30- 36, 2009.

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Research on the simulation of an Automatic Train over speed Protection driver-machine interface based on Model Driven Architecture B. Y. Guo, W. Du & Y. J. Mao State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, China

Abstract The principle of Model Driven Architecture (MDA) was drawn into the emulation research of an Automatic Train over speed Protection (ATP) drivermachine interface (DMI). To realize the functions of the DMI, a new method based on the MDA principle was raised. Specific to the requirement of the DMI, the ICV (Core Interface-Frame Controller-View) model was proposed. This is the Platform Independent Model of the ATP driver-machine interface. ICV is a View-centred GUI model that includes a Core Interface and a Frame Controller. The View was used for the description of interface visualization. The Frame Controller accomplished the communication between the driver and the on-board vital computer (VC) by the display of different views. The Core Interface provided the information bridge among View, the driver and VC. Then the detailed transform rules from the Platform Independent Model to the Platform Specific Model were drawn up. The transform rules were separated into two parts. One part realized the core communication function to ensure the accuracy of the system communication interface by the auto-transform method and, according to the definition of the Platform Independent Model, the other part built each module of the ICV model using manual mode. The ultimate complete ATP driver-machine interface system satisfied the emulation requirements, and has been used for the research of the evaluation and testing on the CTCS-3. Keywords: ATP driver-machine interface, MDA, GUI model, simulation.

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1 Introduction The control of operation signalling for Railway China has developed from manual operation by drivers, who follow the traffic command of ground signals, to automatic speed control by the Train Controlling System, which receives the information sent from the ground [1]. The ATP (Automatic Train over speed Protection) driver-machine interface is displayed at the centre of a LCD monitor, which is configured with a speaker and a keyboard. Drivers are notified with all kinds of information about the train and status of the ATP by sound and graphical information, and then are able to change its working mode and status by input through the keyboard. As a media of displaying the train information and speed command, the human-machine interface is the only interface to communicate with the backend train running control system; it plays an important role in the running process of the train as its normal display affects the arrival time and safety of the train. The CTCS-3 simulation and testing platform is a research platform hosted by the National Key Laboratory of Rail traffic Control and Security, Beijing Jiaotong University, in order to make researches on systems and solutions, and evaluate the equipment testing for the CTCS-3. This system includes the train security computer, track information receiving unit, transponder information receiving unit, speed sensor, human-machine interface and 3D scene, to simulate the train running environment to be as real as possible. The simulation of the ATP driver-machine interface has a great significance in the implementation of a simulation platform of the entire train control system. The principle of Model Driven Architecture (MDA) was drawn into the emulation research of the ATP driver-machine interface (DMI). To realize the functions of the DMI, a new method based on the MDA principle was raised. Specific to the requirements of the DMI, the ICV (Core Interface-Frame Controller-View) model was proposed. Then the detailed transform rules from the Platform Independent Model to the Platform Specific Model were drawn up. The ultimate complete ATP driver-machine interface system satisfied the emulation requirements, and has been used for the research of the evaluation and testing on the CTCS-3.

2 Simulation method of the ATP driver-machine interface based on MDA MDA is the collection of a series of Standards (MOF, UML, CWM and XMI) and Technology (CORBA, Java, C++, etc.), which are the basis for supporting MDA [2]. The core idea of MDA is to form a CIM (Computation Independent Model) based on users’ needs, including the development purposes, performance and requirements of the software to be developed in the system development process [3]. According to the CIM model, using the above standards and technology, the platform-independent, highly summarized models are abstracted and concrete, which are called PIMs (Platform Independent Models). Then, the

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Requirement Requirement Capture Analysis Computation Independent Models

Figure 1:

Design PIM

Coding PSM

Testing Code

15

Release Code

MDA development process, using PIM as driven.

MDA software development cycle.

transform rules are defined based on the specific implementing technology platform. The PIM is transformed to the PSM through the defined transformation rules and tools, and the PSM will be converted into executable code automatically. The development process of the MDA-based system is shown in Fig. 1. Using MDA, the system development process is detached from the building process of two models: one is the establishment of the PIM; the other is the establishment of the PSM, and the key technology is the conversion between the PIM and the PSM. In the beginning of the system development, the PIM should be established, which is independent of the specific implementation technology and platform and is the high-level abstraction of the system. Then, according to the transformation definition, the PIM is converted into the PSM, which is closely related with the specific implementation technology and platform. The framework of the MDA includes the PIM, PIM description language, transformation rules, PSM, PSM description language and several other elements [4]. In traditional software development processes, the model represents not only the demand, but also the realization of specific technologies. Using MDA, models are classified into PIM, representing demand, and PSM, representing the realization of specific technologies, and therefore, the demand and technologies are related. To develop an ATP driver-machine interface simulation system, the requirement must be analyzed above all, and the function of the ATP drivermachine interface could be described using UML. On top of this, the PIM was established, which did not contain any platform-related details. In the description of the PIM, the demand should be summarized and summed up; restraint describing language should be used to achieve the transformation. After the completion of the PIM description, the PIM was mapped to a particular simulation development platform, and then the PSM was obtained. The PIM-toPSM transformation rules were divided into two parts. One part is to carry out the communication function, since the core function of the ATP driver-machine interface is to accomplish communication with the Vehicle Computer [5, 6]. In order to ensure the accuracy of communication, the automatic conversion mode was used in this part [7]. The other part is to complete the construction of the modules and interaction among the modules according to the definition of the ICV model. The manual mode was used in this part. The PSM was generated by a combination of these two conversion methods and then the ultimate simulation system was completed. The comparison between the MMI development framework and the MDA development framework is shown below. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

16 Computers in Railways XII MDA Development Framework

ATP Driver-machine Interface Simulation Framework

CIM

Requirement Analysis of ATP Driver-machine Interface

PIM

PIM of ATP Drivermachine Interface

PSM

Code

Figure 2:

Communication Function of ATP Driver-machine Interface

Construction and Interaction of Modules of ATP Drivermachine Interface

Simulation Code

Development framework of the DMI simulation system.

3 Requirement analysis of the ATP driver-machine interface simulation The Vehicle Computer sent information to the ATP driver-machine interface at a fixed frequency. The ATP translated the information to a readable data, according to the prior agreed rules, and showed the data on the interface as certain rules. The ATP followed multi-level hierarchical design ideas and was decomposed into various views in its logic functions. Each view could be divided into multiple sub-views and each sub-view was a further decomposition of its parent view. Drivers may make driving operations based on the information displayed in all levels of views, including entering data, such as Driver ID, train number, train length and so on, controlling the train independently for functions such as mitigation and change the running status, and responding to the information sent by the vehicle security computer, such as the need for confirmation of the driver when transforming from CTCS-2 to CTCS-3. The information would be sent back to the Vehicle Computer by the ATP after the driver finishes the operation and then responses were sent back to the ATP after being confirmed by the Vehicle Computer, which formed a closed loop for the information communication. The train initial data, such as Driver ID, train number, train length and so on, are input at the first step when the driver started to drive. Then the start button was pressed. The driver should drive in accordance with the interface display. During driving, the information transmitted from the Vehicle Computer was responded to by the driver and the driver could control the train independently.

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The main responding operations are confirmation of operation level and the status of the front track. From the workflow, the functions and use case of the system were confirmed, including: 1. data display; 2. data input; 3. mode selection; 4. carrier frequency mode selection; 5. selection and confirmation of operating level; 6. release selection; 7. departure selection; 8. driver response. The ATP driver-machine interface shows the information sent by the Vehicle Computer and contacts the Vehicle Computer and the train driver. Therefore, it can be determined that the driver and Vehicle Computer are system participants. The system use case diagram is shown in Fig. 3.

4 Establishment of the PIM for the ATP driver-machine interface The ATP driver-machine interface is a graphical user interface. To build the PIM of the ATP driver-machine interface based on MDA is to design graphical user interface models at the system point of view. In this research, combined with an

Figure 3:

Diagram of the ATP driver-machine interface.

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Presentation Communication

Driver Communication Communication

VC

Core Interface Message

Figure 4:

Message

View Frame Controller

Message

ICV model.

important GUI model, the Seeheim model [8], a kind of PIM for the ATP drivermachine interface was presented. That was the ICV, the Core Interface-Frame Controller-View model. The ICV was a kind of GUI model whose centre was View, including the Core Interface and Frame Controller. The visible part of the user interface was described by View, and the tasks from the driver and vehicle computer were accomplished by the frame controller through each view. The core interface was used to offer an information exchanging interface for train the driver and the Vehicle Computer. The model is illustrated in Fig 4. Since multi-level hierarchical design ideas were used in the ATP drivermachine interface, the View decomposed the interface into various views in its logic functions. Each view could be divided into multiple sub-views and each sub-view was a further decomposition of its parent view, but the sub-views did not have to be called by parent views, while some shortcut keys were set. Those frequently used sub-views would be called by shortcut keys rather than by parent views, and this facilitated the driver’s operation. The static characteristics of the View included size, location and its own form of property, while the dynamic behaviours of the View included the internal action and communication between the View and Vehicle Computer. The View is the core of the ICV model. The View of each level could fulfil its specific function. The hierarchical and modular description of the complex ATP driver-machine interface could be actualized by the use of the View module. The information response was as a core in modelling the View mode. The View dealt with the messages from the Vehicle Computer and driver by the information response process, for example, the current speed display and calling the sub-views. The sub-views of each level in the View mode interacted inhouse. Several Views of level 2 and Views of level 3 had the ability of sending information. According to the incoming control information, the corresponding information was sent to the Vehicle Computer. The button information collection process in the model was integrated into the driver module. Since its primary role was to capture the driver’s button information and send it to the ATP driver-computer interface by the way of WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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communication, this part was dissociated in the periphery of the ATP drivercomputer interface model and it was not necessary to build a separate module for this part. The “Driver” would be representing this process instead in the model. The Core Interface module had a dual mission. One part was to receive the information from the Vehicle Computer and the other was to receive the driver’s button information. It provided an interface between the vehicle equipment and the train driver and established a buffer zone. As a result, the efficiency and maintainability of the code have improved. The main function of the Frame Controller was to receive control information from the Core Interface, which is responsible for switch scheduling among each view and to control the operation of each view. This module was divided into two parts. One part was used to receive the control information from driver and open the appropriate view according to the driver’s manipulation. The other part was used to receive the control information from the Vehicle Computer and open the appropriate view according to the incoming message.

5 PSM construction of the ATP driver-computer interface When the PIM was constructed, the transformation work from the PIM to the PSM could be launched. Because the current transformation tools can only accurately converse the elements of the PIM into the PSM elements, it was necessary to manually complete the construction of the PSM to achieve specific functionality. According to the past practice, the research was divided into two parts and each part of work is as follows. The first part was to mainly complete the communication function, including the following aspects. 1. The elements in the ATPInterface Class of Core Interface, which is the core one in the PIM, should be converted into the corresponding function’s elements of the PSM. 2. The operations in the ATPInterface Class of Core Interface, which is the core one in the PIM, should be converted into the corresponding operations of the PSM. 3. The elements in the SendInformation Class of the View in the PIM, which need to communicate with the Vehicle Computer, should be converted into the corresponding function’s elements of the PSM. 4. The operations in the SendInformation Class of the View class in the PIM, which need to communicate with the Vehicle Computer, should be converted into the corresponding operations of the PSM. This part is the emphasis of the ATP driver-machine interface simulation, using Rational Rose to automatically generate codes to complete communication between the Vehicle Computer and the ATP driver-computer interface. The tasks to be done in the other part are as follows. 1. Construct the PSM according to each divided class in the PIM. There were three parts of the PIM: the Core Interface, Frame Controller and View. The Core Interface contained two sub-modules: one is in charge of receiving information from the Vehicle Computer; the other is in charge of WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

20 Computers in Railways XII receiving information from the driver. There are also two sub-modules in the Frame Controller. One is responsible for accomplishing the control on the View from the Vehicle Computer, and the other is to accomplish the control on the View from the rail driver. The View module was divided into several submodules according to its interface and all these sub-modules were classified in accordance with different view parts. Each sub-module should be described while the PSM is constructed. 2. Information exchange among the PSMs should be completed according to the interaction among the various modules in the PIM. Information exchange is the top priority of the ATP driver-machine interface simulation. After the construction of the PSM, interaction among the various models should be accomplished. The Core Interface module had to achieve the interaction between the Vehicle Computer and the ATP driver- machine interface and interaction between the driver and the ATP driver-machine interface. Then the information coming from these two areas were divided into two parts: one was the data message, which would be sent to the View module to display; the other was the controlling information, which will be sent to the Frame Controller to complete the task of calling for the View. The View received data information from the Core Interface and controlling information from the Frame Controller to display the view. The graphical programming language called G language was used in this part. The PSM description was carried through in the LabVIEW platform. Finally, the simulation system would be finished.

6 ATP driver-machine interface running a typical example The simulation platform of the ATP driver- machine interface is part of the Rail Control System Simulation Platform in the lab, and has been used for system research, program research and equipment testing and evaluation on the CTCS-3. Fig. 5 shows the ATP driver-machine interface operation chart. In the picture, it can be seen that the ATP driver-machine interface was shown in the centre of the driving device’s LCD screen. Speakers and keypad are around the screen,

Figure 5:

ATP driver-machine interface operation chart.

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Computers in Railways XII

Figure 6:

21

ATP driver-machine interface operation status and the transforming confirmation menu to the CTCS-3.

and the driver can input some relevant information through the keyboard to change the mode of the ATP system and work status. The ATP driver-machine interface can correctly display various status images. This is shown in Fig. 6.

7 Conclusion The first step of this method was to do a requirement analysis, following which the PIM can be established. Combined with the GUI model, a new model called the ICV (Core Interface-Frame Controller-View) model was proposed as the PIM of the ATP driver-machine interface simulation. The process of transforming from the PIM to the PSM was divided into two parts. One part is to finish the most important section of the ATP driver-machine interface, communication, especially the communication between the Vehicle Computer and the ATP driver-machine interface. The other part was used to establish the PSM according to each module of the PIM and the interaction among the modules. Then the system simulation would be completed. It has been proven that this method saves development time and enhances the portability and accuracy of the system. The ATP driver-machine interface simulation system has been used for the research of evaluation and testing on the CTCS-3.

Acknowledgement This work is supported by the Key Science and Technology Research Project of the Chinese Ministry of Education (No. 109010).

References [1] Wang Xi, Tang Tao. Design and Realization of Train Operation Control System Onboard MMI Based on UML. Journal of System Simulation, 18(2), pp. 338-361, 2006. [2] Heng Xiangan. Research on the Modeling and Simulation Method based on MDA. Changsha: National University of Defense Technology. 2005. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

22 Computers in Railways XII [3] Jishnu Mukerji. Model Driven Architecture-A Technical Perspective. www.omg.org/cgi-bin/doc/ormsc/2001-07-01. [4] Jiang Chun. MDA Method and MDA Modeling Based on UML. Journal of Shenyang Institute of Engineering: natural Science, 4(1), pp. 67-93, 2008. [5] Gronbaek, J, Madsen, T.K, Schwefel H.P. Safe Wireless Communication Solution for Driver Machine Interface for Train Control Systems. System, 4, pp. 208-213, 2008. [6] Ceccarelli A., Majzik I., Lovino D. A Resilient SIL 2 Driver Machine Interface for Train Control Systems. Dependability of Computer, 6, pp. 365374, 2008. [7] Deuk Kyu Kum, Soo Dong Kim. A Method to Generate C# Code from MDA/PSM for Enterprise Architecture. Computer and Information Science, 6, pp. 238-243, 2006. [8] Sun Xiaoping, GUO Tengchong, WEI Mingzhu, etc. A UML-Based ObjectOriented Graphic User Interface Design Model. Computer Science, 30(5), pp. 108-112, 2003.

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A framework for modeling train control systems based on agent and cellular automata J. Xun, B. Ning & T. Tang The State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, P.R. China

Abstract A train control system is a system that is geographically and functionally distributed. Its subsystems have a high degree of autonomy. Because of these characteristics, this paper describes a two-layer framework for modeling train control systems. The upper-layer is defined by agents. The lower-layer is the cellular automata (CA) traffic model to simulate the train following dynamic. The CA model delivers the knowledge needed by the agents to make decisions. The interaction between agents can describe the decision-making processes of train control systems to achieve its functions. Its functions are classified into three levels: Service Control Functionality, Signaling Functionalities and Train Operation Functionality. A case study is used to illustrate the applicability of the proposed framework. The study results show that the proposed framework can be successfully used to analyze the influence on traffic flow, which is caused by the train control system. Keywords: modeling, train control system, agent, Cellular Automata (CA).

1 Introduction A train control system model is an important tool to research train control systems. The previous models are based on the equipments that are used in the practical train control system. For different train control systems, they may have different equipments. In other word, the equipments that constitute the train control system can be tailored towards requirements. This leads to different systems having different system configurations. It is possible to accept the nonuniformity of the configurations in practical projects; however, it is not conducive to understanding the train control system. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100031

24 Computers in Railways XII This problem can be solved by modeling the train control system based on its functions. Its functions are achieved through the equipments in the system. Although the equipments are various and their configurations are distinct, the functions are the same for the different train control systems. It is fundamental to railway control systems that they should be concerned with the positional control of trains [1]. Therefore, the essential purposes of the functions for any train control system are [2, 3]:  To maintain a safe distance between following trains on the same track;  To safeguard the movement of trains at junctions and where crossing a path that could be taken by another train;  To control train movement between and at stations;  To regulate the passage of trains according to the service density and speed required, accounting for the planned schedule. This places the train control system at the heart of the railway [4].

2 The function-oriented model of train control systems According to these essential purposes, the functionality of the train control system can be summarized in three levels:  Service Control Functionality: The functionality in this level is to maintain the quality of transportation service, both in normal and abnormal situations. It will compare the real-time traffic status with the schedule and reschedule in order to reduce the delay.  Signaling Functionality: The functionality in this level is to ensure the safety of the train movement. It will collect the information related to the movement of trains first. Then, based on the information, it will allocate the movement authority (MA) for each train. Moreover, it will send the corresponding signal information to the Train Operation Functionality and the Service Control Functionality respectively.  Train Operation Functionality: The functionality in this level is to operate trains in an effective way. The operation will consider energy saving and comfort as the object of train operation. Next we will introduce the above three kinds of functionality in details. 2.1 Service Control Functionality (SCF) The Service Control Functionality is enforced through the train control system. Where more serious service abnormalities occur, it is necessary to manage the service in real time to ensure that train destinations are appropriately balanced, that bunching/conflicts are minimized, and that staff and stock resources are available when and where required. This function is referred to as Service Control [4]. Service Control Functionality includes the Centralized Manual Control function, Local Manual Control function, Platform Management function and Automatic Train Supervision function.

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The Centralized Manual Control function manages the service on the whole railway line. Normally it will not control the service directly. The control order will be transmitted to the Local Manual Control function first and then it will be transferred to other functions. In particular, the temporary speed restriction order will be sent to other safety critical functions in order to ensure its consistency, completeness and validity. The Local Manual Control function manages the service around one or several stations on the railway line generally. It is not only a transfer station for exchanging information between the Centralized Manual Control function and other functions, but also a commander to control the local service. The local service includes the management of the platform. The Platform Management function is to reduce the dwell time at stations. Dwell times result from a number of delays associated with train and platform design, service regularity, operating practice and passenger behavior. Their effects can be limited by implementing systems and techniques for platform management. The systems and techniques can be found in [4]. The Automatic Train Supervision function takes on the automation of the signalers’ and controllers’ roles. It is therefore responsible for the monitoring and co-ordination of individual train movements in line with the schedule and route assignments [2]. Its function is accomplished through the cooperation among the functions of Automatic Train Regulation, Automatic Route Setting and Automatic Traffic Monitoring. Currently these functions are usually used to operate an alarm to draw a human operator’s attention to the need for action and, subsequently to provide information to support decisions by that operator. 2.2 Signaling Functionality (SF) The movement of trains is in accordance with the signaling information in railway system. The signaling information includes the aspect of signal, slope, curve, the status of points (lock or unlock, normal position and reverse position), train position, train integrity, train route information, and so on. The information should be collected by the SF. In some ways, SF is a set of functions that gather the information related to the movement of trains, select and send to the destination functions who will act upon the information. Among the information, the aspect of signal, slope, curve and the status of points will be collected by the Line Information Collection function; train position will be collected by the Train Location function; train integrity will be collected by the Train Integrity Check function; train route information will be collected by the Interlocking function. First, all of the collected information is the input of other sub-functions in SF, such as the In Cab Signaling, the MA Allocation, the Interlocking and the Automatic Train Protection. The In Cab Signaling function will receive the track-side signal and display it in the cab. That will benefit driver to drive, especially when trains run at highspeed. The MA Allocation function needs the trains’ position and route information. The trains’ position and route information is necessary for the MA allocation function. The Interlocking function needs the aspect of signal, status WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

26 Computers in Railways XII of points and MA information so that it can evaluate this information and permits movements via the signals. The Automatic Train Protection (ATP) function is a safety critical function. It will intervene in real time to slow, even stop a train when the train runs over the permitted speed restriction. In order to decide the permitted speed restriction, the ATP requires the following information [5–7]:  Dynamic data: the current train location and speed (detected by the Speed Measurement function), and master controller position;  Train data: the class, length, acceleration performance, braking performance (for service and emergency braking) and maximum permitted speed of the train;  Route data: gradients, current maximum line speed, the line speed profile ahead (relevant to the particular class of train) including the start and finish points of temporary speed restrictions, the distance to the next signal/marker/data transmission point, the distance to go before the train must slow down or stop (the movement authority). Besides supplying information to the sub-functions in SF, it will provide information for other two functionalities. The Automatic Train Operation function or driver (Manual Driving function) needs the signaling information to guide the operation of train. Automatic Train Supervision, Centralized and Local Manual Control needs to know the actual traffic condition. All information is transmitted in a dedicated data communication network, which can be classified into wired and wireless communication. The wireless communication is used between train and trackside, hand signaling equipment and control center respectively. In other conditions it is wired communication. No matter it is wired or wireless communication, it is safety critical if it transmit safety related information. 2.3 Train Operation Functionality (TOF) In addition to the above two kinds of functionality, the Train Operation Functionality is also a key functionality in train control system. Most of trains on railway lines are operated manually. As technology continues to advance, the Automatic Train Operation function became feasible. It has to operate trains in a comfortable and energy-saving way, depending on the information collected from other functions. The information includes the current train location and speed, train length, acceleration performance, braking performance (for service and emergency braking) and maximum permitted speed of the train, gradients, current maximum line speed, the delay of the front train and so on. It is not safety critical because it only represents the movement control aspects of the driving function. It cannot therefore exist without the Automatic Train Protection (ATP) function, since it relies upon ATP to provide the movement safety functions [8, 9]. At last, the movement control is implemented through the Train Traction/Brake Control function.

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Centralized Manual Control Temporary speed limit order release and cancel

Local Manual Control

Automatic Traffic Monitoring

Platform Management

Automatic Train Supervision Automatic Train Regulation

Hand Signaling

MA Allocation

Automatic Route setting

Management of the Line and Wayside Unit signals, track circuit, points, balises、LEU Line Information Collection (status of points, slope, curve, route information

Train Location

Interlocking (to ensure the interlocking logic) In Cab Signaling

Wireless Communication

Speed Measurement

Automatic Train Operation

Wired Communication

Train Integrity Check Automatic Train Protection

Movement Control

Manual Driving

Train Traction/Brake Control Key

Safety Critical Function

Data Flow

Figure 1:

Non-Safety Critical Function

Function-oriented model of a train control system.

As mentioned above, the three functionalities have the principal task of ensuring the safe separation of trains. Meanwhile, they affect the performance of train control system in aspect of capacity. The train control system should provide a means of improving the performance. So, the train control system has competing requirements placed upon it: those of safety (safety critical functions) and those of operational capacity (capacity related functions). Based on the aforementioned functions, a function-oriented model of train control system is shown in fig. 1. It is a generic model because no special equipment is involved.

3

Two-layer framework for modeling train control systems

3.1 Two-layer framework According to the study in [10], the NaSch model(one of cellular automaton models) has been proposed to simulate the railway traffic. Some complicated traffic conditions, such as mixed traffic, overtaking, can be generated. These WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

28 Computers in Railways XII studies demonstrate NaSch model is applicable to simulate the dynamic of the railway traffic. However, there is a problem in the NaSch model. Since it is too complex to achieve full functions of train control system by using rules, it uses some basic rules to describe the functions of train control system in a relative simple way. This leads the decrease of the accuracy of the model, and that will have bad influence on the study of the train control system when using the NaSch model. Actually, these functions are generally achieved through the interactions between the units, which are distributed in the railway system. So it is doable to model the functions of the train control system through the interaction between agents in multi-agents system (MAS). So, we proposed a two-layer framework for modeling train control system:  The upper-layer is designed by the agent technology based on the function-oriented model of train control system. The interaction between agents can achieve the functions of train control system.  The lower-layer is the cellular automata (CA) railway model to simulate train following dynamic. The CA model provides the knowledge needed by the agents to make decisions and react upon the decisions.

Figure 2:

Sketch of the two-layer framework.

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3.2 Design of train control systems by agents In general, the railway traffic system consists of many autonomous, intelligent units, which are distributed over a large area and interact with each other to achieve certain goals. These units may be completely different: drivers, trains, signals, but all of them have a high degree of autonomy, actively perceive the environment and act upon that environment. Owing to these characteristics, many systems in this domain are developed based on an agent approach [11, 12]. Vernazza and Zunino [13] proposed a methodology which is to use Distributed Artificial Intelligence techniques to overcome the limitation of the centralized methodologies. It exhibits an upper bound on the size of the controlled area because of the requirement of real-time processing. Intelligent agents have successfully solved the train pathing problem on a small portion of railway network [14]. Next, based on this research, Blum and Eskandarian [15] introduced a method to enhance the collaboration of the agents. A protocol is proposed that makes the agents operate as efficiently as possible. One of the most recent references about multi-agent railway system [16], presents a multi-agent system for communication based train traffic control. The system infrastructure has an architecture composed of two independent layers: “Control” and “Learning”. “Control” layer includes three agent types: “Supervisor”, “Train” and “Station”. In order to design a system by agents, several components have to be defined precisely: the agents, the interactions and the environment. 3.2.1 Agents In our generic model, we propose three categories of agents:  SCF agents that achieve service control functionality. They detect conflicts and find a solution to minimize delay time. To find a solution, many intelligent technologies, such as expert system, compute intelligence, machine learning and searching, can be used.  SF agents that achieve signaling functionality. These agents includes the signaling-related information which is the aspect of signal, slope, curve, the status of points (lock or unlock, normal position and reverse position), train position, train integrity, train route information.  TOF agents that achieve train operation functionality. Each TOF agent is the abstract model of an actual train running on the railway network and its dynamic status can be collected by SF agents. 3.2.2 Choice of interaction method: the environment modeling approach Interactions between agents through message exchange are an important part of a multi-agent system. Our interaction model is based on EASI model (Environment as Active Support of Interaction) [17]. In this model, agents share a common communication media, the environment, which is used to support interactions. The environment contains description of messages and agents, which is represented by a set of entities,   1 ,..., m . An entity i is related to a component of the MAS and has a description given by observable properties. In order to find these properties that it is interested in, the agents have WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

30 Computers in Railways XII the ability to put filter in the environment. A filter

f j is the description of the

constrains on the observable properties of the entities that are related to the connection j .In other words, a filter is a reification of a connection, by which a

message transmitted. Let P  p1 ,..., pn  be the set of the n observable

properties of the MAS. An observable property pl is a function that gives for an

i

pl  P, pl :   d l  unknown, null , with dl the description domain of pl . dl can be quantitative, qualitative or a finite data set.

entity

a

value

that

can

be

used

for

the

connect,

Figure 3 shows an illustration of our environment modeling for a scenario in railway. Here are four entities,   1 , 2 , 3 , 4  that are respectively the description of the train operation agent TOA1 and TOA2 , of the service control agent SCA1 and of the message m1 . The train operation agents have three

speed and its connection object respectively. The value pos 1  is the position

properties called pos , speed and connectionObject , which is for position, of a train who is represented by TOA1 ; the value pos 3  is unknown

because the value has not been given; the value pos 4  is null because 4 does not have this property in its description. The value of a property can be modified by the agent in real-time. According to the Definition of Filter in [17], three types of filter can be defined and put into the environment: reception, emission and interception filter.

1： id , "TOA1" ,  class, " CBTC / ATP " ,  pos, val _ pos _ 1 ,  speed , " val _ speed _ 1 ,  connectionObject, " SCA1" 

3： id , " SCA1" ,  pos , unknown ,  pos _ start _ management , " val _ pos _ 3" ,  pos _ end _ management , " val _ pos _ 4" ,  rank , " local " 

4： sub, " delay"  sender, "TOA1" ,  receiver, " SCA1" 

2： id , "TOA2" ,  class, " CBTC / ATP" ,  pos, val _ pos _ 2 ,  speed, " val _ speed _ 2 ,  connectionObject, " SCA1" 

Figure 3:

Example of EASI interaction model.

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Reception filters: to search the value of a specified property for the agent to decide the receiver. For instance, if the agent has an id property, the filter that enables interaction based on the value of this property is a reception filter. env f reception  [id (a) " SCA1" ],[ sub(4 ) " delay" ]

, " reception",0, environment  

This type of filter is put by the environment generally. Emission filters: to match the potential receivers of a message. For instance, the delay message should inform to not only the local service control agent SCA1 but also the central service control agent SCA2 . TOA1 f emission  [rank (a ) " central" ]  [ sons (a ) " SCA1" ],



[ sub(4 ) " delay" ], " emission",0, TOA1  This type of filter is put by TOA1 generally.

Interception filters: to allow the agent to receive a message that has not been sent to it directly, but it is interested in. For instance, the delay message from TOA1 may be useful to the control of TOA2 .

TOA2 is the following train of TOA1 . Hence, TOA2 can put an interception filter to overhear the delay message from TOA1 . TOA 2 fintercepti on  [id ( a ) "TOA2" ],[ sub(4 ) " delay" ]

 [ sender (4 ) "TOA1" ], "int erception",0, TOA2  The example of the three types of filter is illustrated in Figure 4.

f

env reception

f

TOA emission

1

TOA f intercepti on 2

Figure 4:

Transmission scenario of a train delay message.

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32 Computers in Railways XII 3.3 CA model for rail traffic dynamic simulation The lower-layer extends the model in [18]. The state of a cell is not only a symbol of if there is a train but also used to represent other information of infrastructure on the track-side. The information possibly includes the status of signal and point, slope, curve, and so on. More details can be found in [18].

4

Simulation

In this section, we apply the proposed framework in our simulation. The simulation is based on a 8000 m long line, which has three stations A, B and C. The stations A, B and C are located at 1 m , 4000 m and 8000 m respectively. Trains depart from station A successively with interval I under a moving block system and stop at station B for a dwell time Tdw , then leave and run to station C. Finally, they move out of this system after staying at station C for Tdw . The length of the computational time is taken as T  1000 s . The (1) Train acceleration and deceleration is a  1 m / s and b  1 m / s ;

other parameters used in the simulation are as follows: (2) Train length LT  100 m ;

2

2

(3) Safe distance Ls  60 m ;

(4) Maximum speed of train Vmax  20 m / s ;

(5) Speed limit of line SLi  20 m / s, i  ( 1, L) ;

(6) Interval of the train’s departure time at station A I  60 s ( I is a variable when we calculate the minimum time headway); (7) Dwelling time at the station B and C Tdw  30 s , and for the delayed train 106 the dwelling time is Tdw  60 s .

Based on the proposed framework, a train delay scenario is simulated and the simulation results are shown in Figure 5. The Train 106 is delayed at station B in Figure 5(a). If the following train 107 does not percept the delayed information, it results that the following train 107 has to stop outside of station B (The dotted line in Figure 5(a)). After rescheduling, the Train 106 will have a new departure time. In our model, the message of the new departure time (or the delay of the train 106) can be overheard by the following train 107 by an interception filter, which it put in the environment. With this information, it could brake earlier and run with a slow speed as shown by the dotted line in Figure 5(b). The optimization of their speed profile will avoid stop and benefit their energy-saving and comfortable object.

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30 Train 106 Train 107

Train 106 Train 107

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20 Velocity(m/s)

Velocity(m/s)

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3400

3500

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4000

4100

station B

4200

4300

0 3000

3200

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(a)

3600 3800 Position(m)

4000

4200

(b) Figure 5:

5

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Results of simulation.

Conclusions and future research

In this paper we present a two-layer framework for modeling train control system. The upper layer describes a function-oriented model of train control system by using an agent-based approach. The agents in the proposed framework are classified into three types: SCF, SF and TOF agents. The interaction between agents is based on a model called EASI. The model defines an interaction in a generic way to achieve some functions of train control system. The lower layer is a CA model that describes the perception and reaction of agents in upper layer. It represents the actual dynamic of railway traffic. The preliminary simulation result demonstrates the availability of the model. Until now this has been a framework. Through the proposed framework, the agents in the system can get more information. The use of the available information is not discussed in this paper. The future research should focus on how to optimize the performance of the train control system by using the available information.

Acknowledgement The project is supported by National Natural Science Foundation of China under Grant No. 60634010 and Major Program of Beijing Municipal Science & Technology Commission "Comprehensive research and core technology development to improve the urban rail transportation efficiency".

References [1] Short, R.C., Fundamentals of Signalling and Train Control Systems,presented at the IEE Power Division Sixth Vacation School on Railway Signalling & Control Systems, 1996.

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34 Computers in Railways XII [2] Khessib, M., Improved energy exchange by central train control, University of Birmingham, 1989. [3] Nock, O., Railway signalling, A & C Black, London, 1980. [4] Woodland, D., Optimisation of Automatic Train Protection Systems, Doctor of Philosophy, Department of Mechanical Engineering, University of Sheffield, Sheffield, 2004. [5] Barnard, R., SELCAB Automatic Train Protection for British Rail's Chiltern Lines, presented at the Aspect'91 International Conference Proceedings 1991. [6] Dapre, S., Introduction to Signalling - Automatic Train Control, Institution of Railway Signal Engineers, 1999. [7] Rose, J. & Fisher, A., Flexible Automatic Train Control: IRSE, 1989. [8] Taskin, T. & Allan, J., Overview of Signalling and Train Control Systems, presented at the IEE Power Division Third Vacation School on Electric Traction Systems, 1995. [9] Waller, J., Control Concepts in Automatic Rapid Transit Systems, in Rail Engineering – The Way Ahead, London, 1975. [10] Li, K.P., Gao, Z. & Ning, B., Cellular automaton model for railway traffic. Journal of Computational Physics, 209(1), pp. 179-192, 2005. [11] Lind, J., Fischer, K., Bocker, J. & Zirkler, B., Transportation scheduling and simulation in a railroad scenario: A multi-agent approach. Logistik Manage, eds. Kopfer, H. & Jou, R.C., Springer: Berlin, 1999. [12] Böcke, J., Lind, J. & Zirkler, B., Using a multi-agent approach to optimise the train coupling and sharing system. European Journal of Operational Research, 131(2), pp. 242-252, 2001. [13] Vernazza, G. & Zunino, R., A distributed intelligence methodology for railway traffic control. IEEE Transactions on Vehicular Technology, 39(3), pp. 263-270, 1990. [14] Tsen, C. K., solving train scheduling problems using A-teams, Ph.D., Carnegie Mellon University, USA, 1995. [15] Blum, J. & Eskandarian, A., Enhancing intelligent agent collaboration for flow optimization of railroad traffic. Transportation Research Part A, 36, pp. 919-930, 2002. [16] Proenca, H. & Oliveira, E., MARCS Multi-agent Railway Control System, Lecture notes in computer science, pp. 12-21, 2004. [17] Saunier, J. & Balbo, F., Regulated multi-party communications and context awareness through the environment. Multiagent and Grid Systems, 5(1), pp. 75-91, 2009. [18] Xun, J., Ning, B. & Li, K., Multi-objective optimization method for the ATO system using Cellular Automata. Computers in Railways XI Computer System Design and Operation in the Railway and Other Transit Systems. vol. 103, eds. Allan, J., Arias, E., Brebbia, C. A., Goodman, C. J., Rumsey, A. F., Sciutto, G. & Tomii, N., WIT Press: Toledo, pp. 173-182, 2008.

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A new train GPS positioning algorithm in satellite incomplete condition based on optimization and the digital track map X. Jia1,2, D. Chen1 & H. Wang2 1

State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, China 2 School of Electronics and Information Engineering, Beijing Jiaotong University, China

Abstract The train positioning plays a key role in the train control system. The current train positioning is determined by the track circuit or balise, which cost a lot to build and maintain. GPS (Global Positioning System), one kind of GNSS (Global Navigation Satellite System) positioning technology, provides a cheap and real-time option. However, the inherent defect of GPS positioning is the socalled incomplete condition of GPS when less than four satellites are effective. This paper presents a new train GPS positioning algorithm based on the digital track map and optimization method for the incomplete condition of GPS. First, the track piece where the train is located is identified at the moment when the GPS satellite signals become incomplete. Then, a straight-line equation constrained by the pseudo-range equation is deduced. Finally, the estimated train position is obtained by minimizing the sum of the squared errors, which is solved by the gradient descent method and compared with the actual location in the digital track map. After the experiments were carried out in Sanjia dian Station, Beijing Railway Station, to get the field GPS positioning data, the performance of the proposed algorithm was evaluated and analyzed. The results demonstrated that the accuracy and stability of train positioning employing the proposed method were improved in GPS satellite incomplete condition (SIC). Keywords: railway, GPS, incomplete condition, digital track map, optimization method.

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1 Introduction With the rapid growth of the Chinese economy, railway transportation plays a more and more important role in the national social and economical development. In the train control system, the train positioning is one of the key techniques. Obtaining accurate train position data is a prerequisite for the train safety and control. The present train positioning mainly depends on ground equipments, such as track circuits, balise and so on. However, ground equipments cost a lot to build and their security and the maintainability are not easy, which has greatly increased railroad worker's labor intensity. Furthermore, it is obviously advantageous to use GPS positioning for the train control system in reducing cost in infrastructure and maintenance, especially for low-density railways [1]. GPS is one kind of modern navigation technology whose applications are getting more and more widespread in transportation, surveys, geodetic and so on. However, GPS positioning also has its flaws: GPS receivers cannot work in satellite incomplete condition (SIC). When vehicles travel in some areas, such as urban tall building areas, tunnels and multi-level crossing bridges, some GPS satellite signals are often covered. In this case, the number of satellites is less than four or the geometric distribution received from satellite is non-uniform [2]. In particular, when GPS is applied in the train integrity inspection, the GPS antenna installed in the vehicle hook in the train rear part is easily occluded by the compartment [3]. Lin studied the problem of ground emitter positioning by a satellite cluster composed of three satellites and proposed an iterative algorithm based on a digital map for the urban traffic application [4]. Zou proposed a DR (Dead reckoning) positioning algorithm using Doppler and range data as the complementary information when the number of effective satellites was three. [5]. Liu proposed a positioning algorithm using a virtual satellite when the satellite number is three for train integrity inspection, checking with the GPS receivers in the head and tail of a train [6]. In a certain moment when the receiver in the tail of a train only receives three satellites signal because of carriage occlusion, the fourth constraint equation is added by making the height in the head and in the tail the same, which is called the virtual satellite assisted positioning method. Then, the three satellites with visual simultaneous equations can obtain the position solution of the rear. This method is easy to understand with few errors, but still needs three satellites and employs the four-star location model restrictions. Li proposed a GPS autonomous integrity detection method and a train-positioning algorithm assisted by a digital track map [7]. However, the algorithm was also in accordance with the four-star positioning mode. The linearized pseudo-range equation and track map data were used as the fourth constraint. However, these algorithms must be given an initial value, which has a deep influence on the results. The process assumed zero elevation changes, which cannot apply to larger areas of undulating terrain. In this paper, we propose a solution for this GPS positioning incomplete condition, which combines the digital track map information and the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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optimization method [8]. As the train is running on a fixed track, there are some strict regulations: Tracks approximately approach straight lines or curves with big curvature radius; any track has lots of nodes, such as turnouts, signals, insulation sections, kilometer marks and so on. All of this makes digital track maps easily described. In practice, a large number of low-cost GPS track data and a small number of high-precision nodes (turnout generally, signals and other properties points) can be used to describe the digital track map. Track straight lines can be fitted into the equivalent linear equations; curved tracks can be divided into several sections of line segments for approximate description and each track section of the endpoint nodes are high-precision [9]. In SIC, the results of position resolution equations due to the lack of conditions cannot be solved. However, we can use the digital track map as a constraint to achieve satellite positioning in the SIC according to the features of the digital track map. In this way, we not only use characteristic of the digital track map, which is not easily affected by outside influences and has high stability, but also use the optimization method to decrease the positioning error.

2

Positioning model in the satellite incomplete condition

When the number of available satellites is more than four, it is defined as the satellite complete condition (SCC), where the train’s position is calculated by the traditional pseudo position method, as shown in Fig. 1. The distance between satellites and a GPS receiver can be calculated by equation (1). (1)   t  c

c : The velocity of light;

 t : The signal propagation time. Then, the train’s position can be calculated by equation (2):

 j  ( x j  xu ) 2  ( y j  y u ) 2  ( z j  z u ) 2





j j  c d tr  d tsj  d ion  d trop

(x2,y2,z2)

(x1,y1,z1)

Figure 1:

1

2

( j  1,2,3,4 )

(x3,y3,z3)

3

4

(x4,y4,z4)

Schematic diagram of positioning theory.

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(2)

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Figure 2:

Schematic diagram of the track segment.

 j : The geometric distance between the satellite position by satellite broadcast ephemeris and receiver;

x j , y j , z j : The three-dimensional coordinates of satellite J; d tr : Receiver clock error; d tsj : Clock error of satellite J;

xu , yu , zu : The three-dimensional coordinates of the receiver; j : The distance deviation by the ionospheres’ effects of satellite J; dion

j dtrop : The distance deviation by the troposphere’s effects of satellite J.

When the number of available satellites is less than four, which is defined as the SIC, the train’s position is calculated with the help of the digital track map. First, the track segment that the train belonged to is judged, as illustrated in Fig. 2.

xu  X 1 yu  Y1 zu  Z1 k   X1  X 2 Y1  Y2 Z1  Z 2

(3)

k [0,1]

f j 

From equation (2) and equation (3), we can get equation (4).

(x j  (k(X2  X1)  X1))2  ( y j  (k(Y2 Y1)  Y1))2  (z j  (k(Z2  Z1)  Z1))2 (4)





j j  c dtr  dtsj  dion  dtrop  j

( j 1,2,3)



Then the train positioning can be achieved by using the gradient descent method, which calculates k by minimizing the E ( E  ( pfi )2 ). Assuming that

E k

i

 k i  2 *  pf i 

pf k

the initial value of k is 0.5, we get the updated value of k as follows:

k i 1  k i  

k  ki

k  ki

(5)

where  is the learning rate, we set it as 0.1 in the beginning of the algorithm. A heuristic rule is applied to assure the stability of the optimization method. If the i

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E decreases two times continuously, we increase  at 10%. However, if E decreases and increases alternately twice, we decrease it also at 10%.

3 The proposed positioning algorithm According to the model, the steps of the proposed positioning algorithm are as follows: Step 1; Judge the number of satellites received. If the number is more than four, use the traditional pseudo positioning method; if the number is less than four, go to step 2. Step 2; Judge the number of segments the train belongs to and then load the coordinates of the nodes of the segment. Step 3; Using the node coordinates and the satellite information to calculate the position of the train by the descent gradient method. The flow chart of the algorithm is shown in figure 3.

4 Results and analysis The data collection and experimental validation was conducted in Sanjia dian Station, Beijing Railway Station. Experimental equipments were the Novatel (DL-4) GPS Receiver and the cart especially designed for track measurement, as shown in fig. 4.

Figure 3:

The flow chart of the algorithm.

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Figure 4:

Figure 5:

Measurement cart and Novatel (DL-4) GPS receiver.

Digital track map model on the plane coordinate system.

The experimental steps were as follows: Step 1: Select the track section and turnout section in the track map database as the experimental section; Step 2: Collect GPS satellite ephemeris and pseudo-distance and other data using the Novatel (DL-4) GPS Receiver; Step 3: Data extraction and processing; Step 4: In the SCC, calculate the position using the algorithm. The satellite incomplete condition was created by deleting some satellite signals to make the number of satellites be two and three; Step 5: Results comparison and analysis. Track 16 of the station was chosen as the map model to test the proposed algorithm, then its coordinates were transferred from Gauss coordinates into plane coordinates, as shown in figure 5. In this model, "○" means the map nodes where signals or turnouts are placed. The line between two nodes indicates the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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fragment. From the mathematical point of view, the head node and the corresponding tail node constructed a space straight-line equation. In SCC, the position can directly be calculated by equation 2. As in figure 6, the dots on the line in the figure are the true values; the star dots around the line are the value of point locations obtained by a GPS receiver. The positioning error is shown in figure 7, which means that the positioning error is less than 3.5m.

Figure 6:

Figure 7:

Result of positioning in SCC.

Positioning errors in SCC.

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42 Computers in Railways XII In the experiment, the satellite condition was very good and about 10 satellites signals were received. To construct the satellite incomplete condition, we deliberately chose two or three satellite signal data to validate the proposed algorithm. Of course, the positioning errors are different with the different satellite selection. There are many methods in satellite selection, which we do not want to overemphasize due to the page limits. In SIC, the result of three satellites is shown in figure 8 and the positioning error is shown in figure 9, which means that the positioning error is less than 3 m, even less than the positioning error in SCC without using the digital track map.

Figure 8:

Positioning result for three satellites.

Figure 9:

Positioning errors for three satellites.

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Figure 10:

43

The positioning errors for two satellites.

In SIC, the positioning errors of two satellites are obviously influenced by the geometric distribution between the GPS receiver and satellites. The positioning errors are shown in figure 10, which means that the positioning error is greater than 11m. It can be found that the positioning error is much greater than that in the three satellites condition.

5 Conclusions In this paper, we propose a positioning algorithm using the digital track map and optimization method in the SIC. The algorithm broke through the limit that GPS positioning must need four or more satellites. The experimental results show that the positioning accuracy obtained by the algorithm proposed in this paper can meet the positioning requirements if three satellites are available. In addition, the algorithm provides a valuable supplement and improvement for the application of GPS technology in the railway. Due to the limited experimental conditions, we did not do large-scale experimental tests. Only some simulation experiments were carried out, but the results have some reference value. In addition, the positioning error of two satellites is still large, so how to improve the algorithm to make it work better for two satellites or even one satellite still needs further research.

Acknowledgements This research is partly supported by a National Natural Science Foundation of China (NSFC) under grant number 60776833 and by the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RCS2008ZZ001, RCS2009ZT004), Beijing Jiaotong University.

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References [1] J. Liu, W. Wu, Train Positioning Technology of Railway and Mass Transit, Urban Mass Transit, 4(1), 2004. [2] X. Sang, S. Li, Study of GPS Positioning in Incomplete Condition, Computer Engineering and Applications, 42(24), 2006. [3] X. Chen, J. Wang & B. Cai, Research of GPS Application in Train Integrity Monitoring, Journal of Beijing Jiaotong University, 30(2), 2006. [4] X. Lin, Y. He, Location Method and Error Analysis for Three-Star TimeDifference System Using Digital Map, Journal of University of Electronic Science and Technology of China, 36(4),2007. [5] B. Zou, N. Zhang, Study on 3D Satellite Positioning Algorithm, High Technology Letters, 10(2), 2000. [6] H. Liu, Research and Implementation of GPS Aided Train Integrity Monitoring Algorithm, Master thesis of Beijing Jiaotong University, 2008. [7] C. Li, Research on Train Positioning Method Aided by Track Digital Map, Master thesis of Beijing Jiaotong University, 2008. [8] JSR Jang, CT Sun, Neuro-Fuzzy and Soft Computing, Pretence Hall, 2000. [9] Y. Zhang, J. Wang & B. Cai, Research of Virtual Balise Based on GNSS, Journal of the China Railway Society, 30(1),2008. [10] R. Glaus, G. Peels & U. Muller, Precise Rail Track Surveying, GPS World, 2004.

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Simulation of a high-speed train control system based on High Level Architecture and its credibility analysis Wei ShangGuan, J.-Q. Chen, B. Li, L.-N. Guo, M. Li & L.-Y. Chen State Key Laboratory of Rail Traffic Control and Safety, School of Electronic and Information Engineering, Beijing Jiaotong University, China

Abstract The study of the simulation of a high-speed train control system has great significance for the realization of a train control system. This paper studied the basic theory of a high-speed train control system in China. Based on the theory and structure of HLA (High Level Architecture), multi-resolution modelling, simulation real-time management methods and the system architecture of a highspeed train control system simulation was studied systematically, and the simulation result of the on-board vehicle and field centre equipment was shown. With the aim of establishing the credibility of simulation, the methods of VV&A, qualitative and quantitative RAMS analysis and system fault injection were studied, which improved the credibility of high-speed train control system simulation. Keywords: high-speed train control system, HLA, high level architecture, credibility analysis, multi-resolution modelling, fault injection.

1 High-speed train control system The Chinese railway department makes a set of CTCS standards that are fit for Chinese actual conditions by referring to the ERTMS/ETCS standards .The CTCS standards have made an overall technical program and master plan for the great-leap-forward development of China’s railway signalling system. CTCS is classified into five grades, from CTCS-0 to CTCS-4. Its structure is made up of the railway transport management layer, the network transport layer, the ground equipment layer and the on-board equipment layer [1, 2]. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100051

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Figure 1:

The federal structure of the high-speed train control system’s simulation platform.

CTCS is a technical specification classified into grades for different transport requirements to be used to ensure the train’s operational safety. We have determined the need to adopt the CTCS-3 train control system, with high reliability and high safety, as the general technical platform to be consulted for the passenger specific line and the Beijing-Shanghai express railway.

2 High Level Architecture for modelling High Level Architecture is a new framework for distributed simulation, which was developed by the U.S. Department of Defense to meet the needs of interconnection and interoperability problems of a variety of simulation systems with multiple-models, which were developed in various fields. In a HLA simulation system, the federate is a distributed simulation system used to achieve a particular simulation purpose. It consists of several interactive federal members. All the applications participating federal running can be called federal members [3, 4]. According to the functional analysis of the Simulation Integration Platform for the Train Control System, we built the Simulation system’s Federation based on HLA. Federation members are shown in Fig 1.

3 High-speed train control system simulation system based HLA 3.1 Multi-resolution Modelling Multi-resolution Modelling is a modelling method that uses multi-precision and multi-level approaches to model a system. A high-speed train control system’s Multi-resolution Modelling is defined as: in the process of modelling and simulation, making the details of interaction with different levels of information as the criteria, we use the method with different precision and different levels to WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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descript every function of the train control system to improve simulation fidelity or improve simulation efficiency. These precision and levels are defined as the high-speed train control system’s modelling resolution [5]. After understanding the basic structure of the high-speed train control system and the division of the train members, to be directed against a different person’s focus on different sides of train equipment, such as ground equipment and trainground information exchange processes, we research the establishment of the high-speed train control system’s multi-resolution model on the basis of existing research. In this paper, the high-speed train control system is divided into three types of resolution, using details of interaction with different levels of information as the criteria, as shown in Fig 2. Low-resolution information (the top) is embodied by the train moving, obtaining the speed and location information generally. Medium-resolution information is embodied by exchanging information among members. The establishment of this model is favoured to check whether the information channel is established. Because this model does not involve computing information and access, it does not only effectively reflect the interaction of information, but does easily grasp the overall message. The high-resolution model is embodied by the calculation of the various members of the internal information and access, and this model can be used to test the accuracy of the information. 3.2 Simulation of real-time management How do we realize the data exchange between the model points? We process data exchange with the RTI of the HLA to ensure real-time and reliability. The RTI is assistant software system of the HLA. The basic way to improve network trial-time is to advance the RTI performance [6].

Figure 2:

Instantiation of multi-resolution in the high-speed train control system.

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48 Computers in Railways XII 1) With Multithread In HLA, the process mode decides the ways that the application process (member) calls the RTI and the RTI call callback function. At the same time, it decides members share the CPU clock with the RTI. There are three process modes for the RTI: single thread, asynchronous mode and multithread. For single thread mode, the member must use the RTI and call tick() function to complete the callback in a thread. For the synchronous mode, it assists the multithread reentrant. It calls the RTI service with a thread and continuous calls tick() with another thread. Its performance is better than single thread. For the multithread mode, the member does not need to call tick(). Message process and callback could auto complete in the RTI. There is no message starvation state and message obstruction state. Because this makes full use of the CPU clock, it effectively improves network real-time. 2) Further Improvement to the Data Filtering Mechanism Net data excess leads to network obstruction and delay. HLA provides a data filtering mechanism based on class and value to limit redundant data in the system and reduce system source pressure. The communication mechanism that points to multipoint in distributed interactive simulation applies multicast communication well in a distributed interactive simulation environment, especially combined with the data filtering mechanism, leading to reduced network traffic and simulator point communication load. As a result, it improves the system’s scalability. 3) Improve Time Management Algorithm Data manage (DM) and time manage (TM) are the focus and difficulty points of HLA/RTI. The RTI’s time management algorithm used for reference Parallel Discrete Event Simulation (PDES). Its important key is Lower Bound of Time Stab (LBTS). Based on the test data of the network delay aforementioned, we can establish that there is a message transfer delay between nodes, which is caused by time advance. When a member requires the RTI to provide a Time Advance Request (TAR), the RTI provides LBTS to the members who ask for a TAR, according to member’s TM mode (time limit and time limited) and their logic time and time lookahead, and reacts to the request. Improving the time management algorithm’s key improves the time advance request’s reaction. In other words, it is a LBTS algorithm. 4) Improve System Real-time from the Application Level Considering that the time-delay of the RTI responding to the time advance request from the federate is the main factor in affecting the real time of the system, we can reduce the times taken to request, even coordinating the time advance request without the RTI. In that case, the real-time performance of the system will be significantly improved. Based on such consideration, a new improved strategy is researched in this paper. Experimental studies show that the strategy can effectively reduce the time-delay caused by the RTI and improve the real-time performance of the system. Time management service is an important aspect that the HLA distinguishes with the DIS system. In addition, it is the problem of bottleneck that affects the real-time performance of HLA. With the problem of bottleneck unsolved, we can WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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coordinate the time synchronization between the federate by an application layer without the time management service, and the other services that the RTI provides are still valid. Fig. 3 shows that the time management mentioned above integrates into the simulation of the high-speed train control system. Through reducing the number of time advances in the application layer, it can save time. The time advance strategy of the RTI itself is achieved by improving the data filtering mechanism, using multi-thread, improving time management algorithms according to the data needs of the application layer. 3.3 Architecture of high-speed train control system simulation Because of the problem that using simulation becomes more and more complicated, it is meaningful to improve the authenticity, timeliness, usability, reusability and interoperability of the simulation of the high-speed train control system with using High Level Architecture (HLA) [7]. The high-speed train control simulation system is divided into the simulation management subsystem and simulation equipment modules, which are put together into the HLA/RTI environment as federations. Fig. 4 shows the architecture of the system. The simulation management subsystem includes a line database, verify and analysis module and simulation management module. Before the simulation, it can complete the data preparation and configure other federations by the HLA/RTI underlying environment. During the simulation process, it can start the simulation and issue orders. After simulation, it can verify and analyze the data and evaluate the entire platform. Simulation equipment modules include the interlocking module, CTC module, train control centre, etc. As a result of HLA, we can separate the bottom environment and the application layer, and each simulation module can be individually designed according to different needs and concerns with different algorithm models.

Figure 3:

Time management module plan.

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Figure 4:

Architecture of the high-speed train control system simulation based on HLA.

Figure 5:

On-board equipment speed control modelling.

3.4 Simulations The on-board equipment and ground equipment are achieved based on the HLA multi-resolution modelling method and time management strategies in this paper. Fig. 5 shows the simulation of vehicle equipment. Fig. 6 shows the simulation of the 3D view and the DMI module simulation. Fig. 7 shows the ground equipment, CTC module and interlocking module simulation.

4 Analysis of credibility for simulation After the high-speed train control system simulation is built, the analysis of credibility for the simulation system is needed based on the simulation environment. 4.1 Loop simulation model and analysis of VV&A At present, there are two categories for train control system simulation, one is total digital simulation, and the other is loop simulation. In the design and assessment of a complex train control system, the latter is more beneficial, which WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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can not only descript the process of train running, but also can put the real equipments into the simulation. In order to ensure the credibility of the train control system, the VV&A workflow is used, as shown in Fig. 8.

Figure 6:

Figure 7:

On-board view and DMI.

Simulation of CTC and interlocking.

Concept

check

results

model

Figure 8:

VV&A flowchart of simulation.

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52 Computers in Railways XII For the problems of the credibility of complex system, such as the high-speed train control system, after analysis, synthesis, determination and formation of function modules related to the credibility problems, and according to the relationship and importance, the function structure is shown in Fig.9.  There is only one factor in the highest layer of the complex simulation system that is credible for the purpose of solving problems, which is also the overall goal, so this layer is also called the target layer.  The middle layer represents the intermediate links that adopt and implement the programs used to achieve the overall goal, which is generally called the strategy layer, constraint layer or rule layer. Through the analogy, according to the characteristics of the simulation system, the middle layer is designed to the subsystem’s credibility and function module’s credibility, such as the subsystem’s credibility (1,2,…,m) and function credibility (1,2,…,n), which can be further broken down based on the actual system.  The bottom layer is the credibility of the sub-function modules. These subfunction modules are characterized as being more independent and credible and they are easily measured, providing more detail than the middle layer. 4.2 High-speed train control system for qualitative and quantitative analysis of RAMS RAMS is the reliability, availability, maintainability, and safety of the short. RAMS in the train control system is subject to the following aspects: fault source triggered within the system, failure source introduced in the system operation phase, failure source introduced in system maintenance activities. RAMS and their interrelations in the train control system are shown in Fig.10. In order to achieve quantitative analysis of the RAMS in the train control system, they need to define a quantitative expression. The tandem structure of



 Figure 9:

  

Simulation system function structures chart.

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Figure 10:

53

RAMS and their interrelations.

the train control system usually use means time between failures (MTBF) to measure each subsystem and the entire subsystem reliability [8]. (1) 1 MTBFSystem 

q  n

i 1

i

i

The train control system’s maintainability is expressed by the average time required to identify the failure and return to normal state and maintenance convenience. Usually we use mean time to repair (MTTR) to measure this, expressed by the formula: (2)  q  MTTR n

MTTRSystem 

i 1

i

q 

i

i device and i is the failure rate for i device. j 1

where qi is the number for

i

n

j

j

In the quantitative analysis of the system RAMS, the Markov method has powerful functions. It can fully reflect impact from the system testing and maintenance and the time-varying characteristics of real-time response systems. The Markov method can also be calculated using a number of different RAMS indicators, such as the system reliability within a certain time period, the availability of a moment and the MTTF. For the general mathematical model of the Markov chain, suppose { X ( n), n  0,1,2,......} is a value in the E  {0,1,2,......} or E  {0,1,2,......, N } on a random process, the former expressed as an unlimited number of states. In the latter case, it is expressed as a limited number of state spaces. The following formula will be used for the definition of Markov chains. Suppose { X (n), n  0,1,2,......} random sequence of discrete state space for E . If for any m non-negative integer n1 , n2 ,......, nm (0  n1  n2  ......  nm ) and any natural number k , and arbitrary T, to satisfy: P{ X (nm  k )  j | X ( n1 )  i1 , X (n2 )  i2 ,  X (nm )  im } (3)  P{ X (nm  k )  j | X (nm )  im } This illustrates an important property of the Markov process: it has a no aftereffect nature, which is also known as non-memory. The RAMS analysis flowchart based on the Markov chain is shown in Fig. 11. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

54 Computers in Railways XII Start

End

To understand system function and structure ,To determine the meaning of failure

Solve the model, obtain Analysis results

Carry out FMEA

Simplified model

Divide into Safety And Dangerous Fault, Calculate Failure Rate

Establishment Model

Determine the failure detection and commoncause failure the details , divide the corresponding failure rate further

Figure 11:

RAMS analysis flowchart based on the Markov chain. F a u lt p a tte rn b a s e F a u lt case

C ase c o m b in a tio n In te rfa c e

In je c t a lg ris m

DMI

C3 simulation system

In te rfa c e In te rface

In te rfa c e

In te rfa c e D a ta O b ta in a n a ly z e re s u lts E v a lu a tio n m o d u le … …

Figure 12:

General structure of the software fault injection system.

Through the merger of the state, the Markov model can be greatly simplified. In addition, there are a number of other Markov model simplification techniques, for example, the system decomposition and model compression. In the case that the system is relatively large and complex, one can use these technologies. 4.3 Realization of the fault injection system The general structure of the software fault injection system is shown in Fig. 12. This system has three modules, a fault injection module, fault pattern base and evaluation module [9]. The fault pattern base plays an important part in fault injection. A good fault pattern base could improve fault injection quality efficiently. The fault patter base is composed of the fault case coding module, fault tree module, automatic evaluation module and evaluation result module, as shown in Fig. 13. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Fault Pattern Base

Fault case coding module

Fault case coding Data base storage Tree-view model Edit/delete

Automatic evaluation module

Fault tree module

Obtain fault tree Data base storage Component attribute edition

Figure 13:

Obtain the minimal cut sets, the probability of the occurrence of the top event,etc.

Evaluation result module

Fault tree figure Minimal cut sets ...

Fault pattern base function module. Obtain T Read and store system information

Is there new fault pattern?

N

Y Update structural body FAULT[T]

Is there any injected fault?

N

Y Inject fault

Figure 14:

Injection algorism.

The fault injection module finishes one round of fault injection in the following steps: monitor simulation system, collect and transmit operation data, read fault pattern, inject fault, stop injection. The injection algorism injects the structural body, which is coded already, into the simulation system [10]. The process of this algorism is: as soon as the simulation system time is obtained, update the fault pattern structural body; according to the structural body, the fault inject place and fault data are obtained; intercept transmission data and inject fault data. The fault injection module finishes one round of fault injections in the following steps: monitor the simulation system, collect and transmit operation data, read the fault pattern, inject the fault, stop the injection. The injection algorism injects the structural body, which is coded already, into the simulation system. The process of this algorism is: as soon as the simulation system time is obtained, update the fault pattern structural body; according to the structural body, the fault inject place and fault data are obtained; intercept transmission data and inject the fault data. A flowchart of the injection algorism is shown in Fig. 14. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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5 Conclusion This paper has made a design of a high-speed train control system from a multiresolution model to reliability, analyzing step by step the needs of the high-speed train control system, and HLA is used as the simulation supporting environment. The HLA environment can meet the distributed requirement of the high-speed train control system; Improved RTI time management can meet the requirement of real-time. The high level architecture can show the interaction between modules and the data interfaces, while the multi-resolution can build the modules based on the different concerns. Making use of the advantages of the software simulation system, fault injection is used to inject to the system to get the information caused by the fault; the Markov chain method is used to achieve the qualitative and quantitative analysis of RAMS, while the analysis of VV&A provides the basis for performance to improve and optimize the system design and confirm the system capability. Furthermore, the methods researched in this paper can be used to analyze effectively the high-speed train control system.

Acknowledgements This research work was supported by the Key Program of the National Natural Science Foundation of China (No.60736047, 60870016), Independence Research Task of State Key Laboratory of Rail Traffic Control and Safety (RCS2009ZT013), Technological Research and Development Programs of the Ministry of Railways (No. Z2009-059), Science and Technology Foundation of BJTU (No.2008RC023) and Fundamental Research Funds for the Central Universities (No. 2009JBM005). R.B.G. thanks Pro. Cai and Dr. Wang who had devoted their attention to my study and guided the right research direction; thanks for my team partners, they have given me a great deal of instructive advice on my research; and thanks for my family, my family’s self-giving love is my most important power; thanks for everybody who has ever helped me.

References [1] Beijing Railway Administration. CTC-2 train control system used in maintenance [M]. Beijing: Chinese Railway Press. 2007. [2] Xu Xiaoming, Yuan Xiange, Li Ping. Train operation control system controlling ground equipment books column [M]. Beijing: Chinese Railway Press. 2007. [3] Zhang Xuguang. CTCS-3 Train Control System Technology Innovation [M] Program of Transportation. 2008.3 [4] Qin Jiandong, Yan Changfeng, Wangdi. Collaborative ship defense simulation system based HLA and UML [J]. Journal of Wuhan University of Technology,2008,30(2):261-264 [5] Liu Baohong. Multi-resolution Modelling Research and development [J]. System Simulation, 2004, 16(6):1150-1154 WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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[6] Rachel F.M, Cuganasca P.S. Objected-oriented approach for automatic train operation control systems [C]. Computers in Railways IX Proceedings, Wit Press, 2004:421-430 [7] Pataricza I, Majzik G, Huszerl Gy. Várna. UML-based Design and Formal Analysis of a Safety-Critical Railway Control Software Module In G. Tarnai and E. Schnieder (eds.): Formal Methods for Railway Operation and Control Systems (Proceedings of Symposium FORMS-2003, Budapest, Hungary, May 15-16), L. Harmattan, Budapest, 2003:125-132. [8] Decknatel G, Slovak R, Schnieder E. Definition of a Type of ContinuousDiscrete High-Level Petri Nets and Its Application to the Performance Analysis of Train Protection Systems. In Engell S, Frehse, G, Schnieder E, Hrsg. Modelling, Analysis, and Design of Hybrid Systems, Springer, Berlin, 2002:355-367 [9] Decknatel G. Modelling Train Movement with Hybrid Petri Nets. FME Rail Workshop, Stockholm, 1999, 99(5):11-12. [10] Adelantado M., Bonnet S. and Siron P. Multi-resolution Modelling and Simulation with the High Level Architecture. Proceedings ESS’2000, 12th European Simulation Symposium, 2000:1-6

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Research on a hybrid map matching algorithm for Global Navigation Satellite System based train positioning J. Liu1,2, B. Cai1, T. Tang2, J. Wang1,2 & Wei ShangGuan1,2 1

School of Electronics and Information Engineering, Beijing Jiaotong University, China 2 State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, China

Abstract GNSS has been proved to have great potential for Safety-of-Life critical rail applications, particularly the train control technique and railway signalling. In the GNSS based train positioning scheme, although with the aid of inertial sensors (e.g. the odometer, gyro, accelerator and Doppler radar) some systematic and random errors could be reduced or limited by an appropriate measuring method and data fusion filtering, it is significant to improve and guarantee the positioning precision and integrity performance by using the map matching (MM) technique in a cost effective way. In this paper, the structure of an electrical track map database is designed according to the requirements of precision and efficiency, the architecture of a GNSS based train positioning system integrating INS sensors is introduced, and a novel hybrid map matching algorithm is proposed, in which the determined train position is the integration of the position solution from multi-sensor fusion, the identification of the similarity or matching probability, and heading validation, with different track map levels. As the “point-to-curve” and “point-to-point” matching strategy are adopted with the provided feature of track map data, the adaptive performance and completeness of the map matching algorithm is guaranteed and improved. A field test in the Qinghai-Tibet line demonstrates that the proposed algorithm earns high position decision accuracy and integrity with simple implementation, which is of great practical value to precise train control and railway signalling. Keywords: map-matching, train positioning, integrated positioning, GNSS, INS, track map database, similarity, train control. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100061

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1 Introduction The fast developing GNSS (Global Navigation Satellite System), including U.S. GPS, European Galileo, Russian GLONASS and Compass in China, will play a more important role in railway transport, especially the signalling and traffic control. The integration of satellite navigation systems and the ERTMS/ETCS will bring great benefits to both corridor and regional low density lines. Within recent years, a number of R&D determination projects based on GNSS have been carried out world-wide, such as ATCS, ARES, PTS and NAJPTC in North America and APOLO, ECORAIL, LOCOPROL/LOCOLOC, RUNE and GADEROS in Europe [1, 2]. China has been developing the modern train control system, named CTCS (China Train Control System), and has reached the CTCS level 3 [3]. With the implementation of the next generation satellite system Compass, there will be a high demand for the GNSS technique for safety related railway applications in China. The position of the train is the core function of all the railway operations. Quite different demands on an on-board GNSS based train positioning system are required by safety related applications, mainly those concerning signalling and train control, and one important aspect of them is to develop the positioning system as precisely and cost-effectively as possible [4–6]. Due to the disadvantages of single sensor configuration for train positioning systems, a multi-sensor based structure has been an inevitable trend to improve the performance of accuracy, reliability and integrity. In the position sensing and measuring process, there must be some systematic and random noise to increase the deviation between the real train position in the Map Set Space and the practical measurements in Measuring Space, in which the final measuring error is the combination of sensor behaving error and the stochastic interference (Fig. 1). With the analysis of train position sensing, then the train positioning process, which is aiming at the integrity, accuracy and reliability, could be divided into

Figure 1:

Architecture of the train positioning process.

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three main steps to return to the original position state constrained by the rail track map. The three steps, integrity assurance, multi-sensor fusion and map matching, are designed to solve the systematic, random and errors in position decisions. Among the three steps, the map matching calculation, which provides a link by integrating positioning data with spatial track map data to identify the correct geographical position and the track that the train is moving on, is the key component to improve and realize the required performance index. In recent years, there have been a lot of map matching algorithms developed for GNSS based transport applications, and those approaches can be categorised into four groups: geometric, topological, probabilistic and other advanced techniques, which have been introduced and detailed in [7]. For the one-dimensional character of rail trains and the switch based topological structure of rail tracks, the geometric way is most direct approach to realize the matching process, such as the vertical projection from the positioning fix to the connection between candidate track points [8], correlating the angular rate extracted from the map database to the corresponding measurements [9]. In order to be capable of supporting the requirements of various operation conditions, the integrity, adaptive ability and computational efficiency should be concerned in the design of the map matching algorithm. In this paper, based on the analysis of rail track map structure and multi sensor integration, a hybrid map matching algorithm is proposed with similarity extraction, point matching and the heading validation in different map levels, and the algorithm can be implemented into various train positioning solutions.

2 Track map database for train positioning Map-matching not only enables the physical location of the train to be identified, but also improves the positioning accuracy, if precise track map data is available. There must be some kind of error on the track map for the inadequate measuring means and uncertainties derived from the generation process. So the precision of track map is a crucial factor to the map matching approaches. The railway track map is composed of rail track lines and the rail equipments along the lines. Measurements of the track map are always obtained by static measurement in a long period at the key points (such as the switch and signal controller) and dynamic operation along the centre of track lines. With post processing of track position measuring data and corresponding completeness, the map could be expressed or described by the track map database, combining discrete track line points and the attribute data. As the cost of the track map database depends on the accuracy it holds (i.e. higher track database accuracy leads to high complexity and expense), according to different operation conditions, the map is generated at three levels separately, which is as shown in Fig. 2. For level 1, there are only position and attribute data of points of interesting collected, which describe the most significant information of the track lines, with high accuracy measuring, such as switch, signal controller and insulation section. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

62 Computers in Railways XII However, the description of the rail track in this level is a low resolution strategy, and the precision performance could be compensated by the cost effectiveness. For level 2, except the key track points in level 1, some characteristic points of track curve are extracted from raw GNSS track measurements by certain curve feature extraction and data reducing algorithm (i.e. Douglas-Peucker algorithm), then the precision of the track description will be improved to fulfil the matching requirements. For level 3, regardless of the cost of track map building, the detailed curve feature information of rail tracks are introduced by interpolating the key points and characteristic points in level 1 and 2 as the cubic B-spline principle. A factor of precision is employed to constrain the uniform interpolation and evaluate the point matching performance which will be presented in following chapters.

3 GNSS based integrated train positioning Train positioning system could get lots of benefit (i.e. lower cost, better precision and time-space coverage) from the application of GNSS, hence GNSS technique has been integrated into some current train control and positioning systems in the form of core position sensor or the virtual balise. But there are still some safety risks for the satellite based system, such as the limited SIS availability, multipath effect, Signal-In-Space verification and the electromagnetic interference, so the GNSS-only strategy for train positioning cannot cover the performance indices completely, then the multi-sensor integration is found an effective approach.

Figure 2:

Structure of the track map for train positioning.

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It is evident that railway GNSS / INS (Inertial Navigation System) based safety of life applications acquire highly reliable and accurate data provided by onboard sensors. The core function of position (include the geographic location, velocity and headings of a train, in a broader sense) could be realized by fusion of data from GNSS receiver and INS sensors, which can be simply described as follows: odometer, accelerometer and Doppler radar for distance calculation and validation, and gyroscope for gyro-odometry for routing detection on switch [11]. As an important component of train control system, multi-sensor integrated positioning system could be introduced to current train control systems in many ways, depending on the application level, interoperation capability and operation context (high speed lines, low density lines, etc). With current odometer based scheme in train control, two main integration approaches are as follow: I. GNSS/INS enhances the odometer, in which the GNSS/INS based system is taken as a complement to enhance the position determination function of the current existing position sensors (the odometer with calibration from balise), thus the integration could be realized without breaking the current configuration and interoperation of the train control system. II. GNSS/INS substitutes the odometer, in which GNSS/INS based system is employed to realize the whole function of position determination, replacing the current odometer based positioning solution, then that will be more independent and flexible for the integration to innovate the train control architecture. In practical implementation, approach I is more feasible for configuration compatibility. The architecture of GNSS based integrated positioning system in approach I is as Fig. 3. On-board unit calculate the train position with position sensor data and the cubature Kalman filter, which has been proved an efficient nonlinear data fusion algorithm. The final position is determined and calibrated to the track map which is taken as the absolute reference. In this map matching process, track map is used as another “virtual sensor” in the form of a database, with the architecture as described in former chapter.

Figure 3:

Architecture of GNSS based integrated train positioning.

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4 Hybrid map matching for position determination Map matching is a software algorithm that is used to integrate various position sensors data with map data to give a better position estimate of trains. It plays an important role in the positioning system as it employs digital track map database to improve the accuracy and reliable of positioning system, with the principle that trains can only move along the fixed track lines. In practical application, based on the discussion of the structure of track map database, map in level 2 and level 3 are feasible in most conditions. As for the different feature of the map levels, the map matching algorithm is designed and tested separately. To map matching in level 2, as the medium precision map data are provided, the “point-to-curve” strategy is adopted for matching algorithm; the similarity maximization principle is used to obtain the optimal matched position. Assume p f (k )   x f (k )

y f (k ) l f (k ) 

T

is the output of multi-sensor integration at

and l f (k ) is the travelling distance. V j  are track line data in level 2, including

time k , where x f (k ) and y f (k ) are train position in east and north direction,

the key track points and curve characteristic points, and V j   x j y j l j  . The aim of map matching is to determine the matched position pm (k ) for the calculated p f (k ) with maximal probability and similarity. The hybrid matching T

algorithm combines the CKF based data fusion with the similarity identification and heading validation. The algorithm could be divided into three key operations: (1) Obtain the data fusion position. The cubature Kalman filter (CKF) is a Gaussian approximation to Bayesian filter, with more accurate filtering performance than traditional method and less computational cost [13]. As some inertial sensors’ measurements have nonlinear relation with filtering state, the CKF approach is adopted to estimate the position error and compensate the inertial calculation, which is the foundation to match the map database. (2) Acquire the candidate map segment. Use the distance between fusion output p f (k ) and map position V j 

iN j i

to

compute the probability of the candidate segment, with a fixed length “window” of N points. The Gaussian function based probability is pca (k , j ) 

 p f (k )  V j exp    h 2 c 

1

2

   

(1)

The most probable extreme point can be determined by M f (k )  arg max pca (k , j ) j

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(2)

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Then the other endpoint of the candidate segment M e (k ) could be selected by the nearest strategy with train travelling distance. The curve segment M f M e as candidate will be the map data set for matching. (3) Similarity calculation and identification. In order to find the matching position Pm (k ) from segment M f M e , the similarity is employed to be the decisive factor for the “point-to-curve” solution. Firstly, initialize the target model with the fusion position and candidate map segment from step (1) and (2). The distance between p f (k ) , M f (k ) and M e (k ) in east and north are taken as the feature factors, and the initial target model can be given as   x (k )  xmf  qˆ1  C exp  f  h0     y f (k )  ymf   qˆ3  C exp   h0  

2

2

 ,  

 x f (k )  xme qˆ2  C exp   h0 

2

  y (k )  yme  , qˆ4  C exp  f   h0  

2

   

   

(3)

where C is the normalization factor, h0 is the bandwidth. Then the candidate target model could be described with the same parameters. For a candidate element P (n)  M f M e , the model could be   x (n)  xmf  p1  C p exp  p  h0     y p (n)  ymf   p 3  C p exp   h0  

2

2

 ,  

 ,  

 x p (n)  xme p 2  C p exp   h0 

 y p (n)  yme p 4  C p exp   h0 

2

2

   

   

(4)

Finally, combine the target model and the candidate, the similarity function is proposed for evaluation, which is defined as

ˆ (n)  [ p (n), qˆ ]   p i (n)qˆi 4

i 1

(5)

where the similarity ˆ  [0,1] , and the larger ˆ is, the more similar features are identified between fusion position and the candidate model P (n) . The matched position can be Pm (k )  arg max ˆ (n) n

(6)

The similarity based map matching process in level 2 could be described as the upper graph of Fig 4. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 4:

Principle of the map matching algorithm.

(4) Heading validation. For the validation of Pm (k ) to be the final matched output, an independent testing method is adopted with the heading of the train. Under the ideal condition, the heading variation of the train trajectory should be the same as that with map matching. In practical train operation environment, interferences and errors lead to some differences to a certain extent. Given a reasonable error threshold, then the map matched position could be validated and the positioning precision and integrity monitoring will be realized simultaneously. Assume the error threshold is  , and then the validation could be defined as

 h(k )  h f (k )  hm (k )  

(7)

where the h f (k ) and hm (k ) are heading at fusion position and the map matched respectively. To map matching in level 3, where the interpolation map data are available, as the high precision map data are provided with predefined precision factor  d , which is usually at decimetre level, in order to keep a balance between efficiency of map storage and matching computation, the “point-to-point” strategy is used to realize the map matching. Map matching process in level 3 has the same step (1), (2) and (4) as that in level 2. Here in the step (3), candidate segment M f M e provide corresponding



interpolation data set C j | M f M e

 for the determination “which” point would

be chosen as the matching result. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Similar to the acquisition of the most probable extreme point of candidate segment, the matching operation is based on the maximum probability principle, which is as shown in lower graph of Fig 4, where Pm (k ) satisfies   Pf ( k )  C j Pm (k )  arg max   exp   j   h  

2

   

(8)

From the detailed analysis of the map matching in different map levels, the whole hybrid map matching process could be unified into one flow diagram, which is as shown in Fig 5. In the unified process, the judgement of “Interpolation data available” is the key step to vary the different map level based matching strategies. Only when the heading validation is successful, the calculated matching position would be used for output, and evaluation of the positioning precision and integrity.

Figure 5:

Flow of the hybrid map matching process.

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Figure 6:

Map matching compared with sensors and track map.

5 Field test and validation In order to validate the performance of proposed hybrid map matching algorithm in this paper, field tests have been conducted with electrical track map generated from Qinghai-Tibet rail line in June 2009. A high precision GNSS receiver is employed to collect position measurements along practical tracks or at the points of interest, and then the track map levels 1–3 are generated with strict criteria. Extensive map validation collections are also taken to test and validate the proposed map matching algorithm. Fig 6 shows the map matching results compared with the positioning sensor fusion and track map data from a station, where all the position coordinates have been shifting transformed. As is shown in the elliptical area, actually the position from sensors are even close to the track lines, however, the map matching isolate the error and further improves the positioning accuracy. Take travelling distance as the one-dimensional map description, Fig 7 shows how the similarity distributes and varies in the map matching process with 193 frame sensor measurements. The maximum similarity of every epoch indicates the matched position from the hybrid map matching algorithm, and from the similarity based determination process. It can be concluded that the hybrid algorithm preserves the advantages of both the geometric and probabilistic characters. With the “point-to-curve” or “point-to-point” strategy, the “longitude-latitude-altitude-similarity /probability” character space architecture is constructed and applied to every received sensor data frame, and then the position matching is the dynamic form of the space. As the results of the field test and validation shown, the proposed hybrid algorithm earns a high adaptive ability for the track map level to achieve high precision, reliability and completeness in GNSS based train positioning. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 7:

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Similarity variety in the map matching process.

6 Conclusion In this paper, an approach for train position determination with an electronic rail track map is demonstrated, with a novel map matching algorithm proposed for GNSS based train positioning. Based on the architecture analysis of track map database and the GNSS based train positioning system, a hybrid map matching algorithm is proposed with four key steps, where the judgement for map interpolation data is used to distinguish matching strategies in different map level, and the heading validation for correction assurance. The proposed approach holds high precision and computational efficiency, and field tests validated the conclusions, including that the accurate sensor integration and precise track map data are also crucial for realization of GNSS based train positioning and train control.

Acknowledgements This work was supported by National Natural Science Foundation of China (No.60736047, 60634010, 60870016), and the Fundamental Research Funds for the Central Universities (No.2009YJS020).

References [1] Filip A., Bazant L., Taufer J., Maixner V., Mocek H., Train-borne position integrity monitoring for GNSS/INS based signalling, International Symposium on Speed-up and Service Technology for Railway and Maglev Systems ‘2003, Tokyo, Japan, 2003, pp. 88-93.

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70 Computers in Railways XII [2] Polivka A., Filip A., Satellite-Based Positioning for CBTC, the 2nd international conference "Reliability, safety and diagnostics of transport structures and means 2005", Pardubice, Czech Republic, 2005. [3] Cai B., Shangguan W., Li X., Wang J., Research on supporting technology for simulation CTCS-3 based on multi-resolution modelling, Journal of Beijing Jiaotong University, vol. 34, no. 2, 2010, pp. 5-10. [4] Simsky A., Wilms F., Franckart J-P., GNSS-based failsafe train positioning system for low-density traffic lines based on one-dimensional positioning algorithm, 2nd ESA Workshop on Satellite Navigation User Equipment Technologies, Noordwijk, Netherlands, 2004, pp. 1-8. [5] Filip A., Bazant L., Mocek H., Taufer J., Maixner V., Dynamic properties of GNSS/ INS based train position locator for signalling applications, COMPRAIL ‘2002, Lemnos, Greece, 2002, pp.1021-1030. [6] Filip A., Train real-time position monitoring trials at Czech railways, Structural Integrity and Passenger Safety, WIT press, Great Britain, 1999, pp. 152-166. [7] Quddus M., Ochieng W., Noland R., Current map-matching algorithms for transport applications: State-of-the art and future research directions, Transportation Research Part C 15, 2007, pp. 312-318. [8] Jana H., GNSS train position integrity monitoring by the help of discrete PIM algorithms, Journal of Applied Mathematics, vol. 2, no. 3, 2009, pp. 73-79 [9] Saab S., A Map Matching Approach for Train Positioning Part I: Development and Analysis, IEEE Trans. Vehicular Technology, vol. 49, no. 2, 2000, pp. 467-475. [10] Noronha V., Goodchild M., Map accuracy and location expression in transportation - reality and prospects, Transportation Research Part C 8, 2000, pp. 53-69. [11] Maixner V., Mocek H., Taufer J., Bazant L., Filip A., The Simulator of Train Position Locator, COMPRAIL ‘2004, Dresden, Germany, 2004, pp. 477-486. [12] Meng Y., Chen W., Li Z., Chen Y., Chao J., A Simplified Map Matching Algorithm for In-Vehicle Navigation Unit, Geographic Information Sciences, vol. 8, no. 1, 2002, pp. 24-30. [13] Arasaratnam I., Haykin S., Cubature Kalman Filters, IEEE Trans. Automatic Control, vol. 54, no. 6, 2009, pp. 1254-1269.

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Automated system testing of an automatic train protection system B. Friman & T. Andreiouk Ansaldo-STS Sweden AB, Sweden

Abstract The testing of safety critical software is becoming more and more automated. Automated testing has the advantage that the tests can be carried out much more frequently and with more numerous test cases. For low level unit testing, there are several good tools available, such as Aunit. For system testing, however, the test framework normally has to be specifically tailored for each project, since it has to deal with external interfaces, e.g. man-machine-interfaces, and sensor and control interfaces. For efficient operation, it is desirable that an automated framework for system testing shall be able to serve both in a pure software setup, where most of the development is done, and in a hardware set-up, which is as close as possible to the environment where the product shall operate. This paper describes an automated system testing framework for a SIL 4 safety critical train protection system. The testing framework can be used both in the pure SW setup and in the HW set-up, and is able to extract its test cases from readable Test Specification documents and also produce high quality Test Protocol documents. Approximately 98% of the system tests have been automated in this project. The project in question is the development of STM’s (Specific Transmission Modules) for Sweden, Norway and Finland. The STM’s carry out train protection on national equipped lines – lines that are not equipped with the ERTMS (European Rail Transport Management System). A total of approximately 1300 test scenarios are executed by the automated testing framework. Keywords: automated testing, system testing, ETCS, ERTMS, ATP.

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1 Testing of safety critical software Safety critical software is normally tested in detail both on module level, and on system level. A special requirement for SIL 4 software is that the tests must cover all details of the system, and that this coverage has to be documented all the way from the code and to the requirement specification. It must also be proved that the documented tests are valid for the delivered system, meaning that if changes have been done after the tests, then either all tests must be rerun, or part of the tests rerun and a proof being presented that the other parts are unaffected by the changes. In order to limit the costs of rerunning old tests, many providers of safety critical systems have started using tools to automate the unit tests. There are different approaches on how to do this – you can for example develop a second implementation of each module (n-version programming) and make a set-up that runs the twin modules in parallel and compares the outputs. You can also use software tools which support writing of test cases and testing the expected results automatically. There are several tools available for this kind of testing. Some of them are script based. Other, such as A-Unit (for Ada software), use test cases that are written in the form of Ada programs. For system testing however, the test framework normally has to be specifically tailored for each project, since it has to deal with external interfaces, such as e.g. man-machine-interfaces, and sensor and control interfaces. When you are testing on system level, the object you are testing remembers earlier inputs and it is the sequence of inputs and outputs that defines the system behaviour. This means that system testing has to be scenario based. You build a scenario from the world where the system is supposed to operate. For a train protection system, the scenario is a train that runs along a track. It starts and stops, accelerates and brakes, runs forward and backward, and it picks up signal information along the track, information which is used to prevent the train from entering a dangerous area or running at a dangerous speed. When you test a system in the laboratory, you have to build a simulated environment around it. The environment for a train protection system consists of a train, a track and a driver. This environment typically consists of several specially developed hardware systems, and one or more PC computers. Once you have this environment ready and running, you can test the train protection system manually in the lab. During the testing, you operate the different hardware systems, and monitor the result from various displays, PC windows, and logging devices. To automate the system testing, you must: 1. find a way to control and monitor all the equipment from a single program 2. find a way to write the test cases that enables them to be automatically executed and evaluated by this program 3. find a way to automatically create humanly readable test reports. For efficient operation, it is desirable that an automated framework for system testing shall be able to serve both in a hardware set-up as described above, which is as close as possible to the environment where the product shall operate, and in a pure software set-up, where most of the development is done. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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This paper describes how we have done this in the STM projects, at Ansaldo STS Sweden, in Stockholm. STM = Specific Transmission Module, in practice an Automated Train Protection system that runs alongside and in co-operation with ETCS (European Train Control System) onboard systems, in order to provide continued protection on lines equipped with local (national) signalling systems. See ERTMS Subsets 035 [1] and 058 [2] for more information about STM.

2 Manual system testing Manual system testing will still be the primary method for the developers to test new functions and bug fixes in the software in their daily work. It means that the test environment shall both support the manual tests by the developers and the automatic tests by the validation team. The natural way to implement automatic system testing is thus to build it on top of the manual test environment. The following figure shows a typical system test environment:

3 Controlling and monitoring the test equipment from a single program In order to control and monitor the test equipment, we first must find a way to communicate with the PC software associated with the different devices. We

Figure 1:

The photo shows a substantial number of different hardware devices connected to each other and to one or more PC computers.

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STM (device to be tested)

PC

Recorder Log

S/W

Log

S/W

Vital computer

Sniffer ETCS

DMI

EVC

BTM

Test program

track

Figure 2:

S/W

Train and track simulator

The test system overview, now with the controlling and monitoring connections included.

asked the developers of the different software to implement TCP/IP server ports which we could connect to, send control directives to and read logged data from. We must also find a way to push buttons on the DMI (Driver Machine Interface) and to register the information shown on it. To our luck, the ETCS DMI already had a serial port dedicated to testing, which enabled us to send simulated button pushes using an RS 232 connection. Automatic pushing of buttons is absolutely indispensable for automated testing. Had it been required, we have even considered building a device with electrically controlled “fingers” for this purpose. The registering of information shown on the DMI was no problem, since we can pick it up from the high speed bus between the STM vital computer and the ETCS EVC (European Vital computer), with the sniffer.

4 Writing test cases so they can be automatically executed and evaluated As mentioned earlier, system test cases normally are built as scenarios. In a scenario for a train protection system, you describe each event along the track, from the time when the equipment is powered-on, to the time when the test is finished. A common way to do this is in the form of a table, where inputs are specified on the left side, and expected outputs on the right. For automatic testing, all inputs and outputs must be machine readable, but they must also be humanly readable, so that the meaning of the test is comprehensible. For this

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Pos.

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+10

10

+290 (500)

11

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+1295

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+142

Figure 3:

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Information Acceptance criterion •Accelerate to 70 --km/h (Preset speed increase exists = No) Si 160/130, (Linking distance = 1,2*5000 + 100 = 6100 m) 5000m (Linking margin = 0,2*5000 + 100 = 1100 m) (Reference location = 500 m) (Linking distance = 700 - 500 + 1,2*1000 + 100 = 1500 m) (Linking margin = 0,2*1000 + 100 = 300 m) SH 100, 1000m (Linking distance will be updated because current point < primary target point: 1500 < 6100) (Reference location + Linking distance was passed) (Balise erasing = SIG) •MR ceiling speed = 80 km/h DMI indications: --•Indicator C5 = Balise failure 2/Fixed_Yellow •Text Message = 7UU Signal missing •Service brake = Yes (Brake is autoreleased) •Service brake = No •Accelerate to 70 km/h

An excerpt from a test case for automatic execution. Parentheses are used for comments.

purpose, we have created a symbolic language for signal information and driving commands, that both shall be easy to understand, and possible to compile to binary data. The test case scenarios have four columns – Transponder id (group), position (m), Information, and Acceptance criterion. The information column can contain both trackside signalling information (transponder data) and driving commands. As you can see, the scenario positions (Pos.) are relative, which makes it easier to later insert or remove lines in the scenario. The absolute locations will be automatically calculated by the script.

5 Distilling test cases from the test case database In order to automatically distil the files needed to run the tests, from the test case database, we must first export it into a public format. We chose to export it to html, since our database tool – DOORS – had the possibility to export to html. XML would have worked too, if DOORS had been equipped with an XML exporting facility. Below we have used the command to distil all the test cases in the database. Only the end of the summary is visible in the figure: The test base database is approximately 1000 pages long, when printed out. The distilling script requires approximately 45 seconds to convert the htmlversion of the database to the files needed for running the tests. If you want to test not the entire test database, but only a chapter, it is also possible to extract a single chapter or a single test case. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

76 Computers in Railways XII

Figure 4:

After running the distilling script, we get a list of all files generated, and a summary of the number and length of the scenarios.

The distilling script also checks the syntax of all the scenario information, including position information, trackside data, train running commands and acceptance criteria, example of output when a fault is found. Example: *** UpdatePos: Unable to understand: "stop-pos. +70". Chapter = 3.1.4.3.1.2: a. File="out.htm", line=30434. The distilling function also contains a trackside data compiling function. For the ATP-systems we are designing, the trackside data consists of telegrams from transponders which are placed on the rail, and from which the train collect information about signals and fixed speed restrictions along the track. In this example you can see both the symbolic notation and the compiled binary data. 51 400 4 8 9 9 2 12 /Si 130/160, 500m It says: At position with id 51, located 400 m after the start of the test case, there are two transponders, one with the telegram 4 8 9 and one with the telegram 9 2 12, and the tell that the train has passed a signal with main signal speed 130 km/h, distant signal speed 160 km/h, and distance to next signal 500m. The amount of binary data is very small in this example, since Sweden was first in the world with ATP systems, and the transponders at that time could only host 12 information bits each. Modern transponders can host up to 800 information bits, thanks to better coding and CRC-technology. Here is an example from Finland which use 180-bit balises, in this case the complete telegrams, also the CRC-code is included: 2 200 /Si 200/200, 2500m -0,8% Sw: 80, 263m -1% +150m Sw: 35, 4900m +90m |2211 3111 1EEE EEED 3D3D 855E EEEE 1865 2845 EE2A 153E E62C 76D5 66BE EF47 BD74 |3211 3111 1EEE EEED 3D3D 855E EEEE 1865 2845 EE2A 153E E371 6304 CF9E 570E 39DE

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It says that, there is a transponder group at position 200m after the start of the test case, in which there is a signal with id=2, main signal speed 200 km/h, distant signal speed 200km/h, plus information of distance to next signal, and two switches which reduce the allowed speed of the train.

6 Running the test cases in a PC environment At earlier stages of development, most tests are run in a PC environment. All the equipment that the tested ATP needs to communicate is then simulated by PC programs. Some of these simulators are written in Ada, others are written in a script language (X). In our case, we need simulators for the following equipment:  ETCS (European Train Control System) – the European standard ATP system. Written in Ada. Brake curve algorithm by Friman [3] is used.  The train, including acceleration, braking, driving forward, reversing, changing cabin, measuring brake pressure, etcetera. Written in Ada.  The track, including transponders which shall send data when the train passes them. Written in Ada.  A profibus sniffer, which in the real test will be connected to the physical profibus connection between the STM (national ATP) and the ETCS (European standard ATP). Written in script language.  A recorder, which is part of the STM to be developed, and will play a role in extracting test results in the real tests. Written in script language.  ETCS DMI, which in the real test set-up is an LCD device with pressure sense surface to enable pushing buttons. The DMI has an input interface (RS232) that enables automatic pushing of buttons. Written in Ada.  The driver. For automatic testing, also the driver is simulated. Written in script language (X). For all these simulators to work together, there is a script which co-ordinates the entire tests. In this script you can order the test of a separate chapter, or the entire test database. Example: . The “+” means that all the subchapters shall also be included in the test. The test is started by specifying which chapter in the test database shall be tested. If the chapter is on high level, example chapter 3, then a large number of scenarios will be run before the tests ends. If a low level chapter, e.g. 3.1.4.3.1.3 is specified, then a single scenario is run, but also a single scenario can take long time to run, e.g. one hour. A single scenario is separated into several test cases, a, b, c etcetera, which test different requirements belonging to the same chapter. If a single test case is specified (e.g. , then the test coordination script will first run the common initialisation part of the scenario, then jump to test case g. During the test, all output data are saved in an output data file. Here follows an excerpt from an output data file: 20547 20586 20586 20586

(70 (70 (70 (70

km/h) km/h) km/h) km/h)

Service brake = Yes MR ceiling speed = 150 km/h V_PERMIT = 150 km/h V_INTERV = 160 km/h

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78 Computers in Railways XII 20586 20586 20617 20617 20652 20652 20850

(70 (70 (70 (70 (70 (70 (70

km/h) km/h) km/h) km/h) km/h) km/h) km/h)

Button F8 = Loss/On Indicator C3 = 150/Fixed_Green Button F8 = Off Service brake = No Text Message = 6 L U Indicator C5 = Balisfel 1/Fixed_Yellow Indicator C5 = Off

The excerpt above shows the output data between position 20547 and 20850 in a test scenario. The output data is seen as a number of variables which can change value. A logging is done every time a variable changes its value. In the example above we can both see changes on the DMI (e.g. Button F8= Off) and in the brake interface (e.g. Service brake = yes). Since all variable changes are logged, it will later be possible to determine the value of each variable at any given position, just by searching for the last time it was changed before the given position.

7 Test report generation A test report is automatically generated after the end of a test scenario. The test report contains the test cases, the output data, and an evaluation, PASS or FAIL, of each acceptance criteria. The test report generator, starts with the table containing the test cases, then evaluates the acceptance criteria and adds a column with the evaluation result, then merges this table with the output data file, and finally converts the now 8 column wide table into an RTF document.

Figure 5:

The test co-ordination script will start all the simulators, and put the windows of those that shall be visible during the test, on the PC screen.

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Computers in Railways XII Input = svs_3.1.4.3.1.5_a_e4s1.ida Grp Pos Information Acceptance criterion 11

700

SH 100, 1000m

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1995 -

•(Reference location = 500 m) •(Linking distance = 700 - 500 + 1,2*1000 + 100 = 1500 m) •(Linking margin = 0,2*1000 + 100 = 300 m) •(Linking distance will be updated because current point < primary target point: 1500 < 6100) -

•(Reference location + Linking distance was passed) •(Balise erasing = SIG) •MR ceiling speed = 80 km/h •Indicator C5 = Balisfel 2/Fixed_Yellow •Text Message = 7UU Signal missing •Service brake = Yes

Figure 6:

Output = svs_3.1.4.3.1.5_a_e4s1.oda Pos + Spd Test output marg 704 70 MR target distance = 1200 m

1693

70

1992

70

V_PERMIT = 130 km/h Permitted Speed Bar = 130/Grey V_INTERV = 140 km/h D_TARGET = 0 m Target Distance Bar = 0 Indicator C2 = Off Indicator C3 = 130/Flash Slow_Green MR ceiling speed = 80 km/h Release speed = 10 km/h Text Message = 7 U U V_PERMIT = None km/h Permitted Speed Bar = Off V_INTERV = None km/h Intervention Speed Bar = Off Indicator C3 = FEL/Flash Fast_Green Indicator C5 = Balisfel 2/Fixed_Yellow Indicator C7 = Tågöverv/Fixed_White

79

Res. -

-

PASS PASS PASS FAIL (value =No)

Example excerpt from a test report.

Both the evaluation of the test criteria and the merging of test cases with output data require some amount of arithmetic calculation. For evaluation, it must be decided at which position the expected value shall be compared with the logged value. The calculation must then take into account the delays in the ATP system. A similar calculation is done in the merging, in order to decide whether an output data logging shall be on the same line or a different line as a line in the test case scenario. The test report contains all output data, not only those needed to evaluate the acceptance criteria. This is an advantage, because even if a test case is targeted to test a specific requirement, manual analysis of other output data can sometime reveal interesting insight in how the system works. Errors in other requirements can also be discovered earlier, by analysing the output data. In Ansaldo STS Swedish STM project, the customer has decided to allocate some of its own experts to analyse the output data of the automated tests.

8 Running automated tests on the real hardware The scripts which distil the test cases, and those which co-ordinate the automated tests and create the test reports, are written so that they shall be compatible with both the PC based environment and the real hardware environment. You can see a picture of the real hardware test environment in section 2 above. The input files and the output files will look exactly the same. The script will adapt to the changes in the interfaces. For example, in the real hardware environment, a profibus sniffer is used to monitor the output data from the STM, and a serial interface RS232C to send simulated button pushes to the DMI. In the PC based WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

80 Computers in Railways XII test environment, all interfaces use TCP/IP. There are other differences. In the real hardware environment, there are ID-plugs which contain installation parameters for both the ETCS and the STM. In order to test the function of these parameters, the system has to be manually restarted after each change of ID-plug. In order to minimise these manual interceptions, the test case distilling script contains a sorting function so that all test cases which use the same combination of ID-plugs, can be run in an unbroken sequence.

9 Conclusions Automated system testing is today an obvious part of the daily work at the validation department of Ansaldo-STS Sweden. It does the tedious work of repeating old test every week, and enables the personnel to focus their efforts on developing new and exploratory tests. The increased amount of testing also appears to boost project performance. Site acceptance test 1 for STM Finland was successfully completed in record time, in April 2010. Finally, it can be mentioned that the customers have expressed their trust in the automated system tests and how they are repeated and documented.

References [1] ERTMS/ETCS Class 1, Specific Transmission Module FFFIS, SUBSET-035, Alcatel, Alstom, Issue 2.1.1, Date 2003-07-24 [2] ERTMS/ETCS Class 1, FFFIS STM Application Layer. SUBSET-058, Alcatel, Alstom, Issue 2.1.1, Date 2003-11-19 [3] Friman, Bertil. An algorithm for Braking Curve Calculations in ERTMS. Proc. of the 10th Int. Conf. On Computers in Railways, ed. C.A. Brebbia, pp. 421-429, 2006.

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Design and implementation of a distributed railway signalling simulator X. Hei1,2, W. Ma1, L. Wang1 & N. Ouyang1 1

School of Computer Science and Engineering, Xi’an University of Technology, China 2 State Key Laboratory of Rail Traffic Control and Safety (Beijing Jiaotong University), China

Abstract The Distributed Railway Signalling System (DRSS) is a new signalling system, in which all devices including trains, switch point and signals, as well as position checking, interact and exchange information based on some logic constraint relations. These devices operate independently to ensure train safety. Based on this idea, we have presented the concept of modelling these device actions with G-nets (an Object-oriented Petri Net tool) in Comprail 2006. In this paper, a simulation system that we developed is introduced in order to conduct experiments on DRSS and verify its feasibility. The simulator is based on the concept of DRSS and includes mainly six classes and their functionality modules: station layout automatic generation, train operation, position checking, switch point and signal. In addition, the instance generation of all classes and timetable design are considered in the simulator. It is possible to verify and simulate almost all functions with this simulator, such as train protection, route process, interlocking logic verification and terminal device procedure, etc. Keywords: distributed railway signalling system, simulator, object-oriented.

1 Introduction A railway signalling system has been developed over the long history of railways, and has been vital in ensuring the safe operation of trains. However, computers have been used in such safety-critical systems for no longer than 30 years [1], and they have demonstrated a high level of safety and reliability. One drawback of the existing computerized railway signalling systems, however, is WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100081

82 Computers in Railways XII that they require the development of different software for different stations, which tends to introduce unreliable human errors. Further, they are difficult to upgrade due to their lack of standardization, both in hardware and software. A possible solution to overcome these problems is to apply modular-based technology to railway signalling systems. For this, a novel system named Distributed Railway Signalling System (DRSS) was presented in Comprail 2006 [2]. In the new signalling system, all devices including trains, switch point and signals, as well as position checking, interact and exchange information based on some logic constraint relations. These devices operate independently to ensure train safety. It is vital to design the logic functions and control flows for such a safetycritical system. A convenient approach is to develop a simulator based on the designed logic and control flows. In this paper, a simulation system we developed is introduced in order to conduct experiments and verification on DRSS. The simulator is based on object-oriented concept and includes mainly six classes and their functionality modules: information display module, initialization module, train operation, position checking, switch point, and signal. Also the instance generation of all classes, timetable design are considered in the simulator. It is possible to verify and simulate almost all functions with this simulator, such as train protection, route process, interlocking logic verification and terminal device procedure.

2 Distributed Railway Signalling System (DRSS) Compare with traditional signalling system, DRSS needs not the centralized computer for controlling the whole system. All terminal devices work independently and exchange messages via network to ensure safety operation of trains. In the case of a typical interlocking system, these devices include signals, points and track units. Signals indicate whether the train can run or not by displaying green or red. Points are devices for controlling turnouts which determine the direction in which trains move. Track units detect whether or not there is a train on the track. If there is, then other trains are prohibited from entering this section of track until the first train leaves. Consider an interlocking system in a station, the architecture and message interaction flows of DRSS are illustrated in Figure 1. All devices are composed of logic process part and action part. The logic process part receives/sends messages from/to other devices, and makes decision. The action part provides mechanical output according to the orders sent by logic process part, such as displaying green or red for a signal device, turning over the switch for a point. The development process deals with standardized hardware and software for the interlocking devices. Control flows of the interlocking devices are based on their function specification. The development strategy of the DRSS is shown in Figure 2. Structure of standardized devices consists of hardware part and software part. The hardware

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P1 processor and data

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TX1 processor and data

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SS2 processor and data

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SH12 processor and data

Architecture of the DRSS in the case of an interlocking system. DRSS (for a interlocking system) Corresponding to stations

Standardized device models Track Units Signals

Hardware

Points

Software

Classes

Specification(Methods) Data(Attributes)

Figure 2:

Interlocking figure （logic relations between devices and routes)

Initial phase Development phase Internal structure of standardized device models

Development strategy of the DRSS.

specifications include device board design, CPU, digital circuit, input/output, etc. The software specifications include the necessary modules design of typical interlocking devices. Each module is similar with a class or object which inherits from one kind of device class. The device control flows are expressed by methods, while interlocking logic data related to a specific station are expressed by attributes. When the devices are initialized, the logic data will be loaded into the devices, and then the devices operate based on these data. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

84 Computers in Railways XII Once the devices have been verified safe enough, they can be ordered and produced when a new station is constructed. What engineers just need to do is analyzing the logic relations and allocating some basic attributes such as device ID to each device.

3

DRSS simulator design

Authors have proposed a Petri net-based designing approach for DRSS in reference [3]. Toward development of the DRSS simulator, object-oriented methodology is an ideal choice. Thereby UML tools like sequence diagram, class diagram and activity diagram are used for the system design [4, 5].

CDevice

CDeviceSignal Figure 3:

CDeviceTrain

CDevicePoint

CDeviceTrack

Device classes and their inheritance relations. TrainStart TrainSendRouteRequestToSignal SignalRouteProcess&Sig nalLockRelativeSignal SignalSendRouteRequest

TrackReceiveRequest TrackRouteProc ess

PointReceiveRequest PointRouteProc ess

TrackMonitorAndLock

PointMonitorAndLock

TrackSendMess ageToSignal

PointSendMess ageToSignal

TrackReceiveMesFromSignal

PointReceiveMesFromSignal PointSetting

TrackPrepare SendMessage

SendMessage ReceiveMessage SignalChangeGr een

SignalSendMessageToTrain TrainRunning&SignalTrackPoint UnlockAndReleaseResource

Figure 4:

Activity diagram when a train is approaching a signal.

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Computers in Railways XII theTrain:CDe viceTrain

theTrack:CD eviceTrack

OnInitTrain()

theSignal:CDe viceSignal()

OnInitTrack()

85

thePoint:CD evicePoint

OnInitSignal()

OnInitPoint()

SendMsg() SendMsg()

SendMsg()

ReceiveMSG()

ReceiveMSG() LockInterrelatedSignal()

Dispatcher()&ifReservation()

Dispatcher()&ifReservation()

DeviceLock()

DeviceLock() ChangStatus()

SendMsg() TrainRunning() DeviceUnLock()&RecoverStatus()

DeviceUnLock()&RecoverStatus()

SignalUnlock()&ChangeRed()

Figure 5:

Sequence diagram of the four classes and objects.

In the DRSS simulator, there are four device classes and two function classes are designed. Device classes include CDeviceSignal, CDevicePoint, CDeviceTrack and CDeviceTrain, which are shown in Figure 3. These four device classes inherit from CDevice class. Function classes include station layout automatic generation and message. Based on these classes, the instance generation of all classes and timetable design are considered in the simulator. The process when a train comes can be depicted as activity diagrams of these devices. With moving of the train, a signal will start the route reservation process and request to lock the conflict signals. All devices which are related to the requested route check their states and send a response message to the signal. If and only if all these devices are ready for this route request, the route can be reserved and the signal displays green. Figure 4 gives the activity diagram. These procedures for sending and receiving messages and actions of each device are predefined as member functions of class. The sequence diagram is designed as Figure 5.

4 Implementation of DRSS simulator 4.1 System flowchart and modules There are mainly six modules in the simulator: information display module, initialization module, train module, signal module, switch point module and track module. 1) Information display module: displaying system information, train information, timetable window.

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86 Computers in Railways XII 2)

Initialization module: initializing communication module, signal module, switch point module, track module based on the interlocking interlocking logic relations, and initializing train module with train schedule information. 3) Signal module: description of the associated signal, point and track, displaying green or red based on the associated devices. 4) Switch point module: locking/unlocking point, setting position (Normal or reverse) of switch point, indicating the reachable stations when the switch point is in normal or reverse position. 5) Track module: determining whether the train is on some track segment or not. 6) Train module: description the train ID, status (running or stop), the train starting station and destination, and all pass through stations, the current track section and the next, train acceleration and deceleration function. The system process flowchart is illustrated in Figure 6. 4.2 Displaying stations and railway lines For the implementation, an important step is to display the station layout automatically. Consider the universal property, the station layout has to be record as data and a drawing module is needed. The module can be divided into five steps. i. Dividing the main window's client area (size 800 × 500) into 32 × 20 grids, each grid is 25 pixels long side. S ystem sta rt

Sy ste m initialze

S tation a nd line Se le ctiong Ca llin g o th er m od ule s

Train in itia lize

S ignal initialize

P oint initialize

Trac k initialize

M ess age loop a nd wa it

Figure 6:

System process flowchart.

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ii.

Constructing an array with 21 rows × 33 columns, the array subscript (i, j) is corresponding to the main window's client area coordinates (j * 25, i * 25), where i = 0, 1 ... 20, j = 0,1 ... 32. iii. The element value of the array is assigned to an equipment ID or connection mark of two adjacent rail sections when there is a device. Otherwise, the element value is assigned to 0 which indicates that there is not equipment. iv. In the array, equipment ID should be one of three kinds: switch machine ID, track circuit (including the station platform) and signals. Here switch machine's ID is assigned 301-399; track circuit ID is 101199 (track segment) and 201-299 (station platform), and signal ID is 401-499. v. Designing a switch machine table. The fields include switch machine ID, device ID, device ID, device ID, which means that the switch machine is associated with the three equipments followed. vi. Creating data for each device and drawing these devices based on the data table described above, when the station and lines need to be introduced. With the drawing module above, it is convenient to display a different station by designing a corresponding two dimension array. It executes when the simulator initializes and a station and line is selected. 4.3 Layout of the simulator The window is divided into four areas: main client area, system information area, train information area and timetable area, as shown in Figure 7. Station and

Figure 7:

Main window of the simulator.

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88 Computers in Railways XII railway lines will be displayed in the main client area. System running information will be displayed in the system information area. Operation information of all trains will be displayed in the train information area. Timetable area lists the departure as well as pass through time of all trains from the first–run to the last-run.

5 Conclusion The simulator shows the essential features of the DRSS: all device classes process all messages and make decision independently. This process starts with approach event of train. The DRSS simulator provides a platform for almost all experiments and analysis, including exploring the effect of device amount on message process, communication protocols design, etc. In addition, the stochastic failures or events can be inserted into the operation process of trains. This work will be carried out soon. Therefore, it is possible to verify control and schedule logics and simulate almost all functions with this simulator, such as train protection logic, route process logic as well as logic verification and terminal device procedure.

Acknowledgement This work is supported by the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RCS2008K008) and Natural Science Basic Research Plan of Shaanxi Province (2009JQ8010).

References [1] K. Akita, T. Watanabe., H. Nakamura., I. Okumura: “Computerized Interlocking System for Railway Signaling Control; SMILE”. IEEE Trans., May 1985: Ind., 1A-21. [2] X. Hei, H. Mochizuki, S. Takahashi. & H. Nakamura: “Modeling distributed railway interlocking system with object-oriented petri-net”. In 10th International Conference on Computer System Design and Operation in the Railway and Other Transit System, Prague, Czech Republic, 2006, pp.309318. [3] Xinhong Hei, Sei Takahashi, Hideo Nakamura,: Modelling and Analyzing Component-based Distributed Railway Interlocking System with Petri Nets, Institute of Electrical Engineers of Japan (IEEJ) Transactions on Industry, Sec. D, Vol.129 , No.5, 2009.5. [4] Object Management Group, Unified Modeling Language Specification v.2.0, www.uml.org, September 2003. [5] C. Lindemann, A. Thummler, A. Klemm, M. Lohmann, and O. Waldhorst: Performance Analysis of Time-enhanced UML Diagrams Based on Stochastic Processes, In Proc. of the 3rd Workshop on Software and Performance (WOSP), pp. 25–34, Rome, Italy, 2002. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Train tracking problem using a hybrid system model Y. Wang, R. Luo, F. Cao & B. Ning State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, China

Abstract Tracking is an important problem in train operation control. A key requirement for this problem is an accurate knowledge of the train’s position, velocity, and running mode. In this paper a hybrid system model of the train’s movement is introduced, which, for the first time, gives a clear description of the uncertainties during the movement. Based on this hybrid model, a new hybrid estimation algorithm is proposed in order to achieve a more accurate estimation of the train’s states, thereby improving the tracking performance. In the algorithm, the state transition probability matrix is dependent on the operation mode. Simulation results illustrate the good performance of the new estimation algorithm with the hybrid system model. Keywords: hybrid system, automatic train operation, tracking, estimation.

1 Introduction The automatic train operation system is one of the key sub-systems in trains. Accurate estimation of the train’s velocity and position is the basis for the safety of the automatic train operation. With that, the train tracking problem becomes more and more important for obtaining an accurate estimation of the train’s states. Hybrid estimation algorithms have been used in many target tracking applications, including air traffic surveillance [1, 2]. In this paper, a hybrid system model is proposed for modelling the train’s dynamics. Four operation modes, power, speed holding, coast and braking, are modelled as the discrete states of the system, under which the train operates based on a continuous-time dynamic equation. Meanwhile, our model considers

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90 Computers in Railways XII the stochastic factors due to the uncertainties in the train movement. Few literatures consider these, yet they have a great effect on the tracking problems. Based on the hybrid model of the train’s movement, a new hybrid estimation algorithm is proposed to track the train’s movement and estimate the train’s operation state. Interacting the Multiple Model (IMM) algorithm is a popular hybrid estimation algorithm based on multiple-model Kalman filters. It has been shown to give excellent performance with low computational cost in Blom and Bar-Shalom [5]. However, the IMM algorithm and other similar algorithms usually assume constant mode transition probabilities. The estimation algorithm presented in this paper has different mode transition probabilities corresponding to different modes, called the Mode-Dependent-Hybrid-Estimation (MDHE) algorithm. The simulation results show that the proposed algorithm achieves more accurate tracking and estimation performance compared with the IMM algorithm. This paper is organized as follows: Sec. 2 introduces a stochastic linear hybrid system model of train dynamics. Sec. 3 proposes a corresponding hybrid estimation algorithm for the train tracking problem. In Sec. 4, the simulation illustrates the performance of the algorithm. Conclusions are presented in Sec. 5.

2 Hybrid model of train movement A hybrid system is a system whose evolution is driven by both the continuous time and the discrete events. The dynamics of continuous components are described by the traditional differential/difference equations. Only when some conditions are satisfied, jumps of the system’s state are triggered by discrete events. In the train control system, the train’s states change continuously with time, such as velocity and position, which can be described by differential equations [3, 6]. However, they will run into different modes triggered by discrete events, such as the switches between traction and brake. In the train’s movement, there are four operation modes: power, speed-holding, coast and braking. Let M  {1, 2,3, 4} correspond to these four discrete modes. It is assumed that p  0 is the traction power applied at the wheels and P is the maximum power, q  0 is the braking force and Q is the maximum braking force. To describe the train dynamics in each mode, we define x  [ s, s,  s ]T as the continuous states vector, where s denotes the train’s position, s denotes velocity, and s denotes acceleration. In the hybrid model of the train’s movement, the uncertainty inherent in the train’s motion is considered. The uncertainty is due to traction and braking ability, weight bearing, climate factors and so on, which is modelled by different white Gaussian noises with respect to different modes. Let tk  t0  kTs be the sampling time instant started from t0 ,

where Ts is the sample interval, and k  1, 2, . The train dynamics in each

mode are described as follows.

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2.1 Train dynamics

1)

Power Mode. The traction force is equal to the maximum power and the braking force is zero. The control in power mode is given by p  P and q  0 . In the power mode, we model the uncertainties as a white Gaussian noise. The train dynamics are described by 1 Ts Ts 2 / 2  Ts 2 / 2      (1) x(k  1)  0 1 Ts  x(k )   Ts  Power , 0 0   1  1     where Power is white Gaussian noise with mean zero and covariance: 2 ]  0.052 (m s 2 ) 2  Power  E[Power

2)

(2) Different covariances are chosen for different modes by analyzing the train running conditions and moving data. Hold Mode. If the train is running at a constant speed, we call this mode speed holding or simply hold. When the train is in this mode, the traction power changes with various resistances and braking force q  0 . The model is given by Ts 2 / 2  1 Ts 0      x(k  1)   0 1 0  x(k )   Ts  Hold , (3)  1  0 0 0    where  Hold is white Gaussian noise with mean zero and covariance: 2 ]  0.032 (m s 2 ) 2  Hold  E[Hold

3)

4)

(4) Coast Mode. There is no power applied and no braking in coast mode, i.e. p  0 , q  0 . In the coast mode, the model is similar to that the model used in power mode. 1 Ts Ts 2 / 2  Ts 2 / 2      (5) x(k  1)   0 1 Ts  x(k )   Ts  Coast 0 0    1    1 

The process noise in the Coast mode is 2 ]  0.012 (m s 2 ) 2 Coast  E[Coast (6) Braking Mode. In the Braking mode, the speed declines by full braking force, i.e. p  0 and q  Q . The dynamic model is as following: 1 Ts  x(k  1)   0 1 0 0 

Ts 2 / 2  Ts 2 / 2     (7) Ts  x(k )   Ts  Braking  1  1    The process noise Braking is a white Gaussian noise with mean zero and

covariance

2  Braking  E[Braking ]  0.052 (m s 2 ) 2

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(8)

92 Computers in Railways XII 2.2 Measurement model

In train control systems, the measurement of train’s speed and position are taken by the corresponding sensors. All measurements are subject to uncertainty due to the time delay and measurement disturbance. Thus, it can always be approximated by a linear model given by  s (k )  1 0 0  (9) z (k )    x(k )   (k )  , 0 1 0    s 

where  s ( k ) ,  s ( k ) are Gaussian noise with mean zero and covariance:  E[ s 2 ] 0  0.1 0  R   E[ s 2 ]  0 0.05  0

(10)

3 Hybrid estimation algorithm for train tracking We rewrite the train dynamics as a stochastic linear hybrid model as: x(k )  Am ( k ) x(k  1)  Dm ( k ) (k ) z ( k )  Cm ( k ) x ( k )   m ( k ) ( k )

(11) (12)

Where x(k )  R and z (k )  R are continuous state and the measurement variables, respectively. m(k )  M  {1, 2,3, 4} is the discrete state at time k , corresponding to four different operation modes: Power, Hold, Coast, and Braking. The process noise m ( k ) (k ) and the measurement noise m ( k ) (k ) are n

p

uncorrelated Gaussian sequences with zero mean. We use m(k )  j to denote the event that the system is in mode j at time k , and m(k  1)  i to denote the event that the system is in mode i at time k  1 . A continuous-state-dependent mode transition matrix is defined to describe the evolution of mode m(k ) :  ( x(k  1))  { ij ( x(k  1))}i , j 1,2,3,4 (13)

 ij ( x(k  1)) : p[ j | i, x(k  1)]

(14)

for i, j  {1, 2,3, 4} . It is worthy to note that in some linear hybrid estimation algorithms, such as IMM algorithm, the mode transition matrix  is constant and does not depend on the states. We propose an estimation algorithm with different mode transition probabilities corresponding to different modes, called Mode-Dependent-HybridEstimation (MDHE) algorithm. Fig.1 shows a schematic of the MDHE algorithm. MDHE also uses a bank of Kalman filters (KF1 to KF4) to compute the mode probabilities i (k  1) and the continuous state estimate xˆ(k  1) . However, individual Kalman fitters share information about the other Kalman fitters through new initial conditions at each time step. The components of MDHE in Fig.1 are described as follows:

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xˆi (k ) Pi (k )

i ( k )

Compute mode transition probability

Mixing

ij (k )

x j 0 (k ) Pj 0 (k )

Mode probability update

KF1

 j (k  1)

KF2

xˆ j (k  1)

KF3

KF4

Pj (k  1)

Output

 j (k  1) xˆ(k  1) P(k  1) Figure 1: 1)

Structure of the MDHE algorithm.

Mixing probability. This is the probability that the system is in mode i at time k , given that it is in mode j at time k  1 ( i, j  {1, 2,3, 4} )

ij (k  1| k ) 

1  ij i (k ) , cj

(19)

where c j is a normalisation constant, i (k ) is a measure of the probability

2)

that the system is in mode i at time k . It is assumed that i (0) is given, which should be i (0)  1 for a specific mode i , with i (0)  0 for other modes. New initial conditions. For each Kalman filters, the initial states xˆ0 j (k ) and covariance P0 j (k ) are computed by weighting the output of each Kalman xˆ0 j (k )   xˆi (k )ij (k  1 | k )

filters with mixing probability as the weight N

i 1

P0 j (k )   [ Pi (k )  [ xˆi (k )  xˆ0 j ( k )]  [ xˆi ( k )  xˆ0 j ( k )]T ]ij (k  1 | k )

(20)

N

i 1

3)

(21)

where xˆi (k ) and Pi (k ) are the estimation of state and its covariance of KF i at time k . Mode Transition Probability. The mode transition matrix  is constant in the IMM algorithm. In this paper, we utilize the objective velocity-speed profile information to model the mode transition probabilities as modedependent probabilities. Each operation mode has a mode transition matrix and the system switches among these matrixes depending on the objective curve and continuous state. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

94 Computers in Railways XII 4)

Kalman filter. Four Kalman filters run in parallel and each Kalman filter computes the xˆ(k  1) and P (k  1) using the initial conditions xˆ0 j (k ) and P0 j (k ) .

5)

Mode probabilities update. The probability of mode j at time k  1 is computed as follow N 1  j (k  1)   j (k  1)  ji i (k ) (22) C i 1 where C is a normalisation constant,  j (k  1) is the likelihood function,  j (k  1)  N p (rj (k  1); 0, S j (k  1))

defined as

(23)

where rj (k  1)  z (k  1)  C j xˆ j (k  1| k ) is the residual of Kalman filter j , and S j (k  1) is its covariance.

6)

Output. The estimation of state is a weighted sum of the estimates from four Kalman filters. The mode which has the highest mode probability is the mode estimate. xˆ (k  1)   xˆ j (k  1)  j (k ) N

j 1

P (k  1)   {Pj (k  1)  [ xˆ j (k  1)  xˆ (k  1)]}

(24)

N

j 1

[ xˆ j (k  1)  xˆ (k  1)]T } j (k  1)

mˆ (k  1)  arg max  j (k  1)

where mˆ (k  1) is the mode estimation at time k  1 .

(25) (26)

j

4 Simulations We consider an optimal speed-position trajectory of train’s movement as shown in Fig.2. The mode transition matrixes of MDHE are chosen as follows:  0.9 0.06 0.03 0.01  0.06 0.9 0.03 0.01  0.06 0.9 0.03 0.01  0.9 0.06 0.03 0.01      Power   Hold   0.06 0.9 0.03 0.01  0.9 0.06 0.03 0.01      0.06 0.9 0.03 0.01 ,  0.9 0.06 0.03 0.01 ,  Coast

0.01 0.01  0.01  0.01

0.06 0.9 0.03 0.01 0.01  0.06 0.9 0.03  Coast   0.01 0.06 0.9 0.03   , 0.06 0.9 0.03 0.01

0.03 0.06 0.9  0.03 0.06 0.9  0.03 0.06 0.9   0.03 0.06 0.9  .

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We compare the results of MDHE with that of IMM algorithm with constant mode transition matrix as  0.9 0.1 3 0.1 3 0.1 3  0.1 3 0.9 0.1 3 0.1 3 .  I MM    0.1 3 0.1 3 0.9 0.1 3    0.1 3 0.1 3 0.1 3 0.9  60

50

Speed (m/s)

40

30

20

10

0 0

5000

10000

15000

20000

25000

30000

35000

Position (m)

Figure 2:

The optimal speed-position curve of the train.

40

MDTHE IMM

Position error (m)

30 20 10 0 -10 -20 -30 -40 0

200

400

600

800

Time (s) 4

MDTHE IMM

Velocty error (m/s)

3 2 1 0 -1 -2 -3 -4 0

200

400

600

800

Time (s)

Figure 3:

Comparison of tracking accuracy of MDTHE and IMM.

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1.0

ture mode estimated mode Mode probabilitiesfrom MDTHE

Train Driving Mode

4

3

2

0.8

Power Hold Coast Braking

0.6

0.4

0.2

1 0

200

400

600

0.0

800

0

200

Time (s)

400

600

800

Time (s) 0.2520

ture mode estimated mode

Power Hold Coast Braking

0.2515 0.2510

Mode probabilitiesfrom IMM

Train Driving Mode

4

3

2

0.2505 0.2500 0.2495 0.2490 0.2485

1 0

200

400

600

800

0.2480 0

200

Time (s)

Figure 4:

400

600

800

Time (s)

Estimation of modes and their probabilities.

Fig. 3 and Fig. 4 compare the tracking accuracy and the mode estimation accuracy of the algorithms. The tracking accuracy of MDHE and IMM algorithm depends on the design of the mode transition matrix. It is easy to see that MDHE has better tracking performance compared with IMM. The result also shows that the proposed algorithm improves the accuracy of the operation mode estimation.

5 Conclusions In this paper, a hybrid system model is introduced to describe the train’s dynamics. The stochastic factors during the train’s movement are considered in this model. A new hybrid estimation algorithm is proposed for the train to track the objective velocity-position curve more accurately with mode dependent transition probability matrixes. Better tracking performance and the accuracy of the algorithm have been illustrated with simulations.

Acknowledgements The authors would like to acknowledge that this work is supported by the Foundation No. 60634010, RCS2008ZQ003, and W08J0270.

References [1] Seah, C.E. & Hwang, I., Terminal-Area aircraft tracking using hybrid estimation [J]. Journal of Guidance, Control, and Dynamics, 32 (3), pp.83684, 2009. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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[2] Hwang, I., Balakrishnan H. & Tomlin C., State estimation for hybrid systems: applications to aircraft tracking [C]. IEE Proceedings of Control Theory Application, 153(5), pp.556-566, 2006. [3] Howlett, P.G. & Pudney P.J., Energy-Efficient Train Control, Advances in Industrial Control, Springer, London, 1995. [4] Zhu, J. & Feng, X., The simulation research for the ATO model based on fuzzy predictive control, Autonomous Decentralized Systems, ISADS Proceedings. 2005. [5] Blom H.A.P. & Bar-Shalom Y., The interacting multiple model algorithm for systems with markovian switching coefficients. IEEE Transactions on automatic control, 33(8), pp.780-783, 1988. [6] Khmelnitsky E., On an optimal control problem train operation, IEEE Transactions on Automatic Control, 45(7), pp.1257-1266, 2000.

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Latent energy savings due to the innovative use of advisory speeds to avoid occupation conflicts F. Mehta, C. Rößiger & M. Montigel systransis Ltd., Switzerland

Abstract Track occupation conflicts are frequent in standard railway operations today. Train drivers, who are not aware of such conflicts in advance, are forced to stop, which results in additional delays, timetable instability, and a waste of energy. This could be avoided if they were informed about the conflict and had a chance to adapt their driving behaviour accordingly. The innovative computer-based train control system “Automatic Functions Lötschberg” (AF), developed by systransis Ltd, tries to reduce these negative effects by sending advisory speeds to the drivers of conflict affected trains in the Lötschberg base tunnel. This article presents the results of a study done using real operational data from the Lötschberg base tunnel to estimate the energy savings due to the AF sending advisory speeds. These results are then extrapolated to estimate the latent energy savings that could be achieved if a system like the AF were in operation over the entire Swiss railway network. Keywords: advanced train control, energy savings, advisory speeds.

1 Introduction With a length of 34.6 km under the Swiss Alps, the Lötschberg base tunnel is currently the longest land tunnel in the world. The computer-based train control system “Automatic Functions Lötschberg” (AF), developed by systransis Ltd. as a subcontractor of Thales Ltd, has been monitoring and controlling the train traffic through the Lötschberg base tunnel since its opening in December 2007. The topology of the tunnel introduces special challenges in its operation. The northernmost two thirds of the tunnel is a single-track section, which feeds into a two-track section in the south via the high-speed point “W60”. Figure 1 illustrates this topology. Solving track occupation conflicts between trains is especially important since the single-track section needs to be used optimally. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100101

100 Computers in Railways XII

Figure 1:

Topology of the Lötschberg base tunnel.

The tunnel is equipped with ETCS Level 2 as its train protection system which allows continuous tracking of train speeds and positions, and communication to the on board units of each train. One essential function of the AF is forecasting and solving track occupation conflicts by calculating an optimal speed trajectory for a train affected by a conflict that would otherwise have to stop or slow down. It then sends advisory speeds via GSM-R to the train driver who uses them as a recommendation for his onward journey. This gives him the possibility to solve the conflict by preemptively slowing down, instead of eventually being forced to stop. Advisory speeds are sent to the affected train as text messages in regular time intervals of 30 seconds. A final message “vopt = vmax” is sent when the advisory speed limit is to be lifted. The primary goal of sending advisory speeds to train drivers is to reduce collateral delays and minimise train stops caused by conflicts, and thereby maintain capacity and timetable stability. Figure 2 illustrates the approach of solving track occupation conflicts used by the AF. More details on the computational aspects and use of the AF in the Lötschberg base tunnel can be found in Montigel et al. [1] and Montigel [2]. Although not its main aim, a welcome side effect of this optimisation is the reduction of the traction energy needed by trains to travel through the tunnel. This claim is intuitive: energy consumption should be lower if a train is not required to come to a full stop. In order to test this claim empirically, the following study was undertaken to quantitatively estimate how much traction energy was saved in this way using real operational data. Technologies for increasing energy efficiency in the context of railway operation are receiving increased attention. A review of these technologies can be found in [7]. Increasing energy efficiency through energy-optimal train trajectories have also been studied extensively. The possibilities for computing and using such trajectories are described in detail in Albrecht [8], Howlett and Pudney [9], and Franke et al. [10]. Lüthi [4] discusses the energy saved as a result of

integrated real-time rescheduling. Mitchell [11] discusses the impact of advisory systems on energy savings and uses a simulation-based model to quantitatively estimate these savings. The novelty of the work described in this article is that this is the first time that data from real-world commercial railway operations with traffic flow optimisation is available and used to determine energy savings.

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Figure 2:

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Time gained by sending advisory speed. The solid line is optimised and avoids a halt.

2 Methodology used The following general methodology was used to estimate the energy savings as a result of sending advisory speeds. Pre-recorded operational data (i.e. log files generated by the AF) from the tunnel was used to extract all train movements in the tunnel during which an advisory speed was sent to resolve an occupation conflict. This operational data was used to reconstruct the actual train trajectory through the tunnel, and thereby the actual traction energy consumed for each such train run was calculated. In order to compute the energy savings, a comparison of the actual traction energy consumed with the energy that would have been consumed if no advisory speeds were sent (i.e. for the non-optimised case) needs to be done. The functionality of the AF to send advisory speeds has been continuously active since the start of operation of the tunnel. Since such a study cannot warrant turning off this functionality just for test purposes it was not possible to directly measure the energy consumed if no advisory speeds were sent. Therefore, assumptions about the behaviour of train drivers for the non-optimised case needed to be made. Based on these assumptions, a non-optimised train trajectory was generated and used to calculate the energy consumption for the nonoptimised case. This was then used to estimate the energy saved as a result of sending advisory speeds. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 3:

Conflict types for which an advisory speed is sent in the tunnel.

The rest of this section describes the above methodology in greater detail. 2.1 Conflicts considered in this study The AF sends advisory speeds to train drivers in the tunnel in the following three general cases: a. Cross conflicts b. Merge conflicts c. Follow-up conflicts Figure 3 illustrates these three conflict types. The filled train is the one causing the conflict, and the hollowed train is the one affected by it. The hollowed train receives advisory speeds. It should be noted that the AF also solves other types of conflicts in the tunnel, but these are disregarded in this study. Out of these three cases, cross and merge conflicts are of interest as far as energy savings are concerned since they will, in most cases, result in a full halt of the affected (hollowed) train if not solved by the AF optimisations. Follow-up conflicts are not currently considered, as this would significantly complicate the method used, requiring more than one train to be considered per conflict case. Furthermore, such follow-up conflicts are rare in practice since the train dispatcher takes care to avoid them by positioning faster trains before slower ones. Another point to note is that conflicts are often interdependent. Solving a conflict favourably in the present time can avoid future conflicts. This study though only considers individual conflicts in order to avoid too much speculation into the future. 2.2 Area of interest The AF log files are processed to extract the following data for each train affected by a cross or merge conflict in the tunnel:  Train characteristics: engines, weight, class (passenger or freight)  Position and speed reports at various times  Transmitted advisory speeds at various times The analysis is restricted to the section of the travelled train path affected by the receipt of advisory speeds. The start position of this area of interest is the

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Figure 4:

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Area of interest and train trajectories for the optimised and non-optimised cases.

position where the affected train receives the first advisory speed. The end position of this area of interest is the position where the train has finished its reacceleration after the merging point W60 and reaches a stable speed over a defined distance. Figure 4 illustrates how this area of interest is determined using the optimised (actual) train trajectory, and how the non-optimised (estimated) trajectory is calculated using the start and end speeds and positions, and the position of the last signal in front of the point W60. The calculation of the nonoptimised trajectory will be described in detail later. Note that the figure is to be read from right to left since the affected trains travel in the direction of decreasing mileage. 2.3 Energy calculation model Energy consumption is calculated using the following standard formula, by numerically integrating the traction force exerted by the engines ( Fi ) over the respective travelled distances ( si ):

E

F s n

i i

i1

The following components of the traction force are considered:  Rolling resistance (dependent on speed, always positive)  Tunnel resistance (dependent on speed, always positive) WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

104 Computers in Railways XII  

Gradient resistance (positive for uphill tracks, negative for downhill) Free acceleration force (positive for acceleration, negative for braking) The free acceleration force is determined using the position and speed reports extracted from the AF log files. The resistances are assumed to be constants that depend on the actual speed and train class. The sum of all these forces needs to be applied by the engines and is used to calculate the traction energy needed by them. 2.4 Regenerative braking

It is assumed that a certain ratio of the negative free acceleration force could be recuperated. This ratio accounts for the efficiency of the regeneration process, the conductive losses of the overhead wire, and the fact that the recuperated energy can only be effectively used if there is an energy consumer currently connected to an interconnected overhead wire. Two values are used for this ratio: 1. For the optimised case it is expected that a high ratio (i.e. 40%) of the deceleration energy can be recuperated. This is because the AF calculates advisory speeds in such a way that the required deceleration can be achieved solely using regenerative braking. 2. For the non-optimised case a lower ratio (i.e. 20%) is assumed because a larger amount of the total braking force has to be provided using mechanical brakes. 2.5 Estimating the non-optimised case For the non-optimised case it is assumed that no advisory speeds are transmitted. The train driver doesn’t know about the conflict until he has to brake because of the last signal in front of the point W60. The calculation of the train trajectory for the non-optimised case uses the start and end speeds and positions, as well as the position of the last signal in front of the point W60 as described earlier. The trajectory consists of the following phases: 1. Travelling with the start speed until hitting the braking curve of the last signal in front of the point 2. Braking to standstill at the signal in front of the point 3. Accelerating to the end speed The non-optimised curve in Figure 4 illustrates this trajectory. The model used for calculating the non-optimised trajectory is based on the standard train dynamics model contained in Hürlimann [3], which is also the one used in the AF. Brake applications are modelled as constant decelerations, dependent on the class of the train. Coasting (i.e. speed decrease without application of traction force) was not considered. For acceleration, the traction capabilities of individual engines are considered. For each engine type, the traction forces dependent on the current speed are used. The free acceleration is calculated using the train weight, dynamic mass factor, class, and the driving resistances described earlier. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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The resulting free acceleration is then used to calculate the traction energy needed to complete the non-optimised trajectory and thereby estimate the energy saved as a result of sending advisory speeds.

3 Results and discussion For this study 746 optimised train journeys over a three-month period from August 2008 to October 2008 were considered. Of these, 117 journeys involved passenger trains, and 629 involved freight trains. 3.1 Quantitative results The total estimated energy savings for these journeys was calculated to be 45,655 kWh. The average saved traction energy per optimised journey was therefore 61.2 kWh. Although the absolute energy savings in kWh varies strongly for each train run, the percentage of the energy saved (with respect to the energy used to cover the area of interest in the non-optimised case) does not vary as significantly. This can be seen in Figure 5. The percentage of total energy saved to cover the area of interest was calculated to be 12.4%. These estimates are still on the conservative side since, as mentioned before, this study though considers individual conflicts as isolated, and does not take into account that present conflicts, if not solved favourably, can lead to future

Figure 5:

Distribution of the percentage energy saved due to sending advisory speeds.

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106 Computers in Railways XII conflicts. More conflicts would therefore be observed if advisory speeds were not transmitted, leading to a greater energy consumption in practice for the nonoptimised operation than the estimate calculated above. What is worth mentioning again is that energy savings are not directly taken into account in the calculation of advisory speeds. They are purely a side effect of reducing the delays resulting from a conflict. 3.2 Qualitative results Apart from the above quantitative analysis, the data was also qualitatively analysed in order to learn more about factors on which energy savings may depend: 3.2.1 Journey attributes It could be thought that the percentage energy savings depends on the train weight, or the ratio between the initial and advisory speeds. However, the analysis of this data did not reveal any such dependencies. 3.2.2 Train class An interesting question is whether energy savings due to freight and passenger trains differ. Passenger trains travel with higher speeds (160-200 km/h) through the tunnel and have to spend a higher ratio of their energy to overcome resistive forces. Freight trains are heavier and therefore need longer distances to accelerate. The energy consumption was partitioned for the two train classes with the result that the percentage of saved energy for freight trains is slightly higher (12.9%) than that for passenger trains (10.3%). 3.2.3 Regenerative braking The impact of regenerative braking on the consumed energy is often emphasized. Since the total amount of recoverable energy is subject to many influences, it is not easy to draw a final conclusion concerning the effectiveness of regenerative braking. The assumption in this study was that all considered trains are capable of regenerative braking and an average ratio of 20% of the free braking energy could be recuperated in the non-optimised case, and 40% in the optimised case. For the sake of comparison, energy savings with different ratios (0%-0%; 50%100%) was calculated. The results for these ratios deviated not more than 1.5% from the original result. Reasons for this small impact of regenerative braking are: 1. A high ratio of the braking energy is used to overcome resistance forces, which are higher in a tunnel when compared to an open track, leaving only a minor part for recuperation. 2. The distances where regenerative braking can be applied are short compared to the total length of the area of interest. Eventually, regenerative braking seems not to have a big influence on the energy savings provided by the AF. Nevertheless, it may play a relevant role when the total consumed energy is considered.

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3.2.4 Driver behaviour For this study, operational data from the tunnel was used to compute the energy consumption in the actual case, regardless of whether the train driver obeyed the advisory speeds or not. It would be interesting to see how much the saved energy estimated from the actual data deviates from the optimised trajectory calculated by the AF. To do this an ideally optimised trajectory based on the first advisory speed transmitted by the AF was computed. The resulting estimated energy savings were observed to be 10.1% compared to the 12.4% estimated energy savings using the actual travelled trajectory, i.e. not all train drivers obey the advisory speeds strictly and nonetheless consume less energy than estimated by the AF. An explanation for this maybe unexpected result can be that in reality, the train drivers partly applies coasting, which is not considered by the calculations for the actual optimised case. Another point to note is that the aim of sending advisory speeds is to minimise delays, and energy savings is only a side effect of this. The computed optimal trajectory need not therefore consume the least energy. The possibility that the train driver chooses a trajectory that is energetically better is therefore possible.

4 Latent energy savings for the entire Swiss railway network The study described till now estimated that the AF sending advisory speeds saved about 60 kWh of traction energy per conflict. Using this figure, it would be interesting to make a rough extrapolation of the total energy that could be saved, given that a system, such as the AF, were in operation over the entire Swiss railway network. The following assumptions on the daily train traffic from [5] and [6] are used: 1. About 1500 passenger journeys (only long-distance trains) occur each day, each experiencing on average 2 conflicts leading to an unplanned stop 2. About 2000 freight journeys occur each day, each experiencing on average 3 conflicts leading to an unplanned stop. It is easy to see that the resulting latent energy savings for an entire year is in the order of 200 GWh (i.e. 200 million kWh). The current market value of electrical energy is around CHF 0.20 per kWh. Hence, this translates into a monetary saving of about CHF 40 million in energy costs alone, not to mention the added value and monetary gain due to fewer delays and better timetable stability, and reduced maintenance costs due to less wear and tear. Since engine efficiency and conductive resistance of the overhead wire have not been taken into account for these figures, the metered energy saved would therefore, in practice, be more than the estimates just calculated. As mentioned before, it also has to be taken into account that the optimum for energy savings does not necessarily have to match the optimum for operational purposes.

5 Conclusion It can be concluded from this study that there exists a significant potential to save energy in railway operations by introducing a computer-based train control WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

108 Computers in Railways XII system like the AF that uses advisory speeds to resolve occupational conflicts in a railway network. In the case of the Lötschberg base tunnel, as per conservative estimates, it is observed that about 60 kWh of traction energy is saved per conflict due to advisory speeds. This translates to a savings of 12.4% of the traction energy needed to travel through the conflict-affected region, when compared to an estimated non-optimised train trajectory. It is also observed that this percentage of saved energy does not depend significantly on regenerative braking, conflict size, or class of train. Even a rough extrapolation of these results for the entire Swiss railway network yields a significant energy savings potential of about 200 GWh for a year. The resulting monetary savings of about CHF 40 million per year could be therefore well invested in a computer-based train control system like the AF on a nationwide level, which would also provide further benefits such as reduced delays and better timetable stability.

References [1] Montigel M., Kleiner C. & Achermann E., Experience with the Speed and Traffic Optimisation employed in the novel Train Traffic Control Center of the Lötschberg Base Tunnel in Switzerland, Proceedings of Railway Capacity – The Engineering Challenge, 2007. [2] Montigel M., Operations control system in the Lötschberg Base Tunnel, RTR - European Rail Technology Review 02/2009, 2009. [3] Hürlimann D., Objektorientierte Modellierung von Infrastrukturelementen und Betriebsvorgängen im Eisenbahnwesen, Diss. ETH Nr. 14281, ETH Zürich, 2001. [4] Lüthi M., Evaluation of energy saving strategies in heavily used rail networks by implementing an integrated real-time rescheduling system, Comprail 2008 Proceedings, 2008. [5] Information from Media centre SBB, 2009. [6] http://www.reisezuege.ch/, queried 24th November 2009 for timetable period 2009/10. [7] http://www.railway-energy.org, Website for Energy Efficiency Technologies for Railways. [8] Albrecht, T. Energy-Efficient Train Operation, Chapter in Railway Timetable and Traffic, pp 83-106, Eurail Press, 2008. [9] Howlett, P.G., Pudney, P.J. Energy-efficient train control, Springer, Berlin, 1995. [10] Franke, R., Meyer, M., Terwiesch, P. Optimal Control of the Driving of Trains, Automatisierungstechnik 50(12), pp 606-613, 2002. [11] Mitchell, I, The Sustainable Railway – Use of Advisory Systems for Energy Savings, IRSE Technical Paper, 2009.

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Section 2 Traffic control and safety of high-speed railways in Asia Special session organised by N. Tomii

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How the punctuality of the Shinkansen has been achieved N. Tomii Chiba Institute of Technology, Japan

Abstract The high speed railway line in Japan began operation in 1964. The high speed railway is called the Shinkansen and is known for its safety and reliability. In addition, the Shinkansen is well known for punctuality. As a matter of fact, the average delay of trains is less than one minutes every year. The Shinkansen runs along dedicated lines, which seem to be advantageous in keeping punctuality. However, there are lots of disadvantages as well. For example, although traffic is very dense, resources are not abundant. In some Shinkansen lines, trains go directly through conventional railway lines and the Shinkansen is easily influenced by the disruption of those lines. Punctuality of the Shinkansen is supported by hardware, software and humanware. In this paper, we first introduce a brief history of the Shinkansen and then focus on humanware, which makes the punctuality possible. Keywords: high speed trains, punctuality, rescheduling, Shinkansen.

1 Introduction In 1964, a high speed railway line opened in Japan. The new line connects Tokyo, the capitol, and Osaka, the second largest city located 600 km away. The maximum speed of trains was 210km/h, which was almost twice that of other trains in those days and the travelling time between these two cities was halved to only three hours and ten minutes. The new high-speed line was called the Shinkansen and it had a great impact not only on railways in Japan, but also on railways worldwide. From that time on, the Shinkansen was extended to other areas of Japan and the length of Shinkansen lines is about 2,200 km at present. We may well say that characteristics of the Shinkansen are very dense traffic, very high safety and very high punctuality. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100111

112 Computers in Railways XII In the Tokaido Shinkansen line, the total number of trains a day is 323 at present. As a matter of fact, at Tokyo station, you can see trains departing every several minutes. The Shinkansen is proud of ultimate safety with evidence that no passengers in trains were killed or injured for 46 years since the Shinkansen began its operation. Another important characteristic is punctuality. In the fiscal year 2008, the average delay of trains in the Tokaido Shinkansen was just 0.6 minutes, namely 36 seconds. People might think this punctuality is achieved thanks to abundant resources, such as extra train-sets, extra crews, lots of tracks at stations, etc. However, this is not true. As described later, resources are not abundant. The punctuality of the Shinkansen is achieved by hardware, software and humanware. As for the hardware, efficiency and reliability of signalling systems, electrical power transmission, tracks and rolling stocks play quite an important role in keeping punctuality. As for the software, the Shinkansen is equipped with various kinds of computer systems. To name a few, route control systems, operation management systems, track maintenance management systems, rolling stock maintenance management systems, etc, which are indispensable in keeping the punctuality of the Shinkansen. Humanware, however, is very important as well. In this paper, the importance of humanware to keep punctuality is focused upon.

2 Brief history of the Shinkansen An outline of the Shinkansen network is given in Table 1 and Figure 1. In Japan, each line is given a line name such as Tokaido, Shinkansen and Sanyo Shinkansen and so on. Table 1:

Outline of the Shinkansen network.

Line

From

To

Distance

Remark

Tokaido

Tokyo

Osaka

515.4km

Sanyo

Osaka

Hakata

553.7km

Trains start from Tokyo.

Tohoku

Tokyo

Hachinohe

593.1km

Extended to Aomori (2010/12)

Joetsu

Omiya

Niigata

269.5km

Trains start from Tokyo.

Hokuriku

Takasaki

Nagano

117.4km

Trains start from Tokyo.

Akita

Morioka

Akita

127.3km

Trains start from Tokyo.

Yamagata

Fukushima

Shinjo

148.6km

Trains start from Tokyo.

Kyushu

Yatsushiro

Kagoshima

126.8km

Extended to Hakata (2011/3)

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Shinkansen under construction conventional lines Aomori

achinohe Akita

Akita orioka

Yamagata

Shinjo

Tohoku Sendai

oetsuNiigata Nagano

ukushima

okuriku Sanyo

akata

Nagoya Takasaki saka

Tokaido

Tokyo

yushu Yatsushiro

agoshima

Figure 1:

The Shinkansen network.

Although the Tokaido Shinkansen belongs to JR (Japanese Railways) Central Co. Ltd. and the Sanyo Shinkansen belongs to JR West Co. Ltd, trains on these two Shinkansen lines are operated jointly by these two companies because trains on the Tokaido Shinkansen go directly to the Sanyo Shinkansen and vice versa. The Tohoku, Joetsu, Nagano, Akita and Yamagata Shinkansens are managed by JR East Co. Ltd and the Kyushu Shinkansen is managed by JR Kyushu Co. Ltd. The maximum speed of the Shinkansen is 300km/h at present, and an increase of the speed is in the planning stage. Since its start of operation, the Shinkansen has taken a lot of passengers from airplanes. As a result, the flight service between Tokyo and Nagoya and between Tokyo and Sendai were given up after the Shinkansen began operation. At present, the Shinkansen keeps 60% of the market share between Tokyo and Akita and 81% market share between Tokyo and Osaka, for example [1, 2]. Figure 2 shows the annual transportation volume of the Shinkansen network and Figure 3 shows the market share of the Shinkansen for passenger transportation in fiscal year 2007 [3]. As you can see, the Shinkansen bears 82,825 million person kilometres, which is 6% of the market share of all over Japan. The Shinkansen was designed with a brand new philosophy. It is totally different from railways in those days (railways other than the Shinkansen are called conventional railway lines in contrast with the Shinkansen). Major differences between the Shinkansen and conventional railway lines are: WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Passengers (x1,000)

PersonKilometer( xmil.)

350,000 300,000 250,000 200,000 150,000 100,000 50,000 1965 1970 1975 1980 1985 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

0

Figure 2:

Annual transportation volume of the Shinkansen. Ship, 3,834

Airplane,  84,327

Conv.Railway,  322,787 Car, 936,049

Shinkansen,  82,825

Figure 3:

Market share of the Shinkansen.

1. The Shinkansen runs on dedicated lines, all of which were newly constructed. 2. The gauge is standard (1435mm) whereas that of conventional railway lines is narrow (1067mm). 3. Along all the lines, high fences are put to prevent public from approaching the tracks. There are no level crossings. These are established by special laws for the Shinkansen, which specify the rules about construction and operation of the Shinkansen. 4. Train schedules are rather simple compared with those of conventional railway lines. No freight trains are running and no night trains are running, for example. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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The Yamagata Shinkansen and the Akita Shinkansen are a bit different from other Shinkansens. The Yamagata and Akita Shinkansen lines are not regarded as Shinkansens from a legal point of view. Trains of the Tohoku Shinkansen go directly into these Shinkansen lines where trains other than the Shinkansen are also running. The gauges are standard (gauge was broadened so that the Shinkansen train-set can run when the Yamagata and Akita Shinkansens opened. In some part, lines are equipped with three rails so that both trains of the Shinkansen and trains of conventional railway lines can run) but the special laws about the Shinkansen are not applied. So, there are level crossings and no fences along the line etc. Trains are coupled and decoupled at the junction stations (Fukushima for the Yamagata Shinkansen and Morioka for the Akita Shinkansen). One of the most remarkable characteristics of the Shinkansen is high punctuality. The average delay of the Tokaido Shinkansen is depicted in Figure 4[4]. In Japan, if a train is more than one minute behind the planned schedule, the train is considered to be “delayed” (this rule is the same in conventional railway lines). From Figure 4, we can observe that average delay of the Tokaido Shinkansen has been less than one minute for almost twenty years. The figures for the Tohoku and Joetsu Shinkansens are a bit larger than those of the Tokaido Shinkansen because as stated earlier, the punctuality of the Tohoku Shinkansen is easily influenced by the delay of trains in conventional railway lines. However, the figures are also less than one minutes every year recently.

3 Disadvantages in keeping punctuality It may well be said that the Shinkansen is in an advantageous situation in keeping punctuality. In other words, special attention has been paid to prevent various kinds of disturbance from occurring. In fact, collision with cars at level crossings which often happen in conventional railway lines never happen in the Shinkansen (except the Yamagata and Akita Shinkansens). People often commit suicide in railway lines but this seldom happen in the Shinkansen neither.

average delay (min.) 3.0  2.5  2.0  1.5  1.0  0.5  1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0.0 

Figure 4:

Average delay of the Tokaido Shinkansen.

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STAT N Z

STAT N Y

STAT N X AY

Figure 5:

Partial cancellation of trains (grey: planned, black: result).

Still there are a lot of disadvantages as follows: 1. Trains are operated very densely. In the Tohoku and Joetsu Shinkansens, This means if a train is delayed, a lot of other trains are influenced. So, an extensive rescheduling is required. 2. It is difficult to use effective rescheduling methods. Cancelation of trains, which is an effective measure in rescheduling, is usually difficult to use in the Shinkansen. This is because trains run for a long distance and trains have seat reservations. For the similar reason, partial cancelation is never done in the Shinkansen. In conventional railway lines, partial cancelation of trains (see Figure 5) is usually done to absorb delays but this is never done in the Shinkansen because a lot of passengers are inconvenienced at Station Y. Partial cancellation of trains is never done in the Shinkansen. 3. It is necessary to keep enough time at terminal stations for turning out, because cleaning inside trains and exchange of linen etc. are necessary. This means it is difficult to absorb delays at terminal stations. 4. Resources are not abundant. Basically, there are no stand-by train-sets and crews and there are not abundant tracks at stations. For example, Tokyo station of the Tokaido Shinkansen from which 14 trains per hour depart in the busiest time, has only six tracks. In Tokyo station of the Tohoku-Joetsu Shinkansen, there are only four tracks, from which eight trains per hour depart in the busiest time (It is all right to understand that there two Tokyo stations; one has six tracks and the other has four tracks). This becomes a serious constraint in rescheduling. 5. Natural disaster often happens and disrupts punctual train operation. In some part of the Tokaido Shinkansen, they sometimes have a severe snowfall in winter. Snowy weather itself is not a problem at all. However, if there is a snowfall, snow sticks to the surface of train-sets WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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and these cause problems. That is, when the train-sets come to a rather warmer area, the snowballs fall down and hit ballast. Then the stone hit the train-set or houses along the Shinkansen line. So, trains are compelled to decrease running speed in snowy area so that snow does not stick to the train-set. In the Akita and Yamagata Shinkansens, on the other hand, trains run through regions where weather especially in winter is harsh and sometimes trains are delayed because they have to decrease the running speed. 6. Connection with trains in conventional railway lines is considered to be very important. The Shinkansen takes charge of long distance transportation, and timetables of conventional railways are made taking convenient connection with the Shinkansen into full account. This means, however, if trains are delayed in conventional railway lines, the Shinkansen trains have to wait although a limit of waiting time is prescribed a priori. 7. In the Yamagata and Akita Shinkansens, trains go directly to conventional railway lines. In conventional railway lines, trains other than the Shinkansen including freight trains are also running. The Shinkansen trains in these two lines are coupled or decoupled at junction stations as stated above, and this implies that a delay in these two lines is easily propagated to the Tohoku Shinkansen, the Joetsu Shinkansen and the Hokuriku Shinkansen because these Shinkansens share a track in some part. 8. Route control is done by computer systems (PRC: Programmed Route Control system) totally automatically. You may think this is advantageous in keeping punctuality. However, should a system-down occur, it might cause a serious problem. Reliability of the PRC is very high but the higher the reliability is, the less skilled dispatchers are in manual operation of signals. Although a system-down is very unlikely to happen, trains do not run on time if it really happens.

4 Realizing the punctuality of the Shinkansen As described in detail in the previous section, there are a lot of disadvantages to achieve high punctuality in the Shinkansen. In this section, efforts and devices to concur the disadvantages are introduced. 1. As elaborate rescheduling as possible: When trains are delayed, elaborate rescheduling is done. An example of rescheduling is depicted in Figure 6. Gray lines mean planned schedule (left). Let us assume that Train 1 is delayed at Station Z for some reason. A common method of modifying the schedule in this case is shown in black lines (right). The train-set of Train 1 is to be turned back as a deadhead train and stored in the depot located next to Station Y. Another train-set is urgently set up from the depot and driven to Station X and it is assigned to Train 2 so that Train 2 can depart on time. This may be thought to be easy, but that is not true. Usually, there WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

118 Computers in Railways XII are no spare train-set at the depot. So, a lot of subsequent changes in trainset utilization schedule have to be made. In addition, there are no reserve crew as well. So, again a lot of subsequent changes in crew shift schedule have to be made. Figure 5 is just a simple example and in case of disruption, very elaborate rescheduling is done to reduce dissatisfaction of passengers as much as possible. 2. As standard as possible: Standard patterns and rules of rescheduling are prepared: In the case of the Yamagata and Akita Shinkansens, there occurs a serious problem about delay management if trains are delayed in conventional railway areas. Dispatchers have to make a decision whether they keep coupling or give up coupling at the junction station. JR East prepares a manual for such cases (see Figure 6). It describes if the delay is less than a certain threshold x, coupling has to be done, meaning trains in the Tohoku Shinkansen (main line) have to wait. If the delay is larger than x and less than another threshold y, coupling is given up (Figure 7 right). This means trains of the Akita/Yamagata Shinkansen run in the Tohoku Shinkansen line alone. If the delay is larger than y, the train is coupled with the next train (Train 2 in Figure 7) of the Tohoku Shinkansen, because it is very likely that the next train (Train 12) is also delayed. In case of giving up coupling, the problem is again in assigning crew. One more crew has to be squeezed out by drastically changing the subsequent crew schedules. 3. As simple as possible: This could be considered to be an important and basic idea in the operation of railways in Japan. I would say that in Japan it is believed that usage of facilities must be as simple as possible. There is an idea in the background that if usage of facilities is complicated, it might cause operational errors because there are so many trains running. For example, “double single track” is seldom used in Japan. This is because people hate to complicate usage of tracks, which is crucial in guaranteeing safety. Another example is the drivers’ shift. Licences for the Shinkansen are different from those of conventional railway lines. This means a driver of conventional railway lines is not permitted to drive the Shinkansen (and vice STAT N Z

STAT N Z

in

Tra in

in Tra

Tra

STAT N Y

STAT N Y

STAT N X

STAT N X

Figure 6:

An example of rescheduling.

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(b) Result

Delay management at a junction station where trains are coupled.

versa). Hence, it is impossible for a driver to drive a Shinkansen train and a conventional railway line train on the same day. This is to avoid drivers make any mistakes in operation because equipments of train-sets and operational rules are different between the Shinkansen and conventional railway lines. One more example is seat layouts of Shinkansen trains. Series 700 and Series N700 Shinkansen train-sets of the Tokaido Shinkansen were designed so that the seat layouts are totally the same as Series 300, which was mainly used at that time. This is because even if a Series 300 train-set is suddenly substituted with a Series 700 or N700 (or vice versa), it is not necessary to give any further announcement to passengers for their new seats, and it is all right just to say “Please take the seat described on your ticket.” Otherwise, extensive guidance to passengers about their new seats becomes necessary. It might be true that by making usage of facilities complicated, you can maximize the performance of the facilities. For example, in case of the double single track, if a train has an engine trouble between stations, you can continue train operation using the other track, which is not possible in a double track line. So, to cope with such a criticism, a lot of efforts to increase reliability of hardware are made in Japan in compensation of making usage of facilities simple. 4. As much training as possible: Route settings of all trains for all stations are automatically done by the programmed route control system (PRC). PRC has a long history and has an extremely high reliability. However, in case of a system-down, training of manual route setting is always done. It may be improbable that this training is of any use one day, but they continue the training just for a rainy day. 5. As much efforts as possible: As described in the previous section, in some part of the Tokaido Shinkansen line, they sometimes have a lot of snow in WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

120 Computers in Railways XII winter. The worst record was made in 1976. 635 trains were cancelled in that year due to heavy snow and the average delay of trains on snowy days was 20.1 minutes. Running speed must be decreased to avoid catching the snow and the snow on the surface of train-sets has to be removed. This means that trains are very much delayed on snowy days. In former days, people hit the snow on the train-set with a wooden stick to fall it down. Obviously, it took a lot of time and labour. Now, a new equipment using high pressure is used to remove the snow. More than that, JR Central made an intensive research about effective countermeasures to reduce damages caused by snow such as to install sprinklers to make snow wet and monitoring system for the condition of snow to decide when the sprinkler should be started and so on [5]. The result was outstanding. For more than 15 years, no trains were cancelled due to snow and the average delays on snowy days in fiscal years 2006 and 2007 were 1.4 minutes and 1.7 minutes respectively.

5 Conclusions The Shinkansen of Japan is well known for its punctuality. Although there are not abundant resources available, various kinds of ideas and hard training of dispatchers and crews have realized the punctuality. The Tohoku Shinkansen is extended to Aomori this year. The Kyushu Shinkansen between Hakata and Yatsushiro is opened next year and it is planned that some trains go to Kagoshima directly from Osaka. The lines and timetables of Shinkansens become more and more complicated but I am quite sure that this punctuality level will be kept in the future.

References [1] JR East Annual Report 2009: http://www.jreast.co.jp/e/investor/ar/2009/ [2] Annual Report of JR Central (in Japanese), 2009. [3] Facts and Figures of Japanese Railways - 2009 (in Japanese), Institution for Transport Policy Studies, 2009. [4] Environmental Report of JR Central 2009: http://english.jrcentral.co.jp/company/company/others/eco-report/_pdf/kankyo2009-e.pdf. [5] T. Amatani: Countermeasures to reduce delays of trains on snowy days (in Japanese), Technical Report of JR Central, Vol.8, 2009.

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Linkage of a conventional line dispatch system with the Shinkansen dispatch system Y. Yoshino Administration Division, Transportation Department, Kyushu Railway Company, Japan

Abstract With the partial opening of the Shin-Yatsushiro to Kagoshima-Chuo section of the Kyushu Shinkansen in March 2004, management of the connection to the conventional line limited express trains at Shin-Yatsushiro Station. It became important to provide a service in the Hakata to Kagoshima-Chuo section that was comparable to that of the transport system up to then. For that reason, station facilities were made to enable transfers between the Shinkansen and conventional line trains at the same platform. In addition, linkage functions between the Shinkansen and conventional line dispatch systems were set up as follows. - Referencing of conventional line timetables when considering revised Shinkansen timetables - Adding conventional line connection management functions to the Shinkansen programmed route control - Adding conventional line occupation display to the Shinkansen line occupation display and route control monitor - Displaying the conventional line timetable (planned and actual) on the Shinkansen timetable display monitor - Sharing of operation information provision between the Shinkansen and conventional lines - Guidance of trains and operation, including information relating to conventional line train connections on indicators for passengers The work is supported by means such as allowing dispatchers to identify the timetable of the day for the other type of train system and the current train operation status. Keywords: system linkage, transfer, operation control. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100121

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1 Introduction With the partial opening of the Shin-Yatsushiro to Kagoshima-Chuo section of the Kyushu Shinkansen in March 2004, management of the connection to the conventional line limited express trains at Shin-Yatsushiro Station. It became important to a provide service in the Hakata to Kagoshima-Chuo section that was comparable to that of the transport system up to then (Fig. 1). For that reason, station facilities were made to enable transfers between the Shinkansen and conventional line trains at the same platform (Fig. 2). Additional functions were also established so as to enable necessary information exchange between the Shinkansen and conventional line dispatch systems and allow dispatchers to identify the timetable of the day for the other type of train system and the current train operational status.

2 Outline of SIRIUS (super intelligent resource and innovated utility for Shinkansen management) SIRIUS consists of the following subsystems that form a total support from train operation planning to actual daily operation. The system configuration is shown in Fig. 3.

Figure 1:

Kyushu Shinkansen.

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Figure 2:

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Transfer between the Shinkansen and the conventional line.

Figure 3:

System configuration.

2.1 Transport planning system This system makes train timetables as well as schedules for vehicle operation and for drivers and conductors. In making train timetables, the system checks the travel times between stations and conflicts among trains at the stations. Then it plans departure, passing, and arrival times and tracks for each in-service and deadheading train (Fig. 4). It also makes vehicle scheduling and driver/conductor scheduling based on the created train timetables. For vehicle scheduling, the vehicle use plan is made based on the identification check of arrival and

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Figure 4:

Timetabling system.

Figure 5:

Vehicle/crew scheduling system.

departure stations, the check of the night stay station, etc. For driver and conductor scheduling, the work conditions are checked, and driver and conductor scheduling is made (Fig. 5). For each planning, the basic plan, the base for the train timetable revision, and the daily change plan, which is based on the basic plan and includes test runs in association with the passenger fluctuation and inspection, are necessary. This system can make either plan. 2.2 Transport planning control/planned information distribution system Each plan made by the Transport Planning System is controlled as a part of the database of this system (Fig. 6). This system develops the daily train timetable based on the basic plan and the daily change plan, and it distributes the information to the train operation control system. It also receives the actual train running results from the train operation control system to be incorporated in the actual operation results. In addition, this system distributes various plans to each station and crew offices to notify them of the basic and daily change plan, and it also makes various forms such as for business at the station and for driver and conductor’s duties. In this way, this system aids in the accurate and effective performance of duties. 2.3 Train operation control system (programmed route control/centralized controller of speed limit for work-site/CTC) This system implements daily train operation of all lines based on the train timetables received from the Transport Planning Control/Planned Information Distribution System. Programmed Route Control determines train positions based on the information of line occupation, train number and switches and signals received from the on-site interlocking devices and automatically controlled signals based on train timetables. In the case of train delays etc., the dispatcher changes the timetable to recover, and the system adjusts accordingly. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 6:

Figure 7:

Transport system.

Timetable monitor.

planning

control/planned

display

information

125

distribution

Figure 8: Line occupation/route control monitor.

The following terminals are provided to dispatchers; the timetable display monitor; the line occupation/route control monitor that displays line occupation and controls the signals including signal indication (Fig. 7). (Fig. 8) In addition, the large-sized operation indicator panel is provided to indicate the conditions of all lines. With the Centralized Controller of speed limit for work-site’, slow speed signals can be controlled by the dispatcher in the case that trains need to reduce speed due to climatic conditions, such as excessive precipitation, strong wind, and earthquakes, and other necessities so as to ensure safe operation (Fig. 9). Two lines of transmission paths, regular and detour, are provided to the Centralized Traffic Control (CTC) that connects the site and the dispatcher’s office in order to be able to continue train operation should one of the transmission paths be out of order (Fig. 10). WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 9:

Speed limiting system. Figure 10:

Figure 11:

Centralized train control.

Command information distribution system.

2.4 Command information distribution system Working with the operation control system, it sends the timetable change of the day to each station and crew office in real time so as to ensure immediate response to that change of the day. It sends out information from the dispatcher’s office (Fig. 11). 2.5 Passenger information system For passenger information at each station, the train timetable is transmitted from the dispatcher’s office to each station to control the train information display board and the automatic announcement system. The information includes the train operation information in advance based on the train timetable and the line occupancy, in addition to the above-mentioned train information indicator and automatic announcement that are provided at an appropriate timing such as when a train is approaching, arriving, or departing. Not only the train information, but also the business and accident information can be inputted in text and displayed on the board (Fig. 12). WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Passenger information board at station.

3 Function of connection control 3.1 Referring to the conventional line timetable when reviewing the Shinkansen timetable revision The transport Planning Control system can be connected to the system that makes the timetables for the conventional lines so as to review the Shinkansen timetable while the conventional line timetable under review is displayed (Fig. 13). In the opposite way, the Shinkansen timetable can be displayed when reviewing the conventional line timetable. In this way, this system assists mutual linkage and adjustment. 3.2 Conventional line connection control function is added to the Shinkansen programmed route control With the Programmed Route Control, the other layover trains can be registered in the timetable in advance, and if a registered train is delayed and cannot make the connection, an inquiry is outputted asking whether the connection is to be executed or not, and the dispatcher decides whether the train is to depart or not. In addition, with the Programmed Route Control for the conventional lines, the layover Shinkansen trains can be registered in advance to be used for judging the control (Fig. 14). 3.3 Shinkansen: line occupancy information of the conventional line is displayed on the line occupancy/programmed route control monitor To be able to determine the operating conditions of the conventional line train that is to be connected at the same platform, the line occupancy information of the conventional line trains can be displayed on the line occupancy display/Programmed Route Control monitor at Shin-Yatsushiro Station (Fig. 15). This is the same as with the large-sized operation indicator board. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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The Shinkansen timetable can be reviewed while the conventional line timetable is displayed.

Figure 13:

Timetable display for both Shinkansen and conventional lines.

Information of the conventional line layover trains is registered.

Figure 14:

Connection control from the conventional line to the Shinkansen line.

3.4 Displaying conventional line timetable (planned/actual) on the Shinkansen timetable display monitor In addition to the Shinkansen timetable and results, the conventional line timetable and results (for the sections between Hakata and Yatsushiro, Sendai and Kumanojo, and Kami-Ijuin and Kagoshima of Kagoshima main line) are displayed on the timetable display monitor, and the Shinkansen timetable can be changed while checking the current operation conditions and the future prospect of the conventional lines (Fig. 16). On the conventional line timetable display monitor, the Shinkansen timetable and results are displayed, enabling train timetable management in coordination with the Shinkansen. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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In addition to Shinkansen, the line occupancy information of the conventional line is displayed.

Figure 15:

Line occupation display for both the Shinkansen and conventional lines.

The conventional line timetable of the day is displayed.

Figure 16:

Timetable display for both the Shinkansen and conventional lines.

3.5 Sharing operation information between the Shinkansen and conventional lines The Shinkansen’s line occupancy and delay conditions were added to the traditional operation information distribution system that had been established for the conventional lines so as to enable switching those information displays on the monitor. This allowed the personnel to understand the Shinkansen operation conditions with the terminal installed at each conventional line station. In addition, at each Shinkansen station, both Shinkansen and conventional line operation conditions can be checked (Fig. 17). WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Both Shinkansen and conventional line operation 新幹線と 在来線の運行状況を conditions are displayed. 両方表示 Figure 17:

Sharing operation conventional lines.

information

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Shinkansen

and

Holding connection information from Shinkansen to the conventional line. Figure 18:

Passenger information information.

system

includes

connection

train

3.6 The passenger information display system including information of the conventional line connecting trains Data of stops and terminal stations of the conventional line trains to be connected from the Shinkansen trains is added to the timetable distributed to the passenger information display system at each station. Doing so, the terminal station for the conventional line train is noted as a destination, and the information of the stations where the train stops is noted, including the conventional line. In the case that the train is delayed and passengers cannot transfer, the connection guidance can be cancelled by the dispatcher (Fig. 18). WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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4 Conclusion With this system, we have provided stable transportation for about 7 years by improving customer services, such as guiding connection of the conventional line Relay Tsubame limited express and providing timely and appropriate information when operation disruption occur, and also sharing information smoothly between dispatchers of the Shinkansen and the conventional line. Currently, preparation for the entire line operation of Kyushu Shinkansen (Kagoshima Route) including the route between Hakata and Shin-Yatsushiro is proceeding with a spring 2011 operational start target. We are currently working on system development for operation commencement.

References [1] Yamasaki. K., Kyushu Shinkansen Operation Management System (Japanese). Journal of Japan Railway Engineer’s Association, pp 36–41, 2005.

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Train scheduling of Shinkansen and relationship to reliable train operation S. Sone & Y. Zhongping Beijing Jiaotong University, China

Abstract This paper explains why security is important, especially in Asia, as well as safety, and how we established reliable transportation in the Japanese Shinkansen, mainly in relation to train scheduling. The authors also describe several ideas actually taken by Shinkansen in order to realise reliable operation even in the case of possible disturbances. Out of many ideas, some examples of which are shown here, selective adoption according to the purpose of the railway or line is strongly recommended, together with given conditions taken into account. Keywords: disturbance, punctuality, reliable operation, spare time, train scheduling.

1 Introduction Features of east-Asian high-speed railways are very dense passenger flow together with frequent train operation with a big capacity. In order to realise reliable transportation in this circumstance, safe train operation in a narrow sense, which is guaranteed mainly by signalling system, is not enough; secure passenger flow must also be guaranteed even when some traffic disturbances take place. This is the reason why the authors present this paper, which mainly deals with security rather than safety, for the special invited session of "Traffic Control and Safety of High-speed Railways in Asia". Just after the inauguration of Tokaido Shinkansen in 1964, we had many disruptions to train operation due to rain and snowfall, breakdown of the power feeding system, deterioration of track conditions due to excess axleload, etc. In a narrow sense of safety, the Japanese Shinkansen carried more than nine billion passengers without any casualty by train accident, which is by far the safest WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100131

134 Computers in Railways XII railway in the world. The total time spent on Shinkansen trains by all nine billion passengers exceeds 15 billion hours, which corresponds to the total lifetime of 25 thousand people. From these figures, it is not surprising that not a few people died in Shinkansen trains, from disease or even by murder, even if we exclude suicides. Trains and other accidents of the Shinkansen did happen many times, including the overrun of an empty train to the dangerous area of Tokaido Shinkansen's main line, the fall of the bridges of Sanyo Shinkansen by an earthquake, which took place just before the starting time of train operation of the day and derailment of a running train of the Joetsu Shinkansen at high-speed by another earthquake, etc, which means that there having been no casualties so far can be thought of as due to luck. This paper deals with how we have established reliable train operation with the very heavily trafficked Japanese Shinkansen, common to east-Asian countries, in relation to train scheduling.

2 Special features of the Shinkansen in relation to train scheduling 2.1 Frequent train operation with relatively few intermediate stations Passenger dedicated lines tend to have fewer intermediate stations in order to run trains faster than on conventional lines and the number of trains on the lines tends to be greater in order to provide better services; this means that in the case of disruption of train traffic, it is difficult or impossible to stop each train at a track facing platform. 2.2 Uni-directional signalling system with few crossover routes Unlike many other high-speed railways in the world, the track layout and signalling system of the Japanese Shinkansen was modelled on the then modern double-track urban/suburban railways; uni-directional signalling, many passing loops and few crossover routes between down and up tracks. 2.3 Existence of trains of different average speeds due to different number of intermediate stops Compared with commuter trains, whose acceleration and deceleration rate is high, station stopping time is short and maximum speed is low, the additional time of high-speed trains per each additional stop at intermediate station is much longer; typically four minutes in the Japanese Shinkansen against one minute for commuter trains. In order to allocate fast and slow trains on a highly trafficked line, many passing loops are required in the Japanese Shinkansen. Indeed almost all intermediate stations have two platforms each facing one or two side lines off the main passing line. The exceptions to this on the Tokaido and Sanyo Shinkansen are only two, Atami and Shin-Kobe, at the latter of which all trains stop so that no difference of average speed takes place here. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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2.4 Through operation to lines with different characteristics Different characteristics include the quality and quantity of traffic demand, and very many differences of reconstructed existing lines to accept through trains from Shinkansen: The so-called Yamagata Shinkansen and Akita Shinkansen were once narrow gauged local lines. Now the track gauge is widened to international standard, 1435mm, but the loading gauge, electric system, AC20kV, maximum speed of 130km/h, and existence of level crossings with road traffic, etc, are still in the conventional line’s standard.

3 Problems of train operation during traffic disturbance 3.1 Minimum train headway by signalling system In normal conditions, train groups scheduled with a train headway longer (by Tspare) than the theoretical minimum (Tmin) can be realised stably and if a train is delayed (by Td), each following train can follow by the headway of Tmin, this means the initial delay can be absorbed up to Td/Tspare-th train. However, in the case of some disturbance, such as a temporary speed restriction at a place, the following train can pass the same place by headway of Tmin+Tr. If Tr is bigger than Tspare (this is not a rare case), it is often observed that the initial delay diverges and all the following trains must run at the headway of Tmin+Tr, which is longer than the scheduled headway. It is not so easy to find the longest Tr beforehand from the designed data of the signalling system and traction performance. 3.2 Uni-directional signalling system Contrary to European practice, the Japanese double track section is equipped with uni-directional signalling only, with relatively few crossover routes between down and up tracks. This is a big source of problems in rescuing trains in case of a big traffic disturbance. The Tokaido Shinkansen prepared two high-power high-performance diesel locomotives for rescuing purposes, but they have never found the route to arrive at the required spot. 3.3 More trains running at a time than the total number of tracks at intermediate stations In a wide area disturbance for an estimated long time, we want to stop all trains at a platform so that any passengers can get out of the train to the street, but the total number of platforms in the Tokaido Shinkansen is much smaller than the total number of trains running at a time in peak hours. 3.4 Not enough tracks at the most important terminal station Shinkansen’s Tokyo terminal has 10 tracks in all; six for the Tokaido Shinkansen (trains run through to Sanyo Shinkansen) and four for the Tohoku Shinkansen WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

136 Computers in Railways XII (trains run through to Joetsu, Nagano, the so-called Yamagata and Akita Shinkansen). The maximum number of revenue trains per hour per direction, excluding out-of-service trains, is 14 for Tokaido and also 14 for Tohoku. 3.5 Sub-terminal station and required function of it in case of traffic disturbance Tohoku Shinkansen’s Ueno station, which is next to Tokyo terminal, has four tracks all of which face to platform, and at first Tokyo terminal had only two tracks. At that time Ueno was used as a sub-terminal station effectively, some trains originate from and terminate to Ueno, and some other trains run empty between Tokyo and Ueno for arrangement to reuse as a passenger train. The Tokaido Shinkansen has added Shinagawa sub-terminal with four tracks all facing platforms, and three additional sidings for use of draw out and storage tracks, but flexible usage in case of traffic disturbance can hardly be done by thoughtless design of the line profile; it is impossible to run between additional sidings and the Tokyo terminal. 3.6 Cancellation of trains is difficult due to seat reallocation and keeping impartiality among passengers Most seats of all trains are pre-booked to passengers in Japanese practice with relatively few non-reserved seats. In this situation, even when the average loading factor is 50%, no trains can easily be cancelled because of the difficulty in reallocation of pre-booked seats fairly. If cancellation of trains is inevitable by a big disruption, all pre-booked seats are cancelled and used on a first-come, first-served basis with compensation of refund of express surcharge.

4 Train scheduling to keep in mind reliable train operation 4.1 Not to use connected fast and slow trains The scheduling pattern of “connected fast and slow trains” is very popular and reasonable for commuter railways in which a slow train arrives at a transfer station followed by arrival of the fast train at an adjacent track facing the same platform and after transfer of passengers the fast train departs first then the slow train follows. Typical timing of this procedure is; one minute after arrival of the slow train the fast train arrives and its dwelling time is also typically one minute or a little shorter, the slow train can depart about one minute after the fast train’s departure. This means the dwelling time of the slow train is about three minutes. If the same sequence is applied to a pair of high-speed trains, the following intervals of both arrival and departure are about three minutes each and the dwelling time of the slow train is about eight minutes, because the fast train stops for about two minutes. Instead of “connecting fast and slow trains”, simple passing requires much shorter additional dwelling time for a slow train, which is typically 2.5 minutes, arrival to passing, plus one minute, passing to departure, minus 1.5 minutes, plus necessary dwelling time. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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From the passengers’ point of view, a well-organised train schedule is convenient as a “connected” pattern. 4.2 Reducing level crossing interference at the terminal station and intermediate important stations There are six tracks in the Tokyo terminal of the Tokaido Shinkansen. The track layout is the simplest one, as shown in Fig. 1. In this layout, out of 30 combinations of a pair of incoming and outgoing trains, only nine pairs have no level crossing interference and the remaining 21 pairs have interference with each other, as shown in Table 1, according to the timing. If we add the routes as shown in Fig. 2, the number of interference free pairs can be increased from nine to 15, as shown in Table 2. (The track number is from bottom to top 14 through 19.) In Tokyo, there is not enough space to realise Fig. 3, but train scheduling is made so as to avoid interference; arrival and departure time is restricted to prefixed times: 0 min 0 sec., 3 min. 20 sec., 6 min. 40 sec., 10 min. 0 sec., etc. The duration of 3 min. 20 sec. corresponds to the minimum train headway, including the necessary time margin. The Tokyo station of the Tohoku Shinkansen has similar prefixed timing of 4 minute intervals. This is a very easy way to avoid interference and is effective if trains run exactly enough and even if trains are much delayed, this pattern can be applied, although it is not the best way.

Figure 1:

Table 1:

Existing layout.

Interference of Fig. 1.

Table 2:

Figure 2:

Improved layout.

Interference of Fig. 2.

is an interference free route while x route interferes with each other

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138 Computers in Railways XII Much better practice can be observed at private railway commuter lines, such as the Odakyu Electric Railway's Shinjuku terminal: 5 track terminal of two levels in which interference takes place very seldom. 4.3 Various ideas to prevent disturbance from divergence At the Fukushima station of the Tohoku Shinkansen, only one track out of four is used for thorough operation to and from the so-called Yamagata Shinkansen. The seven car Yamagata Shinkansen trains leave Tokyo coupled with the eight car Tohoku Shinkansen train for Sendai and in reverse the Yamagata Shinkansen train couples with the Tohoku Shinkansen train from Sendai on the same track. Even in case of disturbance, coupling and decoupling cannot be made at the same time. The required time to couple/decouple trains is much longer than normal station dwelling time, so it is convenient to be passed by the fast train, Hayate, during this stopping time. This requirement of train scheduling is the biggest restriction of the whole Tohoku Shinkansen train scheduling. 4.4 Train crossing at the intended partial double-track section on the Akita Shinkansen Between Omagari and Akita, 51.7km, of the so-called Akita Shinkansen was once a double track section of narrow gauge Ouu line. This section was converted to two single line tracks in parallel, one narrow gauge and the other standard gauge. However, out of this section, a 12.4km section between Jinguuji and Mineyoshigawa is laid of one standard gauge track in parallel with a dual gauge track so that standard gauge trains can pass each other while running. This particular section was chosen from the train schedule of the Akita Shinkansen where trains cross each other, plus a margin for the possible delay of one train. 4.5 Enough spare time allocated on the Tokaido Shinkansen in highly trafficked hours The fastest Nozomi trains using the newest Series N700 trainset can run between Tokyo and Shin-Osaka in 2 hours 25 minutes with reasonable spare time. Actually, three trains are scheduled to run in this time in early morning and late at night, but during most hours of the day the Nozomi trains, exclusively the N700 trainset, run in an additional 8 to 12 minutes, which is partly necessary due to mixed traffic with slower trains and partly due to giving a recovery margin in case of train delay. Whether an additional 8 to 12 minutes on top of the basic 145 minutes is justifiable or not may be a big question.

5 Conclusion The authors do not think all of these practice are necessary or inevitable because there are many other countermeasures to keep punctuality or to avoid large disturbances, as seen in private railways lines. For instance, the average required WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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time of 155 minutes between Tokyo and Shin-Osaka, 10 minutes longer than the basic time, seems too long if the competitive situation with airlines is taken into account. The Akita Shinkansen’s partial double track section of standard gauge line is very effectively used at the moment, when trains run regularly at 60 minute intervals. This means it is difficult to add trains flexibly without substantially longer travelling time. Fukushima’s case of coupling (of up trains) and decoupling (of down trains) on only one track requires too much restriction to the whole train schedule of Tohoku Shinkansen. Under this track layout, trains from Sendai must cross down the main line twice to couple with the Akita Shinkansen train; this is too restrictive to train operation in the case of disturbance. Another measure to cope with this situation, such as to provide a new route from the Yamagata Shinkansen to the scarcely used track 11 of the Fukushima station, where up trains can couple, should be taken even if the new route crosses down the main line. In east-Asian countries, frequent train operation is required mainly to realise the large capacity, while in European countries, this is required mainly to realise better connections between trains. From this difference, frequent train operation in Asia should accompany reliable train operation, especially in peak demand hours. Necessary techniques for this may be different line by line or time by time: The authors recommend selective application by each high-speed railway section according to the purpose of the line and time, rather than to take the proven best practice from a line of different purpose.

References [1] Timetable of Shinkansen: issued every month in Japan (in Japanese) by several publishers; bi-monthly by Thomas Cook Publishing UK as Overseas Timetable. [2] Track layout of railways: officially undisclosed by railways; but few private enthusiasts published so far including Tokaido Lines, by Ryozo Kawashima from Kodansha Publishing Co. (in Japanese) and Quail Map Series, by John Yonge from Quail Map Co. Exeter, UK. [3] Detailed list of rolling stock for each line or railway; edited by JRR, issued semi-annually for Japan Railways Group and annually for Japanese Private Railways Group by Kotsu-shimbunsha. (in Japanese) [4] Tracks of each station including normal operation practice: http://ja. wikipedia.org/wiki/[station name in kanji such as 東京駅]

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Rescue operations on dedicated high speed railway lines R. Takagi Kogakuin University, Japan

Abstract When disruptions of service take place on dedicated high speed railway lines, it is not uncommon that situations arise in which special “rescue” operations would be necessary. This paper outlines the following; 1) how such situations take place, or how efforts are being made to avoid them; 2) how rescue operations can be done; and 3) possible research and development on how the situations can be reduced using new technologies. Keywords: rescheduling, rescue operations, high speed railways, substitute train protection, on-board energy storage.

1 Introduction When disruptions of service take place on dedicated high speed railway lines, trains may have to be halted at places where passengers on board the trains cannot evacuate. For example, it has been reported on the Asahi Shimbun [1] that, on 29 January 2010, five trains with approximately 3,100 passengers on board had been stranded for nearly four hours on the Tōkaidō Shinkansen in Japan after a power outage caused by the breakage of an auxiliary messenger wire of the compound overhead line equipment. Earlier, it has been reported on the BBC News Website [2] that five Eurostar trains got stuck inside the Channel Tunnel when exceptional weather conditions caused failures of electrical systems on board trains, with nearly 2,000 passengers having to be rescued in a series of special operations. In this paper, the following will be outlined: 1) how such situations take place, or how efforts are being made to avoid them; 2) how rescue operations are currently carried out and can be done; and 3) possible research and development on how the situations can be reduced using new technologies. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100141

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2 The need for rescue operations There may be situations in which high speed trains stop at places other than stations or emergency evacuation points along a dedicated high speed railway line when a disruption to services take place. If this situation is expected to continue for an unacceptably long time, rescue operations must take place to let these passengers out of the trains. Once the disruption takes place, and the information on the nature and the extent of the incident that caused the disruption is available, traffic control is done to avoid such out-of-station halts. However, it is difficult to avoid such situations before the information is available, especially when the average distance between stations and/or evacuation points is long and the frequency of the trains is high.

3 How rescue operations are being done If a train to be rescued can be moved, but something is blocking its way, the first thing to be done is to open a path for the train so that it can be moved to a nearest station or an evacuation point. It will include moving the train in the direction different from the one it was originally travelling towards. This, however, may need bi-directional signalling, which is uncommon for the Japanese high speed railway system, Shinkansen, and is not effective for high speed railways like Shinkansen where train frequency is very high. If the distance for which the train must do the reverse-running is short, the train may actually do so in the rescue operation.

Station S Train A Train running direction: 

Platform P Platform Q

Train C Figure 1:

Train B An example rescue operation at a station (1).

Station S Train A Train running direction: 

Platform P Platform Q Train C

Figure 2:

Train B

An example rescue operation at a station (2).

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If the train cannot be moved by itself, but can be pulled to the nearest station or rescue point if another train or a rescue locomotive is attached to it, this option will be tried. Finally, if the train cannot be moved for various reasons, but another road in a double-track railway is open, a rescue train is prepared and sent to the site where the train is halted. The passengers on board the unmovable train will be transferred to the rescue train at the site and transported to the nearest station or evacuation point. Sometimes it may be necessary to put more than one train on a platform which normally serves only one. An example is shown in Figures 1 and 2. In Figure 1, there are three trains A, B and C, out of which Train C is not on any of the platforms of Station S. By moving Train B slightly forward, a part of Train C can share Platform Q with Train B, and the passengers on Train C can safely alight using the passenger doors towards the front of it.

4 Discussions on how to improve rescue operations, or how to avoid this happening on a massive scale 4.1 Information available to the line controllers If a disruptive event takes place in a railway line which may lead to the requirement for any rescue operations as discussed above, it is always important that the line controllers have information as much and as accurate as possible on the nature and the extent of the event. Especially important is the accurate estimate as to how long it will take to remove the cause of the blockage, which will have an impact on the optimal overall re-scheduling strategy. The rescue operation itself may take time, and it may be better to just wait until the cause of the disruption is removed if the estimated time until the cause is removed is short enough. Unfortunately, currently this estimate is very inaccurate, which causes the decision to start the rescue operation to be very late. The development of technologies to improve the precision of the estimate, including construction of a good incident database and good information acquisition at the control room, is strongly expected. 4.2 Substitute train protection Generally speaking, a high speed railway line is equipped with a train protection system throughout its length, which is in full use during normal operations. However, during rescue operations, substitute train protection systems are frequently used, with special arrangements and restrictions being imposed. For example, for safety reasons the following rules are imposed when a substitute train protection is being used on a Japanese Shinkansen line, as explained in [3]: 1) every train must have a driver and a guard (or two drivers) on the leading driver’s cab; 2) train speed is limited to 110 km/h; 3) every train must stop before entering a station; and 4) every train must stop at all stations. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

144 Computers in Railways XII A substitute train protection is basically a fully manual process, which is prone to human errors. Okada [4] introduced the intention of JR East to develop a new substitute train protection system using the digital train radio, which is expected to be less prone to errors. 4.3 Energy storage onboard trains Energy storage systems are expected to reduce energy consumption of electric railways, especially DC railways. The application of energy storage systems to high speed railway systems will not, however, contribute to any considerable energy-savings; this is because high speed railways are generally AC-fed. Nevertheless, it is expected that the application of energy storage on-board high speed railcars will contribute to the improvement in the rescue operations, mainly because this may make it possible to move trains regardless of the availability of electric power through overhead contact equipment. Disruption of power supply of a railway line will result in major disruption of rail services on the line, because the loss of power means loss of the ability to move for the trains in a certain area. If some amount of energy is stored onboard, the ability to move will not be lost entirely even when the failure of power supply takes place; this will mean it is much easier to plan and carry out rescue operations under such circumstances. Careful design of the railcars, however, must be made if this idea is to be implemented. The addition of on-board energy storage will result in increased weight, and therefore increased energy consumption and wear and tear of the tracks. This must be compared to the advantage gained by the addition.

5 Conclusion Rescue operations are the necessary step in the train re-scheduling when a major disruption of service takes place. As discussed in Section 4, the development of good system to give precision estimate of the time to the removal of the blocking condition is very important. In addition, there are some new technologies that may contribute to the improved rescue operations, especially on-board energy storage. The rescue operation, however, is only necessary when the major disruption actually happens. In this respect, the improvement of the reliability of services, by improving the reliability of individual components that make up the whole railway system, is most important.

References [1] Asahi Shimbun, 30 January 2010 (in Japanese). [2] BBC News Website, 19 December 2009. [3] “Shinkansen Signalling Installations” (in Japanese), Revised Ed., Railway Electrical Engineers’ Association of Japan (2002).

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[4] Okada, K.: “Development and Implementation of Digital ATC Systems” (in Japanese), JR-East Technical Review, 5, pp. (2003). http://www.jreast.co.jp /development/tech/pdf_5/27-30.pdf (accessed 1 May 2010).

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Track measurement by Kyushu Shinkansen cars in commercial service H. Moritaka1 & T. Matsumoto2 1

Omuta Track Maintenance Depot, Kyushu Railway Company, Japan Track Maintenance Division, Track & Facilities Department, Kyushu Railway Company, Japan

2

Abstract The Kyushu Railway Company (JR [Japan Railway] Kyushu) has introduced, for the first time in Shinkansen trains in Japan, a device that can measure all track irregularity using cars in commercial service. With that, special measurement cars were no longer needed, and frequent monitoring of the status of tracks became possible. The track irregularity measurement device employs an inertial measurement method, whereby track irregularity can be measured at a single cross-section. It is mounted with a special attachment base at the center of the bogie frame on rear bogies of the lead cars at both ends of the train. Measurement operations are done by remote control from PCs at the wayside. Devices that can measure track irregularity, body vibration acceleration, and axle box vibration acceleration were mounted to Shinkansen cars in commercial service introduced in August 2009, and use of the devices commenced. Those cars have run 458,299 km as of the end of April 2010, and track measurement was made without problems in the 27,412 km for which measurements were taken. Keywords: Kyushu Shinkansen, track measurement by Kyushu Shinkansen cars in commercial service, the inertial versine method.

1 Introduction The Kyushu Railway Company (JR [Japan Railway] Kyushu) has been proceeding since FY 2005 with the technical development of measurement functions for track irregularity, vibration acceleration, and axle box vibration acceleration to add to Shinkansen cars in commercial service. As a result, the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100151

148 Computers in Railways XII company has completed the mounting of track measurement devices to new Shinkansen rolling stock (U7 trainsets) introduced this fiscal year, and good track measurement has been achieved. This paper will give an overview of general track measurement by Kyushu Shinkansen cars in commercial service.

2 Composition of the track measurement device for the Kyushu Shinkansen Figure 1 shows an image of the track measurement device mounted to a Shinkansen car in commercial service. The track measurement device covered here is composed of five major devices: track irregularity detector, vibration acceleration detector, axle box vibration acceleration detector, position detector, and control PC. The track measurement device is mounted on the lead cars (car Nos. 1 and 6). Technical development of the individual devices was conducted while gaining the consensus of the Rolling Stock Division from the standpoints of high-accuracy measurement, no disruption to bogie running performance, and no reduction to passenger cabin space [1] In this section, we will cover the functions of the individual devices. 2.1 Track irregularity detector The track irregularity detector—the core component of the general track measurement system created—utilizes the inertial versine method contrived by the Railway Technical Research Institute in which high-accuracy measurement of track can be expected without the need for large-scale modifications to rolling stock (bogies and body) [2]. The inertial versine method applies the inertial measurement method that utilizes the phenomenon whereby rolling stock vibrates due to track irregularity, and the results especially are output as versine irregularity and allow simultaneous measurement of gauge and cross level. In that way, the inertial versine method that can measure the five basic items of track irregularity on a single cross section allows the track irregularity detector to be mounted without a base of the body and multiple bogies as with previous measurement methods (versine method and asymmetrical chord offset method).

Control PC Vibration

Axle box Figure 1:

Position Track irregularity

Image of mounting to Shinkansen rolling stock.

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It can thus be said to be a measurement method fitting all-motor-car Kyushu Shinkansen cars in commercial service. Figure 2 shows the specific mounting method for the track irregularity detector. The structure has a special attachment base at the center of the bogie frame, and the detector is rigidly coupled to that. This detector has a double box construction with a steel outer box and sensors (displacement gauge, gyro, accelerometer) on a high-precision aluminum base protected from vibration in the inner box. Bogie strength and running stability were taken into consideration as much as possible in design. The track irregularity detector is mounted on the rear bogies of the lead cars taking into consideration axle load balance, avoidance of danger in impact with obstructions, and workability in the pit line at the depot. 2.2 Vibration acceleration detector The vibration acceleration detector is mounted under the floor near the center of the lead bogie. It detects vertical and horizontal vibration acceleration of the body.

Measurement unit

W2000mm， H225mm， D490mm

Figure 2:

Measurement unit dimensions.

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Axle box detector

Figure 3:

Axle box vibration acceleration detector.

Position detector

Figure 4:

Position detector.

2.3 Axle box vibration acceleration detector Axle box vibration acceleration detector is mounted to the bottom of the axle boxes of both wheels on the front axle of the lead bogie (Fig. 3). It detects vertical and horizontal vibration acceleration of the axle. 2.4 Position detector The position detector is mounted under the body at the rear axle of the lead bogie. That device detects beacons installed every 500 m to detect location information. That information along with 0.25 m sampling pulses makes appropriate alignment of measurement data. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 5:

151

Control PC.

X left No. X left No. X left No. X right No. X right No. X right No. Y left No. Y left No. Y left No. Y right No. Y right No. Y right No. Gross level No. Gross level No. Gross level No. Gauge No. Gauge No. Gauge No. Speed No. Speed No. Speed No.

100m Slab track， R4,000， TCL505， C200 * X: 10m chord longitudinal level irregularity Y: 10m chord alignment Figure 6:

Example of track measurement wave forms for Shinkansen cars in commercial service.

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152 Computers in Railways XII 2.5 Control PC The control PC is mounted in the equipment room between the driver’s cab and passenger cabin. That unit is made up components such as the measurement control part, acceleration control part, power control part, and data recorder. Compactness was pursued thoroughly so it could reside along with other rolling stock control devices, making effective use of empty space so it could be mounted in the equipment room without reducing passenger cabin space. Furthermore, wayside notebook PCs are linked with the onboard control PC by a network to form a system where settings for initial conditions and measurement start/stop can be made by remote control. With that function, measurement personnel do not need to be on the train, and car scheduling for measurement is not needed. The system also compensates the weak point of inertial measurement—low-speed range measurement—by simultaneous measurement between cars Nos. 1 & 6.

3 Wave form of track measurement by Shinkansen cars in commercial operation Figure 6 shows an example of the track irregularity wave form acquired in general track measurement by Kyushu Shinkansen U7 trainsets. The features for this section are as follows. [Features, etc.] ・ Directly fastened Type 8 frame-shaped slab ・ Open section ・ Near 4,000 m radius ETC ・ 200 mm cant ・ 12‰ downhill grade The track measurement wave forms for three passes shown in Figure 6 were acquired from the track irregularity measurement device on car No. 1. The three wave forms match extremely well, and track conditions such as amount of versine in the curve and appropriate amount of cant are appropriately captured. We can thus see that the device has very high measurement accuracy.

4 Conclusion Highly accurate monitoring of track conditions became possible by achieving general track measurement with Shinkansen cars in commercial service, and we can expect further improvement in safety. Initial costs and running costs can also be reduced, and we can expect large expenditure reduction effects. JR Kyushu plans to add the track measurement function to U9 trainsets to be introduced next fiscal year to build an even more complete monitoring system.

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References [1] Moritaka, H., Matsumoto, T. & Yazawa, E., Technical development for general track measurement by Kyushu Shinkansen Cars (Japanese). Journal of the Japan Railway Civil Engineering Association, pp. 921-923, 2009. [2] Moritaka, H., Yazawa, E. & Tsubokawa, Y., Performance evaluation of inertial versine track irregularity detector and investigation of detecting method in low speed range (Japanese). Proc. Of the 46th Academic Lecture Meeting of Japan Society of Civil Engineers: Fukuoka, Japan, pp. 73-74, 2008.

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Development of a high-speed overhead contact line measurement device for the Kyushu Shinkansen N. Kinoshita1, Y. Himeno2 & R. Igata2 1

Strategy Management Department, Kyushu Railway Company, Japan Electric Power Division, Electrical Engineering Department, Kyushu Railway Company, Japan

2

Abstract This report addresses the development of a measurement device for more efficiency in the dynamic inspection of overhead lines, which is one type of equipment inspection for the Kyushu Shinkansen. With Shinkansen lines in the past, overhead lines were measured with special electric and inspection cars using measurement pantographs and lasers. A testing timetable had to be put together during the regular commercial service time. In light of that, the Kyushu Railway Company (JR [Japan Railway] Kyushu) took the following points into consideration, and developed a device for measurement where imaging equipment is mounted to Shinkansen trains in commercial operation to analyze the dynamic state of overhead lines by image analysis. 1) Enabling increased efficiency in maintenance by measuring on the normal timetable during commercial service. 2) Reducing costs by eliminating the need for a special measuring car. 3) Simplifying the components that make up the measurement device. With that measurement device, train location information and speed information can be acquired from ATC (Automatic Train Control) to associate those with test results at the points measured for better data management. This measurement device is used periodically, and the data acquired is utilized for maintenance and management of the overhead line equipment. Keywords: Shinkansen, overhead contact line measurement, image processing, stereo measuring, pattern recognition of shape.

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Figure 1:

Kyushu Shinkansen Kagoshima route map.

1 Introduction With the partial opening of the Kyushu Shinkansen Kagoshima route in March 2004 (between Shin-Yatsushiro and Kagoshima-Chuo Stations: Fig. 1), trains in commercial service were equipped with imaging devices and other equipment. A high-speed overhead contact line measurement device (hereinafter “the measurement device”) incorporating those was developed to diagnose the dynamic state of contact wires and pantographs. For Shinkansen lines in the past, overhead contact lines were measured with measurement equipment using measurement pantographs and lasers on special electric and track inspection cars. The Kyushu Railway Company (JR [Japan Railway] Kyushu), however, decided to mount the measurement device on Shinkansen cars in commercial service in consideration of the following to measure Kyushu Shinkansen overhead contact lines. - Costs can be reduced by eliminating the need for special measurement cars. - Equipment composing the measurement device can be simplified. - Maintenance can be made more efficient with the ability to measure during commercial operation. The measurement device images and records with cameras train line facilities around contact wires and pantographs during commercial service, and it finds the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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required measurement values by image processing. To determine the location of measurement points, speed and distance information from ATC are recorded in parallel with processing to save image data, and the results of image processing are output associated with the location of measurement points. The following development policies were followed to build the system in development of the measurement device. (1) Development of a measurement device that does not interfere with commercial service (2) Acquisition of highly accurate measurement data by putting a compact measurement device on trains (3) Introduction of imaging equipment compatible with acquiring high-speed measurement data (4) Establishment of a system composition that allows for easy function upgrading (5) And easy-to-handle system composition (6) A system composition using general-purpose equipment

2 Measurement items Measurement items for the measurement device are the following dynamic items pursuant to measurement items with conventional electric and track inspection cars (Fig. 2). (1) Contact wire height (2) Contact wire deviation (3) Detection of obstructions around pantograph (4) Shape monitoring of pantograph head and horn (5) Contact wire hard spot detection (6) Power collection status monitoring (video playback confirmation item) Measurement of the static item of contact wire residual diameter is not done with the measurement device. That is measured by a wear measuring instrument on the separate maintenance car. (4) Pantograph head/horn shape monitoring

(2) Contact wire deviation

(1) Contact wire height (5) Detection of contact wire hard spot

Figure 2:

(3) Detection of obstacles around pantograph

Measurement items.

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3 Conventional method for overhead contact line measuring Conventionally, the method to measure the state of overhead contact lines has been to measure manually or use a special car. When measuring manually, an operator uses various measurement equipments to measure the overhead line from the wayside or from an overhead contact line work car. That method allows accurate measurement of static aspects of overhead contact lines such as residual diameter of contact wires. However, measurement is at points, so efficiency is poor, and dynamic aspects such as contact wire height, deviation, and hard spots cannot be measured. Measuring using a special car allows for measurement of static aspects of overhead contact lines by running a special measuring car with measurement devices on it. While efficiency of measurement is higher than that with manual measurement, operation scheduling must be done in a planned manner. Thus, it is difficult to be flexible in terms of route and time for measurement. Measuring with a special measuring car is done by irradiating with lasers, using a special measurement pantograph, or by image processing. When measuring by irradiating with lasers, laser light scans the overhead contact line, and the reflected light is captured to measure the state of the overhead contact line. While highly accurate measuring can be done, equipment such as a mirror control device and high-frequency power source are required in addition to the laser emitter. Thus, a broad space on the roof of the special car is needed for installation. When measuring with a measurement pantograph, a pantograph that does not collect power is installed on the roof of the car in addition to the regular power collection pantograph, and that is used to take measurements. Consideration does not need to be made for insulation with the measurement pantograph body, so devices such as acceleration sensors and micro switches can be directly installed on the measurement pantograph. With measurement using image processing, a CCD camera installed on the roof of the train records the area around the pantograph, and those images are processed to measure the overhead contact line. Contact wires other than those being measured, messenger wires, feeders, and other lines are visible in the images. So, light from a slit light is projected perpendicular to the overhead contact line to differentiate which line segment of the wires in the images is the contact wire to be measured, and an image where one point on the contact wire is reflected is processed for measurement.

4 Composition of the system for the measurement device The measurement device is composed of onboard devices including the camera for imaging and wayside devices for data analysis (Fig. 3). Onboard devices include cameras on the roof of Shinkansen cars, projectors, and onboard PCs. Data imaged while the train is running and speed/distance information acquired from ATC devices are stored on the PC. Cameras are installed on car No. 5, on which a pantograph is also installed (Fig. 4). WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Car No.2 end

Onboard ＰＣ

Car No.1 end New ATC 新ATC

Car No.1

New ATC

Pant2 Pant2

Pant1 Pant1

Car No.2

Car No.3

Car No.4

Car No.5

Car No.6

Onboard devices Wayside devices PC CCD cameras ×2 Line sensor

Maintenance base Tape

Figure 3:

System composition.

Line sensor CCD camera 1

Figure 4:

CCD camera 2

Onboard cameras.

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160 Computers in Railways XII In past overhead line measuring methods (by emitting with a laser or using a measurement pantograph), there were restrictions on space for installing equipment on the roof or in the cabin. So, many issues had to be overcome in applying those methods to rolling stock in commercial service. Moreover, the measuring method by image processing where lighting is modified uses special images recorded by illuminating with a slit light, so images cannot be diverted for use in other measurement items such as pantograph shape monitoring or obstacle detection. To sum up the situation, it would be best to be able to measure multiple items from the images from one camera. The measurement device thus used two CCD camera and one line sensor camera. A projector is installed on the roof of car No. 4 to ensure brightness for the pantograph of car No. 5 and its surroundings. The onboard PC is installed in the equipment room on car No. 6. Video signals from the cameras are converted into optical signals and transmitted to the equipment room by optical cable, and are supplied to the onboard PC. The onboard PC records video data on a hard disk along with speed and distance information acquired from ATC devices. Major specs of the imaging equipment are as follows. a. CCD cameras Pixel count: 648 (H)×492 (V)(max) Frame rate: 60Hz Sensor sensitivity: 0.23 Lux, Max gain, 50% Video b. Line sensor camera Pixel count: 4,096 Scan rate: 4.73 kHz (max) c. Camera units ・Embedded on car so as not to be a source of noise when train is running ・Made to be as compact as possible d. Projectors ・Ensures brightness to allow camera imaging 4 HID lamps (2 lamps×2 units) Wayside devices download image data acquired by the onboard devices, analyze those images, conduct measurement processing, and output data associated with speed and distance information acquired from ATC devices. As the amount of data exchanged between the onboard and wayside devices is enormous, LTO large-capacity media is employed.

5 Principles of overhead contact line measurement Two cameras are used for position measurement of overhead contact lines. Those cameras are located on the right and left sides around the pantograph on the roof of the car, and the baselines of those two cameras are made parallel to the pantograph head. The benefits of two cameras over one are as follows. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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・Just the contact wire in contact with the pantograph can be geometrically differentiated from images where contact wires, messenger wires, and other items show up in various ways. ・Images taken by the two cameras on the right and left sides almost never have contact wires and messenger wires show up overlapped due to their erection structure. That way, the contact wires are not imaged thicker than they actually are, and the center position of the contact wire can be correctly measured. Next, we will explain the contact wire position measuring process (Fig. 5, 6). First, the left and right cameras acquire images of the same pantograph and the surrounding area, and the pantograph in the images from the left and right cameras is detected by pattern matching. Next, multiple line segments of wires in the image perpendicular to the detected pantograph are selected as candidates for the contact wire to be measured, and groups of line segments of wires among those that cross at the same point as the pantograph are found by stereo corresponding point searching. That is determined to be the contact wire to be measured. From the coordinates of the left and right images of the point where the contact wire and pantograph cross, the three-dimensional position (XYZ) of the contact point is calculated by triangulation. Through those processes, the contact wire that is contacting the pantograph is detected from multiple contact wires and messenger wires in images, and the position of that contact point can be measured. The principle of so-called stereo measuring is used. Stereo measuring is the same as a human visually senses the distance to an object. For example, cameras A and B are set apart at distance L as in Fig. 7. In that case, the angle of view recorded by both cameras is a known value. When the object is recorded by camera A, it will be recorded at a position where the image is split into A1:A2. The principle is that the respective formulas for the straight lines connecting camera A and B with the object are solved, and the crossing point coordinates of those two straight line formulas is the position of the object (Fig. 7).

Figure 5:

Measurement procedure.

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162 Computers in Railways XII

Figure 6:

Figure 7:

Detecting contact wire to be measured.

Principles of stereo measuring.

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6 Image processing Next, we will cover specific image processing for individual measurement items. 6.1 Contact wire height With the measurement device, a line sensor camera having resolution power about ten times that of CCD cameras are used to acquire highly accurate measurement data. Specifically, a perpendicular slit image of the pantograph head area is recorded at 1,000 lines per second to generate a spatiotemporal image, and the change in pantograph height is calculated by the top of the pantograph being extracted through image processing and output as the contact wire height. Fig. 8 is an example of a spatiotemporal image from a line sensor camera. The horizontal axis is time, and the vertical matches physical up-and-down movement. The thick line extending horizontally is the trajectory of the pantograph head, and the vertically flowing line is wayside structures momentarily passed. In image processing, the top of the pantograph head is extracted and the height is calculated. 6.2 Contact wire deviation Images of the area around the pantograph recorded from two directions by two CCD cameras installed on the car roof are used to find contact wire deviation. The contact wire and the pantograph contacting that are extracted from the left and right images by image processing, and the three-dimensional position of the contact point is calculated through the principles of the triangulation method based on the coordinate values from the left and right images of the contact point. The distance from the center of the pantograph to the contact point is then output as contact wire deviation.

UP Pantograph(trajectory)

Time axis Down(body)

Pantograph height

･････ Figure 8:

Model of line sensor image manipulation.

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164 Computers in Railways XII 6.3 Detection of obstacles around the pantograph Images recorded by two CCD cameras are analyzed to detect obstacles around the pantograph. When objects other than preset structures (pantograph, overhead contact lines, support fixtures, etc.) in a range set beforehand are discovered, those are detected as obstacles. The position of obstacles and the section and distance information of the point where obstacles are detected are output. The definition of “obstacles” around the pantograph is as follows. ・Obstacles are all objects not contacting the contact wire that are suspected to obstruct the pantograph. ・Overhead contact line support fixtures, etc. in contact with the contact wire are not detected as obstacles even if they are around the pantograph. 6.4 Pantograph head/horn shape monitoring Pantograph head and horn shape are pattern-recognized, and the presence of shape irregularities is determined by comparison processing with the acquired image. The position where shape irregularity occurs (kilometerage) is also found. Places with pattern discordance can be reconfirmed through images by indicating the kilometerage, etc. A binary search function that plays back recorded images in sequence and narrows down the location where shape irregularity occurs is provided as a tool to visually inspect pantograph shape. 6.5 Contact wire hard spot detection Continuous data of pantograph height calculated from spatiotemporal images by the line sensor camera shown in Fig. 8 is in itself the trajectory of pantograph behavior, so the second order differential of that is calculated, and acceleration acting on the pantograph found. Dividing that acceleration with the gravitational acceleration, and the hard spot found. If that result is in excess of a certain value, the point of the contact wire is judged to be a hard spot. 6.6 Power collection status monitoring (item confirmed by image playback) Inspection is performed visually with this function by playing back images. If irregularities with the power collection status are discovered, images and distance/speed information are associated at that point in time and recorded.

7 Use of measurement data Results of measurement processing are arranged by section and distance information, and are output as lists of figures and as graphs (Fig. 9). The horizontal axis of the graph display is kilometerage, and measurement data and points measured can be easily compared. Similarly, if any irregularities are detected, places where they occur can be easily identified. Furthermore, the measurement device allows image data to be cued from the output data of WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 9:

165

Log form output example.

measurement results. That way, still images of the places where problems occur and video of before and after the occurrence can be instantly played back to allow visual confirmation of the state of the overhead contact line at the time irregularities occur.

8 Measurement results The measurement device was installed on Shinkansen cars in commercial service, and measurement processing was done from images repeatedly acquired under conditions with differing running time and running speed in the section between Shin-Yatsushiro and Kagoshima-Chuo. Fig. 10 and Fig. 11 are some of the measurement processing results for that. The top of the two graphs in each figure is the measurement results for pantograph height. Its vertical axis is pantograph height, and its horizontal axis is operating distance with Hakata Station as the starting point. The bottom graph is the measurement results for contact wire deviation. Its vertical axis is deviation from center of pantograph to contact point of the contact wire, and its horizontal axis is operating distance with Hakata Station as the starting point as in the top graph.

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166 Computers in Railways XII Fig. 10 is graphs when running in a straight section. The top graph is contact wire height (height from rails to pantograph), varying between 5,000 and 5,100 mm height. Height is higher at overhead contact line supporting points and lower between the supporting points, demonstrating the change in height with a simple catenary system. The bottom graph is contact wire deviation, showing the zigzagging of overhead contact lines in straight sections where the contact wires slides left and right about ±200 mm. The point plotted like a protruding hair to the left of the center of the graph is where two contact wires in the image are detected. That location is a section where contact wires switch with main wire and side-main wire installed in 300 mm intervals, and those intervals and locations are accurately measured. Fig. 11 is graphs when running in a curved section in a tunnel, and the section between the arrows applies to the curved area. The top graph is contact wire height, and while wiggling change in height cannot be seen compared to the straight section, it shows the amount of sag for contact wire between supporting points is less than in the straight section. The bottom graph is contact wire deviation, showing that the contact wire is skewed to one side at the outside of curve.

Figure 10:

Contact wire height and deviation measurement results example (straight section).

Figure 11:

Contact wire height and deviation measurement results example (curved section).

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9 Conclusion JR Kyushu has developed and put into operation a device that measures the dynamic state of overhead contact lines by acquiring images of around the pantograph while the train is running using cameras installed on Kyushu Shinkansen cars in commercial service. That has brought about improvements in measuring accuracy and reduction in labor required for measuring work. Addition of a contact line residual diameter measuring function is also planned with the opening of the completed Kyushu Shinkansen. Verification tests are being conducted for that at the present time, and further reduction in labor required for measuring work is expected with its introduction.

References [1] Nakahata, Y. & Kinoshita, N., Measurement by utilizing commercial train (Japanese). Railway and Electrical Engineering, Japan Railway Electrical Engineering Association, pp. 65-68, 2004. [2] Kinoshita, N., Development of overhead contact line measurement device by imaging (Japanese). Journal of Japan Railway Engineer’s Association, pp. 57, 2004.

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The analysis of train reliability for the Taiwan High Speed Rail J.-C. Jong1, T.-H. Lin1, C.-K. Lee2 & H.-L. Hu3 1

Civil & Hydraulic Engineering Research Center, Sinotech Engineering Consultants, Inc, Taiwan 2 Department of Marketing and Logistics, Southern Taiwan University, Taiwan 3 Bureau of High Speed Rail, Ministry of Transportation and Communications, Taiwan

Abstract This study briefly reviews the development of the Taiwan High Speed Rail and analyzes its service reliability in terms of punctuality and average delay per train. The concept of risk management is also introduced in this paper to analyze the frequency and the severity of train delays caused by different kinds of accidents. According to the result of the analysis, signal and interlocking failures are the main reasons leading to train delays. Earthquakes and typhoons are also major threats to the system, even though the system tends toward stable. Based on the experiences of the Taiwan High Speed Rail, shortening the maintenance cycle can efficiently alleviate the problem of train delay caused by signal failures. Keywords: High Speed Rail, train delay, risk management.

1 Introduction On 1 October 1964, the world’s first high-speed train commenced service on the Tokaido Shinkansen line between Tokyo and Osaka at a speed of 210 km/h. This date marks the start of the era of High Speed Rail (HSR). Despite the success of Shinkansen, the spread of HSR around the world was relatively slow. Seventeen years later, France launched a HSR service with a maximum speed of 270 km/h between Paris and Lyon in 1981. Another seven years later, the world’s third HSR was introduced in Italy. Afterwards, German and Spain also joined the club of HSR in 1991 and 1992, respectively [4]. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100171

170 Computers in Railways XII In the late 20th century and the beginning of the 21st century, the development of HSR increased rapidly because of economic, environmental and external cost concerns, especially in the Far East [8]. In 2004, the Korea Railroad (Korail) opened its KTX between Seoul and Busan, using TGV technology [13]. Three years later, the Taiwan High Speed Rail (THSR), the first HSR outside Japan to adopt Shinkansen technology, was inaugurated to provide a high speed passenger service between Taipei and Kaohsiung at a maximal speed of 300 km/h. In 2008 and 2009, the Beijing-Tianjin HSR and the Wuhan-Guangzhou HSR were introduced in China. At present, the HSR has become a prevailing transportation mode and several projects are currently under development in different countries, including the High-Speed Intercity Passenger Rail (HSIPR) in the USA [3]. As it spreads around the world, HSR has been recognized as an energysaving, environment-friendly, and efficient mode of transportation [8]. People expect not only high-speed travel, but also safe and reliable service. After three years of operation, the THSR has carried more than 80 million passengers. Incidents leading to injuries and fatalities have never occurred to date. However, train delays are created sometimes. This study collected operation data from the Bureau of High Speed Rail (BOHSR), the supervisor and regulator of the THSR, to analyze the train reliability of the THSR. The study also introduced the concept of the risk management to analyze the frequency and the severity of train delays caused by different kinds of accidents. Through the proposed method, problems disturbing the normal operation of the THSR could be identified. The proposed methodology could be applied to other HSR or conventional railways for identifying, analyzing, and evaluating the risks of train delays.

2 The Taiwan HSR project In the 1980s, Taiwan’s economy was booming, especially in the western region. The growth of the economy led to increasing demands for intercity transportation. According to the investigation report in 1990 [5], the amount of trips between Taipei (the major city in the North of Taiwan) and Kaohsiung (the major city in the South of Taiwan) would increase by 84% until 2011. The huge growth attracted much attention from the government to think about how to alleviate the congestion problem. To overcome the capacity insufficiency problem and to achieve the goal of the “one-day living area” policy in Taiwan, a HSR system was finally selected from many alternatives. The THSR project was initially planned to be built by the public sector. Due to the increased public fiscal burdens, parliament withdrew the budget allocated to the THSR project and decided to have the project built by the private sector with a Build-Operate-Transfer (BOT) model [14]. This kind of infrastructure privatization model is spreading in many developing and developed countries under tight budgetary constraints [6]. With a construction value of $18 billion, the THSR project was undoubtedly one of the most expensive concession transportation projects in the world at that time and perhaps even today. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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In September 1997, the Taiwan High Speed Rail Consortium was selected to be the best applicant for the BOT project. The Taiwan High Speed Rail Corporation (THSRC) was then incorporated in May 1998 as the concessionaire to build and operate the HSR service. The THSRC was licensed by the government to finance, construct, and operate the system for a period of 35 years and a concession for station area development for a period of 50 years [14]. The construction of the THSR started in 1999 and ended in 2006. The rail network links Taipei and Kaohsiung at a total length of 345 kilometers. Currently, eight stations are in operation, including Taipei, Banciao, Taoyuan, Hsinchu, Taichung, Chiayi, Tainan, and Zuoying (a district in Kaohsuing), as shown in Figure 1. The THSRC imported 700T trains, a type of the Shinkansen rolling stock based on the 700 series, from Japan. It was the first time that the Shinkansen exported its system to a foreign country. The 700T train set has a distributed traction system formatted by 12 cars including nine power cars and three trailers. The passenger capacity of the 700T train is 989 seats [11]. The designed maximum speed of the 700T train is 315 km/h, but its commercial maximum speed is 300 km/h. The acceleration rate is 2.0 km/h/s and the deceleration rate is about 2.7 km/h/s. The whole network of the THSR is designed as double tracks. The maximum gradient is 35‰ and the minimum radius is 6,250 meters. The operation control center (OCC) is located at Taoyuan station. One maintenance base is situated near Hsinchu, and two depots are located in the center and south of Taiwan. The main workshop is located at Yenchao between Tainan and Kaohsiung. Normally, double-track operations are used, but the signaling system also provides the flexibility of single-line, bi-directional operations. In addition, the digital automatic train control (D-ATC) system is installed to ensure safety.

Figure 1:

The route and stations of the THSR.

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3 Train services and ridership Table 1 lists the stopping patterns and their associated journey time of the THSR. The stopping patterns combine non-stop, express, and local trains. At the beginning, the THSRC provided train services with many different kinds of stopping patterns. However, at present, almost all trains follow pattern B or E and very few adopt patterns F or G. Currently, pattern B is the fastest service between Taipei and Zuoying with a travel time of 96 minutes. When the THSRC started commercial operations, only 38 train services were provided daily. Afterwards, more and more drivers completed training and the system tended toward stable. The THSRC constantly increased the number of daily services from 38 to 142 to achieve the request of the BOT contract until December 2008. After that, the THSRC reduced train frequency due to the economic depression. The trend of the number of daily services from January 2007 to March 2010 is displayed in Figure 2. Table 1:

The stopping patterns and the associated journey time of the THSR.

Pattern

Taipei

Banciao Taoyuan Hsinchu Taichung Chiayi

Tainan Zuoying

Travel Time (min)

A

81

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r-

J -

Fe -

-

-

The trend of the number of daily train services.

De -

Se -

N

A g-

-

y-

J l-

J

r-

A r-

Fe -

-

J -

-

De -

N

Se -

-

A g-

y-

J l-

J

r-

A r-

J -

Fe -

-

De -

-

N

J

Figure 2:

Se -

60

A g-

G

-

57

J l-

120

F

r-

E

y-

108

A r-

D

J -

108

Fe -

96

C

The Number of Train Services The Number of Train

B

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173

J Fe rA ryJ J lA gSe N De J Fe rA ryJ J lA gSe N De J Fe rA ryJ J lA gSe N De J Fe r-

The Number of Passengers The Number(thousands) of Passenger(thousand)

Since the fares of other modes in the Western corridor of Taiwan are cheaper than the THSR, except airlines, several marketing strategies were implemented to increase the seat utilization rate and the revenue of the THSRC. In addition to the half price promotion during the first two weeks at the beginning of commercial operations, the strategy of “non-reserved seats” has also been adopted since November 2007. The concept of non-reserved seats is that passengers need not book before riding; they can purchase tickets immediately after arriving stations, and then take any train without designated seats. The promotion provided more convenience for business travelers, and the price of non-reserved seats had a 20% discount during the first three months. The THSRC initially provided three cars of non-reserved seats per train, and this increased by one more in January 2008 to mitigate the crowded condition. After the three month period, the discount for non-reserved seats was adjusted several times until settling on a final value of 15%. Additionally, the use of these tickets is now only permitted on weekdays, excluding Fridays and the days before holidays. Another promotion that allowed 20% discounts on all types of tickets on weekdays was implemented from April to November 2008. During the period, the airlines between Taipei and Taichung, Taipei and Chiayi, Taipei and Tainan were cancelled. Only Taipei-Kaohsiung airlines survived and there remained three flights per week. Since November 2008, the THSRC has pushed a new program called “Two-Color Promotion”. It was the first time that the THSR introduced the concept of revenue management. In this program, each train service was denoted by a color, either blue or orange. The blue indicates a 15% discount and the orange means a 35% discount. The THSRC has promoted this program to attract on-peak passengers to take off-peak trains. Figures 3 and 4 depict the number of passengers and the seat utilization rate of the THSRC from January 2007 to March 2010. Generally speaking, the monthly ridership is approximately 2,500 ~ 3,000 thousand passengers and the seat utilization rate was approximately 40% ~ 50% last year. The influence of each promotion can also be observed roughly in these two figures.

Figure 3:

The number of passengers.

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% % % % % % % % % J Fe rA ryJ J lA gSe N De J Fe rA ryJ J lA gSe N De J Fe rA ryJ J lA gSe N De J Fe r-

Seat Utilization Percentage Seat Rate

%

Figure 4:

The seat utilization rate.

Punctuality within 5 mins

Punctuality within 10 mins

100.00% 99.50% 99.00% 98.50% 98.00% 97.50% 97.00% 96.50% Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 Mar-09 Apr-09 May-09 Jun-09 Jul-09 Aug-09 Sep-09 Oct-09 Nov-09 Dec-09 Jan-10 Feb-10 Mar-10

96.00%

Figure 5:

Monthly punctualities within 5 and 10 minutes.

4 The analysis of punctuality and train delays Although the THSR has provided services for more than 80 million passengers since January 2007, no incident leading to injuries and fatalities has ever occurred. However, a few incidents causing train delays have indeed happened during the past three years. This section tries to analyze the punctuality and the train delays of the THSR. The concept of the risk management is also employed to analyze the frequency and the severity of train delays caused by different kinds of accidents. 4.1 Trend of train punctuality Figure 5 shows the train punctualities of the THSRC within 5 and 10 minutes during the past three years. Since the THSRC did not report punctuality within 5 minutes to BOHSR in 2007, this data was not drawn. The figure indicates that monthly punctualities are almost higher than 98%. In July 2008 and August WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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2009, signal failures made punctuality drop below 98%. In March 2010, an earthquake of magnitude 6.4 resulted in a minor train derailment. This earthquake caused damage to the train and running rails, but all passengers were safe. However, more than 20 trains were cancelled or adjusted to run with new stopping patterns after the earthquake. The earthquake led to a steep decline in punctuality to a value of 96.61%, the lowest one since the THSRC’s commercial operations. 4.2 Trend of average delay The delays reported to BOHSR were presented by a frequency distribution with unequal delay interval, i.e., less than 5 minutes, between 5 and 10 minutes, between 10 and 30 minutes, between 30 and 60 minutes, and more than 60 minutes. The average train delay is approximated by the following equation: X 

fM 5

i

i 1

i

(1)

n

Figure 6:

Average delay per train.

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Mar-10

Jan-10

Feb-10

Dec-09

Oct-09

Nov-09

Sep-09

Jul-09

Aug-09

Jun-09

Apr-09

May-09

Mar-09

Jan-09

Feb-09

Dec-08

Oct-08

Nov-08

Sep-08

Jul-08

Aug-08

Jun-08

Apr-08

May-08

Mar-08

Jan-08

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Feb-08

Average Delay per Train (min) Average Delay per Train

where: X = average train delay (minutes) f i = the frequency of the ith class M i = the median of the ith class (minutes); M 1  0 and M 5  60 n = total train services The above equation implies that trains with delays less than 5 minutes are considered to be punctual and that delays over 60 minutes are reset to 60 minutes for simplification. Besides, the medians of the other classes are used to represent the delay time for all trains in the classes. The approximation is not precise, but is a reasonable estimate of average delay. Figure 6 displays the average delay per train during the periods from January 2008 to March 2010. The results during 2007 are not shown in the figure since the number of delay less than 5 minutes is not recorded. The figure shows that the average delay per train ranges between 0 and 0.83, demonstrating that the service of THSRC is very reliable.

176 Computers in Railways XII 4.3 Delays caused by accidents Since BOHSR only requested THSRC to report specific accidents such as collisions, derailments, rolling stock failures, and the accidents causing train delays over thirty minutes, the data collected for this study were limited. Figure 7 presents the number of reported accidents per month from January 2007 to March 2010. The annual moving average (AMA) number of accidents normalized by 10 million train-kilometers is also marked in the figure. There has been a decreasing trend in the AMA over the past three years. In 2007, rolling stocks, tracks, and signal failures were the main reasons leading to train delays. As the operation of THSR gradually reaches to a stable condition, natural disasters such as earthquakes and typhoons become the major threats to train reliability nowadays. In addition, signal and interlocking failures are still potential hazards to reliability. The evidence from March 2009 showed that more than 3,000 minutes of train delays were resulted from only one signal failure. Figure 8 uses another indicator, the total train delays caused by accidents, to represent the trend of reliability. It is easy to notice the contrast between Figure 7 and Figure 8. These two figures indicate that the frequency of accidents decreases, but the number of total train delays increases. That is because the number of train services has increased continuously in the last three years. Any accident might easily affect other trains and eventually cause train delays. 4.4 The analysis of train delay risks The concept of risk has been widely applied to different disciplines. In railway industries, risk can be used to evaluate the threats to the success of a railway project, or the safety of a railway system. However, the applications of risk concept to train delays are seldom found in the literature. In this study, we tried to apply the concept of risk to evaluate the threats to train punctuality. According to the “Operational Rules and Regulations of Railroads” stipulated by the Ministry of Transportation and Communications [10], railway accidents are classified into 17 categories: (1) train or rolling stock collision, (2) train or 1.5

2.5

1.25

2

1

1.5

0.75

1

0.5

0.5

0.25

0

0

Figure 7:

Trend of the number of accidents reported.

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AMA Number of Accident per AMA Number of Accidents per 10 10 Million Train Kilometers Million Train-Kilometers

AMA Number

Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 Mar-09 Apr-09 May-09 Jun-09 Jul-09 Aug-09 Sep-09 Oct-09 Nov-09 Dec-09 Jan-10 Feb-10 Mar-10

The Number of Accident per The Number of Accidents perMonth Month

Monthly Number 3

Computers in Railways XII

AMA Number

3500

770

3000

660

2500

550

2000

440

1500

330

1000

220

500

110 0 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 Mar-09 Apr-09 May-09 Jun-09 Jul-09 Aug-09 Sep-09 Oct-09 Nov-09 Dec-09 Jan-10 Feb-10 Mar-10

0

AMA Train Delays per 10 Million Train Kilometers

Total Train Delays per Monthh

Monthly Number

177

Figure 8:

Trend of the total train delays caused by accidents.

rolling stock turnover, (3) train or rolling stock fire, (4) train or rolling stock derailment, (5) train or rolling stock separation, (6) train running into wrong track, (7) rolling stock runaway, (8) bumper stop collision, (9) false blocking, (10) rolling stock failure, (11) track or civil structure failure, (12) overhead catenary system (OCS) failure, (13) signal and interlocking system failure, (14) train forced to stop, (15) train stops outside home signal, (16) train delay, (17) fatality or injury. Note that the meanings of some accidents are not as clear as their titles. For examples, the accident of “train forced to stop” means that there are some obstacles on the line to obstruct train movement. Train delay represents accidents that are not included in categories (1) to (15) but lead to train delay. Likewise, fatality or injury denotes any other accidents that result in fatalities or injuries. The frequency and the severity of an accident can be calculated by the following equations: Fk  N k TK Sk  D Nk

(2) (3)

where: Fk = the frequency of the kth type of accident N k = total number of the kth type of accident per train-kilometer TK = total number of train-kilometers S k = the severity of the kth type of accident (minutes per accident) D = total amount of train delays (minutes) Figure 9 shows the delay risk matrix of accidents. Since only eight kinds of accidents ever happened in the past, the matrix is only marked by eight symbols. It is obvious that the frequency of “signal and interlocking failure” is higher than the others. The severity of “other accidents leading to train delay” is also high. The reason is that earthquakes have occurred 5 times since 2007, causing almost 4,500 minutes of train delays. The severities of “train or rolling stock derailment” and “train or rolling stock collision” are relatively low since most of WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

178 Computers in Railways XII them happened in depots and did not disturb train operation except the derailment caused by an earthquake on March 2010. Figure 10 shows the risk profile of train delays during the periods from January 2007 to March 2010, where the risk of an accident is calculated by multiplying the frequency with the severity of the accident. The figure demonstrates that “signal and interlocking failure” is undoubtedly the most serious threat to the reliability of THSR. “Other accidents leading to train delay” are also an important risk item, but their causes are diverse and complex. The top two accident types in the risk profile account for almost 80% of all train delays.

900 800 Severity(min)

700 600 500 400 300 200 100 0 0

A C E G

0.05

0.1

0.15

0.2

0.25

0.3

Frequency(per 10 Million Train Kilometers)

Other accidents leading to train delay Rolling stock failure Train forced to stop OCS failure

Figure 9:

B D F H

Signal and interlocking system failure Train or rolling stock derailment Track or civil structure failure Train or rolling stock collision

Delay risk matrix caused by accidents.

Train Delay Risk of Accident (min/10 Million Train Kilometers) 0 20 40 60 80 100 120 140 160 180 200 Signal and interlocking system failure

167.422

Other accidents leading to train delay

127.603

Rolling stock failure

24.292

Track or civil structure failure

16.337

Train forced to stop

15.242

OCS failure Train or rolling stock derailment Train or rolling stock collision

Figure 10:

12.265 2.691 0.000

Risk profile of train delays.

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Table 2:

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Comparison of reliability among different HSR systems in Asia.

Punctuality (within 5 min) Average delay per train Shinkansen 98.3% (2005)1 0.6 min/train (2009)3 2 KTX 94.1% (2008) THSR 99.25% (2009) 0.216 min/train (2009) 1: The punctuality of Shinkansen was collected from Lee [7]. 2: The punctuality of KTX was obtained from Lim [9]. 3: The average delay per train for Shinkansen was collected from the data book of Central Japan Railway Company [1].

5 The comparisons Table 2 lists the reliabilities of different HSR systems in Asia. It shows that THSR has the best performance in terms of both punctuality and average delay per train. However, it should be noted that the comparisons are not completely fair. That is because both train service frequency and operating distance affect service reliability. For examples, the service frequency (13 trains per hour) of the Tokaido Shinkansen from Tokyo to Shin-Osaka in the peak hour is much higher than that (five trains per hour) of THSR. The operating distance of KTX from Seoul to Busan is 412 km, which is longer than the distance from Taipei to Kaoshiung of THSR (345 km). Even though the external conditions are too different to judge which system is better, THSR is undoubtedly a reliable system.

6 Concluding remarks This study collected the punctuality and train delay data of THSR and applied risk concept to analyze the service reliability of the system. The result of the analysis shows that signal and interlocking failures are the main causes leading to train delays in THSR. Although the technologies of THSR were imported from Shinkansen, one of the most reliable systems in the world, the investigation reports of BOHSR pointed out that the reasons causing signal failures are various and undetermined. Even though the facts of failures are still unknown, THSRC has found that shortening maintenance cycle can efficiently mitigate the problems. Through the maintenance strategy, the punctuality has indeed increased after three signal failures in August 2009 until the earthquake happened in March 2010. We believe that the train delays caused by signal failures have been controlled by THSR, and the coming challenge will be how to ensure the safety and reliability while earthquakes and typhoons happen. The proposed methodology to analyze and evaluate delay risks is very useful for operators to improve service reliability. From the resulting risk profile, operators could easily identify the most critical threats to service reliability and concentrate their efforts in mitigating the risks. However, that would require more detailed studies on mitigation measures for reducing the frequency or the severity of a threat to train reliability.

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References [1] Central Japan Railway Company, Data Book 2009, Central Japan Railway Company, 2009. [2] Department of Statistics, Monthly Statistics of Transportation and Communications Republic of China, Ministry of Transportation and Communications, 2010. [3] Federal Railroad Administration (FRA), High-Speed Intercity Passenger Rail (HSIPR) Program; Notice, FRA, 2009. [4] Givoni, M. “Development and Impact of the Modern High-speed Train: A Review”, Transport Reviews, Vol. 26, No. 5, pp. 593-611, 2006. [5] Institute of Transportation (IOT), The Feasibility Study of High Speed Rail on the Western Corridor of Taiwan, Ministry of Transportation and Communications, 1990. [6] Kwak, Y. H., “Analyzing Asian Infrastructure Development Privatization Market”, Journal of Construction Engineering and Management, Vol. 128, No. 2, pp. 110- 116, 2002. [7] Lee, Y. S. “Achievements of KTX Project for the Past Year and Improvement Measures”, Presented in the 5th Congress & Exhibition on High Speed Rail, 2005 [8] Lee, Y. S., A Study of the development and issues concerning High Speed Rail (HSR) – Working Paper, Transport Studies Unit - University of Oxford, 2007. [9] Lim, B. O., “Innovations in Rolling Stock Maintenance Facilities”, UIC 6th World Congress on High Speed Railway, 2008. [10] Ministry of Transportation and Communications (MOTC), Operational Rules and Regulations of Railroads, MOTC, 2008. [11] Shima, T. “Taiwan High Speed Rail”, Japan Railway & Transport Review, No. 48, pp. 40-46, 2007. [12] Taiwan High Speed Rail Corporation, ROD Incident and Accident Reporting and Investigation Procedure, Taiwan High Speed Rail, 2006. [13] Takagi, R. High-Speed Railways : The Last 10 Years, Takagi, Japan Railways and Transport Review, No. 40, pp. 4-7, 2005. [14] The official web site of Taiwan High Speed Rail Corporation, http://www.thsrc.com.tw/en/about/ab_comp.asp.

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Section 3 Communications

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Development of a railway signaling device based on mixed digital and analog signals using digital signal processors R. Ishikawa1 , D. Koshino2, H. Mochizuki2, S. Takahashi2 , H. Nakamura2 , S. Nishida1 & M. Sano1 1 Kyosan Electric Mfg. Co., Ltd., Japan 2 College of Science and Technology, Nihon University, Japan Abstract In Japan, automatic train control (ATC) systems, one type of railway signaling system, transmit train control information by using analog signals based on amplitude modulation (AM) in the audio frequency band. To realize highly functional train control by increasing the data transmission speed, there have been many studies on digital ATC that transmits train control information by using digital signals based on phase shift keying (PSK), and these systems are employed in some railway lines. In practice, however, it is difficult to install digital ATC because it is impossible to ensure another transmission band for digital ATC signals due to the existing track circuit configuration and interoperability conditions, and there are many railway lines in which analog ATC is still employed. To overcome this restriction, we proposed a novel railway signaling system using mixed digital and analog signals. We employed quadrature PSK (QPSK) for digital signals and developed a transmission device using digital signal processors. We evaluated the transmission characteristics by conducting a basic experiment. Keywords: railway signaling, amplitude modulation, phase shift keying, digital signal processor.

1 Introduction In Japan, automatic train control (ATC) systems ensure railway safety by transmitting train control data via the rails based on amplitude modulation (AM) in the audio frequency band. There is currently a great deal of research on digital ATC, WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100181

184 Computers in Railways XII which transmits train control information based on digital modulation schemes, such as phase shift keying (PSK) [1]. Since digital ATC can realize highly functional train control by increasing the data transmission speed, many railway engineers are attempting to install digital ATC. In our previous work, we proposed a high-speed data transmission system employing code-division multiple access (CDMA) and quadrature amplitude modulation (QAM) for digital ATC [2]. In practice, however, there are some problems in installing digital ATC. In railway signaling via rails, the audio frequency band is usually employed for data transmission, and the carrier frequency depends on the track circuit configuration and interoperability conditions. Therefore, since it is difficult to ensure a new channel for digital ATC signals in a railway line in which analog ATC is employed, all equipment must be replaced simultaneously when attempting to install digital ATC. To overcome this restriction, we investigated a novel data transmission scheme for railway signaling. Since a digital modulation signal such as PSK has no amplitude component, we considered that conventional analog ATC can employ it as an AM carrier. Based on this idea, we previously proposed a data transmission scheme based on mixed digital and analog signals, which we call “digital–analog ATC”. In the present study, we attempted to develop a transceiver for digital–analog ATC using digital signal processors (DSPs). In the transmitter, we implemented an AM modulator that uses a quadrature PSK (QPSK) signal as an AM carrier. In the receiver, on the other hand, we implemented some functions such as automatic gain control (AGC) and a Costas loop, which is one carrier synchronization method, for QPSK demodulation. In addition, we conducted a basic experiment to verify these transceiver functions. We also conducted an experiment using a setup including conventional equipment and evaluated the spectral distribution of the AM demodulated signal and the QPSK constellation characteristics.

2 Overview of digital–analog ATC 2.1 Definition of digital–analog signal Figure 1 shows the scheme for generating a so-called digital–analog signal. First, it is necessary to be able to receive the train control signal with conventional ATC equipment. Therefore, the scheme shown in Figure 1 is based on an AM transmitter. It is possible to demodulate an AM signal even if a PSK signal that has no amplitude component is employed as the AM carrier. Based on this idea, we developed the digital–analog signal as a novel signal generation scheme that applies a PSK -based modulation signal to an AM-carrier of an AM-based transceiver. 2.2 Composition of digital–analog ATC Figure 2 shows a block diagram of the digital–analog ATC. In this figure, a digital– analog signal generator installed as field equipment is connected with the rail WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Train control signal transmitted by AM

AM Signal generator

Digital-analog signal PSK modulator

Digital data generator Train control data transmitted by PSK

Figure 1: Generation of digital–analog signal.

Amplifier

BPF

Demodulator (digital or analog signal) Train speed decision logic

Transformer Receiver adaptor

Real train speed measurement

Receiver coil

Comparison of train speed Braking output

TG

Transformer Audio amplifier

Rail Digital-analog signal generator

Figure 2: Block diagram of digital–analog ATC.

serving as the transmission medium. On the train, the received signal is demodulated after passing through an amplifier and a band pass filter (BPF). Since the received signal includes digital and analog signals, either a digital signal demodulator or an analog signal demodulator is installed. The instructed train speed is determined from the demodulated signal and is compared with the actual train speed measured by a tachogenerator (TG). If the actual train speed is faster than this instructed train speed, it is reduced by applying the brake. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

186 Computers in Railways XII 2.3 Characteristics of digital–analog ATC When installing digital ATC, one problem is that all equipment must be replaced simultaneously. With our proposed digital–analog ATC, on the other hand, it is possible to transmit mixed digital and analog signals using the same carrier frequency. Therefore, this scheme has the following benefits: • It is possible to replace existing equipment with digital ATC equipment gradually. • It is easy to conduct verification experiments for installing digital ATC. • It is possible to verify the validity of digital ATC signals by comparing them with analog ATC signals. However, the AM power spectrum is distributed to other frequency bands by employing a digitally modulated signal as the carrier. Therefore, digital–analog ATC requires higher signal power to ensure an adequate signal-to-noise ratio (S/N).

3 Design of transmission device using digital signal processors 3.1 Overview of transmission device development We attempted to develop a transmission device based on the ideas described in the previous section. Since the transmission band is in the audio frequency band, we used digital signal processors (DSPs), which have many applications in audio signal processing. Since one goal of our research is to replace analog ATC with digital ATC smoothly, in our experiments we used a conventional analog ATC receiver, and we developed a digital–analog transmitter and a digital ATC receiver. 3.2 Design of digital–analog transmitter As mentioned above, since a PSK-based signal is employed as the AM carrier for the digital–analog signal, we designed the digital-analog transmitter to include both AM and PSK modulators. In addition, we adopted quadrature PSK (QPSK) to increase the digital data transmission speed. In QPSK, two orthogonal carrier signals are used to transmit digital data. One is given by cos 2πfc t, and the other is given by sin 2πfc t. The two carrier signals remain orthogonal in one period: 

0

Tc

cos 2πfc t × sin 2πfc tdt = 0

(1)

where Tc is the period of the carrier signals, which is equal to 1/fc . By using cos 2πfc t and sin2πfct, the QPSK signal is given by: 1 1 s(t) = √ dI (t) cos(2πfc t) + √ dQ (t) sin(2πfc t). 2 2

(2)

The channel in which cos 2πfc t is used as a carrier signal is generally called the inphase channel, or I channel, and the channel in which sin2πfct is used as a carrier WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Q 1

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Figure 3: Symbol placement in QPSK.

signal is generally called the quadrature-phase channel, or Q channel. The data in the I and Q channels are dI (t) and dQ (t), respectively[3]. Figure 3 shows the symbol placement in QPSK. In this figure, QPSK transmits two bits simultaneously by assigning one bit, 1 or -1, to the I channel and Q channel, respectively. 3.3 Design of a digital ATC receiver As mentioned above, since the digital–analog signal includes an AM signal, we need to implement a function to cancel an amplitude component in a digital ATC receiver. To normalize the amplitude component, we applied automatic gain control (AGC) in a digital ATC receiver developed using a DSP. Specifically, we developed software to implement a function for squared detection, which is an AM detection method. We used a Costas loop as the carrier synchronization method and implemented it in the digital ATC receiver. A Costas loop is based on a phase locked loop (PLL), as shown in Figure 4. Since QPSK has four symbols at intervals of π/2, as shown in Figure 3, the symbol element is cancelled by multiplying the phase by four at the Costas loop. The Costas loop detects an output signal that is proportional to the phase difference between the received signal and a voltage controlled oscillator (VCO) signal and ensures carrier synchronization by adjusting the phase difference to zero. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

188 Computers in Railways XII cos ϕ LPF

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sin 4ϕ

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sin 2 ϕ

Figure 4: Block diagram of Costas loop.

4 Evaluation of digital–analog ATC transceiver 4.1 Specifications The specifications of a digital-analog ATC transceiver developed based on the design in the previous section are shown in Table 1. Although we set the carrier frequency to 3,150 Hz in this study, other frequencies may be used in practice, for example, 5,250 Hz. Similarly, we used 35 Hz as the analog signal frequency, but other frequencies may be used in practice, such as 28 Hz, 64 Hz, etc. The digital– analog ATC transceiver that we developed can freely set these values, as well as the digital transmission speed, by changing the DSP parameters. 4.2 Verification of basic functions in digital–analog ATC transceiver In order to verify the basic functions, such as AGC and carrier synchronization, we conducted a basic experiment in which a transmitter was connected directly to a receiver based on the specifications shown in Table 1. Figure 5 shows the

Table 1: Specifications of digital–analog ATC transceiver. Parameter Carrier frequency

Value 3,150 Hz

Analog modulation method

AM

Digital modulation method

QPSK

Analog signal frequency

35 Hz

Digital transmission speed

400 bps

Sampling frequency of DSPs

48 kHz

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Time[s]

Amplitude[V]

(a) Received signal.

Time[s]

(b) Received signal after applying AGC function. Figure 5: Waveforms at the digital ATC receiver.

waveforms at the digital ATC receiver. In this figure, we verified that the amplitude component of the digital–analog signal was cancelled by applying the AGC function. Figure 6 shows the constellation characteristics after QPSK demodulation. The effectiveness of the AGC function was also verified from this figure because the amplitude of the demodulated signal was approximately constant. In addition, the phase of the demodulated signal was also approximately constant, showing that the VCO output at the receiver could be synchronized with the carrier of the received signal. We verified that the function of the Costas loop could be implemented in software on the DSP. 4.3 Experiment using actual railway signaling devices To verify the characteristics of a conventional analog ATC receiver when presented with the digital–analog signal, we conducted an experiment using actual railway signaling devices, as shown in Figure 7. After the digital–analog signal generated by the digital–analog ATC transmitter passed through a bandpass filter (BPF), which is typically employed as the receiver unit on actual trains, it was split and supplied to a conventional analog ATC receiver and the digital ATC receiver that we developed. Figure 8 shows the signal after passing through the BPF, which had narrow band characteristics, and Figure 9 shows the spectral distribution for the AM demodulated signal. In these figures, since the QPSK signal was influenced by the BPF characteristics, the power spectrum increases to cover a wide bandwidth, not just 35 Hz, which is the analog signal frequency shown in Table 1. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Q

I

Figure 6: Constellation characteristics after QPSK demodulation.

Analog ATC receiver

Signal generator

BPF AM

Digital ATC receiver (DSP)

QPSK modulator Digital-analog transmitter (DSP)

Figure 7: Experimental setup, including actual railway signaling devices.

However, since the power at 35 Hz was much larger than that at other frequencies, it was adequate for detecting the train control signal. We verified that the correct signal corresponding to 35 Hz could be detected with the setup shown in Figure 7.

5 Future deployment of highly functional ATC system As mentioned above, adoption of a digital–analog signal can realize a highly functional ATC system that is free of restrictions due to the track circuit configuration and interoperability conditions. We noted that the PSK signal has no amplitude component, and we employed it in analog ATC using AM. Therefore, once WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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0

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-50 10

20

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Frequency [Hz] Figure 9: Spectral distribution of AM demodulation.

existing equipment is completely replaced with digital ATC, we will be able to use the amplitude component for a digital ATC system in order to increase the transmission speed. At present, we are developing a transceiver using QAM for a digital ATC system. Since QAM can realize high-capacity data transmission compared with PSK by making use of the amplitude component, digital–analog ATC has the potential to realize more highly functional systems by updating the transceiver that we developed. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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6 Conclusion In this study, we proposed a transmission system that mixes digital and analog signals in the same frequency band, called “digital–analog ATC”, as a highly functional ATC system that is free of the restrictions caused by the track circuit configuration and interoperability conditions. We designed a digital–analog ATC transceiver including some functions, such as AGC and a Costas loop, developed using DSPs. From the result of a simple experiment, we verified the basic functions of the digital–analog ATC transceiver. In addition, in a setup including actual railway signaling devices, when a digital– analog signal was given to a conventional analog ATC receiver, the correct signal corresponding to the AM signal frequency could be detected. In future research, we plan to evaluate the proposed system quantitatively by studying the S/N ratio characteristics. We will also investigate a detailed procedure for implementing an actual ATC system.

References [1] S. Irie and T. Hasegawa: “A study on the Railway Signalling System using Spread Spectrum Communication” , IEICE Technical Report, Vol. 93, No. 89, pp. 43–48 (1993). [2] H. Mochizuki, S. Takahashi, H. Nakamura, S. Nishida and R. Ishikawa: “Development of a High-speed Rail Transmission System Using Digital Signal Processors for Railway Signalling”, Eleventh International Conference on Computer System Design and Operation in the Railway and Other Transit Systems, pp. 295–304 (2008). [3] H. Harada and R. Prasad: Simulation and Software Radio for Mobile Communications, Artech House, pp. 90–91 (2002).

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A multi scalable model based on a connexity graph representation L. G´ely, G. Dessagne, P. Pesneau & F. Vanderbeck SNCF, Innovation and Research Department, University of Bordeaux I, France

Abstract Train operations will be greatly enhanced with the development of new decision support systems. However, when considering problems such as online rescheduling of trains, experience shows a pitfall in the communication between the different elements that compose them, namely simulation software (in charge of projection, conflict detection, validation) and optimization tools (in charge of scheduling and decision making). The main problem is the inadequacy of the infrastructure’s monolithic description and the inability to manage together different description levels. Simulation uses a very precise description, while the optimization of a mathematical problem usually does not. Indeed, an exhaustive description of the whole network is usually counter-productive in optimization problems; the description must be accurate, but should rely on a less precise representation. Unfortunately, the usual model representing the railway system does not guarantee compatibility between two different description levels; a representation usually corresponds to a given (unique) description level, designed in most cases with a specific application in mind, such as platforming. Moreover, further modifications that could improve performances or precision are usually impossible. We propose, therefore, a model with a new description of the infrastructure that permits one to scroll between different description levels. These operations can be automated via dynamic aggregation and disaggregation methods. They allow one to manage heterogeneous descriptions and cooperation between various tools using different description levels. This model is based on the connexity graph representation of the infrastructure resources. We will present how to generate corresponding mathematical models based on resource occupancy and will show how the aggregation of resources WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100191

194 Computers in Railways XII leads to the aggregation of properties (e.g. capacity) that can be translated into mathematical constraints in the optimization problem. Keywords: modeling, optimization, railway operations, traffic management, infrastructure representation.

1 Introduction This paper defines an innovative way to represent infrastructure and a methodology allowing the use of different description levels. This is a theoretical prerequisite to any system, which will help experts to address the very heterogenous problems encountered within the re-scheduling operations.

2 Classic representation 2.1 Origins of the classic representation Railway studies arose during the 1970s. Planning problems have been treated since the 1990s and rescheduling is a rather recent topic of interest. The most important developments in the last decade are summarized in the surveys in [1–4]. One can have a look at [5] for earlier studies. However, although many mathematical models and techniques are presented, modeling issues are scarcely debated. With the exception of some formal exercises, such as [6] and an interesting discussion on implicit choice of description and its consequences in [1], the importance of infrastructure representation has been barely mentioned before [7]. Indeed, most studies naturally re-use the same kind of representation designed for industrial purposes, where the adequacy of description strictly corresponds to (only) one application. 2.2 Examples The range of representations goes from an exhaustive one, as in figure 1, to more synthetic representations, where only the main railway nodes and main lines remain, as is the case in figure 2. 2.3 A formal definition for the classic representation Considering the most elementary description level, the infrastructure of the railway network consists of basic track sections (e.g. block or routes sections). These sections join at special points (switchings, joints, stations , etc). The real railway network is usually represented in the same way via an undirected graph (since directions are given by itinerary definitions and signaling; parts of the infrastructure are not dedicated to one-way usage). WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 1: Lyon St Exupery TGV Station.

Figure 2: Railway network around Paris.

Thus, a classic representation on the field corresponds to the graph: Gclassic = (S, P ), with S = {arcs} = {Track Sections} and P = {nodes} = {Special Points}

(1)

If the intention of the description is to be exhaustive, as shown in figure 1, arcs correspond to block sections and nodes represent limits between adjacent blocks. In the case of figure 2, on the other hand, arcs correspond to complete WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

196 Computers in Railways XII lines (and might include some (minor) stations), whereas nodes represent stations or junctions. 2.4 Microscopic versus macroscopic 2.4.1 Why are there different description levels? Railway infrastructure descriptions may require different levels of details, each one corresponding to different objectives, cf. [8]. The following set of online problems (more detailed in [9]) illustrate studies of different size and requiring different precision: 1. fluidification of a complex junction with speed fine-tuning around a limited local area. 2. re-scheduling with intermediate precision of the representation, but modeling interactions between surrounding areas. 3. re-routing of trains along new itineraries (succeeding a major breakdown, for example) within a macroscopic description of vast areas (typically involving different lines). The bigger the area to consider (i.e. spatial distribution and time window of the incident consequences), the less precision in the description (although more time is usually available for computation). Indeed, in practice, if bottleneck areas require precise description, an exhaustive description of the whole network with maximum precision may be counter productive, especially with online applications, since calculations are usually exponential in terms of the number of elements (and mathematical variables). On the other hand, when experts build a timetable off-line, solutions are mostly a guideline; only a moderate level of precision is required. However in operations, the solution must be immediately applicable then precise enough to ensure real feasibility. Consequently, the online rescheduling problem usually requires a more precise description, for the same area. In other words, there is always some kind of trade-off between accuracy, size and available time. This trade-off is hard to balance with a one-size-fit-all description. 2.4.2 Definitions of macroscopic and microscopic representations A microscopic description is a representation where all the elements correspond to the most basic resources; only one train can be affected on each one of them, e.g. a block section. A macroscopic description contains elements that can be aggregations of basic elements. Resources do not necessarily have the capacity of one train. For instance, a resource can represent, as an example, a set of (connected) platforms, block sections, or even a whole complex station. Remarks: • a description is not necessarily homogeneous: several parts can be described at macroscopic level, others with more precision, i.e. with more disaggregated resources. Usually, lines correspond to the first one, while junctions, stations and other bottleneck areas correspond to the latter, e.g. [10, 11]. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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• the macroscopic description covers every level above the elementary microscopic one. Thus, every resource aggregation in a macroscopic description can be disaggregated or aggregated again into other aggregated resources. 2.5 How to choose a good description level? 2.5.1 Academic point of view Given a specific re-scheduling problem, with a given (type of) incident and a given size of area to cover, one is able to choose an adequate description level. Hence, most algorithms use a dedicated (unique) description; some studies consider a representation of infrastructure based on the most detailed (microscopic) level: block sections level on a small area cf. [12]. However, studies consider more often more simplistic (macroscopic) descriptions [13, 14] on a larger scale; usually a main line joining two main stations with some stops (smaller stations) in between. Once again we refer to [1] for a comparative study on the size (and the precision level) used in the main recent studies. In practice, the railway infrastructure description is done by human experts once and for all (nowadays such description takes days for every new study) and hence is unique. However, one cannot assume that some rules of thumb, even combined with expertise, could determine an average adequate description level that fits all incidents and covers the wide range of problems, such as those previously presented (i.e. from fluidification to re-routing problems). In conclusion, it is hardly suitable in practice and a generalization would be particulary uncertain. 2.5.2 Operational point of view As previously explained, post-optimization validation (via simulation) requires the most precise description level. Consequently, the whole process uses at least two description levels, namely one for a (microscopic) simulation tool and one for a (more macroscopic) simulation tool. Needless to say, specifying (off-line) a microscopic description is unavoidable; however, we should not expect experts to provide other (every new macroscopic) descriptions all over again from scratch. Anyway, one must ensure that cooperation between at least two models would be possible. Moreover, nowadays when real incidents occur, the impact of consequences can be hardly predictable. That is why forecasting tools are needed in the near future to help analyze, a priori, an acceptable trade-off between, on the one hand, precision and size of the description, and, on the other hand, calculation time. However, until the very end of the process, any choice will remain uncertain. We claim that any predefined fixed description level is very restrictive for optimization purposes and will probably be inefficient in many cases. Consequently, an automatic or semi-automatic scalable representation would be of great interest if one can rely on an available microscopic description. Moreover, if we assume we can scroll easily from one level to another, why not use it dynamically in the search process itself? Finding a good trade-off would become part of the process. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

198 Computers in Railways XII We can easily imagine that switching to any level of aggregation, starting from the elementary microscopic description to very macroscopic ones, would be more suitable to deal with unpredictable incidents (and their impacts, i.e. the area to consider and the needed level of precision). This would also be easier to adapt facing the specific topology of infrastructure. To conclude, it would be more flexible and easier to generalize to an industrial tool. 2.5.3 Other modeling and mathematical issues Some signaling systems do not use fixed blocks, but moving blocks where trains must respect permanent headways (e.g. ERTMS 3 signaling system). Here, an elementary resource based model is inappropriate. Accordingly, in the most general case of study, any description should be able to manage a mix between basic resources with the capacity of only one train and more aggregated resources. Using different kinds of representations could help one to tackle some complexity issues with the mathematical problems in a counter-intuitive way, e.g. in an area with a complex network of switchings (such as the pre-entrance of main stations), a microscopic description (i.e involving resources with the capacity of one train) may lead to a more compact formulation (with regard to the number of constraints) than managing the whole set of incompatibilities between itineraries. In the first case we can aggregate occupancy (blocking) constraints (one constraint per resource), but in the second case we must deal with every couple of incompatible routes. 2.6 Limits of the classic representation We will now describe why the classic representation does not conveniently fit the above requirements. The classic representation allows elements of different nature to share the same kind of representation. In consequence, coherence may be broken, and aggregation or disaggregation operations may be difficult in practice. This will be illustrated in the following example. In graph A, there are nodes of different nature; the gray nodes represent stations (hence infrastructure resources, such as the aggregation of platforms and ways), whereas the clear node represents only a special point (a virtual landmark that does not stand for a physical resource): the junction between three main lines, as illustrated in graph B. Finally, graph C shows an additional representation where the same network is divided into two parts: one is composed by the high-speed line between Bruxelles and Nimes (dark color), the second is composed by the classic line between Nimes and Perpignan (clear color). The arc representing the high-speed line would correspond to an aggregation of all the nodes (i.e. a spacial point and the stations) plus the arcs (lines) that constitute the high-speed line. All three representations would make sense from an operational point of view, with three different purposes in mind. However, if they correspond to different macroscopic description of the same network, one cannot define a common rule WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 3: Illustration: TGV 9835 itinerary.

that would permit one to aggregate or disaggregate resources and then switch from one representation to another conveniently. In practice, as we expect to implement aggregation methods, we must ensure that the following coherence rule is respected: all elements of the same nature should share the same kind of representation (regardless of the description level). While classic representation is valid with a microscopic description (e.g. the exhaustive description of Lyon Saint-Exupery station, cf. 1), it cannot describe some aggregated resources and respect the coherence rule. Hence, if arcs represent sections of ways and nodes represent remarkable points, how can a station be represented in a macroscopic description? On the one hand, since a station may be connected with more than two resources (unlike arcs), one would need to represent it as a node. On the other hand it is of a different nature to a remarkable point; macroscopic resources (like a station) are aggregations composed of resources that are ways or route sections (platforms, etc . . . ). They are not remarkable points (virtual landmarks); minimal duration constraints must be applied on every train crossing this resource (as for any physical resource). In conclusion, if we want a model that respects the coherence rule (hence allowing easy implementation of an object-oriented model), the resources route section and station must share the same kind of representation, since they are of the same nature. As an aggregation of resources (such as a station) can be connected to many other resources, the most natural choice is to represent every infrastructure resource used by trains (such as ways, platform, station, . . . ) with a node. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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3 A new model representing the infrastructure 3.1 Definition for a new representation Every graph representation of the infrastructure consists of the two following elements: 1. nodes represent infrastructure resources, 2. arcs represent connections between resources. Thus, the railway network representation with this new model is a connexity graph: Gconnexity = (R, C) ,with R = {nodes} = {Infrastructure Resources} (2) and C = {arcs} = {Connexity Relationship} Such a formalism allows one to adapt infrastructure representation to any description level if we use an appropriate methodology. For example, the following illustration shows four representations of a small network surrounding a junction (involving height track sections). We present, from left to right: the microscopic classic representation, its new representation, an example of aggregation (the closest four resources around the junction) and finally an equivalent of this aggregation if we had used the classic representation.

Figure 4: New representation as a connexity graph.

Remarks: 1. If we compare the central node in both classic representation: in the disaggregated version it represents a landmark, while in the aggregated version it has become an infrastructure resource (aggregation of four sections), 2. Every arc in the classic representation becomes a node in the new representation, 3. For every relationship of a connexity (arc), one can define a unique measure point (frontier) between two resources, as we will see later. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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3.2 Methodology and rules of aggregations The next question is how to treat aggregations and consequently disaggregations? Some basic rules must be pre-defined in accord with experts, once and for all. They must define how properties of aggregated resources, and hence, mathematical constraints, will evolve in an aggregation of resources, i.e. define the aggregation functions to implement. These rules must be in relation with the properties and operational constraints one wants to model and what is considered to be relevant in regard of the addressed problem. We will illustrate this methodology with two obvious examples of aggregations where one wants to address a capacity problem, hence one considers mainly maximum flow and maximum storage capacity properties (plus a sequence property). Of course, other aggregations of properties should be eventually defined following the same kind of methodology. 3.2.1 Serial aggregations, itineraries • aggregation of infrastructure resources: serial aggregation is very close to the concept of itineraries; this is the aggregation of an ordered list of infrastructure resources (with a maximum flow property and a static capacity property).  e.g. in figure 5, where: ra = r2.i =< r2.1 , r2.2 , r2.3 > i

• aggregation of measure points: measure points (i.e. arcs, noted frontiers) connecting the aggregated resource to the adjacent resources are the same as before aggregation. e.g. F r(r1 , ra ) = F r(r1 , r2.1 ) and F r(ra , r3 ) = F r(r2.3 , r3 ). • Properties: 1. F low(ra ) = min F low(r2.i ) i  2. Capacity(ra ) = Capacity(r2.i ) i

3. re-ordering is not allowed: the sequence of entrance remains strictly the same for the clearance (with the next trains).

Figure 5: Serial aggregation. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

202 Computers in Railways XII 3.2.2 Parallel aggregation 1. aggregation of infrastructure resources: the best example consists of the aggregation of parallels ways that compose a line (with a maximum flow property) or a storage (with a static ⎧ capacity property). ⎪ ⎨ r2.1  e.g. in figure 6 : ra = r2.i = r2.2 ⎪ ⎩ i r2.3 2. aggregation of measure points: point of measure (arcs) should be melted.   F r(r2.i , r3 ). F r(r1 , r2.i ) and F r(ra , r3 ) = e.g. F r(r1 , ra ) = i

i

3. properties: 

F low(r2.i )  (b) Capacity(ra ) = Capacity(r2.i ) (a) F low(ra ) =

i

i

(c) re-ordering is allowed: the sequence of entrance is not necessarily the same as for the clearance.

Figure 6: Parallel aggregation.

3.3 Conventions regarding schedules Finally, we must define how to connect explicitly schedules with the previous representation. 3.3.1 Convention Each arc in Gconnexity represents a unique point of measure. At this point we evaluate when the head of any train crosses the frontier between two resources (i.e. any effective entrance in a new resource). In order to construct a timetable, one horary (one variable) must be associated with the pass of every circulation on any measure point. This allows every move of every circulation to be described and a timetable to be associated with any graph Gconnexity . Needless to say, every aggregation of resources yields directly to a mathematical model with fewer variables. This permits one to determine a trade-off between WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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precision (the more disaggregated, the more precise), size and calculations (the more aggregated, the faster the computation). 3.3.2 Measure of entrance and resource occupancy The length of trains must not be neglected. One measures the date when the head of a train crosses the measure point; according to the length and the speed of the train, the whole train may completely leave the resource a long time after this measure (especially in the case of freight trains). A circulation occupies a resource until liberation (or clearance), which happens a certain amount of time after its effective exit of the resource. In the same way, the resource occupies a certain amount of time called reservation before the effective entrance. The headway between circulations is then the sum of these amounts of time plus a buffer time (see blocking time theory, in [7], for example). 3.4 About the choice of a mathematical model Once a multi scalable representation, as detailed here, is available, any mathematical model reviewed in [1] can be applied (adapted) on. The alternatives depend on what kind of operational problem is treated, and the types of operational constraint to consider are those that are more convenient and efficient, but in the end it should not depend on the representation nor the convention proposed. On the other hand, different operational problems can be addressed (each with a different level of representation and a different mathematical model), as soon as a microscopic description based on this multi scalable representation is available.

4 Conclusion We have defined a methodology and a representation that permits one to scroll from microscopic to any aggregated modelization. We have shown basic examples of aggregation rules that make automated aggregation possible. Finally, we have defined a convention for schedule that allows one to address the model of timetabling problem (and rescheduling problem). Another aim of this paper was to explain why a complex software system will be needed to help online operations efficiently. We are convinced that a multi-level capable model will play a key role and is the first theoretical prerequisite towards their development. Another prerequisite is a microscopic digital description of railway infrastructures; this would mark the entrance of railway operations in the digital age. Models of description and numerical databases are now under development in Europe, e.g. RailML [15] or Eifel (the SNCF dataset that will be compliant with the present concepts). However, compatibility issues could arise. Consequently, we encourage anyone to consider this new representation, which should enhance compatibility (at least with descriptions that are not of the same level). Finally, as developed through an object-oriented mind, this model can be applied to any traffic management problem (involving resource allocation), and is probably WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

204 Computers in Railways XII also applicable to a more general set of industrial problems outranging the scope of the railway industry, exclusively treated in this paper.

References [1] T¨ornquist, J., Computer-based decision support for railway traffic scheduling and dispatching: A review of models and algorithms. 5th Workshop on Algorithmic Methods and Models for Optimization of Railways, eds. L.G. Kroon & R.H. M¨ohring, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany, 2006. [2] Caprara, A., Kroon, L., Monaci, M., Peeters, M. & Toth, P., Passenger Railway Optimization, Elsevier, volume Transportation, 2006. [3] Cordeau, J.F., Toth, P. & Vigo, D., A survey of optimization models for train routing and scheduling. Transportation Science, 32(4), pp. 380–404, 1998. [4] Bussieck, M.R., Winter, T. & Zimmermann, U.T., Discrete optimization in public rail transport. Mathematical Programming, 79(1-3), pp. 415–444, 1997. [5] Assad, A.A., Models for rail transportation. Transportation Research Part A: General, 14(3), pp. 205–220, 1980. [6] Bjørener, D., The Domain Book: A Compilation of Reports and Papers on Domain Models, Technical University of Denmark, chapter Railways, pp. 157–185, 2007. [7] Hansen, I. & Pachl, J., Railway Timetable and Traffic. Analysis, Modelling, Simulation. Eurailpress, 2008. [8] Lindner, T. & Zimmermann, U., Mathematics-Key Technology for the Future: Joint Projects Between Universities and Industry, Springer: Berlin, chapter Train Schedule Optimization in Public Rail Transport, pp. 703–716, 2003. [9] G´ely, L., Real time train rescheduling at sncf. Robust planning and Rescheduling in Railways, 2007. [10] Burkolter, D., Herrmann, T. & Caimi, G., Generating dense railway schedules. Advanced OR and AI Methods in Transportation, Publishing House of Poznan University of Technology, 10th EWGT Meeting and 16th MiniEURO Conference, pp. 290–297, 2005. [11] Caimi, G., Burkolter, D., Herrmann, T., Chudak, F. & Laumanns, M., Design of a new railway scheduling model for dense services. ISROR, 2007. [12] Brannlund, U., Lindberg, P.O., Nou, A. & Nilsson, J.E., Railway timetabling using lagrangian relaxation. Transportation Science, 32(4), pp. 358–369, 1998. [13] Carprara, A., Fischetti, M. & Toth, P., Modeling and solving the train timetabling problem. Operations Research, 50, pp. 851–861, 2002. [14] Carprara, A., Monaci, M., Toth, P. & Guida, P.L., A lagrangian heuristic algorithm for a real-world train timetabling problem. Discrete Appl Math, 154(5), pp. 738–753, 2006. [15] RailML.org, http://www.railml.org/. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Universal communication infrastructure for locomotives U. Lieske Head of System Integration, PC-Soft GmbH, Germany

Abstract As international freight transport becomes increasingly essential for the competitiveness of the European economy, operators must further address the challenges of efficiency and quality of their vehicle fleets in the years ahead. Here, modern information and communication technologies offer major opportunities for the future. The German PC-Soft GmbH is a specialized company on the market that provides operators with a mobile solution that is situated directly on the vehicle. With 20 years of history and an experienced team of railway consultants and maintenance specialists, PC-Soft develops and implements customer-oriented solutions that support the computer-aided asset management of vehicle fleets. Keywords: asset management, maintenance, teleservice, locomotives.

1 Introduction Manufacturers, operators and service providers know the requirements for high availability of their vehicles with optimum use of resources. Above all, the frequently great distances between service centre and vehicle, the difficult situation regarding availability of resources (spare parts, operating and auxiliary equipment, specialists) require efficient monitoring of the running operation and fast and targeted remedying of faults. To cater even more flexibly to increased teleservice requirements, PC-Soft has developed a unique communication solution, named zedas®mobile [1]. System data and status information relevant to the effective organisation of servicing and maintenance strategies are recorded immediately on the vehicle, processed and electronically made available to service personnel. The central aim is to ensure system availability and optimisation of maintenance strategies based on real operating data and status WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100201

206 Computers in Railways XII information from dispersed systems. The mobile status capture has been developed as an industrial solution that fully meets the demands of tough operating conditions. It can be used without any retroactive effect on process control on any type of vehicle. The possibility of decentralised recording of operating and status data for all components, as well as location-independent provision of information, has been taken into account when developing the system. In the immediate vicinity of the object to be maintained, the system performs the following tasks: – Capturing and processing of operating data – Gaining of status information – Automatic status monitoring and alarming – Reconciliation of plant data with the service centre – Temporary monitoring and analysis of critical plants – Remote diagnosis of systems by external specialists – GPS-aided position capturing and recording – Driving/operational reporting – Warranty monitoring of plants – Calculable and profitable full service contracts

2 Starting position Mobile systems operating over a wide geographical spread, such as locomotives, need online communication links to various back-end systems. The communications technology linking these systems must therefore be open and of universal applicability for different tasks and the technical communication solutions strategically planned and adopted for the long term. Conformity with

Figure 1:

Functionality of mobile plant management with zedas®mobile.

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established technical standards is taken for granted. For the purposes of actual use, the communications technology link is a system infrastructure task and not part of the solution. Exceptions to the above are safety-related applications (e.g. ETCS), most of which require special communication solutions on account of the particular demands on transmission reliability and availability. It makes particular sense to separate the communications infrastructure from the concrete application as various demands are made on the technical solution, the ideal situation being that the application is developed with a bias towards solutions, new and innovative processes are integrated quickly, and allowance is made for upgrading but also for replacing the entire application at a later date without involving great complexity or cost. The communications solution itself needs to have universality and longevity, and the availability of spare parts must be guaranteed over a long period. Extensive work needs to be done on the technical system, e.g. for the installation of power supply and cabling for antennas, therefore it is normally very costly to replace the communications system. Indeed, most information technology applications host several applications in one technical system, e.g. for logging of operational data, remote diagnosis or scheduling, and operate via a shared physical infrastructure.

3 Solution Hence the need, given this backdrop, for a universally applicable communications solution like zedas®mobile which is compatible with international standards (e.g. GSM, UMTS, WLAN). Users engaged in varied tasks for different organisations can communicate with several others. zedas®mobile consists of two components: - an on-board computer [2] fit for industrial applications and railway use (see Figure 2) - a secure, i.e. encrypted, mobile communication link [3] via WLAN, UMTS or GSM (see Figure 3).

Figure 2:

Diagram of compact, industrial-strength onboard computer.

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Figure 3:

Diagram of compact, industrial-strength onboard communication unit.

Various communication modules can be added to the on-board computer, adding flexibility and enabling data to be exchanged via various field bus systems (e.g. MVB, CAN) or via serial interfaces. Direct analogue or digital I/O signal interfaces are also possible. The on-board computer is fitted with a GPS receiver which enables the locomotive to be located and acts as a time standard for all applications. The computer is powerful enough for on-board signal storage and pre-processing, leaving only alarm messages needing to be transmitted to the control centre. Not only does this speed up communications but it also helps to lower the cost of communications. If you have an Ethernet port it is possible for additional on-board computers or control units to be connected directly to the communication box if required. Data can also be exchanged with mobile terminal equipment in close range via WLAN. To all intents and purposes, the locomotive or technical installation is then practically a satellite station in communication with the company network, with security guaranteed by the use of modern encryption methods like Virtual Private Network or Wi-Fi Protected Access (see Figure 4).

4 Conclusion The communications solution discussed above constitutes a universal infrastructure development for locomotives and other mobile technical systems. A sophisticated infrastructure means enhanced efficiency and reduced costs of communication. The solution boasts flexibility, long-term viability and security of collaboration for users in different organisations for e.g. diagnosis and servicing of technical systems. From a maintenance point of view, resulting operations free of breakdowns and owing to status- and load-oriented maintenance and modern teleservice ensure planned system performance and savings on cost-intensive call-outs and manual inspections.

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Figure 4:

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Example application - universal communications infrastructure for a locomotive.

References [1] PC-Soft, www.pcsoft.de [2] EMTrust, www.emtrust.de [3] FMN, www.fmn.de

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Section 4 Computer techniques

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Research on a novel train positioning method with a single image B. Guo1, T. Tang2 & Z. Yu1

1

School of Mechanical and Electronic Control Engineering, Beijing Jiaotong University, China 2 School of Electronics and Information Engineering, Beijing Jiaotong University, China

Abstract Comprehensive train monitoring is an important infrastructure detecting facility that ensures normal operation of the high-speed railway. An accurate position is the basis of precise detection. A research on the autonomous train location method is of great theoretical and practical significance for the positioning of comprehensive monitoring train and enhancing the infrastructure detecting level of the existing line. Comprehensive train monitoring synchronizes all diagnosis parameters by sharing time and position. However, it cannot correct the odometer’s accumulative error with the track circuit’s insulator in high-speed railways. This paper presents a novel position correction method with a single image. It analyses the three dimensional (3D) camera projection model and its disadvantage. A simplification from the 3D to the one dimensional (1D) model is proposed. The actual distance between the landmark and the camera optical center is calculated with image coordinates of the landmark acquired by the camera fixed on top of the train. Then, the actual position of the train can be calculated with the pre-stored landmark position and the calculated distance. Both academic and experimental errors indicate that the position correction method with a single image can satisfy the train positioning requirement. Keywords: train position, single image, projection model, one dimension simplification, landmark.

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1 Introduction Odometry is a familiar method for vehicle location. However, there is a limitation of accumulative error for odometry. The track circuit’s isolator is a traditional and effective way to correct accumulative error in railways. The track inspection car always locates at the tail-end of train. Hence, it cannot use the track circuit’s isolator to correct the accumulative error of the odometer. Otherwise, the track inspection car needs to survey all railway lines, including lines without a track circuit (such as the Qing-Zang line, which is based on GSM-R). It is impossible for these lines to add devices to correct the odometer accumulative error at the target point only for the track inspection car that is running. So a novel correction position method at the target point with recognition of an existing landmark in an image is proposed in this paper. Estimating the 3D pose and position of an object with an image is a key process and a kernel problem in machine vision applications. The advantages of wide range, the lack of intervention needed and high precision make image measurement applicable in many fields. Object positioning with images includes the process of 2D image projection and 3D reconstruction. Firstly, a 2D image of a 3D object in a real world coordinate is produced by the camera. Then, the 2D images can be analyzed and processed for 3D reconstruction and geometric measurement. The interior and exterior camera parameters are a precondition for calculating an object world coordinate in the 3D reconstruction process (Zheng [1]). These parameters are obtained by the calibration process of the camera. However, there is hard calculation load for the interior and exterior parameters [2, 3]. Sun and Wang [4] point out that position with a single image is the simplest and most convenient way for object position. It is not necessary to look for corresponding conjugate image points in binocular image pairs and it also not necessary to carry out a transformation between different coordinates. Ogawa et al. [5] proposed a self-positioning system using a digital mark pattern and a CCD camera. The horizontal distance from the mark pattern is measured using the ratio between the length and width of the mark pattern image. Lee et al. [6] proposed an algorithm to recognize and track the road lane by interpreting a 2D image to a 3D image by angle and position of the CCD camera. Fang et al. [7] proposed an algorithm for vision location on the condition of uncalibrated camera fixation and coplanarity. It gives the 3D calculation model, using the property of projective geometry. For train position, we are only concerned about the longitudinal distance ahead of the train. We propose a 1D simple calculation model based on the 3D calculation model with camera fixation and coplanarity. It greatly reduces the computation load and gives the error analysis. In this paper, we firstly gives the 3D position model with a single image, then the 1D simplification and its error model is introduced. Finally, an experiment result on the railway field is used to validate this method.

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α

215

S

C′ H

F′ D A

E′

C

X′ O′

B′ O

F

X B

E

Figure 1:

3D positioning model of the camera.

2 Train positioning method with a single image 2.1 3D position model with a single image In perspective projection, the straight line connecting the optical center and the image points is used to establish that the corresponding object point is not the one and only. The depth information cannot indicate in image. However, when a fixed camera takes pictures of objects on a plane, the plane provides a constraint in the direction of height. For train position, the objects on the ground can be deemed as coplanar to a certain extent. Under this condition, the points on the ground and on the image are corresponding one by one. Under the constraint of a fixed camera and coplanar image (to a certain extent), the position of an object can be achieved via the camera image so long as the relation between objects and images is determined. Figure 1 shows the 3D positioning model of camera, in which H is the height from the projection center S to the ground, α is the angle between the optical axis and the vertical direction, ABCD is the camera’s field of vision on the ground and AB′C′D is a virtual reference plane vertical to optical axis. The optical axis intersects with the ground level and virtual reference plane at points O and O′ respectively and X is the position of a landmark point on the ground. The virtual reference plane is not in geometric proportion to the ground plane. However, due to its being vertical to the optical axis, the virtual reference plane is in geometric proportion zoom to the image. So E ' X ' E ' F ' E '' X '' E '' F '' :  : O ' X ' O ' F ' O '' X '' O '' F ''

(1)

where E′′O′′X′′F′′ are points on the image corresponding to points EOXF on the ground plane respectively. According to the collinear equation and invariable cross ratio of central projection, we get eqn (2):

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216 Computers in Railways XII EX EF E ' X ' E ' F ' :  : OX OF O ' X ' O ' F '

(2)

According to eqn (1) and (2), we can get

EX EF E '' X '' E '' F '' :  : OX OF O '' X '' O '' F ''

(3)

Eqn (3) shows the proportional relation between points EOXF on the ground and points E′′O′′X′′F′′ in the image. In eqn (3), so long as the position of the pixels of landmark feature in the image is determined, the actual position of this landmark on ground will be worked out. Furthermore, the train position will be calculated. However, while calculating with eqn (3), EF and E′′F′′ must be known. They are intersection points of the extending lines of OX and O′′X′′ with plane boundary respectively, which can be calculated by the equation group of two intersection lines. However, for each landmark point X and X′′, EF and E′′F′′ must be calculated once, which results in a heavy calculation load and long calculation time. This is not of advantage to real-time calculation. 2.2 1D simplification of the 3D model

When we position a train with an image, only the longitudinal position in the direction of train running is concerned. If the 3D model can be simplified into a 1D model, the calculation load and complexity will be reduced. Figure 2 is the schematic diagram of 1D longitudinal positioning with a single image. In this diagram, the camera is fixed rigidly on the frontage top of the locomotive. Within a short distance in front of the locomotive, the position of the camera relative to the ground is determinate when ignoring the track gradient and outer rail super-elevation on the curve, where H is the height from the projection center S to the ground,  is the angle between the optical axis and the vertical direction, the vertical field angle of camera is  and P is the vertical projection of the projection center S on the track plane. In the direction of train running, the nearest point in the field of vision corresponds to point E on the ground and the furthermost point in the field of vision corresponds to point F on the ground. In this figure: | PE | L1  H tan(   / 2)  | PF | L2  H tan(   / 2)

(4)

Therefore, the longitudinal field range of the camera is as follows: L  L2  L1  H [tan(   / 2)  tan(   / 2)]

(5)

The distance from landmark X to point P is

| PX || PE |  | EX | L1  | EX |

(6)

where |EX| means the distance from the target point to the nearest point in field of vision, which can be worked out according to the pixels coordinate of the landmark in the image. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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In figure 2, γ is the angle between the projection ray of landmark X and the optical axis; X′ is the projection of landmark X on the virtual reference plane; EF′ is the projection of the virtual reference plane on the longitudinal 1D section. The distance on line EF′ is in direct proportion to the distance of the corresponding point in the image. Therefore, the distance on the virtual reference plane can be represented as the pixels distance in the image. Assuming O′X′ is y, the direction of O′F′ is positive and the direction of O′E is negative. Assuming | EF ' |  W / 2 , then | SO ' | d , | PE | L1 , | PF | L2 , | O ' E || O ' F ' | 2 d

W /2 W  tan( / 2) 2 tan( / 2)

In triangle SO′X′,

  arctan

(7)

y 2 y tan( / 2)  arctan d W

(8)

Therefore, the distance of landmark X to the projection point of camera P is as follows | PX | H tan(   )

(9)

The real position of landmark X can be calculated with eqns (8) and (9). 2.3 Error analysis for the 1D simplification model

In eqn (9), the factors affecting error include: H,  and  . The assumed height variation is H . The variation of  and  is integrated as angle variation  . Therefore, the error formula is as shown in eqns (10) and (11).

α

S θ γ

F′

H

X′

O′

P

E L1

O

X

F

L L2

Figure 2:

1D positioning model with a single image.

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218 Computers in Railways XII L  ( H  H ) tan(     )  H tan(   ) a11  H tan(     )  H tan(   )  H tan(     ) a11 

H tan( )  H tan 2 (   ) tan( )  H tan(   )  H tan( ) (10) 1  tan( ) tan(   )

a11  H tan( )

1  tan 2 (   ) tan(   )  tan( )  H 1  tan( ) tan(   ) 1  tan( ) tan(   )

Ignoring the infinitesimal of the second order in eqn (10), then L 

H tan( )  H tan 2 (   ) tan( )  H tan(   ) 1  tan( ) tan(   )

(11)

In error equation eqn (11), there are two factors affecting the error: height and angle. The height variation mainly depends on two aspects: firstly, height variation would be caused by super-elevation of the outer rail while the train is passing a curve. In the Chinese railway, the maximal super-elevation on a singleline track is 125mm, and 150mm for a double-line track. The maximal superelevation only appears on small curvature curves. Secondly, high variability would be caused by the swaying of the car body, but this value is less than that caused by super-elevation. Since the camera is fixed on the central line of the car body, considering the two factors comprehensively, it is assumed that the maximal height variation is 75mm. As for angle variation, due to the camera being fixed rigidly with the car body, it will move together with the car body. So the affection on angle α by gradient and car body vibration can be ignored theoretically. The variation of angle γ between the landmark projection line and the optical axis is introduced by the quantization error of pixels. Assuming that the pixels quantization error is 1, the maximal angle error caused by boundary pixels is 0.025 degree. Therefore, for angle variation, only the variation caused by boundary pixels is considered. Putting height and angle variation into eqn (11), the boundary error is 0.249m, which is the maximal theoretical error.

3 Experiment results In order to verify the validity of the above-mentioned 1D simplified calculation model, MV-752 high-speed camera with 752×582 black and white pixels was adopted for the experiment, which has the maximal frame frequency of 350 frames per second. During the experiment, the height from the camera to the ground is H=2.81m, the visual field angle is   14.3 and the angle between the optical axis and the vertical direction is   77.8 , as a result, L1  8.00m and L2  31.80m . Figure 3 shows the picture taken during the test on the railway experiment, in which the white line on the right rail acts as a landmark point. The distance from the real point corresponding to the lower image boundary to the camera is 8m. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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The distance from the first landmark to the corresponding lower image boundary point is 0.56m. There are in total 24 landmark points with 1m interval in the field of vision. Table 1 shows the experiment result and the error of landmark points. The maximal error is -0.15m, which is within the range of error model analysis. The precision can meet the train positioning requirement.

4 Conclusion This paper introduces a 1D simplification method for the 3D position model with a single image. The 1D calculation formula and its error equation are also deduced. Both the theoretical calculation and the experiment result on the railway show that this method has very high precision, and can meet the precision requirement of train position spot correction.

Figure 3: Table 1: No. 1 2 3 4 5 6 7 8 9 10 11 12

Picture with landmark on tracks.

Measurement result and error of the 1D projection position model.

actual row measurement value No. value(m) (m) 534 0.557 0.56 461 1.553 1.56 403 2.513 2.56 354 3.479 3.56 312 4.45 4.56 274 5.474 5.56 242 6.470 6.56 214 7.464 7.56 189 8.470 8.56 167 9.465 9.56 147 10.474 10.56 130 11.428 11.56

error(m) No. -0.003 -0.007 -0.047 -0.081 -0.11 -0.085 -0.09 -0.096 -0.090 -0.095 -0.084 -0.132

13 14 15 16 17 18 19 20 21 22 23 24

actual row measurement value No. value(m) (m) 113 12.481 12.56 98 13.506 13.56 85 14.479 14.56 73 15.456 15.56 62 16.427 16.56 51 17.480 17.56 42 18.410 18.56 33 19.408 19.56 24 20.483 20.56 16 21.510 21.56 9 22.470 22.56 2 23.494 23.56

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error(m) -0.079 -0.054 -0.081 -0.104 -0.133 -0.080 -0.150 -0.152 -0.077 -0.050 -0.090 -0.066

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Acknowledgements This work was supported by the National 863 Program of China (No. 0912JJ0104-ZH00-H-HZ-002-20100105) and the Science & Technology Program of Beijing Municipality (No. D07050601770705).

References [1] Zheng, N., Computer Vision and Pattern Recognition. National defence industry Press: Beijing, pp. 14-20, 1998. [2] Li B., Wang X., Xu, X., Wang, J., A linear three-step approach for camera calibration. Journal of Image and Graphics, 11(7), pp. 928-932, 2006. [3] He, J., Zhang, G., Yang, X., Approach for calibration of lens distortion based on cross ratio invariability. Chinese Journal of Scientific Instrument, 25(5), pp. 597-599, 2005. [4] Sun, F., Wang, W., Pose determination from a single image of a single parallelogram. Acta Automatica Sinica, 32(5), pp. 746-752, 2006. [5] Ogawa, Y., Lee, J., Mori, S., The positioning system using the digital mark pattern –the method of measurement of a horizontal distance. System, Man, and Cybernetics, IEEE SMC’99 Conference Proceedings: Tokyo, pp. 731741, 1999. [6] Lee, J., Choi, S., Lee, Y., Lee, K., A study on recognition of road lane and movement of vehicle using vision system. Proc. of the 40th SICE Annual conference: Nagova, pp.38-41, 2001. [7] Fang, Sh., Cao, Y., Xu, X., A new vision algorithm for uncalibrated camera. Chinese Journal of Scientific Instrument, 26(8), pp. 845-848, 2005.

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Software redundancy design for a Human-Machine Interface in railway vehicles G. Zheng1 & J. Chen1,2 1 2

Institute of Software, Chinese Academy of Sciences, China Graduate University of Chinese Academy of Sciences, China

Abstract The Human-Machine Interface (HMI), which displays the real-time status of electrical systems, interacts with the driver or operator, and collects and reports system fault information, is an important device in railway vehicles. The HMI is a critical component of the control and diagnosis system in the railway vehicle, thus the reliability of the HMI software affects the reliability and safety of the whole railway vehicle. Therefore, it is necessary to design the HMI software with high reliability for railway vehicles so as to ensure the reliability, stability and safety of the railway vehicle operation. This paper analyzes the HMI software function requirements, which include information display, the humanmachine interaction, and communication. A kind of redundancy mechanism is proposed, which employs two structural redundancy methods: N-version programming and recovery blocks. The HMI software is divided into the information display module, the human-machine interaction module and the communication module, and each module is made up of some components. Based on the analysis of the reliability requirement, complexity, and the implementation cost for each component in the HMI software modules, the corresponding redundancy design mechanism is proposed, which consider the tradeoff between the reliability and the cost. In order to evaluate the reliability of the designed redundancy mechanism, a scenario-based reliability analysis method is used to calculate the reliability of the HMI software, which constructs five scenarios and employs the component dependency graph to compute the reliability. The reliability of the HMI software after redundancy design is compared with that before the redundancy design. Keywords: human-machine interface, reliability, software fault tolerance, redundancy design, reliability analysis. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100221

222 Computers in Railways XII

1 Introduction A Human-Machine Interface (HMI) in the driver cab is an important device for a railway driver to interact with the railway vehicle, and also an integral part of the vehicle control system. During the vehicle operation, the driver can monitor the state of the vehicle in real time and send control messages to ensure safety. As a whole, the HMI executes operation states information display, human-machine interaction and communication with other electrical devices in the vehicle. In order to ensure the safety and stability of the railway vehicle operation, it is necessary to design highly reliable software in the human-machine interface for railway vehicles. At present, there are mainly the following methods for the software reliability: error avoidance, error detection and correction, and fault tolerance. Error avoidance employs the standardization design and coding process to reduce software errors. Error detection [1] discovers software errors by setting checkpoints in the software program, moreover, some techniques are used to isolate and correct the errors [2]. Note that it is very difficult to completely ensure software does not have any errors. Fault tolerance [3] is currently a valid technique to improve the reliability of the computer software, which can detect faults automatically and execute the corresponding fault tolerance program. The structural redundancy technique is used commonly in software fault tolerance, which generally includes N-version programming (NVP) and the recovery block technique (RCB) [4]. In the N-version programming technique, N software versions (N>1) are developed independently and work simultaneously after being installed in the same environment, where N versions accept the same input, and the final output is selected from the N outputs by a majority voting algorithm. In the recovery block technique, several recovery blocks are developed for the same software function, where each recovery block accepts the same input and gives an output, and the output is the input of the acceptance test unit. If the output passes the test, the software continues to run, else the software environment is restored, and then the other recovery blocks repeat the above process until a valid result is accepted or there are no other recovery blocks. Considering the functions and the safety requirements of the HMI, several different redundancy mechanisms are employed to improve the software reliability. When developing the HMI software, it is necessary to select different redundancy mechanisms, e.g., N-version programming or recovery block technique, for different function components in terms of the software complexity and cost to implement structural redundancy. After completing the software redundancy design, the reliability of the software is evaluated. In this paper, a scenario-based analysis technique [5] is employed to evaluate the reliability result of a component-based application in the HMI software. The rest of the paper is organized as follows. The components function of the HMI software is introduced in section 2, and the redundancy design is presented in section 3; next, the reliability evaluation of the HMI software is given based on a scenario and, finally, the conclusion is drawn in section 5.

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2 Functions of HMI software in railway vehicles The HMI is connected to the electrical systems over the vehicle communication bus, as shown in the Fig. 1. The HMI monitors the operation status of the vehicle, displays the fault diagnosis results and receives the driver inputs and gives the associate responses. The HMI software functions are given as follows. (1) Information display: displays the status of electrical systems, faults diagnosis results and fault recovery information; (2) Human-machine interaction: gives responses to the operation of the driver on the HMI screen to transit the interfaces, input data, and send control instructions;

Figure 1:

Connection between the HMI and other electrical systems.

Figure 2:

Figure 3:

Structure of HMI the software.

Sub-functions of the HMI software.

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224 Computers in Railways XII (3) Communication: communicates with control units in the vehicle, input/output devices and other electrical equipments to transmit message data and state data over the vehicle bus. The structure of the HMI software is shown in the Fig. 2 in terms of the HMI function. Each function is divided into some sub-functions so as to implement the redundancy design, please see Fig. 3. The sub-functions of the information display are presented as follows: (1) Basic display: displays interface name, line, date, time, vehicle number and other simple information. (2) Status display: displays status information of the corresponding electrical system according to the requirement of the driver. (3) Fault display: displays fault information when an electrical system error happens. The sub-functions of the human-machine interaction are presented as follows: (1) Interface transition: implements the corresponding interface transition when the driver presses the transition key. (2) Electrical system control: calls the communication module to send control commands when the driver presses the control key. (3) Data input: responds to input information from the driver, such as vehicle number, driver number, system time. The sub-functions of the communication are presented as follows: (1) Status receive: receives real-time status information of all electrical systems. (2) Fault information receive: receives the fault information and then transfers it to the appropriate processing. (3) Command transmit: sends control commands to the appropriate electrical system.

3 Software redundancy design for a human-machine interface 3.1 Analysis on the compromise between costs and reliability The HMI software is divided into three modules corresponding to the functions, where each module is made up of some components. The structure of a component is more compact than the one of a module and contains some similar features, which can employ the same redundancy design technique. Therefore, the redundancy design of the HMI software is based on the structure of the components. In terms of the functions of the human-machine interface software in railway vehicles, two structural redundancy methods, N-version programming and recovery blocks, are employed to improve the reliability. When designing the software redundancy, the reliability requirements, the complexity of the various function components and implementation methods for structural redundancy are considered.

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(1) Reliability requirements High reliability is demanded on some components that have a direct effect on the safe and stable operation of the train, and some assistant function components demand relatively low reliability. According to the standard EN50126 [6], the whole HMI software has a reliability requirement. (2) Complexity of components A simple structure component can achieve a high reliability before the redundancy, so there is no need to implement redundancy design on it. Meanwhile a complex component requires redundancy design to improve its reliability. The complexity of a component is considered to determine the need for redundancy design. (3) Implementation costs N-version programming requires N teams to complete the same function component, at the same time; recovery block technique requires several blocks with the same function, which leads to more development cost. N-version programming makes a selection among several outputs. Numerical value may facilitate carries on the selection, while some display functions are unable to make selection. An acceptance test unit in recovery block technique is used to test result. The result of a number of function components cannot be used for testing, so that these components cannot use the recovery block technique. Because some acceptance units are difficult to write, a compromise should carry on between the reliability and the cost. 3.2 Software redundancy design The software redundancy design for three modules, i.e., information display, human-machine interaction and communication, is presented as follows. (1) Information Display The information display module includes three components: basic display, status display, and fault display. Because of the simple structure, basic display component can easily obtain high reliability. The operation of the vehicle will not be influenced even if the basic information is displayed inaccurately, so high reliability is not demanded on this component. This component does not need redundancy design.

Figure 4:

Redundancy design structure of the status display component.

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226 Computers in Railways XII Status display component receives the electrical system status, processes and displays the status information on the screen. Since this component has many kinds of status to process, and each kind of status has a variety of forms, this component is highly complex. Whether the status of electrical systems is good or not affects the operation of the railway vehicle, the HMI can reflect the operation status of electrical systems in real time; therefore, the recovery block technique is employed in this component. Fig. 4 shows the redundancy design structure: According to the process showed in fig. 4, before entering the recovery bock, current electrical status should be stored by memory unit. For the first time, the primary recovery block is chosen to figure out a numerical result. If the acceptance test unit accepts the result, the result is displayed and the following process is executed. If the acceptance test unit does not accept the result, the component returns the access point of recovery blocks and chooses another recovery block. If the results do not pass the acceptance test, this component throws an exception and executes the exception treatment, which means the component has broken down. The fault display component has a high reliability requirement as it shows the operation status of the electrical systems. This information reminds the driver to response to a fault. Before the new fault is diagnosed, the component can query the diagnosed faults. The 3-version programming (3VP) is employed to improve the reliability of the component. Three versions receive the same error number as their own inputs and figure out the display result. The final result is obtained based on the majority voting algorithm [7], which is chosen among the results of three versions. Fig. 5 shows the redundancy structure with three versions. (2) Human-Machine Interaction The human-machine interaction module consists of three components: interface transition, electrical systems control, and data input. The information cannot be displayed if the interface transition component breaks down, which could result in the entire vehicle out of control. Therefore, the interface transition component is required with high reliability. However, the number of the interfaces, which interfaces in the HMI device can switch to, is limit, thus the component should be designed with low complexity so that the reliability requirements can be satisfied easily. The cost to multiple recovery blocks is low because the structure of the interface transition component is not complex. The algorithm of acceptance test unit is described as follows: 1) read

Figure 5:

Redundancy structure of the fault information display component.

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the name of the interface which is switched to; 2) judge whether the name is the same as the one the pressed soft key corresponds to; 3) If two names are not the same, the program return to the access point and choose another recovery block. Note that the algorithm is easy to implement. It is so important for the driver to control the electrical systems via the HMI screen, which is relative with the reliability and stability of the vehicle operation, and even the comfortable level of the passenger. Therefore, the reliability requirement for the electrical system control component is so high. Meanwhile, the complexity of this component is high because there are so many electrical systems to control. This component employs 3-version programming to improve the reliability of this component. The process is the same as the one of the fault display component. When the driver presses the soft key to control the electrical system, three versions receive the same input, and each version sends a control instruction to the electrical system. The selection unit in the component receives these three control instruction, decides which instruction is sent. If the data input component fails, the input, e.g., the vehicle number, is not consistent with the last saved results, but the inconsistence has little effect on the vehicle operation, thus, the reliability requirement of this component is low. Meanwhile, the complexity of the component is not high because the data, which the driver can enter, is so limited. Therefore, it is not necessary to implement redundancy design. (3) Communication The information display module consists of three components: status receive, fault information receive, command transmit. Status receive component is very similar to fault information receive component. They both receive important information which directly reflects the Table 1: Function Module

Information Display

Redundancy design of the HMI function components. Component

Reliability Requirement

Complexity

cost of redundancy design

Redundancy design

Basic Display

low

low

--

--

high

high

high

RCB

high

medium

high

medium

medium

RCB

high

high

high

3VP

Data Input

low

medium

--

--

Status Receive

high

high

medium

RCB

high

high

medium

3VP

high

medium

medium

RCB

Status Display Fault Display

HumanMachine Interaction

Communication

Interface Transition Electrical System Control

Fault Information Receive Command Transmit

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medium

3VP

228 Computers in Railways XII operation status of the vehicle .Because of the large number of electrical systems and devices, they both have much information to deal with. Thus, the reliability requirements and complexity of these two components are high. 3-version programming is employed in these two components to avoid data loss and ensure accuracy of the information. The failure of the command transmit component can cause that electrical systems are out of control, which is so dangerous. This component has medium complexity because the process of information transmission is not complex. A recovery block technique is employed to ensure that control command is transmitted normally. Table 1 shows the components redundancy design on the HMI software.

4 Software reliability analysis The effect of redundancy design is measured by means of assessment of HMI software reliability. The reliability is estimated using Scenario-Based Reliability Analysis (SBRA) [5], which is specific for component-based software whose analysis is strictly based on execution scenarios. In this paper, the reliability of HMI software after redundancy design is compared with the one before redundancy design. SBRA consists of three steps: (1) Usage of scenarios to analyze the dynamic behaviour of HMI software, construct a sequence diagram to each scenario. (2) Calculate some parameters using the sequence diagrams, construct a component dependency graph (CDG) using these parameters. (3) After constructing the CDG model, use an algorithm [5] to analyze the reliability of HMI software.

Figure 6:

Scenarios of the HMI software.

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Table 2:

229

Parameters of each scenario.

Scenario Name

Probability of a Scenario ( PS k )

Average Execution Time of a Scenario ( ECi )

Status display

0.85

3

Fault display

0.03

3

Switch

0.05

11

Input

0.02

2

Control

0.05

3

4.1 Construction of scenarios There are two types of input can stimulate HMI. One is the data receiving from the other system: electrical system status and fault information; the other is the driver input: interface switching input, data input, and electrical system control command. Based on these inputs, five scenarios can be constructed to describe the interactions between components. Fig. 6 shows the five scenarios. 4.2 Component dependency graph construction

Let Sk be an element of the application scenarios set S , k 1,..., S , where |S| is the number of the set S. The probability of the kth scenarios PSk is calculated based on the implementation of HMI software, the probabilities of execution of the 5 scenarios are listed in the following table. Let RCi be reliability of the ith component in the HMI software, i=1,…,9. RCi is calculated based on the one of a single version/ recovery block. Suppose that the reliability of each version is r, the reliability of a component that has implemented 3-version programming is calculated by eqn (1).

RCi  r 3  3* r 2 * (1  r )

(1) Suppose that the reliability of each recovery block is rb, the reliability of acceptance test unit is ra , the reliability RCj of a component implementing

RC j  rb * ra  (1  rb ) * rb * ra

recovery block technique is calculated by eqn (2):

(2)

where j=1,…,9. The reliability result of each component is shown in table 3. Let RTij be a reliability estimate of a transition from component Ci to component Cj. In order to simplify the analysis, supposes the reliability of the interface is 1. Let ECi be the average execution time of the ith component, i=1,…,9, ECi   PSk  Time(Ci ) |S |

k 1

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(3)

230 Computers in Railways XII where PS k is the execution probability of the kth scenario, k 1,..., S , Time(Ci ) is the execution time of component Ci . The average execution time is shown in table 3. Table 3:

Parameters of each component. the average execution time of

Component Reliability before redundancy design

Component Reliability after redundancy design

Basic Display

0.95

0.95

0.52

Status Display

0.9

0.98

0.85

Fault Display

0.92

0.982

0.03

Interface Transition

0.92

0.984

0.05

Electrical Control

0.9

0.972

0.05

Data Input

0.92

0.92

0.02

Status Receive

0.9

0.972

1.7

Fault Receive

0.9

0.972

0.06

0.92

0.984

0.1

Component Name

System

Information

Command Transmit

Figure 7:

CDG of HMI software.

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each component

ECi

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AEappl   PS k  Time( Sk )

231

Let AEappl be the average execution time of the HMI software, |S |

k 1

(4)

where Time( Sk ) is the average execution time of scenario S k . Based on eqn (4), the average execution time of the HMI software is 3.38. The transition probability between components RTij is obtained based on the analysis of each scenario. The component dependency graph of the HMI software is shown in Fig. 7. 4.3 Reliability analysis Based on the scenario-based reliability analysis algorithm [5], the construction process of reliability is shown in Fig. 8. The reliability of the HMI software after redundancy design is 0.95035352. Follow the above steps, the reliability of HMI software before redundancy design is 0.81572.

Figure 8:

Construction progress of HMI software reliability.

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5 Conclusion The redundancy design on the various function components in the HMI software is proposed in this paper based on the N-version programming and recovery block techniques, and the HMI software reliability is analyzed by employing the SBRA method. The result shows that the kind of redundancy design can improve software reliability effectively. Note that only the N-version programming and the recovery block techniques are considered in this paper, the other fault tolerance techniques, such as the N self-checking programming and retry block, will be introduced in order to access higher reliability and reduce the cost in the future work.

References [1] Cobb, P.R., Lennon, C.J. & Long, K.J., System and method for software error early detection and data capture, US Patent: 5119377, June, 2, 1992. [2] Moon, T.K., Error Correction Coding: Mathematical Methods and Algorithms, John Wiley & Sons, New Jersey, 2005. [3] Lyu, M.R., Handbook of software reliability engineering, McGraw-Hill, Inc., NJ, USA, 1996. [4] Pham, H., System Software Reliability, Springer-Verlag New York, 2006. [5] Yacoub, S., Cukic, B. & Ammar, H., A Scenario-Based Analysis for Component-Based Software, IEEE Trans. Reliability, vol.53, no.4, pp. 465480, 2004. [6] CENELEC EN50126, Railway Applications: The specification and demonstration of Reliability, Availability, Maintainability and Safety (RAMS), 1999. [7] Goseva-Popstojanova, K. & Grnarov, A., N-Version Programming with Majority Voting Decision: Dependability Modeling and Evaluation, Microprocessing and Microprogramming, Vol.38, No.1-5, pp.811-818, 1993.

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Study on the method of traction motor load simulation on railway vehicles F. Lu, S. Li, L. Xu & Z. Yang School of Electrical Engineering, Beijing Jiaotong University, China

Abstract Based on the physical model of the motor-wheelset system, the expression of the load torque of traction motor is drawn out. According to the two parts – the damping torque of the load torque and inertia loads – the control method of DC load motor electromagnetic torque is proposed separately. Following the principle that the acceleration time should be the same as the actual time, it indicates how to reduce the traction performance curve to fit the power of the load simulation system in the laboratory. Consulting with the actual parameters of CRH2 EMUs, it simulates and authenticates the system control following the method above. Having controlled the damping load torque on 3.5kW hardware platform, the results show the agreement of performance of simulated vehicles and the actual performance curve. This indicates that the method can accurately and exactly simulate the traction motor load. Keywords: load simulation, traction motor, damping load, inertial load, torque control.

1 Introduction Traction motor load simulation is a method used to obtain the experimental data in the laboratory without experiments on the actual vehicle, by which we can do analysis and research on a traction motor’s characteristic and the control method of the propulsion system. It overcomes the shortcomings of actual-vehicle experiments, such as the high cost, low feasibility, difficulty in changing the condition outside the vehicle and the long cycle of a complete test. By imitating the different kinds of load of the traction motor in different conditions, the key physical quantities’ change can be observed in the corresponding conditions, even under the affection of some certain disturbance. These are important WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100231

234 Computers in Railways XII parameters to research on the vehicle characteristic and control method of the propulsion system. The traction motor load simulation technology is indispensably used in many subjects referring to the propulsion system, such as deciding the power of machines and converters, giving out the method of traction motor torque control, slip, slide and re-adhesion control, inhibiting the fluctuation of the power grid voltage, and research on the affection of harmonic current in the DC circuit.

2 Quality of the load torque Huang [1] put forward the theory of the load simulation system. The load torque of the traction motor can be divided into two parts: the damping load torque and the inertia load torque. By checking the results, the conclusion in his thesis is verified and improved in this paper. Part of the deducing course is shown as follows (the force analysis in Fig. 1 and the variable definition in table 1). By considering the force condition about the whole vehicle, N m Ft  f  M

dv dt

(1)

Translation force equation of single power shaft:

Ft  f m  m

dv dt

Table 1:

The symbols used in the theory analysis.

Symbols

Unit

Nm

1

Ft , Fmw , Fwm

N

f , fm

N

TLf , TLd

Nm

M,m

kg

R, rg1 , rg2

m

Jm , Jw

kg·m2

v, vw , vs

m/s

m ,  w

rad/s

ig ,Gear , 

——

Instructions The number of traction motors

Traction force per power shaft, force between active and passive gears Total resistance of vehicle and that divided onto each power shaft Damping load torque, inertia load torque Total weight of the train and that divided onto each power shaft Radiuses of wheel, active gear, and passive gear Inertial-mass of the active mechanism and that of the passive mechanism Forward velocity, linear velocity of the wheelset and the sliding velocity Mechanical angular velocity of the traction motor and the wheelset

Gear ratio, gear efficiency and creep ratio

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(2)

Computers in Railways XII

Fmw

Active Gear

Wheelset

Tm

m ,  m

vw  v  vs

rg1 rg2 ma

Motor Shaft

235

R

v, a

fm

Wg

Passive Gear

Fw m

Ft

Force analysis of the wheelset.

Figure 1:

Rotation force equation of a power wheelset: Fmw rg2  Ft R  J w

dw dt

(3)

Coming down to the creep ratio between wheel and rail, vw  v  vs  v(1   )  w R

(4)

To the traction motor, the torque equation is established: Tm  TL  J m

dm dt

(5)

Considering about the affection of gear efficiency (traction condition), TL  Fwm rg1  Fmw rg1 / Gear

(6)

Combine the equations (1)-(6), and refer to the power transmission characteristic of the gear: w  m / ig  ig  rg2 / rg1

It is put forward that

TL  TLf  TLd

(7)

(8)

In the equation, TLf 

R f Gear N m ig

J d MR 2  w 2 ]( m ) 2 Gear (1   ) N m ig Gear ig dt

TLd  [

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(9)

(10)

236 Computers in Railways XII The two expressions separately stand for the damping part and the inertia part of the load torque of traction motor. Eqn. (8) is the expression of the load torque. Jˆ is used to express the equivalent rotating inertial-mass besides that of the traction motor in the system: Jˆ 

J MR 2  w Gear (1   ) N m ig2 Gear ig2

(11)

3 Torque control of the traction motor Like the actual condition during the starting course of the train, the traction torque which is generated by the traction motor is firstly added onto the load simulation system, and it directly gives an effect to the load motor. By obtaining the torque and speed information of the traction motor, the load machine immediately gives out the load torque, which should correctly and rapidly imitate the actual load of the traction motor. So, before the control method of the load machine torque is put forward, it should be sure that the control method of the traction motor torque has been proposed at first. The traction motor characteristic curve of the actual vehicle is given by Zhang [2] and shown in Fig. 2. This paper will take the CRH2 (China Railway Highspeed) EMUs as an example to discuss how to reduce the actual traction characteristic curve equivalently so that the curve can be used on the experiment platform with reduced power.

Figure 2:

The traction performance curve of CRH2.

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According to the actual condition and the experimental condition, the torque equations of traction motors are separately established: d Tm (v)  TLf (v)  ( Jˆ  J m ) m dt

Tm (v)  TLf (v)  ( J ad  J ms )

dm dt

(12) (13)

In the equation, Jms means the inherent rotating inertia-mass of the experiment platform and Jad shows the inertia-mass which should be added on to the system with some inertia mechanical equipments such as flywheel referred in studies [3– 5], or with the inertia load torque generated by the load machine. Jad is called the additional rotating inertia-mass. Traction torque can be expressed in the following form: m  nv, v  vb Tm (v)    p / v, v  vb

m  nv , v  vb Tm (v)    p  / v, v  vb

(14)

(15)

Setting v = kvv’, Tm(v) = kTTm’(v’), kv is called the velocity zoom ratio, and kT is called the torque zoom ratio. Replace the corresponding symbols in eqn. (14) with them.

So,

vb   m  nk v v , v   k  v Tm (k v v )  kTTm (v)   v ( p / k ) / v, v  b v  kv

(16)

vb  m nkv  k  k v, v  k  T T v Tm (v)   p k k v / ( ) v T  , v  b  v kv

(17)

v nk m p , n  v , p   , vb  b . kv kT kT k v kT The damping load torque can be expressed in the following form:

It can be inferred that m 

TLf (v)  a  bv  cv 2

TLf (v )  a   bv  cv2

Solving it with the same zoom ratio, a  

k k2 a , b  v b , c   v c . kT kT kT

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(18) (19)

238 Computers in Railways XII Consulting the equations (12) and (13), Tm (v)  TLf (v) 

( Jˆ  J m )kv dm d  ( )  ( J ad  J ms ) m kT dt dt

(20)

Consequently, J ad 

kv ˆ ( J  J m )  J ms kT

(21)

Jad is just the additional rotating inertia-mass, which should be added onto the experiment platform. Fig. 3 shows the original traction characteristic curve and the reduced one with the parameters kv = 3.46, kT = 100. On this condition, the relationship between the actual acceleration and the reduced acceleration is a (v) 

1 a (v ) kv

(22)

For application, the value of the velocity zoom ratio and the torque zoom ratio is decided by the rated electromagnetic torque and the rated speed of the traction motor. The reduced traction torque curve will be made the given torque for the traction motor, and the reduced damping torque curve will be made the given torque for the load motor.

4 Torque control of the load motor This paper only makes a research on the condition of using a DC motor as the load machine. It tells how to control the torque of DC motor to add the load for the traction motor. It is easier to control the torque of DC machine than that of induction machine, so the way to control DC load motor can provide a reference for controlling AC load motor.

Figure 3:

Traction curve before and after being reduced.

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Ia I a* 



K1

1  1s U a  1s 

1 Ra  sLa

Ia

CT

TLd  Tm (n)



1 F  K n J ms s

239

n

Ce

CT 1

* Ld

T

Figure 4:

H ( s)

Closed loop control system of DC generator.

It may be easily inferred from eqn. (8) that the electromagnetic torque of the load machine opposes that of the traction motor. In addition, it is composed by the damping load torque and the inertia load torque. It is relatively easy to control the DC load machine following the damping load curve, for which in this paper it is selectively discussed how to drive a DC machine imitating the inertia load accurately. In fact, the inertia load torque should not surely be imitated by the electromagnetic torque of a load motor. As what has been referred above, inertial mechanic equipments just like flywheels may apply such an inertial torque as well. Not only that, it could greatly simplify the system control. However, according to eqn. (21), the additional rotating inertia-mass is usually great. The volume and weight of the equipment might be unacceptable for a laboratory platform if all the inertia torque is generated by a flywheel. At the same time, the inertia-mass of flywheels is unchangeable if the simulation conditions are changed. So, such a disadvantage may limit the function of the load simulation platform, and will degrade the flexibility of the experiments based on it. Consequently, it is very important to make a research on the technology of electrical inertia-load simulation. Fig. 4 shows the block diagram of a closed loop control system of DC generator in the complex frequency domain. In the dash dotted square, it is the model of the DC machine. The signal Tm(n) is not only the output torque of the traction motor, but also the load torque of the DC load motor. According to the superposition principle, the forward channel of the inertia controller is analyzed specially. The H(s) is assumed as H ( s )  K n J ad

1 Hs s

(23)

In it, Kn (2π/60) is the conversion coefficient from the rotor speed to the mechanical angular velocity. In the following sections, it will be put forward that how to choose a suitable value for the time const τH according to the system output response. In spite of the viscous damping coefficient F, the time constant of rotor of the DC generator is made as τe = La / Ra. Then let τ1 = τe. When the value of K1 is

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240 Computers in Railways XII large enough, the transfer function of the system can be inferred with the Mason’s gain equation: (1   H s )(1   e s ) n 1 Tm



K n ( J ms  J ad ) s

1  K n ( J ms  J ad ) s

J ms J ms  H e s 2  (  H   e )s  1 J ms  J ad ( J ms  J ad )

1Hs .  J 1  H ms s J ms  J ad

,

(24)

Change eqn. (24) into the form shown in Fig. 5 n* is the expected value of the rotor speed, and n is the actual speed response. It is obvious that in case of τH = 0, the two values of rotor speed will be totally the same. By debugging the output response, the best value of the inertial time constant will surely be found out. Affected by such a value, the response time of the derivative control should be short enough and the high frequency noise must be as weak as possible. The best value of τH mainly depends on the inertia-mass of the imitated load, and is also affected by the parameters of the PI regulator and the response time of the control method, and so on.

5 Simulation and experiment The structure of the load simulation system is designed as Fig. 6. The system is composed by a traction motor (induction motor) anda load motor (DC motor),

Tm ( n)

Figure 5:

1 K n ( J ms  J ad ) s

n*

1 Hs J ms H 1 s ( J ms  J ad )

n

Transfer function of the inertia controller.

Flywheel

Figure 6:

Structure of the load simulation system.

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Table 2:

Parameters of the actual and experimental motors.

Items

CRH2-200 motor

AC motor

DC generator

Rated power(kW)

300

2.2

3.5

Rated voltage(V)

2000

380

230

Rated current(A)

106

5

15.2

Rated speed(r/min)

4140

1420

1450

Rs

0.144

3.2

275

Lls

0.0014

0.0166

Separate excitation

Parameters of the equav circuit

241

Rr

0.146

2.2

3.96

Llr

0.0013

0.0166

0.012

Rm

527.7

5.19

CeΦ = 0.165

Lm

0.0328

0.361

CTΦ = 1.58

p

2

2

——

 and s

s

* f

Calculator

1 CT

Figure 7:

Simulation model of the load imitation system.

the shafts of which are joined together in order to act the traction torque and its opponent. Parameters of the traction motors on CRH2 EMUs and motors in the laboratory are listed in table 2. 5.1 Simulation of the load imitation system

Based on the theory of load torque control method above, a model of the low power load imitation system of CRH2 traction motor is established with MATLAB/ SIMULINK. The control system is drawn in Fig. 7. Consulting the experimental equation of the basic resistance of vehicle on flat and straight railway, fb = 8.63+0.07295v+0.00112v2(N/t), the total resistance of the vehicle can be calculated out. Substitute the parameters of CRH2 in equations WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

242 Computers in Railways XII (8)-(10), referring to the rated torque and rated speed of the experimental traction motor shown in table 2. The relationship between the rotor speed of the traction motor and the vehicle velocity is like: nm 

1000ig (1   ) 60(2πR)

(25)

v

Let 250km/h (corresponding actual motor speed: 4912.5r/min), the highest speed of the vehicle correspond the rated rotor speed (1420r/min) of the experimental motor. The rotor speed zoom ratio should be set 3.46. Because of the proportional relation between the rotor speed and the vehicle velocity, set kv = 3.46 and kT = 100. The reduced traction curve is shown in Fig. 3. Set Jad = 17.37 kg·m2 according to eqn. (21), and set k1 = 100, and τ1 = τe = 0.06s. Valuate the inertial filter time constant τH = 0.1. Simulate the starting course of the experimental traction motor following the reduced curve in Fig. 2 and the result is expressed in Fig. 8. Obviously, it takes 375 seconds for the traction motor to reach the speed of 1420r/min (corresponding v: 250km/h and v’: 72.24km/h) at full speed in Fig. 8(a). The acceleration time is generally the same as the actual time. Fig. 8(b) shows the simulative acceleration is about 1/3.46 of the actual acceleration, which is in agreement with eqn. (22) and Fig. 3. Based on all above, the conclusion is that the method to control the torques of traction motor and load motor is reasonable, effective and accurate. 5.2 Experiment of the load simulation system

On the hardware platform, the induction motor is controlled following the method shown in Fig. 7. The DC load motor is separately excited, and its torque is controlled by changing the armature current with constant magnetic flux so that the electromotive force constant and the electromagnetic torque constant will never be changeful, by which the torque of the motor is much more easily inferred. The circuit structure is shown in Fig. 9.

(a) Figure 8:

(b)

Simulation result of the load imitation system, (a) traction and load torque with rotor speed, (b) simulative acceleration of vehicle.

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Figure 9:

Circuit structure of the hardware experiment platform.

(a) Figure 10:

243

(b)

Result of the damping load torque control experiment, (a) speed and torque current of the traction motor, (b) speed and armature current of the load motor.

As continuous running with a speed over the rated value may destroy the structure of the machines, lowering the level of the traction curve or simulating the ramp resistance will be helpful to let the highest speed be lower than the rated speed of motors. The level 8 traction curve is used in this paper as the given traction torque. The balance speed will just be the rated speed in spite of the inherent mechanical resistance of the coupling system. The speed and current waveforms of the damping load experiment are shown in Fig. 10. In Fig. 10(a), the stator phase current changes from about 5.6A (1A/100mV) when starting to lower than 2.8A when the torques are balanced. During this course, the actual torque current IT perfectly follows the given IT*. So, the output torque of the traction motor can be judged following the reduced traction curve of level 8. The final speed is 1023r/min (1500r/min corresponds 3.3V), and the corresponding traction torque is about 4.9N·m. The armature current totally follows the given current in Fig. 10(b), and its value is about 1.52A when the speed is changeless. Consulting the torque constant in table 2, the corresponding torque of the DC load motor is about 2.4N·m. It seems still 2.5N·m lost in the inherent mechanical resistance of the system if the change of motor parameters and the error position of the observed flux linkage are ignored. The results tell WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

244 Computers in Railways XII that the control effect of the damping load torque is the same as what is expected.

6 Conclusion Traction motor load simulation system with double motors and double torque controllers has no closed loop control for the speed, and the speed signal is just one of the input variable which participates in the system control. At the same time, the speed control is one of the most important aims. By dynamic control of the traction torque as well as the load torque, the speed is decided indirectly reflecting the status of the actual vehicle. Although the inertia load is simulated well in the simulation system, limited by the working time of the DSP program codes, the discrete sample time, the highest frequency of the MOSFET and the sample precision of the rotor speed, the hardware platform cannot absolutely satisfy the demands of the control methods. As above, the method of controlling the inertia load has to be improved. What’s more, the paper has given out the additional inertia-mass by eqn. (21), which could be a reference to decide the weights and radiuses of the inertial equipments (such as flywheels). The method of the inertia load torque control by Digital Signal Processor will be discussed in another paper.

Acknowledgements This paper and its related research are supported by Technology Research and Development Plan of MOR (2009J006-M): Research on the method of DC voltage pulsation suppression in high-speed train propulsion system. We express our sincere appreciation for the substantial support.

References [1] Huang, Y.P., A study on load simulation of traction motor of railway vehicle, M.S. thesis, Beijing Jiaotong University, Beijing, China, June 2009. (in Chinese) [2] Zhang, S.G., CRH2 Electricity Multiple Units (China high-speed railway technology: CRH series), China Railway Publishing House: Beijing, 2008. (in Chinese) [3] Li, Z.S. & Dong C., Actuality on mechanical loads emulation basin on electric powered technology abroad. Machinery, 34(5), pp. 1-3, 2007. (in Chinese) [4] Padilla, A.J., Asher, M.G. & Sumner, M., Control of an AC Dynamometer for Dynamic Emulation of Mechanical Loads with Stiff and Flexible Shafts. IEEE Transactions on Industrial Electronics, 53(4), pp. 1250-1260, 2006. [5] Rodic M, Jezernik K & Trlep M., Control Design in Mechatronic Systems Using Dynamic Emulation of Mechanical Loads. Proc. of IEEE ISIE 2005, pp. 1635-1640, 2005. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Formalizing train control language: automating analysis of train stations A. Svendsen1,2, B. Møller-Pedersen2, Ø. Haugen1, J. Endresen3 & E. Carlson3 1

SINTEF, Norway University of Oslo, Norway 3 ABB, Norway 2

Abstract The Train Control Language (TCL) is a domain-specific language that allows automation of the production of interlocking source code. From a graphical editor a model of a train station is created. This model can then be transformed to other representations, e.g. an interlocking table and functional blocks, keeping the representations internally consistent. Formal methods are mathematical techniques for precisely expressing a system, contributing to the reliability and robustness of the system through analysis. Traditionally, applying formal methods involves a high cost. This paper presents a formalization of TCL, including its behavior expressed in the constraint solving language Alloy. We show how analysis of station models can be performed automatically. Analysis, such as simulation of a station, searching for dangerous train movements and deadlocks, is used to illustrate the approach. Keywords: interlocking, domain specific language (DSL), model analysis, alloy, Train Control Language (TCL).

1 Introduction An interlocking system prevents dangerous train movement on a train station by giving a “clear” signal to a train only if the requested route is safe. The interlocking system ensures that the route is safe by reading the status of the elements in the route (e.g. tracks, switches, signals) to see if they comply with the logic of the interlocking system. This logic is depicted by an interlocking table, and realized by the interlocking source code, in the form of functional WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100241

246 Computers in Railways XII blocks of code that are executed (interpreted) by the PLCs (Programmable Logic Controllers) in their control of the station. Since the interlocking system is a safety system of the highest classification, several rounds of formal review and testing are needed. The functional specification is formally reviewed, before the functional blocks are produced and also formally reviewed in several steps. In addition, systematic testing of the station products is performed to ensure that they are correct. Both the review and testing processes are time-consuming and have a high cost. The Train Control Language (TCL) [1, 2] is a domain-specific language (DSL) for modeling train stations. TCL automates the production of functional specification and interlocking source code. From a graphical editor, where train stations can be modeled, model transformations generate other representations of the stations, e.g. interlocking tables, functional specifications and functional blocks of interlocking source code. In this paper we present an extension to our original TCL to automate analysis of train station models. The contribution is the formalization of the TCL language and models, and the analysis performed on these models. Even though the current review and testing processes cannot be eliminated, allowing for automatic analysis on model level may allow reduction of costs in these activities. The outline of the paper is as follows: Section 2 describes the background for this work, the current development techniques, including the review and testing activities. Section 3 introduces TCL and how it automates the production of interlocking source code. Section 4 briefly introduces the constraint-solving language Alloy that will be used for formalizing TCL in Section 5. Section 6 illustrates how the formal Alloy models can be used for automatic analysis of the TCL models. Finally, Section 7 concludes the paper and look at some topics for future work.

2 Background From an input requirement specification, consisting of an interlocking table, a structured drawing of the station and a generic Computer Based Interlocking (CBI), incorporating national rules, a functional specification is produced. The functional specification is a mapping of the interlocking table into a set of logical equations. The functional specification is further developed into a design specification, which is close to the interlocking source code. The functional specification and design specification are formally reviewed following the Fagan inspection method [3]. This method includes a set of rules, guidelines and checklists for use in ABB RailLock. Both the production and review of the functional specification and design specification are performed manually, and are thus of high cost. Following the functional specification and the design specification two teams develop the interlocking source code using different libraries and developing methods. This reduces the chance for common code errors. A formal review of the produced interlocking source code, checking it against the functional WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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specification and design specification, is then performed using the Fagan inspection method once more. An independent party then validates the source code against all safety requirements using a formal mathematical method that is accepted as adequate by the Norwegian Railway Authority. Following the review of the interlocking source code, the source code is deployed and several steps of systematic testing are performed. This includes testing the response of the elements in the station, to ensure that they give the correct responses, and simulating train movement systematically, to verify that the system behaves as expected. The behavior of the interlocking system is described by the dynamic semantics of this system, and we model a set of dynamic semantic rules for the interlocking system in Section 5.

3 Train control language Since the development of interlocking source code is a time-consuming process requiring a large amount of resources, the Train Control Language has been developed to automate this task. This was shown by [1, 2], and in this section we show a summary of this work. TCL is a domain-specific language for modeling stations in the train domain. TCL is defined by a metamodel (see Figure 1), which defines the concepts in the language and how they are connected. The topmost concept is Station, which represents the station, containing the other concepts. A TrainRoute is the route a train must acquire to be allowed to move into or out of the station. A TrainRoute consists of several TrackCircuits, which are a collection of Tracks, where a train can be detected. A Track can

Figure 1:

TCL metamodel excerpt.

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248 Computers in Railways XII either be a LineSegment or a Switch, and these are connected by Endpoints. An Endpoint can either divide TrackCircuits (TCEndpoint) or be within a TrackCircuit to connect LineSegments and Switches (MiddleEndpoint). A TrainRoute starts at a TCEndpoint with a connected Signal and ends at another TCEndpoint with a connected Signal in the same direction. Based on the metamodel, Eclipse Modeling Framework (EMF) [4] and Graphical Modeling Framework (GMF) [5] have been used to develop editors, in particular a graphical editor for modeling the structure of a train station (see Figure 2). The figure also illustrates the concrete syntax of TCL by showing a station with two tracks. A station is created by choosing an element on the toolbar (to the right), dragging it into the canvas (middle) and connecting it to the other elements. Attributes for the elements are then set according to its property (property view at the bottom). When the station model is complete according to the input specification, other representations can be generated automatically by pressing a button (on top). TCL includes three kinds of model transformations, generating one of the three following representations: Interlocking table, functional specification and interlocking source code (functional blocks). The interlocking table is used to compare with the provided interlocking table to visually verify the correctness of the station in an early phase. The functional specification is also used for

Figure 2:

TCL graphical editor.

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verification purposes. The interlocking source code has to be formally verified and tested before it can be used for controlling the station. Notice that for TCL to be put into full production, a formal verification of the language and code generators is needed, to formally confirm that it complies with the same safety standards as the current development process. We will, however, see how analysis can be performed on the TCL models automatically by translating the models into models of the constrain-solving language Alloy. Since the train domain has to follow high safety standards, this will not eliminate the time-consuming process of reviewing and testing the station products. However, by performing analysis on model level early in the development phase, both design and implementation errors can be discovered early and thus reducing cost.

4 Alloy Formal verification and validation involves expressing a system (e.g. a train station) precisely through mathematical terms and proving the correctness of the system. Formal methods have traditionally provided accurate analysis of systems at a high cost. Extensive knowledge of mathematical techniques, with their complex notations and theorem proving raises the threshold for performing analysis. Alloy is a lightweight declarative constraint-solving language for relational calculus [6]. Through the Alloy Analyzer automatic and incremental analysis can be performed without the need for proving theorems or handling complex mathematical notation. Unlike traditional theorem proving, the Alloy Analyzer only offers analysis within a given scope, which is the number of instantiated elements of each type. The small scope hypothesis ensures that such analysis is sufficient, since if a solution exists, it will be within a scope of small size [7]. An Alloy model typically consists of signatures (types), fields (references to signatures), facts (global constraints), predicates (parameterized constraints) and assertions (claims). A type hierarchy is modeled by letting a signature extend another signature. A fact consists of constraints that must always hold. A predicate consists of constraints that must hold if the predicate is processed, and can therefore be used to represent operations. An assertion consists of constraints that is claimed to hold. As an example, Figure 3 shows a signature of a train route corresponding to train route in the TCL metamodel (Figure 1).

Figure 3:

Signature of a train route in Alloy.

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250 Computers in Railways XII In the search for a solution, the Alloy Analyzer populates the signatures with elements up to the given scope where all the facts are satisfied. Two kinds of analysis can be performed: Finding a model instance satisfying a predicate or finding a model, which represents a counterexample to an assertion. If an analysis does not find a solution or counterexample, there may not be any solution or counterexample within the selected scope, or the constraints (facts/predicates) may over-constrain the model. Thus, the constraints can be adjusted and the Alloy model can be built stepwise based on the feedback from the Alloy Analyzer. The Alloy Analyzer requires the maximum number of each type of element (scope) to be specified, and it guarantees that if a solution or counterexample exists within the scope, the analysis will find it. This process does not require any test cases, since it checks a property for all possible solutions within the scope. The space of cases examined by the analysis is usually huge (billions of cases) [6].

5 Formalizing TCL For the formalization of TCL we follow the approach by Kelsen and Ma [8]. They illustrate how to use Alloy to formalize modeling languages and compare it to traditional formalization techniques. As they point out, the Alloy approach offers a uniform notation and automatic analyzability using the Alloy Analyzer. We choose to formalize TCL in Alloy by three separate models; a static model, a dynamic model and an instance model (see Figure 4). Semantic rules on language level can then be separated from the rules on instance level, such that several instances can use the same static rules. Besides that, we get a clear separation between static and dynamic semantics, making them easier to maintain. The static model holds the static semantics for the TCL language, including the concepts and how they relate (from the metamodel) in addition to language constraints. Figure 3 shows how the concept train route is modeled in Alloy by using a signature. This signature relate to other signatures through its fields (e.g. to track circuit and endpoints). Additional constraints restrict the number of valid TCL models instantiated by the Alloy Analyzer.

Figure 4:

Alloy specification divided into three models.

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The Alloy Analyzer populates the signatures with elements when it searches for solutions or counterexamples. Thus an arbitrarily TCL model is instantiated when using the static model. However, since we want to analyze a particular TCL model created by the TCL editor, the number of valid model instances in Alloy must be further constrained to be only this one. We therefore import and extend the static semantics of the static model using an instance model, which specifies one particular station. The instance model therefore specifies the number of model elements in the TCL model and how they are connected (e.g. the exact number of train routes and how they are connected to other elements). The result of these constraints is that Alloy only instantiates one valid model for the analysis, which is the TCL model subject to analysis. To be able to perform proper analysis on a TCL model, the behavior of the station needs to be formally specified as well. This specification is modeled in the dynamic model using a state machine. The dynamic model constrains the behavior of the concepts of the static model, and the Alloy Analyzer satisfies these constraints when it uses the instance model to instantiate an instance. Therefore, the dynamic model imports the instance model and uses the concepts of the static model. The dynamic semantics of TCL involves train movement. Intuitively, trains can move simultaneously on a station as long as they follow the basic rules of the interlocking table (table defining safe train movement). More specifically, a train has to request a train route before it can move into or out of the station. Given that no other conflicting routes are already taken and all track circuits in the route are free, the route can be given to the train. The allocation of the train route involves setting switches to the right position and signals to the correct status before the train gets a “clear” signal. The train moves from track circuit to track circuit within the route until it reaches its destination. The track circuits are occupied and freed during the movement. The state machine defined in Alloy, to describe the behavior of a station, contains a set of states and trains in addition to the instance of the TCL model. The states define the conditions of the station (e.g. position of trains) and the transitions between them define the operation to be performed. There are three operations (represented as predicates): Insert a new train on either side of the station, allocate a route to a train, and moving a train. Through these three operations we can simulate the train movement on the TCL model modeled by the TCL editor. The development of the Alloy models is illustrated in Figure 5. The static and dynamic models are defining the TCL language and are thus only produced once. The static model is generated from the TCL metamodel, while the dynamic model is produced manually. The instance model is different for each TCL model, and is therefore generated once for each TCL model. However, the instance model is generated automatically from the TCL model modeled in the TCL editor using a MOFScript transformation [9]. As a comparison, Jackson presents an Alloy case study on railway safety [10]. In this example constraints are specified such that only safe train movement is allowed. This is very similar to our Alloy approach. However, our approach WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

252 Computers in Railways XII performs analysis on real train stations, which are typically more complex than the example presented in [10].

6 Performing analysis of TCL models From the Alloy formalization of TCL we can perform analysis on the TCL models. The Alloy Analyzer can, as mentioned in Section 4, perform two kinds of analyses: searching for a solution that satisfies a predicate or searching for a counterexample that falsifies an assertion. In our analysis we will use both of these to prove certain properties. To perform analysis on a TCL model, the TCL model is exported and transformed to an Alloy instance model (as described in Section 5) and the Alloy Analyzer is invoked with this model as input. This process has been integrated into the TCL editor giving a user-friendly interface for performing the analysis on TCL models. Figure 6 illustrates the integration with the TCL editor, and how

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to perform the analysis. By right clicking on the station canvas, the illustrated menu is given where one of the menu items can be selected. Only a few options for analysis are included in this interface for now. However, we plan to add more options in the future, including a possibility to specify arbitrarily predicates and assertions. Our analysis is mainly concerned with the behavior of the station (dynamic semantics) in some particular situations. The Alloy Analyzer gives a solution or counterexample by giving a trace through the state machine specified by the dynamic semantics. By following this trace, we can observe how the condition of the station changes, and thus see the train movement through the station. Constraints for the conditions in the first and last state in the trace can be specified (e.g. both the first and last state includes no trains in the station). Intuitively, we specify the conditions for the first state and for the last state in the trace and how many trains are moving through the station. These parameters, in addition to whether we run a predicate or check an assertion, decide what kind of analysis we are performing. As an example, imagine that we have a start condition with a train on track 1 (see Figure 7). Typical test-cases will be to test whether any train routes involving track 1 (train route 1 and 2 in Figure 7) can be given to other trains while the train is located on track 1. This property can be checked through specifying an assertion in Alloy (see Figure 8). This assertion claims that no model can be instantiated where the following constraints are true: The first state in the trace includes a train on track 1, the last state in the trace still constrains the train to be on track 1, and the last state in the trace also includes an allocated route (to another train) involving track 1. The Alloy Analyzer is invoked to find a possible trace through the state machine where such behavior is allowed (a counterexample). Fortunately, for our two-track station, Alloy does not find any counterexample that falsifies our assertion, proving that no train routes involving track 1 can be allocated when a train is located there. Other analyses include the search for the number of active trains the station can include simultaneously without leading to a deadlock. A predicate can be used to search for a solution for a certain number of simultaneous trains. If no solution is found, the specified number of simultaneous trains will lead to a deadlock. For our two-track station, the maximum number of simultaneous trains turns out to be three (solution illustrated in Figure 9). This figure illustrates the

Figure 7:

Two-track station with a train on track 1.

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254 Computers in Railways XII assert checkRouteAllocation { //assert that no model with the following constraints exist no t,t2:Train, tc:tc_01, tr:TrainRoute{ tc in tr.trackCircuits //constraints for first state in trace t->tc in first.trainOnTrack tc in first.occupiedTrack no first.trainOnRoute no first.allocatedRoute //constraints for last state in trace t->tc in last.trainOnTrack tc in last.occupiedTrack t2->tc in last.trainOnTrack t2->tr in last.trainOnRoute tr in last.allocatedRoute } }

Figure 8:

Figure 9:

Assertion on train route allocation involving track circuit 01.

Maximum number of trains on the station simultaneously.

condition of the station in the state (in the trace) where it included three trains simultaneously. Notice that this figure has been created based on the trace information given by the Alloy Analyzer, and is not created by Alloy itself. Arbitrarily analysis can thus be performed automatically by specifying the condition of the first and last states in the trace, the number of trains to be involved and what kind of assertion/predicate to check/process. We have seen two examples of analysis that can be performed on a TCL model. However, we see that these two examples do not differ from other test cases on stations. Thus, a big amount of the testing of stations can be similarly checked by analyzing the TCL models, with considerable less amount of effort.

7 Conclusion and future work This paper presented a formalization of TCL, both static and dynamic semantics, in Alloy such that automatic analysis can be performed on TCL models. We looked at how the process of performing this analysis has been simplified by

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integration with the TCL editor. Furthermore, two examples of analysis were presented to illustrate the approach. As pointed out, this approach may not replace the traditional validation, verification and testing processes. However, it adds extra value by allowing automatic analysis in the early development process, which can be performed both in the designing phase and in the development phase. By simulating train movement traces on different station architectures (models), errors can be discovered and corrected early, making a considerable potential for reducing cost and time-to-market. Furthermore, since this approach analyzes TCL models, it will shift the necessity of validation and verification from the code level to the model transformations. However, validation and verification of the model transformations only needs to be performed once. This approach thus has a huge potential of optimizing the development and testing of interlocking source code. As future work we plan to extend the analysis we perform on TCL models. Since the analysis is performed automatically, we can easily extend it to include other test cases and properties that were earlier checked manually. Furthermore, we are currently working on verifying the interlocking source code generated by the TCL code generators. With verified code generators, parts of the verification and testing process can be performed automatically on model level.

Acknowledgements The work presented here has been developed within the MoSiS project ITEA 2 – ip06035 part of the Eureka framework.

References [1] Endresen, J., et al. Train control language - teaching computers interlocking. in Computers in Railways XI (COMPRAIL 2008). 2008. Toledo, Spain: WIT Press. [2] Svendsen, A., et al. The Future of Train Signaling. in Model Driven Engineering Languages and Systems (MoDELS 2008). 2008. Tolouse, France: Springer. [3] Fagan, M.E., Design and Code Inspections to Reduce Errors in Program Development. IBM Systems Journal, 1976. 15(3): p. 182-211. [4] EMF, Eclipse Modeling Framework (EMF): http://www.eclipse.org/ modeling/emf/. [5] GMF, Eclipse Graphical Modeling Framework (GMF): http://www.eclipse. org/modeling/gmf/. [6] Jackson, D., Software Abstractions: Logic, Language, and Analysis. 2006: The MIT Press. [7] Andoni, A., et al., Evaluating the “Small Scope Hypothesis”. 2003, MIT CSAIL. [8] Kelsen, P. and Q. Ma, A Lightweight Approach for Defining the Formal Semantics of a Modeling Language, in Proceedings of the 11th WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

256 Computers in Railways XII international conference on Model Driven Engineering Languages and Systems. 2008, Springer-Verlag: Toulouse, France. [9] Oldevik, J., MOFScript Eclipse Plug-In: Metamodel-Based Code Generation, in Eclipse Technology Workshop (EtX) at ECOOP 2006. 2006: Nantes. [10] Jackson, D., Micromodels of Software, in Models, Algebras and Logic of Engineering Software, M. Broy and M. Pizka, Editors. 2003, IOS Press. p. 351-384.

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Design and operation assessment of railway stations using passenger simulation D. Li1 & B. Han2 1 2

State Key Laboratory of Rail Control and Safety, China Beijing Jiaotong University, China

Abstract To assess the design of infrastructure and operation efficiency of railway stations, passenger simulation models are useful tools. This paper presents a microscopic passenger simulation model for railways. The simulation process is described as event planning, route choice and behaviour decision. Complex passenger behaviours are modelled, as well as simple motions. The model is calibrated using field data collected from Beijing railway station. Software called SRAIL is developed to validate the model. By using input passenger characteristics, station facilities, train timetables, traffic flow rules and simulation parameters, some useful indicators can be obtained. The indicators can reflect facility usage, delay, congestion, safety and coordination of the station. The total level of service is also evaluated. The first passenger dedicated railway station of the China – Beijing South Railway station is studied as an example. The result shows that the model can assess the station design and operation efficiently. Keywords: railway station, design and operation assessment, microscopic passenger simulation, event planning, route choice, behaviour decision.

1 Introduction The largest scale passenger dedicated railways are being constructed in China. Meanwhile, lots of new railway stations are being built. Most of these stations are passenger dedicated, modern designed, large scale, multi-floor structures and have a multi-modal traffic service. However, engineers are often faced with several problems: how to improve the efficiency of railway stations; how to avoid station travel time increasing time for the entire trip; how to keep large WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100251

258 Computers in Railways XII crowd passenger flows safe in the case of limited resources. Railway operators also hope to improve the level of service by using new technologies in new stations. They want to reduce the risk of operation accompanied by the lack of experience. It is hard work to solve these problems through traditional methods, because there are many factors influencing station efficiency, such as passenger flows, passenger behaviour, layout of facilities and operation strategy. Moreover, the passenger crowd system is nonlinear; passenger flows on different facilities affect each other. Thanks to the development of simulation technology, by using passenger simulation, it is possible to forecast the potential problem of station facilities, operation schedule and emergency plan. The paper is outlined as follows. In section 2, the literature is reviewed. In section 3, a passenger simulation model and its calibration is described. In section 4, the simulation tool SRAIL, based on the proposed model, is introduced. Section 5 is a case study of Beijing South Railway Station, which is the first passenger dedicated railway station in China. Finally, conclusions are provided.

2 Literature review Traditionally, station assessment is done by mathematic method. The station is thought of as a cluster of facilities. By calculating the smallest capacity, the bottleneck is identified. However, the basic problem of capacity calculation is still not solved. Such method lasted for a long time, until the use of simulation in engineering. In particular, in the 1970s when Henderson [1] published the statistics of crowd fluids, many pedestrian simulation models were developed. The advantage of simulation is that the research object is modelled as an integrated system from passenger facility to operation strategy. Although there are only a few researchers studying passenger simulation in railways, pedestrian simulation is widely studied, since it is a common technology. Many specialists from physics, civil engineering and social science have made great contributions in this field. Different methods were used to study pedestrian flows, such as computational physics, hydromechanics, cellular dynamics, artificial intelligence and society. However, much attention has been Table 1: Year 1985 1993 1994 1994 1995 2000 2000 2003 2009

Researcher Gipps [3] Okazaki [4] Lovas [5] Rothman [6] Helbing [7] Hoogendoorn [8] Blue [9] Kirchner [10] Izquierdo [11]

Researches review.

Model Benefit cost Gravity Queue network Lattice gas Social force Gas kinetic CA Floor Field PSO

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Researcher Maw [12] Gordge [13] Schelhorn [14] Still [15] Steps [16] Hoogendoorn [17] Li [18]

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paid to theories until recently, when physicist Helbing’s [2] book “Managing Complexity: Insights, Concepts, Applications” was published. Although the mechanism of pedestrian behaviour is not very clear, useful models and tools were developed. Typical researches are classified as theories and applications, which are listed in table 1. Most of the above tools are used on egress, which has a simple flow. The most widely used tool is Legion, which is based on crowd dynamics. However, it is not especially designed for railway traffic. Many scenarios of railway station operation could not be effectively simulated. Nomad is the first tool specifically for railway. A systematic indicator set is proposed for assessing the railway station, as this is important to facility configuration. Despite a microscopic model, the simulation of complex systems, such as stations, need more detailed work. These include an activity model, route choice model, behaviour model, integrated model and so on.

3 Modelling and calibration 3.1 Model hierarchy To assess the railway station design and operation, it should be very flexible on both infrastructure modelling and simulation dynamics. The model is divided into macroscopic, mesoscopic and microscopic levels (see fig. 1). At each level, models are set up for station facility, passenger and operation strategy. The advantage of this structure is any changes of station design or operation strategy are related to passenger behaviour, so the assessment of station facility design and operation efficiency can be more easily achieved. (1) Station model. The station is defined as a graph G(N, E) at macroscopic level. A node indicates functional blocks such as the railroad, bus, taxi, metro and park system. These nodes are the places where passengers “appear” or “disappear”. A link is the connection between these systems. At mesoscopic level, facilities’ relationship is described as a logic network. Facilities, such as escalators, staircases, concourses and platforms, are modelled as units with different properties so that the passenger can identify them. This level is also designed to deal with the connection between different floors. At microscopic level, each facility system is described as a grid with dynamic cell size. The movable passenger can occupy the cell, and have real time interaction with facilities through it.

Figure 1:

Model hierarchy.

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260 Computers in Railways XII (2) Passenger model. The passenger is modelled as entities with three property groups. The first is basic properties, including physical properties (gender, age, body size, vision range, and walk-ability) and psychological properties (psychological distance, temper). The second is social properties, such as familiarity with environment, educational background, partners, attractiveness and trip experiences. The third is traffic properties, such as trip aim, origin, destination, desired speed, acceleration, position, ticket, and luggage size. The passenger’s process model in the railway station is classified into three steps, thus it can seamlessly interact with the station model. The three steps are event planning, route choice and decision-making. The event planning is as follows: when a passenger enters the station, he should clearly know what his aim is, then make an activity plan of what he would do in the station before leaving. The activity plan is highly related to the traffic aim and time need. To depart, passengers who have a long time before they leave might make a rich activity plan. In contrast, passengers who have little time or just arrive at the station would only do the necessary activities. The route choice is a process when the event is relatively determined. Passengers should try to find a reasonable target to achieve their aim. However, on most occasions, there is more than one target. Passengers should select a target that would maximize their utilities. The last step is decision making: the passenger should decide how he gets the target and which behaviour is reasonable. The decision is made according to the state of the passenger and the station. In this step, passenger behaviour modelling is also very important. Passenger behaviour is designed to have add-ons. It means users can develop their own behaviour models. Although different passengers would have different behaviours, they have some behaviour in common. In a railway station, behaviours are modelled, such as buying a ticket, waiting to board, queuing, checking in, looking at the information screen, alighting and boarding. These complex models are made up of simpler models, such as walking, obstacle avoidance, waiting, wandering, seeking and path following. Actually, such a process is not always from top to bottom. Passengers might change their activities or decisions temporarily according to the situation they are confronted with. For example, passengers with a lot of spare time would adjust their activity, even their walking speed. Passengers who feel bored may wander here and there. Passengers who feel tension may try to get ahead of others in queues. In order to model the various activities, a dynamic activity network is established. The network is the description of all necessary activities. Passengers can either follow the network or separate from the network temporarily, as long as they do not deviate from the target. In fact, there are a lot of factors that may influence a passenger’s planning and decision making process. Some are even not very clear. In this model, the user can define exactly which rule the passenger should obey at each level. (3) Operation model. This manages the operation strategy of the station. By providing a user interface, many operation methods could be implemented from passenger flow line management to timetable adjustment. The result would affect the facility state of the station and behaviour of each passenger. It decides WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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questions such as when the ticket gate opens, how long it takes to get a ticket and where should the train stop. For example, one can adjust the stop time of a train from the interface, or adjust the direction of the automatic fare gates. At service systems, it also controls the reasonable queue to make the simulation more realistic. Connections are also set up from the event and passengers model by the operation model. For example, a train arrival event, passenger generation event and facility state switch event may trigger at the same time. 3.2 Four steps model calibration and validation It is important to perform a passenger simulation calibration before using the model, although it is more difficult to use than the general pedestrian simulation model. The model should not only reflect the basic passenger behaviours under different conditions, but also obey the fundamental diagrams of pedestrian flow. Moreover, the activity of the passenger and his time consumed in the station should be kept consistent with real operation. We present a four step calibration method to ensure the availability of the model. (1) Fundamental diagram test. Passenger flow should obey traffic flow characteristics at macroscopic level, although individual behaviour might be completely different. Special experiments, such as passenger movement on loop facilities (a certain width corridor with unlimited length), are designed. After some warm up time, the passenger movement is simulated under a different crowd level. The density, flow and speed data is recorded. The relationship is compared with an empirical study of prior researchers, as shown in fig. 2. It is found that the capacity flow is about 110p/min/m when space is 0.5m2/p. This is very close to the fundamental diagram of the HCM. The capacity value is also equal to the practical measurement in Beijing. (2) Self organization test. One of the most famous characteristics of passenger flow is self organization phenomenon. Unlike other traffic modes, when the flow approaches the capacity or on other occasions, some special phenomena, such as lane formation, bubbles, bottleneck oscillations and moving stripes, can appear; this is not deliberately designed. Taking the bubbles and bottleneck oscillations as examples, the proposed model is tested. The bottleneck is set to a 0.5m narrow 120

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Figure 3:

Bottleneck oscillations and bubbles.

door. When the bi-directional passenger flows pass the bottleneck during a large crowd scenario, instead of deadlock, passengers from one side pass the door first and after a while the opposite side, until the bottleneck has cleared. The result is shown in fig. 3. This phenomenon is widely found in prior researchers’ studies. In the test, the highest frequency found is about 45 seconds, which reduces multi-nominally with an increase of the door width. It is also found that even in very high densities, some spaces would not be effectively used. These spaces were called bubbles. This is consistent with the real world. (3) Field data test. It is generally accepted that people’s traffic behaviour is different under different areas, environment and cultures. It is also found that passengers’ use of different facilities is very different. So it is necessary to validate the model using field data test. Firstly, a data collector and analysis tool is developed for the validation. The video data is first collected from the CCTV in the station. Then each passenger’s coordinates at different times are extracted from the video. The data relationship curves, for example for evacuation versus time and distance versus flow, are analyzed and compared with the simulation result in the same scenario. Secondly, a special purpose survey is carried out, such as for time consuming investigation. Each surveyor would select a passenger randomly, and try to follow him. The surveyor would record the time of each activity and each target position. For example, at the entrance, ticket vendor, waiting room or gate. Other data, such as station structure, timetable and parameters, are also obtained from the station operation agency. The simulation scenario is carefully imported in to the model and, after a 24h simulation, the simulation data for time consumption is collected and analyzed. By comparing the result with the field data statistics, the model is validated or revised. (4) Empirical formula test. Railway operators have summarized much useful “knowledge” about passenger flow, facility use and operation method. For example, the unidirectional flow is more effective than mixed flow, long distance corridors can ease passenger flow congestion and sometimes a set of obstacles might be useful to improve the safety of the flow. Besides, some empirical formulas were also given, such as the station egress time confirmation. Although these formulas are not absolutely correct, they reflect the effects of some factors relatively. The results with different input parameters should be consistent with some existing knowledge. The model is tested with special experiments. For example, the escalator width is changed in different scenarios, while keeping other parameters the same. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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The empirical knowledge told us that the level of service is lower when the width is narrower, and passengers would rather gather at escalators than select the staircase. Some field data can also be collected to make the knowledge quantitative.

4 The SRAIL system SRAIL is a passenger simulation system for railway stations, developed using the proposed model. The system is made up of a station editor module, data collector module, passenger flow generation module, passenger activity design module, passenger behaviour simulator, station service simulator, data replay module, 3D simulator, simulation data analyzer and auto-report system. 4.1 Input The input of the system depends on how complicated the simulation scenario is. Basically, it includes the station facilities profile, passenger flow generation profile, activity profile and system parameters. The station editor module provides a tool to edit station facilities, such as the entrance, exit, concourse, escalators, staircase, gate and platform. The user should also define the position and parameters of the station service. The passenger flow generation system provides three types of model. Passengers could be generated by probability distribution, by train timetable or by OD-matrix. This is dependent upon how accurately the operators know the rule of passenger arrival flow. The passenger activity profile gives the user the opportunity to change operation strategies. For example, in most of the railway stations in China, passengers should wait for the train before checking their ticket; this is called “wait first then check”. However, new passenger dedicated railway stations reserved the “check first then wait” method. This can be edited conveniently by the activity module. 4.2 Simulator The passenger behaviour simulator is the core simulator of the passengers’ motion. According to the model, passengers’ behaviours are determined. The station service simulator is also very important, because it controls the changeable facilities or services of the station. In the railway station, there might be a lot of service systems with queues or without queues. The station service simulator controls and updates the queue systems. In some stations, a ticket gate’s open time is related to the train departure time, thus the simulator provides the connection between them. It can also maintain user defined service systems, such as a security check. 4.3 Output The system provides a lot of useful indicators as output. Basically, it can be divided into three categories: quantity indicators, time indicators and integrated WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 4:

Assessment indicator system of SRAIL.

indicators. Quantity indicators include density, flow, speed and queue length. Time indicators include time consumed at each trip step and the aggregate time of level of service at a specific region. Integrated indicators include level of service, comfort and station bottleneck position. These indicators can reflect facility usage, delay, congestion, safety and coordination. In order to assess the station more fully, a complete indicator system has been designed, which is shown in fig. 4. Not all of these indicators are required in a simulation. This depends on what problem is faced and what problem causes most concern.

5 Case study 5.1 Object station Beijing is a city with nearly 300 million inhabitants. There are six passenger train stations. As the first passenger dedicated railway station, Beijing south railway station connects Beijing and Tianjin city, which are the two most important cities in the north of China. The station opened before the 2008 Olympic Games. It has five floors with two metro line (M4&M14) floors, one transfer floor, one platform floor with 24 tracks and one high level waiting floor with more than 20 waiting areas. After it became operational, the time taken to travel between Beijing and Tianjin decreased from 2 hours to 29min and now takes 30 minutes. Every day, more than 162 trains depart from the station. It is one of the busiest railway stations in China. Nearly all of the high speed trains from Beijing depart from this station. After the M4 came into operation in October 2009, the passenger volume of the station was more than 55,000 per direction per day. An overview of the station is shown in fig. 5. 5.2 Simulation experiment Before the M4 was opened, the operators of the railway station needed an assessment of the capacity of passenger facilities. The utilization of the underground transfer hall should be evaluated after the line is opened. The highest passenger load of the station should be determined to decide the use of emergency plan. To solve the problem, simulation experiments are designed. The key point is the underground transfer hall, so this floor should be paid much attention. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 5: Table 2: Scheme

1 2 3

Train number

70 100 150

265

Overview of Beijing south railway station. Simulation schemes’ parameters.

Peak hour train number mornin Evening g 10 8 20 12 30 22

M4 entran ce

M4 exit

SNE SNE SNE

EW EW W

Passenger arrive ratio M4

Bus

Taxi

Flow cross point

0.55 0.66 0.50

0.34 0.24 0.34

0.16 0.10 0.16

6 5 5

S: South; N: North; E: East; W: West

According to the current usage of the hall, the hall is divided into four zones: departure hall 1, departure hall 2, metro-rail transfer areas and others. In addition, in order to evaluate the highest passenger load, low, middle and high passenger volume schemes was designed. Three simulation schemes were designed according to different train numbers, passenger flow scale and operation method, as shown in table 2. Other parameters are investigated and input into the model. The peak hours are selected (7:00-9:00 in the morning and 17:00-19:00 in the evening). It is assumed that all train occupancy is 100%. According to the survey, passengers arrive at the station from 0 to 100 minute before train departure for long distance travel, because there are only a few trains per day. For short distance travel, passengers arrive at random. About 30% of passengers buy tickets before they arrive at the station. Station staff and people only at the station to greet people or buy tickets are not considered in this simulation. The delay of the train is randomly distributed, while all the trains should depart or arrive between 6:00 and 23:00. According to actual data, only platforms 2, 3 and 4 with six tracks could be used. On the second floor, two box offices (with a total of 28 service windows) are available. The desired speed of the passenger obeys the Gauss distribution G (1.5, 0.25). The passenger arrival probability of a train obeys the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

266 Computers in Railways XII exponent distribution and the time spent when buying a ticket obeys the uniform distribution with N (30, 10) second. The simulation clock is set to 0.5 second. The ticket gate is only available 15 minutes before the train departs, and only correct ticket holders could pass the ticket gate. The ticket gate pass time is about 2s per passenger. The simulation starts at 4:30 and ends at 23:59. The simulation is shown in fig. 6. 5.3 Result After simulation, the Instantaneous Maximum Passenger Number (IMPN) of the entire station and of each the concerned zones, Maximum Density (MD) and occupation time of Level of Service-A (LOSAT), is as recorded in table 3 and figs. 7 and 8. From the simulation result, it is found that the three schemes have the same peak time segment with train views. The time when the maximum passenger number appears, as well as the passenger volume in the station, is different. In scheme 1, the morning peak time is at about 8 a.m. with two peaks; the maximum passenger number is 3011 at 5:11 p.m. In scheme 2, the morning peak comes earlier at 7 a.m. with three peaks. The maximum passenger number

Figure 6:

The instance density of the transfer hall.

Table 3: Station Departure hall

The statistics indicators.

IMPN Crowd Point IMPN Departure hall 2 Departure hall 1 MD Departure hall 2 (p/m2) Departure hall 1 Transfer Areas LOSAT Departure hall 2 Departure hall 1

Scheme 1 3011 2 788 660 1 1.4 0.025 78.99% 84.88%

Scheme 2 3964 4 1248 1042 1.6 2 0.058 52.26% 73.44%

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Scheme 3 5134 3 1324 1064 1.7 2.4 0.06 62.08% 79.42%

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6000 5000 4000 3000

Scheme1 Scheme2

2000

Scheme3 1000 0

Figure 7:

Figure 8:

Instantaneous maximum passenger number.

Level of service time occupation distribution.

appears at 6:06 p.m. with 3964 passengers. The morning peak time lasts longer than the evening peak time. In scheme 3, the morning peak is about 6-7 a.m. and the maximum passenger number is 5134 at 6:14 p.m. The evening peak is stable in the three schemes at about 5:00 p.m. This result can be explained, as many long distance trains “depart at night and arrive early”; these cause the high density in the morning. At departure halls, the instantaneous passenger number is also recorded. It is found that passenger volume rises very fast but reduces stage by stage. This might be because passengers who would go in many directions will share the same departing hall. Most of the passengers would like to gather in the underground departure hall 1. Passenger volume in departure hall 1 accounts for 65.8% of the total passengers in the underground. An interesting phenomenon is that the peak time of departure hall 1 is just the low volume time of departure hall 2. This is because of the uneven use of the departure halls. Departure hall 2 serves more tracks than departure hall 1. At the last scheme, the maximum density of departure hall 1 and departure hall 2 is 2p/m2 and 1.6p/m2; this is about 30 times the average density of the entire floor. One reason for this is that passengers take a rest and have to spend almost the longest time in the departure WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

268 Computers in Railways XII hall, another is the facility layout of this floor. This density is very close to the capacity of the facility, which means the station would be almost in saturation state under this level. It is found that the crowd point in the three schemes is 2, 4 and 3, respectively. Although the passenger volume is the highest in scheme 3, the crowd point is not the most serious. This is because a special passenger flow line operation method is used in the last scheme. The mixed directional flow is changed into unidirectional flow by changing the escalator run direction. Comparing the level of service occupation time of the facility, it is found that the level of service is not reduced very sharply with the increasing of the passenger volume. Even though the maximum density appears in departure hall 1, the level of service at departure hall 2 is worse than departure hall1 from the view of the whole day operation. This means the use of departure hall 2 is more balanced at this time.

6 Conclusion A passenger simulation model and its implementation in China are described in this paper. A four steps model calibration and validation is presented for similar simulation applications. An indicator system is proposed to assess the station. A simulation tool, SRAIL, is developed based on the proposed model. SRAIL provides a user friendly interface and contains a lot of useful modules. The tool has been already used on station design tests, station egress capacity evaluations, passenger flow line improvements and station operation optimizations in many projects in China. An integrated simulation of station passenger flow and station yard operation is being studied and will be used in the future.

Acknowledgements This work has been financed by the National Natural Science Foundation of China (NFSC), project ID: 60674012; National Key Technology R&D Program (2009BAG12A10); Beijing Jiaotong University Research Fund, Project ID: 2007RC039. We would like to thank the related committee.

References [1] Henderson, L.F., The statistics of crowd fluids. Nature, 229, pp. 381–383, 1971. [2] Helbing, D., Keltsch, J. & Molnár, P., Modelling the evolution of human trail systems. Nature, 388, pp. 47–50, 1997. [3] Gipps, P. G., Marksjo, B., A Micro-simulation Model for Pedestrian Flows. Mathematics and Computers in Simulation, 27(2), pp. 95–105, 1985. [4] Shigeyuki Okazaki, A Study of Simulation Model for Pedestrian Movement with Evacuation and Queuing. Proc. of the Int. Conf. on Engineering for Crowd Safety, eds. Roderick A. S. &Jim F. D., Elsevier: London, pp. 271– 280, 1993. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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[5] Lovas G. G., Modeling and Simulation of Pedestrian Traffic Flow. Transportation Research B, 28B (6), pp. 429–443, 1994. [6] Rothman D. H. & Zaleski S., Lattice-gas models of phase separation: Interfaces, phase transitions, and multiphase flow. Reviews of Modern Physics, 66, pp.1417 1417–1479, 1994 [7] Helbing, D. & Molnar, P., Social force model for pedestrian dynamics, Physical Review E, 51(5), pp.4282–4286, 1995. [8] Hoogendoorn, S. P., Gas-Kinetic Modeling and Simulation of Pedestrian Flows. Transportation Research Record, 1710, pp. 28–36, 2000. [9] Blue, V. J., Cellular Automata Model of Emergent Collective BiDirectional Pedestrian Dynamics. Proc. of the 7th Int. Conf. on Artificial Life, eds. M.A. Bedau, J.S. McCaskill, N.H. Packard &S. Rasmussen, MIT Press: Cambridge, pp. 437–445, 2000. [10] Kirchner, A., Namazi, A., Nishinari, K. & Schadschneider, A., Role of Conflicts in the Floor Field Cellular Automaton Model for Pedestrian Dynamics. Proc. of the 2nd int. conf. on Pedestrians and Evacuation Dynamics. Eds. Edwin R. Galea E.R. U.K., pp. 51–62, 2003. [11] Izquierdo, J. & Montalvo, I., Forecasting pedestrian evacuation times by using swarm intelligence. Physica A. 7(388), pp.1213–1220, 2009. [12] Maw, J. & Dix, M., Appraisals of station congestion relief schemes on London Underground, Proceedings of PTRC Seminar, England, 335, pp.167–178, 1990. [13] Gordge, R. M. & Veldsman, A., Planning for pedestrians: Friendly, safe and viable transportation station environments, In: Freeman & Jarnet, (eds.), Urban Transport Policy, Balkema, Rotterdam, Netherlands, 481– 487, 1998. [14] Schelhorn, T., Sullivan, D. O, Haklay, M. & Thurstain, M., STREETS: An agent based pedestrian model, Proc. of Computers in Urban Planning and Urban Management, Franco Angeli, Milano,1999 [15] Still G. K. Crowd Dynamics. PhD Dissertation, Warwick University, 2000. [16] MacDonald M., STEPS Simulation of Transient Evacuation and Pedestrian Movements User Manual. Unpublished Work, 2003. [17] Hoogendoorn, S.P. & Bovy P.H.L., Pedestrian route-choice and activity scheduling theory and models, Trans. Res. Part B, 38, pp.169–190, 2004. [18] Li, D., Modelling and Simulating Passenger Flow of Urban Railway Traffic, PhD Dissertation, Beijing Jiaotong University, 2007.

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Modeling of an interoperability test bench for the on-board system of a train control system based on Colored Petri Nets L. Yuan1, T. Tang1, K. Li2 & Y. Liu2 1

State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, China 2 School of Electrical and Information Engineering, Beijing Jiaotong University, China

Abstract The interoperability of train control systems is an essential feature for high-speed railways. It must be proven that the on-board system of the train control system has the ability to allow the safe and uninterrupted movement of each line, which accomplishes the specified performance. A third-party interoperability test bench should be built for the customer to test the interoperability of the on-board systems, which are manufactured by different appliers. In this paper, a formal model was applied on the design and the verification of the test bench. The design errors can be detected using this formal model, thus the correctness of the test bench functionality was ensured. A structured Colored Petri Nets model was proposed to describe the test bench in the aspects of system, modules and processes. The model includes three sub-models: test bench, interface and onboard system. Colored Petri Nets was used for system modeling and CPN-TOOLS was used to support the simulation and the formal analysis. The hierarchical modeling method not only reduces the complexity, but also enhances the reliability and reusability. On the basis of these models, the architecture, the information flow and the algorithms of the test bench can be verified during the system design and development. The simulation results showed that the design errors can be found and some algorithms can be verified and corrected in the modeling and simulation process. Keywords: test bench, CPN, formal method, train control system.

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1 Introduction Train control systems based on communications are advanced signaling systems, which are an important part of high-speed railways, as they ensure the safety and the efficiency of the high-speed railway. Communication-based signaling systems control the train operation through radio communication, such as ETCS level 2 and the CBTC system. The high-speed trains, which are equipped with the on-board system, must be able to run on many lines of the high-speed railway network in the future in order to enhance the operational and deployment flexibility of the transportation network, for example, European Corridors and the DPL (Dedicated Passengers Line) of China. This requires the train control system to have the interoperability features. It must be proven that the on-board equipment has the ability to allow the safe and uninterrupted movement of highspeed trains, which accomplish the specified performance. There are some differences in the technical detail between each manufacturer, because of the different understandings of the specification, for example, the sequence of the message between the train and trackside. Therefore, an interoperability test is necessary to validate whether the on-board system can run on other lines, and this is used in tests in the reference laboratory. Interoperability testing in the laboratory is different from testing by manufacturers. Interoperability tests are not based on manufacturers’ design documents, but the specifications issued by the administration. The laboratory provides a test bench, which can be connected with the real equipment to run all test sequences, and therefore provide a standard environment to verify whether the on-board equipment meets the specifications. To verify the consistency between the functions of the equipment and the specifications, the interoperability test does not concern the internal implementation details of the equipment, but the external characteristics of the device. Therefore, the interoperability test is a third-party test, and it is also a test that mainly serves the users. The interoperability test bench should not only ensure the accuracy of the test, but also should prove the accuracy of itself for the authority of the laboratory. In addition, the test bench itself should be open, that is, the principle of the test bench is understandable. All of these are the requirements for the design and verification of the test bench.

Figure 1:

The interfaces between the test bench and the SUT.

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Petri Nets is a formal, graphical modeling method. Petri Nets’ advantages are its visual graphical modeling and its preciseness of theoretical analysis. So, it is widely used in various fields, especially in describing the complex systems or the logical relationship between processed activities, such as concurrency, competition, synchronization, etc. However, the Petri Nets models of a largescale system will be too complex to analyze, and the correctness of Petri Nets models are based on the experience of the designer. The hierarchical model is a common way to solve the problem of the state space explosion of the Petri Nets. By hiding the internal structure of the subnet, the designer can focus on the higher abstraction level design and the subnets can be designed concurrently and reused easily, and the resulting model has a good hierarchy (Jensen [1]). In this paper, a structured Colored Petri Nets model was used to describe the test bench and to decompose and refine it in the aspects of system, modules and processes. The model started from the system context level and described the interfaces and interactions between the test bench and the SUT. Then, the modules in the test bench are further refined in the second level, to show the state transition of the internal modules. In the third level, the module working processes are refined respectively. Colored Petri Nets was used for system modeling and CPN-TOOLS was used to support the simulation and the formal analysis. On the basis of these models, the architecture, the information flow and the algorithms of the test bench can be verified during the system design and development. The simulation results showed that the design errors can be found and some algorithms can be verified and corrected in the modeling and simulation. The models that describe the test bench can not only be used to prove the correctness of the test bench, but also be used to show the principle of the test bench to the people participating in the test (David et al. [2]).

2 Functional model of the interoperability test bench 2.1 The structure of the interoperability test bench and the basic principles of modeling The interoperability of high-speed railways includes many aspects, e.g., vehicles, electrical system and operations. The interoperability in China focuses on whether the trains, which are equipped with different on-board equipment supplied by different manufacturers, can run continuously and safely on the line, which is equipped with trackside equipment from other suppliers, and meet the functional specifications and required performances of the system. Thus, the interoperability test concerns the ability to exchange information and to use the information that has been exchanged between trains and trackside. The interoperability test is focused on the external behavior of the on-board system. In addition, the interoperability test should not require manufacturers to complete some additional interfaces for the test. Therefore, all tests should be completed in the available interfaces of the SUT. In addition, the interoperability test should not require manufacturers to provide internal design documents. The test should validate the equipment only through its input and output behavior. Therefore, the interoperability test should be a black-box test and a data-driven test. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

274 Computers in Railways XII MONITOR

TEST SEQUENCES

Interface 1

SCENARIO CONTROLLER

Interface 2.1

Interface 2.2

BALISE ELEGRAM GENERATION SIMU.

MESSAGE GENERATION SIMU.

TRACK CIRCUIT GENERATION SIMU.

Interface 3.1

Interface 3.2

Interface 3.3

Interface 4.1

EUROBALISE SIGNAL GENERATOR

Interface 4.2

Interface 2.3

Interface 4.3

Interface 2.4

SPEED SENSOR SIMULATOR Interface 3.4 Interface 4.4

Interface 2.5

TIU SIMU. Interface 3.5 Interface 4.5

Interface 2.6

DMI PROMPTOR Interface 3.6 Interface 4.6

EURORADIO COM. SIMULATOR

TRACK CIRCUIT GENERATOR

ODO ADAPTOR

TIU SIMULATOR

DRIVER

Interface 5.1

Interface 5.2

Interface 5.3

Interface 5.4

Interface 5.5

Interface 5.6

BALISE

EURORADIO

TCR

ODO

TIU

DMI

SYSTEM UNDER TEST (ONBOARD)

Figure 2:

Structure of the test bench.

According to the on-board equipment specifications of the train control system, the structure of the interoperability test bench includes a test sequence database, a Scenario Controller module (SC), online executive modules and interface modules. During the execution of the test, the test bench will send test data to the SUT in real-time, according to test sequences data, thus creating an external running environment for the on-board system, so that the tested device’s functions can be executed. The test sequences stored in the database are formed by concatenation of the set of test cases according to the test specification. The Scenario Controller is the main module of test execution, which is responsible for reading all the test data from the test sequence database, configuring other online executive modules, controlling the start and the end of the test, and monitoring the entire testing process. The online executive modules’ responsibility is to generate messages according to the configuring data by SC, determining the proper time and occasion for sending messages to the on-board equipment. The Speed Sensor Simulator (SSS) simulates the dynamics behavior of the train and calculates the speed and the position of the train in real time. It provides not only speed information to the on-board equipment, but also position information of the train to other modules of the test bench. The interface modules are used to connect the test bench and the SUT, as the equipment to be tested may come from different suppliers; therefore, it needs to adapt the interfaces between the test bench and the real SUT, to ensure the communication between the test bench and the SUT. Considering the functional decomposition of the test bench and the improvement of the maintainability and reusability, the test bench is divided into several functional modules, which can run on different computers. As the test bench uses the data-driven approach, all the modules work together to drive the on-board system in real-time through the test sequence data, so it is essential for there to be synchronization between the modules’ internal states. To ensure functional correctness of the test bench, a formal method is used to model and WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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analyze the test bench at the beginning of the design. In the analysis process of using Petri Nets for modeling, a hierarchical modeling approach is used, that is, the modeling process was divided into three levels: system level, module level and process level. This is not only to decompose the complex model, and simplify the problem, but also to model with Petri Nets throughout the whole process of the test bench’s development. As the development of the software and hardware of the test bench is in accordance with the preliminary design, detailed design, module interface design, sub-module design, coding and debugging, modeling should be hierarchical to meet the phase of the development of the test bench. The hierarchy of the modules is: (1) System level. This level includes all the components of the test bench. The components are taken as transitions and the information exchanged between modules are taken as places. The relationship and the information flow between the components are considered in this level. (2) Module level. The transitions in the system level were refined in this level. The states and the transition of the states are considered in the modules. The internal states of the component are taken as places, and the events that triggered the state transitions are taken as transitions. (3) Process level. The transitions in the module level were refined further here. The modules of this level are similar to the program function design of the components of the test bench. Considering the network’s hierarchy, the lower level network is actually the refinement of the higher level Petri Nets model. That is, if the total Petri Nets is N = (P, T, F) and the set Y is a transitions boundary set, N[Y] = (P[Y], T[Y], F[Y]) is a higher level module and the subnet, which just contains the elements of the set Y, is the lower level sub-model. In this way, the internal behavior of the subsystem has been further described in lower level models (Girault and Valk [3]). During the process of the development of the test bench, the module of the system level was designed to check whether the system design and the interface definition are correct. In addition, the structure and the functional partitioning of a component were focused in the module level modeling and the implementation of the functions of the component was focused in the process level modeling. By refining the transitions of a Petri Nets, we can gradually get the hierarchical Petri Nets model of the interoperability test bench to show the internal logic of reasoning and operation mechanism within the bench. Using the top-down modeling method, we can reduce the complexity of a system; make the model intuitive and easy to control and so on. The sub-net model of a hierarchical Petri Nets model is actually the operational analysis of each subsystem, and the necessary parts of the whole Petri Nets model. To ensure the properties of the total Petri Nets model of the system, such as activity, boundedness and consistency, each sub-Petri Net model must satisfy the following conditions: (1) If the places are removed, the structure of the network should be noncircular. (2) Subnet models should be marking graphs.

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276 Computers in Railways XII 2.2 System level models Using the top-down hierarchical approach to develop the interoperability test bench, we should discompose the organizational structure and identify the important sub-modules and then describe the entire structure of the sub-modules, as well as the relationships of the sub-modules model, and finally refine the submodels according to the requirements of the system. In this level, we focused on modeling the interface between the various component modules of the test bench. With modeling, we defined the interface relationships and the data flow between the test bench and the SUT at different stages. The test bench module is divided into four sections: scenario controller, executive modules, interface modules and the SUT. The information exchanged between modules is also divided into the offline data and the online data. The offline data mainly refers to the configuration data of the test bench before the test. The online data mainly refers to the dynamic data of the modules in the process of the test. By modeling the system level, we can make a clear understanding of each module relationship in the configuration stage before the start of the test, and of the interaction relationship of each module in the process of testing. As shown in Figure 3, the transitions in the shadow corresponded to the application logic of each module and the places in the shadow corresponded to the information exchange between the modules. In the model of this level, the messages, which were received from and sent to the external modules, are merged into a place; this means that we do not have to be concerned with specific information and can just focus on the source, the destination and the type of the data (i.e. offline or online).

Figure 3:

The system level model of the test bench.

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The right part is the simulator of the on-board equipment. This simulator is a part of the test bench when we debug the test bench and the test sequences. So the test bench for the modeling process should be considered as the SUT behavior. 2.3 Module level models Modeling in this level, the functions of each component were refined. The external interface of this component is refined to a message or information of train location. According to the state division of the test bench, there are initial state, waiting state, ready state, operational state and implementing state in the component, the name of which is MGS (MESSAGE GENERATION SIMULATOR), as shown in Figure 4. Each state corresponded to a place. The state transition was triggered by the Scenario Controller. The operation of the state corresponded to a transition. In this model, the transition will be refined in the next level to describe the detail functions. Figure 4 shows an example of the module of MGS. 2.4 Process level models In the modeling of this level, the function of each module is refined in further detail. This model refines the upper network in order to refine the inner function of the component. The nets of this level do not consider the external interface, because the external interface level has been considered in the previous level model and the function, which is external interface communication, is ensured by the previous level model. The example of the model shown in Figure 5 is for the execution of the test sequence.

Figure 4:

The model of the module level (MGS).

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Figure 5:

An example of the model of the process level.

3 State reduction and model analysis of the test bench State space analysis is also known as Occurrence Graphs (OG) or Reach ability Graphs/Trees (RG/RT). State space analysis researches the accessibility of Petri Nets models to construct a directed reachability graph. The reachability graph included each state node, which is the reachability state place, and each arc that binds each element. State space analysis of the Colored Petri Net can be done through constructing the reachability graph of the reduced nets. In addition, for modeling with the hierarchical Petri Nets Model, we can analyze the reachability in the form of a reachability graph. The correctness of the test bench design can be verified by researching the reachability of the model states. The reachability graph was constructed through the research of the transition path from any initial state and the research of all possible replacements. We can also construct state space by a fully automated tool, such as CPN TOOLS. The state space can explain many analysis and verification issues related to the system behavior. The state space analysis can validate whether the system owns the expected properties through the node, path and subnet forms. For example, there had been a design error in the early stage of the test bench design, which was a lack of an essential state, and the error was found through the deadlock of the state. Once the problem was identified, a new state was added into the model of the test bench design and the deadlock was eliminated, so the test bench design was improved. The major restriction for the state space analysis is the dimension of the state space. The increase of Petri Nets model (such as the increasing of transitions) may lead to the exponential growth of the state space. To complete the model analysis, we should consider the reduction method to simplify the net model. First, the reduction rules were defined. Then, the places and the transition of the model were merged and eliminated through applying the rules to the Petri Nets model to simplify it. Reasonable reduction rules should be made so that the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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resulting model is still in keeping with the properties of the original model. The reduction rules included are as follows: merging continuous places and continuous transition, eliminating equivalent places and equivalent transition, eliminating self-circular places and self-circular transition, etc. The model reduction is essentially a transform process to apply repeatedly the set of the reduction rules. Repeated application of reduction rules can maintain the feature we considered, until the system becomes irreducible. Reducing the model can mask some details that are irrelevant to the designer. The initial model of the test bench was reduced through applying these rules and this made it possible for the analysis to be performed using CPN-TOOLS. After describing the test bench using formal model, the model checking must be done to verify the model. Assuming the model has a finite state space, model checking confirmed that the system will not execute against the state rules through detecting all the possible routes of the system state space. The system state of a reliable test bench should be unique at any time; therefore, the correctness and completeness of the state transition must be validated. For the test bench, the boundedness of the model and the equality of each state transition were mainly considered. The boundedness of the model shows that the resource of places was limited to avoid the system exception. In the reduced model, there is no specific description about the trigger conditions of the transition, because the model should describe abstractly the state transition of the test bench. Actually, each transition was bound with certain conditions and the sequence and the frequency of the transitions will affect the whole system. This will be shown in the status report as the fairness of the occurrence of the transitions. The experience of the implementation of the test bench has shown that the model-based design and analysis method supported effectively the development of the test bench. The bug of the design was found in the early stage and the efficiency of the development was achieved.

Figure 6:

The application for monitoring the execution of the test sequence.

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4 Conclusion The interoperability test bench is used to test the on-board equipment, which is from different manufacturers, and to validate whether the system meets the specifications. In this paper, a structured CPN model was proposed to describe and analyze the test bench during the development of the test bench. The model showed the behaviors of the test bench in the aspect of system, module and process. The model was simulated and verified with CPN-TOOLS to check the accessibility of the states and analyze the deterministic of the state transition; therefore, the correctness of the different designing stages was validated. Modeling provides the basis for the development and debugging of the test bench, and ultimately promoted the realization of the test bench. It also showed that this formal method can be used effectively for interactive system design. The models can be used for the system quality assurance and the system certification.

Acknowledgements We wish to acknowledge the support of the National High-Technology Research and Development Program ("863" Program) of China No. 2009AA11Z221, National Science & Technology Pillar Program of China No. 2009BAG12A08.

References [1] Jensen, K. Coloured Petri Nets. Basic Concepts, Analysis Method and Practical Use (Vol.1-3). Monographs in Theoretical Computer Science, Second Edition, Springer-Verlag, 1997. [2] David V., Didier R., Morm B. A Petri Net based model for assessing OH&S risks in industrial processes: modelling qualitative aspects [J]. Risk Analysis, 2004, 24(6): 1719-1735. [3] Girault C., Valk R. Petri Nets for systems engineering: a guide to modeling, verification, and applications. Publishing House of Electronics Industry: Beijing, 2005. [4] Ma M., Chen G. Stochastic Petri-Net of auto-test system and performance evaluation. Measurement & Control Techniques Journal, Vol.25, No.10, pp. 19-2l, 2006. [5] Cai J., Wang D., Li B. Extended hierarchical color petri net-based test case generation for composite services. Journal of southeast university (Natural science Edition), Vol.38, No.4, pp. 598-604, 2008. [6] Hu J., Li H. Design & implementation of Petri-net-based coordinator in industrial hierarchical control scheme. Computer Integrated Manufacturing Systems Journal, Vol.13, No.12, pp. 2316-232l, 2007. [7] Pan X., Li T., Lui Q. A Hierarchical model of Petri Net and a modelling tool for its design. Computer Applications and Software Journal, Vol.25, No.8, pp. 33-35, 2008. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

Section 5 Planning

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How regular is a regular-interval timetable? From theory to application P. Tzieropoulos, D. Emery & D. Tron Group EPFL-LITEP - Intermodality and Transport Planning École, Polytechnique Fédérale de Lausanne, Switzerland

Abstract Initially, choice of regular-interval timetable was mostly addressing operational concerns, aiming to increase the network throughput and to smooth the day-today tasks of the personnel. Separation between infrastructure management and train operations, induced by the European Union since the early 90s, and the future opening of the rail services to competition, pushes more and more infrastructure managers to operate their network with regular-interval timetable. Thus, the interest of measuring the degree of regularity. The paper defines the different steps needed for going from conventional operations to fully coordinated regular-interval timetable (the so-called clockface timetable). It starts by defining the basic notions, and shows some fundamental properties of regular-based timetables. Then, based on the definitions, a methodology is developed to measure and assess the regularity of a timetable, for a line and over a full-scale network. This is because, in practice, implementation of a perfectly regular timetable is not possible and, perhaps, neither desirable. Constraints related to demand or to resources lead to cancel train paths during off-peak periods or to provide extra stops or longer dwell times (and thus slowing down travel time) during peak hours, for instance. More specifically, the paper presents a methodology for determining the interval used to evaluate and compare reference and actual timetables, per train class and by corridors. Tolerances in measuring are dealt with. The developed methodology has been used to develop assessment software, which has been used in a real life application. Keywords: regular-interval timetable, coordinated cycling timetable.

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1 Introduction A regular-interval timetable provides identical train paths for each service, scheduled at regular time intervals. A coordinated, or so-called clock-faced timetable [3], is based on the same principle and adds to it scheduled and guaranteed connections in selected main stations. Nowadays, several European countries operate their train services on the basis of a regular-interval timetable. Those who do not yet, are gradually coming to this type of operation, too. Initially, the choice of regular-interval timetable was mostly addressing operational concerns. Systematic operations help both increasing the network throughput, and smoothing the day-to-day tasks of the personnel. Separation between infrastructure management and train operations, induced by the European Union since the early 90s, and the ongoing opening of the rail services to competition, pushes more and more infrastructure managers to operate their network on regular-interval timetable. Even based on a regular-interval principle, a timetable almost never strictly adheres to this principle. Early morning and late night services usually diverge from the standard train path design. Reinforcement train paths are often necessary during peak periods. Cost concerns may lead train operators to alleviate off-peak service by cancelling some train paths. Finally, especially in suburban and regional services, political pressures may also generate some diversions from the standard train path service by imposing extra stops. Transgressions of the regular-interval pattern may negate (and often do) the main expected advantages from the regularity. To actually assess the cost of those transgressions, one needs to go for a detailed analysis and comparison of the actual timetable against a perfectly “orthodox” one. This is a cumbersome process that, to the knowledge of these authors, has never been conducted. In order to help planners and transport authorities to proceed with an initial fast assessment of the regularity of a timetable, an evaluation methodology has been developed and implemented as a software package [5]. The developed software has been applied to the French Rhône-Alpes Region, which in 2008 rescheduled its regional services on a regular-interval basis [6]. To design the methodology and to develop the software, it was first necessary to specify precisely the notions of structure, regularity and connectivity. This was done by referring back to the theory of regular-interval timetabling, and by developing specific notions as needed in the process. The paper sets the theoretical framework of regular-interval timetables, shows the fundamental properties of the latter, presents the options taken for measuring the regularity, and highlights the advantages and drawbacks of the methodology.

2 Definitions Urban services have been operated with constant headings almost since their beginning. Often, this has also been the case of shuttle services. Dutch railways have been probably the first to apply this principle at the scale of the national network services in the late 1940s. It was called rigid timetable, by then. Some WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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European networks came to the same principle during the last quarter of the 20th century. They opted for it in order to both achieve productivity gains by systematising their operations, and to offer full time coverage of the services, much alike the car which is available for a journey at any time of the day. By sending the message to the customers that train services are also available at any time during the day, railways aim to enhance their competitive stance. Some basic definitions are needed here to set the scenery. The first is the one of service, kind of product mould for the operator. In this context, a service is composed by [6]:  a directional path in the network (defined by its origin, destination, and route),  a stopping pattern (defining the intermediate stops and their duration),  a commercial identity, which may be related with o o o o

travel time objectives, choice of rolling stock assigned to this particular mission, fare policy, package of extra services, etc.

Usually, any given service has its dual one, the return path. A structured timetable is the one that keeps the service typology under control [6]:  with a finite (and not too large) number of services, to ensure that the transport supply remains readable for customers and operators as well;  with fairly distinct services, that are easily identifiable; supplying a range of products that are easy to identify makes consumer choices simple (and helps improving the marketing, too);  with each particular train assigned to a given service (by avoiding planning “outlier” trains, that are hard to recognize by both customers and operators and which degrade the readability of the whole transport supply). With a structured timetable, customers still need consulting the timetable, though they can easily identify local, fast, high-speed trains, etc. A regular-interval timetable is a structured one and, what is more, with successive identical services planned at fixed time intervals [6]; services are periodical, and the time interval is the period. Theoretically, periodicity may not be the same for various services although, to fully benefit from the systematic properties, periods are usually unique or integer multiples of a basic time interval. Theoretically too, the time interval may be of any value and, for independently optimised shuttle services, it reflects the round trip time on the route, or – depending on supply level requirements – a multiplier or an integer fraction of it (Figure 1). However, for a network with interconnected lines, there is a strong impetus to opt for a unique time interval, often set to a round value, e.g. 60 minutes. In this case, customers only need to remember the departure minute of their usual service: if it is 12, for instance, for a fast train leaving town A for town B, they know this same service is available at 7:12, 8:12, 9:12, and so on. A coordinated regular timetable (or clock-faced timetable) is a regularinterval timetable that fulfils three additional constraints [6]: WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

286 Computers in Railways XII Let be: l: line length [km] f: time interval between [h] services [h] r: turnover time for a unit cu: the unit capacity of a vehicle/ vessel/train-set [pass] K: carrying flow (line [pass/h] capacity) Then:

l

f 

cu K

(1)

(NB: Substitute passengers by tons for freight operations) Figure 1:   

Basic structure of a regular-interval timetable for a shuttle line (space - time diagram) (Source: [7]).

a common axis of symmetry for all the lines in the network, balanced transport supply in opposite directions, with identical travel times, scheduled and guaranteed transfers in selected major stations.

3 Fundamental properties There are mainly two readings of Equation (1):  either K is the actual flow to be carried, and f is the maximum interval between two successive services, derived from the equation;  or f is the minimum headway, and K is the line theoretical capacity. Now, let define: vc as the commercial speed of the service on the line (including turnround time in terminuses) n as the size of the rolling stock (number of units in operation) The rolling stock necessary to provide the service can be computed by means of Equation (2), and the turnover time for a unit by means of Equation (3): r 2l n (2) (3) r f vc By combining the three equations above, we get the fundamental relationship for a shuttle service operated with regular interval as (Equation (4)): K (4) n2 l vc  cu This equation links the size of the rolling stock, to the unitary capacity of a vehicle/vessel/train-set, the length of the line, the commercial speed, and the transport supply level. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Irregular timetable 3 train sets

Figure 2:

5 train paths per direction

287

Regular-interval 2 train sets

Structuring reduces the need for resources.

Structuring the timetable generates productivity gains, especially when no additional constraints degrade the optimisation (Figure 2). However, setting the interval to any given value (i.e. not linked to the turnover time) may be detrimental to productivity gains – at least partly. In the case of multiple services running on the same line, each service follows the same regular-interval logic, and the various services are “stacked one upon another”, provided that the infrastructure allows for such a superposition. In this case, an extra constraint comes into play: regular intervals should be identical for all services or, at least, modulo between regular intervals should be null (i.e. longer intervals should be a round multiple of the shorter ones, such as – for instance – 30/60/120 minutes). This further reduces the optimisation potential. Nevertheless, real-life experience shows that switching to regular- (hourly-) interval operation usually led to eventual gains, sometimes substantial, in resources’ productivity. One of the most interesting properties of regular-interval timetables stems from their periodicity: any particular event is repeating with a period equal to the interval. Therefore, if ever trains meet at any moment in a station, this meeting will occur repeatedly, every hour if the interval is set to 60 minutes. Setting such a meeting in a central node of the network is straightforward: one has just to plan this unique meeting once, by scheduling nearly simultaneous arrivals of all trains in the node, letting enough time for passengers’ exchanges, then letting the trains go. The timetable for each line joining the meeting station is wedged in time by means of the arrival/departure times of trains in the central node. If trains running in opposite direction cross at the central node at a given time, and provided that running times are identical for both directions (which is fairly the case in modern networks), crossing of trains will occur at half-the-period time intervals along the line and through time (Figure 3). This symmetry propriety can be used to extend the meeting of trains in any station that is distant from the central station to an integer multiple of half the period. If running times make it possible to apply the principle to a triangle of 3 lines, what happens to the central node is exactly repeated to the 2 other nodes of the triangle (Figure 4).

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288 Computers in Railways XII Interval n+1

Interval n

Interval n+2

Main Station

Figure 3:

The symmetry propriety.

Central Station

Central Station

55 min

55 min 55 min

55 min

Station B

60+55 min

60+55 min

Station A

Figure 4:

Station B

Station A

Coordinated 3-nodes network, with link travel times being an integer multiple of the 60-minutes period.

Thanks to coordination, railway services offer – besides the time coverage provided by the regular operation – spatial coverage. Railway services become available to join any place at any time.

4 Building elements of a regular-interval timetable

Perrache

Legend: every hour every 2 hours

Figure 5:

Part-Dieu

Givors

Saint Étienne

Firminy

The first step is to define the fundamental structure of the future transport supply as a more or less abstract set of services, the service backbone (Figure 5).

The services backbone (Source: [6]).

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08 34 52 22 55 17 05 35

289

00 30 00 00 56 26 46 16 08 12 42 04 38 51 21 57 57 01 31 09 39 54 43 13 47 26

08 38 42 12 16 28 47 08 38 55 34 51 55 24 55 46 50 20 42 12 20 08 49 19 02 25 55 59 24 01 11 07 37 12 43 51 16 15 45 41

35 10 40 24 24 48 18 33 19 29 17 03 33 11 21 52 00 25 41 11 19 37

38 42 15 46 33 03 58 28 15 45 38 21 51

07 04 34 21 51 54 24 06 36

Lyon-Perrache

39 18 37 06

44 11 56 26

00 30 52

52 37 07 44

04 43 51 Firminy 12 05

19 49 12

49 19 34 04

07 22 52 15

04 23 53 08 38

34 49 19

48 21 51 06

09 05 35 50 20

36

41

St-Etienne-Chateaucreux

Figure 6:

Givors-Ville

Reticular diagram (Source: [6]).

Designing the basic timetable framework is the second step. This is generally done for a 2-hour time slice and becomes the fundamental raw material used to build the final timetable. Often, the best way to represent the basic framework is a reticular diagram (Figure 6) that shows the network topology. Each line represents a train path able to be repeated every hour, or every two hours. Next steps involve building the 24-hour timetable for a working day, by repeating the basic framework throughout the day, setting up the early morning and late night services. The whole process is repeated for Sundays and holidays.

5 Assessment methodology and indicators Two main indicators have been developed to capture structural differences among timetables [8]:  a structure index, reflecting how well the different services comply with the service backbone;  a regularity index, reflecting how well the final timetable complies with the periodicity defined in the basic framework. In analysing operational timetables, we may find [8]: A) Regular train paths belonging to a service, planned at regular time intervals a) either produced by strictly replicating the train path of the reticular diagram b) or being “loose” copies of the initial service, i.e. exhibiting slight differences either in travel times or in servicing intermediate stations; B) Gaps in regularity, i.e. missing train paths that should exist according to the periodicity of the service; C) Train paths belonging to a service, but planned at irregular time intervals WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

290 Computers in Railways XII a) strictly complying (travel times and stopping patterns) with the definition b) loosely replicating the initial service; D) Outliers, i.e. train paths that cannot be traced back to a given service a) within the normal operational range b) at the fringe of the operational range, (first and last trains). Strict compliance with the service backbone or with the regularity (periodicity) is self-explained. To assess loose compliance or not compliance at all, one needs to define tolerance rules. Here is an example of tolerance ranges [8]:  [0 min; + 4 min] interval for the departure time at the origin of the service  [-4 min; +2 min] interval for the arrival time at the end station of the service  no more than 1 extra or less stop in intermediate stations. In the developed software, users cannot change those rules but are free to set the tolerance thresholds to those that fit best their scope [5]. The assessment methodology is quite sequential. It involves 6 steps [8]. 5.1 Set up a reference reticular diagram This will be the reference frame; assessment of compliance will be done by comparing the actual timetable against this reference. The reticular diagram includes implicitly full information on the service backbone, which makes it possible to compute both indexes: structure and regularity. For a given timetable, the underlying reticular diagram may be known or not. In the latter case, some preliminary analysis is needed to reverse-engineer the basic framework out of an existing timetable, which may involve some arbitrary decisions. 5.2 Set up the tolerance thresholds That may be as simple as accepting the default values. Alternately, as already mentioned, users may set their own tolerance thresholds (Figure 7).

Figure 7:

Setting the tolerance thresholds (Source: [5]).

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5.3 Define the O/D relations that will be used in computing the indexes This step actually comes to modelling the network as a set of lines. This operation is largely arbitrary and reflects the user’s view of the network. Subjectivity, here, is unavoidable. Notwithstanding, experience shows, however, that analysts with fair knowledge of the network come up with pretty close, often identical solutions. Knowledge of the service backbone can help as some diametric lines in the reticular diagram may result from operational concerns and does not necessarily reflect functional objectives. Moreover, users may assign a weight on each line, to take into account volume of demand, or the strategic role of a given line. 5.4 Define the operational range for each O/D relation A thumb rule may be that the operational range starts with the first departure of a train path that belongs to a regular-interval planned service, and ends with the last arrival at destination of a train path belonging also to a regular-interval planned service. Implementation for such a rule may be automated, provided that assignment of a train path to a given service is also automated. Alternately, and depending on the design of operations, the operational range may also be based on a fixed number of train paths, or be a fixed time interval, let us say from 6 a.m. to 8 p.m. Ideally, operational range should not be shorter than 13 hours. 5.5 Assign and label; identify the missing train paths For each O/D relation and within its operational range, the software assigns to a service every train path and labels it; it also identifies missing train paths within a service as well as outliers. As already seen, there are 4 labels for train paths [8]: - A, train paths belonging to a service planned at regular time intervals - B, missing paths that would exist if a service was planned at regular intervals - C, paths that can be assigned to a service, but not planned at regular intervals - D, outliers that cannot be traced back to a service. Based on this qualification of train paths, we can define: - a regularity index as being the ratio - a structure index as being the ratio - and, possibly, a reinforcement rate with the ratio

RI 

SI 

A A B

AC AC  D RR 

C A

Depending on the tolerance thresholds, measured regularity and structure may be strict (with 0 tolerance) or loose (with some tolerance allowed).

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292 Computers in Railways XII 5.6 Synthesize and display the results for the whole network Both regularity and structure indexes are computed for a line and for a service (Figure 8). In what it is proposed, there is already a first aggregation: indexes are computed for the full set of services on a given O/D relation. Structure Index

Regularity Index

Number of missing train paths

B

Total number of train paths if all regular-interval paths are provided

A

Actual number of train paths planned at regular interval

Figure 8:

D A + C

Number of outliers

Total number of actual train paths

Number of train paths assignable to the structure (service backbone)

Reading key for the regularity and structure indexes.

The issue of further aggregating the results to build up a unique index for the whole network is still left open. The development team felt that such an additional aggregation will result in unacceptable information loss and that it is actually purposeless. Transport policy makers are sufficiently aware and capable of analysing results on a per line basis; providing a unique performance indicator offers no significant gains in making an overall assessment of the situation.

6 Limits and drawbacks Perfectly regular interval timetables obtain 100% on both indexes (Figure 9). By cancelling 2 off-peak train paths (at 10 a.m. and 3 p.m.), the regularity index drops to 86%, but the timetable structure index remains still at 100%.

Station D Station N

Station M

Station L

Station K Station O

06

07

08

09 RI 

Figure 9:

10

11

14  100 % 14

12

13

14 SI 

15

16

17

18

19

20

14  100 % 14

The perfectly complying example (Source: [6]).

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Station D Station N

Station M

Station L

Station K Station O

06

07

08

09 RI 

Figure 10:

10

11

12  86 % 14

12

13

14 SI 

15

16

17

18

19

20

16  100 % 16

Adding 4 train paths for extra peak services (Source: [6]).

Adding peak-period extra trains (at 6:30, 7:30, 16:30 and 17:30) gives no change in any of those 2 indexes. The reinforcement rate, however, jumps from 0% to 33% (Figure 10). Now, if those 4 extra trains provide additional stops to stations K and N, they do not comply with the structure anymore and the structure index drops to the 75% level. This is one of the limits of the methodology. Actually, the 4 extra train paths are identical and can be assigned to a new service; counting them as outliers falsely reduces the structure index. By counting them as a second service, the regularity index drops indeed to 57% (12+4 planned trains for a possible total of 14+14 train paths), while the structure index remains at 100%. This issue is related to the arbitrary identification of the services. Preventing it in this particular case is easy enough: one needs only to be systematic in service identification while reverse-engineering the service backbone. The software package does precisely this. However, in most complex cases and with the tolerance thresholds set to non-zero values, the issue is harder to settle, and user’s decisions here are critical. Station D Station N

Station M

Station L

Station K Station O

06

07

08

This is incorrect!! 09 10 11 12 13 14 15 RI 

Figure 11:

12  86 % 14

SI 

16

17

18

19

20

12  75 % 16

Falsely taking into account the 4 extra trains (Source: [6]).

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7 Conclusions Seamless presence of the rail services is a core objective for a regular-interval timetable. Users should trust the system and be sure that a service is available, all day long, without having to read and decode timetables. Breaches in regularity reduce the system’s trustworthiness: customers would need again to consult the timetable before using the services. This raises the need to assess the regularity of actual timetables. Real life constraints however result in a more-or-less distorted application of the principle and actual timetables often display some irregularities. It is important for the transport authority to assess how well the initial objective of regularity has been achieved in its actual implementation as an operational timetable. Here lies the interest of providing a general methodology to fast and efficiently measure the regularity. The developed methodology has been eventually implemented in an operational tool [5]. Policy makers can use it to assess the degree of completion of their objectives and, also, to compare alternative timetables. However, the tool reflects the limits of the methodology, which force the user to accept a couple of subjective hypotheses in order to run it. Subjectivity being a part of policy making, having to assume it should not be a major impediment.

References [1] Daniel Émery (2009), Mesure du cadencement, Note technique N° 2, Retour d’expérience sur la mise en service du cadencement 2008 en RhôneAlpes, EPFL-LITEP, Lausanne (restricted diffusion) [2] Mohideen Noordeen (1996), Stability analysis of cyclic timetables for a highly interconnected rail network, PhD Thesis N° 1435, EPFL, Lausanne [3] Werner Stohler (2003), Why is an integrated clockface-driven railway system more efficient than a divided competition-oriented railway system? SMA und Partner AG, Zürich [4] Werner Stohler (1993), La planification de la gestion et de l’exploitation ferroviaire, in Rail International, Paris, 10/1993; pp. 64-70 [5] David Tron, Panos Tzieropoulos (2009), How regular is a regular-interval timetable? An operational tool to assess regularity, Swiss Transport Research Conference STRC 09, Monte Veritá, Ascona [6] Panos Tzieropoulos, Daniel Émery (2009), De la théorie à la pratique, in Préconisations, Retour d’expérience sur la mise en service du cadencement 2008 en Rhône-Alpes, EPFL-LITEP, Lausanne (restricted diffusion) [7] Panos Tzieropoulos, Daniel Émery, Jean-Daniel Buri (2009), Regularinterval timetables; Theoretical foundations and policy implications, presented in the 12th World Conference on Transportation Research, Lisbon [8] Panos Tzieropoulos et al (2008), Qualité du cadencement, in Diagnostic, Retour d’expérience sur la mise en service du cadencement 2008 en RhôneAlpes, EPFL-LITEP, Lausanne (restricted diffusion)

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Port Hinterland traffic: modern planning IT methods A. Radtke IVE mbH - Ingenieurgesellschaft für Verkehrs - und Eisenbahnwesen mbH, Germany

Abstract This paper will present the latest requirements and methods of a sophisticated and integrated timetable and infrastructure planning tool. The related methodology, taking into accounts both passenger and freight services, will also be discussed. The paper will handle the planning and analysis of timetables, rolling stock, signalling and infrastructure, through the integration of operational simulation into the planning process. This will include:  Timetable construction,  Possession planning (timetable for construction sites),  Capacity calculation (UIC 406),  Railway operation simulation,  Vehicle dynamic calculation/energy consumption,  Infrastructure asset management and infrastructure planning and  IT-Integration capability. The port of Hamburg is one of the most important ports in Europe and is an important hub for international trading. The growth rate of the goods volume was increasing yearly until the year 2008. At that stage, the prognostic volume of the handling of goods will be doubled in some years. The railway is responsible for a high proportion of the transportation to and from the Hamburg port and other ports in Lower Saxony. Therefore, the number of daily trains running to and from the port will increase. However, the current railway infrastructure of the metropolis region of Hamburg and other regions in Lower Saxony, especially the track southwards, are already being used very intensively. The prognostic increase for the number of trains running in the network is expected to reach the capacity of the existing infrastructure. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100281

296 Computers in Railways XII Keywords: timetable integration.

construction,

railway

planning

methodology,

IT

1 Introduction For Germany, it is of great importance to have a fully developed traffic infrastructure. It is not only an important aspect for the economical development of the country, but for the people with their need for mobility as well. Having, in particular, ecological factors in mind, it is impossible to follow the ongoing demand for newly build infrastructure. Therefore, the focus lies on a goaloriented transport policy that stresses the maintenance and optimization of the already existing traffic systems instead of prioritizing new developments. The growth in the amount of traffic in the ports of Northern Germany, however, shows that the sole optimization of already existing railway infrastructure will not be enough to meet the traffic demands in the future. Over the previous years, the German economy observed a constantly growing export volume, and the imports increased even more. Germany’s external trade profited from the enlargement of the European Union over the last few years with numerous eastern European countries joining. The growth of the global economy and the German gross domestic product strengthened the external trade. These developments require increasing capacities of (railway) transporting. Further development of the Northern German railway network was planned in a time when the long-distance passenger transport used to determine the direction of development. The realisation of the railway lines Cologne – Frankfurt and Nuremberg – Ingolstadt was already finished.

2

Targets and basic parameters of the investigations

In Northern Germany, these developments increased the meaning of the big ports (Hamburg, Bremen, Bremerhaven and Wilhelmshaven). These ports play an important role when it comes to handling continental and intercontinental freight traffic. Handling capacities and storage areas – especially for the booming container handling – are enlarged to meet the needs of the increasing demand. It is not only the accessibility from the seaside; the hinterland-connection plays an important role as well when it comes to handling the growing transport volume and economical developments in the future. Here, the rail freight traffic can be seen as the key factor. A train’s efficiency is mostly determined by the route. The axle load is important as well since it has a direct effect on the efficiency and the profitability of the freightage. A high line capacity is reached when all trains on one track travel at approximately the same speed. However, the efficiency of the track decreases with the growing differences of the maximum speed of trains. To counteract this development, a timely or regional separation of the individual

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types of trains can be used. Using a timely separation, fast trains operate during the day and slow trains at night. An optional regional separation differentiates between tracks for fast and slow trains. Due to the fact that the railway infrastructure is rather durable, principles for the use have to be determined before the development of the original infrastructure. Civil engineering construction works like bridges, tunnels and the permanent way may have a life expectancy of more than hundred years. The rail track itself can be accounted with a life expectancy of up to 60 years. The specific characteristics of the line routing and the Control and Rail Automation Technology (needed for the safe and economical management) are comparatively expensive in the field of railway infrastructure. Planning dependability is needed when it comes to reasonable operation of railway infrastructure under the aspect of efficiency and sustainability. Traffic concepts have to be planned permanently and in the long run need to guarantee an efficient utilization of the railway construction. Above all, the permanent existence of infrastructure in a quality that is suitable and meets the technical requirements is to be guaranteed. Each year, investments have to be made to compensate for the wear and depletion that occurred during that year in order to guarantee the constant quality and availability of the track system. Knowledge of the expected investments in the railway network enables companies working in the field of railway construction to predict and last their capacities according to the demands. It has to be differentiated between new construction, extension and renewal. The peculiarities of railway construction sites occur due to the wheel-rail system and especially when extending and renewing tracks. A long planning supply and a quick construction site operation are ideal to keep the railway operation and therefore are the core function of the railway company running as smooth as possible.

3

Investigation area

The various studies for the hub of Hamburg and other ports in Lover Saxony include the development of a different infrastructure and operational concepts for several time periods and a capacity analysis. The investigation area is described in Figure 1. The area is limited in the North by the border to Denmark, in the East by the stations Puttgarden, Schwerin, Ludwigslust and Magdeburg, in the South by the stations Osnabrück, Minden, Hannover and Braunschweig, and in the West by the stations Emden and Rheine [1]. The area contains railway infrastructure of the German Railway (DB Netz AG), of the Hamburg Port Authority (HPA), the East-Hannover Railway (OHE) and the Elbe-Weser Railway and Transportation Company Ltd. (EVB). These studies examine the railway network and focus on the port-hinterlandtraffic. The investigation area covers the federal states of Lower Saxony, Hamburg and Bremen as well as parts of Schleswig Holstein, Saxony-Anhalt and North Rhine-Westphalia. Figure 2 shows exemplarily the railway tracks in the port of Hamburg.

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Figure 1:

Investigation area (Puttgarden, Schwerin, Ludwigslust, Magdeburg, Osnabrück, Minden, Hannover, Braunschweig, Emden and Rheine).

Figure 2:

Hamburg Port Authority (source: Google Earth).

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Within the investigation area, a detailed analysis of the sub-networks, tracks and nodes was always combined with the consideration of requirements for the hinterland-traffic. Investigation railway line networks:

 Hamburg – Hannover  Node Hamburg  Node Bremen  Oldenburg – Wilhelmshaven  Extension Hamburg S-Bahn Network  Regio S-Bahn Network Lower Saxony/Bremen In addition, models were used to represent how railway connections to the following ports could be made, taking existing and future traffic flows into account. Investigation area ports:

 Hamburg  Bremerhaven and Bremen  Wilhelmshaven  JadeWeserPort  Emden  Leer  Papenburg  Oldenburg  Brake  Nordenham  Cuxhaven  Stade Afterwards, various single measures were by means with timetable construction and railway simulation separated and within the network connection analyzed. These were followed by a number of different questions. Models were used to test for example track extensions, the improvement of signalling equipment and changes of line routing. Development measures (examples only):        

Stelle – Luneburg Y-Trasse Langwedel – Uelzen Oldenburg – Wilhelmshaven Uelzen – Stendal Oebisfelde – Stendal – Berlin Improved signalling equipment Stelle – Celle Multiple-track line extension Stelle – Uelzen – Celle

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Line routing parallel to the Autobahn/Highway until Hamburg Use of secondary line in the area of Hamburg – Celle (see figure 1, OHE) All analyses were conducted with the proven timetable construction and railway simulation tool, RailSys version 7/8.

4

The software tool

4.1 The software system RailSys RailSys is a comprehensive timetable construction and simulation package for various planning purposes. The system is used by various railway undertakings and a number of consultancy companies and universities around the world. RailSys is focussed on timetable construction and optimisation of timetables (capacity and performance), rolling stock utilisation, engineering design, and infrastructure management. The following sections highlight the main features only [2]. 4.1.1 Software history – RailSys The RailSys core system was developed by the Institute of Transport, Railway Construction and Operation (IVE) at the University of Hannover, Germany. The development of the first model started on mainframe computers using Fortran 77 in the eighties. This developed into a PC based model in 1996/1997, which was based on a new design and concept and was written in C++ to make use of modern technology in a well structured new approach (Simu++). Object oriented, re-useable programming concepts were applied. At the moment (May 2010) RailSys version 8 is used at selected customers (RailSys Classic and RailSys Enterprise). Version 8 includes a database and multi user functionality. Several web-based services are also available such as RailSys Map and RailSys CRM (Customer Relationship Management) [3]. Figure 3 shows the main components of RailSys Classic and Enterprise: RailSys Enterprise consists of the components shown above and two additional web-based modules. The component RailSys Map is used to visualize operational data (infrastructure and timetable data); the CRM-module can be used for third party requests concerning train paths. The TOC request train slots and other information from the RIU. In the past, this process in general was a time consuming manual task using telephone, pen and paper or simple spreadsheets. The web based RailSys-CRM (Customer Relation Management) solution offers far more possibilities to support this process taking into account the increased time pressure for the planning tasks. TOC and RIU can save time of unnecessary (multiple) data entry and, therefore avoid mistakes. Furthermore, streaming less flow of data enables the RUI to perform the time table construction on the basis of the original requests and follows up changes in a much better quality to construct a non discriminating timetable. Using this technology, the RIU can provide all necessary information

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RailSys ® Classic/Enterprise

301

Planning

Timetable Management

Infrastructure Data Management

(Timetable Construction)

Possession planning

Evaluation Management

(Operation of track possessions)

Simulation Management

RailSys® database

Rolling stock circulation planning

RailSys® interfaces

Multi User RailSys Enterprise for Timetable Construction, Simulation and Infrastructure planning

Figure 3:

RailSys system (overview).

to rail regulation authorities to prove the non discriminating timetable construction according to the agreed timetable construction rules (see Figure 4). 4.2 Workflow The exact microscopic modelling of the railway infrastructure with the RailSys System creates a database, which contains all tracks and all signalling systems information for the research area (see Figure 5). The infrastructure data is available in the RailSys data format (HPA), and had to be transformed by an interface (DB Netz AG) or integrated into the RailSys system manually (OHE and EVB). The timetable data on the infrastructure of the DB Netz AG was transformed from the timetable construction system RUT-K (DB Netz AG) into the RailSys data format. The timetable data on the infrastructure of the OHE and EVB was integrated in the RailSys system manually. The result is a base timetable which considers all passenger and freight train runs with information about arrival times, departure and dwell times at all stations in the research area. The next step was the determination of existing and prospective bottlenecks for the metropolis region Hamburg and the hinterland (see chapter 3). Following this is the development of conflict solutions by operational or infrastructural measures. Furthermore, possible deviations and alternative routes were considered.

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Figure 4:

RailSys: consistent flow of data.

Various concepts will be developed for a step by step realisation of measures or a combination of measures which solve these bottlenecks. In the focus stands the realisation of short term measures with effects until 2015. However, long term measures until 2025 will be considered as well. The long term planning is important on the one hand to start with the planning in time to guarantee a realisation of these measures (realisation period in German is normally more than 10 years) and on the other hand to evaluate the sustainability of the short term measures. The projects were partly dealt with in the multi user mode provided by RailSys Enterprise, so several persons could carry out the complex planning tasks at the same time. Figure 6 shows an example of constructional operation partial planning, including some track blockings due to constructions. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 5:

Figure 6:

303

Railway network in RailSys of North Germany.

Example of constructional operation partial planning.

5 Results and implementation The manifold results of these studies carried out with the use of modern IT technologies using the example of port-hinterland traffics can be summarized as follows:  Detailed modelling of all infrastructure variants in networks and their effects WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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 



Timetable construction and simulation for all train runs in networks Infrastructure modelling and timetable construction supported by multi user technology Comparison of the possible conflict free number of train runs with the prognostic number of train runs for the time horizon 2011, 2013, 2015 and 2025 and the daily distribution Determination and analysis of bottlenecks and suggestion to solve these conflicts by operational or infrastructural measures Formulation of recommendations for the development of a future based and suitable infrastructure

According to the listed conditions and thoughts, the study introduces following results:  The realised analysis of the current traffic densities and the expected increase in demand result in the outcome that the proposed Y-Trasse and the three-track extension on the route Hamburg – Hannover in the section Stelle – Lüneburg do not lead to the required increase in freight transportation capacity.  To achieve a further increase in freight transportation capacity using the Y-Trasse, the extension of the section Lauenbrück – Buchholz from three to four tracks and in the region Isernhagen a connection to the track Celle – Lehrte is necessary  Development measures serving as an alternative in enabling a capacity increase are introduced in this study.  To improve the hinterland-connectivity of the ports of Bremerhaven and Bremen the “Bundesverkehrswegeplanung” plans various measures. All bottlenecks in the railway network cannot be eliminated but the planned measures can help improving the capacity of the track hinterlandconnectivity.  As a short-term measure for capacity increase in the relation Hamburg – Hannover an improvement of the signalling equipment could be used. Additionally, preparatory work could be done to redirect some trains on existing secondary lines.  The three track extension between Stelle and Lüneburg will lead to another capacity increase in the medium term.  It has to be decided now if the Y-Trasse should be realized in 2015 with the extensions shown in this study or if the requirements of the freight transportation should be followed, and therefore an alternative new constructed track meeting the needs of freight transportation should be realized between Hannover and Hamburg.  The node Bremen has to be looked at much closer concerning the freight transportation coming from the ports in Lower Saxony and Bremen. According to today’s information, extension measures are necessary.  If the installation of a new S-Bahn-network in the region Bremen is decided, where no track infrastructure is planned yet, then the situation will be intensified. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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References [1] Güterverkehr in Niedersachsen, Bauindustrie Niedersachsen/Bremen 2007, IVE [2] Radtke, A. “Timetable management and operational simulation: methodology and perspectives”, presentation of COMPRAIL 2006, Prag, Czech Republic, (2006), proceedings page 579 – 589 [3] Timetable Construction and Simulation Tool RailSys® Enterprise and RailSys® Map and CRM: www.rmcon.de

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Generating optimal signal positions E. A. G. Weits & D. van de Weijenberg Movares Nederland B.V., The Netherlands

Abstract In The Netherlands railway traffic is growing. As the growth has to be largely accommodated on existing tracks, short headways are increasingly important. Headways are mainly determined by signal positions. Since signal positions are subject to many diverse constraints, finding a good signal positioning scheme by hand is a time-consuming task and it is nearly impossible to prove optimality. Therefore, an algorithm that generates an optimal signal positioning scheme, taking care of all constraints, has been designed and implemented in a computer program for infrastructure planners. The algorithm calculates the sequence of signal positions that minimises the weighted sum of headways for a set of trains, each pair of trains with a common track yielding possibly two headways. The first step of the algorithm consists of a tree search leading to an enumeration of groups of similar signal sequences. Secondly, a linear programming problem is applied to all groups in order to find the best solution within each group. A validation study showed that the signal positioning scheme produced by the algorithm slightly outperforms the results found manually, as long as the computer program is restrained to the same number of signals as used in the manual solution. In a number of cases, the computer program suggested better solutions using a larger number of signals. The results of the validation study have led to adoption of the computer program for use in projects. At the same time further research to improve the computational speed has started. Keywords: railway capacity, signalling scheme, signal positions, headways.

1 Introduction The Dutch railway network is heavily utilised and the number of passengers is growing by between 3 and 5 percent a year. Therefore, the intention is to increase the frequency of departures from 4 to 6 times per hour, for intercity

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308 Computers in Railways XII trains as well as for local trains. In this situation it is important to shorten headways as much as possible. The key to short headways is the introduction of short blocks, blocks that are (much) shorter than the typical braking distance of a train. The ERTMS (European Railway Traffic Management System) will provide an opportunity for short blocks. However, also traditional signalling systems offer opportunities. A drawback is, besides increased costs, the difficulty in designing a signal positioning scheme that minimises the headways. In this article we consider a railway line section of arbitrary length that consists of multiple, parallel tracks. For this line section, we aim to find a signal positioning scheme (a list of signal positions) so that headways are minimised. See Figure 1 for an overview of a typical line). Signal positions are heavily constrained by national signalling conventions (including national safety rules). These national signalling conventions differ from country to country. Therefore, little international literature has been published on the subject of finding optimal signal positions. Notable exceptions are some papers published in China, of which [1] comes close to the research that is reported in this paper. There are, however, some relevant differences, concerning the problem statement as well as concerning the solution method. (The present problem statement explicitly includes the implications of diverging points and the possibility of allocating a braking distance to two successive blocks. See section 2.1.) General information on headways can be found in [2]. In this book Hanson and Pachl describe how headways can be calculated and how headways are related to the capacity of line sections. The Dutch signalling conventions are summed up in several documents, written by ProRail, the Dutch infrastructure manager of the railway network [4–6]. These documents contain information about how the signalling system works. More historical and legal information about the Dutch signalling system can be found in [3, 7].

Figure 1:

Line section with speed profiles per track and overall speed profile.

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The next section, section 2, gives a detailed description of the problem, including the relevant Dutch details. Section 3 describes how the problem is solved. After that, section 4 gives the results of this research. Section 5 presents the conclusions.

2 Problem description 2.1 Scope and main characteristics of the Dutch signalling rules The geographical scope is restricted to a railway line section of arbitrary length that consists of multiple, parallel tracks. See Figure 1 for a scheme of a typical line section: The line section, as well as the signal positions, is considered in one direction only. The signals of all parallel tracks have to be placed at the same position, which means that signal fronts are assumed. The line section starts at some fixed departure signal (front) and ends at some fixed arrival signal. The number of signals that are placed between the departure and arrival signal is not fixed. Furthermore, it is possible that trains enter or leave the line section along the way. In The Netherlands signals can show three aspects: red, yellow and green. Figure 2 illustrates this. When the main block is occupied by a train, the signal at the beginning of the occupied block, the entrance signal, shows a red aspect. This means that other trains should stop before this signal. However, because the braking distance of trains is rather large, it is not sufficient to just show this red signal. Therefore, the previous signal (the 1st approach signal) shows a yellow aspect. Whenever a train passes a yellow signal, it should start braking and make sure it stops before it passes the red signal. A block that is long enough for all trains to be able to brake from the maximum speed to 0 km/h within the block is called a long block. However, sometimes the distance between the yellow and red signal is not enough to brake from the maximum speed to 0 km/h. Such a block is called a short block. If this is the case, another signal, the 2nd approach signal, also shows a yellow aspect. This last signal also shows a number that corresponds to a target speed. It is assumed, according to Dutch practice, that each train brakes within one or two blocks. This means that minimum block lengths for long blocks are also valid for two (possibly short) successive blocks.

Figure 2:

Aspects and blocks (colour online only).

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Figure 3:

A detail of a headway diagram.

Signal positions are subject to constraints. There are several kinds of constraints. The first type is called a negative constraint, which is defined by an interval that is not allowed to contain a signal. The second type, a positive constraint, consists of an interval in which a signal must be placed. In addition, the third type of constraint relates to the distance between tot successive signals (i.e. the minimum block lengths as already discussed above). The first two types of constraints apply uniformly to all parallel tracks. However, the last type of constraint may be different from track to track, depending on the speed profile (the maximum allowed speed) of the track. The maximum speed at which a train is allowed to enter a block, determines the distance to the next signal or the distance to the signal after the next signal. It is assumed that a speed profile (the maximum allowed speed) per track is given. The speed profile of the line section is then defined as the maximum of the speed profiles per track. The headways can be computed locally (i.e. at a certain block of the line section) as well as globally (taking the maximum over all shared blocks of the line section). In this article the headways are calculated globally, since these headways reflect the need for an optimal positioning of signals along the entire line section. Figure 3 shows the elements that play a role in the calculation of headways. Refer to Hanson and Pachl [2] for an explanation of the terminology. 2.2 Search space and objective function First of all let us denote by P the set feasible sequences of signal positions. The set P is determined by all constraints mentioned in the previous subsection. Next, for each pair of trains, the shared sections are determined. The number of these shared sections can be 0, 1 or more and each section consists of a number of successive blocks. For each shared section, two headways ( H ) are computed. The first headway corresponds to the situation that one train follows the other, and the other headway corresponds to the situation with the other train in front. The objective function is now the weighted sum of headways. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

min Wt1 ,ts H t1 ,ts pP

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311 (1)

t1 ,t 2

In eqn (1) t1 , t2 denotes the situation that train t2 is following t1 , having a certain section in common. The weights are denoted by the symbol W . Note that this objective function only considers headways and not running times which sometimes play a role too. However, since in general short headways imply short running times, the running times are not included in the objective function. 2.3 Headway calculation For the trains t1 and t2 , having a certain section in common let H b ,t1 ,t2 be the headway for the succession t2 after t1 at block b . To calculate the minimum headway the approach point of train t2 is important, which is defined as the first point where t2 has to run with a speed that is lower than normal because of train t1 . The minimum headway can now be calculated as follows. It is assumed for the time being that all blocks except block b cause no problems. A few seconds (due to a release process) after the rear of train t1 has left block b, train t2 must be before its approach point of block b to make sure that it does not have to run slower than normal because of train t1 . Therefore, two running times are calculated. The first running time is the running time of t1 from its approach point of the first shared block until the exit signal of block b ( eb ,1 ). The second running time is the running time of t2 from its approach point of the first shared block until his approach point of block b ( ab,2 ). The difference between these two running times is the minimum headway at block b . Taking the maximum over all blocks gives the global minimum headway for the train sequence ( t1 , t2 ):

H t1 ,t s  max H b ,t1 ,t2  max(eb ,1  ab , 2 ) b

(2)

b

Sometimes the routes of the trains split in block b . If this is the case a virtual exit signal has to be placed at this point where the routes split, to make sure the headways are valid.

3 Approach to solving the problem The approach consists of two parts: 1. An enumeration of discrete paths, each path representing a group of similar signal sequences.

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312 Computers in Railways XII 2. A LP procedure for finding the best solution within each group. The following sections describe the two parts. 3.1 Part 1: the construction of disjoint groups of signal sequences In the first step disjoint groups of signal sequences are constructed that share the following properties:  The number of signals (number of blocks) is constant.  For each signal a single interval of positions is given.  In each signal interval the speed profile does not change, so that shifting the signal within the interval does not influence the speed at which a train may enter a block.  In each signal interval the gradient does not change, so that the minimum gradient (downwards counts as negative) does not change as a signal is shifted within the interval.  For all blocks enough information is given to be able to determine the approach points for all trains. The relevant information says whether a block is a long block or a short one. If a block is a short block, then, in some cases, it is determined whether the block's length allows for braking to stand still from 130, 80, 60 or 40 km/h. The last property will now be explained in more detail. From Figures 2 and 3 we learn that the location of the approach point depends on whether the 1st approach block is a short or long block. If the 1st approach block is a long block, the approach point is at sight distance of the 1st approach signal. If the 1st approach block is a short block, the 2nd approach signal also shows a yellow aspect as long as the main block is occupied. Therefore, in many cases the approach point is at sight distance of the 2nd approach signal. However, the yellow aspect in the 2nd approach signal is accompanied by a number indicating a target speed (4, 6, 8 or 13 for 40, 60, 80 and 130 km/h, respectively). It may be the case that a train enters the 2nd approach block without an intention of surpassing the target speed. Then the approach point shifts to the location where for the first time the target speed truly restricts the speed of the train. There are two situations in which a train is not immediately restricted. The train may enter the 2nd approach block with low speed (e.g. just after leaving from a station) or it may enter the block while braking according to plan. In these cases it is relevant what the target speed is. Since the target speed is directly determined by the length and gradient of the 1st approach block, it is therefore necessary to determine what the 'speed of the block' is (i.e. does the block's length allow for braking to stand still from 130, 80, 60 or 40 km/h). 3.2 Part 2: finding the best solution within each group In the second step the objective function is linearised. Starting from an initial signal sequence (IS) a better one (S) is computed applying an LP algorithm. If necessary, the LP algorithm is iteratively applied, until no improvement is obtained. The following paragraphs describe this process. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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In the first step disjoint groups of signal sequences have been constructed. Within such a group, every signal is placed in a corresponding interval (BI,EI) which leads to an initial positioning of signals (IS). As a result of the first step, the relation between the shifting of the signals and the minimum headway is continuous. The initial positions of the signals lead to some set of initial minimum headways (IH) at each block, which can be calculated as explained in section 2.3. The minimum headways of a block b can be lowered in two ways: 1. Shifting the exit signal of block b to the left. 2. Shifting the approach point of block b to the right. The exit signal (Se) of a block can easily be shifted (unless a virtual exit signal is placed, which means the exit signal cannot be shifted). However, the approach point of a block can only be shifted if this approach points corresponds to a signal (Sa), which is not always the case. When it is assumed that every train drives with a constant speed within the specified intervals, the influence of shifting a signal to the minimum headway depends on two factors: 1. The speed of the trains at the shifted signals. 2. The size of the shifts. If S e corresponds to the distance over which the exit signal is moved to the right, the increase of the minimum headway is as follows:

S e Vt1 ,Se

(3)

If S a corresponds to the distance over which the approach signal is moved to the right, the increase of the minimum headway is as follows:

 S a Vt 2 , S a

(4)

When we take the above equations together, the objective function can be linearised as follows. If the approach point of block b corresponds to a signal, we find

H b ,t1t 2  IH b ,t1 ,t 2 

S e (b)  S a (b)  Vt1 , Se ( b ) Vt 2 , S a (b)

(5)

and if the approach point of block b does not correspond to a signal:

H b ,t1t 2  IH b ,t1 ,t 2 

S e (b) Vt1 , S e ( b )

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(6)

314 Computers in Railways XII 3.3 The LP-problem

min Wt1 ,ts max H b,t1 ,ts

Combining sections 2.2 and 2.3, the objective function reads as follows: pP

(7)

b

t1 ,t 2

To convert the above min-max problem to an LP-problem which is easily solvable, some adjustments have to be made. These adjustments with the explanations are described in Winston [8]. The objective function then becomes

min Wt1 ,ts Z t1 ,ts pP

(8)

t1 ,t 2

In introducing the variables Z, the following constraints are added:

Z t1 ,t2  H b,t1 ,ts

(9)

3.4 Implementation The solution method explained in the previous subsections was implemented in a computer programme called DeSign. The programming language is Java. For the LP subproblems the MILP solver lpsolve 5.5 is used (to be found on http://lpsolve.sourceforge.net/).

4 Results The programme was applied to 7 signal positioning problems for which a good (i.e. reviewed and accepted) manual solution was available. For each application one reference signal design for one direction was selected. Figure 4 and 5 show one of the applications. The application shows a four track line section the SAAL line that connects Schiphol and the province of Flevoland (passing the station Amsterdam Zuid). The line section has a length of 5.3 km. The results obtained were evaluated w.r.t. two criteria. The first criterion is the validity of the results. The second criterion deals with the practical usability of the computer programme. 4.1 Validity Two questions are posed. First, are the solutions in the eyes of the experts plausible? This question in fact concerns the validation of the model assumptions rather than the model itself. The main issue was that perhaps relevant objectives might not have been included in the objective function. The experts considered all model solutions with the same number of signals as the manual solution. It turned out that all solutions generated by DeSign but one were accepted by the expert as good, plausible solutions. The solution for Arnhem oostzijde suffered WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 4:

Dwangpunten

315

Track lay out, maximum speeds indicated by colours (colour online only). 0 5 9. 8 5 0 2 0 . 9 5 0 2 0 . 9 5

0 3 2 . 9 5

0 9 9. 7 5

0 9 6. 7 5

8 9 6. 6 5

5 1 2. 7 5

4 7 5. 6 5

1 6 5. 6 5

3 2 0. 6 5

6 1 5. 5 5

29 46 0. 9. 54 55

6 7 1. 5 5

0 8 2. 4 5

4 0 8. 3 5 5 5 7. 3 5

6 8 6. 8 5 0 0 .8 7 5

0 1 .9 6 5

3 8 .9 5 5

8 2 .5 5 5

3 7 .0 5 5

8 1 .6 4 5

5 5 .7 3 5

FIS

DeSign Figure 5:

0 2 0 . 9 5

0 9 6. 7 5

9 8 6. 6 5

0 3 9. 5 5

6 1 .5 5 5

2 6 .0 5 5

3 3 .5 4 5

5 5 7. 3 5

Constraints (dwangpunten) – negative constraints in red and the positive one in green – manual solution (in project of type FIS) and solution of DeSign for SAAL (colour online only).

from the fact that in this case the model assumption that each train can brake to stand still in maximally two blocks, prevents a ‘good’ solution. The second question was: are the solutions generated by the programme optimal relative to the objective function? The second question could not be answered due to the lack of optimal reference solutions. Instead it was evaluated to what extent the model outperformed the manual solutions. The comparison between the model solution and the manual solution was split into two aspects. The first aspect was the reduction of headways the model solutions showed for the same number of signals as the manual solution. The second aspect was the further reduction of headways the model solutions showed when the number of signals increased. Table 1 shows the results.

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316 Computers in Railways XII Table 1:

Average reduction per headway.

5.2

Reduction for same number of signals (s) 0

Reduction for same number of signals (%) 0

Further reduction for max. number of signals (s) 7

Further reduction for max. number of signals (%) 5.5

3.5

–

–

–

–

3.9

4

3.5

11

9.5

5.9

12

11.5

9

8.5

5.3

6

2.5

0

0

5.1

8

5.5

0

0

3.4

2

2

0

0

7.6

7

3

3

1

Application (including direction)

Length of line section (km)

Wormerveer (dir. Zaandam) Arnhem oostzijde (arr.) Den Bosch zuidzijde (dep.) Den Dolder (dir. Utrecht) SAAL (eastwards) Schiphol (arr. from Leiden) Schiphol (dep. to Amsterdam) Utrecht zuidzijde (arr.)

4.2 Practical usability Apart from interface issues, the main issue was the computation time. In all but one application in Table 1, the computation time was limited to about 1 minute on an ordinary PC. The computation time for Utrecht zuidzijde already increased to several hours. Extension of the Utrecht example to a line section of 15 km led to computation time of one day or more for even the lower numbers of signals. As the programme is meant to be part of a design process, the conclusion was that the present maximum length of the line section is 7 to 8 km.

5 Discussion of the results and future work An algorithm that generates optimal signalling positions for a given line section has been constructed and implemented. The main conclusion of the research is that the computer program DeSign based on the algorithm yields valid results. In one example DeSign did not yield valid results, because the assumptions underlying the model were too restrictive. The computer program DeSign yielded small but significant improvements. The more important contribution, however, seems to be that with the computer program infrastructure planners can prove optimality of their designs (relative to an accepted set of assumptions and constraints. In particular, they can easily show to what extent increasing the number of signals above the present number

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is useful. Moreover, the can much more easily than before devote time to sensitivity analysis, varying the constraints. The main present drawback concerns the computation times. Future work will be directed at reducing the computation times by introducing a branch and bound feature in part one of the algorithm.

References [1] Baohau, M., Jianfeng, L., Yong, D., Haidong, L & Kin, H.T., Signalling layout for fixed-block railway lines with real-coded genetic algorithms, Transactions Hong Kong Institution of Engineers, 13(1), pp. 35-40, 2006. [2] Hanson, I.A. & Pachl, J., Railway, Timetable and Traffic: Analysis Modelling - Simulation, Eurailpress, Hamburg, 2008. [3] Middelraad, P., Voorgeschiedenis, Ontstaan en Evolutie van het NSLichtseinstelsel, NS Railinfrabeheer, Utrecht, 2000. [4] ProRail, Algemene voorschriften 131: Het lichtseinstelsel 1955, 6e editie, Utrecht, 2006. [5] ProRail, Algemene voorschriften 132: Remafstanden bij de seingeving, 1e editie, Utrecht, 2005. [6] ProRail, Algemene voorschriften 133.1: Plaatsing en Toepassing van Seinen, 2e editie, Utrecht, 2006. [7] Regeling Spoorverkeer, Bijlage 4 (Seinenboek), 4 juni 2007 [8] Winston, W.L., Operations research: Applications and algorithms, Thomson-Brooks/Cole, Belmont, 2004.

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A method for the improvement need definition of large, single-track rail network analysis and infrastructure using “Rail Traffic System Analysis” T. Kosonen Head of Railway Project Planning Unit, Finnish Transport Agency, Finland

Abstract A major change in Finnish rail freight transport flows caused a need to have a thorough capacity analysis of the network and a need to estimate how infrastructure should be improved to match the future traffic situation. For this purpose a new method was developed. Its main goals were to have an approach that combines different levels of traffic planning, is suitable for single track lines, takes into account the commercial aspects of the traffic and takes into account the network related dependencies. A large study was successfully done with the method and it showed that it could match its goals. Therefore, it was taken into regular use and it is integrated into the long term planning process of the Finnish rail network. Keywords: capacity, calculation, cost/benefit, single track, planning, network.

1 Introduction The total length of the rail network in Finland is about 5 900 km, of which about 90% is single track. Almost all of the track sections are mixed traffic, only a few sections are dedicated to passenger or freight traffic only. The annual amount of passenger trips is about 67 Mio and the total amount of annual freight is about 45 Mio tonnes. About 25% of all freight traffic is Russian-related. Recent changes in Finnish forest sector strategies and wood export customs decisions made by the Russian government had created a need for significant change in the Finnish freight transport system. Major traffic flows had to be WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100301

320 Computers in Railways XII turned around. Traditionally, the wood needed in eastern Finland’s large paper mills has been imported from Russia. Now it has to be transported from western and northern Finland. This caused major changes to the use of the network. To evaluate the impacts of this change and to define the improvements needed for it, a large transport study was started by Finnish Rail Administration in 2008 [1]. The method used in this study was developed during this project and it is called “Rail Traffic System Analysis”. Background work for method development was done in earlier studies that took place in 2005–2007 and handled similar kinds of issues on a smaller scale [2, 3].

2 Concept of “Rail Traffic System Analysis” Traditional capacity analysis methods (for example UIC 406) are usually based on the idea of theoretical capacity and its utilization [4]. Usually, analysis based on the calculation of the percentage of theoretical capacity and how much is still available for additional traffic is currently used. This can be done by compression of timetables or other similar methods. These methods are very simplified and handle only one line section at a time. Adjacent sections are handled separately and optimized independently. This is why these methods are not able to handle network level studies in an accurate way. These methods also do not take into account the commercial aspects that are always present in railway traffic. Every train has to have certain commercial interest or else it makes no sense to operate the train at all. System analysis is a method that answers the question of capacity and its availability, but at the same time is able to handle network level studies and commercial aspects. It consists of four or five different steps as follows: Step 1: Traffic flow estimation Step 2: Train amount calculation Step 3: Timetable definition and planning Step 4: Traffic quality analysis Step 5: (Cost/benefit analysis)

3 Macro level studies System analysis starts from a high level in step 1. At this stage general economic forecasts, land use plans and railway transport customers’ interviews are used to form different scenarios of future traffic flows. This can be done both for passengers and for freight. The result of this step is traffic flow estimations for passengers and different goods types in the observation area. In step 2 traffic flow estimates are transformed into daily traffic amounts. This is done for freight by dividing tonnage flows with average train weights and operation days per year. In passenger traffic, the same thing is done for dividing passenger flows by average carrying capacity for different train types. Seasonal peaks are taken into account so that the maximum traffic needs on the network can be illustrated. The result of step 2 is daily future train amounts in the observed track network in different scenarios. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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KOLARI

2 2 2

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6 6 6

8 14 16

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VARTIUS

KONTIOMÄKI

7 13 15

YLIVIESKA

8 16 16

18 22 26

IISALMI

7 13 11

SEINÄJOKI

Tonnage flow estimate

Figure 1:

Train amount forecast

From goods flow to train amounts.

4 Detailed planning and analysis Step 3 consists of the definition of commercial boundary conditions for future trains and is based on future timetable planning for the observed network. Commercial boundary conditions are typically time slots in which the train has to leave its origin and/or in which it has to arrive at its destination station. In Finland this step is done in co-operation with operators or transport customers in freight traffic. In passenger traffic, usually the regular interval timetable is used so a separate boundary condition definition is not usually done. Traffic planning is done in a capacity allocation priority order that is presented in the network statement. In Finland the order is the following:  Synergic passenger traffic entity  Express passenger trains  Transport for processing industry  Local and other passenger traffic  Other regular freight traffic  Freight traffic not requiring strict transport times  Other traffic In practise this leads to the situation where the passenger traffic regular interval timetable is planned independently, first and freight trains are added on top of it. If available capacity does not allow train timetable planning according to the defined commercial boundary conditions, this is marked down as a serious WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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KO LARI

2 2 2

2 2 2 2 2 2

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6 6 6

8 14 16

27 35 37

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PIETARSAARI PÄNNÄINEN

Tavarajunat 2006 Ennuste 2015 Ennuste 2030

14 18 22

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OULU

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32 46 48

23 27 17 29 21 23

28 38 40

10 12 16

VARTIUS

KONTIOMÄKI

7 13 15

YLIVIESKA

8 16 16

18 22 26

IISALMI

7 13 11

Future timetable definition and planning

SEINÄJOKI

Train amount forecast

Figure 2:

From train amounts to train paths.

signal of insufficient capacity. Traffic planning is done with simply usable planning software (usually Swiss Viriato). The result of step 3 is planned timetables for estimated future traffic in different scenarios. The future traffic quality on different scenarios is analyzed in step 4. This is done by collecting a large amount of train run describing data from the timetables planned in the earlier step. For this process a macro that produces these figures automatically from the timetable database is used. The key figures observed are the following:  Absolute train running time on a defined section  Average running times of all the trains on a defined section  Deviation of train running times on a defined section  Average speed of a train on a defined section  Average speed of all the trains on a defined section  Deviation of train average speed times on a defined section  Absolute non-commercial stop time of a train on a defined section  Average stop times of all trains on a defined section  Total non-commercial stop times of all the trains on a section (daily, weekly and yearly) The data from different sections is compared with each other in order to locate the problematic areas of the network. If a large network is observed, usually a map illustration of the figures is necessary to be able to form an accurate picture of the traffic quality. This can be done with GIS systems. The situation of capacity usage on a single line section can be observed, for example, from the deviation of the running times of a train group. If there is a lot of free capacities, all the trains with similar properties get approximately similar train paths, have almost the same running time and the deviation is small. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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If there are certain problems with capacity, the deviations in the running time are larger as the trains that are planned first get the smoothest train paths and the rest have to manage with the worse paths that are left. The bigger the deviation is, the worse the capacity situation is. The problem is the situation where the capacity is in full use. In that case no additional trains can be added to the timetable. It means that their running behaviour figures will not be taken into account, as they do not exist. These situations must be handled separately. If the traffic quality is too low on certain parts of the network, it is possible at this stage to observe the impacts of different infrastructure improvement actions to the traffic system. This can be done by changing the infrastructure properties and repeating steps 3 and 4 again. This iterative process can be repeated until an adequate level is reached. The result of step 4 is a representation of the capacity and traffic quality situation in different future traffic scenarios. An additional result can also be a list of required infrastructure improvement actions to reach a tolerable traffic quality level in the future. During steps 3 and 4 the relations between different infrastructure improvements can be pointed out. Usually some improvements are beneficial only if some other improvements are done first. It is very important to notice these relations on a network level so that the infrastructure upgrade actions can be prioritized.

5 Economical aspects Step 5 is used if there is a need for cost/benefit analysis of the infrastructure improvement actions. The main figures produced in step 4 are usable for calculating operating costs for different traffic models. In Finland we have used an operating cost model that was originally created by Swedish Banverket [5].

Running time min Running time avg Running time max

Running time [h:min]

Large deviations of running time show that capacity is limited or totally in use

Track sections

Figure 3:

Example of running time deviations.

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Change in the total yearly non-commercial stop times (h) 2008–2015 without improvements

Figure 4:

Example of a non-commercial stop time change on a network level.

It is based on the basic parameters, such as train composition and travel time of a train. It has preset values for the cost of the rolling stock, personnel, emission values, etc. With this, the operation cost for a certain timetable structure can be calculated and different alternatives can be compared with each other. A macro that calculates operation cost figures straight from the timetable database is currently under development. With the operation cost difference in the studied alternatives and the infrastructure improvement cost needed to achieve it, the cost/benefit ratio can be calculated.

6 Conclusion The “Rail Traffic System Analysis” method has proven to be a usable and most credible tool for capacity analysis in the Finnish network. Its best features are the possibility to take commercial aspects of the rail traffic into account and the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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possibility to provide illustrative and reliable results of railway capacity in such a complex environment as a single track network. It combines different approaches of rail traffic planning together, starting from macro level studies with traffic flows and ending in detailed planning of single train operations. This makes it possible to have a thorough understanding of the real needs of the rail system in the future and how they can be achieved. The following results have been produced for the Finnish network:  Future traffic amounts on the network  Future capacity bottlenecks of the network  Comparison of the effectiveness of different future traffic models and routing alternatives  Infrastructure improvement needs of the network  Prioritization of infrastructure improvement action  Cost/benefit ratio for infrastructure improvement actions With this method the complex case of major freight traffic flow change on the Finnish network could be handled and a three phase program for rail infrastructure upgrade to years 2010–2025 formed. It has been decided that this method will be used regularly in the future to check the relevance of the upgrade program and to adjust it in the direction that is most beneficial.

References [1] Iikkanen, P., Kosonen, T., Mukula, M., Kiuru, T., A 16/2009 Etelä-Suomen rataverkon tavaraliikenteen kehittäminen, Finnish Rail Administration, Traffic system unit, Helsinki 2009 (in Finnish). [2] Iikkanen, P., Kosonen, T., Rautio, J., A 4/2005 Kaakkois-Suomen rataverkon tavaraliikenteen kehittäminen, Finnish Rail Administration, Traffic system unit, Helsinki 2005 (in Finnish). [3] Iikkanen, P., Kosonen, T., Rautio, J., Mähönen, N., A 5/2007 PohjoisSuomen rataverkon tavaraliikenteen kehittäminen, Finnish Rail Administration, Traffic system unit, Helsinki 2007 (in Finnish). [4] UIC leaflet 406, Capacity, UIC International Union of Railways, France 2004. [5] Banverket guidance for calculation – Appliance for socio-economic calculations in the railway sector, BVH 706, Sweden 2007 (in Swedish).

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Automatic location-finding of train crew using GSM technology F. Makkinga1 & B. Sturm2 1 2

Innovation Rail division, Movares, The Netherlands NS, The Netherlands

Abstract Passenger carrier NSR (Dutch Railways Passengers) on a daily basis deploys approximately 1000 drivers and 1300 guards to run approximately 5000 trains. Normally speaking, the current deployment is in line with the crew schedule as laid down in the transport management system. This schedule is generally immediately (manually) updated to suit the situation. In the event of major disruptions, however, problems may occur as a result of which the disruption management organisation loses sight of the current personnel deployment. As a consequence, a situation can arise whereby the crew schedule no longer reliably reflects the current situation. This can lead to errors in the crew rescheduling and possibly to the cancellation of trains because crew have not been organised on time. For NSR this was an undesirable situation and the reason to launch the investigation into how this bottleneck could be solved. A research and development project was undertaken by NSR and Movares with the aim of developing a method for the automated detection of train crew on trains and the registration of deviations in respect of the crew schedule. During this project, a system was developed that – on the basis of GSM technology in combination with the monitoring of trains via the infrastructure – automatically detects which train crew members are located in which train. In the spring of 2009, a very successful test was implemented using the system. Keywords: planning, crew scheduling, location determination.

1 Introduction Dutch Railways, Passengers division (NSR) is far and away the largest passenger carrier in The Netherlands. Every day, NSR carries approximately one million WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100311

328 Computers in Railways XII passengers with approximately 5000 trains. NSR serves approximately 280 stations. The Dutch railway network covers a total length of approximately 5000 km, of which more than 2000 km are twin-track and almost 1000 km singletrack. 1.1 Complexity To successfully manage the complex transport process in The Netherlands, trains and the accompanying train crew are planned accurately to the nearest minute. Every train has train crew on board: one driver and one or more guards. Train crew change trains at the end of the train journey, or en route, at one of the approximately 30 larger stations. During their shift, train crew are not linked to a single route, but several times a day switch to trains operating on another route. Every day, approximately 1000 drivers and 1300 guards are at work in trains. 1.2 Management of the train service The operational management of the train service is undertaken in collaboration between the operator (NSR) and the railway controller (ProRail). NSR monitors the deployment of train crew and wherever necessary makes adjustments (Makkinga [1]). The operational management is supported by a transport management system according to which execution of the timetable and the deployment of rolling stock and train crew can be monitored and as necessary adjusted. During the peak hours of the day, approximately 300 trains are operated simultaneously. Due to the intensity of train traffic (short follow-on times) but also because train crew are not linked to fixed routes, train traffic and crew deployment are relatively susceptible to disruptions (Jespersen-Groth et al. [2]). In the event of major disruptions (intersections or track sections becoming blocked), the result can be that during the execution dozens or even more than one hundred work lines for train crew members have to be revised. A work line for a driver or guard describes on which train he or she is consecutively set to work today, together with the times and the stations. If a work line has to be revised, the responsible officer (the so-called crew dispatcher) comes up with the change, duly notifies the driver or guard in question by telephone, and registers the change (following acceptance by the driver or guard in question) in the transport management system. 1.3 Who is where? For adjusting crew shifts, it goes without saying that it is crucial to know on which train or at which station a driver or guard is currently located. Normally speaking, the current deployment is in line with the crew schedule in the transport management system. This schedule is generally immediately updated if made necessary by the situation. For example, if current execution deviates from the schedule as in the event of a delayed train, or if the schedule requires adjustment, for example because it has been decided to run an extra train. In the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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event of major disruptions, however, the situation can arise that the crew schedule no longer reliably reflects the current situation. Train crew may then possibly be located on a different train or at a different station than shown in the crew schedule. One major cause is the often poor telephone connectivity during disruptions, for both train crew who are required to notify passengers, and for crew dispatchers, who are hard at work rescheduling crew. This can lead to situations whereby the train crew themselves take decisions on their own deployment, without those decisions at that moment being known to the crew dispatcher or recorded in the crew schedule. It is also possible that in the event of major disruptions, changes to the timetable and crew deployment are not immediately fully processed in the schedule. The fact that the crew dispatcher has no clear picture of the current situation leads to errors in crew rescheduling, and sometimes even to the cancellation of trains because crew have not been arranged in time. 1.4 Automatic localisation of train crew For the operational management of rolling stock deployment, a tracking and tracing system has been in use for a number of years, which compares the rolling stock schedule with measurements of actual rolling stock deployment, and as necessary, updates the schedule on the basis of the findings. This led to the need for a comparable method of detecting on which train a driver or guard is currently operational, comparing this information with the crew schedule, and as necessary, updating that schedule on this basis. Preferably, these processes should be fully automated.

2 Successful implementation of the innovation project In the second half of 2008, on behalf of the Transport Control department of NSR, engineering firm Movares carried out an innovation study. This study aimed to identify the possibilities of determining which train crew members are on board which train in real time, fully automatically. The further requirement was imposed that no equipment was to be built into the train. Besides the already available PDA and GSM telephone, crew were not allowed to be supplied with additional equipment. As a result, the space for solutions was considerably limited. Within these parameters, the most likely solution for determining the presence of crew members on a train seemed to be matching position reports from trains with position reports from the GSM telephones of the train crew. In January 2009, in a collaborative venture between NSR, NS Information Management & Technology (NS IM&T), Movares, SmartPosition and InTraffic, a development programme was launched. As client, NSR formulated the requirements and parameters. Movares submitted its knowledge of the railway infrastructure, and developed the system concept together with NS IM&T, the organisation that was also responsible for project management. Software company SmartPosition supplied its knowledge of GSM technology and, in close WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

330 Computers in Railways XII

TNL Train no. & Location train, time stamp, lat,lon

GNL GSM & Location GSM ID,time stamp,Cell ID

LBS Location Based Service

WER Display & Registration Staff ID, time stamp, train

Database Staff ID, GSM ID

Figure 1:

Trial system layout.

collaboration with the software company InTraffic, implemented the system. In five months, the collaborative venture succeeded in developing, testing and assessing a system in practice, in the innovation project. With the system developed, it is possible to reliably determine in real time which train crew members are located in which train.

3 The system The technical feasibility of the selected suggested solution was tested with the system layout in figure 1. 3.1 Where is the train? The train position details required for the trial were obtained from ProRail. ProRail collects this information with a network of approximately 10,000 measuring points in the railway network. The measuring points are located approximately 500 metres apart, but there are sections where the separation between the measuring points is considerably larger (never more than approximately 15 km). Because ProRail delivers position data in respect of the railway network, and because LBS (see par. 3.3) has no knowledge of this railway network, a conversion to geographical coordinates was necessary. This is provided by the TNL system. 3.2 Where are the train crew? Drivers and guards are localised according to the position of their GSM. In this process, use is made of the data from the GSM masts with which the GSM devices have a connection. To be able to receive this information on the LBS WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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platform, an application was developed known as GSM Number and Location (GNL). This application on the GSM sends the current cell ID of the GSM mast to which the crew member’s GSM device is connected, at a fixed frequency, to the LBS platform. The frequency with which the messages are sent can be preset. During the trial, the GSM sent a message to the LBS platform every 90 seconds. 3.3 Location Based Services platform A central element of the trial layout is the LBS system. This system matches train position data with GSM position data. On the generic Location Based Services platform from SmartPosition [3], an algorithm was implemented with this in mind to specify this matching. Because of the specific know-how required for this application (e.g. using cell ID data), it was decided to outsource this task, in this case to SmartPosition. To limit the resultant dependency, an attempt was made to restrict the complexity of the matching functionality and interfacing. With that in mind, alongside the cell ID data, LBS is only supplied with geographical train position data. LBS does not have data about the railway network, timetable or the crew schedule. 3.4 Confrontation with the crew schedule An interface between LBS and the transport management system to make it possible to confront the matching results with the crew schedule is not part of the trial layout. Instead, a simple display and registration component (WER) was developed, to make it possible to consult the matching results. A component was also developed to make it possible to analyse the matching results (see section 5). 3.5 The matching of GSM and train movements The localisation of GSMs was restricted to matching with trains or, if that was not possible, pointing out the movement of the GSM at a speed greater than a specified (preset) threshold value. The latter requirement is important to be able to identify the suspected presence in a train, even if it is not possible to specify precisely in which train. This situation can arise if a second train is travelling in the same direction or if train position data are missing because there are no measuring points in the vicinity to detect train passages. Presence anywhere other than in the trains of NSR is not detected. The matching algorithm was designed to also identify the breaking of a previous match (interpretation: GSM no longer in train), and an extended period of non-confirmation of a previous match (interpretation: train has probably reached its final destination or is stationary, unplanned).

4 The practical trial Over a period of four weeks, the system was tested in an area in the centre of The Netherlands. The trial area is shown in Figure 2. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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N E

W S

Figure 2: Table 1: From Ermelo Bussum Zuid Bilthoven

To Bilthoven Lunteren Apeldoorn

Trial area.

Distance table for the trial area. Distance (km) 40 44 43

Comment Northeast-Southwest Northwest-East Southwest-Northeast

Amersfoort station is at the centre of the trial area. Table 1 provides an outline idea of the size of the trial area. The trial area has no relevance whatsoever for the timetable. It matches a socalled traffic controller’s area. In other words, the operation of signals and points in this area takes place from a single regional office of ProRail. The trial system was provided only with train position data from this area. For trains passing through the area, the only information provided was position reports from the moment of entry into the area until the moment the train left the area. Given the central location of the area and the fact that train crew are employed on a range of different routes, a large proportion of the approximately 3300 drivers and 4500 guards employed at NSR regularly pass through this area. However, the trial only involved drivers and guards operating from Amersfoort, one of the approximately 30 crew bases. The application (GNL) was only installed on the GSM responsible for providing GSM position details to LBS, belonging to the participants in the trial. The trial system was therefore used within this group of drivers and guards for determining on which train they were located, at least in as much as they were on a train at that time travelling through the trial area. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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SOLL

WER Display & Registration Staff ID, time stamp, train

Crew schedule Staff ID,time stamp, train, on/off shift

Analysis Function Personnel & GSM data Staff ID,GSM ID, GSM-number

TNL Train no. & Location train,time stamp,lat,lon

Report on the matching results

Figure 3:

System analysis.

During the execution of the trial, every identified match between GSM number and train number was immediately compared with the transport management system. If a discrepancy was identified, by way of verification, telephone contact was immediately sought with the train crew member in question.

5 The findings The system was tested for a period of four weeks (May/June 2009). During the first three weeks, the LBS system was adapted on the basis of errors in the system software and errors in the matches between crew members and train. In addition, it was noted that given a frequency of transmission of GSM position reports of once every 90 seconds, the GSM battery could rapidly become exhausted before the shift (approximately 8.5 hours) ended. The GSM cannot be recharged during the journey. The guard constantly has the GSM in his possession, and needs it to carry out his tasks in the train and on the platform. For that reason, the trial was subsequently restricted to four hours in any day. Another restriction on use of the GSM device as a source of GSM position data is that the speech communication via the GSM, a common occurrence during disruptions to the train service, hinders the transmission of cell ID data. After three weeks, the system was considered stable and suitable for implementing extensive testing. In week four, the system was no longer altered, and between 3 June and 7 June 2009, daily trials were held between 12.00 and 16.00 hours. The transmitted matching results for that week are summarised in WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

334 Computers in Railways XII Table 2:

Number of detected participants per day. Date Mon 18 May Tue 19 May Fri 22 May Sat 23 May Sun 24 May Mon 25 May Tue 26 May Wed 27 May Wed 3 June Thur 4 June Fri 5 June Sat 6 June Sun 7 June Average

Total 104 111 112 95 101 120 111 115 112 114 115 104 105 109

this paragraph. Figure 3 shows the structure of the analysis system. For each match specified by LBS, it was verified whether this matched the crew schedule as laid down in the transport management system. 5.1 Participation in the trial In total, 218 drivers and guards operating from Amersfoort participated in the trial. During the hours in which the trial system was operational, their presence on trains was detected in the trial area. Table 2 shows the numbers of participants detected on trains over a number of days. 5.2 The reliability of the matching results During the implementation of the trial, it was assumed that a match recorded by LBS between GSM number (and the corresponding employee according to the administration) and the train number was correct if: - it matched the crew schedule, or, in the event of a deviation, - it was confirmed by the driver or guard in question, by verification. In analysing the matching results, it rapidly emerged that the crew often also travelled by train off shift. The crew often travelled by train from and to their crew base, and also on their days off, crew often travelled by train. If the GSM mobile is then switched on, the system can identify a presence on trains which (it goes without saying) is not reflected in the crew schedule, and which may also not be verified. For such situations, the matching results have been corrected. Table 3 shows the matching results for a number of selected days. The column with the heading ‘number of employees’ shows the number of detected WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Table 3: Date 26 May 27 May 3 June 4 June 5 June 6 June 7 June Total and average

335

Matching results. Number of employees 7 35 31 32 36 28 22 191

Result 100% 100% 97% 100% 97% 96% 100% 99%

employees (drivers or guards) in the trains. The column with the heading ‘result’ shows the percentage of correctly identified ‘presence of crew members’ in the train. 5.3 Timeliness of the matching The matching of crew with a train is possible as soon as a train leaves a station located in the trial area (for example Amersfoort) or as soon as a train enters the trial area. On the basis of 191 measurements, it was calculated that: a. a 68% matching occurs within 5.5 minutes of departure/entering the trial area b. a 95% matching occurs within 11 minutes of departure/entering the trial area c. a 99% matching occurs within 16.5 minutes of departure/entering the trial area Because of the limited scale of the data set, for a reliability of 95%, an error margin of around 14% is included in the specified times. For the first case, this means that 5.5 minutes, which equates to 330 seconds, includes an error margin of 47 seconds and for the second case, 11 minutes, an error margin of 94 seconds. For correction, the above means that 11 minutes following departure of the train (or entry into the trial area), 95% of train crew members has been chartered out, and linked to a train by the trial system.

6 Conclusion The trial showed that it is possible on the basis of cell ID data and train position data to detect in real time, with an automated system and with a reliability of 99%, in which train crew members are located. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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7 Future work In a system whereby the GSM registers and transmits the cell-ID every 90 seconds during the entire shift of the train crew, it emerged that the load on the GSM battery is unacceptably high. This could perhaps be implemented more intelligently by installing smarter software on the GSM. In addition, speech communication via the GSM, a common occurrence during disruptions to the train service, hinders the transmission of cell-ID data. Further investigations are therefore necessary into possibilities for tackling these problems. One option would be to obtain cell-ID data from telecom providers. Other possibilities include the selective registration and transmission of cell-ID data, specifically only during major disruptions to the train service, by managing the GSM application by means of an SMS broadcast to the train crew. Meanwhile NSR has started a project which aims to speed up the rescheduling of crew after a disruption has occurred. An important part of this project is the installation of a software module for automatic crew rescheduling. This module will be based upon operations research algorithms (Potthoff et al. [4]). Also for the successful use of such a module, it is important that correct location data of the crew are available. The improvement of those data, including further necessary investigations, will therefore be part of this project.

References [1] Makkinga F, Network control for improved performance – A new concept for on-line scheduling and dispatching, Proceedings of Comprail, pp 943 – 952, 2002 [2] J. Jespersen-Groth, D. Potthoff, J. Clausen, D. Huisman, L. Kroon, G. Maróti and M.N. Nielsen, "Disruption Management in Passenger Railway Transportation", in: R.K. Ahuja, R.H. Möhring and C.D. Zaroliagis (eds.), Robust and Online Large-Scale Optimization, Lecture Notes in Computer Science, 5868, Springer-Verlag, Berlin (2009), pp 399-421. [3] LBS system of Smartposition, system information available on http://www.smartposition.nl/site/nl/services/117/lbs-platform [4] Potthoff, D., Huisman, D., Desaulniers, G. Column generation with dynamic duty selection for railway crew rescheduling, Econometric Institute Report EI 2008-28, December 19, 2008

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Alignment analysis of urban railways based on passenger travel demand J. L. E. Andersen & A. Landex Department of Transport, Technical University of Denmark, Denmark

Abstract Planning of urban railways like Metro and especially Light Rail Transit often result in multiple alignment alternatives from where it can be difficult to select the best one. Travel demand is a good foundation for evaluating a railway alignment for its ability to attract passengers. Therefore, this article presents a computerised GIS based methodology that can be used as decision support for selecting the best alignment. The methodology calculates travel potential within defined buffers surrounding the alignment. The methodology has three different approaches depending on the desired level of detail: the simple but straightforward to implement line potential approach that perform corridor analysis, the detailed catchment area analysis based on stops on the alignment and the refined service area analysis that uses search distances in street networks. All three approaches produce trustworthy results and can be applied as decision support in different stages of the urban railway alignment planning. Keywords: public transport, urban railways, metro, light rail transit, alignment, catchment area, service area, travel demand, travel potential, GIS, planning.

1 Introduction Conventional railways are usually large and rigid with few degrees of freedom in planning of alignments. This is due to the characteristics of such rail systems: high average stop distance and stop positioning dominated by strategic requirements of service (e.g. stop in the big cities the railway passes). However, smaller flexible urban railways like Metro and especially Light Rail Transit (LRT) have much lower average stop distance and the stop positioning may not be evident when consistently running in build-up areas. Therefore, it is often seen that the screening phase of a new urban railway consists of multiple WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100321

338 Computers in Railways XII strategic alignment options or alternatives (e.g. see [1]). It may be difficult to choose the best alignment between multiple high quality alternatives and a decision support tool is often required. Traffic modelling of each alternative will usually provide the best decision base. However, traffic modelling is very time consuming and expensive and is, therefore, usually not introduced until a later phase of the planning process where the number of alternatives are low or nonexisting. A quick-to-implement decision support for selecting alignment alternatives that can be used in an earlier planning phase is, therefore, desirable. Among other important decision elements of the urban railway alignment planning such as transfers, travel time and construction cost travel demand has the highest influence. This is because travel demand constitutes the customer base in the surrounding areas of a railway line. Therefore, a decision support methodology based on passenger travel demand to aid the selection of the best alignment between multiple others is relevant. In the following such methodology – with different approaches depending on the level of detail – is presented and evaluated for its applied use in the planning of alignments for urban railways. A case example will be introduced to show the applied use of the methodology. The case example is based on a light rail solution since this type of urban railway gives rise to most alignment alternatives. 1.1 Introduction to case example The case example is taken from Copenhagen, Denmark and deals with a light rail proposal going from the city centre to the main airport running on the northern part of the island of Amager. The focus area of the case can be seen in figure 1. INDRE ØSTERBRO

REFSHALEØEN

REFSHALEØEN

INDRE ØSTERBRO INDRE NØRREBRO

NYHOLM

NYHOLM

INDRE BY

City Centre INDRE BY CHRISTIANSHAVN

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Regional trains Metro TØMMERUP

Airport

TØMMERUP

KØBENHAVNS LUFTHAVN SYD

Figure 1:

KØBENHAVNS LUFTHAVN SYD

Focus area of case example – the northern part of the island of Amager (left side), and the existing high quality public transport in the focus area (right side).

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There are already rail connections between the city centre and the main airport by regional trains and Metro. However, these are relatively fast connections with few stops whereas a light rail solution is intended to service more locally on the island of Amager and will not (and cannot) compete for travellers going all the way between the city centre and the main airport.

2 Passenger travel demand Travel demand can be used to investigate the need for public transport services in specific areas. Travel demand for public transport can be an indication of potential passengers hence the term passenger travel demand. There are many different factors that affect travel demand. Some are very dominant and have a regular impact (residences, workplaces, student places etc.) while some are only dominant in a time specific period thus having an irregular impact (stadiums, beaches, amusement parks etc.). Furthermore, the passenger travel demand is dependant on the socio-economic composition of the examined area (car ownership, income, ages, family types, driver licenses etc.). For instance, the passenger travel demand is more likely to be utilized in areas with low car ownership than in areas with high car ownership.

0

00 75 >

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1030. It is not reasonable to generate and evaluate all of these strategies. Thus, the strategic scope has to be reduced in a way that it allows to compute all of the possible migration strategies. This is achieved by regarding realistic

Figure 4:

Number of possible migration strategies.

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390 Computers in Railways XII planning constraints. These constraints are e.g. the fixed choice of an ETCS Level to be equipped, whether the Class B system stays in operation or not, and if some of the migration paths can be excluded for a line section or vehicle pool. In eqn (1), these considerations reduce g and p and therefore deliver a reduced Nred.opt.section:

g * p  N opt .sec tion  N red .opt .sec tion  g red . * pred . , g red .  g ,

(3)

pred .  p.

Due to the fact that a migration goal which results in changing ETCS Level every few kilometres would not be accepted, further simplifications can be found. Some line sections might be re-combined to a number of sred clusters equipped with homogenous ETCS Levels for a given migration goal. Together with eqn (3), this leads to a reduced amount of options Nred.options:

( N opt .sec tion ) s  N options  N red .options  ( N red .opt .sec tion ) sred , sred  s.

(4)

The assumptions of eqns (1), (2), and (4) lead to the following result for a reduced strategic scope Nred.total:

N red .options * N red .order  N red .total  N total  N options * N order . (5)

Although the strategic scope has been reduced, e.g. to Nred.total < 108, it still delivers reliable results due to regarding realistic planning constraints. The makes it possible to compute all migration strategies. Therefore, a very good baseline the choice and optimization of the migration is gained. To handle the found strategies properly, the strategic scope is saved as a Petrinet (fig. 5). Starting from the initial situation, different paths to migration goals

Figure 5:

Example of the strategic scope.

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are shown. Each combination of a path and a goal for the migration forms a strategy to be evaluated. The states within the Petri-net describe the status of the whole corridor or rail network at a specific point in time. The transitions between the states symbolize the activities carried out during a migration strategy. The states have been put into different categories. This eases the evaluation and optimization of the strategies. First, it is distinguished between states in which the migration is accomplished and such where the migration has not been finished and interoperability has not been achieved yet. It is also taken into account, if interoperability already exists, but the migration process is not yet accomplished. Finished migration processes could be separated into three different categories. The first category is characterised by a minimum of ETCS equipped vehicles, i.e. a minimum of investment. In the second category, all line sections and vehicles only use ETCS, i.e. all Class B systems have been removed. The last category consists of states with an accomplished migration that cannot be put in one of the foregoing categories.

6 Evaluation and optimization of migration strategies The generation of the migration strategies delivers the changes of the examined assets over time. For the infrastructure, the point in time of the equipment of each line section is known as well as the number and type of elements required doing so. Due to that, differences between ETCS Levels are regarded in detail. The vehicle pools are treated equally. The amount of vehicles for each pool is considered as well as the point in time of the retrofitting with ETCS or replacement of legacy vehicles with new ETCS vehicles. The following figures show three example strategies, derived from the corridor modelled in fig. 2. The first example in fig. 6 shows the strategy with the least investments, i.e. the minimum amount of vehicles is equipped with ETCS. The Class B systems stay operational and the deployed ETCS Levels match, but do not exceed the requirements.

Figure 6:

Migration strategy with minimum investment.

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392 Computers in Railways XII The second example, shown in fig. 7, is a strategy with solely ETCS in operation after the migration. Therefore, all vehicles have to be equipped with ETCS, all line sections operate with ETCS Level 2, and the Class B systems are removed. This strategy could lead to a higher line performance. The last example in fig. 8 shows a possible compromise between performance and investments. All sections are equipped with ETCS Level 2. The Class B systems only stay operational on those sections, where parts of the assigned rolling stock do not require interoperability. This way, the investments in the equipment of vehicles are minimized.

Figure 7:

Migration strategy with complete replacement of Class B systems.

Figure 8:

Migration strategy with compromise between performance and investment.

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From this baseline, a cost driver is assigned to each asset and element. Additionally, gained value or income can be taken into account, e.g. if the equipment of several line sections leads to new train connections or shorter travel times. This yields the possibility to derive different performance figures, like the net present value, life cycle costs, or migration costs. For each strategy, the critical path is identified. So, each activity of the strategy can be started as late as possible and as early as required. This leads to a cost optimization of the strategies. For each migration goal the optimal path can be found by comparison of all paths leading to that goal. Depending on the chosen performance figure, this could be e.g. the one with the lowest migration costs or the highest net present value. Thus, at his stage of the optimization, only the migration goals have to be compared. Fig. 9 shows how the investments of the foregoing examples relate to each other. Now it is possible to take more criteria into account, e.g. to evaluate whether an increased line performance pays off or not. Due to the automatic generation, evaluation, and optimization of the migration strategies, consequences of changes along the planning constraints can be computed quickly. For example, if the deadline of the equipment of a line section is rescheduled, this might have a huge effect on the equipment of vehicles. This could be useful either to adapt to the changes, or to negotiate about the rescheduling. In addition, different perspectives of the optimization can be compared, i.e. the infrastructure, the vehicle, and the integrated point of view. The best strategy for the infrastructure side is not necessarily the best for the vehicle side. The knowledge about these strategic options would be a good and transparent baseline for negotiations among the stakeholders to find a mutually agreed strategy.

Figure 9:

Compared investments of the strategies.

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7 Summary The method delivers the possibility to handle complex ATP migration problems, e.g. the introduction of ETCS on corridors. The dependencies of the equipment of vehicles and infrastructure are modelled in detail. Different scenarios of migration goals can easily be generated and compared. Each migration strategy is evaluated and optimized. Due to the detailed modelling, several performance figures can be applied for the evaluation, e.g. the net present value or life cycle costs. Furthermore, different perspectives of the optimization can be compared. Thus, the method could well be used for the negotiations along the stakeholders of the migration.

References [1] The Commission of the European Communities, C(2006) 5211 ENCOMMISSION DECISION of 7 November 2006 concerning a technical specification for interoperability relating to the control-command and signalling subsystem of the trans-European high speed rail system and modifying Annex A to Decision 2006/679/EC concerning the technical specification for interoperability relating to the control-command and signalling subsystem of the trans-European conventional rail system, The Commission of the European Communities, Brussels, 2006. [2] Bolli, M., Rothbauer, M. F., ERTMS/ETCS – the future has begun, Signal und Draht (101), pp. 6-12, 3/2009. [3] Boehmer, A., Schweinsberg, R., Germany’s national implementation plan for the introduction of ERTMS/ ETCS, Eisenbahntechnische Rundschau, pp. 660-664, 10/2008. [4] Obrenovic, M., Jäger, B., Lemmer, K., Methodology for the LCC-Analysis and the Optimal Migration of the Railway Operations Control on the Example of ETCS, Comprail, Prague, 2006. [5] Bartnicki, K., Rahn, W. H., Integration of relay interlockings on ETCS corridors, Signal und Draht (102), pp. 24-27, 3/2010. [6] Eisenbahn-Bau- und Betriebsordnung (EBO), BGBl., S. 1563, 1967 II. [7] Deutsche Bahn AG, Konzernrichtlinie Infrastruktur gestalten, Modul 413.0301, Frankfurt am Main, 2002. [8] Gralla, C., Koulischer, J., Schunke-Mau, C., Zoetard, P., ETCS for the multi-system ICE 3, Signal und Draht (101), pp. 30-33, 3/2009. [9] Mindel, K., Migration from LZB to ETCS in Germany, Signal und Draht (93), pp. 45-47, 9/2001.

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Synthesis of railway infrastructure J. Sp¨onemann & E. Wendler Institute of Transport Science, RWTH Aachen University, Germany

Abstract This paper addresses the problem of generating a cost-optimal railway infrastructure by stating and solving a linear optimization problem. Railway infrastructure is represented by a network consisting of nodes and arcs. The nodes represent stations; the arcs lines connecting the stations. An input instance of the network design problem for railway infrastructure consists of two parts. The stations, which have to be connected in a certain way, and a traffic demand, which relates each pair of nodes (A, B) to a number of trains of different types, has to be routed from A to B in a given time horizon. A newly designed network answers two questions: what is the topology of the network, i.e. which stations are connected to each other and how does the line look like in each connection (e.g. single track, double track, single track with one overtaking station etc.)? The observed kind of routing problem can be stated and solved as a multi-commodity flow problem. In order to get the design of the network using a routing routine, a complete network is constructed. Finding a routing in such a complete network is then equal to designing the network, since the routing chooses the arcs needed and so designs the desired network. To solve the problem efficiently it is stated as a mixed integer program (MIP), which is solvable by standard MIP-solvers. Keywords: railway infrastructure, strategic long-term planning, network design, multi-commodity flow, MIP.

1 Motivation A solution of the problem of synthesizing railway infrastructure (SRI) answers the question: what does a cost-optimal network of railway infrastructure for a given traffic requirement look like? Planning a complete new network of infrastructure from scratch is one obvious reason why research in this field pays off. Another one is the strategic long-term planning of infrastructure done by railway infrastructure WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100371

396 Computers in Railways XII managers. During this planning process, estimated future traffic flows are routed on an existing railway infrastructure to identify bottlenecks or capacity surpluses. After that the infrastructure has to be redesigned to meet the future requirements. As stated by Ross [1], currently long-term infrastructure planning is mainly a creative process. The results of this paper create a basis for methods that can provide provable optimal decisions for such planning processes. The following section, which is the main section of the paper, describes what has to be done to state the SRI problem as a MIP. The third section, which is followed by some conclusions, discusses MIP solving in general and the first results of the solving process.

2 Model The network of railway infrastructure that has to be designed is very intuitively represented by a graph G = (V, A), with nodes N representing the stations and arcs A representing the lines connecting the stations. Before the model of the problem is presented in detail, let us first have a look at the demands that the input and output of the problem are placing on the structure of the model. An input instance of the SRI problem contains the following information: • a set of railway stations defined by their distances to each other, • a set of train types, which are distinguished by parameters such as maximum speed, length, acceleration and deceleration rates, • a traffic demand consisting of pairs of stations and an associated number of trains – of possibly different types – which should run between these two stations, • a quality parameter, which limits the degree of utilization for each stationto-station connection, • a set of stages of extension of lines, which are constructible between two stations, defined by their life cycle cost and capacity, and • a time horizon. The questions a solution for this problem has to answer are • Which stations have to be connected directly to each other? • What does the connection of two stations look like (e.g. single track, double track, single track with one overtaking station etc.)? • What does the routing of traffic demand look like, i.e. which route is used by which train to reach its destination station? It is important to distinguish the two parts of the problems’ structure that are mentioned above. On the one hand there is the network design problem, which determines the topology of the network, and on the other hand there is the routing problem of the traffic flow. Let us first consider how the routing problem for traffic flow can be modeled and after that how the network design problem can be solved. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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2.1 The routing problem It is easy to see that finding the best route for a given demand of traffic flow is equal to searching for the minimum cost flow in a network. 2.1.1 The minimum cost flow problem In the minimum cost flow problem the objective is to find the (s, t)-path with the least cost shipment for a given flow demand, where s and t are the source of the respective target nodes. Ahuja et al. [2] define the minimum cost flow problem as follows:  Minimize cij xij (1) (i,j)∈A

subject to 

j:(i,j)∈A

xij −



xji = b(i)

∀i ∈ N,

(2)

∀(i, j) ∈ A,

(3)

j:(j,i)∈A

lij  xij  uij n 

b(i) = 0,

(4)

i= 1

where N is the set of nodes, A the set of arcs, xij the flow, cij the cost per unit flow and lij , uij the capacity bounds on an arc (i, j) ∈ A. The b(i) in eqn (2) is defined in the following way: ⎧ ⎪ ⎨< 0, node i is demand node with demand −b(i), b(i) ∈ = 0, node i is transshipment node, ⎪ ⎩ > 0, node i is supply node with demand b(i).



The SRI deals with multiple commodities of traffic flow. Each commodity is defined by a start and destination station, as well as by an amount of trains. This leads to a special kind of network flow: the multi-commodity flow. 2.1.2 The multi-commodity flow problem Ahuja et al. [2] state the multi-commodity flow problem as an optimization problem of the form:  Minimize ck xk (5) 1 k K

subject to



xkij  uij

∀(i, j) ∈ A,

(6)

k = 1, 2, ..., K,

(7)

∀(i, j) ∈ A and k = 1, 2, ..., K,

(8)

1 k K

N xk = bk 0

xkij



ukij

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398 Computers in Railways XII where G = (V, A) is the network graph, K is the number of commodities, xkij the flow of commodity k ∈ K on arc (i, j), xk denotes the flow vector of commodity k ∈ K, ck the corresponding cost per unit flow vector and N the node-arc incidence matrix, which is used in eqn (7) analogous to eqn (2) to define whether a node is a demand, supply or transshipment node. Eqn (6) restricts the sum of all flows on each arc (i, j) by the upper bound uij . The values of ukij also enable the possibility to bound the flow of each commodity on each arc separately. 2.2 The network design problem The key idea to solve the network design part of the SRI is to use the solution of the embedded routing problem. To do so a multi-commodity flow problem is solved, getting as input a complete network with multiple arcs. As mentioned in the beginning of section 2, the solution of the embedded network design problem of the SRI answers not only the question of which stations have to be connected to each other, but also what a connection looks like. Thereby, different stages of extension for one line, such as single track, double track, single track with one overtaking station and so on are distinguished (see Figure 1). 2.2.1 The multi-arc network The multi-arc network used for the SRI contains one arc for each stage of extension, which is constructible between two stations. Each of these arcs possesses a different capacity and cost depending on the lines’ design and the corresponding life cycle cost. Before the arc capacity is defined in the next subsection, an example is given that shows the working method of solving the network design problem by solving the multi-commodity flow problem on a complete graph with multi-arcs. 2.2.2 Example Given the graph G = (V, A), see Figure 2(a), with the set of nodes V = {A, B, C, D} and the set of arcs A = {AB0, AB1 , AB2, BC1 , ...}, where each arc XYi of a connection XY has got a different capacity and different cost. Also given three traffic flows C0, C1 and C2 (commodities) with a start and a destination station and an amount of trains (demand). Different train types are indicated by an index. C0 = ((A, D), [50 , 01, 02]), C1 = ((B, C), [00 , 151, 22]) and C2 = ((C, D), [00 , 01, 32]). A routing found by solving a multi-commodity flow problem could, for example, route the flows 50, 50 + 151 + 22 and 50 + 32 via arcs

Figure 1: Different stages of extension for one line. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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B

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AB0 , BC2 and CD1, shown in Figure 2(b). The resulting stages of extension of the lines between the stations are displayed in Figure 2(c). To calculate the capacity consumption of mixed flows, such as 50 +32, one has to keep in mind that different train types have different characteristics, such as maximum speed, acceleration and deceleration rates, and because of this they consume WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

400 Computers in Railways XII different amounts of capacity. In terms of flow units this has the impact that, for example, the following holds: 5i = 5j . Furthermore, different mix ratios of trains consume different amounts of capacity and there is no train type whose capacity consumption is expressible by a linear combination of the other train types consumptions. This specific characteristic of the capacity consumption is examined in the next section. 2.3 Arc capacity Capacity consumption of one train on a track section can be expressed using the well-known minimum headway time, introduced by Happel [3]. It is the time zij a train j at least has to wait when it wants to enter a track section that is currently occupied by another train i. A visualization of the minimum headway time using blocking time stairways is shown in Figure 3. For more information on the blocking time theory, see Pachl [4]. Wendler [5] calls this minimum headway time in the context of queuing theory service time because it is the time frame while one train occupies the service channel – i.e. the track section – and a following train cannot be served. To calculate the mean minimum headway time of a mix of trains on a track section, the order of trains arriving at the track section is important, since the minimum headway time can only be derived for pairs of consecutive trains. Because SRI deals with future traffic demands there is no fixed timetable for the trains

d

Zij

train i

train j

t

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occupying the infrastructure. Because of that, all possible successions ij of trains i and j are considered and weighted with probabilities pij of the event that train j follows train i. This is shown in eqn (9), where xi is the number of trains of type i, xj is the number of trains of type j, and N is the number of all trains. This is a very simple estimation, which will be refined in future SRI solver implementations. The mean minimum headway time z¯ij and the expected service time ETB over all trains in the time horizon, respectively, can be derived by summing up products pij · xij (eqn (10)). Eqn (11) shows the expected capacity consumption on a given track section a and mix of trains of different train types |K|. xi · xj xi xj · = , N N N2  pij · zij , z¯ij = ETB = pij :=

i

i∈K j∈K

(10)

j

  xai · xaj

N a · ETBa = N a ·

(9)

N a2

a · zij

(11)

By means of the preceding definitions it is now possible to state the SRI problem as an optimization problem. 2.4 SRI: multi-commodity flow with multi-arcs For a network G = (N, A), a set of commodities C and set of flow types (train types) F T the optimization model of the SRI is formulated as follows: Minimize



ca · xaused

(12)

a∈A

subject to Na ·

  xai · xaj N a2

i∈K j∈K



xaij −

a∈Out(n)

a · zij  0.6 · tU



xaij = bnij

xaused =

(13)

∀n ∈ N, ∀i ∈ F T, ∀j ∈ C

(14)

a∈In(n)

xaij  0 

∀a ∈ A,

∀a ∈ A, ∀i ∈ F T, ∀j ∈ C

1, if ∃i : xia > 0, 0, else.

(15) (16)

where xai is the sum of all flows on arc a of the flow type for train type i, and xaij is the flow of type i of commodity j on arc a. For a node n, the functions In and Out return the set of all arcs (n′ , n) ∈ A and (n′ , n) ∈ A, respectively. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

402 Computers in Railways XII Because one unit of flow corresponds to one train, it seems to be incorrect to not restrict the variable domain to positive integers. However, since the used traffic demands are derived from future traffic flow estimations it is sufficient to choose positive reals as the domain, as stated in eqn (15). This relaxation furthermore simplifies the solving process. The objective is to minimize the life cycle cost for the arcs that are used to route flow. The variables xaused , defined in eqn (16), are used to ensure that arc costs arise if and only if there is flow on the arc. Usually, cost functions in flow problems depend on the amount of flow, but since the track, which corresponds to an arc, has to be constructed independently of the number of trains running on it, the cost model described above is chosen. Eqn (14) contains the known flow conservation constraint, cf. eqns (2) and (7). Eqn (13) is called capacity constraint. The left-hand side describes the occupation time of the mix of trains routed via track/arc a, cf. eqn (11). This capacity consumption is bounded to an amount of 60% of the observed time frame tU . This value is taken out of UIC Code 406 [6]. This is a leaflet of the International Union of Railways, which standardizes railway capacity analysis. There exist of course more sophisticated capacity models, but for the sake of simplicity the approach according to UIC Code 406 is selected. Since the capacity constraint is non-linear, powerful LP/MIPsolvers are not applicable. To overcome this difficulty the model is transformed into a mixed integer program. 2.5 SRI: MIP model A MIP is a linear optimization problem that contains variables with an integer value domain. Because of this integrality a MIP is much more difficult to solve than a LP. The MIP resulting from the following transformation of the optimization problem given in eqns (12)–(16) is furthermore a binary MIP (BMIP), because the integral variables are even binary variables. To get rid of the non-linear capacity constraint, possible train/flow type mix ratios that fully utilize the capacity of an arc are calculated for each arc, using ETB , see eqn (11). These mix ratios are denoted as configurations. In the case of three different train types 0, 1 and 2, the set of configurations for an exemplary arc a has the following form: Conf a := {[760, 01, 02], [740, 11, 02], ..., [00, 641, 00], ..., [00, 01, 422]}.

(17)

In the MIP model there is one binary variable yca for each configuration c ∈ Confa . This holds for every arc a ∈ A. For each arc a at most, one of these variables can be selected by setting its value to 1. This enables the chosen configuration and for each flow type i the sum of the flow of type i on that arc is bounded by the value of the ith component of the selected configuration. This means that one configuration [x0 , x1 , x2]a , if selected for an arc a, restricts the flow on a for each flow type i to the value of xi . This is modeled by the new linear capacity constraint in eqn (19) of the MIP model. The objective is to minimize the cost of the selected arcs, which is equal to maximizing the cost of the arcs that are not a picked. Therefore, variables yoff/on are introduced and set to 1 if the arc is not used. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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This restructuring of the objective function provides a faster solving process. The constraint shown in eqn (21) ensures that an arc is either switched off or exactly one configuration is enabled.  a Maximize ca · yoff/on (18) a∈A

subject to 

yca · v c (i)

∀a ∈ A, ∀i ∈ F T,

(19)

xaij = bnij

∀n ∈ N, ∀i ∈ F T, ∀j ∈ C

(20)

∀a ∈ A

(21)

c∈Conf a

j∈C





xaij  xaij −



a∈In(n)

a∈Out(n)



a yca + yoff/on =1

c∈Conf a

xaij  0

∀a ∈ A, ∀i ∈ F T, ∀j ∈ C

yca

∈ {0, 1}

∀a ∈ A, ∀c ∈ Conf

a yoff/on

∈ {0, 1}

∀a ∈ A,

a

(22) (23) (24)

where G = (N, A) is the network, Conf a the set of all configurations associated with arc a, C the set of commodities, F T the set of flow types and function v c returns for a given i ∈ F T the value of the ith component of the configuration c.

3 Solving The previous section presents a MIP model of the SRI problem. To solve the problem professional solver software, which provides academic user licenses, is used. Two solvers with different advantages were used to solve the problem as follows. 3.0.1 Gurobi The Gurobi Optimizer is a linear programming mixed integer programming solver that exploits modern multi-core processors. Gurobi is currently the performance benchmark winner, so it provides the fastest solving times. The disadvantage of Gurobi is the interface. It allows only the usage of restricted sets of functions, parameters and attributes, which can be accessed via the programming languages C, C++, Java, .NET or Python. Despite this restriction it is a powerful solver that additionally supports some modeling systems, such as MPL and AMPL, and is able to read and write LP and MPS files. For further information see the Gurobi homepage [7]. 3.0.2 SCIP SCIP stands for Solving Constraint Integer Programs and was developed at the Konrad-Zuse-Zentrum for information technology in Berlin. Since the complete WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

404 Computers in Railways XII source code is available the solver allows total control of the solution process and unrestricted access to any information at any stage of the solution process. The user can define, write and include their own pricers, branching rules, presolvers, heuristics and so on. Basic principles and further information about the concept of constraint integer programming and SCIP are provided by Achterberg [8] and the SCIP homepage [9]. 3.1 Solving mixed integer problems There is a wide range of methods that are useful for solving (mixed) integer programs efficiently. The kernel method, which is state of the art and the cause of that focussed here, is called branch-and-bound. 3.1.1 Branch-and-bound The goal of the branch-and-bound method is to find an assignment of values of the integer variables that forms an optimal solution of the MIP. One way to achieve this is to enumerate all possible assignments of values by a so called explicit enumeration tree. This results, even in the binary case for a small set variables, in a huge number of tree nodes. So it is desirable not to explore the whole tree. To achieve a so called implicit enumeration tree, bounds are calculated at each node of the branch-and-bound tree during tree building. With the help of these bounds it is possible to prune branches of the tree, so that they need not be explored. The most common method for finding bounds is to solve the linear programming relaxation of the given MIP. In the case of a maximization problem the optimal solution of the relaxation provides an upper bound on every solution of the MIP and is the basis for the branching decision in the current node. Branching in the binary case means creating two new branches of the branch-and-bound tree by assigning the values 0 and 1 to the variable that is chosen to branch on. For more detailed information about branch-and-bound, see Wolsey [10]. 3.2 Results Current implementations run on examples with 20 stations, 3 different train types, 4 different track types and 10 traffic flows. Gurobi returns a result within a 1% optimality gap in less than 7 minutes. The optimality gap is calculated using the best upper in best lower bound in the current stage of the solving process. Proving the optimality, i.e. reaching a gap of 0%, currently takes a great deal of time. This is caused by the huge amount of binary variables. The described example contains about 2 · 106 binary variables and 4.5 · 104 continuous variables. To overcome this explosion in the number of binary variables ongoing implementations are focussed on approaches such as column generation, described by Desrosiers and L¨ubbecke [11], which try to minimize the number of binary variables needed to calculate the optimal solution. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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4 Conclusions This paper shows how to synthesize networks of railway infrastructure out of estimated future traffic flows. To do so the problem is modeled as an optimization problem by interpreting it as a multi-commodity flow problem on a complete graph with multi-arcs, so that a found routing determines the arcs needed. To make the optimization model applicable to professional solver software the problem is transformed to a MIP and respectively BMIP. This transformation results in a large number of binary variables, which again results in long solver running times. To overcome this difficulty ongoing research focusses on approaches, such as column generation, which try to minimize the number of binary variables needed to calculate the optimal solution.

Acknowledgement This publication is a result of research done in the DFG Research Training Group 1298, AlgoSyn: Algorithmic synthesis of reactive and discrete-continuous systems, funded by the Deutsche Forschungsgemeinschaft (DFG).

References [1] Ross, S., Strategische Infrastrukturplanung im Schienenverkehr. Deutscher Universit¨ats-Verlag: Wiesbaden, 2001. [2] Ahuja, R.K., Magnanti, T.L. & Orlin, J.B., Network flows : Theory, Algorithms, and Applications. Prentice Hall: Upper Saddle River, New Jersey, pp. 649–686, 1993. [3] Happel, O., Sperrzeiten als Grundlage f¨ur die Fahrplankonstruktion. ETR, 8(2), pp. 79–90, 1959. [4] Pachl, J., Timetable design principles (Chapter 2). Railway Timetable & Traffic, eds. I.A. Hansen & J. Pachl, Eurailpress: Hamburg, pp. 9–42, 2008. [5] Wendler, E., Timetable design principles (Chapter 6). Railway Timetable & Traffic, eds. I.A. Hansen & J. Pachl, Eurailpress: Hamburg, pp. 106–117, 2008. [6] International Union of Railways: UIC Code 406 - Capacity, pp. 18–20, 2004. [7] http://www.gurobi.com/, May 2010. [8] Achterberg, T., Constraint Integer Programming. Ph.D. thesis, Technische Universit¨at Berlin, Berlin, 2007. [9] http://scip.zib.de/, May 2010. [10] Wolsey, L.A., Integer Programming. Wiley-Interscience: New York, pp. 91– 108, 1998. [11] Desrosiers, J. & L¨ubbecke, M.E., A primer in columnn generation (Chapter 1). Column Generation, eds. G. Desaulniers, J. Desrosiers & M.M. Solomon, Springer: New York, pp. 1–32, 2005.

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Dimensioning of a railway station for unknown operation O. Lindfeldt & A.-I. Lundberg Division of Traffic and Logistics, Department of Transport and Economics, Royal Institute of Technology, Stockholm, Sweden

Abstract Every now and then new railway stations are brought into operation on existing lines. This is a good way of increasing the availability of railway services and attracting more passengers. However, from a capacity point of view, this procedure can be quite tricky, since new stations and additional stops thoroughly alter the traffic properties of the line. The addition of a station like this in Solna, north of Stockholm is under discussion. Here, most of the regional trains, but probably not the long-distance trains, would stop for passenger exchange. A new line, connected to the main line just north of Solna, would also contribute to the traffic flow through the new regional station. The essential question in this project was to determine the number of platform tracks needed to cope with the traffic flow. However, it has proven difficult to find a representative timetable structure to use in the dimensioning work, both the total number of trains and the distribution between stopping and passing trains turned out to be uncertain. A combinatorial method was therefore applied. Using this approach, a large number of timetables, i.e. possible traffic situations, were generated and tested (automatically) for the number of platform tracks needed. Constructing and using this simple model forced the engineers to understand and describe the fundamentals of this operational/scheduling/dimensioning problem. The procedure hence gave useful insights about the system properties and a direct knowledge of the sensitivity of different factors that are essential for the number of tracks needed at a railway station like this. Keywords: station design, station capacity, timetable, combinatorial method.

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408 Computers in Railways XII

1 Introduction Every now and then railway stations are added and brought into operation on existing lines. This is a good way of increasing the availability of railway services and attracting more passengers. As with most investments in railway infrastructure, this type of extension is much easier to dimension correctly when the future timetable is known, or can be decided, before the station is designed. A special case is dedicated commuter lines with completely homogeneous traffic. In these cases, all trains can be assumed to stop for passenger exchange at the new station. The design, i.e. track and platform configuration etc, is therefore merely a question of frequency of service, dwell times and delays. The exact timetable is less important and the station’s operation can be assumed to be similar to that of already existing, adjacent stations. However, most Swedish railway lines are operated with mixed traffic. Longdistance, regional and freight trains are mixed. The construction of additional stations on these lines implies great uncertainties connected to the timetable. The track configuration, including the number of platform tracks, parallel movement facilities etc, has to be carefully designed so that the overall capacity is not affected negatively by the new station. When the traffic is mixed, it is not so easy to foresee which trains are going to stop and which are not. Even if the number of stopping trains per timetable cycle is known, it is also necessary to know the exact sequence of stopping and passing trains to achieve a proper station design. Stockholm Central station is a combined through and dead-end station served by two major lines from the north and one from the south. The two north lines, the East Coast line and the Mälar line, are quadruple- and double-track respectively. On the four-track East Coast line, the commuter traffic is separated from other traffic whereas the two-track Mälar line is operated with a full mix of traffic. This mix of different speeds limits capacity and implies a high level of disturbance sensitivity on the Mälar line. The demand for more and reliable traffic motivates an extension into quadruple track and planning is currently ongoing. Two alternative locations of the two new tracks have been evaluated:  Along existing tracks all the way from Kalhäll to the junction in Tomteboda.  Along existing tracks from Kalhäll to Barkarby and then through a tunnel eastwards to the East Coast line, see figure 1. The second alternative, which also implies an extension into six tracks of the East Coast line south of the junction in Ulriksdal, also gives the opportunity to extend the existing Solna commuter station into a combined station for commuter and regional trains. Such a station would serve both commuter and regional traffic from the East Coast line and regional traffic from the Mälar line, whereas commuter traffic on the Mälar line would continue to use the old line through Sundbyberg.

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East Coast line

Mälar line

Stockholm C

Figure 1:

Extension of Mälar line through a tunnel between Barkarby and the East Coast line.

This type of complex system gives rise to several questions regarding how the extended station should be designed to give sufficient capacity and other operational properties. This article describes a deterministic method that systematically evaluates different timetable layouts, i.e. combinations of frequency of service and stopping/passing patterns. Solna station is used to exemplify the method since the conditions are clear and the traffic situation is neither too simple nor too complex for this kind of analysis. This introduction is followed by a short overview of related studies and literature. The section “Method and modelling” then describes conditions and assumptions regarding infrastructure design, timetable generation, and the capacity allocation model. The results are then presented, followed by some concluding ideas about the proposed way of modelling and further developments.

2 Related studies and literature The Stockholm area has undergone several infrastructure planning processes during the last decade. Lindfeldt [5] gives an overview of different capacity issues that were faced during the design of the new commuter line, Citybanan, through Stockholm. The evaluation method applied for the two connecting junctions has several similarities with the method presented in this article. Also in these cases, the design had to be performed subject to uncertainties about future timetables and operation. Berg von Linde [1] evaluates the unfavourable interaction of two closely located bottlenecks south of Stockholm Central station. One of these bottlenecks, WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

410 Computers in Railways XII Flemingsberg station, has only one platform track for north-bound traffic and this imposes considerable constraints on the timetable. Lindfeldt [7] also makes use of combinatorial methods to evaluate a great number of timetable variants for mixed traffic on double-track lines. He uses these methods to analyse the effect of frequencies of service, speeds and distances between overtaking stations on line capacity. Also here, the idea is to determine the interrelations between infrastructure and timetable. Schaafsma and Bartholomeus [8] present a new control concept for the Schiphol bottleneck in the Netherlands. Schiphol has several similarities with the planned Solna station. Both are located between two junctions, have several platform tracks, dense traffic and high utilisation. Several operation procedures that are already in use at Schiphol, e.g. the first come first served operation, the cross platform strategy and the stay in lane principle, will all be applicable at a future Solna station as well. These procedures can be brought to maximal efficiency if the infrastructure design is performed with them in mind. Several studies have been made of routing through existing stations and alternative methods are proposed in the literature. Hansen [3] gives a clear introduction to the complexity of train routing through stations. He compares analytical approaches based on queuing theory and max-plus algebra respectively. He concludes that these methods give similar results regarding the location of bottlenecks and the occupation of route sections. However, significant differences in the amount of buffer time and the ability of the track network to compensate for delays call for further development of both methods. Yuan and Hansen [9] propose a sophisticated method of determining station capacity indirectly through estimation of knock-on delays caused by route conflicts. Their model takes into account variations in track occupancy times due to fluctuations in train speeds, varying dwell times etc. Kroon et al. [4] face the computational complexity of the problem of routing trains through railway stations. They show that when the layout of a railway station is fixed the amount of computational time is polynomial in the number of trains. Carey and Carville [2] consider the problem of routing trains through large, busy stations. They use scheduling heuristics similar to those adopted by train planners using manual methods. They are hereby able to include rules, costs and preferences used by the expert planners. The method is similar to that described in this article since the trains are slotted one by one according to their desired arrival times. None of the reviewed papers explicitly focuses on the designing of infrastructure or how the requirements regarding station design depend on the traffic situation, which is the main objective in this article.

3 Method and modelling The design of a railway station depends strongly on operational factors such as timetable, disturbances (delays) and occurrence of shunting movements etc. This study aims to explicitly show how the timetable affects the number of tracks WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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needed at a through station that is operated with a mix of stopping and passing trains arriving from two independently operated lines. For the sake of simplicity, no disturbances are taken into account and all kinds of process times and headway times are assumed to be deterministic. All modelled trains are assumed to have approximately the same characteristics such as speed, retardation, acceleration, dwell times etc. 3.1 Infrastructure A simplified schematic track layout is shown in figure 2. Stockholm Central station is located to the left, connected to four tracks above ground and two underground tracks dedicated for commuter traffic (still under construction). This commuter line (City line) and the (existing) Mälar line are both connected to the East Coast line at Tomteboda, whereas the two new tracks for Mälar line are planned to be connected at a junction further north (to the right). Solna station is located between these junctions and the objective of this article is to find a feasible track layout for this station. Sundbyberg Tomteboda

Solna

Kista

Ulriksdal

East Coast line

Figure 2:

Infrastructure layout.

One important condition for the operation is that the two middle tracks are dedicated for commuter trains on the East Coast line, so the task is to find the number of platform tracks connected to the two-line tracks on each side of the commuter tracks in the middle. Due to symmetries in the operation and the surrounding infrastructure, it is reasonable to also assume symmetry in the station design. A

Figure 3:

B

C

D

Possible station designs. The two mid-tracks are dedicated for commuter traffic and are not evaluated in this study.

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412 Computers in Railways XII Figure 3 shows four possible configurations with one, two and three platform tracks for non-commuter trains in each direction. Depending on the number of passing trains, it might be feasible to construct tracks without platforms, alternatives A and C. 3.2 Timetables and timetable generation The station design depends on several timetable factors, mainly the number of trains operated on each line per time unit. These numbers are unknown, or uncertain, during the planning process. The situation is made even more difficult since neither the distribution between stopping and passing trains can be predicted. The method described below is one way to evaluate how different timetables affect the number of platform tracks needed. Combinatorial methods are used to generate all possible timetable variants that follow from a few basic assumptions. This is done in two steps. First, a traffic situation is defined by four factors:  Total number of trains/h on East Coast line.  Number of stopping trains/h on East Coast line.  Total number of trains/h on Mälar line.  Number of stopping trains/h on Mälar line. Several timetable variants may correspond to each traffic situation. These timetable variants arise because:  Stopping trains on the East Coast line can be chosen in different ways from the total number of trains on this line.  Stopping trains on Mälar line can be chosen in different ways from the total number of trains on this line.  The phase shift between the timetables for the two lines can be varied. For a given traffic situation the total number of timetable variants is given by:  N EC   N M  *  N timetable     n EC   nM

 N EC ! NM !  * f  * *f n EC !( N EC  n EC )! nM !( N M  nM )! 

(1)

In eqn (1) ni denotes the number of stopping trains and Ni the total number of trains on a line i. f is the number of phase shifts between the timetables of the two lines. Different traffic situations give rise to different numbers of timetable variants. Based on demand forecasts and experience from earlier operation, four basic assumptions were made in order to limit the evaluation space:  Total number of trains on East Coast line: 14-18 trains/h.  Total number of trains on Mälar line: 4-8 trains/h.  The traffic pattern is repeated every 30 minutes and so the period of evaluation is limited to 30 minutes. This also implies that 30 different relative time shifts between the timetables of the lines appear, f = 30 in eqn (1).  Arriving trains are evenly spread on each line. Table 1 shows the number of timetables that arise in each traffic situation when these assumptions are combined with different numbers of stopping trains. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Each cell corresponds to a traffic situation. The first column shows a pair of numbers on each row. These are the total number of trains/h on the Mälar line and how many of these stop at Solna station. In the same way, the lowest row shows the corresponding data for the East Coast line. It can be seen that each traffic situation consists of 30 – 15 120 timetable variants.

Mälar line

Table 1:

Number of timetable variants for different traffic situations. 8 8 8 4 8 0 6 6 6 4 6 2 3 0 4 4 4 2 4 0

30

630

630

30

30

180

3780 3780

180

180 12600 180

180 15120 15120 180

30

630

630

30

30

2100

30

30

2520 2520

30

30

630

630

30

30

2100

30

30

2520 2520

30

90

1890 1890

90

90

6300

90

90

7560 7560

90

90

1890 1890

90

90

6300

90

90

7560 7560

90

30

630

630

30

30

2100

30

30

2520 2520

30

630

30

630

60

1260 12060

30

630

630

14 0

14 4

14 10

2100

30

30

2520 2520

30

30

30

2100

30

30

2520 2520

30

60

60

4200

60

60

5040 5040

60

30

30

2100

30

30

2520 2520

14 14

16 0

16 8

16 16

18 0

18 6

18 12

30 18 18

East Coast line

3.3 Capacity allocation procedure Each timetable variant implies a unique pattern of station capacity that is required for conflict-free operation. The utilisation of each platform track is heavily dependent on the headway times that are applied during timetable construction. Ideally, these times should be chosen with regard to the prevailing delay level and the acceptance for knock-on delays (Yuan and Hansen [9]). For the sake of simplicity, the values below are applied in this study. They correspond to values commonly used in Swedish planning (Berg von Linde [1]).  Minimum timetable headway times: o 200 s outside platform block sections. o 300 s on platform block sections after stopping trains. o 200 s on platform block sections after passing trains.  Reaction time and time supplement for acceleration for stopping trains is 60 s. This time is only applied when a stopping train is followed by a passing one. Using these headway times the number of platform tracks can be estimated for each timetable variant through a direct track allocation procedure programmed in MATLAB. The trains are simply assigned to platform tracks in the order they arrive (first in first served), cf. Carey and Carville [2]. The model endeavours to choose the lowest available track number which results in efficient utilisation and a minimum number of tracks. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

414 Computers in Railways XII This type of capacity allocation was performed both for the platform tracks and for the exit tracks. It is important to also check the exit tracks since some timetable variants might result in an exit flow of trains that requires more than two line tracks beyond the station.

4 Results Different performance evaluations are possible. The calculations result in a discrete distribution of tracks needed for each traffic situation. These distributions might most conveniently be represented by mean and standard deviation measures. Together they give an idea of the required number of tracks for each traffic situation. The exact track utilisation can also be plotted for traffic situations of special interest. Such a plot shows both the number of tracks needed for each timetable variant and their degree of utilisation. Throughout the study all station tracks have been modelled as platform tracks. It is therefore impossible to tell whether some of the tracks can be constructed without platforms, i.e. for passing trains only, or not. Such an analysis requires additional modelling. 4.1 Platform tracks Table 2 shows the mean number of required platform tracks for the studied traffic situations. Bear in mind that the calculation includes only tracks dedicated for long-distance and regional traffic in one direction. The entire design is given by symmetry assumptions and the fact that two mid-tracks are dedicated for commuter traffic. The values in table 2 are mean numbers of tracks needed for the timetables that originate from each traffic situation. For example, the traffic situation

Mälar line

Table 2: 8 8 8 4 8 0 6 6 6 4 6 2 6 0 4 4 4 2 4 0

Mean number of required platform tracks. 2

2.96

3

3

2

2.99

3

2

2.90

3

2

2.81

3

2

2.84

2

3

3

3

2

2.99

3

3

2

2.99

3

3

3

2

2.99

3

2

3

2

2.99

3

3

2

2.98

3

3

2.96

3

3

2

2.78

3

3

2

2.98

3

2

2.94

3

3

2

2.73

2.99

3

2

2.97

3

2

2.91

3

3

2

2.67

2.99

3

2

2.96

3

2

2.85

2.99

3

2

2.63

2.98

3

2

2.90

3

2

2.85

2.99

3

2

2.56

2.95

3

2

2.88

3

2

2.78

2.98

3

2

2.48

2.91

3

2

2.81

3

2

2.71

2.96

3

14 0

14 4

14 10

14 14

16 0

16 8

16 16

18 0

18 6

18 12

18 18

East Coast line

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14 6 + 4 4

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needs 2.78 platform tracks. This means that two tracks are enough in 22% of the available timetables for this traffic situation, whereas three tracks are needed in 78% of the timetables. In this traffic situation it is reasonable to believe that a two-tracked station would impose restrictions on the timetable or cause scheduled delays due to lack of station capacity. The table shows several interesting results. First, no timetable variant in the 110 examined traffic situations needed fewer than two or more than three platform tracks. The design question is therefore limited to a choice between two and three tracks/direction. It is also clear that stopping trains on the East Coast line are those who impose a need for a third track. Traffic situations with a mean lower than 2.75 tracks are marked in the table. These are borderline cases where either two platform tracks or two platform tracks and one passing track without a platform could be considered. These alternatives imply lower investment costs at the cost of additional timetable constraints and/or scheduled delays. The validity of the calculated values provides that all conditions and assumptions are correct. The most important assumption is probably that each platform track can be utilised every 300 seconds. Such operation requires relatively high punctuality. Under Swedish circumstances, with long delays and low punctuality, the presented values for the number of tracks needed are rather underestimations. For traffic situations of special interest it is also useful to study the track utilisation in detail. One such example is shown in figure 4. Since the model systematically chooses a lower track whenever possible, the utilisation will always be highest for track 1 and lowest for track 3. Note that there are timetable variants (~40%) that do not need a third track. For these timetables the utilisation is higher for track 1 and/or 2. 1 0.9 Track 1

0.8

Track utilisation

0.7 0.6

Track 2

0.5 0.4 0.3 Track 3

0.2 0.1 0

Figure 4:

0

100

200

300 400 Timetable variant

500

600

Track utilisation for timetables originating from the traffic situation 14 4 + . 4 4

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416 Computers in Railways XII 4.2 Exit tracks A station consists of platform tracks and entrance and exit track sections leading into and out of the station. The station analysed in this article is actually a rather simple through station located between two junctions. The number of entrance tracks is the same as the number of connecting lines. This fact, together with relatively low utilisation of the Mälar line, indicates that the conflicts on the entrance side will be limited and that it is reasonable to assume all arrivals to be conflict-free. Due to the mix of stopping and passing trains the situation will be different on the exit side of the station. On this side, the traffic flow will be less regular and so conflicts may occur that need a third track to be resolved. It is therefore also of interest to check the exit capacity. Table 3 shows the share of timetable variants within each traffic situation that could be scheduled conflict-free with only two exit tracks. Two exit tracks are enough in cases where all trains stop or all trains pass. This is reasonable since the exit flow of trains will then be identical to the entrance flow. Share of timetable variants where two exit tracks are enough. Traffic situations lower than 0.20 marked.

Mälar line

Table 3:

8 8 8 4 8 0 6 6 6 4 6 2 3 0 2 2 2 1 2 0

1

0,37

0,36

1

1

0,1

1

1

0,13

0,18

1

0,2

0,12

0,15

0,31

0,17

0,13

0,33

0,17 0,097

0,1

0,33

1

0,25

0,06

1

1

0,056

1

1

0,083 0,034

1

1

0,5

0,53

1

1

0,26

1

1

0,31

0,23

1

1

0,5

0,46

1

1

0,33

1

1

0,31

0,32

1

1

0,47

0,36

1

1

0,29

1

1

0,29

0,27

1

1

0,41

0,26

1

1

0,26

1

1

0,31

0,23

1

1

0,63

0,64

1

1

0,54

1

1

0,49

0,51

1

1

0,6

0,52

1

1

0,48

1

1

0,48

0,45

1

1

0,59

0,46

1

1

0,48

1

1

0,48

0,43

1

14 0

14 4

14 10

14 14

16 0

16 8

16 16

18 0

18 6

18 12

18 18

East Coast line

Low values are shown for traffic situations with a mix of stopping and passing trains. Some of these will hardly manage without a third exit track or added scheduled delays through extended dwell or passing times. The conclusion is that combinations where half of the trains from both lines stop are the most difficult to schedule with only two exit tracks. However, serious problems do not occur until the Mälar line is operated by more than 6 trains/h.

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417

Conclusions and further work

This article proposes a heuristic approach to find a feasible design for a railway station whose future operation, i.e. number of stopping and passing trains, is uncertain. The results show that three platform tracks/direction are needed to accommodate long-distance and regional traffic, even for moderate traffic intensities. Further studies are recommended to determine whether one of these platform tracks could be replaced by a track without a platform, to be used by passing trains only. A separate analysis of the sensitivity to assumed headway times is also to be considered. The modelling of timetables could be extended to also take into account less regularity in the arrival processes. Such timetables are more realistic due to speed differences between trains, operation of adjacent bottlenecks etc. The meshes that connect the platform tracks to in- and outgoing lines could also be further evaluated. Finally, a station design that is hereby found to be feasible should also be evaluated with respect to delay propagation and disturbances.

Acknowledgements The analyses presented in this article were performed by the Royal Institute of Technology (KTH) as a consultancy assignment for the Swedish Rail Administration (Banverket). It is part of the planning process for the extension of the Mälar line.

References [1] Berg von Linde O., Projekt Tegelbacken – en kapacitetsbetraktelse, Tåg Otto HB Rapport 2002-19, 2002. (in Swedish) [2] Carey, M., S. Carville, Scheduling and platforming trains at busy complex stations, Transportation Research Part A 37, pp. 195-224, 2003. [3] Hansen, I.A., Station capacity and stability of train operations, In: J. Allan, R.J. Hill, C.A. Brebbia, G. Sciutto & S. Sone, (eds.), Computers in Railways VII, pp. 809-816, WIT Press, Southampton, 2000. [4] Kroon, L.G., H.E. Romeijn, P.J. Zwaenveld, Routing trains through railway stations: complexity issues, European Journal of Operational Research 98, pp. 485-498, 1997. [5] Lindfeldt, O., Train traffic in greater Stockholm. The demand for a new twin track railway through Stockholm. In: M.C. Ford (ed.), Proceedings of Railway Engineering 7th International Conference and Exhibition, London, Great Britain, 2004. [6] Lindfeldt, O., Evaluation of punctuality on a heavily utilised railway line with mixed traffic, In: Allan, J., Arias, E., Brebbia, C.A., Goodman, C.J., Rumsey, A.F., Sciutto, G., Tomii, N., (eds.), Computers in Railways XI, pp. 545-553, WIT Press, Southampton, 2008. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

418 Computers in Railways XII [7] Lindfeldt O., Analysis of capacity on double-track railway lines, Transport Planning and Technology. In press, 2009. [8] Schaafsma, A.A.M., M.M.G.P. Bartholomeus, Dynamic traffic management in the Schiphol bottleneck, In: I.A. Hansen, F.M. Dekking, R.M.P. Goverde, B. Heidergott, L.E. Meester (eds.), Proceedings of 1st International Seminar on Railway Operations Modelling and Analysis, Delft, The Netherlands, 2005. [9] Yuan, J., I.A. Hansen, Optimizing capacity utilisation of stations by estimating knock-on train delays, Transportation Research Part B 41, pp. 202-217, 2007.

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The simulation of passengers’ time-space characteristics using ticket sales records with insufficient data J.-C. Jong & E.-F. Chang Civil & Hydraulic Engineering Research Center, Sinotech Engineering Consultants, Inc, Taiwan

Abstract It is a very common approach for any business to analyze their historical sales records to adjust operation strategy. Recently, Taiwan Railway Administration, a government-owned railway operator, faces serious competitions from other transportation systems. It becomes very urgent for the operator to modify its timetables to meet demand patterns for increasing revenue. The key issue is how to estimate passengers’ time-space characteristics. For railroads with advanced automatic fare collection systems and simple service patterns, the estimation of passenger flow may not be difficult since detailed travel information is available. However, for systems with mixed traffic and insufficient ticket sales records, it requires a scientific method to deduce actual travel patterns from limited information. This study tried to establish such a model to reconstruct the timespace distribution of passenger flow. The model has been applied to Taiwan Railway Administration to estimate passenger flow. The result is very useful for decision makers to assess the utilization of train capacities and to adjust service plans, such as adding/deleting train services, changing stopping patterns, or modifying service termini. The proposed model can be applied to other railroads with mixed traffic operations and insufficient ticket sales records. Keywords: passenger flow estimation, ticket sales records.

1 Introduction Taiwan Railway Administration (TRA) is the oldest railway operator that provides intercity, regional and commuter train services in Taiwan. In early years when TRA was the only inter-city transportation provider, timetable preparations WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100391

420 Computers in Railways XII were production-oriented. TRA provided transportation services mainly according to resources availability (e.g., trains and crews). Passenger demand was only a small issue in TRA’s consideration list. During last few decades, several transportation systems such as freeways and high-speed rail commenced their services successively. These new transportation systems were strong competitors for TRA. Recently, TRA attempted to employ marketing-oriented approach for preparing more attractive timetables to increase revenue. To do this, TRA should accurately estimate the demand characteristics of its customers, including boarding time, origin station, and destination station. In general, three different approaches are often used to estimate passenger demand, including marketing research, transportation planning model, and historical data analysis [3]. Among them, historical data analysis has great potential for accurately characterizing passengers’ travel behaviour in a railway system with a relatively low cost. The cost for collecting data decreases as the computerization level of ticket sales systems increases. For a modern computerized ticketing system, the sales record log is automatically generated every day. Consequently, many researches prefer this approach for estimating passenger demand. However the ticketing systems in TRA are not as advanced as the Automatic Fare Collection (AFC) systems in modern urban transit systems. Currently, TRA accepts four different kinds of tickets. Only tickets with designated train numbers have detailed travel records. The others have limited information, such as lack of train number or exact time to enter/leave the railway system. This study aims at developing a simulation model and a computer program to reconstruct passengers’ time-space characteristics. The model estimates passenger walking time and assigns each passenger to an appropriate train based on limited information stored in the sales records. When the simulation is completed, the number of passengers on each train and each railway section can be calculated. Capacity insufficiency or service oversupply can also be identified. The computer program provides several 2D and 3D charts to display the time-space transitions of passenger flow. The resulting information is very useful for railway operators to adjust their train service plans and timetables to increase revenue.

2 Literature review Analysis of historical ticketing records can be used to investigate passengers’ travel behaviour in a railway system. Previous studies usually apply this method to urban railway systems where detailed system logs of ticket gates are available. For example, Myojo [4, 5] proposed a model to estimate passenger flow in a large and complicated urban railway network using origin-destination (OD) matrix data from ticket gates. The passengers’ route choices (including trains and train lines) were determined by a logit model (A similar approach can also be found in Hirai and Tomii [2]). To verify the proposed method, the study compared the estimated results with the number of passengers reported by train conductors who used a visual count. The correlation coefficients between them WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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are about 0.95~0.83. Nagasaki et al. [6] also proposed a similar approach to estimate passenger flow but formulated passengers’ route choices as a shortest path problem. Nagasaki’s model considered not only journey time and transfer barrier factors but also congestion factors. In addition, his model also evaluated train schedules from the viewpoint of passengers. Note that both Myojo’s and Nagasaki’s studies used aggregated OD matrix data without exact arrival/departure time for each passenger. Therefore, both models cannot reproduce the detailed time-space characteristics of passenger flow. In contrast to the macroscopic models proposed by Myojo [4, 5] and Nagasaki et al. [6], Barry et al. [1] proposed a microscopic methodology to estimate OD tables from the AFC records of MetroCard in New York City. The results were used for other purpose such as traffic assignment in transportation planning model. Zhao and Rahbee [7] also used AFC data to analyze the behaviour of each passenger and integrated the records with Automatic Data Collection system and Automatic Vehicle Location system to estimate rail passenger OD matrix. Both studies did not estimate passenger flow on rail links and trains. Since TRA is not equipped with advanced AFC systems, the information stored in the ticketing systems is insufficient. In addition, TRA provides train services with different classes, each of which has different stopping patterns and operation speeds. Even in the same class, the stopping patterns and service termini for different trains are not identical. Consequently, the models found in the literature cannot be directly applied to TRA. To overcome the problem, this paper introduces a microscopic simulation model to estimate passenger flow with insufficient data and mixed traffic. When the simulation is completed, detailed information about the number of onboard passengers and the flow on each section during different time intervals can be estimated. The following section will introduce the features of TRA ticketing records. The detailed model is discussed in section 4.

3 Features of TRA ticketing records In early years, TRA used a manual approach to deal with ticket transactions. All activities including ticket sales, checking, and inspection were performed by TRA staff. At that time there were no electronic records. In the past few years, TRA installed several ticketing systems, including booking systems, ticket vending machines, ticket gate systems, etc. However, these systems are designed mainly for accounting purpose. The information stored is insufficient to analyze passenger behaviour. Since the study aims to precisely estimate passenger flow, it is important to understand the status and limitations of the ticketing records in TRA before developing the model. There are many types of tickets in TRA. They can be divided into two major categories. The first ticket type has a designated train number. Passengers with these tickets are only allowed to board a specific train as the train number marked on the tickets. For convenience, these tickets are referred to as designated tickets. Note that designated tickets do not imply that seats are WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

422 Computers in Railways XII reserved. Passengers may purchase designated tickets without reserved seats at lower prices. The second ticket type has no designated train number. Passengers with these tickets can board any trains of the same train class as shown on the tickets. These tickets are called non-designated tickets. In general, designated tickets are only used for express trains that provide intercity services. Designated tickets with reserved seats can be purchased two weeks before the train service and seats can be booked at the same time. Nondesignated tickets are used for any trains, such as commuter trains, local trains, and even express trains. Non-designated tickets have several variations, including one-way tickets and prepaid tickets. Both smart cards (RFID based) and season tickets (magnetic based) are prepaid tickets. Figure 1 shows the classifications of TRA tickets. The information stored in each ticket will be introduced in the following two sections.

Figure 1:

Classifications of TRA tickets.

3.1 Electronic records of designated tickets Electronic records of designated tickets exist in ticket sales and gate systems. Trip information stored in ticket sales system includes the date of train service, train number, origin station, destination station, etc. Gate system records the time when the passenger passes through ticket gates. TRA provides several convenient ways for purchasing designated tickets. The process for purchasing tickets can be divided into three stages: booking, payment, and ticket pick-up. Transactions at each stage are all recorded. Figure 2 shows the flow chart for purchasing designated tickets. The simplest way to purchase a designated ticket is via ticketing offices or vending machines at stations since the three stages can be finished at the same time. If passengers book tickets through internet or phone voice, they have several options to pay for the tickets. The method of payment will decide how to pick up their tickets. The transaction records in internet/phone voice booking system, ticket vending machine system, ticketing offices and post offices were all collected for the study. The main feature of designated tickets is that the train number is determined once the ticket is booked. Therefore, it is easy to find out when and where the passenger gets on and gets off the train. Note that the records of cancelled tickets must be taken into account because tickets may be cancelled by passengers for any reasons at any time before the train departs. If the model ignores the logs of cancelled tickets, passenger flow will be overestimated. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 2:

423

Flow chart for purchasing designated tickets.

3.2 Electronic records of non-designated tickets A passenger with a non-designated ticket is allowed to board any train of the same class as shown on his/her ticket. The trip information stored in the sales records only consists of train class, origin station, and destination station. Due to lack of train number, it is difficult to judge which train the passenger actually boards. This study attempted to estimate the train number by other information. The information depends on the type of the non-designated ticket and is illustrated below: (1) One-way Ticket: One-way tickets can be purchased via ticketing offices or ticket vending machines at stations. A passenger with a one-way ticket is allowed to board any train of the designated class once on the transaction day. Two records may be useful to guess the train that the passenger should take. The first one is the ticket transaction time and the other one is the time stamp record when the passenger passes through a magnetic ticket gate. Unfortunately, not all stations in TRA are equipped with ticket gates. In such circumstances, passengers must show their tickets to station crews in order to enter the paid area. Even at stations where ticket gates are installed, passengers are not required to go through them. Therefore, transaction time is the only reliable reference to estimate the train that a one-way ticket passenger should board. Note that the purchase log and the cancel log may exit in different ticket sales systems. For example, a passenger may purchase a non-designated ticket via a vending machine and then cancel the ticket through a ticketing office. Accordingly, the serial number of the nondesignated ticket is the only key to trace the two databases. (2) Season Ticket: Season ticket is a kind of prepaid ticket. A passenger with a season ticket is admitted to board any trains fifty times within two months for a specific origin and destination pair. The passenger is required to enter/leave paid areas through magnetic ticket gates. Thus, the time stamps and the station names are the keys for estimating passenger flow. (3) Smart Card: Smart Card is also a kind of prepaid ticket. It is based on RFID technology and has its own gates. The characteristics of Smart Cards are

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424 Computers in Railways XII similar to season tickets. For some reasons, the electronic records in Smart Card system have no time stamps when passengers enter stations. 3.3 Summary Since the ticketing systems in TRA are designed mainly for accounting purpose, the electronic records are insufficient for estimating passenger flow. This study surveyed other electronic records in ticket gate system to supplement the information. Table 1 summarizes the important data used in this study for estimating passenger flow. Records for designated tickets and one-way tickets are from ticket sales system. “Quantity” is the amount of tickets in one purchase. If the value is negative, it means that those tickets have been cancelled. Records for season tickets and smart cards are collected from the ticket gate system. Thus, there is no “quantity” or “ticket cancellation” information. In general, train number provides precise information about the train that a designated ticket passenger intends to board. Thus, designated tickets have sufficient information to estimate passenger flow under the assumption that all passengers follow TRA’s regulations. Secondly, we can deduce what train a season ticket holder may take based on actual train schedules and the time stamps of passing through ticket gates. The accuracy rate of determining the boarding train for a smart card record is lower than that for a season ticket record since the entering time for the smart card holder is not recorded. Finally, the estimation of the boarding train for a one-way ticket record is the least precise since only transaction time information can be used to judge which train the ticket holder may take. Table 1:

Useful information for different ticket types.

Designated Ticket Origin station Destination station Train number Train departure time Quantity

One-way Ticket Season Ticket Smart Card Origin station Origin station Origin station Destination station Destination station Destination station Transaction time Leaving time Entering time2 Item Train class Leaving time3 Quantity Serial number1 1. “Serial number” is used to trace the records of cancelled tickets. 2. “Entering time” is the time when a passenger enters the paid area through a ticket gate at his/her departure station. 3. “Leaving time” is the time when a passenger leaves the paid area through a ticket gate at his/her arrival station.

4 Simulation model This study developed a simulation model to estimate passenger flow characteristics based on the limited information summarized in Table 1. The assumption and the framework of the proposed model are explained in this section. Some important issues for the simulation process are also addressed.

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4.1 Assumption The assumptions for the proposed simulation are summarized below: (1) The rail network in TRA is not complicated. The great majority of trips have only one route between origin and destination stations. The model assumes that passengers always choose a train that stops at both their origin and destination stations without any transfers. (2) A passenger who boards a train without any ticket is prohibited. Such illegal behaviors should not happen frequently. These trips can not be counted since there are no electronic records for illegal passengers. (3) It is also an illegal behaviour that a passenger purchases a train ticket with lower class and then boards a higher class train. The model assumes that passengers always board trains according to their tickets. (4) Passengers with one-way tickets will arrive at platform and prepare to board a train within a certain time window after purchasing the tickets. (5) Passengers with season tickets will arrive at platform and prepare to board a train within a certain time frame after passing through magnetic ticket gates. (6) Passengers with smart cards will take trains that arrives their destination stations within a certain time window before they leave the stations. 4.2 Framework Figure 3 shows the framework of the simulation model. The proposed model consists of two major components: Train Traffic Simulator (TTS) and Passenger Flow Simulator (PFS). The first one simulates the movement of trains. The second one simulates the flow of passengers. To make the model widely applicable, the TTS accepts two different types of inputs: the planned timetable and the actual train schedule. The planned timetable is easier to collect than the actual train schedule, but the assignment of passengers to trains for the former is less precise than that for the latter. If train punctuality is close to 100%, the simulation results will be similar. The PFS extracts useful information from various databases and combines them into four tables. The trips of cancelled tickets must be deducted from normal trips to avoid overestimation. Passengers must board a train to move from their origin stations to destination stations. The process to assign each passenger to an appropriate train is the core of the simulation. Passengers with designated tickets are assigned to the designated train. The assignment of passengers with non-designated tickets is more complicated. The study defined “walking time” to represent the time for a passenger to walk to platform after purchasing a ticket or passing through the entry gate. The model employs a uniform distribution to generate walking time for each passenger. Actual value will be determined by parameters a and b in Equation (1). Wi  U (a, b) (1) where Wi =the walking time of passenger i a = minimum walking time to platform b = maximum walking time to platform WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

426 Computers in Railways XII

Figure 3:

Framework of the simulation model.

For one-way tickets and season tickets, the example of assigning a passenger to an appropriate train is shown in Figure 4. Assume that a passenger purchases a ticket from station D to station B at T1 . The model then generates walking time and adds it to T1 . Let T2 be the time when the passenger arrives at platform. The model will search forward for the first train of the recorded train class that stops at both stations D and B, and then assigns the passenger to the train. For example, the departure time of train 1 is earlier than the arrival time of the passenger at the platform of station D. It is impossible for the passenger to board train 1. Train 2, train 3 and train 4 do not stop at both stations B and D. Thus, train 5 is the most likely train that the passenger may board in this example. The assignment of passengers to trains for smart card tickets is similar to that for one-way tickets and season tickets. However, the entering time of smart cards is not recorded. Thus, the model will search backward for an appropriate train from the time that a smart card holder leaves his/her destination station. The concept is shown in Figure 5. In this example, train 1 is the most likely train that the passenger may take. When all passengers are assigned to trains, the number of passengers on each train and each rail section can be calculated. In addition, every train has detailed time and space information. Passenger flow at any time and any space can be estimated. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Twalking

T1

T2

Figure 4:

Train assignment for one-way tickets.

Twalking

T2

Figure 5:

T1

Train assignment for smart card.

5 Case study This study developed a computer program and used real data from TRA to demonstrate the proposed model for estimating passengers’ time-space characteristics. The input data and simulation result are explained as follows: 5.1 Input data Since the operation control center in TRA did not have output module at the time the study was carried out, the actual train schedules were not available. Instead, the study employed the planned timetables as the input data. The sales records and timetables on January 4 and 5, 2009 were used for the case study. There were about 220 stations, 800 trains, and 400,000 records per day in TRA.

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428 Computers in Railways XII 5.2 Simulation result The program provides three different kinds of 2D and 3D charts to illustrate the simulation results. Basically, there are two types of passenger flow estimated by the model, i.e., node flow and link flow. The analysis of node flow focuses on the number of passengers that enter and leave stations, while the analysis of link flow focuses on passenger flow through each rail section. Figure 6 displays the estimated hourly passenger inflow and outflow at Taipei station (the busiest station in TRA). This information is quite useful for planners to figure out the distribution of passenger flow and to identify the peak hour for Taipei station.

Figure 6:

Hourly passenger inflow and outflow at Taipei station.

The number of onboard passengers is a key to estimate section flow. An example of passenger volume on a train along its journey is depicted in Figure 7. With this figure, the transition of passenger volume along its journey can be recognized. Figure 7 shows not only the total passenger volume, but also the components of passengers. According to the definitions in TRA, a trip shorter than 50 km is classified as a short-distance trip. If the length of a trip is between 50 km and 200 km, it is classified as a middle-distance trip. A trip whose length is more than 200 km is defined as a long-distance trip. The figure can be used to locate the Maximum Load Section (MLS) and to find out the trip combinations of different lengths on the MLS. The information may help railway operator plan and modify their train services to satisfy passenger demand. For example, providing shorter distance trains if the passenger volume near the two ends of the rail line is too low. Figure 7 only displays the passenger volume on a specific train. To explore the variations in section flow, the study aggregated onboard passenger flows for all trains, section by section and hour by hour. In other words, onboard passenger volumes of the same section in the same hour were added together. The results are shown in Figure 8. The x-axis, y-axis, and z-axis represent space, time, and hourly passenger volume, respectively. This 3D chart clearly illustrates passenger flow on each section in each hour. Figure 8 also shows significant difference of passenger demands between weekday and weekend. This 3D chart may assist planner to realize passengers’ time-space characteristics.

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N

er

e ger

r

On board passengers on train #1015 along TRA rail section

七 汐汐南 台萬 樹 山 鶯桃 內中 埔 楊富 湖新 竹 新香 崎竹 談大 後 龍 新通 苑 日大 台清 沙 龍大 追彰 花 大員 永社 田 二 堵 福堵止科港 山 華 橋林 佳 歌園 壢壢 心 梅岡 口豐 竹山頂南 文山龍 港沙埔霄裡 南甲中水 鹿 肚分 壇 村林 靖頭 中 水 屯 港 S i

Long distance trip

Figure 7:

Middle distance trip

Short distance trip

Onboard passenger volume of a specific train along its journey.

(a) Weekday Figure 8:

(b) Weekend

3D time-space distribution of passenger flow.

6 Conclusion and suggestion In early years, TRA assembled all station masters to hold a meeting for discussing how to modify timetables once a year. The decisions were based on their experiences without quantitative evaluation methods. Moreover, station masters only understood passenger flow at their stations. Passenger flow between adjacent stations was not supervised by station masters. The primary objective of the study is developing a simulation tool to estimate passenger flow in a rail network with insufficient ticket sales data. Through the proposed model, railway operators can calculate the hourly passenger flow at each station, the onboard passenger volume for each train, the passenger flow on each section, and the 3D time-space distribution of passenger flow. The simulation results are very useful for decision makers to adjust their train services to increase revenue. The proposed methodology can be applied to other similar railway systems with mixed traffic and different types of tickets. Analysis of historical sales records has a restriction that it can not consider potential demands because the analysis result may be influenced by historical timetables. For example, if the passenger inflow at a station is low, the reason may be no demands or no suitable train services. Therefore, the analysis of WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

430 Computers in Railways XII historical sales records is just a reference only. To investigate potential demands, it is suggested that railway operator collect and analyze the log of booking failure. The reason of booking failure may be no available seats or even no train services at the preferable time for the OD pair of a potential customer. The information will be a useful reference to decide whether to increase train capacities or to add train services for increasing revenue.

References [1] Barry, J. J., Newhouser, R., Rahbee, A., and Sayeda, S., “Origin and Destination Estimation in New York City Using Automated Fare System Data”, Proceedings of the 2001 TRB Planning Applications Conference, Corpus Christi, Texas, 2001. [2] Hirai, C. and Tomii, N., “An Estimation Method of the Number of On-board Passengers Applicable to Evaluation of Traffic Rescheduling Plans”, Quarterly Report of RTRI, Vol. 42, No. 4 pp.195-200, 2001. [3] Jong, J. C. and Suen, C. S., “A Train Service Planning Model with Dynamic Demand for Intercity Railway Systems”, Journal of the Eastern Asia Society of Transportation Studies, Vol. 7, pp. 1598-1613, 2007. [4] Myojo, S., “Daily Estimation of Passenger Flow in Large and Complicated Urban Railway Network” Proceedings of the 7th World Congress on Railway Research, 2006a. [5] Myojo, S., “Method to Estimate Passenger Flow Using Stored Ticket Gate Data”, Quarterly Report of RTRI, Vol. 47, No. 4 pp.178-181, 2006b. [6] Nagasaki, Y., Asuka, M., Koyama, K., “A Fast Method for Estimating Railway Passenger Flow”, Computers in Railways X, pp. 179-187, 2006. [7] Zhao, J. and Rahbee, A., “Estimating a Rail Passenger Trip OriginDestination Matrix Using Automatic Data Collection Systems”, ComputerAided Civil and Infrastructure Engineering, p376-387, 2007.

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Headway generation with ROBERTO A. D. Middelkoop ProRail Rail Development, The Netherlands

Abstract A traditional and expensive solution for bottlenecks in a railway network is to build more infrastructure. To handle future growth of passenger and freight transport demand, the Dutch rail infra manager ProRail is looking for alternative ways to solve capacity and quality bottlenecks. One of the ideas is to evaluate and improve the timetable development process. By applying design principles and by other conditions, buffer times are used in different timetable construction phases. It is not clear whether and where the use of buffer times may cause a double claim on capacity. An important design principle is the use of headway times to separate two trains in the timetable safely. The specific values for headway situations are mostly unknown. Planners use general values, based on their experience. The current timetable planning tools require headway times as input data. Given the large number of potential train combinations, it is almost impossible to know every headway time before timetable construction starts. To improve the knowledge and application of headway times, ProRail started the development of ROBERTO, a tool for generating a large number of headway times. The aim is to compute headway times for specific situations and to determine general headway times for use on a more global level. The input for ROBERTO is generated by simulating train characteristics, block section occupation times and signalling aspects. ROBERTO combines possible conflicting train pairs and calculates the headway times. All results are fed into the planning systems and the effect on capacity and quality will be evaluated. Keywords: timetable design, headways, simulation.

1 Introduction This paper describes the development of a new planning support tool ROBERTO, for generating a large number of headway times. After a short introduction of the Dutch Rail Network and its challenge for the near future in WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100401

432 Computers in Railways XII Section 2, it explains the programme of Triple C in Section 3. This programme consists of a number of measures, which amongst others change the planning and the operation principles of the railway traffic. A particular part is to reallocate buffer times in the timetable. Therefore, historical and experience based headway times are not accurate enough and there is a need to improve the quality of headway times and to make them available in the timetable development and planning systems. Section 4 describes the approach to develop the new tool, Section 5 describes the first application on an important railway corridor in the network. The paper ends with conclusions and future activities

2 The Dutch rail network The size of the infrastructure of the Dutch railway network is moderate compared to most other European countries. The length is 2800 km and it contains about 6500 km of tracks. In recent years three new infrastructure lines have been built. A new high speed line Amsterdam-Schiphol-RotterdamBelgium, a new connection between the harbour of Rotterdam and Germany (Betuweroute) and a new extra double track connection between Amsterdam and Utrecht will become available. Anticipating the availability of these capacity extensions ProRail and the Train Operating Companies (TOC) have introduced a new timetable structure in 2007. The main part of the traffic is used for passenger transport (about 85%). On a daily basis some 5400 trains carry 1.2 million passengers. Each day over 300 cargo trains transport 100 kton of freight. All trains from 29 TOC’s produced over 140 million train kilometres in 2007. Although freight transport is growing strongly, it is still a minor part (about 8%) of the total train kilometre performance. 2.1 Travelling in the near future In the near future Dutch society is facing a mobility problem. Transport demand is expected to increase. It will be difficult to reach city centres, main harbours and to establish good connections to the rest of Europe. Both passenger and freight transport might encounter loss of travel times. In addition, the railway transport demand will grow strongly, especially in the western and most urbanised part of the network (Randstad), as a result of the new timetable structure, the operation of the new lines and road congestion. Nowadays the occupation rate of the Dutch rail network is already high [1]. In the Randstad intercity trains run every 15 minutes there and regional trains run every 15 minutes, connecting the four largest cities. It will be difficult to facilitate future growth. A traditional and expensive solution for bottlenecks in a railway network is to build more infrastructure. The challenge is to find solutions that are more cost effective. ProRail, the Dutch rail infra manager has the ambition to facilitate this growth and contribute to the improvement of mobility and reachability. Therefore, ProRail has introduced a programme called Triple C. The idea is to increase railway transport by offering high frequent travel opportunities and to give WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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freight lines dedicated routes through the country. There are a number of bottlenecks to solve before the network may handle the foreseen transport volumes. The ambition is to be ready in 2012 and apply the principles on a first corridor. Depending on the market demand soon other corridors may follow. New solutions from the Triple C approach will be introduced the coming years as soon as possible.

3 Triple C project The project Triple C, where C stands for ‘Change’ (in Dutch Triple A), aims on changing or redesigning the timetable development processes. It covers capacity analysis, timetable development and operational processes. A multidisciplinary design team investigates how these processes may be changed to facilitate improvements. Furthermore, they look at conditions concerning maintenance, traffic control, safety, noise, environmental, legal and regulation issues. This new approach tries to increase transport capacity in a cheaper, smarter and quicker way and focuses on tailor made but robust solutions. It looks for ways to improve the occupation rate first before deciding to build new infrastructure. It also aims at developing innovative solutions and to organise a strong feedback from the operational level to the planning. The next section illustrates the three categories with some examples. 3.1 Changing capacity extension The measures focus on increasing track occupation rates, rail infrastructure extension and river crossings. Examples are: - Signalling block shortening - Higher passing speed for freight trains in a node - Fast overtaking situations - Opening times waterway bridges - Alternative waterway crossings - Advanced traffic management systems to optimise train traffic on punctuality, energy consumption and throughput. 3.2 Changing capacity allocation The infra manager is responsible for the optimal use of the rail and transfer capacity. In case of conflicting capacity claims of different transport companies, this should be the criterion to decide on the final timetable construction. Therefore example measures in this category are: - New or adapted regulation - Priority rules for capacity allocation - Adjust product specification like train types and train lines - New braking regulations

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434 Computers in Railways XII 3.3 Changing planning and operational processes Timetable planners use planning principles in order to construct a timetable that is feasible during operation. In reality, the structural feedback from the operation to the planning is rather new, but first results are very promising. It triggers a discussion about changing planning principles. Concept measures from this category are: - Compute and reallocate headway and buffer times - Small conflicts in planning handled by traffic control - Minimise crossing movements in big nodes - Flexible departure tracks - Support driver and traffic control with on-line information - Maintenance inspection outside the rush hour - Quicker door closing process One of the ideas from the last category is to determine the amount of buffer time in the timetable explicitly and to reallocate the buffer times. By knowing the technical minimal times exactly, the planning process becomes more transparent. The effects of buffer time reallocation and small planning conflicts on capacity and quality may be quantified better. For a lot of situations headway and buffer times are not available on an appropriate level and it is very time consuming to compute them for all situations. This has been the reason to start the development of a tool that generates a large amount of headway times: ROBERTO.

4 Improving the planning process First, this chapter gives an overview of the planning process at ProRail, including the decision support systems. The application of these tools has evolved from not only supporting long term timetable development to also support short term timetable design processes. They are used at ProRail, the Dutch rail infrastructure manager, and at NSR, the main Dutch train operating company. [5]. Then it describes which part is improved by the introduction of ROBERTO. 4.1 Planning process and tools The mid term planning process at ProRail consists of four stages. After these stages, the planning process continues with the construction of the 24-hour timetable, rolling stock, shunting and crew planning. Finally, the capacity allocation process integrates all train paths for all train operating companies. The first stage is a definition of the transport demand in terms of passenger and freight volumes, train lines, frequencies and more. The second stage is to make a definition of the expected capacity. In fact, this is a description of the infrastructure of the rail network, with an appropriate level of details for the stations and the tracks. The properties of the signalling system are incorporated by means of headway times. The third stage is to generate a countrywide feasible timetable using the CADANS-algorithm [2], which is incorporated in the DONSWIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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tool [4, 5]. In the Netherlands, such a timetable is a regular one or two hourly pattern representing a rush hour or an off-peak period. Also known as clockface pattern timetable or Taktfahrplan in German. The result is a set of records in which each train has an arrival, departure or pass through movement, at a station on a certain moment. The last step is to generate a feasible set of routes in the main stations (about 70) given the network timetable, resulting in track occupation charts. This stage uses the STATIONS algorithm [3]. 4.2 DONS approach: designing a network timetable The system DONS (Designer of Network Schedules) supports the construction of a network timetable. It translates the user input into groups of constraints, describing the relation between train events caused by running times, dwell times, headway times buffer times, passenger and rolling stock connections and generates a solution. The result is a set of records where each train has an arrival, departure or pass through movement, at a station on a certain moment. The user has option to give the solver a lot of solution space or to limit this. For instance, when the infrastructure constraints are switched off any timetable should be possible. If in this case no solution is found the market demand is inconsistent and should be changed. The system gives information which set of relations is impossible. On the other hand, the user may also start with an earlier found solution, fix the train times and ask the system to show whether new trains fit in the timetable. If not, the result of former iterations is saved and only new trains should be changed. It is an iterative approach where the tool generates feasible solutions or shows where planners should relax constraints to solve infeasibility. A feasible timetable means that there are no planning conflicts. Potential conflicts occur amongst others where two trains claim the same infrastructure elements in their path through the network. Most planners and also their timetable planning systems use a microscopic infrastructure model, including switches and the signalling system, to check for conflicts. The planned times are based on a technical minimum time added with a buffer time and rounded to minutes. The underlying network infrastructure model in DONS is on a mesoscopic level. Main elements are the tracks in the nodes, the links and how WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

436 Computers in Railways XII they are connected. It does not describe the switches and the signals, but separates succeeding and crossing train movements by headway times. Unless it is possible to specify a headway situation for a unique train combination in DONS, planners use more general definitions based on their experience and scarce historical facts. The origin and the exact values for underlying headway situations are mostly unknown. The calculation of a headway time is very time consuming, mainly caused by the manual input of infrastructure data. Given the large number of potential train combinations, it is almost impossible to know all correct headway times before timetable construction starts. This number may even grow when more variants of rolling stock combinations should be used. Therefore, in practice these planning norms are simplified to one or more general levels. With the tool ROBERTO it is possible to generate a large number of headway times automatically and to feed them into DONS and other the planning systems. The aim is to investigate whether simplification to a more general level makes sense and to decide where to use specific or general headway times.

5 Development of ROBERTO To improve the quality of the headway times and to base planning standards on real facts, ProRail started the development of ROBERTO (in Dutch this is an abbreviation of running and headway times calculation tool). The tool supports the calculation of a large number of headway times automatically. This section explains the elements of a headway calculation first, then it describes the ROBERTO development. 5.1 Calculation of a headway time Headway times describe the time distance between two trains in the timetable planning. Each train needs a free track section ahead to guarantee a safe train run. Due to the low adhesive power of the wheel rail contact (steel to steel) and the condition to have an absolute braking distance to any preceding train, braking distances in railways are relatively long compared to road transport. This distance has to be free of other trains in case of normal operation. The signalling system secures safe access to the required infrastructure ahead of a train by showing red, green or yellow aspects sometimes accompanied by a speed limit. To explain the elements that contribute to a headway time, Figure 2 shows a situation where the location of the conflicting infra element lies outside the platform area. The platform area, represented by a central line P, is the reference location for the moments recorded in the timetable. This is a situation where an arriving train 2 has to be separated from a departing train 1. The same principles hold for other combinations of arriving, departing and passing trains. Basic elements in a headway situation are:  Operation time for setting the route of train 1  Reaction time driver WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Running time train 1, from reference location P (here platform area) to point that releases the conflicting block section C.  operation/reaction time to set a new route for train 2over the conflicting element  Running time train 2, from position T, which is at approaching distance of the relevant signal to platform area P When two trains share one or more infrastructure elements in their routes, a conflict may occur in case they claim for use this element in an overlapping time interval. Train separation may be described using the blocking time model [6]. In a time-distance graph, a train path is visualised by a line, but on a more detailed level it consists of a set of block section occupation times represented by a profile of rectangles. To find a minimal headway time the second train profile has to be shifted as close to the first profile as possible. This is where the line representing sight distance for train 2 touches the rectangle profile of train 1 [Fig. 3]. In other words: shift as long as the running time of the second train is equal to the original running time. 5.2 ROBERTO The aim of ROBERTO is to compute a large number of headway times automatically for all possible pairs of trains that share one or more elements of the infrastructure. Input for the tool are running times, track element occupation times and signalling aspects relations. The computation of a headway time requires input from a microscopic infrastructure level. To realise this in a short time ROBERTO uses output from the simulation model FRISO (Flexible Rail Infrastructure Simulation model of Operations). The approach is the following:  Build a simulation model (with FRISO) containing a part of the infrastructure network  Define a set of trains, e.g. the trains of the timetable pattern, coming from planning systems WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 3:

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SN3B

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Time distance diagram with block section occupation.

Generate the running times by single simulation runs, log train behaviour and information of the signalling system.  Feed this data to ROBERTO (XML-file).  Select ROBERTO parameters and run it.  Analyse ROBERTO output  Convert and feed the ROBERTO output to the planning system DONS The data flow has the following components: IA/DONNA Company Databases Infrastructure Timetable Routes

FRISO Simulation Model Running times Track occupation Signalling aspects

ROBERTO DONS/DONNA Headway tool Timetabling system Headway times Headway times Norms, standards

The user chooses to compute all given or a selection of trains. ROBERTO makes appropriate combinations of the trains and calculates the time differences on all shared infrastructure objects. Note that for two trains following each other more objects are shared than two trains running in opposite directions, with exception of single track use. For succeeding trains, time differences on each common signal become available. The last step is to define the critical/minimal headway time and to adjust it to the reference location of the timetable. To find a minimal headway time the second train profile has to be shifted as close to the first profile as possible without changing the original unhindered running time. For inspection of the speed profile of a train there is a speed-time diagram, for inspection of the critical headway time and place there is a time-distance diagram WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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with a visualisation of the block section occupation times (Fig. 3). The planning system DONS uses headway times on different levels. For instance, there is the most global level, meaning there is a headway time valid for any train pair in any location and there is the most detailed level where a headway time for specific trains on specific locations may be defined. In total there are 5 levels of detail. The system has a mesoscopic model for the infrastructure of the network. Each station or junction is a node and each connection is a link. Both node and links know the amount of tracks inside them and which train movements are potentially conflicting. The results of ROBERTO are based on a microscopic infrastructure model. Therefore, there is an extra step/interface which converts the critical headway times to the DONS nodes and the right level.

6 Future work In a first step, the results are validated. The results and the calculation performance are promising. In a triangular part of the network between Den Bosch – Eindhoven and Tilburg (an area of approximately 60 kilometres), having 22 trains, ROBERTO finds about 1200 headway times in 20 minutes. Next step is to calculate the headway times for the main corridors and to analyse effects on capacity and punctuality performance. Finally, the tool will be connected to all planning systems, to support both timetable development and capacity allocation.

References [1] Poort, J.P.: Limits on utilization (in Dutch). NYFER (2002) [2] Schrijver, A., Steenbeek, A.: Timetable construction for Railned (Dienstregelingontwikkeling voor Railned). Center for Mathematics and Computer Science Amsterdam (1994) [3] Zwaneveld, P., Dauzere-Perez, S., Van Hoesel, S., Kroon, L., Romeijn, H., Salomon, M., Ambergen, H.: Routing Trains Through Railway Stations: Model Formulation and Algorithms, Transportation Science 30 (1996) 181194 [4] Vromans, M.J.C.M...: Reliability of Railway Systems. ERIM PhD Series Research in Management 62 (2005) 56-63 [5] Kroon, L et al.: The new Dutch Timetable, the OR revolution. Interfaces (to appear 2009) [6] Hansen, I.A., Pachl, J.: Railway Timetable & Traffic (2008)

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Development and implementation of new principles and systems for train traffic control in Sweden B. Sandblad, A. W. Andersson, A. Kauppi & G. Isaksson-Lutteman Human-Computer Interaction, Dept of Information Technology, Uppsala University, Sweden

Abstract The trend towards higher speed, more frequent traffic and many traffic operators requires new strategies and solutions for efficient train traffic control and utilization of track capacity. Operative control is today focused on controlling the infrastructure. In earlier research we have shifted the control paradigm from today’s technology oriented into a more traffic oriented one. This is done by real-time re-planning. The continuously updated traffic plan is normally executed by automated systems. After tests and evaluation in a simulated laboratory environment, the Swedish Rail Administration (Banverket) decided to develop and deploy an operative system to be installed at a traffic control centre. This system, called STEG, implements the main research results. Features of the new system are a dynamic planning view in form of a time-distance graph, decision support that helps the controller to identify disturbances and conflicts and automatic systems for execution of the traffic plan. The traffic controller can re-plan traffic (time aspects, track usage) via direct manipulation of the graph lines in the interface. Track maintenance and other activities can also be planned. The system automatically calculates all consequences of the changes and shows the effects on all trains within the actual time-distance space. A very careful process has been used to go from research results and prototypes to a fully operational system. The process has been very user centred and numerous iterations have been performed. Through this elaborate work, we have been able to ensure that the intentions of the prototypes have been correctly implemented in the final product. Keywords: train traffic control, dispatching, traffic planning, user interfaces, automatic execution. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100411

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1 Background Tomorrow’s train traffic, with higher speed, more frequent traffic, mixed traffic and many independent traffic operation companies, requires new principles and technical solutions for efficient train traffic control. Today’s control systems are often designed to support the operator’s possibilities to react on alarms, conflicts and disturbances and to solve acute problems and conflicts. However, in order to perform efficiently, operators should be able to follow the dynamic development of the traffic system over time and prevent disturbances. In order to achieve this, we must change the control paradigm from technical control of the infrastructure into higher level traffic planning tasks. This is done by replacing the traditional control commands by real-time re-planning (Andersson et al. [1], Sandblad et al. [7], Wikström et al. [9]). Advanced laboratory prototypes have successively been implemented and tested. By connecting user interface prototypes to a train traffic simulator (Sandblad et al. [8]), it has been possible to perform experiments with the design of new user interfaces and decision support tools, and to test and evaluate new control strategies for the train traffic control operators. Based on numerous laboratory experiments, a step has now been taken in order to build a fully operational system and to test and evaluate this in a real traffic control centre environment.

2 Earlier research studies Our research has been based on a very detailed description and analysis of how train traffic is controlled today, the mental models of the dispatchers and the strategies they use for decisions and control tasks. The research has consisted of mainly the following steps:  Observations and interviews with dispatchers and other professionals at the traffic control centres. Analysis of the findings and identification of problems and development areas.  Seminars with experienced and responsible professionals from the national rail and traffic control administrations. Here the visions and restrictions for future development of control systems have been specified.  Iterative specifications and evaluations with the help of a working group consisting of experienced operative traffic control professionals.  Tests and evaluations in a laboratory control room environment using a train traffic simulator system. In order to support real-time planning of train traffic we provide the traffic controller with an interactive computerized time-distance graph. Prototypes of new user interfaces that support the new control strategy have been designed, implemented and preliminary tested in the laboratory environment at Uppsala University. The interface is designed to integrate all decision relevant information into one unified interface and to support continuous awareness of the dynamic development of the traffic process (Kauppi et al. [5]). WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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The computer based time-distance graph is designed in such a way that it visually supports the operators’ situation awareness of the current status and the projection into the future (Endsley [3]). The user interface, with its planning view, can support early detection of upcoming conflicts, identify possible replanning alternatives and their predicted effects. The new control strategy has a potential to better support the traffic controller’s ability to handle continuous replanning, with the goal to always have a functional traffic plan at hand. This plan can be automatically executed except when technical malfunctions hinder this. Automatic functions that support execution of the traffic plan must be transparent, predictable and easy to understand. The automatic functions must never change the controllers’ traffic plan but are only allowed to strictly execute the actual traffic plan. The traffic plan mainly consists of time table and track usage information, including maintenance work. Detailed interface design, easy to interpret, concerning the automation helps to keep the human in-the-loop and to avoid automation surprises (Bainbridge [2]). By re-planning, the operator is in control of what the automatic function will do and when. Hence, the operator is continuously in full and active control. We have also evaluated different approaches to include decision support systems in operative train traffic control (Hellström et al. [4], Kvist et al. [6]). We have found that more advanced automated decision support systems are today not a realistic alternative of several reasons. More research and development of methods are needed in this field. We have decided to focus our efforts on supporting the controllers through better presentation of information, improved information observability and quality, help with early detection of conflicts and disturbances, identification of possibilities and limitations for replanning and evaluation of effects of alternative actions.

3 From research prototypes to an operational system Experiments with the new control strategy, operator interfaces, decision support systems and automatic execution functions have been performed in our laboratory environment with satisfying results (Sandblad et al. [10]). Many important aspects can be investigated in the simulated environment at Uppsala University, but some issues must be evaluated in a real operative environment. To work in a laboratory environment, and to control a simulated traffic system, will always mean that we have simplified the situation. The real traffic system is more complex and stochastic compared to our laboratory models. The work tasks of the controllers are also more complex and diversified then what we can create in the laboratory, e.g. concerning communication with other persons in the complex and dynamic environment. It will never be possible to evaluate all relevant aspects of the new control system in a pure simulated environment. We also face large practical and economic problems when the laboratory prototypes shall be implemented and deployed as a part of the real train traffic system. It will not be possible to develop a complete traffic management and control system only for test purposes, but we must implement the new control functionalities on top of the existing basic control infrastructure. Our prototype WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

444 Computers in Railways XII system has a focus on planning, re-planning and automatic execution of control commands. Not all other tasks are supported by functionality in our system. When malfunctions in the infrastructure hinder the automatic execution, the controllers must go back to the old control system. Thus, the new control system, control by re-planning, must for test purposes be implemented as a complementary module to the existing system. The research and technical implementation questions that we try to answer in the project are mainly:  Does the new control paradigm, principles, tools and interfaces really contribute to more efficient traffic control and a better work environment for the traffic controllers?  Is it possible to implement the new control principles and tools as an integrated part of the already existing infrastructure? What of the original ideas must be changed in order to make the implementation possible and economically realistic?  How can our research prototype support requirement specifications and evaluation for the implementation and development process?

4 The STEG project 4.1 Project phases The STEG project has been divided into several different steps or phases. On a high level the following main steps have been identified:  Benefit-cost analysis.  Risk analysis and assessment of the project as such, including backing procedures if certain parts of the project fail.  Identification of test site. A test site was selected that fulfilled a number of requirements, e.g. availability of different track structures, single track, double track, mixed traffic types, more complex stations, connections to other traffic control regions, availability of input data for track diagrams, technical specifications etc.  Requirement specification for the test system.  Several different technical investigations concerning compatibility, availability of input data, technical platform, technical performance, safety, security, communication etc.  Specification and test of control algorithms, e.g. for the automatic execution of control commands from the traffic plans.  Technical development according to specified development model, including a user centred process.  Implementation. Operative tests and evaluation. 4.2 The development phase Of special interest in a research context is the process to come from the research prototype to a fully operational system without loss of essential requirements and WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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functionality. This has successfully been achieved through a very close cooperation between researchers, designers and developers, including numerous iterations. We have earlier found that it is not so easy to create a system that to full extent meets all relevant requirements. In this project there has been a very strong focus on usability issues, minimizing the operator’s mental workload, support of the operator’s situation awareness, avoiding automation surprises etc. In order to achieve all this in an optimal way, it is not possible to supply the technical development team with a comprehensive list of requirements, and passively wait for the final system. Every step in the development process must be followed, analysed and evaluated by skilled interface designers with a deep knowledge in appropriate knowledge areas. Of course, this requires both enough time and resources together with a development team that is open to continuous iterations, tests and modifications.

5 The STEG system today The STEG system is today implemented as an additional module on top of the regular train traffic control system. This allows the traffic planner to go back to the old traditional system at any time. Via STEG, the traffic planner can continuously observe the dynamic development of the traffic within the actual track segment. The planning view in the time-distance graph is automatically scrolled downwards as time evolves. Identified conflicts with respect to track usage on the train lines or in the stations are automatically indicated in the interface. Such conflicts can now be early identified and eliminated by the traffic planner by re-planning of time table and track usage for each train involved in the conflict. Other sets of information shown in the interface are track structure, train positioning, detailed information concerning trains and stations etc. The user interface is continuously updated by dynamic data from the train traffic and signalling system. The results of all re-planning actions and the total effects of the valid traffic plans are always shown in the interface. See figure 1. Through manipulation of the time-distance graph lines, directly in the user interface, the time-table and the track usage can be re-planned whenever the traffic planner finds this appropriate. See figure 2. When the traffic plans are without conflicts, they can be automatically executed. This is done by a separate system that executes the plans exactly as they are specified by the traffic planner. By not allowing the automatic algorithms to change the traffic plans, all “automation surprises” (Bainbridge [2]) can be eliminated. The automatic execution system is “non-autonomous” and is never allowed to change the traffic plan. In earlier systems, where the automatic systems could change the plan, train order, meeting-points etc., the traffic planner often turned the automatic system off in order to avoid confusion. The human traffic planner is now always in full control of the situation.

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446 Computers in Railways XII

Figure 1:

The figure shows the united interface, including the planning view in the time-distance graph, graph lines for each individual train, the time axis, the history below the present time line, the track structure, train and station information, planned maintenance work, etc. The planned traffic can always be seen together with the original time-table lines, so that delays, etc. can be easily detected. Conflicts of different natures are also visualized and can be detected and solved early.

Figure 2:

The figure shows re-planning of a selected train. The traffic planner can easily change the arrival time, departure time or track usage for the selected train and station. This is done by the selection of a graph line and manipulation of the nodes using the mouse buttons and thumb-wheel. The new plan can be seen directly in the user interface.

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6 Preliminary tests and evaluation The STEG system has been used for operational tests at a Swedish train traffic control centre. Via STEG, traffic planners can monitor and control by replanning one segment of the main rail line from Stockholm to southern Sweden, including one double-track line, one single-track line and some less frequently used freight traffic lines. The experiences so far, when the test period is not finished, are mixed. On one hand, the system works according to the intentions and requirements. On the other hand, a number of different problems have appeared which have made the tests somewhat problematic. We have found that the basic concept, control by re-planning in real time and automatic execution of the traffic plans, is working in practice and is accepted by the traffic planners. However, we have also been faced with a number of problems of a more practical nature. Some of the more important problems and obstacles, which have disturbed the tests so far, are:  Technical errors in old interlocking systems, difficult and expensive to eliminate.  The user interface should show more relevant states of the automatic execution system. Otherwise the traffic planner will not be able to predict the effects of technical malfunctions.  A larger presentation area would improve the usability.  Lack of a complete integration with the ordinary traffic control and signalling system leads to robustness problems. More advanced tests and evaluation procedures are planned for the remaining test period. These include e.g. data analysis, observations, interviews, questionnaires and video recordings with following analysis of the planners’ behaviour. The evaluation will focus on two main questions: does the system contribute to better traffic performance and does the system contribute to more efficient work of the traffic planners. The full results of the evaluation will be presented later.

7 Future research From our earlier research and experimental studies in the laboratory, which has been shortly discussed above, we have a more or less complete solution for the new proposed control paradigm, control by re-planning. When the operative test system is being specified and developed, it is not possible to implement the full prototype system. Some parts are not relevant to the operative test environment, other parts are not possible to implement because of limitations in the existing infrastructure etc. Some of the more important, and from a research point of view most interesting, problems to be solved in the future are:

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448 Computers in Railways XII 7.1 Traffic planning on a national level It is not enough to solve the re-planning problem for individual traffic segments. In Sweden today the traffic is controlled as several isolated traffic segments in each of the eight regional control centres. In order to obtain continuously updated traffic plans that cover the total traffic system, a national system and organisation must be created that integrates all separate traffic plans. On this national level more strategic planning decisions can be taken, which better coordinates the local and regional activities. 7.2 Automatic execution functions The purpose of the automatic execution functions is to generate and deliver control command sequences to the underlying control system in time. Because of the lacking quality in traffic predictions, the algorithms must have large margins. This results in a non optimal performance. Measures to improve precision in data are most important, since this can significantly improve the total performance. The traffic controllers could e.g. be allowed to update the traffic plans closer to real-time. The actions of the automatic functions must be clearly shown in the user interface in order to support good situation awareness and avoid automation surprises. 7.3 Detailed track diagrams Today it is unclear how much track diagram information that is needed for the controllers. The presentation must be detailed enough to support the understanding of conflicts, status of the infrastructure, restrictions and degrees of freedom in the re-planning activities etc. Different level of detail in the presentation will be tested in the future. 7.4 Traffic control in complex stations The design of support systems, e.g. interface elements and decision support functions, for traffic control in more complex stations is not investigated enough. In our operative test environment we will not cover complete traffic regions, and because of that we do not now need advanced solutions. On the other hand, we will not be able to evaluate the total performance. In order to specify a complete traffic plan, from start to end station, also complex stations must be covered by the re-planning tool. 7.5 Work environment and design of the workplace We have a rather detailed picture of what kind of presentation system that is needed for optimal performance and a good work environment. This should require very large presentation areas with high resolution and quality and without disturbing frames. Because of economic and practical reasons, we will not be able to implement an ultimate technology. The exact lay-out of the work place WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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will be made so that the individual traffic controller has an optimal solution concerning ergonomics and information overview. 7.6 Remaining design and implementation questions There are some important problems concerning the available technical solutions that we will not be able to solve within the STEG project. This will restrict our possibilities to develop efficient support systems and to evaluate the new control paradigm. The two most severe limitations in this respect are:  Train speed and position. There are no technical solutions available that now allow us to observe train speed and position with high precision. Today the best precision is the identity of the block section. In the future positioning systems with a high precision will be available.  Communication with train drivers. Today we are not able to automatically communicate new traffic plans to the train drivers. This means that the train drivers will drive according to old and obsolete plans. By doing so they will not be able to perform optimally. In very urgent cases the controllers can phone the train drivers to inform them about changes in traffic plans, train stops etc. It is also not possible for the train drivers to easily inform the traffic controllers about late departures, speed restrictions caused by machine problems etc. In the future we will have efficient communications links for such purposes, e.g. when ERTMS/ETCS systems have been fully implemented.

Acknowledgements This project has been financially supported by the Swedish National Rail Administration. We especially thank all professional traffic controllers and planners who have been engaged in the research and test activities.

References [1] Andersson A.W., Sandblad B., Hellström P., Frej I., Gideon A. (1997) A systems analysis approach to modelling train traffic control. Proceedings of WCRR’97, Florence, Italy 1997. [2] Bainbridge, L. (1983). Ironies of automation. Automatica, 19, 775-779. [3] Endsley M.R. (1996). Automation and situation awareness. In R. Parasuraman & M. Mouloua (Eds), Automation and Human performance: Theory and applications (pp. 163-181). Mahwah, NJ: Lawrence Erlbaum. [4] Hellström, P., Sandblad, B., Frej, I., Gideon, A. (1998). An evaluation of algorithms and systems for Computer-Aided Train Dispatching, Computers in Railways VI, Wessex Institute of Technology, 1998. [5] Kauppi A., Wikström J., Hellström P., Sandblad B., Andersson A. W., (2005). Future train traffic control, control by re-planning. In J.R. Wilson, B. Norris, T. Clarke, and A Mills (Eds), Rail Human Factors supporting the integrated railway (pp. 296-305). Ashgate Publ. Ltd. 2005. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

450 Computers in Railways XII [6] Kvist T., Hellström P., Sandblad B., Byström J., (2002). Decision support in the train dispatching process. Computers in Railways VIII, Wessex Institute of Technology, 2002. [7] Sandblad B, Andersson AW, Byström J, Kauppi A. (2002). New control strategies and user interfaces for train traffic control. Computers in Railways VIII, Wessex Institute of Technology, 2002. [8] Sandblad B. et al. (2000). A train traffic operation and planning simulator. Computers in Railways VII, Wessex Institute of Technology, 2000. [9] Wikström J., Kauppi A., Hellström P., Andersson A., Sandblad B. (2004) Train traffic control by re-planning in real-time. Computers in Railways IX, Wessex Institute of Technology, 2004. [10] Sandblad, B., Andersson, A.W., Kauppi, A. and Wikström, J. Implementation of a Test System for Evaluation of New Concepts in Rail Traffic Planning and Control. In: Wilson, J., Norris, B., Clarke, T. and Mills, A. eds.: People and Rail Systems. Human Factors at the Heart of the Railways. Ashgate Publ. Comp., 2007

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Section 6 Maglev and high speed railway

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A model for the coordination between high-speed railway lines and conventional rail lines in a railway passenger transportation corridor Y. Bao School of Traffic and Transportation, Beijing Jiaotong University, China

Abstract With the construction of high-speed railway lines in China, there are growing concerns about the rational transport cooperation between high-speed railway lines and conventional rail lines. A rational transport cooperation scheme can improve railway capacity utilization, train speed, service and the organization quality of railway transportation. The previous research mainly focused on the aspect of the management of railway or passenger organization, which ignored the interaction of them. Based on the planning of a railway transportation corridor and the structure and distribution of passenger flows, we addressed the problem of rational cooperation of the railway passenger transportation corridor, aiming at identifying the train varieties, quantities and the routes of trains on high-speed railway lines and existing conventional rail lines in a railway transportation corridor. A bi-level programming model for the division is proposed. The upper model is to minimize the total transportation cost, and the lower one is an equilibrium model determined by passengers. Then a solution algorithm based on a genetic algorithm (GA) is designed. Finally, the application of the model and the algorithm are illustrated by a numerical example. Keywords: high-speed railway, railway passenger transportation corridor, coordination, bi-level programming, genetic algorithm.

1 Introduction The coordination of high-speed railway (HSR) and conventional railway (CR) is the issue to identify train routes, train quantity and the distribution on the two WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100421

454 Computers in Railways XII lines in a railway corridor. The coordination is a mode choice problem. Meanwhile, it is a route choice problem. Therefore, the problem of coordination is to identify the routes of different trains on the two parallel lines. HSRs are developing very quickly in China, and they are built between metropolises, but there are already CRs between them, so the coordination between HSRs and CRs should be research in order to fully use resources and avoid competition. An agreement was reached by the research of 1990s, and HSRs finished the transportation of passengers on HSR lines and most mid and long-distance passengers from CR lines, and CRs finished freight transportation and the rest of the passengers travelling by the slow trains on CRs. However, with the construction and operation of HSRs, the research background and prerequisites have changed. For example, the research in the 1990s supposed that train units on HSRs were bought from other countries, but now, China can produce train units by herself. HSRs are quite different from CRs in infrastructure, operation, and character, so the alternative is different. Therefore, the coordination between HSRs and CRs is highly desirable. The coordination is concerned with passenger transportation service quality, the revenue and the development of railway transportation. Previous researches about the coordination of HSR and CR cover different aspects and emphases, including form the aspect of passengers by the disaggregate model [1–4], or from the aspect of the railway operator [5], or the above two aspects [6]. The research method contains the logit model [1–3], the passenger flows assessment model, based on the railway network [7], the game model about the selection of a competitive transportation corridor [8, 9], the enumeration method, the satisfaction optimization, and so on. However, most researches about the coordination of HSR and CR are from the aspect of passengers or from the aspect of the railway operator, and few researches have been done from both aspects, and most studies failed to research the feedback of the decision made by the railway operator from passengers. This paper established a bi-level programming model to describe the relationship between the decision made by the railway operator and the reaction from the passengers about the decision. The remainder of the paper is organized as follows. Section 2 explains basic assumptions about the problem. In Section 3, individual route choices and the railway operator’s decision are investigated by a bi-level programming model. Section 4 introduces a genetic algorithm to solve the problem. A numerical example is provided in Section 5 to illustrate the application of the models. Section 6 presents concluding remarks.

2 Basic assumptions The following assumptions are made in this study: (a) whether high level train units run on CR lines or whether low level train units run on HSR lines is determined by the profit of the railway operator, and is not determined by policy or other factors; (b) passengers’ choices are based on the maximum travel utility; (c) the same type of trains (train units) have the same seating capacity and can be

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assigned to HSR lines and CR lines; (d) the operation of the two directions of a line is the same, so we only research one direction.

3 Bi-level programming model A railway transportation corridor consists of parallel railway lines and some stations. Stations can by classified in to two types, 1) a train could run on another type of line at the station; 2) a train could not run on another type of line at the station. We establish a railway network ( S , E ) . Tables 1 and 2 are the parameters and the decision variables used in the model, respectively. The upper level is to maximize the profit of the railway operator, with the restriction of train limited running distance, the capacity of lines. The lower level is to maximize passengers’ utility, since passengers’ utility is described by passengers’ cost, so the objective of the lower level is to minimize passengers travelling cost. The upper level：

Max     xu r  (Qijxu r  M u  ul  Ful  Cul )  d xu (i, j ) Xu

u xu 1

 |e

a ,bS

lab

|   xu relab  d xu max

 Xu

u xu 1

The lower level：

MinZ  

xu relab

  (elab )

  

u xu  X u

m Qijxurh p 1

ijpxu r

Qijxu r  Qij  P ( xu r )

P ( xu r ) 

h

Xu

u xu 1

(2)

(3)

 Vrp

 Vxu r

e e

(1)

 Vxu r

(4)

(5) (6)

The lower level is the utility of passengers, and it determines passengers’ travelling scheme choice. Passengers’ travelling scheme choice is the basis of coordination, since it affects the load factor of a train, so it plays a vital role of the coordination in a railway transportation corridor. Passengers’ travelling scheme choice is affected by passenger time value, fare, travelling distance, train frequency, train departure time, the character of HSR and CR lines, etc, and it is determined by the utility the passengers get from their travel; in addition to the utility of fare and travelling time, it also includes comfort, safety, punctuality, and so on. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

456 Computers in Railways XII Table 1: Parameter and set

S

elab

| elab |  (elab ) u

tu r R xu VSul

Definition of parameters and sets. Definition The set of stations, station i, j  S , and a , b are the adjacent stations Arc, line type l ( l  1 , HSR line; 2, CR line) The length of arc elab The maximum capacity of arc elab

Train type, u  1 , train unit only run on HSR lines; 2, train unit on HSR lines which can run on CR lines; 3, train on CR lines which can run on HSR lines; 4, train only run on CR lines The running time of train type u Train operation scheme The set of train operation schemes, r  R Train number x of train type u , xu  1, 2, , X u

ul

Train speed of type u on line l

Mu Ful

The capacity of train type u

The occupation of train type u on line l

Cul C pa C pe

t pa t pe fu d xu (i, j )

The fare of per train kilometer of train type u on line l The cost of per train kilometer of train type u on line l The fare of a passenger from his origin to his railway trip start station The fare of a passenger from his railway trip end station to his destination The time of a passenger from his origin to his railway trip start station The time of a passenger from his railway trip end station to his destination Frequency of train type u

Tpi

xu The transfer time of passenger p at station i

d xu max

The maximum travelling distance of train

Qij

The volume of passengers from

The running distance of train

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i to j

xu

Computers in Railways XII

Table 1:

Qijxu r

Continued.

The volume of passengers from of scheme r The hierarchy of passengers Time value of passengers

m vh

h (h  1, 2, , m)

Qijxu rh

Table 2:

x r

i to j by train xu with

hierarchy

Passengers of hierarchy h from i to j travelling by train xu of scheme

Parameter and set

457

r

Definitions of variables.

Definition

u

Whether train xu is operated on scheme r , 0, No; 1,

u

Yes Whether train xu of scheme r occupies arc elab ,0 ,

 x re

lab

 ijpx r u

 pi

No;1, Yes Whether passenger

p chooses the train xu of

scheme r , 0, No; 1,Yes Whether passenger p transfer at station



i , 0, No;

1 , Yes; The profit of railway

3.1 Calculation of passengers’ utility According to random utility theory, the utility of passenger p to the choice r is

U rp .

U rp  Vrp   rp U rp ——the utility of passenger p for choosing travelling scheme r ;

(7)

Vrp ——the fixed utility of passenger p for choosing travelling scheme r ;

 rp ——the random error of passenger p for choosing travelling scheme r .

In this paper, passengers’ travelling schemes are the travelling schemes of travelling directly by different routes or by transferring at different stations. Taking fig. 1 as example, there are 6 travelling schemes for passengers travelling from O to D, 1) passengers travel directly from O to D by the train running on HSR line; 2) passengers travel directly from O to D by the train running on CR line; 3) passengers travel directly from O to D by the train first running on HSR line, then changing to run on CR line at station A; 4) passengers travel directly from O to D by the train first running on CR line, then changing to run on HSR WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

458 Computers in Railways XII

Figure 1:

An illustration of passengers’ travelling schemes.

line at station A; 5) passengers first travel by the train on HSR line to station A, then transfer to the train on CR line at station A; 6) passengers first travel by the train on CR line to station A, then transfer to the train on HSR line at station A. Here we define four characters to calculate Vrp , economy ( X 1 ), expeditiousness ( X 2 ), comfort ( X 3 ) and accessibility ( X 4 ). Each travelling scheme has a fixed utility by the four characters. If we use Vr to replace Vrp , then

V 1   11 X 11   12 X 12   13 X 13   14 X 14 V 2   21 X 21   22 X 22   23 X 23   24 X 24 Vn   n1 Xn1   n 2 Xn 2   n3 Xn3   n 4 Xn 4

(8) (9) (10)  rq -parameter, the preference of passenger for the character q of

r , q  1, 2,3, 4 ; X rq - the character q of travelling scheme r .

travelling scheme

3.1.1 Calculation of travelling scheme characteristics (1) Economy Passengers need to pay for their travel, and the fare is used to indicate the economy of the travel. Fare consists of the access fare from origin to the start station, the riding fare on the train, and the egress fare from the end station of the travel to passenger’s destination. X 1  C pa  Ful  d xu (i, j )  ijpxu r  C pe (11) (2) Expeditiousness Expeditiousness is an importation factor affecting passengers’ choice, especially to businessmen. Here, travel time is used to express the expeditiousness of a trip. Previous studies use passengers’ travelling distance divides train speed. However, there are two limitations in this method, 1) the value to different type of time should be different, i.e., passengers’ transfer time for waiting the connecting train is different from the time riding on the train; 2) transfer breaks the continue trip, and it may take threat to passengers in their mind. So we think we should distinguish different types of time during the trip. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Total travel time includes riding time on the train, train stop time and passengers’ transfer time. However, the addition time for the late of the train is not considered. (12) X 2  d xu (i, j ) / VSul     pi  Tpi

 —parameter of the transfer time changed to riding time. (3) Comfort With the raise of living standard, people's consumption idea has changed. They pursue much more comfortable travelling environment. Comfort is an important character that affects passengers’ choice behavior. Since travelling is a consuming of passengers’ physical strength, additional time is needed to recover from the fatigue, when the travelling time is up to some hours. So passenger recovering time from fatigue reflects the comfort of a trip. The recovering time is associated with travelling time and travelling environment, and travelling environment is determined by train type. Passenger recovering time is calculated by the following equation (Peng [5]). gu (t)  M /[1exp(t)]   (utu )/ tu   (utu ) / tu

(13) (14) (15)

 u - nondimensional parameter, when train type is u , and t  0 , the recovering time is M /(1   u ) ; and u - the strength coefficient of recovering time for one travelling hour,  u  0 , M - the limited recovering time, in general, M is 15 h;

1

the unit is h .

Figure 2:

The illustration of passengers’ travelling time and recovering time.

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460 Computers in Railways XII Table 3:

Passengers’ recovering time from fatigue when travelling between Beijing-Shanghai by different types of trains.

Train type High-level train on HSR Low-level train on HSR Normal train on CR

Passenger travelling time (h) 5

Recovering time (h) 0.96

13 22.72

8.97 14.93

When M  15 h, the recovering time travelling by different type of trains is as fig. 2. From the comparison we can see the advantage of HSR in recovering passengers’ fatigue. Taking Beijing-Shanghai HSR line for example, 1) if we assume that the travelling time of high-speed train is 5 h, the recovering time of passengers travelling by HSR is 58 min; 2) if passengers travelling by through trains (train type Z) on CR line, the travelling time is 13 h, and the recovering time will be up to 9 h; 3) if passengers travelling by normal trains on CR line, the travelling time is 22 h and 43 min, and the recovering time will be up to 14 h 56 min, nearly to the general limited recovering time M . The above research is based on through trains, when passengers transfer, if the travelling time is tu on train type u , in this case, parameter  u and  u should be calculated by weighted average method. Therefore, passengers’ comfort is calculated by eqn. (16).

X 3  g ( u ) (t )

(16)

(4) Accessibility Most current approaches about passengers’ choice behavior to different transportation modes, in the mainland of China, focus on the characters of the mode itself, rather than research from the whole trip. The usual methods to improve the market share of a mode are enlarging the network, improving the covering area of the mode, shorting the travelling time, improving the service, etc., but ignoring the accessibility of the mode. From the research of abroad and Taiwan in China, accessibility is an importation factor that affects the market share of different transportation modes [10, 11]. With the development of highspeed train units, the riding time is lower and lower. Therefore, the access time to the station and the egress time from the station occupies a large part in the whole travelling time, especially to those cities that are always in heavy traffic. In this study, station accessibility is represented by passengers’ agree time, egress time, the frequency of trains and the transfer time. X 4  t pa    Tpi   pi   / f u  t pe (17)

 - parameter of train departure frequency.

4 Algorithm for solving the model Genetic algorithm is introduced to solve the model, and the steps are as follows. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Step 1: Representation and initialization of chromosomes for the upper-level model Select a feasible solution N0 for the upper-level model, let X r  {  x r } represent a chromosome for the upper-level model. Each code in u

the chromosome is the value of

x r ,

generate

POP _ SIZE _ U

chromosomes,

which is { X r / X r  ( x1r , x2r ......xnr ), r  1, 2,......POP _ SIZE _ U } . The steps used to generate chromosomes could be described as below. Select a random direction d from Rn; If N0+d is feasible, let it be a new chromosome, otherwise, generate a new d, until N0+d is feasible; Repeat steps (1) and (2) to get POP _ SIZE _ U chromosomes. Step 2: Evaluation and Selection Execute each chromosome to get each chromosome’s fit value based on step6, sort the chromosomes on the basis of fit values. Select POP _ SIZE _ U chromosomes to execute the following steps. Step 3: Crossover Create a new population of POP _ SIZE _ U number by applying the following operations. The operations are applied to choose from the population with a probability based on fitness. (i) Darwinian Reproduction: Reproduce an existing chromosome by copying it into the new chromosome. (ii) Create two new chromosomes from two existing chromosomes by genetically recombining randomly chosen parts of two existing chromosome s using the crossover operation applied at a randomly (according to Pc _ U ) u

chosen crossover point within each chromosome. Step 4: Mutation Create a new population of POP _ SIZE _ U number by applying the following operations. The operations are applied to choose from the population with a probability based on fitness. (i) Darwinian Reproduction: Reproduce an existing chromosome by copying it into the new chromosome. (ii) Create one new chromosome from one existing chromosome by mutating a randomly (according to Pm _ U ) chosen part of the chromosome. Step 5: Iterations Iteratively perform the above steps (2) ~ (4) until the termination criterion Gen _ Num _ U has been satisfied. Step 6: Solving the lower-level model based on the input from the upperlower model Step 6.1 Representation and initialization of chromosomes for the lower-level model Select a feasible solution N0 for the upper-level model, let X L r  { i j p x r } represent a chromosome for the lower-level model. Each code in the chromosome is the value of  ijpxu r , generate u

POP _ SIZE _ L chromosomes,

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462 Computers in Railways XII which is { X / X  ( x1 , x 2 ...... x n ), r  1, 2 , ...... P O P _ S IZ E _ L } . The steps used to generate chromosomes could be described as below. Select a random direction d from Rn; If N0+d is feasible, let it be a new chromosome, otherwise, generate a new d, until N0+d is feasible; Repeat steps (1) and (2) to get POP _ SIZE _ L chromosomes. Step 6.2: Evaluation and Selection. The same with step 2. Step 6.3: Crossover. The same with step 3. Step 6.4: Mutation. The same with step 4. Step 6.5: Iterations. The same with step 5. r

r

r

r

r

Step7: Return the result Qijxu r by the lower level to step 5.

5 Numerical example The rail network topology is shown in fig. 3. Capacity of each train unit on HSR is 600 p (passenger), and 1220 p on CR. We assume that the occupation rate of each train is 90% on HSR lines, and 85% on CR lines. The fare of each passenger per train-km is 0.30 and 0.14 CNY on HSRs and on CRs of highspeed trains, and 0.12 CNY on CRs of normal trains. The cost of per train-km unit is 127.1 and 184.1 CNY of high-speed trains on HSRs and CRs, and 92.4

Figure 3:

Illustration of the rail network used in the numerical example. Table 4:

A A B C D E F G H

B 500

Passengers’ travelling demand. C 1000 0

D 800 1200 0

E 3000 1500 2000 1500

F 1500 600 1500 1000 2000

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G 900 800 800 1200 1000 0

H 5000 2500 3000 1800 3500 3000 2000

The weight of services attribute for different types of passengers.

Passenger hierarchy

Passenger income level/CNY

Percentage/%

h=1

5000

20

>25

0.2

0.4

0.15

0.25

Computers in Railways XII

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Table 5:

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464 Computers in Railways XII

Table 6: Section A-C A-D A-E A-F A-H B-D B-E B-H C-E C-F

The number of trains of different sections. CR 0 1 1 0 1 1 1 2 0 0

HSR 2 0 4 2 7 0 0 0 6 3

Section C-H D-E D-G D-H E-F E-G E-H F-H G-H

CR 0 3 1 1 0 1 2 0 1

HSR 4 0 0 0 6 0 7 5 0

CNY of normal trains on CRs. The organization cost for each passenger is 0.0381 Chinese Yuan (CNY), when they transfer. Meanwhile, we assume that the volume is not a constraint for train units running on CR lines. The genetic algorithm has been implemented by Microsoft Visual C++ 6.0 (more than 4200 code lines) and runs on a Pentium Duo, 3.4GHz PC, with 512MB RAM memory. The time cost is 56 seconds. The results are given below.

6 Conclusions This paper addresses the coordination issue of HSR and CR in a railway transportation corridor from the aspect of passengers and railway operator. A bilevel programming model is established to coordinate the difference of passengers' choice and the decision made by railway operator. A genetic algorithm is designed to solve the model. The model and algorithm is demonstrated by a numerical example. The proposed model can be extended in several directions. In the upper-level level, freight transportation can be included, especially to those valuable goods. Meanwhile, in the lower level, passengers’ departure time can be added as a constriction. Another possibility is to add other transportation modes in a transportation corridor, such as air transportation, and freeway.

Acknowledgements This study was jointly funded by National Natural Science Foundation of China (No.60736047) and Beijing Jiaotong University (No.141078522). The author deeply appreciates the support.

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References [1] Saad N Aljarad & William R Black, Modeling Saudi Arabia-Bahrain corridor mode choice, Journal of Transport Geography, 3(4), pp.257268,1995. [2] Bhat C.R., A Heteroscedastic Extreme Value Model of Intercity Mode Choice, Transportation Research Part B, 29(6), pp. 471-483, 1995. [3] Bhat C.R., Covariance Heterogeneity in Nested Logit Models: Econometric Structure and Application to Intercity Travel, Transportation Research Part B, 31(1), pp. 11-21, 1997. [4] Chaug-Ing Hsu & Wen-Ming Chung, A Model for Market Share Distribution between High-speed and Conventional Rail Services in a Transportation Corridor, The Annals of Regional Science, (31), pp.121-153, 1997. [5] Qiyuan Peng, Transportation organization of passenger special line, Science Press, 2007. [6] Jie Tang, The coordination and optimization method between high-speed railway and conventional railway, Master's Thesis, Central South University, 2008. [7] IljoonC hang, A Network-based Model for Market Share Estimation among Competing Transportation Modes in a region corridor, Ph.D. thesis, The University of Maryland, 2001. [8] Jianmei Zhu, Game Model of Selection of Competitive Transportation Corridors, Journal of Southwest Jiaotong University, 38(2), pp.336-340, 2003. [9] Chiung-Wen Hsu, Yusin Lee & Chun-Hsiung Liao. Competition between high-speed and conventional rail systems: A game theoretical approach, Expert Systems with Applications, 37, pp. 3162–3170, 2010. [10] Clever, Reinhard, Airport and station accessibility as a determinant of mode choice, Ph.D. thesis, University of California, Berkeley, 2006. [11] Martijn Brons, Moshe Givoni & Piet Rietveld. Access to railway stations and its potential in increasing rail use. Transportation Research Part A, (43), pp.136–149, 2009.

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Derivation of the safety requirements for control systems based on the interoperability property of the Maglev train W. Zheng1, J. R. Müeller2 & K. Li1 1

School of Electrical and Information Engineering, Beijing Jiaotong University, China 2 Institute for Traffic Safety and Automation Engineering, Technical University of Braunschweig, Germany

Abstract With the prospect of new and different Maglev train lines to be constructed, the interoperability properties of the Maglev train have become a new issue. The safety performance requirement of the Maglev control equipments for interoperability operation was derived based on the objectives of the crossing boundary between different lines and the corresponding procedures have been modelled with stochastic Petri nets. Firstly, the whole objectives of the crossing boundary of different Maglev lines were defined taking the operation efficiency and safety target into consideration. The train would cross the boundary without decreasing the speed. The operation efficiency and the safety property of the crossing procedure should be guaranteed. In addition, based on the interoperability objectives, the interoperability operation procedure of the Maglev train was specified and the Maglev control equipments used for the interoperability were designed. The control equipments were used to transmit the data between different control systems of different lines. Thirdly, the process of the train passing the boundary of different lines was modelled with the stochastic Petri nets based on the different operation stage of the train. Finally, by means of the simulation of the model, the safety performance requirements of the Maglev control systems were derived based on the defined crossing success rate. Keywords: interoperability, Maglev train, Petri nets, modelling.

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468 Computers in Railways XII

1 Introduction The Maglev train is one of the new vehicles and it is still in the stage of research and development. In the near future, the new operation of the Maglev train lines will be built and it is necessary that the different lines should be linked to the operation network to improve the flexibility and efficiency. Based on this, the same Maglev train should be able to operate in different lines, and this is called the “interoperability” of the Maglev train in China. “Interoperability” means that different lines equipped with different control systems exist and are interlinked together. The train should be able to cross the boundary of different lines, meeting the safety requirements and be efficient. With regard to interoperability research, the Europe Union drew up several documents to define and explain the requirements. Document [1] specifies the safety requirements and document [2] describes the performance requirement. In China, the interoperability research is also undertaken in the laboratory of universities and commercial operation lines. However, there has been no research up to now about the interoperability of the Maglev train. In order to guarantee that one Maglev train can cross the boundary of different lines, the crossing process and changed data flow should be specified and analyzed, and all the factors influencing the crossing success rate should be identified so that it can be used as a guideline for the interoperability specification design. In this paper, the research is mainly focused on one of the interoperability properties, the ability of crossing boundary of different lines, and the corresponding system design approach is presented. The crossing process of the Maglev train is modelled with Petri nets and the relationship between the crossing boundary success rate and the system equipments dependability is analyzed quantitatively.

2 The objectives of the crossing boundary and system design The objectives of the crossing boundary of different lines for the Maglev train are the basis of the system design and analysis. 2.1 Objectives The objectives are described as follows: 1) The action of crossing boundary of different lines could be proposed by the former system. The target system makes the decision of accepting or rejecting the train based on its own operation conditions. 2) If the train is allowed to enter into the new line, the train should be able to cross the boundary with the original speed by the way of “stepping” style and it shall be able to operate in the new track based on the diagram. 3) During the process of passing the boundary, if the train’s speed exceeds the limitation, the former and target systems should be able to cooperate to guarantee the safety of the train. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 1:

469

The schematic diagram of the crossing boundary process.

4) During the process of passing the boundary, if any of the components fail, the two control systems of two lines should guarantee that the train stops at a specified point. 2.2 The process of crossing the boundary of different lines The schematic diagram of a crossing boundary is shown in Fig. 1. 1) As shown in Fig.1, A is the crossing triggering point. When the train reaches this point, CCS-A will transfer the necessary data to CCS-B, such as the information of the train and the demand of the route. The information of the train consists of the train identification, its weight and its length, etc. The route request consists of the track data and segment data, etc. After receiving the route request from CCS-A, CCS-B will reply to CCS-A and inform the DSC-B to set the rout in system B for the train. 2) After the CCS-A has changed the necessary data with CCS-B, the train will execute the step action in point B. DSC-B will send the route data to DSC-A at this point. If the current speed in the operation speed curve for stopping point C is beyond the speed in the minimum speed curve for the next stopping point E, the train will be stepped to E point and it should operation under the operation speed curve of point E. 2.3 Control system design Based on the process of crossing the boundary, it is clear that the new communication net should be designed to enable the data to be changed between the different control systems for different lines. The control system of Maglev train is composed of the CCS (Centralized Control System), the DCS (Decentralized Control System) and the VCS (Vehicle Control System). The DCS is composed of the DCC (Decentralized Control Computer), the DSC (Decentralized Safety Computer), the DPS (Decentralized Propulsion Shut-off) and the DSM (Decentralized Switch Module). The main part of the VCS is the VSC (Vehicle Safety Computer）. The structure of the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

470 Computers in Railways XII control system is shown in Fig.1. Wu [3] specified the functions of each component in the control system. The CCS is connected with the DCS by a control core network and the different components of DCS are interlinked with the safety protection network. Since the control systems of different lines have to exchange the data, it is necessary to add the communication systems between the control systems. As shown in Fig. 2, A-net and B-net are the designed communication systems for the crossing. The function of A-net is to guarantee the communication between CCS-A and CCS-B. If A-net fails before the train arrives at B point in Fig.1, the crossing action will fail. During the time of train running from B point to the place of the boundary, B-net should be intact so that the DSC-A and DSC-B can communicate with each other all the time so that the train can cross the boundary safely. If B-net fails during this period, the crossing action will also fails. Based on the process of crossing the boundary, it is obvious that the dependability property of the A-net and B-net has close relationship with the success rate of crossing boundary.

3 Petri net model of the crossing boundary process As one of the modelling languages, Petri nets has been applied widely in the modelling and performance analysis of traffic control, mechanical engineering, software engineering, medicine and chemistry fields. David and Didier [4] described how the Petri nets can be used in the application of industry engineering. Zheng et al. [5] and Zheng [6] used Petri net to model the level crossing and the computer interlocking and analyze the safety performance of the control system. German [7] presented the basic elements of the Petri nets and Lin [8] specified the performance analysis principle and approach of the stochastic Petri net. Based on the crossing boundary procedure as shown in Fig. 2, the Petri net model of the crossing process is described in Fig. 3.

Figure 2:

The frameworks of the former system and the target system.

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Figure 3:

471

Petri net model of crossing boundary process.

Slovák et al. [9] introduced the hierarchical modelling approach firstly. Based on this approach for system dependability analysis, two views of the procedure of crossing boundary of the train can be modelled: one is the crossing process of the Maglev train including the failure process and the other is the function and dependability property of the control system. 1) The modelling of crossing boundary process: the state of the Maglev train is composed of places “Operation preparation”, “Departure position”, “Crossing position”, “Step position”, “Cross success”, “Target” and “Cross failure state”. The place “Crossing position” and “Step position” means the point A and point B of Fig.1. About the transition, the “Interval time” is a deterministic one because the headway of the train is fixed. The transitions “operation time”, “Crossing triggering time” and “Crossing step time” are defined to be exponential and their parameters mean the corresponding operation time of the train. 2) The modelling of the failure of crossing boundary: the state of a failure process is composed of the place “Initial” and “failure state”. The parameters of the transition “recovery rate” represent the recovering time in case of the crossing failure. 3) The modelling of function and dependability of A-net and B-net: for A-net and B-net, the state is both composed of two places “Intact” and “Failure state”. The transitions between these two places are exponential and the parameters of the transitions mean the failure rate and recovery rate respectively. The place “Intact” also can act as the function place. The place “A-net intact” is in charge of the function of crossing triggering process and it play its role by a testing arc connected to the transition “Crossing triggering time”. The place “B-net” is in charge of the function of stepping process and it is also linked to the place “Crossing step time” with a testing arc.

4 Simulation analysis The TimeNET4.0 for Windows is a tool for edition and simulation of Petri net models [10]. To the model shown in Fig. 3, based on the assumed necessary WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

472 Computers in Railways XII parameters, the expected average marking time of the place “failure state” can be obtained by simulation. Then it is divided by the “recovery rate” and the result will be the failure rate of crossing boundary. The parameters in the model are assumed as follows: headway of the Maglev train: 0.5 hour; the whole operation time: 2 hours; the average crossing triggering time: 0.5 minutes; the average stepping time: four minutes; the average recovery time of crossing failure: 2 hours. Simulation results are shown in Fig. 4 and Fig. 5. As presented in Fig. 4, the failure rate will decreased with the higher dependability of the A-net and B-net.

Failure rate of crossing boundary[%]

0.001 B-net B-net B-net B-net B-net

0.01

FR=1.E+00 FR=1.E-02 FR=1.E-04 FR=1.E-06 FR=1.E-08

0.1

1

10

100 1.E+00

1.E-01 1.E-02

1.E-03

1.E-04 1.E-05

1.E-06 1.E-07

1.E-08

Failure rate of the A-net[1/hour]

Failure rate of crossing boundary [%]

Figure 4:

The relationship between the crossing failure rate and the dependability of the control systems.

0.0021 0.0020 0.0020 0.0019 0.0019 0.0018 0.0018 0.0017 0.0017 0.0016

Exchange data period=0.2 Min Exchange data period=0.5 Min Exchange data period=1 Min Exchange data period=2 Min

0

2

4

6

8

10

12

14

16

The time period of steping procedure [Minuter]

Figure 5:

The relationship between the failure rate of the crossing boundary and the triggering and stepping time.

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It is assumed that the headway of the train is 30 minutes and every operation process is composed of one action of crossing boundary. Then in one year, the whole time of crossing boundary for the trains is 17520. If only one time failure of crossing boundary is allowed in one year, then the failure rate should at most be 5.7 × E-5. From Fig.4, it can be derived that in order to meet this target, the failure rate of net-A and net-B should be lower than 1.0 × E-6 per hour. In Fig.5, it is assumed that the failure rate of the A-net and B-net is 1.0 × E-6 per hour, and it is clear that if the triggering time and stepping time of the train are relatively longer, the failure rate of crossing boundary will be decreased slightly.

5 Conclusion Based on the interoperability objectives and safety requirements, the functions and the structures of the control systems for the crossing boundary of different Maglev train lines were designed. By the results of simulation of the model, the dependability requirements of the added control systems could be identified based on the defined target of success rate of crossing boundary. The triggering time and stepping time of the train have little effect to the failure rate of crossing. The quantitative analysis of the crossing process can be used as the guideline for the formulation of the interoperability specification.

References [1] Safety Requirements for the Technical Interoperability of ETCS in Levels 1&2, ERTMS/ETCS-Class 1, SUBSET-091, 2009. [2] Performance Requirements for Interoperability, ERTMS/ETCS-Class 1, SUBSET-041, 2005. [3] Wu, X.M., Maglev Train. Shanghai Science and Technology Press: Shanghai, pp. 98-134, 2003. [4] David, V. & Didier, R. B., MORM-A Petri net based model for assessing OH&S risks in industrial processes: modelling qualitative aspects. Risk Analysis, 24(6), pp. 1719-1735, 2005. [5] Zheng, W., Müller, J. R., Slovák, R. & Schnieder, R., Estimation of traffic risk of passive level crossing based on stochastic Petri net models and social economic data，Proc. of the 3rd Int. Conf. on Transport Simulation, Queensland, Australia, pp. 35-38, 2008. [6] Zheng W., Modeling and hazard analysis of railway station protection system based on stochastic Petri nets, Proc. of the 8th Int. Conf. on Reliability, Maintainability and Safety, Chengdu, China, pp. 493-496, 2009. [7] German, R. Performance Analysis of Communication Systems – Modelling with Non-Markovian Stochastic Petri Nets, John Wiley & Sons: Chichester, pp. 36-57, 2000. [8] Lin C., Stochastic Petri Nets and System Performance Evaluation, Tsinghua University press: Beijing, pp, 19-27, 2005.

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474 Computers in Railways XII [9] Slovák, R., May J. & Schnieder E., PROFUND modeling for holistic risk and availability analysis by means of stochastic Petri nets applied to a level crossing Control System. Proc. of Formal Methods for Railway Operation and Control Systems, L’Harmattan: Budapest, pp.221-232, 2003. [10] TimeNET. www.pdv.cs.tu-berlin.de/~timenet/

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Dynamic characteristics modelling and adaptability research of the balise transmission module in high speed railways H. Zhao1, S. Sun1 & W. Li2 1

School of Electronic Information and Engineering, Beijing Jiaotong University, China 2 Beijing Jiaoda Signal Technology Co., Ltd, China

Abstract General simulation requirements within the train control system simulation framework were described. Dynamic characteristics were modelled at four resolution layers, ranging from low to very high resolution. The research focuses mainly on the dynamic behaviour and simulation representation at the signal emulation layer. The Balise Transmission Module Hardware-In-the-Loop simulator was justified with respect to dynamic behaviour modelling. Furthermore, the relationship between Balise Transmission Module dynamic characteristics and train speed was verified by simulation and test data. The speed factor is derived and analyzed. Finally, quantitative evaluation issues of high-speed adaptability were explored based on the deduction of decoding failure probability under a certain bit error rate and the availability targets for a certain line. Keywords: Balise Transmission Module, dynamic characteristics, adaptability, high speed railway.

1 Introduction Balise Transmission Module (BTM) is a part of the train control system onboard constituent, and has the main functions of generating tele-powering signals to the balise, to receive and process up-link signals from the balise. BTM has been successfully used in China Passenger Dedicated Lines (DPL) with the maximum speed of 250Km/h and high-speed lines with a maximum speed of WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100441

476 Computers in Railways XII 350Km/h, such as the Beijing-Tianjin DPL and Wuhan-Guangzhou DPL KeJiYun[2008] NO.143 [1]. Valuable test data and operation experience were acquired with respect to the BTM used under the high-speed operation conditions. However, Eurobalise specification recommended that the maximum applicable speed for the reduced size balise is only 300Km/h, as recommended in SUBSET-036-V2.4.1 [2]. Issues about BTM static feature simulation, test and application rules were studied in the past (Yang et al. [3], Zhen and Zhao [4] and Wang et al. [5]), but none of the in-depth research was found openly about BTM used in high-speed lines. This paper discussed the high-speed adaptability assessment issues through dynamic characteristics modelling and simulation. Section 2 described the dynamic characteristics modelling at four resolution layers, ranging from low to very high resolution. The relationship between BTM dynamic characteristics and the applicable train speed was verified by test data in Section 3. Finally, Section 4 formulated high speed adaptability evaluation criteria.

2 BTM dynamic characteristics modelling 2.1 Train control system simulation requirement The train control system is a real-time distributed complex system. The simulation requirement differs as far as different end-users with a diversified focus are concerned. For example, operators own different viewpoints from suppliers, as do maintainers and constructors. The intention to adopt simulation varies from functional test to interoperability test, control strategy optimization, safety assessment, efficiency evaluation or training. Furthermore, the requirement also changes within different phases of system life cycle. Clearly, system modelling should satisfy such diversified needs. Classification of the simulation requirement should be considered deliberately; proven technology, i.e., the multi-resolution modelling method, was supposed to be a good choice. 2.2 Multi-resolution BTM modelling 2.2.1 Overview In general, a train control simulation system is capable of reproducing the movement and operation status of trains despatched in pursuit over a pilot line with no less than three stations. In this context, three kinds of simulator are usually required, including the multi-train simulator, single train simulator and component Hardware-In-the-Loop (HIL) simulator. A multi-train simulator is able to simulate multiple trains running by their own working plan simultaneously. A single train simulator is often used to manifest the detailed behaviour of a train and its onboard equipment, especially the driver-machine interaction. The component HIL simulator is an effective test rig for the interoperability test, providing the HIL test environment for a specified component, such as BTM, Train Interface Unit, etc. Besides, component designers may additionally need the physical mechanism simulator to provide WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Table 1:

477

Multi-resolution model of BTM dynamic characteristics.

Target Multi-train simulator Single train simulator BTM HIL simulator Physical mechanism Simulator

Dynamic characteristics Layer Feature Application see 2.2.2 Protocol see 2.2.3 Signal see 2.2.4 Filed

Modelling method FFA FFA TBM

see 2.2.5

FEM

Resolution Low Medium High Very High

the finest granularity emulation of the electromagnetic coupling characteristics happening at the component interface. Table 1 shows the BTM dynamic characteristics model at these four resolution levels. A detailed explanation of Table 1 is as follows. 2.2.2 Dynamic characteristics modelling at the application layer The BTM model at this layer has low resolution. The Functional Failure Analysis (FFA) (Nicholson [6]) method is used to analyse the features presented in the multi-train simulation process. We apply the five typical guide words (“Commission”, “Omission”, “Early”, “Late” and “Value”) to the failure analysis of the major top level function of BTM, i.e. the telegram reporting function. Getting rid of the meaningless outcome, the accurate description of five derived malfunctions are: the transmission of an erroneous telegram interpretable as correct, the loss of the telegram intended for full performance, erroneous reporting of a valid telegram in a different track, transmission of the valid telegram before the time window, transmission of the valid telegram after the time window. We believe, in addition to the normal function, the derived malfunctions depict properly the highest level BTM characteristics. 2.2.3 Dynamic characteristics modelling at the protocol layer The communication protocol between the BTM and the Vital Computer (VC) onboard is the major concern of the single train simulator at this layer. We cannot make a general discussion of the dynamic communication behaviour, as the protocol is supplier dependent. However, FFA should be a suitable way to model it. For example, the guide word “Value” can be used to the communication baud rate and CRC result. 2.2.4 Dynamic characteristics modelling at the signal layer It is necessary for the BTM HIL simulator to model the received up-link balise signal under specific train speed in the laboratory while the antenna and simulated balise are kept still. Test Based Modelling (TBM) is suggested at this layer, which is the same as the transmission test procedure described in Test Specification for Eurobalise Form Fit Function Interface Specification (FFFIS) SUBSET-085-v2.2.2 [7]. Firstly, the tele-power radiation pattern is evaluated by recording the flux of the tele-power signal at every geometrical test position. Secondly, the threshold curve is recorded while the BTM functions well with WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

478 Computers in Railways XII respect to the strength of the received up-link signal. An example of the radiation pattern and threshold curve is shown in Fig. 1. Then, the up-link signal for the dynamic test is created by a certain algorithm SUBSET-085-v2.2.2 [7], based on the results of two previous steps. The dynamic up-link signal can be generated by taking all the environmental factors into account, such as lateral displacement, debris, mounting height and speed. It is also important that the dynamic up-link signal can be used for BTM testing under standstill conditions. Actually, the signal is created by converting the up-link signal threshold at the diversified geometrical test position to the signal envelope at (0, 0, maximum height). The BTM HIL simulator can be realized thus far (Zhen and Zhao [4] and Wang et al. [5]). Taking advantage of the BTM HIL simulator, we can experiment on the specific BTM. The following data can be acquired: (1) telegram and user data; (2) location report; (3) number of non-overlapping good telegrams; (4) BTM function reporting time. These data can be further expressed graphically with respect to operational speed or any other factor of interest; then the corresponding BTM behaviour may be modelled, as in Section 3. In this process, the following two concepts were defined. 1) Static contact length: Ls. This denotes the effective action length along with the X-axis direction, while the BTM can correctly decode and output the balise telegram within the main lobe region under the specified static conditions. For the weakest balise, the formula is:

Figure 1:

Example of signal radiation pattern and threshold curve.

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Ls  X l 2  X l1 ,

 X l1 , X l 2  [ x : I th ( x)  I low ( x)  0]

479 (1)

where Xl1, Ll2, Ith and Ilow, are as shown in Fig. 1. 2) Dynamic contact length: Ld. This denotes the effective action length along with the X-axis direction, while the BTM can correctly decode and output the balise telegram within the main lobe region under the specified dynamic conditions. For the weakest balise, the formula is: (2) Ld  XL2  XL1  (Tbtm Tdis Tdeb Temd )  v，  XL1, XL2 [x : Ith (x)  IBal (x)  0]

(3) Ld  Ls  (Tbal  Tbtm  Tdis  Tdeb  Temd )  v where Tbal denotes the start-up time of the balise; Tbtm denotes the start-up time of the BTM decoding function together with the Antenna Unit; Tdis denotes the delay time due to random displacement between the antenna and balise; Tdeb denotes the delay time due to change of debris; Temd denotes the delay time due to changes of the spatial electromagnetic environment.

2.2.5 Dynamic characteristics modelling at the field layer The Finite Element Method (FEM) is suitable for modelling the dynamic behaviour of the BTM in the highest resolution. BTM designers need to know more about the electromagnetic field distribution and quantitative coupling property where specific application scope, such as train speed and balise discreteness, is concerned.

3 Speed factor in BTM dynamic characteristics A high resolution model built at the signal layer provides a suitable basis for the discussion of the influence of operational speed upon BTM dynamic characteristics. The BTM HIL simulator described in Section 2.2.4 is capable of acquiring a “number of non-overlapping good telegrams (denoted as Ng)” with respect to “simulated train speed”. Taking the BTM developed by Beijing Jiaoda Signal Technology Co., Ltd (BJST) as an example, test data were as shown in Figs. 2 and 3. The nominal mounting height for the antenna unit is 463mm, i.e. H=463mm. The lateral displacement is 110mm in all test cases, i.e. Y=110mm. Every test case was repeated 10 times. The test result of each repeat had an effect on the final test results. The test result was summarized as follows.  The BTM received a fewer number of telegrams as speed was increased.  Ice on the antenna had no significant effect on the BTM function (Fig. 2).  Compared with the condition of the metallic plane under the simulated balise, the BTM received more than 0.5 frames of telegram at nominal condition for the weakest balise.  Compared with the weakest balise, the strongest balise contributed 1 to 2 more frames of telegram for the BTM function (Fig. 3). The BTM dynamic characteristics had significant relation with train speed while the number of non-overlapping good telegrams was evaluated. To be clearer, the dynamic contact length was evaluated instead, as shown in Fig. 4 for debris test cases. Obviously, the dynamic contact length decreased 26mm per 100 Km/h averagely when speed was increased. Let Ld denote the dynamic WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

480 Computers in Railways XII contact length in metres, whereas Ls is the static contact length, namely the contact length when the speed was approaching zero. Let v denote the speed in m/s and  denote the speed factor. The following formula was derived by linear curve fitting, Ld  Ls    v (4)

4 where   9.36  10 s . Clearly, this formula is the same as the formula (3). The speed factor  is actually the effect of the start-up time of the balise and the BTM function together with the Antenna Unit, as well as the disturbance factor of Tdis, Tdeb and Temd defined in formula (3). From another point of view, Ld is a product of the dynamic action time and speed, where the dynamic action time can be derived from Ng, as shown in formula (5). Here, 1023 is the number of bits in one frame of the balise telegram; and 564480 is the mean data rate in bits per second.

Ld 

N g 1023  v

(5)

564480

Combining formulas (4) and (5), we can get the relation between Ng and v, which is the mathematical expression of Figs. 2 and 3.

Ng 

564480  Ls     1023  v 

(6)

4 High speed adaptability evaluation criteria High speed adaptability for the BTM can be evaluated based on the above modelling and reasoning results. Firstly, the number of non-overlapping good telegrams for the specified BTM and antenna under specific conditions, namely Ng, is an important figure. It is in inverse proportion to speed, as shown in formula (6). Secondly, according to mathematics statistics theory, the BTM decoding failure probability, denoted as Pber, is able to be derived when the nonoverlapping telegrams with a total number of Nt is received under the contaminated environment with certain Bit Error Rates (BERs) [2]. Supposing

Number of non-overlapping good telegrams

9

Nominal Matellic Ice

8 7 6 5 4 3 2 1 150

Figure 2:

200

250

300

350 400 Speed：Km/h

450

500

550

BTM performance under debris (H=463mm/Y=110mm).

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Number of non-overlapping good telegrams

12

Weakest 11

Strongest 10 9 8 7 6 5 4 3 2 150

200

250

300

350

400

450

500

550

Speed：Km/h

Figure 3:

BTM performance with respect to the strongest and weakest balise (H=463mm/Y=110mm).

750 Dynamic contace length:mm

Nominal Metallic Ice 650

550

450 150

Figure 4:

200

250

300

350 400 Speed：Km/h

450

500

550

Dynamic contact length under debris (H=463mm/ Y=110mm).

Table 2: Pber Nt

Decoding failure probability with respect to Nt. 1E-6 2.0

2.71E-8 3.0

9.20E-11 3.5

that the BER within the dynamic contact length is evenly 10E-6, the relationship between the two figures is expressed in Table 2 (Zhao et al. [8]). A basic receiver is assumed to be used for the BTM in the above cases. Thirdly, Pber can be determined by specific availability targets of certain lines within the entire specified range of railway conditions and train speeds. For example, if the mean BTM failure rate due to BER will be less than 2.66 times per annum, a mean figure of 10E8 balise passages with error free telegrams delivered by the BTM to the VC should be ensured in the Beijing-Shanghai High Speed Line (the line is supposed to be about 1400Km, 2 balises per kilometre, 260 trains per day. The annual number of balise passages will be 2.66E8). Then, Pber should be 10E-8. Fourthly, we can find the required total number of nonoverlapping telegrams Nt, approximately 3.0 in this instance. Finally, if we determined Ng by the BTM HIL simulator in the laboratory, where BER can be controlled to zero, Ng should be no less than Nt in principle. Therefore, the high WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

482 Computers in Railways XII speed adaptability evaluation criteria for the BTM are simplified to the comparison of Ng and Nt.

5 Conclusion BTM dynamic characteristics can be modelled at four resolution layers by FFA, FFA, TBM and FEM, respectively. For specified BTMs and antennas under specific conditions, the number of non-overlapping good telegrams is in inverse proportion to speed, whereas the dynamic contact length decreases linearly with speed. High speed adaptability for the BTM can be evaluated by comparison of the number of non-overlapping good telegrams received by the BTM with the predetermined value. This value is derived from specific availability targets of certain lines.

Acknowledgements We gratefully acknowledge the financial support provided by The National Natural Science Foundation of China (Grant No. 60736047) and the National 863 High-tech R&D Program (Grant No. 0912JJ0104). We also would like to thank the CEDEX Eurobalise Laboratory Spain for providing the test facilities and test reports for the contractual BTM test activities.

References [1] Ministry of Railway China, KeJiYun [2008] No.143, Balise Application Principle for CTCS Level 2 (V1.0). Train Control System Specification for Passenger Dedicated Line, Ministry of Railway, Beijing, China, 2008. [2] Union Industry of Signaling, SUBSET-036-V2.4.1 Form Fit Function Interface Specification for Eurobalise. Brussels: Alstom Ansaldo Bombardier Invensys Siemens Thales, 2007. [3] Yang, Z., Fan, P., & Xue, R., Balise System Used in High Speed or Speedincrease Line. China Railway Science, Beijing, 23(2), pp.42-47, 2002 [4] Zhen, J., & Zhao, H., Research on Balise Transmission Module Test System, Journal of Beijing Jiaotong University, 32(2), pp. 80-83, 2008. [5] Wang, R., Zhao, H., & Wang, S., Research on Up-link Signal Simulator Used for BTM Test in Balise system. Journal of the China Railway Society, Beijing, 30(6), pp. 46-50, 2008. [6] Nicholson, M., Lecture Note: Fundamental Safety Engineering, University of York, 2008. [7] Union Industry of Signalling, SUBSET-085-V2.2.2 Test Specification for Eurobalise Form Fit Function Interface Specification. Alcatel Alstom Ansaldo signal Bombadier Invensys Rail Siemens: Brussels, 2007. [8] Zhao, H., Li, W., Zhao, M., & Liu, Z., Dynamic Characteristic and HighSpeed Adaptability of Balise Onboard Equipment. (To be published) China Railway Science, Beijing, China, 2010. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

Section 7 Metro and other transit systems

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CBTC test simulation bench J. M. Mera, I. Gómez-Rey & E. Rodrigo CITEF (Railway Technology Research Centre), Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Spain

Abstract Due to its safety characteristics, signalling equipment requires a great amount of testing and validation during the different stages of its life cycle, and particularly during the installation and commissioning of a new line or upgrade of an existing line, the latter being even more complicated due to the short engineering periods available overnight. This project aims to develop a tool to reduce the above-mentioned efforts by simulating the CBTC trackside, fulfilling the interfaces between subsystems and elements of these subsystems, and using some real elements. In this way, a testing environment for signalling equipment and data has been developed for the CBTC system. The aims of the project that were set out at the beginning of the development and completed with the present simulator are as follows:   

Real CBTC equipment trials and integration: CBTC on-board equipment, CBTC Radio Centre, etc. Other signalling elements trials and integration: interlockings and SCCs. CBTC track data validation.

In order to achieve these objectives, various simulation applications have been developed, of which the most important are the following: Infrastructure, Automatic trains, Train systems, Planning and Control Desk, etc. This system has been developed, and is currently adding new modules and functionalities, for companies of the Invensys Group: Westinghouse Rail

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486 Computers in Railways XII Systems in the UK and Dimetronic Signals in Spain, which are using it for the new CBTC lines under their responsibility. Keywords: computer techniques, management and languages (simulation), advanced train control (CBTC), equipment test.

1 Introduction The increasing expansion of underground railway networks seen in recent years to meet the growing demand has highlighted the need to integrate new signalling and rail traffic management systems, such as CBTC [1, 2], which enable line capacity to be increased as well as line operating safety. Therefore, in order to obtain a safe and reliable operation, numerous tests need to be performed, but the high costs of infrastructures as well as rolling stock make it extremely difficult to immobilize both in order to use them for testing and training. For this reason, and because sometimes it is impossible to create high risk situations to demonstrate the procedure to follow, the use of simulators is more than justified in the world of railways. Within the scope of railway simulators, we can find different functionalities, such as driving simulators and operational simulators, for testing real equipment, and analyzing data, etc. The main aims of the project with which we are dealing, are to develop a tool to reduce the effort needed to bring a new line into service, and at the same time avoid immobilizing infrastructure and rolling stock. The tool may even be used for carrying out tests prior to the physical existence of the new line. For this reason, our simulator is included among those developed for testing real equipment and analyzing data. In order to develop the simulator, all the elements needed as well as their real interfaces have been simulated, it being possible to replace each of these elements by their real equivalents. In order to attain these goals, a test environment for signalling and data equipment has been developed within the CBTC system. The aims set at the start of the project, which are being completed with this simulator, are as follows:  

Integration and testing of real CBTC equipment, such as: BPs, ATP, etc. Integration and testing of other signalling elements, such as: Interlockings, SCCs, etc.

2 System architecture design The set objectives require an independent module-based software structure to be developed so that each of the modules corresponds to a real element and can therefore be replaced by it. This layout is shown in Figure 1. The system is based on the ERTMS/ETCS simulator developed for Invensys Rail [3–5], and shares several of its modules with it. The most important elements that have been reused are: WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Automatic Train

Dynamic

DMI

ATP

Train Systems TIU

Antenna Radio

Automatic Driver

Track Circuit APR Reader Code Reader

Infrastructure

BP APR Balise

Signals Analysis Tool

Interlocking

Track Circuit

Switches

Planning and Control Desk

SCC

Real Data







Figure 1:

General layout of the CBTC Simulator.

Planning and Control Desk (PCD): this application allows the Simulator user to generate, configure, launch, etc., the different scenarios. It has had to be adapted to offer the possibility to generate and work with both an ERTMS and a CBTC scenario. Infrastructure: this is automatically generated from a configuration file containing a description of all the elements making up the infrastructure, using a specified language: track circuits, points, balises, signals, etc. The logic of each of these elements as well as their functionality has also been simulated. Specific infrastructure components have been developed for CBTC. Automatic Trains: it is possible to have up to thirty automatic trains running on the line. The train systems elements are simulated, that is, the pneumatic and electrical behaviour is modelled through their respective circuits. The vehicle dynamics have also been simulated. The driver’s actions are simulated automatically. By using automatic trains, both the performance of the on-board equipment and the infrastructure

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488 Computers in Railways XII can be checked. For example, the correct working of the BP with several trains connected to it can be checked. Of the new elements developed for adaptation to CBTC, the following are particularly important:  





APR Balise: these elements contain the telegrams from the APR trackside balises and send their contents when stimulated by the simulated train. Track Circuit with Speed Codes: these elements simulate track circuit occupation, whether it be untimely or due to an oncoming train. In addition, they load a speed code in accordance with the conditions contained in the interlocking and this is sent to the train when it invades the interlocking. Interfaces with the real modules: since various types of real equipment have had to be integrated, like the ATP or the DMI, elements have needed to be developed that can send and/or receive, as need be, the data that each piece of equipment must exchange with the simulated part. Analysis Tool: this tool allows analysing the data loaded in the BP. By taking the messages exchanged between an automatic train and the BP, a series of graphs and checks are generated that can easily check whether or not the engineering rules with which the signalling was designed are being met, as well as determining if the BP is performing properly under the circumstances specified for the analysis.

The modules that have been integrated are described below:  



ATP: the on-board train equipment has been integrated in its Host version, that is, in its software version to be run in a PC. DMI: the driver interface has also been integrated in its Host version. BP: the Block Processor Host has also been included in the simulator.

The system also continues to use the same idea to separate the communications in an independent module inside each application, as can be seen in Figure 2. In this way, maximum integration capability is achieved for real equipment, since it is ensured that the design does not change when real equipment is inserted. Communication between different applications is achieved through a ‘Host’ whose mission is to control communications and tell each application where it can find the required data. The use of the Components Technology developed by CITEF has also been maintained in respect of the base system. One component is a DLL (Dynamic Linked Library) which has a specific function. For example, a balise needs to send its content to a train when stimulated by such. Each real element has its WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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TCP/IP

Communications Module

Body of the Application

Figure 2:

Figure 3:

General layout of an application.

Communication mechanism between components.

equivalent in a component. The components involved in each application are stored in a Components Container called a Variables Register, which enables them to communicate with one another through the exchange of variables, as can be seen in the diagram in Figure 3.

3

Interfaces with real equipment

A new inter-application communication mode has had to be developed to achieve interaction with real equipment, such as the ATP Host and the DMI Host. These communications are socket-based. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

490 Computers in Railways XII The sockets enable any data flow to be reliably and orderly exchanged between two applications. To define a socket, an IP address and a port are necessary, apart from setting up a transport protocol capable of being interpreted by both applications. Since IP addresses are being used, sockets can be used between applications that are running in different computers. In the example in Figure 4, it can be seen that applications A and B are connected by a socket that joins the Port2 ports of the IP1and IP2 IPs. A socket can be made to look like a direct pipeline between two applications so that one will supply the data that the other needs to receive. The sockets enable a client-server architecture to be implemented. The name client is given to the application that initiates communication and server to the application waiting for the other to initiate said communication. That is, in the example in Figure 4, if application A makes a communication request to application B, and application B accepts it, the socket is established between both, and A plays the role of client and B the role of server. In spite of the fact that using sockets allows two-way communication, the developed system uses a one-way system so that the client only sends data and the server only receives data. Where two-way communication is required, two sockets are implemented so that both applications are servers and clients at the same time. In Figure 5, applications A and B are connected to two sockets. Socket 1 is used to send data from B to A and inside it. A is the server and B the client; to the contrary, Socket 2 is used to send data from A to B and inside it. A is the client and B the server. The real equipment integrated is accompanied by a simulation layer that lets sockets be implemented in it and serves as a data exchange interface in a format that is adapted to the core of the real equipment. Figure 6 shows the general layout of communications with the integrated real equipment. The ATP and the DMI are the elements for which sockets have had to be used.

Figure 4:

Figure 5:

Scheme of a socket.

Scheme of implemented sockets.

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The design developed by CITEF in the simulator incorporates new components whose function is to replicate the data handled by the real equipment by means of communication with a central component called an ATP Router. It is this that is connected to the ATP and the DMI through sockets. The elements developed for this purpose in the simulator are detailed below, indicating the kind of data exchanged with the ATP Router: 

  

TIU Socket: sends/receives the discreet signals handled by the ATP. For example, when the driver operates the emergency brake, the TIU receives this data, which in turn is received by the TIU Socket, which transmits it to the ATP Router for it to be sent to the ATP. Dynamic Socket: sends the speed and forward movement data that calculates the dynamics for the ATP Router to be able to send it to the ATP. APR Socket: sends the telegrams from the balises the train passes over on its journey for them to be transmitted to ATP. Socket Speed Codes: sends the speed codes that the ATP must receive as the train keeps occupying track circuits.

The ATP Router establishes the following sockets with the DMI and the ATP:    

ATP Despatch Socket: all the data required to be received by the ATP is sent through this socket; that is, balise telegrams, discreet, dynamic speed codes, messages from the BP and messages from the DMI. DMI Despatch Socket: the data to be shown to or requested of the driver at any instant as indicated by the ATP at any instant is sent through this socket. DMI Reception Socket: the actions taken by the driver on the DMI to be transmitted to the ATP are received from this socket. ATP Reception Socket: three types of data are received; the messages sent to the BP by the ATP, the discreet messages that the ATP orders the TIU to activate or deactivate and the data the ATP sends the DMI so that the latter can show it to the driver.

4 Data analysis One of the most important parts of the development undertaken is the Analysis Tool. This tool can be used to program analyses of the data exchanged between the BP and the ATP. The process followed to achieve this purpose is described below: 

A scenario is taken without automatic trains and without any set routes.

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Train Systems

TIU

Socket TIU DMI

Dynamic

Socket Dynamic

Router ATP

APR Balises

Socket APR

Track Circuits with Speed Codes

Socket Speed Codes

ATP

Signals

Switches

Infrastructure

Antenna Radio

BP

Figure 6:

Communications with real equipments.

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



 





493

In the real SCC the user sets the route to be analysed. The interlocking receives the order to set the route and sends it to the field elements that are positioned to correspond to the route in question, and when the conditions set are met, the interlocking sends a signal to the BP to indicate that the route has been authorised. The Analysis Tool is listening to the communication between the interlocking and the BP, and is thus able to detect which route has been set. When the route has been authorised, the user can request an analysis of the route in question, selecting the required analysis options. A train is automatically inserted in the simulator forward of the set route and begins to run. The train must receive 2 APR balises before entering the route. On receipt of the first balise, the train must initiate communications with the BP, and after the second APR, the BP can send the train a movement authorisation. Throughout the simulation, the messages exchanged by the train and the BP are listened to by the Analysis Tool, and, so, the tool can decide when the conditions set for the analysis have been met. At this moment, the train will brake automatically, shut off communications with the BP, and will be eliminated from the scenario. The Analysis Tool will analyse the messages that it has been listening to during the simulation and generate the graphs and checks requested by the user.

5 Advantages and functionality of the system It may, therefore, be stated that the main advantages and functionalities of the system described in this article are as follows: 

  



A reduction in the efforts required to bring lines equipped with the CBTC system into service. It offers the possibility to test different configurations of a single scenario to see which option is most advantageous. Since neither the infrastructure nor the rolling stock need be immobilized in order to carry out tests, a considerable reduction in costs is obtained. Since the same interfaces are used as with the real equipment, this means that functional tests can be performed on real equipment, with the possibility of simultaneously including one or more pieces of real equipment. It may be used to verify trackside data before it is installed, and in addition, if need be, obtain results showing where the erroneous data is located.

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Figure 7:

Some snapshots of the simulation system.

6 Future developments It is planned to extend the system in several stages in order to achieve the following objectives: 

  

To test real Target equipment, that is, the hardware version with the configuration to be installed on the track. To perform this, there will be a stage where the ATP, DMI and BP Host will be replaced by Target equipment. Target interlockings will also be tested and will also be required to be integrated. To use the system as a driving simulator by incorporating a virtual cab and a visual environment. To test the real ATO equipment, both in its Host version and Target version. Therefore, this equipment will need to be integrated into the simulator.

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7 Conclusions Since the CBTC Test Simulation Environment developed by CITEF implement exactly the same interfaces built into real equipment, it can guarantee that the behaviour of the simulated and real equipment is absolutely identical. Moreover, if we take into account that the use of simulators in a railway environment has been fully justified throughout this article, and more so in this particular example of a CBTC environment, we may state that using this test bench tool is an overriding guarantee for bringing new CBTC lines into service as well as ensuring that the different track and on-board equipment will run smoothly under absolutely any circumstances. It is also a tool for preparing data, testing and detecting any possible failures in track data. We may state that in spite of the development costs for this type of tool, the cost of track tests is reduced considerably thanks to this simulation environment, since the number of track tests is reduced, thereby reducing the use of infrastructure and rolling stock set aside for this purpose. This cost saving becomes more hidden if it is borne in mind that it is a polyvalent system, as it can be used for any line that implements CBTC. This system is being developed, and is currently adding new modules and functionalities, for companies of Invensys Rail: IRNE in the UK and IRSE in Spain, which are going to use it for the new CBTC lines under their responsibility.

References [1] 1474.1 IEEE Standard for Communications-Based Train Control (CBTC) Performance and Functional Requirements [2] 1474.2 IEEE Standard for User Interface Requirements in CommunicationsBased Train Control (CBTC) Systems [3] Gómez-Rey, A, Mera, JM, et al. ERTMS Driving and Operation Simulator under Distributed Architecture in a Virtual Reality Environment. Proceedings of ITEC’2001. Lille, France. April 2001. [4] Mera, JM, Gómez-Rey, I, et al. ERTMS/ETCS TEST SIMULATION BENCH. 10th International Conference on Computer Aided Design, Manufacture and Operation in Railway and other Advanced Mass Transit Systems. COMPRAIL X. Prague, Check Republic. June 2006. [5] Mera, JM, Gutiérrez, LM, et al. Simulation of the ERTMS / ETCS Railways Control and Protection System; Levels 0, 1 and 2. 8th International Conference on Computer Aided Design, Manufacture and Operation in Railway and other Advanced Mass Transit Systems. COMPRAIL VIII. Lemnos, Greece. June 2002.

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Development of the new CBTC system simulation and performance analysis R. Chen & J. Guo School of Information Science & Technology, Southwest Jiaotong University, China

Abstract This paper introduced the new developed CBTC (Communication Based Train Control) system simulation and performance analysis (hereafter called the simulator) in Southwest Jiaotong University from the system architecture, functions, modeling and algorithms. The simulator is a software program to simulate the movement of trains in the system. It applies the same signal rules of a project without any real hardware or software. A directed graph of track layout and the CBTC moving block train control model used in the simulator are introduced. The main purpose of the simulator is to calculate the system headway and trip speed under the current system configuration and block design. Headway is defined as the time interval between the successive trains moving along the same track in the same direction through the same point. Minimum Design Headway is a key parameter in a system. In this paper, the principle of the in-line station headway and the turn-back station headway calculation are described, and the examples of Chengdu line 1 are provided. Keywords: CBTC, train control, headway, simulation, performance analysis, safety braking model, safety distance, directed graph.

1 Introduction With the rapid development of urban railway transit, the new moving block technology CBTC has been the trend of the urban railway Automatic Train Control (ATC) system. To carry out the system simulation and evaluate the system performance is an important step in the system design stage. The main purpose of the simulation is to calculate the system headway and trip speed with the current system configuration and block design. Headway is WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100461

498 Computers in Railways XII defined as the time interval between the successive trains moving along the same track in the same direction through the same point. Minimum Design Headway is defined as the shortest headway at which the system is able to operate trains continuously. It is determined by the most restrictive point on the guideway. Minimum Design Headway is a key parameter in a system. This paper introduced the new CBTC system simulation and performance analysis tool developed in Southwest Jiaotong University from the system architecture, functions, modeling and algorithms. The Simulator is a software program to simulate the movement of trains in the system. It applies the same signal rules of a project without any real hardware and software. The new Simulator is developed with Visual C++, Matlab and GUNPLOT to provide a friendly graphical user interface and easily to configure the operation scenarios. It is not only used for the performance analysis and block design optimization, but also to provide an open platform for the studying of the key algorithms in CBTC system.

2 Software structure and system function Fig. 1 illustrates the system function and the software structure in the most abstract level. There are three core modules in the System Simulation and Performance Analysis: ModeLib, Simulation and Analysis. ModelLib is the train control model library in the software. It includes the following main models called by the Simulation module in every simulation step: Safety Distance model, Braking model, Accelerating model, Station Stop model, Station Departure model, etc. Train control model library is configured offline to be suitable with the particular project using Matlab.

Figure 1:

System function and the software structure.

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Simulation module simulates the movement of all the trains in the system. The simulation scenarios can be configure in the database or dynamically input from the Configuration Workstation. Input information for the Simulation is Guideway database, Vehicle parameters and Simulation scenarios. The Simulation module has an interface to ATS (Automatic Train Supervision) or ATS simulator to display the real-time system status, such as the wayside status, route status, train type, train location, etc. Another type of simulation view is provided by the graphic Time/Distance and Velocity/Distance plots. The analysis module imports the Simulation Data recorded by the Simulation module (including: position, velocity, acceleration, grade, speed restriction, target speed, distance to go, etc) and perform the system parameter calculation. Headway/Distance plot is the most important output of the analysis report, from which we can find out the Minimum Design Headway and where the most restrictive point is in the system.

3 System principle 3.1 Guideway database The guideway database is based on the directed graph, a standard mathematical topology representation. It has a set of Nodes and Edges. Any location where the track diverges, converges, changes the direction of travel or ends is called a Node. The track that connects two nodes together is called an Edge. Typically, the switch and the track end are represented by the node in the topology. Each Edge has a default direction that travels from a source node to a destination node. Normally, the edge direction is same with the track direction. Each Edge and Node has a uniquely ID in the system. The position of the trains and the wayside objects in the system can be defined as vector.

Figure 2:

Train location in the directed graph.

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500 Computers in Railways XII Fig. 2 is the example of Directed Graph in Chengdu line from Wenshu Monastery Station to Luomashi Station. Position of train 1 and train 2 is and respectively. To have a standard and open mathematical topology representing the guideway database is an important technology for the interoperability of CBTC system. When a train transfers from one CBTC system to another, it downloads the new guideway database at the entrance and report the position to the new Zone Controller. Zone Controller will then calculate the movement authority and send back to Onboard Controller. Both of these messages use the method to define the position in the moving block system database. 3.2 System simulation 3.2.1 Safety braking model Fig. 3 is a typical safe braking model recommended by IEEE 1474.1 [1]. In the figure, the emergency brake curve is the worst-case, open-loop, speed/distance curve a train will follow once the ATP has initiated an emergency brake application. This emergency brake curve must always be less than or equal to the safe speed curve, where safe speed is defined as the speed above which a critical hazard (derailment or collision) could occur. In this model, safety factors are accounted for in the emergency brake curve, train position uncertainties, and other additional measurement tolerances incorporated in the CBTC system design, and there is no requirement to add additional safety margins. The ATP over speed detection curve is the speed-distance curve that the ATP subsystem uses to immediately initiate an emergency brake application, if the ATP subsystem detects that the measured speed exceeds this curve at the measured train location. When the ATP subsystem has initiated an emergency brake application, the ATP subsystem is no longer in the control loop, and the

Figure 3:

Typical safe braking model.

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train will emergency brake at or below the emergency brake curve. The emergency brake curve includes an initial propulsion runaway period, until propulsion is disabled. The ATP profile curve is the speed-distance curve that is an ATP over speed allowance below the ATP over speed detection curve. The ATP profile is the base curve used by the ATP subsystem. 3.2.2 Safe train separation Fig. 4 is the Safe Braking Model used in the Simulator. The model defines the principle, assumptions, process and parameters of Safety Distance calculation. All these parameters are imported from the configurable database and can be adjusted to meet the particular project requirements. This Safe Braking Model is an application of the typical safe braking model recommended by IEEE 1474.1 to calculate the Braking Distance, Safe Braking Distance and the Safety Distance. Braking Distance is the distance to the normal stop point with the normal brake rate. Safety Braking Distance is the braking distance in the worst case. The relationship is,

SafetyBrakeDist  BrakeDist  SafetyDist SafetyTrainSeparation  SafeBrakeDist  PositionUncertain

(1) (2)

In the simulator, ATO curve is N seconds (it is configurable) afterward the ATP enforcement curve. System will always try to drive the train along with the ATO profile in the simulation.

Figure 4:

Safe braking model and the train separation in the simulation.

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502 Computers in Railways XII Since the headway is defined as the non-interfering train separation, the Disturb Point is the position of ATO starts to reduce the speed according to braking model. As expressed in eqn (2), Safety Train Separation is the Safety Braking Distance plus the additional Position Uncertainty. 3.2.3 Train movement and train speed calculation System uses a continuous time slice approach to calculate the train position and velocity. Each time the simulator advances one time slice and calls the train control model to calculate the position information for all trains. The below functions describes the relationship between time, velocity, and acceleration,

vi 1  vi  ai t ; ai t 2 S i  vi t  ; 2 S i 1  S i  S i .

(3)

t , is the time slice; vi

where

, is the train initial speed at the time slice;

vi 1 , is the new train speed in this step, also the initial speed for next time slice; ai , is the acceleration for this time slice, to simplify the calculation, the constant

S i , is the distance train travelled in the time slice;

acceleration is used for one time slice;

S i , is the initial position of the train; S i 1 , is the new position of the train. Every step when the current train velocity is calculated, Simulator will compare it to the ATO profile to determine the drive mode: braking, accelerate, or coasting. 3.3 Minimum headway calculation As defined before, Headway is defined as the time interval between the successive trains moving along the same track in the same direction through the same point. Minimum Design Headway is defined as the shortest headway at which the CBTC system is able to operate trains at its maximum ATO speed continuously. It is determined by the most restrictive point on the guideway. 3.3.1 Minimum interstation headway Interstation headway is the time interval of the successive trains without considering the station stop. Fig. 5 illustrates s the principle of the minimum interstation headway calculation. In this figure, ATP profile is not showed. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 5:

503

Minimum interstation headway.

The disturb point of train 2 is the train at its maximum ATO speed and start to reduce according to the brake model defined in section 3.2.2. The time of train 2 travelling from the disturb point to the position X (rear of train 1) at its maximum ATO speed is the minimum headway of position X. Then we can have,

THeadway 

S SservicBrake  S SafetyDis tan ce  STrainLength  DUncerta int y VMax _ ATO

(4)

where

THeadway

, is the minimum headway;

S ServiceBrake , is the service brake distance for train 2 at the maximum ATO speed;

S SafetyDis tan ce STrainLength

, is the safety distance calculated by the brake model;

, is the length of train;

DUncerta int y

, is the position uncertainty of the proceeding train;

VMax _ ATO

, is the maximum ATO speed of train 2. From the above principle, in the simulator, minimum headway of each position can be calculated by simulating only one train. Since the simulated train is always trying to run at the maximum ATO speed, system can scan back to find out the position and the time of the disturb point, and then calculate the actual time it travelled through to get the minimum headway.

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504 Computers in Railways XII

Figure 6:

Minimum station headway model.

3.3.2 Minimum station headway Fig. 6 is the model to determine the minimum intermediate station headway. According to this model, the constraint for the non-interfering train separation requires that Train 1 has cleared the station by a SD beyond the station stopping point (Position uncertainty of the proceeding train is taken into consideration), at the time that Train 2 is a braking distance away from the station stopping point (i.e. the disturb point). System can be configured to use the full service brake rate or a certain constant brake rate to calculate the braking distance. From this model, we can have

THeadway  TEntry  TDwell  TExit  TPr ocess

(5)

where

TEntry

, is the time for the train to travel from the disturb point to the station stop point along with the ATO profile;

TDwell , is the station dwell time; TExit , is the time for the train to travel from the station stop point to its rear is an safety distance plus the position uncertainty beyond the station stopping point;

TPr ocess , is the system process delay time. Fig. 7 is the example of minimum interstation and station headway calculation of Chengdu line1 from Luomashi Station to Centaury City Station. Please note that the used guideway parameters and singling system parameters are not exact the same with the real project. If we assume the system is based on moving block principle, we can see the most restrictive point of minimum headway in the system is always at the station area for the reason of station dwell WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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time. With this figure, we can directly know the minimum design headway and where the most restrictive point is in the system. If the minimum design headway is greater than a project requirement, most likely, it can be optimized at this point. 3.3.3 Minimum turn-back headway Minimum turnback headway is calculated by multiple trains operation through the switches. The following scenario is the Chengdu line 1 Shenxian Lake Station with turnback for 2 trains by the same route.

4 Conclusion The main purpose of the System Simulation and Performance Analysis tool is to calculate the system headway and trip speed with the current system configuration and block design. It is characterized by scanning all the simulation

Figure 7:

Example of the minimum headway calculation.

Figure 8:

Turnback headway calculation.

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506 Computers in Railways XII

Table 1: Time T0

T1=T0+N1

Turnback headway calculation.

Train 1 Train 1 departs from Shenxian Lake arrival platform Train 1 clear switch W2109 (associated axel counter).

T2=T1+N2

ATS commands W2109/W2111 move to normal position W2109/W2111 moved to normal position. Movement authority of Train 2 extended to S2125.

T3=T2+N3

T4=T3+N4

T5=T1+N5

T6=T5+N6

T7=T6+N7

T8=T5+N8

Train 1 Clear switch W2105 (associated axel counter). ATS commands W2105/W2107 move to reverse position W2105/W21071 moved to reverse position. Train 1 arrives the turnback position

T9=T4+N9

T10=T8+N10 T11=max(T7,T1 0)+N11

T12=T11+N12

T13=T12+N13

Train 2

Action Time

N1 seconds travel time to clear W2109. N2 seconds for system processing delay. N3 seconds for switch moving. N4 seconds for system processing delay N5 seconds travel time to clear W2105. N6 seconds for system process delay N7 seconds for switch moving.

Train 2 arrives at the Shenxian Lake arrival platform Train 1 direction changed. Movement authority of Train 1 extended to X2102. Train 1 departs from the turnback position. Train 1 Clear switch W2107 (associated axel counter). ATS sets route for Train 2 to the turnback track.

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N8 seconds travel time N9 seconds travel time (from the disturbing point) N10 seconds for direction change N11 seconds for system processing delay

N12 seconds travel time to clear W2107. N13 seconds for system processing delay

Computers in Railways XII

Table1: T14=T13+N14

T15=T14+N15

T16= T9+N16 T17=max(T15,T 16)+N17

507

Continued.

W2109/W2111 moved reverse position, W2105/W2107 to normal position. Movement authority of Train 2 extended to turnback position. Train 2 dwell time expired Train 2 departs from Shenxian Lake arrival platform Minimum Turnback Headway = T17

N14 seconds for switch moving.

N15 seconds for system processing delay N16 seconds dwell time N17 seconds for system processing delay

data to calculate the actual distance and time the train travelled, and the train movement is based on train control model library. We used Chengdu line 1 as the example for the design and development validation. Besides assisting the system design, the tool also provides as an open platform for the train control model optimization study. Some future study is now carried on this platform, such as energy saving, automatic design optimizing of headway, automatic design optimizing of system capacity, etc. In addition, the tool itself is under the improvement to have faster simulation speed, friendlier user interface, more flexible to build in a new mathematical train control model, easier to create the guideway database, etc.

References [1] IEEE Std 1474.1 - 2004, IEEE Standard for Communications-Based Train Control (CBTC) Performance and Functional Requirements. 2005. [2] Bavafa-Toosi Y., Blendinger C., Mehrmann V., Steinbrecher A. & Unger R., A new methodology for modeling, analysis, synthesis, and simulation of time-optimal train traffic in large networks. IEEE Transactions on Automation Science and Engineering, 5(1), pp. 43-52, 2008. [3] Tang, T. & Huang, L., A survey of control algorithm for automatic train operation. Railway Journal, 25, pp.98-102, 2003. [4] Chen, L., Ning, B. & Xu, T., Research on modeling and simulation of vehicle-on-board automatic train protection subsystem of communication based train control system. Vehicular Electronics and Safety, 2007. ICVES. IEEE International Conference, pp. 1-5, 13-15, 2007. [5] Liu, W., Li, Q. & Tang, B., Energy saving train control for urban railway train with multi-population genetic algorithm. Information Technology and Applications, 2009. IFITA '09. International Forum, pp. 58-62, 15-17, 2009. [6] Ke B. & Chen N., Signalling block layout and strategy of train operation for saving energy in mass rapid transit systems. IEE Proc.-Electr. Power Appl., 152(2), pp.129-140, 2005. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Efficient design of Automatic Train Operation speed profiles with on board energy storage devices M. Domínguez1, A. Fernández1, A. P. Cucala1 & J. Blanquer2 1

Instituto de Investigación Tecnológica, Escuela Técnica Superior de Ingeniería (ICAI), Universidad Pontificia Comillas, Spain 2 Metro de Madrid, Spain

Abstract Energy usage in electrical railway systems is being studied in order to find technologies and developments for increasing energy efficiency. It is not only an environmental problem, but also concerns railway infrastructures as an economical aspect. The problem is finding which system to invest in for decreasing energy consumption and costs. In this paper, two possibilities are studied. The first one is the redesign of the ATO (Automatic Train Operation) speed profiles of metro lines. The speed commands in service nowadays were selected based on time and comfort criteria. In addition, in this paper the consideration of energetic criteria is taken into account. Complementing the previous possibility, the implementation of an on board energy storage device is evaluated. The regenerated energy of electrical brakes in metropolitan railways is not used if there is no other train starting up at the same time, and it is wasted with heating resistors. With the aim of taking advantage of regenerative energy, the economical and energetic advantages of investing in an on board storage device, despite its additional mass, are studied. Both approaches are finally jointed, obtaining speed profiles that are even more efficient with the implementation of the device. Optimal Pareto curves, where the best solutions are placed, are modified taking into account on board storage devices. A simulator is needed for the proper simulation of all the possible speed commands. It has been developed and validated with measurements in line 10 of the Madrid Underground. Solutions show that about 25% of energy savings are expected with only the speed profiles redesign. In addition, it is shown how these WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100471

510 Computers in Railways XII optimal speed profiles are modified when an on board energy storage device is also taken into account. Keywords: regenerative energy, ecodriving, electrical brake, metropolitan trains, ATO speed profiles, optimal design.

1

Introduction

Energy efficiency in railway systems is nowadays a key topic being studied in order to reduce energy consumption and costs. With this end in view, different technologies, developments or strategies are being researched, and tested from the point of view of driving optimization, capacity and optimal use of regenerative braking. In order to find speed profiles which optimise energy use, mathematical models have been applied to principally optimal control techniques. In [1] the optimal speed profile it is calculated with the maximum principle. The study in [2] considers the problem of the optimal driving strategy based on a generalised equation of motion that can be used in discrete and continuous control. The result is a theoretical approach to the search for the switching points of the driving mode. The authors of [3], seeing the difficulties of resolving the optimal control problem with numerical techniques, developed a discrete dynamic programming algorithm. They use kinetic energy instead of speed and obtain an analytical solution in real time. In [4] Bellman’s dynamic programming has also been used to optimise the running profile of a train. The authors transform the original problem into a multistage decision process accomplished by linearization and time-uniform discretization. These approaches include simplifications in their track, trains and driving models. This means that they are not appropriate for the optimal design of Metro ATO speed profiles given the short inter-stations in metropolitan lines, and the differences of a few seconds between the ATO profiles to be designed. Therefore, accurate models are needed. The difficulty involved in the analytical resolution of the problem means that approaches based on simulation are an alternative. They do not require simplifications and enable an accurate calculation of running times and energy consumption, as [5] manual driving modelling for freight trains. A number of optimisation techniques have been used in combination with simulation. In [6], genetic algorithms (GA) are used. A fitness function with variable weightings was used to identify optimal train trajectories. The influence of the weightings is clear. Artificial Neural Networks have also been used. In [7], it is proposed that they are used to obtain the optimal coasting speed. The objective function is formulated by considering the cost of energy consumption and the cost of passenger travelling time. In [8], Chang et al. include Pareto efficiency in differential evolution to find a trade-off between punctuality, consumption and comfort. However, these models cannot be applied to the realistic case of the Madrid Underground. The features of the ATO system considered (see [9]) make necessary a different approach which, rather than using a continuous control curve, optimises the discrete configuration parameters of the equipment and takes also into account operative and comfort restrictions and the highly irregular track gradients. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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With respect to regenerative braking, different approaches are also found. The regenerated energy of electrical brakes in metropolitan railways is not used if there is no other train starting up at the same time in the same electrical section, or there are not inverting substations, with the result that the energy is wasted heating resistors banks. This means that it is needed to equip the system with devices which allow storing energy in the train or at substations [13]. These devices can be supercapacitors, flywheels and SMES (Superconducting magnetic energy storage). Their advantages against regeneration between trains, is that it is not needed another train available to use the regenerated energy [10]. Moreover they can be used for voltage regulation [11] and reducing energy demand without having an effect in transport efficiency and punctuality [12]. The storage devices at substations require energy to be transferred using the lines, which leads to transmission loses. So, it is avoided by placing the device on-board vehicles [14]. This paper takes and combines two of those strategies. First of all, the redesign of the ATO speed profiles of a line of the Madrid Underground has been carried out. In the Madrid Underground, trains are operated according to the speed commands they receive from balises. These commands define a particular speed profile and running time, with associated energy usage (consumption). The design of speed profiles usually takes into account running times and comfort criteria, but not energy consumption criteria. In this paper, a computer aided procedure for the selection of optimal speed profiles, including energy consumption, which does not have an effect on running times is presented. It is a continuation of the work in [9]. To this end, the equations and algorithms that define the train motion and ATO control have been modelled and implemented in a very detailed simulator. This simulator includes an automatic generator of every possible profile and a graphical assistant for the selection of speed commands in accordance with decision theory techniques. It has been developed and validated with measurements in line 10 of the Madrid Underground. Secondly, it is evaluated the implementation of an on board energy storage device analyzing the advantages in a new design in which the regenerated energy can be stored and feed the train, without forgetting the additional mass of this device. With that new design, speed profiles even more efficient with the consideration of the on board storage device, are obtained. Some authors have suggested to optimize the charge/discharge of the energy storage devices and speed profiles together [15]. With the aim of developing a realistic study, in this paper it is the train and the speed profile he follows what leads the operation of the storage device. Thus, a redesign has been carried out obtaining new modified optimal Pareto curves, where the best solutions are placed.

2 Models and simulator 2.1 Operation When designing an energy efficient driving pattern, the decision variables are running time and energy consumption whilst the comfort criteria must be met. The proposed design method is based on the accurate simulation of all the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

512 Computers in Railways XII E. STORAGE 450

400

350

ATO Config.

)

300

s ue o (

Track data

250

CONSUMPTION

200

150

100

50

0 0

s, v

ATO

Balise: command

Figure 1:

ao

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

MOTOR

Train data

Fmotor

DYNAMICS

E

ao, v, s

Track Running Resistance data Coefficients

Block diagram of the modularized model simulator.

possible combinations of ATO speed commands for each inter-station in order to obtain precise results of these variables. To achieve this accuracy, the simulation model has been modularized. Each module represents the different subsystems of a real train (Figure 1). The simulator is composed of five modules: ATO equipment simulator, motor, train dynamics model and train consumption model as well as the model of an on board energy storage device. This modular architecture allows the validation of each module separately and an easy adjustment for specific features of a particular ATO equipment. To this end, the simulator input interfaces are designed to enable the definition of track layout, train characteristics, and ATO system configuration. The ATO model represents the control logic of the driving. At each simulation step, the position and speed of the train is inputted there. Then, an acceleration set value is calculated depending on the state of the train: motoring, braking to target speed, braking to stop etc. This value is sent to the motor module which translates it as the ratio between required force and maximum traction force corresponding to the speed at each simulation step. Motor needs the mass of the train plus the rotational inertial effect and the traction effort available depending on the speed to calculate the force needed to follow the acceleration set value of the ATO module. Then, a jerk limitation checks there are no abrupt changes in force in transitions like traction-braking or brakingtraction in order to assure the comfort of passengers. Subsequently, the new acceleration, speed and position of the train must be calculated. For that purpose the resistance to train movement is needed. The track gradient resistance Fg is the resistive force due to gravity, positive for ramps and negative for slopes and it is calculated from a list with the initial and final points of downhill and uphill sections, their values, and the slope transition curves. Curves are treated as equivalent slopes added to the actual ones. At each simulation step, an average of the gradient where the train is situated is calculated. Finally, the energy consumption E is recalculated according to the time increment ∆t and the current I at each simulation step. A constant line voltage U WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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is assumed. The current corresponding to the maximum force is the only one known. The consumed one could be calculated, assuming a constant efficiency, but this assumption would not be realistic. Therefore, a model including the variation of the efficiency depending on the ratio between the required and the maximum force is needed. Train velocity, acceleration, traction or brake force and energy consumption are computed at each simulation step and they would be the input data for the next simulation step. 2.2 Measurements In order to record real data of trains in Line 10 of the Madrid Underground, a laptop was connected on board to the Traction Control System while trains were travelling with flat-out. These measurements have been used to adjust the simulator and validate some data. For example, the differences between the theoretical motor curves and the real ones (measured) have been found and they are shown in Figure 2. The empirical curves are now used instead of the provided one. A comparison of complete simulations and measured data of running times and energy consumption was also carried out in order to validate the simulator. An average difference of 7.7% in energy usage and 1.4% in running times is obtained. 2.3 Simulations The simulator combines all the possible commands that the ATO system provides. Thus, all the possible speed profiles for each inter-station are obtained. The solution space is plotted in a time-consumption graph with every profile characterized by its running time and consumption. Moreover, the simulator indicates which profiles are not available to be implemented because of comfort or operational restrictions. An example is given in Figure 3. In the Madrid Underground, four alternative speed profiles per inter-station need to be programmed in the Traffic Regulation System. This set of profiles has increasing running times from the first (flat out, the fastest) to the fourth (slowest). If the optimal profiles are chosen, they will also have decreasing Current

Current (A)

Traction Effort (N)

Traction effort

Measured traction force

Measured current

Theoretical maximum traction force (maximum load)

Theoretical current (maximum load)

Theoretical maximum traction force (tare)

Theoretical current (tare)

Speed (km/h)

Figure 2:

Speed (km/h)

Experimental and theoretical motor curves.

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514 Computers in Railways XII Solution Space F1 Possible speed profiles

Consumption (kWh)

10,5

Selected speed profiles Uncomfortable speed profiles Pareto curve

8,5 6,5 4,5 2,5 97

Figure 3:

107

117

127

137 147 Running Time (s)

157

167

177

187

Solution space of Fuencarral platform 1 with uncomfortable speed profiles.

energy consumption according to the shape of the Pareto curve as it is shown in Figure 3. Consequently, this is a multicriteria problem where the aim is to find an appropriate trade-off between energy consumption and running times. Decision theory techniques have been used to solve it. The proposed procedure follows three criteria: domination, sensitivity and uniform distribution of running times. The following description of the procedure will be illustrated with a realistic application of the Madrid Underground. The energy consumption and running times of the current speed profiles and the proposed ones will be compared in order to value the achievable energy saving.

3 Case study The procedure has been applied to the redesign of all the ATO speed profiles of Line 10 of the Madrid Underground. Some considerations must be taken into account: - Four speed profiles per inter-station are looked for. - The selected speed profiles must be comfortable. - The first profile is the flat out. - The maximum running time gap between the fastest and the slowest speed profile is limited in practice, so the slowest profile must be moved and placed before the flat slope of the Pareto curve if it is necessary to observe this restriction. The advantages obtained with the redesign are: - A temporal uniform distribution of the four speed profiles for each interstation. An example is given in Figure 4 where proposed design and current one are compared. The speed profiles 3 and 4 of the current set consume the same energy with different running times. It takes the second and third profile almost the same time to travel the inter-station and the flat area of the Pareto curve is hardly well-spent. In contrast, the new design proposes profiles over the Pareto curve with a similar gap time between them which favours a proper operation of the traffic regulation system. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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- Comfort. The example in Figure 5 is clear. The speed profile in service nowadays in Lago platform 1 is quite uncomfortable because of the consecutive periods of coasting and motoring. The simulator allows verifying the comfort of the selected profiles. - An important energy saving without almost affecting running times is achieved. Figure 6 shows how up to 35.1% savings is reached in Santiago Bernabéu platform 1 maintaining the running time between stations. The simulation results show that as an average 20.6% of savings are expected with the sets of profiles redesign being even 25.0% with the newly designed speed profile number 4 (see Table 1). These results are achieved with only a 2.6% of running times modification. Although the results are based on simulations yet, it is a reliable value since the simulator has been validated with real measurements.

Solution Space NM1 6,5

Possible speed profiles Consumption (kWh)

5,5

Selected speed profiles Current speed profiles

4,5 3,5 2,5 1,5 78

Figure 4:

Figure 5:

80

82

84

86 Running Time (s)

88

90

92

94

Temporal uniform distribution of the proposed speed profiles in contrast with the current design.

Proposed speed profile instead of the uncomfortable current one.

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516 Computers in Railways XII Solution Space SB1 6,5

Consumption (kWh)

Possible speed profiles 5,5

Selected speed profiles Current speed profiles

4,5

35.1% savings 3,5 2,5 1,5 62

67

72

77

82

87

92

97

102

107

Running Time (s)

Figure 6:

Energy saving achieved with the newly designed profiles.

4 Consideration of an on board energy storage device As shown before, with a proper design of the ATO speed profiles it is possible to achieve important savings due to the selection of the optimal strategies of speed holding or coasting-motoring which decreases the traction consumption. Too furthermore, taking into account the advantages of the regenerative brake, the total energy consumption of the train could decrease considerably. One of the possible technologies for a well spending of this energy is the use of on board energy storage devices. Therefore, a study of the convenience of the implementation in trains of these devices has been carried out. Trains could store their own regenerated energy while braking and use it during the next starting. In order to evaluate the convenience of using them, the previous design has been carried out again considering on-board storage devices, so optimal curves are modified. With the aim of obtaining realistic results, an on board device with actual features has been look up on the bibliography. The selected technology to be incorporated is “MITRAC Energy Saver” of Bombardier [16]. It is working from September 2003 in a LRV on Mannheim. It is composed by 640 UltraCaps with a capacity of 1800F each. Its mass is 477kg with a maximum power of 300kW. Simulations have also been carried out in a European metropolitan system in an 8 vehicles train and a tare of 165t with 6 devices of 1.5kWh each [17]. In the present study 4 devices have been assumed with a total mass of M=477x4=1905kg, a maximum power of 300kW and 4x1.5=6kWh possible energy to storage. Moreover, 95% efficiency has been assumed [18]. 4.1 Particular cases Before showing the obtained results, two particular (but real) cases are going to be detailed. One of them is an inter-station situated in an uphill section (Lago platform 1) and the other one in a downhill section (Batán platform 2). Both, the two flat out speed profiles as well as the track gradient are shown in Figure 7. The redesigns have been carried out taking into account an on board energy storage device which is 50% charged before travelling an inter-station. In Figure 8 the solution space of the uphill inter-station is shown. It is possible to see the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

Computers in Railways XII

Lago Platform 1

80

Maximum speed Speed profile

70 60

Speed (km/h)

60 50 40 30

50 40 30 20

20

Maximum speed

10

Speed profile 34477,7

Position (m) 34977,7

35477,7

Elevation (m)

0 33977,7

ATO track gradient Real track gradient

Figure 7:

10 0 15559,08

16059,08

Position (m) 16559,08

17059,08

ATO track gradient Real track gadient

Elevation (m)

Spped (km/h)

Batán Platform 2

80

70

517

Particular cases. Solution Space L1 Possible speed profiles

24,5

Consumption (kWh)

Selected speed profiles 22,5

Uncomfortable speed profiles 20,5

Current speed profiles 18,5

Speed profiles with storage device Potential speed profiles to be selected with storage device

16,5 14,5 136

146

156

Design without storage R. Time (s) E.Consumption (kWh) 137.3 23.32 139.1 22.89 140.5 22.92 142.2 22.92

Figure 8:

166 176 Running Time (s)

Design with storage R. Time (s) E.Consumption (kWh) 137.4 20.75 144.3 17.64 143.1 18.33 146.6 17.64

186

196

Differences R. Time (%) E.Consumption (%) -0.07 11.05 -3.70 22.95 -1.85 20.00 -3.09 23.03

Possible design in Lago platform 1.

comparison between the design previously done without storage and the current one. Speed profiles characteristics are shown in the table as well as the energy saving expected: up to 23% with the fourth profile. A comparison with the profiles currently in service is not possible because they are not observing the comfort restrictions defined for the new design. Being the inter-station on a downhill section, the potential saving is higher because of the necessity of using electrical braking. An example is given in Figure 9 where almost a 50% of saving would be possible to achieve with the second profile. Doing the comparison with the profile number 2 currently in service, the consumption could decrease almost 70%. It is important to notice that in both cases the speed commands which lead to the selected profiles, change when a storage device is considered. That is to say that the optimal profiles are moved not only in “y” axis but also could have a different running time (besides the additional time that the mass storage can mean). On the contrary, there are inter-stations where the optimal Pareto curve is just moved in consumption as the example in Figure 10. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

518 Computers in Railways XII Solution Space B2 Possible speed profiles

5,5

Consumption (kWh)

Selected speed profiles 4,5

Current speed profiles Speed profiles with storage device Potential speed profiles to be selected with storage device

3,5

2,5

1,5 143

153

163

173

183

Running Time (s)

Design without storage R. Time (s) E.Consumption (kWh) 144.2 5.50 146.5 3.24 150.4 2.18 154.0 1.64

Figure 9:

Design with storage R. Time (s) E.Consumption (kWh) 144.2 3.77 148.1 1.67 150.9 1.29 154.0 0.88

Differences R. Time (%) E.Consumption (%) -0.03 31.36 -1.09 48.52 -0.33 40.85 0.00 46.34

Possible design in Batán platform 2. Solution Space F1

12

Possible speed profiles

Consumption (kWh)

10

Potential speed profiles to be selected with storage device Selected speed profiles

8 6

Uncomfortable speed profiles

4 2 0 98

108

118

Figure 10:

128

138

148 158 Running Time (s)

168

178

188

Possible design in Fuencarral platform 1.

5 Results Table 1 shows average results of savings. With the implementation of an on board 50% charged storage device, energy consumption would decrease up to 40% regarding to the previous design proposed. It would be 47.5% of savings regarding to the current situation, that is to say, to the speed profiles in service nowadays in Line 10 of the Madrid Underground. Moreover, the additional mass of the device only increases the running time of the flat out in 0.03%.

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Table 1:

Profile 2 Profile 3 Profile 4

519

Summary table. Average energy savings with the proposed designs.

Design without storage regarding to current design R. Time (%) E. saving (% ) -0.49 16.38 -2.24 20.38 -5.19 25.05 -2.64 20.60

Design with storage regarding to design without storage R. Time (% ) E. saving (%) 0.08 32.29 -0.07 33.69 0.05 35.85 0.02 33.95

Design with storage regarding to current design R. Time (%) E. saving (%) -0.41 43.38 -2.32 47.21 -5.14 51.92 -2.62 47.50

6 Conclusions A detailed simulator of the particular ATO system of the Madrid Underground has been developed in order to obtain a realistic simulation that allows calculating slight differences between alternative speed profiles. In the case study of Madrid Underground, these differences can be a few seconds. Thanks to that, it has been possible to carry out a realistic design of the speed profiles of Line 10. The newly designed profiles against the speed profiles currently being used result in 20% of savings as an average. Too furthermore, taking into account the implementation of an on board storage device, up to 47.5% of savings could be expected regarding to the currently speed profiles. The design has been carried out with an initial charge of 50% supposed. It would be possible with negligible increase in the running times of the fastest speed profiles (flat out) due to the additional mass of the storage device.

References [1] Khmelnitsky, E., "On an Optimal Control Problem of Train Operation," IEEE Transactions on Automatic Control, vol. 45, pp. 1257, 2000. [2] Howlett, P., "The Optimal Control of a Train," Annals of Operations Research, vol. 98, pp. 65, 2000. [3] Franke, R., Terwiesch, P., and Meyer, M., "An algorithm for the optimal control of the driving of trains," Proceedings Of The 39th IEEE Conference On Decision And Control, Vols 1-5, pp. 2123-2128, 2000. [4] Ko, H., Koseki, T., and Miyatake, M., "Application of dynamic programming to the optimization of the running profile of a train," Computers in Railways IX, vol. 15, pp. 103-112, 2004. [5] Lukaszewicz, P., "Energy Consumption and Running Time for Trains," in KTH, Department of Vehicle Engineering: Royal Institute of Technology, Stockholm, 2001, pp. 153. [6] Bocharnikov, Y. V., Tobias, A. M., Roberts, C., Hillmansen, S., and Goodman, C. J., "Optimal driving strategy for traction energy saving on DC suburban railways," IET Electric Power Applications, vol. 1, pp. 675, 2007. [7] Chuang, H. J., Chen, C. S., Lin, C. H., Hsieh, C. H., and Ho, C. Y., "Design of optimal coasting speed for saving social cost in mass rapid transit systems," 2008 Third International Conference On Electric Utility WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

520 Computers in Railways XII

[8]

[9]

[10]

[11]

[12] [13] [14] [15]

[16]

[17]

[18]

Deregulation And Restructuring And Power Technologies, Vols 1-6, pp. 2833-2839, 2008. Chang, C. S., Xu, D. Y., and Quek, H. B., "Pareto-optimal set based multiobjective tuning of fuzzy automatic train operation for mass transit system," IEE Proceedings-Electric Power Applications, vol. 146, pp. 577583, 1999. Domínguez, M., Fernández, A., Cucala, A. P., and Cayuela, L. P., "Computer-aided design of ATO speed commands according to energy consumption criteria," Computers In Railways XI - Computer System Design And Operation In The Railway And Other Transit Systems, vol. 103, pp. 183-192, 2008. Ramos, A., Peña, M. T., Fernández-Cardador, A., and Cucala, A. P., "Mathematical programming approach to underground timetabling problem for maximizing time synchronization," Revista CEPADE, pp. 95, 2008. Sagareli, S. and Gelman, V., "Implementation of new technologies in traction power systems," presented at Proceedings of the 2004 ASME/IEEE Joint Rail Conference (IEEE Cat. No.04CH-37550), Siemens, "SITRAS® SES Acumulador de energía para el transporte de cercanías." Gunselmann, W., "Technologies for Increased Energy Efficiency in Railway Systems," 2005. Chymera, M., Renfrew, A., and Barnes, M., "Energy Storage Devices in Railway Systems," IEE, 2006. M. Miyatake, K. Matsuda, and Haga, H., "Charge/discharge control of a train with on-board energy storage devices for energy minimization and consideration of catenary free operation," presented at Computer in Railways XI, Toledo, Spain, 2008. Steiner, M. and Scholten, J., "Energy storage on board of DC fed railway vehicles PESC 2004 Conference in Aachen, Germany," Pesc 04: 2004 IEEE 35th Annual Power Electronics Specialists Conference, Vols 1-6, Conference Proceedings, pp. 666-671, 2004. Steiner, D. M., Klohr, M., and Pagiela, S., "Energy storage system with Ultracaps on board of railway vehicles," 2007 European Conference on Power Electronics and Applications, Vols 1-10, pp. 982-991, 2007. Gay, S. E. and Ehsani, M., "On-board electrically peaking drive train for electric railway vehicles," presented at 2002 IEEE 56th Vehicular Technology Conference Proceedings (Cat. No.02CH37359)

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Research on the load spectrum distribution and structure optimization of locomotive traction seats W. Wang, M. Wang & Z. Liu Laboratory for Structural Strength Testing, Beijing Jiaotong University, China

Abstract As a result of long-time service, cracks started to emerge in traction seats of the 6K locomotive. There is an urgent need for more reliable traction seat structures. The stress-time history of key points of traction seats was measured under 6K locomotive operation condition. By methods of mean stress amendment and load identification, the load spectra for six traction seats were compiled. The statistic inference and the fitting test were processed on the load spectra, and the Weibull distribution function and the maximum load were deduced in order to have a more comprehensive understanding of the load distribution. With the help of ANSYS code, the optimization structures of traction seats were designed and the dynamic stress test was carried out. Combined with the S-N curve and Miner Law, the equivalent stress amplitudes of key points relative to service life were calculated. The results show that all of the equivalent stress amplitudes are less than the fatigue limit and the optimization structures meet with the operation requirement. Keywords: load spectrum, equivalent stress amplitude, Weibull distribution, traction seat.

1 Introduction The traction mechanism, transmitting the locomotive traction force, is one of the most important parts, ensuring the safety of the locomotive. As a result of long service under heavy loads, fatigue cracks emerge in traction seats of the 6K locomotive. The quantity and size of cracks are increasing seriously, which has caused a security risk to the locomotive in service [1]. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100481

522 Computers in Railways XII The “Z”-style traction rod is the typical type of 6K locomotive. Six traction seats are welded to the wagon underframe and six traction rods are connected to three bogies, respectively. Statistics revealed that about 76% of cracks initiated from the welds between the cover plate of the traction seats and underframe beam [2]. Figure 1 shows the traction system and the connection between the traction seat and the underframe structure. Based on the data processing of the stress-time history signal of the traction seats, the load spectra are compiled for each traction seat. Using the MATLAB tool, statistical inference and adaptive testing are processed to study the distribution characters of the load spectrum parent, and the maximum load is deduced by the probability method. Finally, the optimal structures are defined in terms of ANSYS code, as well as load spectra, and the equivalent stress amplitudes of key points of optimal structures were calculated.

2 Testing and compilation of the traction seat load spectrum 2.1 Test condition The test line, about 288 km, is the operation line for the 6K electric locomotive. The track includes numerous curves, turnouts and an 11~13‰ slope stretch of about 30km. The load of the test locomotive is 4,500 tons, which is the same as the maximum traction tonnage. Data acquisition is continuous in order to ensure the integrity of test data. 2.2 Load identification Traction seat load is the tension and compression load from the traction rod. The load Fx can be identified by strain gauges along the axle [3-5], or

Fx 

Figure 1:

 2  D  . 4

Traction system of the 6K locomotive.

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(1)

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where D is the section diameter of the traction rod and  is the stress value obtained by the axial strain gauges. 2.3 Load spectrum compilation method In order to get more accurate stress distribution, the spectrum compilation should take the stress average and amplitude as a binary random variable [6], which can be obtained by the rain flow counting process of the measured dynamic stresstime history and then be grouped by eqn. (2).

 m max   m min   Dm  Nm    D   a max   a min  a Na

(2)

where Dm and Da are the group interval of stress amplitude and mean stress,

respectively,  m max and  m min are the maximum and minimum of mean stress,

respectively,  a max and  a min are the maximum and minimum of the stress

amplitude respectively and N m and N a are the total number of mean stress and amplitude series ,respectively, which here takes 8. In order to facilitate the optimization design of the structure, eqn. (3) is used to change the two-dimensional stress spectrum into a one-dimensional spectrum.

1ai 

ab b m

(3)

where  a , m are the stress amplitude and mean stress,  b is the tensile

strength of the material and  1ai is the equivalent stress amplitude of each series for symmetric cycle. 2.4 Test results of the stress spectrum and the load spectrum

Table 1 shows the equivalent one-dimensional stress spectrum of the left 1# traction rod. According to eqn. (1), the stress data in Table 1 can be transformed into the load spectra, namely the traction seat load spectra, also shown in Table 1.

3 Statistical inference of the load distribution characteristics In order to fully understand the parent distribution of the load spectrum of the traction seat, statistical analysis of the load spectra is performed. Weibull distribution is assumed according to the approximate shape of load amplitudefrequency histogram and then hypothesis testing is carried out to determine the mathematical model. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

524 Computers in Railways XII Table 1:

Stress spectrum and load spectrum.

Stress amplitude (MPa)

Frequency

Cumulative frequency

Load amplitude (KN)

7.4 12.1 16.9 21.7 26.5 31.0 35.5 40.5

4217 2975 758 174 47 11 4 3

8189 3972 997 239 65 18 7 3

20.88 34.15 47.70 61.41 74.81 87.68 100.41 114.36

3.1 Weibull function The probability density function of the three-parameter Weibull function can be expressed by eqn. (4). b 1    X  X b   X  X0  b 0  X  X0   .exp      F ( x)   X a  X 0  X a  X 0    X a  X 0    X  X0 0 

(4)

where X 0 is location parameter, b is shape parameter and b  0 , X a is scale parameter and X a >0; The expectation of Weibull function is

E( x )   xf ( x )dx  X 0  ( X a  X 0 )  ( 1  

0

Var( x )   ( x   ) 2 f ( x )dx  ( X a  X 0 )  [ ( 1  The deviation of Weibull function is 

(  ) is the Gamma function. 0

where

1 ) b

(5)

2 1 )   2 ( 1  )] (6) b b

3.2 Distribution function deduction of load Chi-square minimization method, namely,  2 minimization method [7, 8] is used and parameter estimation of Weibull distribution for the load spectra is performed with the help of MATLAB tool. The mean value and deviation of the load test subsample can be obtained based on the load amplitudes and frequencies, which can be supposed to be parameters of Weibull function as the subsample is in large quantity. In order to determine three parameters of Weibull function, another equation should be found except eqn. (5) and eqn. (6). Here taking K . pearson value as an objective WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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function,  2 minimization method is used to determine the optimal value of shape parameter b . Table 2 shows the fitting data and adaptive testing results of the relevant parameters of Weibull function. The data in Table 2 show that the correlation R value is greater than "the

minimum value of correlation coefficient" in the R test and in the  2 test, K . pearson statistic values are less than the critical value, which verify that it is acceptable to suppose that the load statistical parent meets Weibull distribution. 3.3 Inference of the maximum load The load spectra may not include the maximum service load due to the test limit. The probability calculation is a typical method to get the maximum load [9]. Table 3 shows the maximum load of each traction seat deduced from statistical inference. It can be found that the maximum amplification factor is 1.206 and the minimum is 1.083 compared with test data.

4 Optimization of traction seats and fatigue life prediction 4.1 Optimization design of traction seats Taking the left 1＃ seat for example, the optimal structure is provided based on the maximum load. Fig. 2 shows the FEA results of both the improvement seat Table 2:

Parameter fitting and adaptive testing results of the Weibull function for the load spectra. Parameters

Traction seats Right 1# Right 2# Right 3# Left 1# Left 2# Left 3#

b

X0

0.7099 0.5573 0.9487 0.9295 0.8331 0.5963

Table 3:

Xa

15.8788 17.0824 12.6846 14.6116 14.1651 16.2452

Correlation

K. pearson

R

Statistics

19.317 18.6873 19.8244 23.4233 19.9945 18.244

4.44 5.47 6.11 5.70 4.29 3.11

0.99942 0.99837 0.97169 0.99054 0.96381 0.99964

The maximum loads of each traction seat (Unit: KN).

Traction seats Right 1#

Maximum Load Statistical inference

Right 2#

112.6 3

Right 3#

104.4 9

Left 1#

103.7 1

132.8 8

Test Load

93.36

95.51

88.33

Amplification factor λ

1.206

1.099

1.174

Left 2#

Left 3#

116.5

105. 31 97.2 7 1.08 3

5 114.3

6 1.162

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98.59 1.182

526 Computers in Railways XII

(a) Original seat

(b) Optimized seat Figure 2:

FEA analysis of the original and optimal design of traction seats.

and the original one. The comparison shows that the maximum stress of the weld connecting traction seat and the wagon underframe decreases from 190.3 MPa to 63.1MPa, and the maximum stress of the improved structure moves to the transition arc of reinforcement cover plate and the value is 103. 5MPa. 4.2 Fatigue life prediction of the optimal traction seat In order to predict the fatigue life of the optimal traction seats, the dynamic stress test was carried out under the same operation condition and the equivalent stress amplitude was obtained according to the reference [8].Table 4 lists the equivalent stress amplitude results of the left 1# traction seat key points (location shown in fig.3), in which the extension life of 1,600,000km, 2,000,000km and 3,000,000km corresponds to 8 years, 10 years and 15 years in service respectively. It can be seen that the equivalent stress amplitudes of measured WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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points are all less than the admissible fatigue stress of 70MPa, which means the optimized structure can be used safely in further 8 ~15 years.

5 Conclusion (1) The dynamic stress-time history of key points of traction seats was collected while the locomotive was in service and the load spectra were compiled respectively for the six traction seats based on the load identification method. (2) The statistical inference and adaptive testing were processed to learn the distribution characters of the load spectrum parent. The results show that the load spectra of the traction seats meet Weibull function. The maximum load is deduced by the probability method as well. (3) By modelling and performing analysis in ANSYS code, the optimal traction seat structures were defined. The online dynamic stress test and the fatigue life prediction were carried out. The results show that the fatigue life of traction seats can prolong to 8-10 years after the structure optimization. Table 4:

Equivalent stress amplitudes of left 1# traction seat. Service life

1,600,000 km

Point location

2,000,000 km

3,000,000 km

Connection weld between cover plate and underframe (non traction rod side, inner side) Connection weld between cover plate and underframe (non traction rod side, outside)

34.2

36.4

40.9

27.7

29.6

33.2

Transition arc of the cover plate( inner side)

36.0

38.4

43.1

Transition arc of the cover plate( outside)

26.0

27.7

31.1

2

1

4

Figure 3:

3

Location of the key points for measurement.

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528 Computers in Railways XII

Acknowledgements The work is supported by “the Fundamental Research Funds for the Central Universities” and “Beijing Nova Program”.

References [1] Huang, Z. & Zhang, H., Traction device's summarization of 3B0 locomotive, Diesel Locomotives, 40(11), pp.24-26, 2004. [2] Yuan, J., 6K electric locomotive profile. Electric Locomotives and Mass Transit Vehicles, 11(1), pp. 37-41, 1988. [3] Wang, F., Study on Fatigue Life and Reliability of Fatigue Life of Turn 8Gtype Device Cross-braced Bogies, Northern Jiaotong University, Master Thesis: Beijing, 2003. [4] Liu, H., Mechanics of Materials (4th Edition), Higher Education Press: Beijing, pp40-45, 2004. [5] Shi, C., Research of Load Identification and Distribution of SW-200 Bogie Frame, Northern Jiaotong University, Master Thesis: Beijing, 2006. [6] Xiong, J. & Gao, Z., Rain Flow - Back method and two-dimensional fatigue load distribution of hypothesis testing, Aviation Journal, 17(3), pp.297-301, 1996. [7] Xu, Q., Software Development of Stress Spectrum Processing and Spectrum Analysis System on Locomotive Bogie, Northern Jiaotong University, Master Thesis: Beijing, 1999. [8] Lv, P. & Liu, Z., Research on statistical inference methods of stress spectrum of bogie, Journal of Northern Jiaotong University, 22(1), pp.44-50, 1998. [9] Stephens, R.I., Fuchs, H., Stephens, R.R. & Fatemi, A, Metal Fatigue in Engineering (2nd Edition), John Wiley and Sons: Hoboken, pp55-70, 2000.

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Generation of emergency scheme for urban rail transit by case-based reasoning F. Li, R. Xu & W. Zhu School of Transportation Engineering, Tongji University, China

Abstract Case-based reasoning (CBR) is a method that uses previous experiences to solve new problems. The characteristics of CBR make it suitable for complex problems related to knowledge reuse. Based on the analysis of the characteristics of emergency events in urban rail transit, a generation method using CBR for emergency scheme is proposed. Certain related technologies, such as case representation, case retrieval, case adaption, case revising and case retaining, are conceived and discussed. A numerical example is used to illustrate the application and efficiency of the proposed method, which can take full advantage of historical correct experiences and benefit to the intelligentization of emergency scheme generation for urban rail transit. Keywords: urban rail transit, emergency scheme, case-based reasoning, case retrieval.

1 Introduction Accidents, failures, unpredictable disasters, and sudden increase of passenger flow due to special events happen in urban rail transit (URT) now and then. These emergency events, which impact widely and long and evolve uncertainly, will bring negative impact on the whole rail network unless properly dealt with. In terms of URT, on one hand, operation organization and passenger evacuation are constrained by the spatial layout because of the relatively closed space in URT system. On the other hand, emergency response process for URT actually is a process of interaction and cooperation between different departments, which is difficult to do well due to the distinct differences existing in the communication, coordination and authorization. Therefore, it is visible that the emergency decision in URT is complex and difficult. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100491

530 Computers in Railways XII In recent years, a lot of researches have been carried out on the safety and security of URT, most of which studied the construction of emergency response system and the disposal method for specific emergency events, while few aim to the generation of emergency scheme. Zhang and Liu [1] designed the management mode of emergency plan based on case-based reasoning and rulebased reasoning, but did not give the specific reasoning algorithms. Cui et al. [2] constructed a framework for the multi-agent-based emergency response in the subway, but did not study generation of emergency scheme. Wang [3] proposed a generation method of emergency scheme based on emergency planning, but the emergency plans were compiled according to the type and level of emergency events before emergency events occur. It is hard to directly apply pre-established plan to emergency response, and specific plan still involves many unstructured problems. Therefore, it is necessary to generate emergency scheme according to emergency status and characteristics. Case-based reasoning (CBR) is one of the well-known AI methods. It solves new problems by finding and reusing solutions from similar problems successfully solved before and often shows significant promise for improving the effectiveness of complex and unstructured decision making [4]. Based on the analysis above, this paper applies case-based reasoning (CBR) methodology into the generation of emergency scheme for URT. Certain related technologies, such as case representation, case retrieval, case adaption, case revising and case retaining, are conceived and discussed.

2 CBR in emergency scheme generation Those previous experiences in emergency disposal in URT consisting of emergency characteristics descriptions and relative solutions are called cases and are stored in a case base in a certain pattern. The process of emergency scheme generation using CBR can be divided into four stages that is retrieval, adaptation, revising and retaining (fig.1) [5]. When an emergency event occurs, the system matches the new problem against cases in the case base using a specific retrieval method, and finds the most similar case from the case base. And then, aiming to the differences between the current problem and the retrieved case, the system revises the retrieved case using professional knowledge of emergency disposal. The revised solution can be confirmed as the emergency scheme for the current problem at that time. Subsequently if necessary, according to the performance of the emergency scheme and experiences and lessons learned in the disposal process, the proposed solution could be modified to avoid the same mistakes. The description of the emergency and the modified solution could be saved as a new case in the case base with the aim of reusing in the future.

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Figure 1:

531

Case-based reasoning cycle in emergency scheme generation.

3 Case representation Cases should be represented in a standard structure and rules, so as to be retrieved and adapted. A typical case usually contains two parts: the problem and the solution. The problem that describes the characteristics of the problem and related information could be represented by pairs of feature-value; the solution expresses the measures, disposal process and related information to the problem. Currently, the main methods of case representation includes: logical representation, production representation semantic network method, frame method. There are various emergency events in URT, such as train fault, fire and so on, and the number and names of the referred features vary from one to another. Framework method is used to represent emergency and fire is taken as an example to describe case representation, as shown in table 1.

4 Case retrieval Case retrieval is the core of CBR. Methods for case retrieval are nearest neighbour (NN), induction, and knowledge-guided induction and template retrieval. These methods can be used alone or combined into hybrid retrieval strategies [6]. In this study, we take NN to retrieval the similar case. It is the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

532 Computers in Railways XII Table 1:

The description of fire case.

Emergency feature or solution

Description

Value type

case ID

the order number of the case in the case base

numeric or ordinal

fire, electrical breakdown, etc.

nominal

peak or off-peak hours

nominal

coach, platform or station hall

nominal

type of emergency occurrence time occurrence location duration

the expected duration

casualties

number of injury and the death toll

event level burning area smoke components smoke concentration

description the severity of emergency the area of burning region the main components of smoke the concentration of smoke

safety facilities

the status of fire safety facilities

cause emergency scheme implementation result

the cause of the emergency

numeric or interval numeric or interval Nominal Interval Nominal numeric or interval numeric or ordinal Nominal

measures, disposal process, et.al good , bad

method for retrieving the most similar case or several similar cases from the case base. Similarity is a measurement of the degree of the similarity between the current case and the retrieved case. A complete case retrieval process using NN is generally composed of two steps: Firstly, calculate the similarity of features between the new case and the source case. Secondly, calculate global similarity according to the weights and the similarity of features obtained. The similarity of features between the two cases could be calculated as follows [7]: 1 | V j*  Vij |  sim(C *j , Cij )= 1  0

if V j* and Vij are numeric or ordinal

if V j* and Vij are numinal and V j*  Vij

if V and Vij are numinal and V  Vij * j

(1)

* j

where C * and Ci are the new case and the i th retrieved case respectively, V j* and

Vij are the values of the j th feature for the two cases, and sim () is the similarity function. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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If the value of the feature is interval, the similarity function of features is shown in eqn. (2) [7]. sim(C j , Cij )  1  *

V 2

1

* j

 Vij

  V

* j

2

 Vij

  2

(2)

where V j* and V j*  are the lower limit and upper limit of the j th feature for the

current case respectively, and Vij and Vij are the lower limit and upper limit of

the i th retrieved case in the case base. Global similarity is the weighted sum of the similarities of features, and it could be computed by the following formula.

  sim(C , C n

sim(C , Ci )  *

j 1

j



* j

ij

n

j 1

) (3)

j

where sim(C* , Ci ) represents global similarity between C * and Ci , and  j is the weight of the j th feature for both the current case and the retrieved case, and n denotes the number of the finally selected features in calculating similarity. The initial weights of features could be determined by using analytic hierarchy process or the experience of experts. It must be noted that all of features values should be normalized before similarity calculation. In order to improve the retrieving efficiency, the current case only matches with the retrieved cases with same type of emergencies because only the solution to the same type of emergency event may be suitable for current event. In the reasoning process,  is set as a threshold to ensure the number of selected cases is not too much. Only global similarity between the current case and the retrieved case is greater than  , can the retrieved case be screened.

5 Case adaptation, revising and retaining The emergency scheme of the most similar case could be regarded as the initial scheme for the current problem. In general, the most similar case, which is still different from the current problem, should be revised to fit the current situation. However, because case revising usually needs professional knowledge, there is no universal revise method. Cases are usually modified manually. The CBR system usually could retrieve several similar cases simultaneously. Operators could revise initial scheme by using the knowledge of other similar cases to improve the performance of emergency scheme. Case retaining is also the process of learning for the CBR system. After emergency handled, the performance of the emergency scheme is evaluated. Combination of the description of emergency event, emergency scheme and implementation result is saved as a new case in the case base. The knowledge of the CBR system can be updated over time, which ensures the growing of system handling ability. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

534 Computers in Railways XII

6 Example In this section, a simple example is presented. Suppose a fire occurred in urban rail transit, and the description of the input case and related cases in the case base are listed in table 2. In table 2, C* denotes the current case, and the listed features are the important features selected in similarity calculation, and the values of the features has been already normalized. The initial weights of the selected features are presented in table 3. The new case and the retrieved cases in the case base.

Table 2: Case ID occurrence time occurrence location event level

C1

C2

C3

C4

C5

C*

peak

off-peak

peak

off-peak

off-peak

off-peak

station hall level 1

station hall level 1

platform level 2

station hall level 1

station hall level 1

coach level 1

burning area 0.1-0.15 0.2-0.3 0.1-0.2 0.2-0.4 0.15-0.25 0.25-0.3 safety 0.95 0.85 0.8 0.8 0.95 0.9 facilities scheme ID scheme1 scheme2 scheme3 scheme4 scheme5 implement good good good good better result Table 3:

weight

The initial weights of the selected features.

Occurrence time 0.05

Occurrence location 0.05

Event level 0.1

Burning area 0.1

Safety facilities 0.15

Take C * and C1 as an example to illustrate the calculation of case reasoning. The five similarities of five features pairs can be calculated by eqn. (1) - eqn. (2). * sim (C1 , C11 )  0 sim (C2 , C12 )  1 *

sim (C3 , C13 )  1 *

sim(C4 , C14 )  1  *

 0.25  0.12   0.3  0.15 2   0.85  2

1

sim(C5* , C15 )  1  0.9  0.95  0.95

Then, the global similarity can be calculated by eqn. (3):

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  sim(C , C

535

n

sim(C , C1 )  *

j 1

j

 j n

* j

1j

)

 0.84

j 1

Finally, the global similarities between the input case and all related cases existed in the case base can be calculated, and the results are listed in table 4. Table 4:

global similarity

The global similarity of each case.

C1

C2

C3

C4

C5

0.84

0.97

0.49

0.95

0.85

From table 3 we can see that the most similar case is C2 . If the threshold  is set to 0.9, C1 , C3 and C5 will be filtered out. The solution of C2 can be selected as the initial scheme for the fire. C4 can be served as a reference to operators for revising the initial scheme in order to make it more suitable to the current condition.

7 Conclusion In this paper, the case-base reasoning (CBR) is introduced into the solution to existing problems in emergency decision-making of urban rail transit as a new approach. The procedure and methods of generating emergency scheme by CBR are analyzed in detail. The numerical example indicates that valuable knowledge in previous practice could be reused. However, generating emergency scheme successfully depends on a well-constructed case base which contains a large number of cases with the same type. Selecting appropriate features to improve retrieval’s accuracy and efficiency is the issue that we plan to explore in the future.

Acknowledgements This research has been financially supported by the National High Technology Research and Development Program of China (863 Program) (Grant No. 2007AA11Z236). The authors gratefully thank anonymous referees for their useful comments and editors for their work.

References [1] Zhang, J. & Liu, Z., Case-based reasoning and rule-based reasoning for emergency preparedness information system. Journal of Tongi University (Natural Science), 30(7), pp. 890-894, 2002. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

536 Computers in Railways XII [2] Cui, Y., Tang, Z. & Wu, X., Study on the multi-agent-based system of accident disposal in the subway. Journal of the China Railway Society, 26(3), pp. 8-12, 2004. [3] Wang, Z., Urban rail transit emergency treatment auxiliary decision-making technology’s several problems research. Tongji University: Shanghai, pp.32-45, 2008. [4] Ahn, H. & Kim, K., Global optimization of case-based reasoning for breast cytology diagnosis. Expert Systems with Applications, 36(1), pp. 724-734, 2009. [5] Watson, I. & Marie, F., Case-based reasoning: A review. The Knowledge Engineering Review, 9(4), pp. 50–60, 1994. [6] Elina, P., Timo, S. & Tuomas, K., Synthesis of separation processes by casebased reasoning. Computers & Chemical Engineering, 25(4), pp. 775-782, 2001. [7] Li, Y., Xie, M. & Goh, T.N., A study of mutual information based feature selection for case based reasoning. Expert Systems with Applications, 36(3), pp. 5921-5931, 2009.

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Application and perspectives for interoperable systems in Italy and Europe R. Bozzo1, R. Genova2 & F. Ballini2 1 2

Department of Electrical Engineering, University of Genoa, Italy Inter-University Centre of Transport Research, Genoa, Italy

Abstract A rapid transit system represents one of the main growth areas of the railway business developed to solve the enhanced mobility request. New solutions have been applied in order to simplify and to improve public transport services, without changing trains and providing a fast, direct link from the city to the outskirts. The solutions are interoperable transport systems (like “light rail” very similar to the “Stadtbahn” approach, “tram-train” and “train-tram”). The “tram-train” concept indicates vehicles which operate on railway lines in suburban areas, but that are also able to work on a tramway net to supply a capillary service in the urban area. In central Europe a few cities have planned different tram-train solutions: Karlsruhe, Saarbrücken, Chemnitz and Kassel realised the “heavy” model (train performances and technical standards similar to railway rolling stock). Other cities choose “light” systems, adopting vehicles more similar to the tramway design (Nordhausen, Zürich, Vienna, etc.). The tram-train represents the missing link between urban tramway and railway systems, whose transport capability depends only on how the infrastructures are used (railway or tramway nets). Another non conventional and lower cost model is the “Stadtbahn” which have diverted in-town sections of their system underground; in some of these cases, tunnels have been built to accommodate full metro trains if desired (Stuttgart, Frankfurt a/M, Bochum, Köln, etc.). In Italy “metrotranvia” lines are to be found in Milan and Turin, based on the light rail model: in Turin the rolling stock have been built with peculiar characteristics (floor height 550 mm for boarding at platform level). Other interesting Italian interoperable systems are the “Tram delle Valli” in Bergamo WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100501

538 Computers in Railways XII and the Sassari tramway (950 mm track gauge, the same as the interconnected railway local net “Ferrovie della Sardegna”) and, in the near future, the renewed interurban tramway line Milano-Desio, (extended to Seregno) with estimated costs amounting to 214 million Euros. Finally, the Torino-Torre Pellice, will be the first tram-train application in Italy Keywords: local public transport, interoperable systems, economic impacts.

1 Means of transfer chosen by the users Since the end of the Second World War mobility demand has undergone deep seated changes, both from the quantitative and qualitative point of view, mainly following the changed conditions of work and settlement. Today journey times have taken on, together with comfort and safety, an ever greater importance, just as the demand for transfers not only from the outskirts to the centre but from metropolitan areas to the central zones of cities is continuously increasing. This phenomenon requires suitable responses from public transport, which in recent years has shown an interesting recovery but not sufficient, however, to supply a valid alternative to private mobility, now so well rooted in the habits of citizens especially in erratic journeys or those which require “breaks of load”. For transport supply one does not require only a suitable sizing, but also economic and environmental sustainability, with a careful look at the visual impact and street furniture. Therefore, the problem appears to be extremely complex both for transport companies and for the public administration. Technological innovation plays an essential and at the same time delicate role, making a higher number of solutions available compared to the past, but facing decision makers with delicate problems in the choices to be taken. 100%

5.7

4.7

5.9

7.5

8

92.2

92.8

90.1

87.7

82.8

10.6

90% 80% 70% 60.1

60% 50%

Motorbikes Private cars

40%

Public means 30% 20%

29.3

10% 0%

2.2

2.5

3.9

4.8

9.2

Up to 5,000 from 5,000 from 25,000 from 50,000from 100,000 more than inhabitants to 25,000 to 50,000 to 100,000 to 250,000 250,000 inhabitants inhabitants inhabitants inhabitants inhabitants

Figure 1:

Means of transfer chosen on the basis of the size of the urban centre.

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Today, the trends in progress are various, first of all the revival of the tramway system lived as an instrument for the best exploitation of town planning for cities, which are being redesigned through it. In more detail, Italy appears with a high propensity to the use of private means (two or four-wheeled) indeed the rate of motorisation is among the highest in the European Union, with 590 cars per thousand inhabitants in 2005, passed only by Luxembourg. Figure 2 shows the results of a survey conducted by ISFORT between 2004 and 2006 with regard to the satisfaction expressed by the citizens of mediumlarge towns with regard to the public transport service delivered, respectively, by bus and tram (graph on left) and by underground (graph on right). As it is possible to see, the judgement expressed in 2006 for bus and tram appears negative (little or not at all satisfied) for about 35% of those interviewed, with a slight improvement compared to the previous year but a marked worsening compared to the year 2004, where dissatisfaction with the service was manifested by about 29% of the sample. The underground service was judged considerably better with a quota of dissatisfied on average of around 20% with a minimum of 16% in 2006. Since an action aimed at reviving public transport requires in depth study, with separation of opinions between road and rail systems such as trams, there emerges a marked satisfaction for mass rapid transport systems [1].   100%

11.8

18.2

100% 26.2

23.3

60%

59.7

45.8

41.9

24.7

0%

Very satisfi ed

22.7 5.9 2004

Figure 2:

11.4

 

2005

Very sati sfied Quite satisfied

50.1 38.7

40%

Dissatisfi ed 24.4

20%

10.4 0% 2006

47

60%

Qui te sati sfied Not very s atisfi ed

40% 20%

40.8

80%

80%

19.6 4.1 2004

14.2 6.3 2005

37.1

No t very  satisfied Di ssati sfied

11.4 4.6 2006

Satisfaction expressed by users in medium-large towns for the public transport service [2].

For medium-small centres, where mass rapid transport systems are not in themselves applicable, technology however makes interoperable systems available, which allow one to limit investments in infrastructures using the existing networks and drastically reducing the breaks of load.

2 The reasons at the base of the disaffection for public transport in Italy The reasons at the base of the disaffection for public transport are of both a social and technical nature. Indeed, along with the simple habit of total autonomy in the journey, the points of major criticality are, in order of importance in the users' perception: WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

540 Computers in Railways XII 1.

2. 3. 4.

5.

journey times, considered non competitive compared to those of the private means and often very difficult to quantify with certainty in advance; accessibility of the public means (understood as ease of use) is lower than the private means, and the need for breaks of load; capillarity of the service; insufficient level of comfort, mainly attributable to overcrowding but also to parameters now assessed as fundamental, such as the level of cleaning, the efficiency of the air conditioning plant, the level of safety and the difficulty of entering the carriage due to the survival of vehicles with the floor not lowered; correspondence of the service with the real needs, with particular reference to the frequency of the runs, the design of the routes and the networks.

The last point, in particular, is confirmed by the data contained in the National Transport Account 2007 and summarised in figure 3, from which a heavy imbalance emerges between supply of and demand for transport. This imbalance is more evident in the urban service (71.7 billion places per km supplied against 11.6 billion passengers per km transported) compared to the extraurban one (70.4 billion places per km supplied against 18.1 billion passengers per km transported). Even though, 25.7% of coverage of the places supplied for the extraurban service does not seem to be an appreciable result either for the covering of the costs, or for the satisfaction with the service. Within towns, this coverage is even lower: just 16.2%.

80.0 60.0 71.7

70.4

40.0 20.0 11.6

18.1

0.0 Urban Places km supplied

Figure 3:

Extraurban/suburban Travellers km transported

Comparison between passenger km transported and places km supplied (billions) [3].

To all this one must add the slow evolution of the concept of sustainable mobility toward s an idea of “enlarged comfort” of towns, where the reduction of private mobility and recourse to public transport systems with low emissions WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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becomes functional to urban upgrading. This derives from a greater environmental awareness among the population, but also from the awareness that such a renewed context represents the driving force for the growth of the wealth of the territory. However, a public transport system cannot limit itself to satisfaction of environmental parameters and user satisfaction, it must also be efficient to bring together the liveability of towns with the economic sustainability of services. These objectives may be reached through the use of innovative, high-capacity systems that make that level of service available that allow one to acquire market shares (new users) and therefore to improve the urban centre, both from the point of view of congestion by traffic and from the point of view of atmospheric and acoustic pollution.

3 Comparison of the costs and technologies for the public transport Table 1 permits a comparison of the innovative and traditional technologies available for the running of Public Transport of people in urban areas and shows a comparison between the transport capacities of different systems obtained hypothesising a frequency of service equal to about three minutes. It is clear how depending on the level of mobility demand expressed by the urban zone the service is sized and the choice of the most suitable means of collective transport is made. An exact consideration of the levels of present use and a correct estimate of those that may possibly be attracted with innovative modes and/or means of transport is indeed presupposed fundamental for both the economic and technical sustainability of the service. As highlighted in table 1, the consumption, expressed for uniformity of comparison in grams of oil equivalent, are very different from one another depending on the transport technology examined. The typology of hybrid bus considered is that of the most innovative which allow an energy saving of about 20% compared to traditional thermal vehicles. Also for what concerns trolley buses the consumption was estimated for those of the latest generation equipped with energy recovery systems (e.g. supercapacitors) and for which one estimates a saving of electrical energy of the order of 25%. With particular reference to trolley buses with supercapacitors, the first series of vehicles, of 18 metres, were delivered at the start of 2009 to the ATM in Milan. Analysing consumption per km travelled one sees how the “classical” underground is that which expresses the highest values: 538 grams of oil equivalent per km, while the trolley bus is the one with the lowest energy consumption with a maximum value for the vehicle of 24 metres equal to 359 goe/km. Finally, considering the specific consumption (which also takes into account the capacity of the means) one sees how the data favour high-capacity systems: WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

542 Computers in Railways XII Table 1:

Type of vehicle

Comparison between urban transport systems – passengers transported consumption and costs.

Estimated Passengers transported /h consumption/km (goe/km*) per direction

Estimated specific consumption (goe/pkm**)

Cost of vehicle €k

Cost of infrastructure €k/km 0 - 100

12 m bus

1500

368

24.2

310

18 m bus

2500

423

20.4

380

0 - 100

24 m bus 12 m hybrid bus 18 m hybrid bus 24 m hybrid bus 12 m trolley bus 18 m trolley bus 24 m trolley bus

3,500 – 4,000

478

17.3

500

0 - 100

1500

294

19.3

460

0 - 100

2500

338

16.3

570

0 - 100

3,500 – 4,000

382

13.8

900

0 - 100

1500

276

18.2

500

400 - 600

2500

317

15.3

800

400 - 600

3,500 – 4,000

359

13

1000

400 - 600

Tram

4,000 – 6,000

488

15

2,000 – 3,000

7,000 – 10,000

15,000 – 30,000

538

10

9000

12,000 – 50,000

Underground

* goe/km: grams of oil equivalent per km ** goe/pkm: grams of oil equivalent per passenger km

Figure 4:

Stuttgart “Stadtbahn” system [4].

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the lowest specific consumption expressed in grams of oil equivalent per passenger km (10 goe/pkm) is that of the underground, while the highest value is that of the thermal bus of 12 metres (24.2 goe/pkm). It is clear that for the underground plant that transport up to 30 thousand passenger hours in each direction with a capacity up to 1200 passengers per train (standard composition with 6 vehicles) the energy consumption attributable to each passenger km transported is drastically cut winning the challenge of the most “environmentally friendly” means among those considered, followed by the other plant with a fixed path, trams, which have a specific consumption of 15 goe/pkm and by trolley buses of 24 metres (16.9 goe/pkm) which are vehicles that have only recently come onto the market. The innovations of the tramway sector have been fundamental for the progressive integration with the existing railway systems and with the classical underground and the creation of interoperable systems, in particular of the tramtrain system [5] which represents one of the most interesting progresses in the transport field in recent years and the new trend for the sector. With the term “tram-train” one defines a family of rolling stock that carry out services on railway stretches in a peripheral or territorial environment, but which are able, thanks to suitable links, to use normal tramway lines, supplying a capillary service of connection within town centres as well as between town centres far from one another. The system [6] is competitive and efficient, particularly thanks to the following characteristics: • minimisation of investment costs thanks to the use of already existing railway infrastructures, possibly reusing disused railway lines; • possibility of increasing the number of stations along the railway line keeping the same journey times for a train; • reduction of running costs compared to an equivalent railway system, the tramway system needing only one operator on board; • increase of the commercial speeds in suburban stretches and drastic reduction of the breaks of load. Different solutions have been adopted by the realities which have adopted tram-train systems. In Karlsruhe, Saarbrücken, Chemnitz and Kassel the so called “heavy” models have been preferred i.e. with trains that are similar in characteristics and dimensions to railway electric locomotives (or railcars), while in other cases “light” models have been adopted i.e. with a more reduced profile and more similar to a tramway vehicle. Karlsruhe, already in 1992, presented itself as a pilot project for the running of tram-trains [5]: today there are 11 lines active of the “S” type with a total extension of the network of 468 km. The 36 trains first supplied of GT8100C/2SY type were joined in the years 1997-1999 by another 79 GT8100D/SY-M, some of which equipped with toilet facilities. In the town of Saarbrücken 28 “Flexity Link” trains have been operating on the 25.5 km of network with 23 stops/stations since 1997.

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544 Computers in Railways XII Table 2:

Comparison between different transport systems.

System

Transport

Infrastructural

Vehicle

Vehicle

(vehicl)

capability

costs

length

power

[pass/h

[k€/km]

[m]

[kW]

4,000 – 6,000

5,000-10,000

30-50

250-500

8,000 – 12,000

20.,000

20-30

400-800

8,000 – 30,000

12,000-50,000

12-25

300-600

3,000 – 5,000

typically existing

30-40

400-600

direction] Tram Light

rail

(*) Underground(**) Tram-train (*)

Length and power referred to single unit, (*) multiple coupling up to 3 units, (**) multiple coupling up to 8 units.

Figure 7: Tram-train in Saarbrücken

Figure 5:

“Heavy” model train system.

In Chemnitz, from which originates the transport model going by the same name, since 2002 10 Variobahn tram-trains have been operating which serve the 23 km of regional network between Altchemnitz and Stollberg. In the early nineties (after the fall of the Berlin wall and with the return of the name of the town of Karl-Marx-Stadt to Chemnitz)a feasibility study was carried out that envisaged the creation of a mobility system on a territorial scale by means of the reconversion of non-electrified railway lines for the running of tram-trains. The RegioTram project of Kassel saw the opening in 2007 of 4 lines (RT1RT2-RT3-RT4) with an extension of 122 km on which 18 Alstom RegioCitadis

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version E/E (750 cc - 15kV 16 2/3 Hz) and 10 Alstom RegioCitadis version E/D (750 cc – diesel) operate destined to the non-electrified lines. In France the “T4” line was opened in November between Aulnay-sous-Bois and Bondy in the suburbs of Paris. The plant is operated by the same SNCF on the site of a previous railway, while the other three Parisian lines are run by RATP. Alstom will deliver to SNCF a good 200 tram-trains, which will benefit besides the area of Lyons, the countryside of the Loire, the Rhône-Alpes region, the Ile-de-France and the town of Strasbourg. In Italy the use of tram-trains is envisaged on the railway stretch from Turin to Torre Pellice: the Piedmont Region has foreseen a contribution of € 20 M for the purchasing of three vehicles and for adjustment of the infrastructure. In the urban environment its running on the tramway network of the Piedmont regional capital is foreseen. The tram-train can also be associated with all the interoperable tramways, among which are to be recalled Line 10 in Basel, which connects Basel with the small town of Rodersdorf running along a stretch also in French territory on which there is the stop of Leymen: the non urban stretch runs on the route of an old reconverted railway line. Still in Basel line 8 which will be extended as far as Weil am Rhein in Germany, confers the town network a unique feature of internationality.

Figure 6:

The railway line Bern-Worb Dorf.

In Berne Line G can be cited which arrives as far as Worb Dorf; in Vienna the Wiener Localbahnen runs the urban service on a tramway network and the same goes for the Forchbahn in Zurich, which is inserted, at the level of service model of rhythmic timetable in the S-Bahn town system. From the point of view of applications, in 2004 the five transport companies operating in the basin of the Rhine and Neckar gave life to a unique consortium, the RNV (Rhein-Neckar-Verkehr GmbH) which, thanks to the infrastructures present and their total interconnection, offers a territorial service complementary to the railway network run by DB.

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546 Computers in Railways XII

4 Conclusions In conclusion we can say that the use of interoperable systems for local public transport (extra urban and suburban) is far more developed in other European countries than Italy where this development is hampered by rigid regulation on the use of rail networks. As we mention in this article, interoperable systems have a positive impact on efficiency, effectiveness and passenger comfort and are often critical to the success of local public transport areas within which they operate. Consequently, their presence significantly affects the number of people who turn to public transport services rather than the convenience of private transport. It is also critical in curbing congestion, air and noise pollution.

References [1] S. Migliaccio, Trasporti pubblici di qualità, MobilityLab, n. 22, Bimestre Luglio – Agosto, 2008. [2] ISFORT - “Audimob” observatory of mobility of Italians. - Data 2005 [3] National Transport Account ed. 2007 – Data 2005. [4] R. Bozzo, R. Genova, I sistemi “STADT-BAHN”: un modello di ferrovia cittadina, MobilityLab n. 17, Settembre - Ottobre 2007. [5] M. Novales, M. R. Bugarin, A. Orro, Un nuovo concetto nel trasporto urbano : il tram-treno, Ingegneria Ferrovia, n. 10, p. 741, 2001, [6] F. Perticaroli, Sistemi elettrici per i trasporti, Seconda edizione – Casa Editrice Ambrosiana, Milano, Gennaio 2001.

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Section 8 Energy supply and consumption

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A method to optimise train energy consumption combining manual energy efficient driving and scheduling C. Sicre1, P. Cucala1, A. Fernández1, J. A. Jiménez2, I. Ribera2 & A. Serrano2 1

Instituto de Investigación Tecnológica, Escuela Técnica Superior de Ingeniería, Universidad Pontificia Comillas, Spain 2 Renfe Operadora, Spain

Abstract This article presents a combined simulation and optimisation technique to optimise the energy consumption of a single manual-driving train service with inter-stations along a High Speed Line. For this purpose, the best energy efficient driving strategies along every stretch between two stations will be simulated with an accurate and detailed train simulator to obtain the run time and energy consumption Pareto curves of each stretch. Afterwards, an optimisation tool will distribute the available slack time for the service among the stretches, which will minimise the overall energy consumption of the service. The potential of the method will be shown with a case study at the end of the article. Keywords: manual energy efficient driving, ecodriving, coasting, timetable, Pareto curves, slack time.

1 Notation and terminology Flat-out time: minimum run time that a train can achieve satisfying all the constraints of a service Slack time: Time that added to the flat-out time constitutes the commercial run time of a stretch, and whose aim is to ensure punctuality in case of delays Grade: Longitudinal track inclination, positive uphills, negative downhills, [‰] HSL: High Speed Line WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100511

550 Computers in Railways XII Neutral section: Piece of rail track without electric feeding Pareto curve: Curve consisting of the drivings that, for a fixed run time, consume the least amount of energy

2 Introduction Reduction of energy consumption on railway systems has recently turned to be a global concern as a contribution to reduce the global warming. Many fields can contribute to this aim, for instance, infrastructure, signalling, maintenance and operation. This paper will focus it from the operation point of view, in particular, by means of manual energy efficient driving and efficient scheduling. 2.1 Manual efficient driving There have been many contributions about manual energy efficient driving. Basically, there are three different ways a train can be driven: applying traction, coasting (no traction effort) or braking. The problem about manual efficient driving is when to switch to each way, fulfilling the constraints of the service. For modelling the manual driving, different authors have translated the experience of the drivers into different manual driving parameters; in [1], four parameters are defined to build a driver model for calculating energy consumption. In [2], the authors introduce the maximum and minimum coasting speed parameters. With relation to the previous parameters, in [3] it is introduced a distance, measured from a departure station, before it the train is not allowed to coast. Researches about finding the best strategy to drive a train started on the late 60s, when the optimal control for a linear train model was solved analytically applying Pontryagin’s Maximum principle [4], concluding that the optimal driving strategy consisted of four sections: starting with maximum acceleration, holding speed, coasting, and finally braking with maximum deceleration. This conclusion was reinforced in [5] and [6], with running tests that lowered the energy consumption by 13% and 20-25% respectively. A different approach for finding the best driving is by training the drivers; in “A toda Vela” project [7], a group of AVE (Spanish High Speed) drivers took part in a contest consisting of consuming the least amount of energy along the service Madrid-Sevilla without affecting the punctuality commitment. The results show achievable savings up to 9,5%. Other attempts to find the best efficient strategy have been focused on calculating the best coasting points along the rail track, like [3, 8, 9], or determining an optimal holding speed lower than the speed limit, like [10] and [11]. Other techniques deal with the reduction of the unnecessary braking; one of them is explained in [12] and consists of avoiding the use of the brake during sections where the grade would otherwise increase the speed above the desired holding speed. To achieve that purpose, the train must start coasting early enough to reduce the same speed that will be recovered along the downgrade. Another similar technique consists in holding speed without braking, [13]; when WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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the train is allowed to increase the holding speed up to the speed limit of the line, it maintains the holding speed always that tractive effort is needed, otherwise, instead of braking, it starts coasting, increasing its speed. The train then can either reach the speed limit, when it will brake in order not to exceed it, or start decreasing its speed due to upgrades, reaching again the holding speed, when tractive effort will be again applied. 2.2 Design of efficient timetables The timetable of the service must be feasibly designed attending to the train, line characteristics and users demand. It is built adding a slack time to the flat-out time, to ensure the punctuality in case of any delays. There are researches that have been focused on designing slack times taking into account behavioural responses [14]. Others have been focused on minimising interchanges waiting times, like [15], where the authors consider a fixed value of slack time for the whole service and the available headway for each route, distributing it the best way for avoiding delays, or [16], where evolutionary techniques are used for the same purpose. Related with energy consumption, in [17] the timetable is designed by adding a slack time to the flat-out time with the aim of reducing the energy consumption for a fixed service time. More ambitiously, the work in [18] looks for a compromise between timetable synchronization and energy minimisation.

3 Model ecodriving - timetable The objective of this work is to minimise the global energy consumption of a single train service with inter-stations along a HSL by manual energy efficient driving and timetable design, which is carried out both by a simulation and optimisation models. A general train service consists of a head station, n inter-stations and a terminal station, resulting in n+1 stretches. The purpose of the optimisation model is to distribute the fixed available slack time present in every timetable among the different stretches of a service in the most efficient way. Besides, the aim of the simulation model is finding the most energy efficient drivings along each stretch, calculating run times and energy consumptions with a high grade of accuracy. A train service is always associated with a timetable, where the commercial time of a stretch is built up by the addition of the flat-out time and the slack time. This slack time is usually calculated as a percentage of the flat-out time plus an additional time that depends on the length of the service, according to UIC Code 451-1. The difference between the slack time and the time delays during a service will be the available time to perform an energy efficient driving. The optimisation model will distribute the slack time of the service among its n+1 stretches minimising the global energy consumption. To achieve it, there are needed the run times and energy consumptions of the best energy efficient drivings of every stretch, and this is extracted from the simulation model. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

552 Computers in Railways XII The simulator will calculate run times and energy consumptions of manual drivings by the efficient combination of the driving techniques described in the introduction. Each driving is associated with a data pair of run time and energy consumption, so the drivings that, for a given time, consume the least amount of energy, will constitute the so called Pareto curve for that stretch, [19]. This curve relates each run time with the minimum achievable energy consumption, Figure 1. The results of the simulator are therefore n+1 Pareto curves. Those Pareto curves, together with the slack time for the whole service, will be the input data of the optimisation model for distributing the slack time in the best energy efficient way. After obtaining the optimal slack time for each stretch, it will be only necessary to add it to its corresponding flat-out time to obtain the associated commercial time. The most efficient drivings fulfilling the whole service energy optimisation will be located on the Pareto curves. Figure 2 shows a block diagram for the whole process.

Figure 1:

Run time-energy consumption and associated Pareto curve.

Manual Driving strategies

Simulation model

Rail Track Data

Pareto curve of each stretch

Generator of manual energy efficient drivings based on simulation

Train Data

Energy consumption

Flat-out driving stretch i

Best manual energy efficient driving for stretch i considering the whole service optimisation

Optimisation model Optimal distribution of the available slack time along the stretches of the service

Slack time for the whole service

Slack time for each stretch

Pareto curve stretch i Slack time stretch i Flat-out time

Figure 2:

comercial time stretch i

Run time

Block diagram for optimising train energy consumption.

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4 Simulation model 4.1 Description of the simulator In order to calculate accurately run times and energy consumptions, a detailed step-based simulation model has been developed [20, 21]. The step-based simulation divides the train movement into a succession of intervals which duration/length is the step itself. Each interval is characterised by an initial and a final state, providing that the final state of one step will be the initial state of the following. Step-based simulation is utilised when the train is varying its speed, and its grade of accuracy is inversely proportional to the duration/length of the step, which may be as small as needed, with the constraint of the computational demand. Nevertheless, due to the fact that one of the main requirements of the simulator is calculating run times and energy consumptions accurately, the step must be kept small. During each step, the acceleration is considered as constant (which is a reasonable assumption when the step is small), so the state variables at the end of a step can be calculated from the state variables at the beginning of it and the equations of the uniformly accelerated linear motion: Time step-based simulation:

s f  s0  v0  t  0.5  a  t 2

v f  v0  a  t

t f  t0  t

Being s0, v0, t0, sf, vf, tf the distance, speed and time at the beginning and the end of the step respectively, a the acceleration, and ∆t the time step. s f  s 0  s

Space step-based simulation:

v f  v0  2  a  ( s f  s 0 ) 2

t f  t0 

v f  v0 a

Being s0, v0, t0, sf, vf, tf and a the same as previously, and ∆s the distance step. This simulation model is based on object-oriented programming and has been implemented in C++. 4.2 Input data The input introduced into the simulator consists of the three upper left blocks shown in Figure 2: train, infrastructure/service and manual driving strategy. The train is modelled as a length distributed and includes rotary inertia to account for the effect of rotary masses on acceleration. The maximum electric tractive and braking effort curves are modelled with respect to the speed WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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(A) Figure 3:

(B)

(A) Traction, braking effort and running resistance curves as a function of speed. (B) Available headway for the commercial service.

(Figure 3A), and their efficiencies may depend both on the speed and the powering/braking ratio, or either be constant. The running resistance is modelled as a second degree polynomial with respect to the speed (Figure 3A). The model also takes into account the consumption of auxiliary equipment and comfort systems and slippage between rail and wheels. It has also the possibility of using regenerative braking and/or onboard energy storage devices. The infrastructure and the service are defined with the following data: a. Check points, stopping stations, stopping times and timetable. b. Speed limits, both permanent and temporary. c. Grades and grade transitions. d. Neutral sections, tunnels and track width. e. Curves, distinguishing among straight sections, curves and clothoids. f. Headway: it is a space-time margin for a specific service out of which it is not desirable the train to be, to avoid introducing delays in the line (Figure 3B). To enable the simulation of manual driving strategies, a driver module has been implemented, where different strategies can be managed with a set of configurable parameters and manual efficient driving techniques that were described in the introduction. The most efficient techniques to reduce the energy consumption are holding speed without braking and performing a coasting process before braking to reach a speed limit or a stopping station. Hence, a typical efficient driving may consist of several sections of different holding speeds combined with a final coasting process, as it is shown in (Figure 4) for the stretch Madrid-Zaragoza, where three different sections of holding speed were combined with a final coasting section before the braking process. The simulation process for obtaining the best manual driving strategies along a service begins with the definition of the track, train and service data described in the previous sections. The track is divided into the n+1 stretches that forms the service studied. Next, a guided search is developed along each stretch independently. This is carried out by combining the manual driving parameters and strategies outlined in the introduction. Each combination configures a driving that will produce a concrete pair of run time and energy consumption. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 4:

555

Typical driving consisting of different holding speeds without braking sections and a final coasting section.

One of the main contributions of this simulation search technique is that a wide range of manual driving strategies can be analysed combining different sections of holding speed with different coasting intervals. This richness of definition of manual drivings contrast with the simplification usually found in the literature, which is admissible for short metropolitan services, but not for HSL. Furthermore, calculating energy consumption by an accurate simulator is a guarantee of reliable results. Other methods even avoid calculating energy consumption during the optimisation process, using instead Artificial Neural Networks. Another advantage of this method is that there can be simulated as many strategies as desired, so it will be always possible to find an efficient driving strategy that lasts a desired time. Once simulated the best manual driving strategies for each stretch, they are obtained n+1 distributions like the one shown in Figure 1. The drivings that, for a given run time, provide the minimum energy consumption, will set up the Pareto curve, consisting of the best manual drivings for each run time. The output of the simulation process is the n+1 Pareto curves that will be the input to the optimisation model. Whether the train uses regenerative braking, energy storage devices or none of them, the shape of the Pareto curve will be different, so depending on the use of these devices, the optimal driving strategy may vary.

5 Optimisation model The timetable optimisation model will use the Pareto curves and the available slack time for the service to distribute it in the best energy efficient way. It will be assumed that the whole slack time is available for efficient purposes. Each Pareto curve will be modelled as a polygonal whose segments can be adjusted in order to achieve more precision, Figure 5.

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Figure 5:

Theoretic and modelled Pareto curves.

The mathematic formulation of the optimisation model is the following: Indexes i Number of stretches along the service, i = 1,…,n+1 j Number of segments of the polygonal Pareto curve n Number of inter-stations Parameters Slope of segment j from the polygonal of stretch i ai , j bi , j

Ordinate of segment j of the polygonal of stretch i

C

Slack time for the whole service [s] Minimum slack time for stretch i

Cf i P

Rmi

Stopping time at station [s] Flat-out time for stretch [s]

S i, j

Minimum run time for the whole service [s] Equation of segment j of the polygonal of stretch i

R

Commercial time for the whole service [s]

Rm

Variables Slack time for stretch i [s] Ci Ri

Commercial time for stretch i [s]

yi, j

Indicates activation of segment j from the polygonal of stretch i

Equations The commercial time of a stretch is the sum of the flat-out and the slack time. Ri  Rm i  C i

(1)

Equation of each segment j from the polygonal of stretch i. S i , j  a i , j  Ri  bi , j

(2)

The commercial time of the service is equal to the addition of the commercial times of each stretch and the stopping time at the inter-stations. n 1 (3) R  R  nP

 i 1

i

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C   Ci

The sum of slack times is equal to the slack time for the whole service. n 1

(4)

i 1

Slack times must be bigger than a certain fixed value to guarantee punctuality. Ci  Cf i

(5)

Ei   y i , j  ai , j  Rmi  Ci   bi 

Energy consumption of stretch i.

(6)

j 1

y

Only one segment in each Pareto curve is activated. j 1



i, j

1

(7)

The objective function consists in the minimisation of the total amount of energy n 1 j (8) y  (a  ( Rm  C )  b ) min i 1 j 1

i, j

i, j

i

i

i

The way the Pareto curves are defined ensures that the solution obtained is achievable; n out of n+1 commercial times will be located at the junction of two segments of the modelled Pareto curves. The position of the last one will be inside a segment, which may not coincide with a simulated driving. In that case, it will be selected the most similar one, (Figure 5). The optimisation model has been implemented in Gams, and the result is the slack time to be added to each of the n+1 flat-out times, what will determine the commercial time of the n+1 drivings that minimise the energy consumption of the whole service.

6 Case study To evaluate the effectiveness of the exposed method, a real case will be presented, consisting on a service operated by Renfe, which will be MadridZaragoza, with stops at Guadalajara and Calatayud, and developed by the train Talgo-Bombardier Class-102. Firstly, there are simulated the optimal manual drivings along its three stretches, obtaining the Pareto curves for MadridGuadalajara, Guadalajara-Calatayud and Calatayud-Zaragoza. Finally, there is proposed a redistribution of the available slack time among the three stretches in order to optimise the global energy consumption. The timetable has a fixed commercial duration Ri for each of the three stretches. Simulating the flat-out driving for the Class-102 on each of them they are obtained the flat-out times Rmi and the slack times Ci, so the sum of the three slack times is the available slack time for the whole service C. All these values are shown on the left side of 0, as well as the energy consumption reached with the flat-out driving. Now there are simulated the efficient manual driving strategies for each stretch in order to obtain their Pareto curves (Figure 6). The three Pareto curves are introduced in the optimisation model together with the available slack time for the service (0:10:30). The new optimal distribution of slack times and the savings got by this method are shown on the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 6:

Simulated driving strategies (Top: Madrid-Guadalajara. Left bottom: Guadalajara-Calatayud. Right bottom: CalatayudZaragoza).

Table 1:

Results of run times and energy consumptions for commercial timetable and optimised timetable.

right side of 0, which fixes the new optimised timetable that minimises the energy consumption of the whole service. It must be noted that the saving predictions are compared to the flat-out drivings, which are the least efficient ones, but it has been checked on real tests that this is the most typical driving developed by the drivers, whose only current concern is the punctuality of the service. It must also be noted that those savings are obtained only with minimal changes on the timetable (the time for the overall service maintained) as only the

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arrival and departure time of the inter-stations are modified, and performing an efficient manual driving, which are actions that cost no money at all.

7 Conclusions This work has proposed a method based on simulation and optimisation to minimise the energy consumption of a single train service with intermediate stops along a HSL following manual energy efficient driving and efficient scheduling. The fully detailed simulator guarantees accurate and trustful results of run time and energy consumption of the drivings simulated, and the way the manual driving strategies are defined allows to explore almost all the feasible driving strategies that can be developed by human drivers, allowing a complete search of the best manual energy efficient driving for an isolated stretch. With the optimal distribution of the available slack time along the different stretches of a service, it is possible to guarantee not only the best efficiency for each of its stretches separately, but the least energy consumption for the whole service.

References [1] Lukaszewicz, P., et al. Driving techniques and strategies for freight trains. In Computers in Railways VII. Seventh International Conference on Computers in Railways. COMPRAIL 2000. 2000: WIT Press. [2] Bocharnikov, Y.V., et al., Optimal driving strategy for traction energy saving on DC suburban railways. IET Electric Power Applications, 2007. 1(5): p. 675. [3] Acikbaas, S. and M.T. Soylemez, Coasting point optimisation for mass rail transit lines using artificial neural networks and genetic algorithms. IET Electric Power Applications, 2008. 2(3): p. 172. [4] Ichikawa, K., Application of optimization theory for bounded state variable problems to the operation of train. Bulletin of the Japan Society of Mechanical Engineers, 1968. 11(47): p. 857. [5] Yasukawa, S., et al., Development of an on-board energy saving train operation system for the Shinkansen Electric Railcars. Quarterly Report of the Railway Technical Research Institute, 1987. 28(2-4): p. 54. [6] Van Dongen, L.A.M. and J.H. Schuit. Energy-efficient driving patterns in electric railway traction. In International Conference on Main Line Railway Electrification (Conf. Publ. no.312). 1989: IEE. [7] Renfe, A.v., A toda vela, la conducción económica. Aprovechamiento máximo de la energía de tracción. 2004. [8] Chang, C.S. and S.S. Sim, Optimising train movements through coast control using genetic algorithms. IEE Proceedings - Electric Power Applications, 1997. 144(1): p. 65. [9] Howlett, P., Optimal strategies for the control of a train. Automatica, 1995(4): p. 519-532. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

560 Computers in Railways XII [10] Pudney, P. and P. Howlett, Optimal driving strategies for a train journey with speed limits. J Austral Math Soc Ser B, 1994. 36: p. 38-49. [11] Liu, R. and I.M. Golovitcher, Energy-efficient operation of rail vehicles. Transportation Research Part A: Policy & Practice, 2003. 37(10): p. 917. [12] Lagos, M., et al. Computer-aided train operation: CATO. In Computers in Railways VII. Seventh International Conference on Computers in Railways. COMPRAIL 2000. 2000: WIT Press. [13] Hee-Soo, H., Control strategy for optimal compromise between trip time and energy consumption in a high-speed railway. IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans), 1998. 28(6): p. 791. [14] Carey, M., Optimizing scheduled times, allowing for behavioural response. Transportation Research: Part B, 1998. 32B(5): p. 329. [15] Golshani, F. and T. Thomas, Optimal distribution of slack-time in schedule design. Traffic Engineering & Control, 1981. 22(8-9): p. 490. [16] Chung Min, K. and C.S. Chang, Timetable Synchronization of Mass Rapid Transit System Using Multiobjective Evolutionary Approach. IEEE Transactions on Systems, Man & Cybernetics: Part C - Applications & Reviews, 2008. 38(5): p. 636. [17] Lancien, D. and M. Fontaine, Computing train schedules to save energy. Revue General des Chemins de Fer, 1981. 100: p. 679. [18] Albrecht, T., et al. A new integrated approach to dynamic schedule synchronization and energy-saving train control. In Computers in Railways. Eighth International Conference. 2002: WIT Press. [19] Domínguez, M., et al., Computer-aided design of ATO speed commands according to energy consumption criteria. Computers in Railways XI Computer System Design and Operation in the Railway and Other Transit Systems, 2008. 103: p. 183-192. [20] Law, A.M. and W.D. Kelton, Simulation Modeling and Analysis. Third edition ed. 2000, New York: McGraw-Hill. [21] Goodman, C.J., L.K. Siu, and T.K. Ho. A review of simulation models for railway systems. In International Conference on Developments in Mass Transit Systems. 1998: IEE.

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Driving equipment with three-phase inverters and asynchronous traction motors for trolleys and trams V. Radulescu, I. Strainescu, L. Moroianu, S. Gheorghe, E. Tudor, V. Lupu, F. Bozas, A. Dascalu, G. Mitroi & D. Braslasu ICPE SAERP S.A., Romania

Abstract The company ICPE SAERP S.A. is the main producer of electric drives for urban traction and for railway vehicles in Romania. The products of our company are subject to the last 48 years of permanent evolution, based on the semiconductor’s development and of the microprocessors control techniques. The improvement of the passenger’s comfort and the downsizing of the exploitation costs is a must for public transportation companies, relating to trolleybuses and trams. Both can be achieved by using modern electric drives (DC choppers or three-phase inverters), which can reduce power consumption and increase control of the vehicle. The main products for electric traction are: drives for traction motors of the vehicles (DC choppers for DC series motors and three-phase inverters for asynchronous and for synchronous motors) and converters for auxiliary services of the vehicles with two development directions, battery chargers (DC converters) for drive supply (24Vdc or 110Vdc) and threephase inverters for auxiliary asynchronous motors (steering, compressor). ICPE SAERP SA has delivered 310 pcs. traction equipment with DC chopper with GTO thyristors or IGBT transistors that equip the trolleybuses from Astrabus srl Arad for the final customers RAT Bucharest, Transurb Galaţi and Ratuc Cluj – Napoca. This paper presents driving equipment with three-phase inverters for asynchronous motors for trolleys and trams that ICPE SAERP has delivered in the past three years. The paper also presents the equipment’s performances, the analysed principle for electrical power diagrams. Keywords: three-phase inverter, asynchronous traction motor, electrical vehicle.

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1

Introduction

The nominal input voltage for the driving equipment with a three-phase inverter and asynchronous traction motors is 750 Vdc or 600 Vdc, with a voltage variation of +25%...–30%, to which the atmospherically voltages are added. The nominal input voltage for the microprocessors command block is 24 Vdc with a variation of +25%...–30%. This voltage comes from the battery on the urban vehicle (tram, subway frame, trolley). The driving equipment is conceived so that it can function in harsh conditions: high mechanical vibrations, a temperature domain of –40....+ 55oC etc. This equipment has a LC input filter, a three-phase power inverter and asynchronous traction motor. The three-phase inverter design is based on high voltage IGBT transistors. The existing control software allow four main functions: o Control – by reading the current state of the vehicle’s electrical driving system and the commands given by the system [3]; o Adjustment – through the commands sent to the three-phase inverter – INV3 – that actions two (for trams) or one asynchronous traction motor (for trolley). During the traction electrical breaking with energy saving regimes, the couple is always adjusted. In case of electrical breaking, the voltage limitation from the filter is ensured by connecting the dynamic break, so that the total break couple would be the one asked by the driver from the. The adjustment block assures also the anti-slide protection when starting the vehicle and anti-blocking when the vehicle uses electrical breaking. o Communication – on an internal level, between the Master and Slave control cards but also with an external computer, for the diagnosis; o Diagnosis – by collecting and memorizing the significant data for the status of the whole system; supplementary, an alphanumeric display with 4 lines of 20 characters is available, that reflects the current status of the whole system.

2

The ICPE SAERP equipment for the driving of the tram or light subway wagon

The electrical power diagram of the driving equipment for the tram with two motor bogie, each bogie being equipped with two three-phase asynchronous traction motor and two inverters with IGBT transistors, each inverter supplying one asyncronous motor with variable voltage and frequency. This is presented in the Figure 1. The input DC voltage, collected by a pantograph 1 (pozitive phase) from the line, is applied to a surcharge discharger 3 and respectively through an inductance 4, a radio parasites filter 5 and after that a main contactor 8. In parallel on the capacitor 5 a voltage transducer 7 is connected, to measure the contact line voltage.

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Power diagram for the electrical drive with two inverters and 2 asynchronous motors. Figure 1:

563

564 Computers in Railways XII By connecting the main contactor, it charges the two three-phase inverters (transistors 18-23 and 41-46) for the driving of the two traction motors 26 and 49 mounted on the bogies. To limit the initial current shock to the capacitive filter 13 and 36, we introduced two resistors 11 and 34, that are short-circuited by the contactors 10 and 33 after a certain time. The first three-phase inverter through two phase current transducers 24 and 25 supplies the three-phase asynchronous motor 26, that has a overspeed and position transducer 27, that measures the motors speed and it’s rotation direction. With the help of a transistor 28 and of a rheostatic breaking resistor we can control the voltage on the supplying line during the electrical braking operation. The inverter gives the command to supply the traction motor with variable voltage and frequency, following the statoric field of the motor, by an inverter command block 31, made with a master microprocessor and one or more slave microprocessors. The inverter command block 31 assures the traction and energy saving electrical breaking regimes and when the network does not receive all breaking energy, the start of the rheostatic break command is given, as a difference between the asked breaking effort and the one with energy saving. To action the traction motor on the second bogie we use a similar scheme as presented, for the driving of the first bogie, using elements 32...54. To action the whole tram, we use a general command block 55, with a master microprocessor and one or more slave microprocessors, receiving a series of information from all the blocks and the traction elements: through a data highway 56 the data and information is transmitted between the main command block 55 and the inverter command blocks 31 and 54. Also through this highway the data needed for an intelligent display block 57 (placed on the board of the vehicle) and for a laptop computer 58 (for the diagnosis data extraction from the command blocks) are transmitted. With the help of a tram command controller 59, placed on the vehicle’s board, the driver commands the run regimes (forward and backwards), normal breaking, emergency breaking and stop breaking, and also starting acceleration and breaking deceleration, this data are transmitted to the main command block through a data highway 60. By pressing the 61 and 62 buttons on the board, the driver can disconnect the first or second inverter, if one of them malfunctions; in this way the driver can ensure the safe return to the garage. With the help of an emergency breaking device 63, we can ensure the emergency breaking at full value when it’s needed.

3

ICPE SAERP’s equipment for trolley electrical drive

Figure 2 presents the electrical principle scheme for the driving of a trolley equipped with a three-phase inverter and a three-phase asynchronous traction motor. The nominal input voltage of 750 Vcc or 600 Vcc is transmitted to the trolley’s equipment by 2 current collectors 63 and 64 and fuses 65 and 66. This voltage is applied to the discharger 3. At the discharger’s terminals 3, the radio parasites filter that is made of two inductances 67 and 68, a capacitor 5 and the thermic fuse 6. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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AFISOR INTELIGENT

Figure 2:

Power diagram for trolleybus electrical drive.

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64 63

565

566 Computers in Railways XII The voltage from the output terminals of the inductance 67, through a fast main switch 8 that provides short-circuit and overload protection, and through an inductance 12, on one side and through the fuse 70, of an auxiliary charge contactor 10 and a charge current limitation resistor, it’s applied to a rectifier with four diodes 71, 72, 73 and 74, so that no matter the contact line polarity, the voltage applied to A terminal has the same polarity. In this way, the trolley can easily run in the garage or on track, being supplied by the lines of the trolley’s that run in the opposite direction, having a different polarity. Usually the closest line to the sidewalk has a minus polarity. In parallel on the capacitor 13 is a fast discharge resistor of the capacitor 14 and a input voltage filter transducer 15. Figure 2: Power diagram for trolleybus electrical drive After an optimum short time, one of the main contactors 16 is closed, that allows, after the capacitor 13 has charged to a reasonable voltage, the supply of a three-phase inverter (made of 6 IGBT transistors – 18...23), through a total current transducer 17. The three-phase inverter supply through two phase current transducers 24 and 25, an asynchronous three-phase traction motor 26, that is connected to a overspeed and position transducer that measures the motors overspeed and determines the rotation direction. With the help of a rheostatic breaking transistor 28, of a direction diode 77 and of a rheostatic breaking resistor 29, we can introduce rheostatic breaking; the breaking current is measured by a rheostatic breaking current transducer 30. The inverter is commanded to supply the traction motor with variable voltage and frequency, following the statoric field of the motor, by a inverter command block 31, made with a master microprocessor and one or more slave microprocessors. The inverter command block 31 assures the traction and energy saving electrical breaking regimes, in which case are commanded two thyristors 75 and 76, that allow the transfer of the electrical breaking saved energy to the contact lines. When the network does not receive all the commanded breaking energy, the start of the rheostatic break command is given, as a difference between the asked break and the one with energy saving. The functioning of the transistor inverter is ensured by the command of the transistors through this command block that receives a series of information from the total current transducer taken by the inverter 17, from the rheostatical breaking current transducer 30 and from the over speed and position transducer 27. The command, surveillance and diagnosis of the entire trolley are done by a main command block 55, that receives a series of information from the inverter command block through the data highway 56, but also from the other equipment and electrical and mechanical elements: pedals 78 and breaks 79, thermic fuses, input voltage transducer 7, auxiliary services static source 84, body voltage sensor 96, closed doors sensor 97, stationary break contact 98. In the driver’s cabin there is an intelligent display 57 that receives information through the highway 56, and when needed can be connected to a laptop computer 58 to collect all the events from the main command block’s diagnosis system. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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567

The three-phase inverter command block

Figure 3 presents an example of command, adjustment, control and diagnosis block with microprocessors, used for the command of inverters on trams and trolleys. The main microprocessor command block for the three-phase force inverter 31 is done with a microprocessor 100 that contains a RAM memory with a battery 101 to memorize the events. Figure 3 presents the command block for the inverter 1 from Figure 1. The analogical measurements from the inverter input voltage transducer 15, from the current transducers: inverter total current 17 and phase currents 24 an 25 enter an interface as analogical inputs 102, in which each analogical input signal is conditioned, filtrated, standardized and then applied to an analog to digital converter, so that the output signals can be read by the microprocessor that sends them by a highway 103 to the microprocessor 100. The logical inputs, usually YES or NO: command the inverter 61, command the emergency break 63 and a signal from a radiator temperature thermostat with IGBT transistors inverter 104; enter an interface block as logical inputs 105 where they are applied to optocouplers and then transmitted through the highway 105 as logical signal applicable to the microprocessor 100. The signal from the speed and position transducer 27 enter a rotor over speed and position interface block 106, where it’s conditioned, filtrated, standardized, so that the output signal can be used by the microprocessor, being transmitted through the highway 103 to the microprocessor 100. The inputs from the microprocessor 100 sent through the highways 56 (from the central command block) and 103 are processed by the microprocessor that has programmes for the voltage, current and traction motor over speed regulators and are transmitted on a command highway 107 to a command block 108, that through transistor command highway 109 commands the IGBT transistors (18, 19, 20, 21, 22, 23) from the inverter. The commands for the transistors enter a block that reads large currents in the transistors 110, that can stop the transmission of impulses to the transistors, by doing so blocking the inverter, that transmits through the highways 107 and 103 to a detection and signalising block of the malfunction 111, that commands an optical/ acoustical warning in the central command block 55. The microprocessor command block for the command of the rheostatical break transistor is realized with a microprocessor 112. The analogical inputs from the voltage transducer 15 and from the rheostatical break current transducer 30 enter an analogical inputs interface 113, in which every analogical signal is conditioned, filtered, standardized and then applied to a analogical-digital converter, so that the output signals can be used by the microprocessor, that are transmitted through a highway 114 to the microprocessor 112. The logical inputs, YES or NO: command the break from the controller 59, from the emergency break button 61 and from a radiator temperature thermostat with thyristor 115, enter a logical inputs interface block 116, where they are applied to optocouplers and then transmitted though the data highway 114 as logical signals applicable to the microprocessor 112. The inputs from the microprocessor 112 received through the highway 114 are processed by the microprocessor 112 that WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

568 Computers in Railways XII

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Block diagram for the command, adjustment, control and diagnosis of the electrical drive on trams and trolleys. Figure 3:

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General view, box drawing LFT- AS. Figure 4:

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570 Computers in Railways XII contains programmes for the voltage and current regulators, are sent through a command highway 117 to a command block 118, that through a rheostatical break thyristor command highway 119 commands the IGBT thyristor 28. The commands for the thyristor enter a large current thyristor sensor block 120, that can stop the transmission of impulses to the thyristor, by doing so blocking the thyristor, that transmits though the highways 117 and 114 to a detection and warning block that the rheostatical break thyristor 121 malfunctioned, and commands an optical/acoustical warning to the central command block 55 through the data highway 56 and the main information is sent to the diagnosis laptop 58. The nominal voltage filter 24 Vdc, 122 supplies two converters cc/cc – 123 and 124 – that supply with corresponding voltages the microprocessors and the block components of the inverter command block 31.

5

Execution of the electrical equipment for the asynchronous motor trolley

The driving equipment with inverter is placed on the roof of the trolley. The signal processor command of the traction inverter is included with the power electronics (IGBT), and the command is done by serial transmission of the commands to the SATREC-MMA block. The components of the electrical equipment from the LFT-AS are: o Traction inverter block IVF; o EMC continuous current network supply filter with ultra fast safety fuse o Ultra fast automated switch QL o Network voltage sensor SPTR o Line contactor KL and pre-charger circuit KR+RP o Services contactors K1…K3 o Crossing operating contactors KD o Ultra fast safety fuses 1250Vcc F1, F2, F5…F9 o Auxiliary services static converter type CS11T-SA o Static supply converter for the air conditioning converter CS11T-CL The roof box project contains all the equipment mounted on the roof. The positioning was made so that the mass of the entire equipment would be reduced and the gravity center would be moved as far back as possible.

6

Conclusions

According to their final destination, the equipment must satisfy all the European standards and requirements regarding safety and comfort for the urban traffic. The reliability must be very high and maintenance operations must be simple and easy to do for medium trained personnel. For this is necessary to have a high level of diagnose and the possibility to isolate the damage very quickly. To fulfill this requirements, ICPE SAERP, traced very sharply the development of the semiconductor technology, and electrotechnical materials, the final goal being to increase the performance of the products. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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The control of the drives are made entirely based on specific designs developed inside the company, beginning from the PCB design and physical stage of the boards, and in the end, final testing and development of the software for control and diagnosis. The electrical drive for trams or trolleys, based on asynchronous motors are state of the art designs, which use the last technology for control and power diagrams. The final performance concerning energy saving and the comfort of the passengers is proved, the power savings against a classical drive is about 30%. The analysis made by various authorities in Europe, reached the conclusion that the pollution in the big cities may be reduced if an well organized urban transportation will be created, with modern means of transportation, especially those that use underground and surface subway networks (on special routes, including light-subway), completed by connection lines that use tramcars, trolley buses and hybrid fuel cell based electrical autobuses that are parallel connected with modern electrical storage batteries. A strong urban transportation network will reduce substantially the number of automobiles and so the pollution can be strongly diminished. In addition to this, through a well-organized urban electric transportation, we can assure an optimum run of passengers from the big cities, with an acceptable comfort and safety. All of this implies large investments distributed on long time periods and respectively subsidies from the city halls that should cover the exploitation costs after renouncing of most of the diesel autobuses and powerful expansion of nonpolluting electrical transportation. This is well covered in the European Recommendation COM (2007) 551 final [1, p. 1, 9, 19, 20]:  Local authorities cannot face all these issues on their own: there is need for cooperation and coordination at European level. The vital issue of urban mobility needs to be addressed as part of collective effort at levels: local, regional, national and European. The European Union must play a leading role in order to focus attention on this issue.  Extension, rehabilitation and upgrading of clean urban public transport such as trolley buses, trams, metros and suburban rail as well as other sustainable urban transport projects should continue to be promoted and supported by the UE.  According to a recent study, over 40% of the urban tram and light rail fleet in the EU-15 and 67% of the fleet in the new Member States is over 20 years old and ought to be replaced before 2020.  At EU level several sources of financing are available, for instance the Structural Funds, the Cohesion Fund and loans from the European investment Bank. As in the past, the EU’s Cohesion Policy will remain an important source of funds in the eligible region during the period 2007-2013.  According to the programming documents, European Regional Development Fund – ERDF- and Cohesion Fund will contribute to almost € 8 billion for urban transport during the 2007-2013 period.

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572 Computers in Railways XII  The cohesion instruments in the current period 2007- 2013 provide a more broad and solid basis for co-financing urban transport and collective transport across Europe. The ERDF and Cohesion Fund regulations make explicit reference to clean urban transport and public transport but also, for the first time, to integrated strategies for the clean transport.

References [1] Green Paper. Towards a new culture for urban mobility. Commission of the European Communities. Brussels, 25.9.2007-COM (2007) 551 FINAL. [2] Radulescu, V., Strainescu, I., Gheorghe, S., Tudor, E., Moroianu, L., Serbu V., Goia, C., Bozas, F., Dascalu, A., Braslasu, D., Tanase, M., Mitroi, G., Badea, S., Sburlan, I., Ungurasu, C., Lupu, V., Radulescu, B., Driving equipments made by Icpe Saerp for urban electric transport vehicles, Proc. of the Urban Transport XIV, Urban Transport and the Environment in the 21st Century, eds. C.A.Brebbia, Malta, pp.203-211, 2008 [3] Strainescu, I., Tudor, E., Serbu, V., Bozas, F., Badea, s., Speed control of subway and trams, Proc. of the Urban Transport XIV, Urban Transport and the Environment in the 21st Century, eds. C.A.Brebbia, Malta, pp.515–223, 200

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Development, testing and implementation of the pantograph damage assessment system (PANDAS) A. Daadbin1 & J. Rosinski2 1

School of Computing Engineering & Information Sciences, Northumbria University, UK 2 Transmission Dynamics, UK

Abstract Pantograph failures due to complex interactions between the overhead line (OHL) and pantograph structure cause significant problems to the railway industry worldwide. Despite many efforts undertaken worldwide, no successfully implemented pantograph monitoring system has, up until now, been introduced for long-term operation on routinely operating trains. This paper describes the development, design and test results from the first fully proven Pantograph Monitoring System, which is now deployed on routinely operating trains in the UK. The system uses two subcomponents: the Digital Processing Module (DPM), which is directly clamped on the live 25kV pantograph structure, and the Receiving Signal and Relay Unit (RSRU) which is installed in a secure location inside the carriage. A pantograph mounted unit is interfaced with the accelerometers that are attached in vicinity of the carbon strip. The DPM uses Bluetooth communication to report any unexpected events to the RSRU. The DPM has an on-board GPS module and acquires and stores time domain data corresponding to the 100 highest events captured during daily train operation. The data is downloaded to the RSRU on a daily basis. Any high alarm events are instantaneously transferred to the train to warn the operator and the control centre about potentially a harmful event that requires immediate attention. The ‘hot spots’ caused by the overhead line are mapped and trended to allow successful implementation of predictive maintenance of the OHL. The system uses the GPRS mobile network to allow instantaneous access and remote interrogation from any location worldwide. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100531

574 Computers in Railways XII The system described in this paper represents the newest developments in pantograph monitoring and it is now in routine operation. Keywords: pantograph failure, monitoring system, condition monitoring, Bluetooth communication.

1 Introduction Pantographs are the single contact point between the rolling stock and the catenary. Good contact must be maintained under all running conditions to ensure seamless collection of power. The higher the speed, the more difficult it is to maintain good contact. In Europe the overhead line infrastructure is designed for a lifespan of 30-50 years plus. This has resulted in the selection of specific materials, such as pure carbon or copper and graphite impregnated carbon, for the critical pantograph contact strips. However, these materials present the drawback of wearing very rapidly, increasing the need for intense regular maintenance. Traditionally, European railways support a maintenance strategy based on inspecting and replacing pantograph heads rather than focusing on the overhead infrastructure. Problems with the overhead line during contact with the pantograph strip can promote wear and damage to the pantograph carbon element. There are reports of pantograph heads needing replacement after a single journey on high speed trains. Therefore, a monitoring system for an accurate identification of the overhead line geometry faults and their locations is extremely valuable.

2 Pantograph environment Due to the nature of the environment in which the pantograph operates, it is very difficult to put any monitoring in place. Figure 1 show the role of the pantograph in connecting the supply from the overhead line to the train.

Figure 1.

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In Figure 2 a close-up view of the pantograph is shown at the resting position. When the power needed, the mechanism is raised until the carbon strips get closer to the overhead line and make contact. Taking measurements of the interaction between the train pantograph and the overhead line catenary has long been a challenge, due to the need to overcome the hostile environment and the problems of isolation. Such measurements require a telemetry system to transmit information from pantograph mounted transducers, at a potential of 25kV, to recording equipment located in the vehicle body. Transmission Dynamics, in conjunction with Serco Rail, has developed and successfully implemented the Pantograph Monitoring System, which is now deployed on routinely operating trains in the UK. The following section deals with the details of the components involved and typical signals used in on-line monitoring.

3 Monitoring system The system uses two subcomponents. The first subcomponent is the Digital Processing Module (DPM) shown in Figure 3.

Figure 2.

Figure 3. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

576 Computers in Railways XII

Figure 4. This DPM is directly clamped on the live 25kV pantograph structure, as shown in Figure 4. The DPM is interfaced with the accelerometers attached in the vicinity of the carbon strip. The DPM houses integrated battery cells and is equipped with an array of three solar panels. Intelligent power management ensures that batteries are replaced only twice per full year of operation. The second subcomponent is the Receiving Signal and Relay Unit (RSRU), which is installed in a secure location inside the carriage. The DPM uses Bluetooth communication to report any unexpected events to the RSRU. The DPM has an on-board GPS module and acquires and stores time domain data corresponding to the 100 highest events captured during daily train operation. The data is downloaded to the RSRU on a daily basis. Any high alarm events are instantaneously transferred to the train to warn the operator and the control centre about potentially harmful events that require immediate attention.

4 Typical data The data collection showed that the signals were free from any electromagnetic interference. The typical signal from the two accelerometers on the carbon strip is shown in Figure 5. It shows the acceleration caused by the impact load on the pantograph. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 5.

Figure 6:

OHL fault location stamp.

The instantaneous monitoring enables the ‘hot spots’ caused by the overhead line to be mapped and trended to allow successful implementation of predictive maintenance of the overhead line (OHL), Figure 6.

5 Conclusions The Pantograph Damage Assessment System (PANDAS) described in this paper represents the newest developments in pantograph monitoring and it is now in routine operation. Its features are: • •

Directly mounted on a 25kV live pantograph Bluetooth radio communication with train

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578 Computers in Railways XII • • • • • • • • •

On board GPS for accurate event location stamp Battery/Soar Panel Powered – long operation life On board data storage and signal processing Using two accelerometers and two ‘dummy’ channels Overnight download of all events to train mounted SD memory card Connected to GPRS mobile network for immediate access and interrogation Remotely re-programmable via mobile network Robust design – proven in Service EMI certified for operation in demanding Rail Environment

This innovative monitoring system reduces the maintenance costs not only for the pantograph but also for the overhead line electrical equipment.

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Section 9 Dynamics and wheel/rail interface

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Strategies for less motion sickness on tilting trains R. Persson1 & B. Kufver2 1 2

KTH, Sweden Ferroplan, Sweden

Abstract Many railways have put tilting trains into operation on lines with horizontal curves with small radii. Tilting trains have vehicle bodies that can roll inwards, reducing the lateral acceleration perceived by the passengers. Tilting trains can therefore run through curves at higher speeds. However, excessive tilt motions can cause motion sickness in sensitive passengers. On the other hand, too little tilting will cause discomfort from high lateral acceleration and jerk. The present paper presents new tilt algorithms aimed at balancing the conflicting objectives of ride comfort and less motion sickness. An enhanced approach is taken, where the amount of tilt depends on the local track conditions and the train speed. The paper shows how selected tilt algorithms influence certain motion sickness related carbody motions. Speed profiles designed to avoid local peaks in the risk of motion sickness are another possibility. The speed profiles for both tilting and non-tilting trains are today set from safety and comfort perspectives only, thus minimizing the running time. The present paper shows how speed profiles could be used to balance the conflicting objectives of running time and less risk of motion sickness. The result is derived from simulations and put in relation to today’s tilt algorithms and speed profiles on the Stockholm–Gothenburg main line in Sweden (457 km). Keywords: tilting train, tilt algorithm, tilt strategy, passenger comfort, motion sickness, running time simulation.

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582 Computers in Railways XII

1 Introduction Growing competition from other modes of transportation has forced railway companies throughout the world to search for increased performance. Travel time is the most obvious performance indicator that can be improved by introducing high-speed trains. Trains with a capability to tilt the carbodies inwards in track curves constitute a less costly alternative than building new lines with large curve radii. The idea of tilting trains on lines with curves with small radii was discussed as long ago as the 1930s [2, 3]. The inward tilt reduces the centrifugal force to which the passengers are subjected, allowing the train to pass curves at higher speed while maintaining ride comfort. Carbody tilting is today a mature and relatively inexpensive technology [4]. Experience shows that tilting trains can cause motion sickness in sensitive passengers [5-9]. The difference in risk of motion sickness between non-tilting and tilting rolling stock has attracted particular interest. Roll and vertical motions are the two carbody motion components that show the largest increase compared to non-tilting trains and are a consequence of the tilt applied [10]. However, too little tilting will cause discomfort due to high lateral acceleration and jerk. The present paper presents new tilt algorithms aimed at balancing the conflicting objectives of ride comfort and less risk of motion sickness. Most existing tilting trains use a fixed relation between the track plane acceleration and the amount of tilt. As in [1], the present enhanced approach applies an amount of tilt commensurate with the local track conditions and speed of the train. Modified speed profiles especially designed to avoid local peaks in the risk of motion sickness are another possibility. Today’s speed profiles are designed to minimize the running time taking safety and comfort parameters into consideration. The present paper shows how the risk of motion sickness could be considered when setting speed profiles.

2 Vehicle motions Measured vehicle motions give important information, in particular as regards the difference between tilting and non-tilting trains. Figure 1 shows a Power Spectral Density (PSD) diagram for carbody roll acceleration; one motion component with a large increase from non-tilting to tilting trains in on-track tests [10]. A four-car long-distance tilting train, class BM73, from Norwegian State Railways (NSB) was used as the test train. The non-tilting cases were run with the same train, but with the tilt switched off and at speeds corresponding to nontilting trains. The measurements were taken on the Norwegian line between Kristiansand and Vegårdshei, a line containing numerous curves with 300 m radii. The main difference as regards carbody roll acceleration is found between 0.02 Hz and 0.5 Hz where the tilting train shows larger amplitudes than the nontilting one. Carbody vertical acceleration shows similar differences to carbody roll acceleration. PSD diagrams for all six motion components are shown in [10].

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Figure 1:

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Power Spectral Density (PSD) for carbody roll acceleration at on-track tests between Kristiansand and Vegårdshei in Norway [10]. The non-tilting case was run with the same train, but with the tilt switched off and at speeds corresponding to non-tilting trains.

The effect of vertical and roll acceleration on motion sickness could not be adequately separated as the two motions were strongly correlated in the on-track tests.

3 Evaluation criteria 3.1 Single source comfort criteria The requirements in respect of lateral acceleration are often set indirectly by limiting the permissible track plane acceleration. The EU-funded research project Fast and Comfortable Trains (FACT) studied the comfort-related quantities for plain track on the basis of certain European track standards [11]. For conventional trains, the results regarding lateral acceleration ranged from 0.78 m/s2 to 1.41 m/s2 with an average of 1.00 m/s2. The corresponding results for tilting trains are lower, ranging from 0.48 m/s2 to 1.00 m/s2 with an average of 0.63 m/s2. Japanese Railways have used 0.80 m/s2 as the limit since the 1960s [12]. This limit was challenged by [13], and [14] indicated that a more liberal limit could be applied as only 10% of the standing subjects reported discomfort at 1.0 m/s2 and only 5% of the seated subjects reported discomfort at 1.2 m/s2. The limit on lateral acceleration has now been relaxed to 0.90 m/s2 for certain trains with seated passengers only. However, they also found that combined comfort criteria were more effective than limits on single sources (see Clause 3.2).

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584 Computers in Railways XII Among European railways, the requirements regarding lateral jerk are set in the same indirect way as for lateral acceleration. The calculated lateral jerk for conventional trains ranged from 0.24 m/s3 to 0.71 m/s3 with an average of 0.48 m/s3 [11]. The corresponding results for tilting trains are considerably lower, ranging from 0.15 m/s3 to 0.50 m/s3 with an average of 0.27 m/s3. The introduction of natural tilting trains in Japan raised motion sickness as a comfort issue. Correlation between roll motions and motion sickness was reported [9] and a limit of 5 degrees/s was set so as to avoid discomfort. Calculated carbody roll velocities for tilting trains ranged from 2.3 degrees/s to 7.6 degrees/s with an average of 5.1 degrees/s when FACT studied the comfortrelated quantities for plain track on the basis of certain European track standards [11]. 3.2 Combined comfort criteria British Rail Research has described how to combine motion components in a curve transition to one comfort criterion [15-16]. The method differentiates between seated and standing passengers, but is here only referred to for seated passengers. The PCT Comfort index calculates the percentage of dissatisfied passengers on the basis of eqn (1). A reasonable acceptance value is in the interval from 3 to7.

 





PCT  max (8.97  y1s max  9.68  y1s max  5.9);0  0.12  ( 1s max )1.626 (1)

y = Lateral acceleration in carbody [m/s2], y = Lateral jerk in carbody [m/s3] and  = Roll velocity in carbody [degrees/s]. where

A similar combined comfort criterion has been developed in Japan [14]. In addition, this method differs between seated and standing passengers, here given for seated passengers. The TCT Comfort index calculates the discomfort on a 1 to 4 scale, where 1 is not uncomfortable and 4 extremely uncomfortable, the result is expressed as eqn (2). Note that the two combined comfort criteria of lateral acceleration and jerk have approximately equal weight.

TCT  0.4  y  0.4  y  0.02    0.04    0.8

where parameters are as above plus [degrees/s2].



(2)

= Roll acceleration in carbody

3.3 Motion sickness Motion sickness is not correlated to a single curve but is rather an accumulated effect from several curves. A limit on one or more motion components is presumably not generally appropriate. A model where motion doses are accumulated over time and recovery can be quantified may be a better choice [17]. Carbody vertical acceleration gave the highest correlation to motion sickness in several on-track tests [10]. However, the motion amplitudes WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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measured during the test were lower than those proven to cause motion sickness during laboratory experiments. This indicates that other motion components such as roll velocity and lateral acceleration contribute to the motion sickness experienced on tilting trains.

4 Optimization algorithms 4.1 General The approach taken in the present study is to reduce the difference in motion between non-tilting trains and tilting trains by applying just as much tilt as is necessary to avoid discomfort. This approach will minimize both carbody vertical acceleration and roll velocity at the expense of lateral acceleration and jerk. Comfort criteria must therefore be set to avoid unacceptable amplitudes on the latter ones. Three different discomfort criteria can be distinguished. 1. Lateral acceleration 2. Lateral jerk 3. Combination of lateral acceleration and jerk The acceptance values of these criteria were discussed in Sections 3.1 and 3.2. The combination of lateral acceleration and jerk is a simplification of the combined criteria, where the roll motions have been omitted. These motions will be minimized anyhow by the general approach of applying just the amount of tilt necessary to avoid discomfort. An example of how the three comfort criteria result in an acceptance area is shown in Figure 2. One option to fulfil the comfort criteria would be to modify the ratio between tilt angle and track plane acceleration. However, the potential of such an approach is limited as the ratio must be set large enough to fulfil the comfort criteria at the largest permissible track plane acceleration. A non-linear ratio between tilt angle and track plane acceleration would be slightly better, but the potential is also here limited by the requirement to fulfil the comfort criteria in the most demanding curve and curve transition. Instead, an enhanced approach is

Lateral jerk [m/s3]

0.5 Combined criteria

0.4 0.3 0.2

Acceptance area

0.1 0 0

Figure 2:

0.2

0.4 0.6 0.8 Lateral acceleration [m/s 2]

1

1.2

Comfort acceptance area as a function of lateral acceleration and jerk.

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Figure 3:

Distribution of circular curves (curve transitions excluded) with radii less than 6000 m as a function of the total length of the Stockholm–Gothenburg line.

suggested, where the amount of tilt depends on local track conditions and the train speed. Such a solution is possible with computer-controlled actuators, route files (defining track alignment and applied cant) stored onboard, and train positioning systems. The present paper uses the Swedish mainline between Stockholm and Gothenburg as an example. The track may be characterized by the curve distribution, which can be expressed as a percentage of the total length of the track. The curve radius indicated is the mean radius in a group, e.g. the curves in the 1000 m group range from 900 to 1100 m. The Stockholm–Gothenburg line has a variety of curves ranging from 352 m radius and up. The curve distribution for the line is shown in Figure 3. The total length of the circular curves (transition curves are excluded) with radii less than 6000 m constitutes 19% of the line. The total length of the line is 457 km. Applying a motion sickness dose perspective on a railway line is an interesting approach [17]. This means that the risk of motion sickness is estimated as a function of time. The influence of different tilt algorithms and speed profiles may be quantified as motion sickness doses by means of the following process: The train speeds at each point on the line are simulated in an Excel-based simulation program. These simulated train speeds are used as input to quasi-static motion calculations, according to [11]. The calculated motions are then accumulated over time to give motion sickness doses. 4.2 Today’s speed profiles Today’s Swedish speed profiles for tilting trains are set by allowing maximum 1.60 m/s2 track plane acceleration and 0.52 m/s3 maximum rate of change of track plane acceleration. Today’s tilt control applies a fixed ratio between track plane acceleration and tilt angle, which gives about 0.60 m/s2 lateral acceleration perceived by the passengers at maximum track plane acceleration and about WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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0.20 m/s3 lateral jerk perceived by the passengers at maximum rate of change of track plane acceleration. These passenger comfort values are slightly better than what could be accepted according to the comfort criteria in Figure 2. The difference may be used to reduce the risk of motion sickness. The following three control possibilities can be distinguished: 1. Original control, fix ratio between tilt angle and track plane acceleration 2. As 1, but with reduced ratio that just meets the comfort criteria 3. Optimized control on a curve by curve basis that just meets the comfort criteria. The effects on certain motion components of the different control possibilities are shown in Table 1. The table contains data for three curves and their transitions. The original control always applies tilt in proportion to the track plane acceleration, while the optimized control considers each curve separately. The first curve has a rather large radius and there is no need to tilt at all; the Table 1:

Motion components in selected curves.

2 2 2 2 Element Radii [m] Cant [mm] y [m/s ] z [m/s ]  [deg/s] y [m/s ] z [m/s ]  [deg/s] Transition 2.0 1.0 Circular 5440 25 0.11 0.010 0.30 0.006 Transition 2.0 1.0 Transition 3.7 2.9 Circular 1401 125 0.36 0.154 0.67 0.138 Transition 3.4 2.6 Transition 2.3 2.0 Circular 998 140 0.60 0.297 0.80 0.283 Transition 2.3 2.0

Track data

Original control

Optimized control

70

Original control Reduced ratio tilt / track plane acceleration 60

Optimized control

Net Dose on z [m/s]

50

40

30

20

10

0 0

50

100

150

200

250

300

350

400

450

500

Distance [km]

Figure 4:

Net Dose on carbody vertical acceleration for today’s speed profile.

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588 Computers in Railways XII carbody roll motion in the case of the optimized control is purely a result of the applied cant. The second curve has fairly short transitions making the combination of lateral acceleration and jerk decisive as regards the need for tilt. The third curve is close to the permitted track plane acceleration; considerable tilt must be applied to satisfy the lateral acceleration requirements. All three curves are examples where a lower tilt angle could be applied to reduce the carbody vertical acceleration and the carbody roll velocity without compromising the set comfort criteria. As mentioned in Section 3.3, motion sickness is not an effect of one or a few curves, but rather an accumulated effect of several curves. The Net Dose method both takes this and recovery during periods with fewer motions into account. Figure 4 shows the Net Dose calculated on carbody vertical acceleration for the Stockholm–Gothenburg line. The difference in Net Dose on carbody vertical acceleration may look small, but the case with reduced ratio between tilt angle and track plane acceleration gave a significant positive effect on the risk of motion sickness in an on-track test [7]. The optimized control gives about twice as large a reduction of the maximum Net Dose calculated on vertical acceleration as in the on-track test. 4.3 Improved speed profiles Another objective of the present study is to reduce the running time without increasing the risk of motion sickness. About 9% running time may be saved if trains and speed profiles are improved [18]. Maintaining the original control would not only require a larger maximum tilt angle, but would also result in an increased risk of motion sickness. The present study shows that the maximum Net Dose value of carbody vertical acceleration will increase by 26% if the tilt control is kept as today. The following control possibilities are available to maintain the maximum Net Dose value of vertical acceleration as today with the improved speed profile:  Reduced ratio between tilt angle and track plane acceleration. This will cause the maximum lateral acceleration perceived by passengers to increase to 1.5 m/s2 and the maximum lateral jerk to increase to 0.6 m/s3.  Optimized control on a curve by curve basis. The values become 1.4 m/s2 and 0.5 m/s3 respectively. None of the options above are attractive, as comfort would be dramatically reduced compared with today’s tilt control.

5 Speed profiles as a tool to minimize motion sickness risk The optimized tilt control was thus found to be unable to combine good comfort and low risk of motion sickness for the improved speed profile. This is a consequence of the maximized speed profile, as full tilt must be applied in many curves. One possibility to regain some scope or the optimized control is to reduce the speed again. This contradicts the purpose of using the improved speed WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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profile, but the impact on running time may be small if speed is reduced only at selected locations. The following control options are available to maintain the same maximum Net Dose on carbody vertical acceleration as today if 8% running time savings are accepted.  Reduced ratio between tilt angle and track plane acceleration. This will result in a maximum lateral acceleration perceived by passengers of 1.1 m/s2 and a maximum lateral jerk of 0.4 m/s3.  Optimized control on a curve by curve basis to just satisfy the comfort criteria in Figure 2. The maximum lateral acceleration and jerk become 0.8 m/s2 and 0.3 m/s3 respectively. These options look much more attractive from a passenger comfort perspective, but at the expense of 1% lost running time. The largest reduction of maximum Net Dose is obtained where the Dose takes its largest values. Figure 5 shows one example with a speed reduction from 190 km/h to 180 km/h between kilometre 60 and 115 on the Stockholm–Gothenburg line.

6 Discussion and conclusions The Net Dose calculations in the present paper are based on carbody vertical acceleration, as this was the motion component that showed the best correlation to the risk of motion sickness [10]. Using carbody roll velocity instead of vertical acceleration would shift the focus from circular curves to transitions, but the track sections with the largest risk of motion sickness remain the same. It could also be discussed whether quasi-static motion calculation is suitable. Support for

80 Original speed, original control Enhanced speed, original control Optimized speed, optimized control

70

Net Dose on z [m/s]

60 50 40 30 20 Section with reduced speed

10 0 0

50

100

150

200

250

300

350

400

450

500

Distance [km]

Figure 5:

Net Dose on carbody vertical acceleration for the improved speed profile.

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590 Computers in Railways XII doing so can be found in [11] and [19]; the latter indicates that weighting of carbody vertical acceleration to the risk of motion sickness must be extended towards quasi-static motions. The present paper presents tilt algorithms aimed at balancing the conflicting objectives of ride comfort and less risk of motion sickness. An enhanced approach is taken, where the amount of tilt depends on local track conditions and train speed. The enhanced approach proved to be effective as long as partial tilt could be applied in the majority of the curves and still satisfy the comfort criteria. Local speed reductions were found to be effective for regaining useful scope work area for the enhanced approach, but at the expense of lost running time.

Acknowledgements The financial support from the Swedish Governmental Agency for Innovation Systems (VINNOVA) and the Swedish National Rail Administration (Banverket) is gratefully acknowledged. The authors thank Bombardier Transportation for permission to publish this paper.

References [1] Kufver, B. & Persson, R. On enhanced tilt strategies for tilting trains. Proc. of Comprail 2006, CMP/WIT Press: Southampton, pp. 839-848. ISBN 184564-177-9. [2] Deischl, WVV. Linienverbesserungen oder gesteuerte Achsen? Verkehrstechnische Woche, 31(9), pp. 97-108, 1937. [3] Van Dorn, W. & Beemer, P. Suspension for vehicles. US Patent 2.225.242, 1938. [4] Persson R., Goodall R. & Sasaki K. Carbody Tilting – Technologies and Benefits. Proceedings of the 21st IAVSD Symposium, Vehicle System Dynamics, Volume 47, No 8, pp. 949-981, 2009 [5] Hughes, M. Tilt nausea is bad business. Railway Gazette International, 153(4), p. 249, 1997. [6] Cléon LM., Quetin F., Thibedore T. & Griffin M. Research on motion sickness. Proc. of World Congress on Railway Research 1999, Tokyo. [7] Förstberg, J. Ride comfort and motion sickness in tilting trains - Human responses to motion environments in train and simulator experiments. PhD thesis, TRITA-FKT Report 2000:28, KTH, Stockholm. [8] Brume, M. Why are Pendolinos so nauseating? Rail Professional, 93, p. 5, 2004. [9] Ohno, H. What aspect is needed for a better understanding of tilt sickness? Quarterly Report of RTRI, Volume 37. pp. 9-13, Japan, 1996. [10] Persson, R. Motion sickness in tilting trains - Description and analysis of the present knowledge. ISBN 978-91-7178-680-3. KTH, Stockholm, 2008.

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[11] UIC. Competitive and sustainable growth programme, Fast and Comfortable Trains (FACT), Track for tilting trains - Report D8. http://www.uic.org/download.php/fact/d8.pdf. [12] Urabe, S., Koyama, M. & Iwase, Y. Evaluations of train riding comfort under various speeds at curves. Quarterly report of RTRI. Volume 7. No 2. pp. 34–36, 1966. [13] Suzuki, H. Recent research and developments in the field of riding comfort evaluation. Quarterly report of RTRI. Volume 37. pp. 4-8, Japan, 1996. [14] Suzuki, H., Shiroto, H., Tanka, A., Tezuka, K. & Takai, H. Psychophysical evaluation of railway vibration discomfort on curved sections. Quarterly report of RTRI. Volume 41. pp. 106-111, Japan, 2000. [15] Harborough, PR. Passenger comfort during high speed curving - analysis and conclusions, BRR TR DOS 017, British Rail Research: Derby, 1986. [16] CEN. Railway applications – Ride comfort for passengers – Measurements and evaluation, EN 12299. CEN. Brussels, 2009. [17] Kufver, B. & Förstberg, J. A net dose model for development of nausea. Proc. of 34th meeting of the UK Group on Human Response to Vibration, Dunton, Essex, England, 1999. [18] Persson, R. Tilting trains – technology, benefits and motion sickness. Licentiate Thesis. ISBN 978-91-7178-972-3. KTH, Stockholm, 2008. [19] Persson, R. Weighting curves to motion sickness. Proc. of 44th meeting of the UK Group on Human Response to Vibration, Loughborough, England, 2009.

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Railway vehicle and bridge interaction: some approaches and applications G. Mikheev, E. Krugovova & R. Kovalev Laboratory of Computational Mechanics Bryansk State Technical University, Russia

Abstract The present paper describes the CAE-based approach for analysis of dynamics of a coupled model of a flexible railway bridge and a train. The approach is being implemented in Universal Mechanism (UM) software. The railway bridge is considered as a flexible multibody system. The dynamics of flexible bodies are simulated using data imported from finite element analysis (FEA) software. An application of the approach to the investigation of dynamics of a railway vehicle and a bridge supposes taking into account the flexibility of the bridge. Comparison of flexible deflections and stresses for the full and reduced FE-models for static and moving loads are presented. The simulation results for a high-speed train on a bridge that is modelled as a reduced FE-model with 50, 100 and 200 flexible modes, as well as comparison of simulation results for separate and coupled approaches to vehicle-bridge interaction (VBI), are shown. Keywords: vehicle-bridge interaction, flexible bridge model, moving load.

1 Introduction Computer simulation is an effective approach to analyze the dynamics of railway bridges under train motion along them [1–4]. The main object of investigations can be both a bridge and a railway vehicle. From the point of view of bridges, purposes of researches could be the detection of resonance phenomena on railway bridges, dangerous operation conditions such as train speed and weight, specific bridge design and so on. As for high speed trains, a dynamic analysis is necessary because of resonance phenomena of the structures due to regularly spaced axle groups of the train. In the case of resonance, excessive bridge deck

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594 Computers in Railways XII vibration can cause loss of wheel/rail contact, destabilization of the ballast and exceedance of the stress limits. Analysis of the dynamics of the railway bridge and time histories of stresses and strains are required for the calculation of their durability. In this case, stress loading blocks are the results of dynamic simulation. These blocks are calculated based on time histories of bridge stresses obtained for selected modes of loading. The loading depends on the weight and speed of rail vehicles, track irregularities on the bridge and so on. As for railway vehicle dynamics, it is important to consider the additional flexibility of the bridge in both vertical and lateral dynamics on safety, stability and ride comfort. Usually, research of the dynamics of railway bridges is carried out based on a simplified description of the vehicle-bridge interaction. The widespread approach supposes analysis of a finite element model of a bridge under action of the moving loading that simulates a train. In most cases, constant values of forces that correspond to the weight distribution of the train vehicles are considered. Thus, the dynamics of the vehicles are not taken into account within the simplified approach. Besides, such models do not take into account the mutual influence between vehicles and bridges. It is their main disadvantage. In this paper, methods for creating complex models, including a full 3D model of railway vehicles and trains and 3D models of flexible bridges, are considered. The methods are implemented in Universal Mechanism (UM) software [5].

2 Equations of motion of a flexible body Equations of motion of a flexible body are derived using the floating frame of reference method (Shabana [6]). Linear flexible displacements of the body are described by the component mode synthesis method (Craig and Bampton [7] and Craig [8]). According to this method, flexible displacements are approximated by a sum of modes:

u   h j w j  Hw ,

(1)

j

where u is a matrix-column of nodal degrees of freedom of the flexible body, hj is a matrix-column of the mode and wj is a modal coordinate that defines flexible displacements corresponding to the mode with number j. Modes of flexible body are calculated by an external FEA program and imported to UM software. A subsystem technique is used for including a flexible body into a multibody system. The idea that flexible displacements of a body can be represented by the sum of a number of mode shapes, scaled by modal coordinates, can be extended to stresses in the body as well. Modal coordinates can be used as the scaling factors on the stress solution of each mode shape and the superposition of these scaled stresses represents the body’s stress state instantaneously.

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3 Simulation of a rail-bridge interaction Rail-to-wheel contact forces are applied to a flexible structure as a running load. Both lateral and vertical force components are considered. A flexible body interacts with other bodies of a multibody system via joints and force elements. Joint points and points of attaching force elements are usually located at nodes of a FE-model of the flexible body. Such an approach cannot be applied to simulation of an interaction of a railway vehicle with the flexible bridge, because loads move and act between nodes of the FE-mesh of the bridge. In UM software, a rail is modelled as a massless visco-elastic force element

(see Figure 1). Transversal yr and vertical zr deflections of the rail, as well as their time derivatives, depend on the position and velocity of point K of the force element attachment to ground. Rotation of the rail around a longitudinal axis is not taken into account. If a rail lies on a flexible bridge, total rail stiffness is obtained as a sum of stiffness between the rail and the bridge (sleepers, roadbed and so on) and stiffness of the flexible bridge itself, Figure 2. During integration of equations of motion, a position of point K is defined by the current position of a wheel.

K

Figure 1:

Figure 2:

Massless model of a rail.

Model of interaction of a railway vehicle with a flexible bridge.

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2

1 4

3

P

3

4

P12

P22 4

P1

L12

3 L22

P2 L2

L1 K1

K

K2

K1 2

1

Figure 3:

1

K

P21

P11

L

L1

K2

L11 2

1

L21 2

Simple algorithm of nodal force decomposition.

Therefore, it is necessary to compute the position and velocity of any point on the surface of the flexible bridge, as well as to apply calculated force to any point on a surface of the bridge. Since the FEA-approach supposes force applying to a flexible body in nodes of FE-mesh only, the simple algorithm of the decomposition of wheel-to-rail contact forces between the nearest nodes of FE-mesh was used. Let us consider the algorithm in detail. A control area consists of surface polygons of a finite element model of the bridge, which is created around a rail-wheel contact point. The position and velocity of point K are calculated as linear interpolation of the corresponding values of the nearest nodes. Interaction forces are also distributed between the nearest nodes, Figure 3.

4 Separate and coupled approaches to simulate VBI Let us discuss two typical approaches for the analysis of vehicle-bridge interaction and the stress-strain state of a bridge. The so-called separate approach is the typical one that is used in many papers. It supposes considering a dynamical model of a railway vehicle and a model of a bridge separately. It means that wheel-to-rail contact forces are obtained from simulation of a railway vehicle without taking into account vehicle-bridge interaction. As a result, the dynamical analysis time histories of contact forces are saved. Then the obtained wheel-to-rail contact forces are applied to the FE-model of a bridge as running loads at the points that correspond to the positions of the wheels, Figure 4. Since vehicle dynamics is simulated without any reference to a bridge, the separate approach cannot give us any vehiclerelated performances, such as safety, stability or ride comfort, that would describe exactly vehicle-bridge interaction. So the separate approach can be used as a good approximation for the bridge response, but it is completely useless with regard to obtaining the vehicle dynamical response to running through the bridge. The so-called coupled approach supposes the mutual vehicle-bridge dynamics, Figure 5. Total displacements of rails are obtained as a sum of displacements between the rail and the bridge due to sleepers and roadbed and flexible displacements of the bridge itself. The obtained total displacements finally influence the contact wheel-to-rail forces that in fact act on wheelsets and the bridge and thereby couple vehicle and bridge dynamics. So the coupled approach connects vehicle and bridge models in the integrated model and WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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provides results concerning not only the bridge, but also vehicle response on vehicle-bridge interaction, which can also be the focus of applied researches.

5 Applications 5.1 Verification of reduced FE-model: static test Since the component mode synthesis method supposes significant reduction of degrees of freedom of the flexible bodies, then compare results of static calculations with the full FE-model of the bridge in NASTRAN [9] and reduced FE-model in the Universal Mechanism software. Let us consider the model that is depicted in Figure 6. A locomotive of 138,4 tonns stands in the middle of the first span of the bridge. The locomotive has 6 wheelsets. So, locomotive static load can be modelled as 12 lumped forces of 113 142 N that represent the wheel loads.

a) Vehicle dynamics

Figure 6:

b) Obtaining contact forces c) Applying moving load Figure 4:

Separate approach.

Figure 5:

Coupled approach.

Single locomotive on a bridge: computer model and design diagram.

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598 Computers in Railways XII Nine interface nodes in places of attachments of spans to piers (three nodes per pier) were chosen according to the component mode synthesis method. The flexibility of the piers was not taken into account. The model of the bridge includes 40902 nodes and 45200 shell and beam finite elements. The full model has nearly 245 400 d.o.f. The reduced model has 98 d.o.f. The cross section of the bridge is presented in Figure 7. Some of the flexible modes used, which are the result of orthonormalization of the component modes, are shown in Figure 8. Eight control nodes were chosen to compare results for the full and reduced FE-models of the flexible bridge, Figure 9. The comparative simulation results are given in Table 1. It is clearly shown that the relative error is less than 2% for flexible deflection, and less than 5% for stresses.

Figure 7:

1.65 Hz

Cross section of a two-way bridge.

11.5 Hz

Figure 8:

Figure 9:

11.8 Hz

Some flexible modes of the bridge.

Control points on the bridge.

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Table 1: Deflection, mm Node 1 2 3 4 5 6 7 8

Reduced FE-model -23.10 -23.20 -1.28 -1.09 -8.30 -1.07 -18.10 -18.10

Full FE-model -23.10 -23.20 -1.30 -1.09 -8.30 -1.09 -18.10 -18.20

Figure 10:

599

Node deflections and stresses. Longitudinal stress, MPa Reduced Full FE-model FE-model -1.88 -1.91 -1.92 -1.96 -0.17 -0.17 -1.62 -1.61 1.23 1.23 2.03 2.05 -1.43 -1.44 -1.45 -1.47

Relative error, % Deflection

Stress

0 0 1.54 0 0 1.83 0 0.55

1.57 2.04 0 0.62 0 0.98 0.69 1.36

Computer model for the moving load test.

5.2 Verification of the reduced FE-model: moving load test The second verification of the reduced FE-model of the bridge was done for the moving load. The model consists of a two-section electric locomotive on a flexible bridge, Figure 10. The FE-model of the bridge consists of 17907 nodes and 17641 beam and shell finite elements. The full FE-model has more than 105 000 d.o.f. and the reduced one has only 200 d.o.f. The vehicle velocity is 80 km/h. Irregularities of the railway track were ignored. A comparison of the flexible deflections for the moving load for the full FEmodel of the bridge in MIDAS software [10] and the reduced FE-model of the bridge in UM software is given in Figure 11. Flexible deflections due to a moving load (without deflection due to the weight of the bridge itself) in the middle of the span in the upper chord of the bridge are given. As is presented in Figure 11, flexible deflections for the full and reduced FE-models of the bridge are nearly the same. This means that reduced models of the bridge can be successfully used for the simulation of vehicle-bridge interaction within multibody system dynamics simulation codes.

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Figure 11:

Results for moving load test: full and reduced flexible models.

5.3 Trains on the bridge Let us consider the two examples of the simulation of the motion of trains on a bridge. The examples are focused on highlighting the following questions. The first one is how many flexible modes of a bridge should be taken into account to obtain more or less accurate results. The second one is how great an effect the mutual vehicle-bridge interaction has. Is it really necessary to consider significantly more complex mutual models or is it enough to represent a vehicle simply as a moving load according to the separate approach? UM software supports the simulation of railway vehicles of all types: diesel and electric locomotives, freight and passenger cars, trains and special railway vehicles. Dynamic models of railway vehicles undergo no simplifications for VBI simulation in comparison with usual vehicle dynamics analysis. So, simulation results related to vehicles can be considered as valid and accurate enough. The correctness of mathematical models and used numerical algorithms in Universal Mechanism software were proven by the Manchester benchmarks (Iwnicki [11], Universal Mechanism [12]). Railway vehicles can be simulated as rigid or rigid-flexible multibody systems. A high-speed passenger train and a heavy-haul train on the same bridge will be considered below. The model of the bridge that is described in Section 5.1 was used, see Figures 7–9. 5.3.1 High-speed train The high-speed train consists of 10 vehicles (totally 294 d.o.f.), Figure 12. The train is modelled as a typical multibody system and runs at 200 km/h. Let us compare some results for the flexible bridge described with the help of 50, 100 and 200 fixed interface eigenmodes calculated according to the component mode synthesis method.

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Figure 12:

Figure 13:

601

Simulation of high-speed train motion on the flexible bridge.

Flexible deflections and stresses for 50, 100 and 200 fixed interface eigenmodes, coupled approach.

Flexible deflections and stresses for the node in the middle of the first span of the bridge are shown in Figure 13. All models include 54 constraint modes and from 50 up to 200 fixed interface eigenmodes. It is obvious that the model with 200 eigenmodes is the most accurate one. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

602 Computers in Railways XII 5.3.2 Heavy-haul train A model of a heavy-haul train consists of 2 locomotives and 8 freight cars (totally 892 d.o.f.), Figure 15. The train runs at 100 km/h. Let us compare the obtained flexible deflections and stresses for the flexible bridge (200 fixed interface eigenmodes for the flexible bridge were used) for the separate and coupled approaches, see Figures 16 and 17. The comparison of results for the separate and coupled approaches is presented in Figures 14, 16 and 17 and shows that there is no significant difference between the simulation results for the mentioned approaches. Please note that the difference for the separate and coupled approaches is larger for the heavier heavy-haul train despite its speed being two times smaller. Please note that the considered models include the relatively stiff steel concrete bridge. The difference between the results of the separate and coupled approaches might be more evident for other bridges of different design.

Figure 14:

Flexible deflections for the separate and coupled simulations.

Figure 15:

Simulation of heavy-haul train motion on the flexible bridge.

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Figure 16:

Figure 17:

6

603

Flexible deflections for the separate and coupled simulations.

Flexible stresses for the separate and coupled simulations.

Conclusions

Reduced FE-models of bridges show generally very good agreement with the full FE-models for static test. The relative error between full and reduced FE-models is less than 2% for flexible displacements and less than 5% for stresses. Simulation results for the moving load test also showed a very good agreement between full and reduced FE-models that proved the practical possibility of using reduced FE-models of bridges within software codes for the simulation of vehicle-bridge interaction. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

604 Computers in Railways XII A different number of considered flexible modes for a FE-model of a bridge (50, 100 and 200 d.o.f.) expectedly influences both flexible deflections and stresses. The maximum difference is obtained for stress amplitudes. The considered model of the high-speed and heavy-haul trains on the bridge did not show any significant difference between the separate and coupled approaches for simulating a vehicle-bridge interaction. The proposed approach proved to be an effective tool for a detailed analysis of the vehicle-bridge interaction, taking into account the flexibility of the bridge.

Acknowledgements This research is supported by the Russian Foundation for Basic Researches under grant no 08-01-00677-а. Simulation results for the moving load test with the full FE-model (Section 5.2) in MIDAS software were kindly provided by Eng. Mikhail Malgin, The Paton Electric Welding Institute, Kiev, Ukraine.

References [1] Gong, L. & Cheung, M. S., Computer simulation of dynamic interactions between vehicle and long span box girder bridges. Tsinghua Science and Technology, Volume 13, Number 81, 2008. [2] Xia, H., Zhang, N. & De Roeck, G., Dynamic analysis of high speed railway bridge under articulated trains. Computers and Structures, 81, pp. 2467–2478, 2003. [3] Yang, Y. B., Yau, J. D., & Wu, Y. S. Vehicle-Bridge Interaction Dynamics, World Scientific Publishing Co. Pte. Ltd., 2004. [4] Gong, L. & Cheung, M. S., Computer simulation of dynamic interactions between vehicle and long span box girder bridges. Tsinghua Science and Technology, Volume 13, Number 81, 2008. [5] Universal Mechanism software, http://www.umlab.ru [6] A.A. Shabana. Flexible multibody dynamics: review of past and recent developments. Multibody System Dynamics, 1, pp. 189-222, 1997. [7] Craig, R.R. Jr. & Bampton, M.C.C., Coupling of substructures for dynamic analysis. AIAA Journal, Vol. 6, No. 7, pp. 1313-1319, 1968. [8] Craig, R.R. Jr., Coupling of substructures for dynamic analysis: an overview. In AIAA Paper, No 2000-1573, AIAA Dynamics Specialists Conference, Atlanta, GA, April 5, 2000. [9] MSC.NASTRAN, http://www.mscsoftware.com [10] MIDAS Family Programs, http://www.midasuser.com [11] Iwnicki, Simon D. The Manchester benchmarks for rail vehicle simulation / ed. by S. Iwnicki. - Lisse: Swets & Zeitlinger, 1999. [12] The Manchester benchmarks for rail vehicle simulation in Universal Mechanism software. http://www.umlab.ru/download.htm

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Certain aspects of the CEN standard for the evaluation of ride comfort for rail passengers B. Kufver1, R. Persson2 & J. Wingren2 1 2

Ferroplan, Sweden Bombardier Transportation, Sweden

Abstract The work of the European Committee for Standardization (CEN), Working Group CEN/TC256/WG7, concerns ride comfort for passengers. A European prestandard from 1999 for the measurement and evaluation of ride comfort for rail passengers has been revised by the working group. A draft standard prEN 12299 (Railway applications – Ride comfort for passengers – Measurement and evaluation) was sent for enquiry during 2006. From the CEN members, the national standardisation bodies of 28 countries, more than 300 technical and editorial comments were received. WG7 then produced a revised draft standard, which in 2009 was accepted as a European standard. The present conference paper discusses certain parts of EN 12299:2009, with a focus on data processing, the application of computer methods and interpretation of results. Keywords: ride comfort for passengers, CEN, European standards, EN 12299.

1 Introduction The European Committee for Standardization (CEN) has a Technical Committee TC256, defining European standards for the railway sector. In 1999, a European prestandard for measurements and evaluation of ride comfort for rail passengers ENV 12299 [1] was published. The prestandard defines methods for quantifying the effects of vehicle body motions on ride comfort for passengers. These methods have originally been developed by Office for Research and Experiments of the International Union of Railways (ORE) (NMV, NVA and NVD methods) [2] and British Rail Research (BRR) (PCT and PDE methods) [3].

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606 Computers in Railways XII Recently, the prestandard ENV 12299 has been revised by Working Group CEN/TC256/WG7. Active experts in WG7 have been nominated from the national standardisation bodies of France, Germany, Italy, and Sweden, and come from the companies Alstom, Bombardier, Deutsche Bahn (DB), Ferroplan, Siemens, La Société Nationale des Chemins de Fer Français (SNCF), Trenitalia and the Swedish National Road and Transport Research Institute (VTI). An enquiry version of the new standard was submitted to CEN during 2006 and more than 300 technical and editorial comments were received and taken into account for the final version of the new standard EN 12299 [4] which was approved and published in 2009. The aim of the present conference paper is to present certain parts of the new standard, with a focus on data processing, application of computer methods and interpretation of results.

2 Basic principles in the comfort standard EN 12299 Comfort is measured in an indirect way. Motions of a vehicle are mostly measured by accelerometers and gyros fitted to the vehicle body at certain positions. Direct tests based on test subjects are not defined in EN 12299 [4], even though certain guidelines are given in an informative annex. The Mean Comfort Complete Method NVA (described in Clause 5 of this paper) makes use also of accelerometers in the interface between the seat pan/seat back and the passenger. Vehicle conditions, accelerometer positions, test speed, selection of test sections, relevant time intervals etc. are defined for each method. The accelerometer and gyro signals shall be band-pass or low-pass filtered. The weighting curves Wc and Wd for lateral and longitudinal motions are the same as in ISO 2631-1 [5], while the low-pass filter Wp (used in the PCT and PDE methods) and the weighting curve Wb for vertical direction are special filters for railway applications. Post-processing of the filtered signals, such as sliding window calculations, rms calculations, averaging procedures and statistical analysis is defined for each method. The scope of the standard is to define relevant methods for the evaluation of ride comfort. In an annex, the procedures for vehicle assessment with respect to one of the comfort methods are defined.

3 The mean comfort standard method NMV The Mean Comfort Standard Method quantifies comfort during a continuous five-minute run for a seated passenger. Weighting curves Wb and Wd are used, extracting vibrations in the frequency range 0.4 Hz – 100 Hz. Hence, the method neglects quasi-static acceleration due to curving. The method is validated for fairly straight lines. The accelerations are measured in the longitudinal (x), lateral (y) and vertical (z) directions. After frequency weighting, sixty continuous (and not overlapping) five-second weighted rms accelerations are calculated for each direction. From WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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the sixty rms values, the 95th percentile (i.e. the 4th highest value) is used for further processing. Finally, the 95th percentiles of the weighted accelerations in the three W

directions ( a XPd 95 etc) are combined with an rss (root-sum-square) calculation according to eqn (1), valid for a 5-minute period.

Wd Wb 2 2 N MV  6  (aWXPd 95 ) 2  (aYP 95 )  ( aZP 95 )

(1)

The resulting NMV value may be interpreted according to Table 1. Based on experiences from France, Germany and Sweden, the scale is slightly modified compared with the corresponding scale in the prestandard ENV 12299 [1]. The NMV method has many similarities with traditional vibration analysis according to ISO 2631-1 [5]. The controversial point is the use of 95th percentiles where only the 4th highest value is considered. The consequences are that the three hypothetical 5-minute vibration patterns in Table 2 are considered equally comfortable, which seems doubtful. Another problem is that it is not possible to connect the resulting NMV value to a certain location along the track and the local track irregularities, since the three 95th percentiles of the x, y and z accelerations may occur during three different five-second time intervals (and consequently at three different locations).

4 Continuous comfort CCx, CCy and CCz Since the NMV method makes use of the 95th percentiles only, there is a substantial loss of information. Therefore, CEN/TC256/WG7 proposes that all five-second rms values are reported from comfort tests. This will enable further analysis and comparisons between different vibration measurements. These fivesecond rms values define a times series for x, y and z directions, respectively (called Continuous Comfort CCx(t), CCy(t) and CCz(t)). Table 1:

Scale for the NMV comfort index in EN 12299 [4]. NMV < 1.5 1.5 < NMV < 2.5 2.5 < NMV < 3.5 3.5 < NMV < 4.5 NMV > 4.5

Table 2:

Very comfortable Comfortable Medium Uncomfortable Very uncomfortable

Three hypothetical five-minute vibration patterns for one direction (each of sixty five-second rms values, m/s2).

Series A Series B Series C

First highest rms value 0.3 0.3 0.9

2nd 0.3 0.3 0.9

3rd 0.3 0.3 0.9

4th 0.3 0.3 0.3

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5th 0.1 0.3 0.3

ith 0.1 0.3 0.3

60th 0.1 0.3 0.3

608 Computers in Railways XII Table 3:

Preliminary scale for the CCy(t) and CCz(t) comfort indexes.

CCy(t), CCz(t) < 0.20 m/s2 0.20 m/s2 < CCy(t), CCz(t) < 0.30 m/s2 0.30 m/s2 < CCy(t), CCz(t) < 0.40 m/s2 CCy(t), CCz(t) > 0.40 m/s2

Very comfortable Comfortable Medium Less comfortable

A preliminary scale for assessments of individual CCy(t) and CCz(t) values is given in EN 12299, Table 3.

5 Mean comfort complete methods NVA and NVD The Mean Comfort Complete Methods (NVA and NVD) quantify comfort during a continuous five-minute run, in analogy with the Mean Comfort Standard Method (NMV). The NVA method is based on accelerometer measurements not only at the floor (vertical direction), but also in the interfaces between a seated passenger and the seat pan (lateral and vertical directions) and seat back (longitudinal direction). This makes the method substantially more cumbersome to use, both in real comfort tests and in computer experiments. The NVA comfort index is based on 95th percentiles of the measured accelerations. The NVD method is validated for standing passengers. Accelerations are measured at the floor only. The NVD comfort index is based on median values of the measured accelerations in all three directions and on the 95th percentile of the measured accelerations in the lateral direction. The ORE B153 expert committee achieved the best correlation between comfort ratings and vehicle motions when the maximum values and not the 95th percentiles were used [2]. However, it was believed the method would be too sensitive to anomalies if it was based on the exceptional values. Whether the method is based on maximum values or 95th percentiles does not really affect the sensitivity to outliers, and does not eliminate the fact that Series A and Series B in Table 2 would be rated equal with the NVD value. Both Mean Comfort Complete Methods have the same substantial loss of information in the statistical analysis as the Mean Comfort Standard Method: Most five-second rms values have no influence at all in the final calculation. In addition, both methods have the characteristic that it is not possible to connect the resulting NVA or NVD value to a certain location along the track since the relevant 95th percentiles (and median values) may occur during different fivesecond time intervals.

6 Comfort on discrete events PDE Comfort on discrete events, PDE, is based on research at British Rail Research (BRR) [3]. The tilting APT and non-tilting HST were used for test runs, where test subjects were instructed to press a button if any aspects of the lateral ride were considered “Uncomfortable” or “Very uncomfortable” on a scale “Very

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comfortable” – “Comfortable” – “Acceptable” – “Uncomfortable” – “Very uncomfortable”. BRR found that comfort disturbances were reported at large track irregularities or transition curves. These two cases were analysed separately. For large track irregularities, it was found that the percentage of passengers indicating discomfort depends on two variables: Mean lateral acceleration (due to curvature and cant) and peak-to-peak lateral acceleration. The PDE method was slightly modified in ENV 12299 [1], with the aim of less manual application. A 2 Hz low-pass filter WP was introduced and a procedure using a two-second sliding window was defined. Within this window, peak-topeak lateral acceleration ÿpp(t) and mean lateral acceleration |ÿ2s(t)| shall be calculated according to eqns (2) and (3).

 T   T ypp (t )max yP,* Wp ( ),  t  , t   , 2   2   T   T  min yP,* Wp ( ),  t  , t    2   2  y2s (t ) 

1 T

 y

t

T 2

T t 2

* P,Wp

( )d

(2)

(3)

* where T=2 seconds and yP,Wp ( ) is the low-pass filtered lateral acceleration of

the vehicle body. From these running peak-to-peak and mean lateral accelerations, running PDE(t) for standing and seated passengers can be defined, eqns (4) and (5), respectively. The PDE functions represent the percentage of the passengers rating the ride as uncomfortable or very uncomfortable. It may be noted that the PDE functions may take values above 100, but such high values are outside the interesting range of application.

 (t )  max 8.46  y

 (t )  21.7;0

PDE (t )  max 16.62  ypp (t )  27.01  y2s (t )  37.0;0 PDE

pp

(t )  13.05  y2s

(4) (5)

The comfort index PDE(t) is a continuous signal as a function of time and can be reported as such. For the assessment of a particular local event (which will affect the two-second sliding window during about 4 seconds), the local maximum of PDE(t) shall be used. Examples of the shape of the PDE(t) function are given in Figure 1. Note that even though the discrete events generate distinct peaks of the low-pass filtered lateral acceleration ÿP,Wp(t), the shape of the PDE(t) function may be less transient. Originally, the PDE functions were derived and validated for circular curves and straight track only. Comfort disturbances on a transition curve, or within WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

610 Computers in Railways XII 3 seconds, from a transition curve were neglected [3]. When eqns (4) and (5) are applied on acceleration data measured on a transition curve, which ENV 12299 [1] and EN 12299 [4] allow, both a mean value of lateral acceleration |ÿ2s(t)| and a lateral peak-to-peak acceleration value ÿpp(t) will be quantified within the twosecond sliding window. This can be seen for the transition curves in the time intervals 2s ES II type: 30kV). Improvement of water-tightness (ES type: Spray water -> ES2-type watertightness) were achieved. Adopting a switching and lock mechanism that is resistant against breakdowns. Opening direction indicator output at time of control failure. Adoption of point control relay and circuit control device. Torque securing tolerance for turnout displacement.

5.2 Simplification of installation and maintenance management a) b) c)

Reduction of spare parts by permitting interchangeability between left-side and right-side uses. Elimination of wiring work inside the switch. Monitor unit that can be shared with ES type.

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5.3 Designs for longevity and environmental considerations a) b)

Make covers that can open and close and make it possible to replace each part individually (see figure 12). Reduce downtime and cut cost. Ability to do overhauling at the manufacturers’ workplace.

5.4 Expansion of area where it is used a) b) c) d)

Operating temperature -20~+60 deg C. Reduce switching time (6 seconds -> 3.5 seconds). Motor power supply from 200V+-10% to 105V+-20%. Can be used in cant sections and AC-electrified sections.

5.5 Revised characteristics of the monitoring device The revised characteristics of the monitoring device are as follows. a) Records the turning angle of the switching roller in ES2 type so that the position of the switch rod and lock rod will be known. b) Monitoring device can be used by both ES2 type and ES type. c) When a switching order is given but the points do not switch, the switching data are recorded. An example of the switching data is shown in figure 13.

Figure 12:

Figure 13:

Point machine with the cover open.

Example of the switching data (applies to both ES2 type and ES type).

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6 Future research topics The ES-type point machine, for which a servo-motor was adopted, made possible the supervision of various items, recording of data and analysis of breakdowns. However, a switch consists of both the turnout and the switching machine, and the cause of failure to change can be the turnout itself. The turnout is inspected periodically, but is not monitored at all times. Torque values recorded with the monitoring device have been utilized to investigate the cause of breakdowns. In the future, trouble with the turnout will also be monitored, using techniques now being researched. In addition, we will do research on how to know the signs of trouble before there is a breakdown.

7 Conclusion When the ES-type point machine with a servo-motor was introduced in 2002, we could monitor not only the lock, but also the torque, stroke, voltage and other information. The next step was development of the ES2-type point machine, an improved ES type with greater stability. In the ES2-type point machine, monitoring device could be used by both ES2 type and ES type. Furthermore, it could record the turning angle of the switching roller in ES2 type. In the future, trouble with the turnout mechanism will also be monitored through techniques now under research. We will also research how to know signs of trouble before there is a breakdown.

References [1] Gregor, Treeg, and Sergej, Vlasenko, Railway Signalling & Interlocking, pp.165-166, 2009. [2] Kazue, Yasuoka, et al., “Practical application of the developed new pointmachine”, International Symposium on Speed-up and Service Technology for Railway and Maglev Systems (STECH’06), 2006. [3] JR Higashinihon Shingosetubi (jou) [JR East Signal Equipment (Upper Volume)], JR East General Education Center Electric Group, pp.10-25 - 1040, 2001. (in Japanese) [4] Tentetsu-souchi [switch-and-lock movement apparatus], Japan Railway Electrical Engineering Association, pp.62-66, 1998. (in Japanese)

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A heuristic approach to railway track maintenance scheduling L. M. Quiroga & E. Schnieder Institute for Traffic Safety and Automation Technologies, TU Braunschweig, Germany

Abstract Travelling safely and comfortably on high speed railway lines requires excellent conditions of the whole railway infrastructure in general and of the railway track geometry in particular. The maintenance process required to achieve such excellent conditions is largely complex and expensive, demanding an increased amount of both human and technical resources. In this framework, an optimal scheduling of maintenance interventions is an issue of increased relevance. In this work a method for optimization of the tamping scheduling is presented. It is based on a heuristic algorithm, which finds a very detailed tamping schedule where each planned intervention is fully specified. The algorithm tries to maximize an objective function, which is a quantitative expression of the maintenance process’s objectives defined by the railway company. It first finds an upper bound for the objective function value, and then returns the best feasible solution found. The method is validated by means of a case study based on real data of the 240 km track of a French high speed TGV line. The results presented show that the value of the best solution found is very near to the upper bound (the difference is smaller than 1%), with a calculation time of under 1 second using a standard computer, so we think the heuristic has a great performance potential. Keywords: track maintenance, heuristics, tamping, scheduling.

1 Introduction Measuring and keeping railway geometry under control are fundamental tasks of a railway infrastructure maintenance process. Railway geometry is representative of the travelling comfort and the derailment risk, so if its deviation exceeds a certain limit value, the travelling speed on that sector must be reduced. Therefore, railway WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100631

688 Computers in Railways XII geometry is both a measure of travelling quality and safety. For these reasons the French railway operator SNCF has been measuring periodically the geometrical characteristics of its high speed network since its commissioning, i.e. for more than 20 years now. Figure 1 shows the measurements of the longitudinal levelling (in French Nivellement Longitudinal, NL) for a 1 km track sector for the last 20 years. The NL parameter is the longitudinal mean deviation of rails in respect to the ideal position, and it is considered representative of the general railway geometry deterioration [1]. By default the deterioration grade increases with time, reflecting the track geometry deterioration. Due to confidentiality reasons, the measurement units are not shown. Degradation decrements take place only when some maintenance intervention is performed. In figure 1 the maintenance activities most relevant for track geometry are included: tamping interventions. In the figure, the bar heights represent the fraction of the railway sector affected by the maintenance activity. Tamping yields a visually obvious effect, yielding a sudden drop in NL. The figure shows some very interesting behaviour: in the autumn of 2001 a tamping action has taken place. However, afterwards, an extremely fast degradation of the NL has set in. Some possible reasons for such counterproductive interventions are water under ballast, adverse weather conditions, or poor intervention quality (operator incompetence). This is a good demonstration of the stochastic characteristic of the ageing and restoration process. The effect of these characteristics on the proposed process model is that both the NL value after a tamping intervention (equation 1) and the degradation speed coefficient (equation 3) are modelled as normally distributed variables. Furthermore, in 2005 the NL improved several times (measurement line with negative gradient) but no tamping action was registered. Possible reasons for negative increments on NL without interventions are measurement errors, mainly offset errors, i.e. NL is not always measured on exactly the same 200 mts. Eventually, it could also

1

Tamping Measurements

0.9

0.8

NL

0.7

0.6

0.5

0.4

0.3

0.2 1998

1999

2000

2001

2002

2003 2004 Time[Years]

2005

2006

2007

2008

Figure 1: Course of longitudinal levelling degradation for a railway sector. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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be that interventions are not registered in the database. The details of how this influences the model can be found in [2]. It can be shortly described as the addition of stochastic noise to represent the measurement errors, and the assumption of interventions when the negative increment is greater than a certain threshold. Technical and human resources required for performing tamping interventions are a major cost factor in high speed railway systems [3]. Furthermore, due to high logistic costs constraints, most track geometry maintenance activities need to be planned up to one year in advance. In this context, a crucial question to be answered is the following: with the available human and technical resources, and considering the current track railway geometry deviation, when and where should tamping interventions be performed? This paper presents a method for answering this question. It consists of two main components: a track geometry deterioration forecasting method, and a heuristic for interventions scheduling. Additionally, it needs a series of input data, such us a database with the available track geometry measurements, some characteristics of the tamping machines available, and the topology of the railway network to be maintained. Section 2 presents the proposed forecasting method, section 3 describes the heuristic algorithm used for schedule generation, and in section 4 the method is validated be means of a case study with real data of a French high speed line. Finally 5 presents some concluding remarks.

2 Railway track geometry forecasting 2.1 Row data preprocessing Railway geometry is measured periodically by means of special measuring coaches equipped with mechanical and/or electrical sensors. As it can be observed in figure 1, the periodicity of the measuring runs has been irregular since line commissioning, so the first problem for forecasting railway geometry deviation is the irregular sampling rate. To overcome this, we interpolate the measured points using splines, and then resample with the sampling rate of the last years. This is a compromise solution minimizing information loss in the last measurement years and keeping the addition of artificial measurements in the first years at an acceptable level. The resampled data is then used to tune the forecasting algorithm. 2.2 Process model The process model used for forecasting is the one presented in [2]. It relies on 2 assumptions, namely: 1. The degradation value NLinitn achieved after the nth tamping intervention can be described as a normally distributed stochastic variable, i.e. NLinitn ∼ N (µNLinit (n), σNLinit (n)). WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

(1)

690 Computers in Railways XII 2. The evolution of the degradation value between two tamping activities can be described by an exponential function of the form NLinitn · ebn

t−tn

(2)

where n is the number of cumulated tamping interventions since track renewal, tn is the time at which the nth tamping activity has taken place, and bn is a is a normally distributed stochastic variable, i.e. bn ∼ N (µb (n), σb (n))

(3)

The first assumption relies on the study on the effects of tamping interventions on high speed railway lines presented in [1]. The second assumption is based on the model presented in [4], which postulates that geometry degradation grows exponentially between tamping interventions. According to these assumptions, for the model to be applied we would need to find expressions for µNLinit (n), σNLinit (n), µb (n), and σb (n). To obtain these functions, we need a database with track geometry measurements on many railway sectors for many years, including tamping activities performed. For each of the sectors recorded in the database, the curve NLinitn · ebn t−tn that best fits, i.e. minimizes the quadratic error for the measurements between the nth and the (n + 1)th tamping interventions, for n ∈ 1, . . . , Nmax , where Nmax is the number of tamping interventions performed in the lapse of time recorded in the database for that sector. In doing so it must be taken into account that it is known that track geometry exhibits a transient behaviour in the first months after a tamping intervention, so we do not consider measurements taken in the three first months after an intervention. Doing this at each sector available in the database, the mean value and variance of NLinitn and bn can be estimated. Furthermore, it is common knowledge that the degradation of NL depends on the annual track load rather than on time. In case that the track load had changed within the time period registered in the database, a transformation could be used to standardize the data, i.e. to unmake the effects of the track load modification, basing on the results presented in [5]. The next step is to find the functions µNLinit (n), σNLinit (n), µb (n), and σb (n) which best fit the estimated values. 2.3 Forecasting algorithm To describe the forecasting procedure, a few definitions are necessary:  + h) as the forecast of NL at t + h with the information available Defining NL(t at time t the algorithm can be described as follows: 1. If the time elapsed since the last tamping intervention is longer than TIME_MIN and there is no intervention planned before time t + h, then find the function of the form of equation 2 which best fits the degradation curve  + h) by extrapolation. since the last tamping intervention, and obtain NL(t 2. If the time elapsed since the last tamping intervention is shorter than TIME_MIN and there is no intervention planned before time t + h, then WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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consider equation 2 with b = bn , where n is the current cumulated number of tamping interventions, i.e. the mean value of b after n tamping interventions. 3. If a tamping intervention is planned before time t+h, then consider equation 2 with b = bn , where n is the current cumulated number of tamping interventions, i.e. the mean value of b after n + 1 tamping interventions. Summing up, the algorithm looks for the curve best fitting the geometry degradation course since the last tamping intervention, but if this was too recent it just takes the mean curve for the current number of accumulated tamping, according to the model of section 2.2. The same happens if a tamping intervention is planned within the forecasting horizon. For the parameter TIME_MIN a value of one year seems to be reasonable. For a more detailed description of the forecasting method see [6].

3 Interventions scheduling method 3.1 Problem definition In order to formalize the problem definition, we model the railway net as a graph. The edges are the railway tracks and the nodes are the railway switches. The edges are in turn divided into sectors of 200 m. Then a criteria has to be established to assess the benefit of performing a tamping intervention at each of the sectors. In 3.2 some possible objective functions are presented. Furthermore, the following constrains has to be taken into account for scheduling tamping activities: 1. Tamping interventions take place in the night service interruptions, i.e. approx. 4 to 5 hours per night are available. 2. The number of tamping machines for the whole railway net is limited, so at each line the tamping machines are available for a limited number of nights per year, i.e. N nights. These nights are in general consecutive, so we call a tamping campaign the set of N consecutive nights at which a tamping machine is available for a given line or net. 3. Each tamping machine has limited travelling speed ST rav and tamping speed ST amping . 4. TSetUp is the preparation time needed between arrival at the starting sector and the intervention start time and TT akeDown the time needed between finalization of the intervention and departure to the end depot. 5. For tamping on and near switches special machines are needed. Furthermore the first and the last 200 m of a tamping intervention are transient sectors used to smooth the transit from a probably deteriorated sector to a fleshly tamped one. According to expert opinion, the number of transient sectors should be kept low. This leads to a further constraint: to minimize the number of transient sectors, each night tamping interventions can only take place in contiguous sectors, and they all must belong to the same edge (switches can not be tamped). WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

692 Computers in Railways XII 6. In order not to disturb normal train operations, the tamping machine must be allocated at a side track, the so called depots, before the first scheduled train runs. Additionally, on the fist intervention night the machine has to be picked up from a specific depot, and after the last intervention it has to be given back also at a specific depot. The problem the heuristic scheduling algorithm solves is to find a feasible solution consisting a set of N interventions (one per night) which maximizes the defined objective function, which should be a mathematical representation of the railway operator’s objectives. An intervention consists of the following elements: • An intervention number i, i ∈ 1, .., N • Start depot Di and end depot De • Start tamping sector Si and end tamping sector Se Furthermore, for an intervention to be feasible, the inequality TSI ≥ (Dist(Di , Si ) + Dist(Se , De ))/ST ravel + TSetUp +TT akeDown + Dist(Si , Se )/ST amping

(4)

must hold, where TSI is the night service interruption time, Dist(Di , Si ) is the distance between initial and end depot, Dist(Si , Se ) is the distance between initial and end intervention sectors. What inequality 4 expresses is that the blocking time must be enough for the maintenance team to travel with the machine to the intervention start sector, get ready to start working (duration of the procedure to block the track, TSetUp ), perform the intervention, get ready to leave the track (duration of the procedure to unblock the track, TT akeDown ) and travel to the end depot. For the interventions to be unambiguously defined, an arbitrary sense is assigned to each edge, and the sectors are numbered in the sense of the edge. According to this sector enumeration, a further constraint can be set for an intervention to be valid: Si ≥ Se . 3.2 Objective function The objective function is a key part of the whole scheduling method. It should express the objectives of the railway track maintenance process, which may vary significantly from one company to another. Next three possible implementations are presented. Total reduction track geometry deviation. The benefit of a tamping intervention is directly proportional to the current geometry degradation NL. This means that the degradation speed, i.e. ∂NL ∂t , is not taken into account. This is the approach used in [7]. Expected time to failure. The benefit of a tamping intervention is inversely proportional to the time it is going to take to reach the maximal allowable WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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geometry degradation value NLmax . This time to failure can be estimated using the forecasting method presented in section 2. Expected NL at next campaign. Let h be the time between campaigns (in general one year, eventually six months). Suppose we are interested in finding out the optimal schedule for a campaign starting next week, i.e. at time t = t . Then an estimate of the value of NL at time t = t + h, i.e.  NL(t + h), could also be a measure of the benefit of tamping it. The  more NL(t + h) exceeds NLmax , the more value it would have to perform  tamping next week. Likewise, the more NLmax exceeds NL(t + h), the lesser it is worth to perform an intervention on that sector next week. The  punctual forecasting NL(t + h) represents the expected value. However, being the process model stochastic (see section 2.2) a confidence interval could also be included in the objective function. In the case study presented in section 4 we use the expected NL at next campaign to enunciate the objective function. 3.3 Heuristic as an optimization method In general, to find a solution for an optimization process a process model is used. When the model is highly complex and there is no standard optimization method, like in this case, there are two possibilities: to adapt the model for it to fit to a standard optimization method, or to adapt or create a new method to fit to the model. In the literature some approaches to the railway track maintenance scheduling problem can be found, e.g. [7–9]. What [7] and [8] do is to adapt the process model by relaxing some constraints and then apply commercial linear programming optimization packages, as illustrated by approach B in figure 2. Our approach is more similar to [9]. We take the model as described in 2.2 and apply a heuristic algorithm, i.e. approach C in figure 2. The heuristic returns two results: an upper bound for the total solution value, and a feasible solution, namely the best one it has been able to find. The upper bound is a value which is guaranteed to be equal or greater than the optimal feasible solution. The results presented in section 4 show that the value of the best solution found is very near the upper bound (the difference is smaller than 1%), which gives us a hint of the heuristic’s great performance potential. 3.4 Heuristic According to the problem definition in 3.1, the heuristic can be described as follows: 1. Let an intervention be Maximal if it is feasible, i.e. equation 4 holds, and changing its Se for the next sector, i.e. Se would turn the intervention into infeasible, i.e. inequality 4 would no longer hold. The first step of the heuristic is to find for each edge i the set of all maximal interventions and calculate for each of them its value according to the objective function WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

694 Computers in Railways XII Complex model

Model simplification

Heuristic

Simplified model

(sub)optimal solution of the complex model

Optimisation

Approach A Approach B Approach C

optimal solution of the simplified model

Figure 2: Some possible approaches to complex optimization problems.

described in 3.2. Remember that as stated in 3.1 all interventions must start and end in the same edge, i.e. in the same track, so the correspondence is unambiguous. 2. Let im be the number of edges of the graph representing the network. The second step is to find, for each edge i ∈ 1, . . . , im and each n ∈ 1, . . . , nmi , where nmi is the number of interventions needed to tamp the whole edge i, the set Mi,n consisting of n maximal interventions in edge i which maximizes the objective function. To put it in a nutshell, Mi,n is the optimal solution if we only consider edge i and exactly n interventions are to be scheduled. This is the part of the heuristic requiring the most computational power, because at each edge i the set Mi,n0 may not be the set Mi,n0 plus some other intervention, but a completely different set, so for each n ∈ 1, . . . , nmi all possible combinations have to be explored. However, the fact that interventions are not allowed to have common sectors (that would mean performing an intervention twice in the same sector) keeps the number of combinations within an acceptable bound, even for edges with 300 sectors, as shown in the case study in 4. 3. The third step is to find the set of sets of maximal interventions L = Mi1 ,n1 , Mi2 ,n2 , . . . , Mim ,nm contained in the sets Mi,n found in step 2, that maximizes the objective function, under the constrains that each set belongs to a different edge, i.e. ij = il ∀j, l ∈ 1, . . . , m, and the total number of interventions is equal to the number of interventions to be scheduled N , i.e. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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m

nk = N . This step is quite straightforward, because in this case it k holds that the solution for n days is contained in the solution for n + 1 days. The set L is the set of disjointed (i.e. with no common sectors) feasible interventions which maximizes the objective function. The only additional requisites it has to fulfil to be a feasible solution is that the start depot of the first intervention and the end depot of the last intervention coincide with the specified ones (see 3.1), and that the end depot of each day equals the start depot of the next day, i.e. Di (j) = Di (j + 1) ∀j ∈ 1, . . . , N − 1. 4. Let us define bridge interventions as interventions for which the start depot is not equal to the end depot, i.e. Di = De . Because of the procedure used to calculate it, the solution L does not include any bridge interventions. Then the conversion would consist in finding a set of bridge interventions such that the start depot of the first intervention and the end depot of the last intervention are as specified, and that all depots included in solution L are visited at least once. This is nothing but the well-known travelling salesman problem. But a necessary condition to solve this problem it to know the cost of going from one node to another. To calculate this cost in this case is very difficult, because the number of possible combinations is enormous, so we choose to perform a local search. To assess the cost of introducing a bridge intervention from edge j to edge k, we do the following: for each Mi,n ∈ L, consider the n different sets which result of subtracting one single intervention to Mi,n . Then add the best possible bridge intervention from j to k to each of them. This will result in n different sets, each of them containing a bridge intervention from j to k. After doing this for all edges, the best solution, i.e. the one that maximizes the objective function, is chosen and the cost of going from depot j to depot k is the decrement of the objective function generated by the introduction of the bridge intervention. Being the described process merely a local search, we can not guarantee that the costs calculated are the minimum possible, but in practice this drawback is minimal, as illustrated in 4. 5. The fifth and last step of the heuristic is to convert the solution L into a feasible solution by solving the travelling salesman problem posed in step 4. This is done by means of the bench and bound method. This method has the advantage of finding the optimal solution without necessarily exploring the whole search tree. However, as the costs calculation described in step 4 may not be optimal, the solution achieved may as well not be optimal. But we can easily calculate how much better the solution could potentially be, because the objective function value of the solution L is an upper bound for all feasible solutions.

4 Case study In this section we present an example of how the proposed scheduling method can be applied in reality. The problem characteristics are next described. Furthermore, the network is depicted by figure 3. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

696 Computers in Railways XII 60 Km

120 Km Figure 3: Railway network used for the case study.

• The modelled railways network consists of 120 km double way track, i.e. a total of 240 km track, with 3 double switches which divide the network into 8 tracks of about 30 km each. • The network has 2 depots (secondary tracks where tamping machines are stationed during the day), being the distance between each other 60 km. • Depot 2 must be the initial depot of the first intervention as well as the end depot of the last intervention. • One tamping machine with a travelling speed of 80 km/h and a tamping speed of 1.4 km/h will be available for 20 nights. • We have a database with track geometry data from the last 15 years for each track sector of 200 m, so we consider a total of 1200 sectors. According to the problem definition in 3.1, the solution space can be calculated as SolSpace = (NDepots · NSectors )NN ights = (2 · 240 · 5)

≈ 10

(5)

This should clarify that exploring the whole solution space is simply out of the question. The first step of the scheduling method is to define the objective function which best expresses the railway company interests. Therefore let S be a set of N scheduled interventions, and TS = {TS , TS , . . . , TSmax } the set of sectors included in S, i.e. the sectors for which a tamping intervention is scheduled. Also let f be the objective function. Then the objective function evaluated for S, f (S) is defined as

f (S) =

TS max 

 NL(t + h)

(6)

TS1

 where NL(t + h) is the expected NL at next campaign as defined in 3.2. In our case study, tamping campaigns take place once a year, so h = 1 year. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

Computers in Railways XII 1.8

Estim NL(t0+h)

1.6

1.4

NL

1.2

1

0.8

0.6

0.4

0.2 0

20

40

60

80 Sector number

100

120

140

Figure 4: Expected one-year-ahead NL for one railway track.

Table 1: Obtained interventions schedule for the whole network. Interv. Id 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Track Id 1 1 2 2 5 5 5 6 3 3 3 3 4 4 4 7 8 8 8 3

Di D e 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 Total value

Si 1 29 196 236 626 664 700 864 339 366 394 422 451 493 531 919 1093 1123 1160 313

Se 25 54 223 262 654 690 725 892 363 392 421 450 480 520 557 949 1118 1149 1188 337

Length 25 26 28 27 29 27 26 29 25 27 28 29 30 28 27 29 26 27 29 25

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Value 7.8 8.56 7.79 9.39 8.14 7.72 6.54 8.26 8.47 8.49 11.8 13.43 7.93 9.99 10.46 8.99 7.01 8.66 9.77 10.72 179.92

697

698 Computers in Railways XII  The estimation NL(t + h) is calculated by applying the forecasting method of section 2 to the database. Figure 4 shows the interpolated values of the estimation  +h) for the 150 sectors of one track. The curve is so irregular that significant NL(t differences can be appreciated even between contiguous sectors. The interventions schedule obtained can be found in table 1. Di and De are the start and end depots, respectively, and Si and Se are the initial and end intervention sectors, respectively. The length is expressed in sectors (each sector is 200 m long), and the value is calculated according to equation 6. In bold are the two bridge interventions, namely interventions 9 and 20. The reason why some interventions are longer than others (lengths vary between 25 and 30 sectors) is that some sectors are nearer to the depots than others, so the travelling times are shorter and then longer time is available for the tamping interventions themselves. The best solution found has a value of 179.92, while the upper bound for the solution value was 180.18. This means that in the worst case our solution has 0.14% lesser value than the optimal solution. The heuristic has been implemented in C++ language, under the GNU/Linux operative system. The calculation time is under 1 second using a desktop PC with Pentium IV processor and 1 GB RAM memory.

5 Conclusions In this work a heuristic based method for railway track tamping interventions scheduling has been presented. To our best knowledge, it is an innovative approach which goes beyond the state of the art both by incrementing the precision of the obtained interventions schedule and reducing dramatically the calculation time. This makes it possible to fine tune the maintenance strategy by evaluating the benefits or drawbacks of potential modifications in the maintenance process. Furthermore, the presented method could also be used to optimize the tamping in such a way that NL values are nowhere higher than a given NLmax . In fact this could be achieved by setting a non-continuous objective function, with a step at NL = NLmax . Future work includes the development of a Monte Carlo simulation environment for the railway ageing and restoration process, for integrated optimization of planning and scheduling of railway track maintenance processes.

References [1] Meier-Hirmer, C., Mod`eles et techniques probabilistes pour loptimisation des strat´egies de maintenance. Application au domaine ferroviaire. Ph.D. thesis, Universit´e de Marne-la-Vall´ee, 2007. [2] Quiroga, L. & Schnieder, E., Monte carlo simulation of railway track geometry deterioration and restoration. Proceedings of the European Safety and Reliability Conference, ESREL 2010, 2010. [3] Esveld, C., Modern railway track. MRT-Productions, 2001. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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[4] Veit, P., Track quality - luxury or necessity? Railway Technical Review Special: Maintenance & Renewal, 2007. [5] Ubalde, L., L´opez Pita, A., Teixeira, P., Bachiller, A. & Gallego, I., Track deterioration in high-speed railways: influence of stochastic parameters. Proceedins of the Railway Engineering 2005, 8th International conference and exhibition, 2005. [6] Quiroga, L. & Schnieder, E., Modelling high speed railroad geometry ageing as a discrete-continuous process. Proceedings of the Stochastic Modeling Techniques and Data Analysis International Conference, SMTDA 2010, 2010. [7] Oyama, T. & Miwa, M., Mathematical modeling analysis for obtaining an optimal railway track maintenance schedule. Japan journal if industrial and applied mathematics: JJIAM, 23(2), pp. 207–224, 2006. [8] Oh, S., Lee, J., Park, B., Lee, H. & Hong, S., A study on a mathematical model of the track maintenance scheduling problem. Computers in Railways X, pp. 85–97, 2006. [9] Lake, M., Ferreira, L. & Murray, M., Minimising costs in scheduling railway track maintenance. Computers in Railways VII, pp. 895–902, 2000.

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Track test monitoring system using a multipurpose experimental train H. Matsuda1, M. Takikawa1, T. Nanmoku2 & E. Yazawa2

1 2

East Japan Railway Company, Japan Railway Technical Research Institute, Japan

Abstract In order to inspect the condition of tracks, which support railway cars, track irregularity is measured four times a year along conventional lines by track inspection cars, called “East-i”. Track irregularity and the track materials that cannot be inspected with the track inspection car are regularly inspected by track patrolling on foot and/or using hand-held type inspection instruments. With the purpose of reducing track patrol labor and maintenance costs, as well as to improve inspection quality, we have been developing a track monitoring system that is installed on a commercial car and monitors the track with greater frequency. This paper provides the outline for a track measurement device that uses the inertial mid-chord offset method and a track-material monitoring device, both of which have been developed to monitor the track, and presents the results of installing these devices on the multipurpose experimental train (called “MUETrain”) on conventional lines. Keywords: track monitoring system, inertial mid-chord offset method, trackmaterial monitoring device, multipurpose experimental train.

1 Introduction Tracks are composed of rails, sleepers, ballast, and other materials. Track irregularity advances and track material deteriorate as a result of repeated car load and severe environmental conditions. So, it is important to monitor, inspect and understand track irregularity and the degree and process of deterioration of track materials. In order to inspect track conditions, track irregularity is measured four times a year along conventional lines in East Japan Railway Company by track WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100641

702 Computers in Railways XII inspection cars called “East-i”. Track irregularity and the track materials that cannot be inspected with the track inspection car are regularly inspected by track patrolling on foot and/or using hand-held type inspection instruments. However, this is time-consuming and labor-intensive when inspecting over long distances. So, an efficient inspection method is needed to reduce time and cost. Moreover, it is necessary to maintain a continuous awareness of track conditions, and to know about potential problems to improve efficiency of inspection. We have been developing a track monitoring system, which is installed on a commercial car and monitors the track with greater frequency. This paper provides the outline for a track measurement device that uses the inertial midchord offset method (hereinafter referred to as IMOM) and a track-material monitoring device, both of which have been developed to monitor the track, and presents the results of installing these devices on the multipurpose experimental train called “MUE-Train” on conventional lines. MUE-Train is a test train converted from former Keihin-Tohoku line 209 series cars to be used to test and develop new technology for use on future trains.

2 Track measurement device using the IMOM 2.1 Outline of a track measurement device using the IMOM The track measurement device using the IMOM is composed only of a sensor box under the car-body and a small equipment box on board which together collect data which can measure track irregularity (e.g., gauge, alignment, longitudinal level, cross level, twist) [1]. The current track inspection car is a complex and large device with a two-bogie measurement system and rail displacement sensors mounted on all the axle boxes. Consequently, it cannot be installed on a commercially used car. The track measurement device using IMOM was selected for its compact size and ease in mounting. With this device we measure the track irregularity using IMOM. The displacement of the device is obtained by measuring the acceleration of the car body and integrating it as well as using two axis rail displacement sensors to measure relative displacement between the device and the rail, measured by laser. It is possible to downsize and lighten the device, and reduce its cost from the current one that occupies a single car since track irregularity is determined by measurement at a single point. Moreover, the current track inspection car involves the dismantling of related parts installed together with the axle box when inspecting it in the car factory and the reassembly after the inspection is finished. However, dismantling and the assembly operations became unnecessary for the new device as it is external. 2.2 Development of a body-mounted device using the IMOM This device has already been used, mounted on a bogie, by Kyushu Shinkansen [2]. However, it would be difficult to mount on certain bogies due to dimensional constraints. So, we developed a body-mountable device with fewer restrictions to WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 1:

703

Composition of a body-mounted device.

Figure 2:

MUE-Train and device.

mounting positions (Figure.1). In this case, the device is somewhat away from the rail, thus necessitating a wider measurable range, resulting in the possibility that foreign objects might also be caught within its range. It is thus important to make improvements so that the object being measured can be properly ascertained. 2.3 Outline of the test measurement We have been implementing running tests of the MUE-Train mounted with this device in the metropolitan area since January 2009 to check its measurement precision and durability. Figure 2 shows the installation of MUE-Train and the device. MUE-Train is a 7-train set, and this device is mounted as shown in Figure 2 near the bogie on the car 6 side of the car 7. The total mileage was about 12,000 km as of the end of April in 2010. Up to now, no trouble has occurred with this device, and it does its job well. 2.4 Reproducibility of repeated measurement Accuracy in reproducibility of repeated measurements along the same section has been verified. Figure 3 shows the 10 m-chord longitudinal level irregularity, WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

704 Computers in Railways XII while Figure 4 shows the 10 m-chord alignment irregularity. The same section was measured seven times along the same section about every 40 days. “σ” is the standard deviation of the reproducible error in a section concerned when based on the waveform of the highest rung. The standard deviation of the reproducible error of repeated measures is intended to be smaller than or equal to 0.3 mm for Shinkansen, and for conventional lines smaller than or equal to 0.5 mm. As seen in Figure 3, 10 m-chord longitudinal level irregularity reproducible error measurement precision proves to be under 0.3 mm in all cases. Moreover, velocity dependency is not seen. Next, as seen in Figure 4, the reproducible error of 10 m-chord alignment irregularity is 0.32 mm maximum, a level of accuracy sufficient for practical use along conventional lines. Velocity dependency is also not seen in 10 m-chord alignment irregularity. The accuracy of the optical-fiber gyroscope greatly influences the accuracy of alignment irregularity as shown in a

67km/h 基準波形 Reference 92km/h σ=0.30mm 44km/h σ=0.25mm 85km/h σ=0.17mm 50km/h σ=0.21mm 105km/h σ=0.28mm 103km/h σ=0.21mm 2mm

Figure 3:

50m

Reproducibility of 10 m-chord longitudinal level irregularity.

66km/h 基準波形 Reference 67km/h σ=0.30mm 89km/h σ=0.32mm 48km/h σ=0.32mm 72km/h σ=0.26mm 77km/h σ=0.25mm 87km/h σ=0.32mm

2mm

Figure 4:

50m

Reproducibility of 10 m-chord alignment irregularity.

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curve section of this device. Therefore, further accuracy improvement can be expected by additionally adopting a gyroscope of higher accuracy. 2.5 Consistency with the current track inspection car By comparing the results of measurement using this device and those of the current inspection car, data integrity was verified. Figure 5 (upper) shows 10 mchord longitudinal level irregularity, while Figure 5 (lower) shows the 10 mchord alignment irregularity. The results from this device show the value measured three days after the measurement of that of the track inspection car, the measurement speed of both being about 60 km/h. In 10 m-chord longitudinal level irregularity, the waveform of this device is identical to that of the track inspection car. In 10 m-chord alignment irregularity, both shape and amplitude correspond roughly to each other’s waveforms, though small differences in detail can be seen due to difference of detection method and the rail side positioning. It one can see that in both longitudinal level and in alignment, results are consistent with the current track inspection car.

3 Track material monitoring device 3.1 Outline of track material monitoring device The purpose of the track material monitoring device is to take images and automatically detect abnormalities: missing fishplate bolts, loosened rail fastening, shifted rail pads, metal flow at glued-insulation rail, etc (Figure 6).

10 m-chord longitudinal level irregularity

Current track inspection car Device using IMOM

2mm Current track inspection car

50m

10 m-chord alignment irregularity

Device using IMOM

Figure 5:

Comparison with current track inspection car.

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706 Computers in Railways XII

(a) Missing fishplate bolt

(b) Loosened rail fastening

(c) Shifted rail pad

(d) Metal flow at gluedinsulation rail

Figure 6:

Examples of abnormality of track material.

This device, which takes images of the track material with a line sensor camera, has already been put to practical use both domestically and abroad. However, not many cases have been seen regarding its function of automatically judging abnormality in track material. If it could photograph abnormalities with greater frequency and could judge automatically, the reliability of the track equipment would improve and it would reduce the labor of track patrol. The device is composed of two cameras: 1) a line sensor camera for taking pictures of the track material: and 2) a device for acquiring altitude information (three-dimensional image) in the vicinity of the rail. As part of this development, we plan to utilize altitudinal information to conduct automatic judgments on abnormalities. 3.2 Outline of running tests The track material monitoring device has been installed on the MUE-Train, and data collected along the Tohoku and Nikko lines since January 2009. Figure 7 shows the device installed on the MUE-Train. During this time, the device was installed on only one side of the rail.

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Figure 7:

707

Track material monitoring device installed on the MUE-Train.

Figure 8:

Examples of photographic image.

3.3 Test results Figure 8 shows a photographic image of track material. Figure 9 shows a three dimensional image of track material around the rail. In this figure, higher areas are shown as white, while lower areas are black. However, the top of rail was not considered at this time. The cross section along the line is showed on the screen below. In this test, the altitude information in the vicinity of the rail were measured to an accuracy of 1 mm up to 110 km/h. Tightness of the rail fastening can be evaluated by means of measuring the height of the upper spring crip with respect to the rail foot.

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X

Cross section

Top surface of bolt

Fastening (X=6mm)

Figure 9:

Top surface of bolt

Loosening（X=9mm

Examples of three-dimensional image of track material.

4 Conclusion For the track material monitoring device, we obtained measurements to an accuracy of 1mm for altitude information in the vicinity of the rail up to 110 km/h. At present, an automatic judgment function is not included in this device, but with the accumulation and verification of data, it is to be included in the future. On the other hand, the track measurement device using IMOM achieved the high level of accuracy required for practical use. Moreover, it will continue to be put to use and its durability will be tested. Eventually, the on-board portion of the device should be made more compact for efficient use on commercial cars in the future.

References [1] Yazawa, E. & Takeshita, K., Development of Measurement Device of Track Irregularity using Inertial Mid-chord Offset Method. QR of RTRI, Vol.43, No.3, pp.125-130, 2002 [2] Moritaka, H., Matsumoto, T. & Yazawa, E., Measurement of Track Irregularity using Kyushu Shinkansen Cars. Shinsenro, Vol.63, No.12, pp.26-28, 2009 (in Japanese)

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Section 12 Safety and security

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Verification of quantitative requirements for GNSS-based railway applications H. Mocek, A. Filip & L. Bažant Railway Infrastructure Administration, LIS, Czech Republic

Abstract The objective of this paper is to verify whether requirements for GNSS-based railway telematic applications are met through GNSS Safety of Life (SoL) services. Measurement methodology was developed for this purpose. An analysis of the achievement of railway requirements was subsequently performed. This technique represents a contribution to the certification process of the GNSS system, which must prove that the required parameters are fulfilled. The analysis consists of: 1) evaluation of static measurements to verify GNSS system behaviour under standard conditions of GNSS Signal-In-Space (SIS) reception, and 2) analysis of dynamic tests focused on train position and protection level determination under variable conditions of GNSS SIS reception in a real railway environment. Experimental tests have been carried out using GPS/EGNOS receivers that meet requirements for the SoL service according to the RTCA DO-229D standard. Keywords: GNSS, Signal-In-Space, certification, EGNOS, non-precision approach, Rayleigh distribution, overbounding, reliability, availability, SIRF III.

1 Introduction Certification of European navigation satellite system Galileo must be carried out before this GNSS system can be used in railway applications, especially in safety-related ones. A contribution to the certification process was previously performed by determining the minimum quality requirements for GNSS-based railway applications (Mocek et al. [1]). The next step in the certification process should be the verification that the proposed requirements are fulfilled. This paper evaluates the fulfilment of railway requirements on the basis of experiments with GPS/EGNOS receivers that meet requirements for the SoL applications WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100651

712 Computers in Railways XII according to the RTCA DO-229D standard [2]. Significant improvements of SIS reception and availability of position determination are demonstrated by means of SIRF III technology.

2 Railway requirements for GNSS While in non-safety related applications it is possible to consider the fulfilment of requirements through GNSS SoL services, such as EGNOS Precision Approach (PA), Non-Precision Approach (NPA), Galileo SoL Level A/ B, in safety railway applications it is obvious that the GNSS SoL service cannot meet the demanding requirements of railway safety and dependability. This is evident from: 1) requirements for the functional and technical safety included in railway safety standards, e.g. EN 50129, and 2) interpretation of the Galileo SoL quality measures in terms of RAMS (Filip et al. [3]). Therefore, the following text will deal with the analysis of requirements for non-safety related applications. Requirements for four selected applications are summarized in table 1. These requirements were derived on the basis of methodology developed in Mocek et al. [1]. From table 1 it is obvious that the first three applications have similar requirements for horizontal positioning accuracy, horizontal alert limit and maximum standard deviation, but different requirements for update time interval. The last application “Diagnostics of infrastructure” has completely different Table 1: Non-safety railway application

HAmax HAL

max

PTPL,ff Kmin t max

MTBFmin MTTRmin MDTmax MUTmin Amin Pfm,max

Requirements for GNSS-based railway applications.

Application 1: Performance charging of railway infrastructure 8.6 m 22 m 3.5 m 1-3.8x10-17 8.7 10 min 4.17x10-2 h-1 24 h 0.19 h 70 h + (1ASIS)Ty ASIS Ty – 70 h 99.2% - (1ASIS) 5.42x10-7 year-1

Application 2: Application 3: Application 4: Position Fleet Diagnostics of monitoring of management infrastructure trains / wagons 10 m 8.6 m 5.7x10-3 m 25 m 22 m 0.01 m 4m 3.5 m 0.0035 m 1-6.3x10-22 1-3.8x10-17 1-10-4 9.9 8.7 4.291 30 s 5 min 30 min 1.39x10-2 h-1 1.39x10-2 h-1 2.08x10-2 h-1 72 h 72 h 48 h 0.25 h 0.25 h 0.22 h 30 h + (130 h + (139 h + (1ASIS)Ty ASIS)Ty ASIS)Ty ASIS Ty – 30 h ASIS Ty – 30 h ASIS Ty – 39 h 99.7% - (199.7% - (199.6% - (1ASIS) ASIS) ASIS) 7.63x10-9 year-1 1.9x10-7 year-1 1.4x10-4 year-1

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requirements for these quality measures. Therefore, the analysis of the proposed requirements will be summarized together for the first three applications and performed separately for the last application. With regard to various railway environments, the value of Horizontal Alert Limit (HAL) can be increased to 50-100 m for many railway applications, including safety-related ones. This should have a positive impact on the reliability of the GNSS position determination. Horizontal Accuracy Horizontal Alert Limit Estimated standard deviation of the model cumulative max distribution function that overbounds the GNSS position uncertainty along semi-major axis of the error ellipse in xy plane Update time interval t Probability of correct position determination of the Train PTPL,ff Position Locator (TPL) Confidence coefficient Kmin Failure rate max Minimum value for required Mean Time Between Failure. In MTBFmin practice, the actual MTBF is much higher. Mean Time to Repair MTTRmin Repair rate max SIS availability on the track ASIS > 1% Mean Down Time MDTmax Mean Up Time MUTmin Minimum service availability Amin Maximum probability of major failures Pfm,max Time interval (1 year) Ty = 8760 h HAmax HAL

3 Evaluation of static measurements Static measurements were performed in the laboratory at the known position of the GNSS antenna. The aim of these experiments was to evaluate the correctness of calculation of GNSS data that will be further used to verify the dependability requirements of railway applications. The determination of dependability from a railway user point of view means the evaluation of the reliability and availability of GNSS position determination. Since the attributes of dependability primarily depend on the accuracy of GNSS positioning and GNSS position error is unknown for the user, it will be necessary to deal with the evaluation of probability distribution of position error and find out whether this distribution satisfies the assumptions given in [4]. Fig. 1 shows the record of static measurement from the GPS/EGNOS receiver PolaRx3 in the SBAS (Space Based Augmentation System) En-route/ NPA mode. The duration of this measurement is 26 375 s (7.3 hours). The elevation mask was set to 5 degrees and with a recording time interval of 1 s. Fig. 1 shows the time dependence of the Horizontal Protection Level (HPL), the real WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

714 Computers in Railways XII Horizontal Position Error (HPE), the estimated maximum standard deviation max and the number of received satellites (SV). The reference antenna was situated in a stationary point with good visibility to satellites. The minimum number of visible satellites was 6 and the average number of received satellites was 8.6. During these tests, under very good SIS reception conditions, the values of horizontal positioning error HPE were less than 2.7 m. For the calculation of GNSS integrity risk it is supposed [4] that the horizontal position error is chi-square distributed and the integrity risk in the horizontal plane is calculated on the basis of the Rayleigh distribution with parameter max. The GNSS receiver estimates the standard deviation max in each epoch of measurement (fig. 1). Values of max are used to calculate the horizontal protection level HPL with a probability of missed detection PMD = 5x10-9 and the corresponding coefficient KMD = 6.18 [2]. For a verification of assumptions in [4] it was necessary to carry out more detailed analysis of HPE probability distribution. The histogram of HPE is shown in the fig. 2(a) bar graph. In order to investigate the characteristics of the probability distribution of HPE, the position errors xerror, yerror in orthogonal directions x, y were tested for the hypothesis that both variables have Gaussian probability distributions. Based on the results of the Jarque-Bera test, Liliefors test, chi-square goodness-of-fit test and analysis of the curves of Q-Q plots, see fig. 2(b) and fig. 2(c), Gaussian distributions can be assumed for these errors with the following parameters: xerror ~ N(x = 0.308 m, x2 = 0.08 m2), yerror ~ N(y = 0.91 m, y2 = 0.14 m2). Then the resulting horizontal position error HPE has two-dimensional normal probability distribution with the probability density 16

HPL HPE max

HPL [m], HPE [m], max [m], SV

14

SV

12

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SV 6

4

max

HPE 2

0

0

0.5

1

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Figure 1:

2

2.5 4

x 10

Time dependency of measured data from the receiver PolaRx3 in En-route/NPA mode.

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Probability density (pdf)

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(a)

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Histogram of horizontal position error / Probability density

1.4 1.2

rel. hist HPE pdf N(HPE, HPE)

N( HPE, HPE)

1 0.8

pdf Rayl(min(max))

Rayl(min(max))

0.6 0.4 0.2 0

0

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3

HPE [m] QQ plot of xerror vs. standard normal QQ plot of yerror vs. standard normal 3

Quantiles of yerror

Quantiles of xerror

2 1.5 1 0.5 0 -0.5 -1 -1.5 -5

Figure 2:

0

2

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-1 -5

5

0

5

Standard normal quantiles

Standard normal quantiles

(b)

(c)

Histogram of horizontal position error and Q-Q plots of orthogonal position errors xerror, yerror versus standard normal (colour online only).

f ( x, y ) 



1

2 x y 1  

2

e

where  is correlation coefficient  



1

2 1 

2



 

  x   2  x x  y  y y  y x  2   2  x y  y2   x



2  

, (1)

 xy .  x y

The probability that the HPE exceeds the horizontal alert limit HAL (the double integral cannot be expressed explicitly) corresponds to the failure of position determination (Mocek et al. [1]), which is considered as horizontal integrity risk in [4]:

P HPE  HAL H 0   1  PTPL , ff  1 

 f ( x, y)dxdy .

x  y  HAL 2

2

(2)

2

The resulting error in the horizontal plane generally does not have the character of the Rayleigh distribution, since conditions for such distribution are not fulfilled: 1) errors in the orthogonal directions must be normally distributed, 2) they have to be independent, 3) they have zero mean values and 4) they have WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

716 Computers in Railways XII the same variances. From eqn. (2) it can be derived that the failure of GNSS positioning corresponds to the Rayleigh distribution only under these conditions:

P  HPE  HAL H 0 ,   0,  x   y  0,  x   y     e



HAL2 2 2

 1  cdf Rayleigh HAL,    Pfail ,Rayleigh .

(3)

However, not all of these conditions are fulfilled in the case of given static GNSS data. Two-dimensional normal distribution of HPE can be approximated by normal distribution instead of Rayleigh: HPE ~ N(HPE = 1 m, 2 = 0.13 m2). The probability density of this normal distribution is depicted  HPE with the blue solid line in fig. 2(a). The red dashed line shows the probability density of the Rayleigh distribution with the smallest standard deviation min(max) = 1.3 m. Normal distribution very well matches to the HPE, as is evident from figs. 2 and 3, and this was also demonstrated by the numerical integration of eqn. (2) for several values of HAL. The probability distribution of the GNSS positioning error is supposed to be bounded by the Rayleigh distribution with parameter max. Since the probability distribution of HPE is approaching the Gaussian distribution, the probability distribution of the position error has to overbound to this Gaussian distribution, as is illustrated in fig. 3. 10

Probability |HPE| > HAL

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Figure 3:

Failure probability of position determination: static measurement, Gaussian distribution, considered Rayleigh distribution from GNSS.

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By using inequality (4) it is possible to prove that the normal distribution is in this case bounded by the Rayleigh distribution, given that max  0.87 m:

erfc( x) 

ex

2



2

x x  2



4

.

(4)

Pfail ,Rayleigh  Pfail ,Gauss HAL,  HPE ,  HPE  ,

We want to prove that

(5)

Pfail ,Gauss HAL,  HPE ,  HPE   1  cdf Gauss HAL,  HPE ,  HPE 

where

 cdf Gauss  HAL,  HPE ,  HPE  

Let’s use a custom substitution of x



 HAL   HPE 1 erfc 2 2 HPE 

 (6) .  

HAL   HPE . After substitution of 2 HPE

eqns. (3) and (6) to eqn. (5), and applying inequality (4) we get

 max

       HPE  2 HPE x  max    0.87 m. (7)     4  2 2  2  x  ln   ln x  x          

This overbounding is valid from the specific alert limit exceeding value of HALmin. The dependence between parameter max and HALmin can be derived on the assumption that the probability Pfail , Rayleigh is equal to the Pfail ,Gauss . After using eqns. (3) and (6) we obtain

 max 

HALmin

1  HALmin   HPE    2 ln  erfc  2 2  HPE   

.

(8)

HALmin value cannot be explicitly expressed from eqn. (8). It must be determined by numerical iteration. For min(max) = 1.3 m the value of HALmin = 0.46 m can be obtained. Since the minimum standard deviation also satisfies the condition given by eqn. (7), the probability of failure of GNSS positioning is WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

718 Computers in Railways XII always considered higher for horizontal alert limit greater than HALmin. Rayleigh distribution very well overbounds calculated Gaussian distribution, especially for higher values of HAL. This is also valid for all Rayleigh distributions supposed during the whole measurement. After verification of behaviour of the GNSS system with regard to the positioning accuracy, we can proceed with the analysis of requirements for railway applications. For the first three applications, the maximum position error does not exceed the estimated maximum standard deviation max. Horizontal alert limit in the range of 22 m to 25 m fits well with the use of the protection level of the EGNOS system with regard to the occurrence of major failures. The real position error does not exceed HAL or HPL. Reliability and availability of GNSS positioning for the given static measurement is 100%. Regarding the last application “Diagnostics of infrastructure” there is not possible due to its character to make a significant change of acceptable value of HAL. Horizontal accuracy of the EGNOS system was identified as 1.6 meters in 95% of cases. This value is much higher than desired HAL of cm level. Requirements for this application cannot be met by means of EGNOS system. Other GNSS systems or future Galileo system will be also unable to meet such stringent accuracy requirement, because the accuracy of SBAS differential systems is in the range of meters. Since the application does not require frequent sending of position information (30 min) and the probability of occurrence of major failures is much lower in comparison with the other applications, the solution for this application could potentially be the usage of the GNSS RTK mode (cm level accuracy) in combination with longer static measurements.

4 Dynamic performance verification 4.1 Experiments carried out on the track Experimental dynamic verification with the GNSS receivers PolaRx3 in Enroute/ NPA mode was realized with a mobile robot on the test track Pardubice Nemošice. Another measurement was carried out with the measuring rail vehicle on the track Pardubice – Brno – Střelice. The purpose of these tests was to verify position and protection level determination with respect to dynamic behaviour of the GNSS receiver in real conditions on the track. Dependability attributes for each application have been also derived. Static measurement described in the previous chapter was characterized by a very good SIS reception. Position and protection levels were continually computed and provided from the GNSS receiver. The situation is completely different for dynamic measurement under real railway conditions, when the GNSS position and protection level determination are influenced by different availability of SIS due to local obstacles along the track. GNSS receiver does not determine its position or protection level in some cases of partial or full SIS blocking. However, the greatest influences on the positioning have the transition states characterized by intermittent reception of SIS, see fig. 4. HPL values can

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then reach several tens of meters. This reduces the possibility of using such information. Nevertheless, if continuous SIS reception is guaranteed for a sufficiently long time after the transition state, soon there is a significant reduction of HPL to an acceptable value that is lesser than the alert limit of the application. Dependability attributes depend on mutual relationship between protection level, alert limit and position error. The availability of position and HPL determination based on SBAS En-route/ NPA mode is ASIS = 91.4% (TSIS  8 h). These values have been determined for the entire track with consideration of 1 second GNSS update rate. However, applications can obtain information with the longer time interval t. Cases where the position is not determined or the alert limit is exceeded during the time interval t occur only for the second application. Availability requirements with using GNSS SoL service are met, since the actual availability for all applications is higher than the required availability, see values of Amin in table 2. Reliability of correct position determination and probabilities of different failure modes (safe, dangerous, detected, undetected) are also shown in table 2. Minimum reliability Rmin during the time interval TSIS is calculated on the basis of term Rmin (TSIS )  e  maxTSIS . Based on the numerical values in table 2, requirement for Rmin is fulfilled for 1 second update time interval from GNSS receiver.

100

HPL [m]

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10 5 0

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0.8

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Figure 4:

1.6

1.8

2 4

x 10

Time dependency of the measured data on the track.

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720 Computers in Railways XII Table 2:

Dependability attributes for railway applications.

Application no. Availability of position and HPL on the track Amin – required Amin – real Rmin(TSIS) Reliability (correct position determination, i.e. no failure) HPE  HPL  HAL, R(TSIS) = Safe undetected failure HPL < HPE  HAL, PFSU(TSIS) = Dangerous undetected failure HPL  HAL < HPE, PFDU(TSIS) = Safe detected failure, i.e. false alarm HPE  HAL < HPL, PFSD(TSIS) = Dangerous detected failure, i.e. true alert HAL < HPL < HPE, HAL < HPE < HPL, PFDD(TSIS) =

1 2 3 ASIS = 91.4%, TSIS = 7.9 h 90.6% 91.1% 91.1% 100% 96.7% 100% 71.9% 89.6% 89.6% 95.7% 96.2% 95.7%

0%

0%

0%

0%

0%

0%

3.9%

3.4%

3.9%

0.4%

0.4%

0.4%

4.2 Significant limitation of satellite signal reception To analyze the usage of GNSS SoL service in adverse SIS reception conditions a lot of dynamic measurements were performed with a car in pre-selected critical areas. SIS reception was often insufficient and mentioned transition states have occurred very frequently in these areas. Numerical values of dependability attributes from one specific experiment are expressed in table 3. The availability of positioning and protection level determination was only 47% during this measurement under very limited SIS reception conditions. Availability requirements are met, but reliability requirements are not met. This is caused by frequent occurrence of false alarms. The number of false alarms much exceeds that of true alerts. The reason for the occurrence of so many false alarms is that the protection levels in such adverse conditions of SIS reception do not reach the values smaller than the proposed alert limit of the application. No case of undetected failures was found. Reliability could be only achieved by increasing of HAL to a tenfold value, which cannot be accepted for these applications. Viable solution for the usage of GNSS for railway applications in such critical environment can bring integration of GNSS with other sensors. 4.3 Availability of GNSS navigation modes Improvement of availability of EGNOS NPA and PA navigation modes has been demonstrated in area of limited SIS reception by means of SIRF III technology. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Table 3:

721

Typical dependability attributes for the environment with adverse reception of SIS. Application no. Availability of position and HPL on the track Amin – required Amin – real Rmin(TSIS) Reliability R(TSIS) = Safe detected failure, PFSD(TSIS) = Dangerous detected failure, PFDD(TSIS) = Table 4:

Satellites 0 1–3 4–6 >6

1 2 3 ASIS = 47%, TSIS = 26 min 46.1% 46.6% 46.6% 100% 75.7% 100% 98.2% 99.4% 99.4% 54.8% 58.0% 54.8% 44.4% 40.4% 44.4% 0.8% 1.6% 0.8%

Availability of GNSS navigation modes.

SIRF III receiver 1% 0% 1% 98%

PolaRx3: NPA mode 50% 0% 15% 35%

PolaRx3: PA / autonomous 22% 4% 32% 42%

Table 4 shows that the receiver SIRF III receives mostly more than 6 satellites regardless of the adverse SIS reception conditions. It is obvious that the number of received satellites from SIRF III receiver is much higher in comparison with PolaRx3 receivers. The first receiver PolaRx3 was set in the NPA mode, while the second PolaRx3 receiver was configured in the PA/ autonomous mode. Table 4 also shows that availability of EGNOS SBAS navigation modes depends on the reception of SIS from geostationary satellites. The SIS reception of SBAS mode is available only from 3 geostationary satellites. For this reason, the NPA mode is unavailable for more than 28% of PA/ autonomous mode. The future Galileo system will receive SBAS signal from all satellites that will also increase the availability of GNSS position determination and related quality measures.

5 Conclusion This paper deals with the practical analysis of quantitative requirements for quality indicators of selected railway applications using GNSS SoL services. This analysis represents a part of the certification process of the GNSS system. Since the GNSS system is unable to meet demanding requirements for railway safety-related applications, the analysis was carried out only for non-safety related railway applications. First of all, static measurements were performed and assumptions of the GNSS system behaviour under standard conditions of SIS reception were verified. Dynamic tests were then realized for subsequent analysis of vehicle WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

722 Computers in Railways XII position and protection level computation in adverse conditions of SIS reception in real railway environment. Experimental tests were carried out using GPS/ EGNOS receivers that meet requirements for the SoL applications. In order to use GNSS system in railway safety-related applications, there is necessary to determine dependability attributes of the GNSS system and subsequently use them for design and verification of a safe train position locator consisting of several diverse sensors. Dependability assessment of the EGNOS system is the subject of our current research. It is particularly based on long-term experimental measurements and subsequent evaluation of measured data using the theory of random processes in time and frequency domains.

Acknowledgement This work was supported by the Ministry of Transport of the Czech Republic under contract no. CG743-037-520.

References [1] Mocek, H., Filip, A. & Bažant, L., Galileo Safety-of-Life Service Utilization for Railway Non-Safety and Safety Critical Applications. STECH'09, Niigata, Japan, June 16-19, pp. 148-149, 2009. [2] RTCA DO-229D. Minimum operational performance standards for GPS WAAS Airborne Equipment. RTCA, Inc., Washington, D.C., 2006. [3] Filip, A., Beugin, J., Marais, J. & Mocek, H., Safety Concept of Railway Signalling Based on Galileo Safety-of-Life Service. COMPRAIL 2008, Toledo, Spain, Sept 15-17, pp. 103-112, 2008. [4] Galileo Integrity Concept. ESA document no. ESA-DEUI-NG-TN/01331, 2005.

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Modelling and design of the formal approach for generating test sequences of ETCS level 2 based on the CPN X. Zhao1, Y. Zhang1, W. Zheng2, T. Tang1 & R. Mu2 1

State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, China 2 School of Electronic and Information Engineering, Beijing Jiaotong University, China

Abstract ETCS Level 2 (European Train Control System Level 2, ETCS-2) has drawn particularly attention from researchers and industries. A new CPN model-based formal approach for test cases and sequences generation is proposed in this paper to increase the test automation degree of the ETCS-2 system and subsystems. In this paper, a set of modelling rules is presented firstly to make the Coloured Petri Net (CPN) model more suitable for test generation. Then, an automated test approach is described in detail, which includes an automatic test case generating algorithm and a type of automatic test sequence searching algorithm. The generated set of test cases satisfies specified coverage. The test sequence searching algorithm guarantees the results satisfying the minimum number of test sequences covering all test cases. The output of this approach is a set of well-formed XML (Extensible Markup Language) file to increase the automation degree of the test executing process. Finally, a partial model of ETCS-2 On-Board subsystem is built and analysed using the CPN Tools as a case study. The model-based formal approach is implemented on this model and the test cases and test sequences are all generated in a form of XML. The conclusion show that the CPN-model based testing approach can be used to improve the automation of the testing procedure and the generated test cases can meet the relative requirement. Keywords: ETCS-2, CPN, test generation, formal method.

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724 Computers in Railways XII

1 Introduction In recent years, the safety-critical system has been come closer to peoples` life. Safety critical system (SCS) is a computer, electronic or electromechanical system whose failure may cause injury or death to human beings as in Chen [1]. ETCS-2 is a typical SCS. In order to ensure the correctness of its behaviour function, there are two commonly used techniques: validation and testing. Testing is the only method which can be used to verify the dynamic behaviours of SCS in running time as in Wegener et al. [2]. With more and more attention has been paid to the testing automation of the Safety-Critical System. How to improve test automation and testing efficiency, and reduce testing costs and risk factors of the testing process has increasingly become the focus and hot spots of the research in the testing field. Model-based testing (MBT), which is to compare the I/O behaviours of a valid behaviour model with that of a system to be tested (the system under test, SUT), has been closely watched in recent years. Model based test generating, which is a method to generate the test cases and test sequences according to the formal model of SUT, is the most important content of MBT. Since 1970s, there had been many test generating methods based on variety of models, such as U-method in Chan and Vuong [3], D-method in Sidhu and Leung [4] and Wp-method in Fujiwara and Bochmarm [5]. But these methods cannot describe the time constraints. Since the 1990s, with the gradual maturity of many formal modelling theory, such as the Temporal logic in Lamport [6], Time Input/Output Automata (TIOA) in Alur and Dill [7] and Timed transition system in Henzinger et al. [8], many Model based test generating methods based on these models has been presented, including Test time Automa in Badban et al. [10] and TIOA based testing method in Hessel et al. [9] etc. However, most of these methods can not describe the Concurrent behaviours of the SUT, also the test cases and sequences generated through these methods are too abstract to be executed, and the generating process is not automatic. Kim et al. generate the test cases separately according to the control flow and data flow on the basis of UML state charts model in Kim et al. [11]. However, its limitation on describing the communication between the numbers of objects causes the low test generating coverage. Nogueira et al. [12] and Helke et al. [13] did the test sequence generation based on the Communication Sequence Process (CSP) model and Z model, but these models are too abstract which makes the generating result more unexecutable. Table 1 is simply comparing among the formal modelling languages which have been used in test generation. According to these advantages of CPN described in Table 1, this paper presents a test cases and sequences generating approach on the basis of CPN, and applies this approach to the ETCS-2 system testing. The paper is organized as follow. In Section 2, we define the test case, test subsequence, test sequence and test coverage degree in a formal way according to the CPN definition. In Section 3, we describe the test generation method, including the test case generating method, the test subsequence generating method and the test sequence WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Table 1: Formal Language TIOA UML CSP Z CPN

725

Compare among the modelling languages.

Modelling Level Abstract Abstract/Concrete Abstract Abstract Abstract/Concrete

Verification capability Strong No Strong Strong Medium

Executable No Yes No No Yes

Modelling process Easy Easy Hard Hard Easy

Data Type Medium Rich Simple Simple Rich

generating method. In section 4, a XML format for describing test cases and test sequences is proposed. In Section 5, together with the example of On-Board subsystem in ETCS-2. Finally, we evaluate the whole method and discuss possible improvements in the future.

2 CPN based modelling method for test generation 2.1 Coloured Petri Net and relative definitions Coloured Petri Nets (CPN) is an extended Petri Nets which is a graphical and mathematical modelling tool proposed by Kurt Jensen. And it can be used to model systems with complex procedures as in Jensen [14] and applicable to describe many types of systems. The locations that can be used to carry information in the graph element of CPN are showed in Fig. 1.

Figure 1:

Information Location in CPN graph element diagram.

In Fig. 1, the “INIT MARK” location in (i) carries the initialized data; the “PLACETYPE” location in (i) carries the colour types information; the “ACRVAR” location in (ii) carries the name of the variable to be passed; In (iii), the “GUARD” location carries the data selection and comparison information; the “TIME” location carries the time restriction information; while the “ACT” location carries the data computing information. In the following part, these locations are used to be searched for information that needed by test data generation process. On the basis of the definition of CPN in Jensen [14], some relative formal definitions will be introduced and these will be the foundation of the following work. Definition 1. Test Case Based on the CPN A Test Case Based on the CPN is an eight-tuple TCCPN = {IA, ID, OA, OD, SC, SCD, EC, ECD}, where: WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

726 Computers in Railways XII IA is a finite subset of input ports, I A  PN and n  I A :[ PT (n)  in] . OA is a finite subset of output ports, OA  PN and n  OA :[ PT (n)  out ] .

IA and OA must be in the same subpages: I  O  Ps, s  S ； ID is a set of the input data, and corresponding with the IA. OD is a set of the output data, and corresponding with the OA. SC  {GFSC , IFSC , IP} represents the start condition, and is a finite set of fusion places and internal input ports. EC  {GFEC , IFEC , OP} represents the end condition, and is a finite set of

GF , IF  FS ,[f  GF , FT ( f )  globle]  [f  IF , FT ( f )  page]

fusion places and internal input ports, where:

IP, OP  PN ,[p  IP, PT ( p)  in]  [p  OP, PT ( p)  out ]

Both in SC and EC, the GF set and IF set can not be empty at the same time, and the situation (GFSC＝GFEC) ∪ (IFSC＝IFEC) should not exist in one test case. SCD is the data set of the start condition corresponding to SC, and ECD is the data set of the end condition corresponding to EC. Definition 2. Test Subsequence A test subsequence is a six-tuple TSsub = {SS, SCSS, SCD, ECSS, ECD, w}， where: SS is a finite set of test cases which are in order, and the order reflects the sequence of the test cases to be executed in the subsequence. Here, tc1 represent the first test case to be executed and tcn represents the last test case to be executed in the subsequence. SCSS is a finite set of the start conditions, SCSS  {GFSC , IPtc1} , where

GFSCSS  GFSCtci ,1  i | SS | which means that the start condition set GFSCSS is the combination of the start condition; the start condition of a test subsequence IPtc1 is the same with IP set in the first test case in the subsequence. SS

ECSS is the set of end condition: ECSS  {GFEC , OPtc } , where SS n

GFECSS  GFECtci ,1  i | SS |

SCD and ECD is the data set corresponding to SCSS and ECSS separately. Note: For each subpage S, there is a test case set TCS corresponding to it. If

TC

 TC

sub S makes the there is a subset tci , tc j  TCsub  [ IFSCi  IFSCj  IFECi  IFECj ] (0  i  j if (x==trigEvt ) then   6                                                                else if …  7                                                                else _STATES  8      end for  9        within /*initial _STATES process*/  10     _QueueS(ih, s) = (#s … /* the message queue associated with sc*/  11                      [](#s>0)& receive.ih!head(s) ‐> …  12     _GStVar(ih,v) = …          /*the global variable process to store the state value of sc*/  WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

822 Computers in Railways XII 13     _Atribute(ih, w) = …   /*the global variable process to store the attribute value of sc*/  14   end if      /*Composition of all instances in _HANDLER*/ 

       {|...|} (||| 

15 SYS=(|||ih

ih



 _SCTRL(ih)){|generate,...|} (|||ih

HANDLER

 _GStVar(ih,v)) {|...|} (|||ih

HANDLER



 _QueueS(ih, s)) 

HANDLER

 _Atribute(ih, w)) 

HANDLER

3.2.1 Transforming the class instance A class instance, either active or passive, is translated into a controller process. In our translation from xUML to CSP, each class becomes a process specification _SCTRL(ih), as shown in line 3 of Algorithm 1. Each one of these processes consists of four parallel parts: the first part is the translation of the state machine associated with class (lines 3 to 9), the second part formalises the state machine inherited from the superclass (the detail discussion is omitted here for conciseness), the third part models the message queue associated with the state machine as an event pool (lines 10 to 11), and the fourth part denotes the global variables used to store the state location and update the value of the attribute of the state machine (lines 12 and 13), respectively. Finally, the CSP code associated with the composition of all instances is given in line 15. 3.2.2 Transforming the state machine In this section, based on the semantics of the xUML state machine, we design a transformation of the xUML state machine model to a CSP specification in a compositional manner. Our transformation rules are designed to inductively process the three types of state found in xUML: basic states, OR-states and AND-states. The core transformation rules (defined using the Epsilon Transformation Language (ETL) of the Epsilon tool) are presented is Listing 1. 1 operation StateMachine2CTRLProcess (sr : UML!StateMachine) 2 : CSP!StMachineProc { 3 var root : new CSP!StMachineProc; 4 root.ProcID := sr.name + '_CTRLS'; 5 var states := UML!Vertex.all.select 6 (sm1|sm1.containingStateMachine() = sr); 7 for (st in states){ 8 var Cont := st.container; 9 if (Cont.state.isDefined()) { 10 var stProc : new CSP!StateProc; 11 if (Cont.state.isOrthogonal) { 12 SimConcurent2StProc(st, sr, stProc); 13 } else { 14 SimComp2StProc(st, sr, stProc); 15 } 16 root.letProc.add(stProc); 17 } … 18 } 19 var startP : new CSP!StateProc; 20 startP := getStartState(sr); 21 root.first := startP; 22 return root; 23 }

Listing 1: Transformation rules for the state machine.

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The operation StateMachine2CTRLProcess is responsible for transforming the xUML state machine into a localized (let … within) process. In terms of our CSP metamodel, this mainly involves a creation of new instances of CSP!StMachineProc. In line 4, we set the name of the name of state machine, e.g., track_CTRLS. From lines 7 to 18, each of the states of the state machine are iterated and operations are called to transform the state types. Note that st, sr, stProc are process parameters and denote the instance of UML!Vertex, UML!StateMachine and CSP!StateProc, respectively. Each application of the above operations returns a process instance stProc, which is added to the localized process by the statement root.letProc.add(stProc). Finally, we compute the initial state of the class diagram using the getStartState operation and we link the new process to the original root.

4 Formal analysis of an interlocking model In this section we exemplify the verification of the xUML Micro interlocking shown in Section 2.1. Listing 2 illustrates a partial translation of the point state machine (presented in Figure 4(b)) in terms of CSP. Lines 9 to 18 denote the process normal_detected_undefined_STATES, which reacts to the external event tout (modelling the time event after (30)) and reaches the target process error_STATES. At the same time the concurrent process normal_requested_right_STATES, representing the behaviour of concurrent region request, is terminated by the process STOP (shown in lines 19 to 23). The resulting process STOP||| error_STATES is equivalent to error_STATES, which is used to model the transition from state undefined to stop. The synchronization communication statement envGenerate.ih?ok is used to keep the consistent pace between the concurrent states, i.e., request and detected. 1 point_CTRLS(ih) = let 2 Initial_0_STATES= 3 normal_STATES 4 normal_STATES= 5 normal_requested_Initial_0_STATES 6 ||| 7 normal_detected_Initial_0_STATES 8 … 9 normal_detected_undefined_STATES= 10 receive_normal_detect.ih?x -> 11 if(x==tout) then 12 write.ih!error_STATE -> 13 envGenerate_detected.ih?ok -> 14 error_STATES 15 else if(x==at_right) then 16 …… 17 else if(x==at_left) then 18 …… 19 normal_requested_left_STATES= 20 receive_normal_request.ih?x -> 21 if (x==tout) then 22 envGenerate_requested.ih?ok -> STOP 23 else if(x==to_right) then … 24 error_STATES= WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

824 Computers in Railways XII 25 receive.ih?x: EVENT -> 26 envGenerate_detected.ih?ok -> 27 envGenerate_requested.ih?ok -> error_STATES 28 within Initial_0_STATES

Listing 2:

Partial CSP code for the state machine point.

The CSP code for the composition of all instances in the Micro interlocking model is shown below. Here, different instances of track (i.e., t1, t2, t3), route (i.e., r1, r2), point (i.e., p1) and signal (i.e., s1) are executed in parallel. SysCTRL = (point_CTRLS(p1) ||| signal_CTRLS(s1) |||track_CTRLS(t1) |||track_CTRLS(t2) |||track_CTRLS(t3)|||route_CTRLS(r1)|||route_CTRLS(r2)) [|{|envGenerate, envGenerate_detected, envGenerate_requested|}|] (element_CTRL_point_CTRL(p1)|||element_CTRL_signal_CTRL(s1) |||element_CTRL_track_CTRL(t1) |||element_CTRL_track_CTRL(t2) |||element_CTRL_track_CTRL(t3))

To demonstrate the verification approach, we analyse the model against a very simple property. Basically, we check if the interlocking model never gets to a deadlock situation – where no routes can be further reserved or cancelled. Deadlock checking is implemented with respect to the processing of signals in active objects. For example, we need to check that  SYSLinkSysState

|externalGenerate|

||

ExternalSignals

 

is deadlock-free. The following partial listing shows the corresponding CSP process ExternalSignals, which is used to define the property. External_Point_p1 = externalGenerate.p1!at_right -> externalGenerate.p1!to_right -> external_Point_p1 ……

The verification results obtained in the FDR2 tool are shown in Figure 5.

Figure 5:

Snapshot of deadlock-free verification in the FDR2 tool.

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5 Conclusions and future work Modelling languages, like Executable UML (xUML), can be used for the definition of railway interlocking systems. In particular, modelling languages typically use testing and simulation of the analysis of the system. They tend not to provide analysis based on formal, more rigorous, methods. This is especially needed for the analysis of safety-critical systems, like railway interlockings. In this paper, we have presented our approach towards the formal analysis of railways signalling specified with xUML. Our approach focuses on the use of model transformation, an integral part of Model-Driven Engineering. Starting from an xUML model, we translate that to the Communicating Sequential Process (CSP) language, used as input to the FDR2 formal verification tool. This enables the formal analysis of the model using FDR2. Future work will mainly focus on the provision of a transparent verification methodology. For instance, currently, the verification results of the analysis of the system are provided in terms of the CSP model. We want to be able to: (i) specify verification properties in terms of the xUML model; (ii) generate counter-examples, executions of the model that violate the property, provided by FDR2 in terms of the xUML model (using sequence diagrams of UML).

Acknowledgements The research is supported from the National Science Foundation of P. R. China under grant No. 60634010, the State Key Laboratory of Rail Traffic Control and Safety of Beijing Jiaotong University within the frame of the project (No. RCS2008ZZ005) and the Technology Funding Project (Beijing Jiaotong University, No. 2007RC101，2007XM004). This research is also funded by the European Commission via the INESS project, Seventh Framework Programme (2008-2011).

References [1] European Committee for Electrotechnical Standardization (CENELEC). Railways Applications: The speciation and demonstration of dependability, reliability, availability, maintainability and safety (RAMS), 1997. [2] Jouault, F., Alliaire, F., Bézivin, J., et al., ATL: A model transformation tool. Sci. Comput. Program, 72(1-2), pp. 31-39, 2008. [3] http://www.eclipse.org/m2m/atl/atlTransformations/ [4] Varró D., Automated formal verification of visual modeling languages by model checking. Softw. Syst Model, 3(2), pp, 85-113, 2004. [5] Extensible Platform for Specification of Integrated Languages for Model management (Epsilon). http://www.eclipse.org/gmt/epsilon [6] http://www.eclipse.org/gmt [7] Hoare, C. A. R., Communication Sequential Process. Prentice-Hall, Englewood Cliffs, 1985. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

826 Computers in Railways XII [8] Abrial, J. R., The B Book: Assigning programs to meaning. CUP, 1996. [9] Software Design Group at MIT: Alloy Analyser 4.1.2, 2008. [10] Holzmamm, G. J., The model checker SPIN. IEEE Transactions on Software Engineering, 23(5), pp. 1-17, 1997. [11] http://www.fsel.com [12] Cimatti, A., Giunchiglia, F., Mongardi, G., et al., Formal verification of a railway interlocking system using model checking. Formal Aspects Comput, 10, pp. 361-380, 1998. [13] Garmhausena, V. H., Campos, S., Cimatti, A., et al., Verification of a safety-critical railway interlocking system with real-time constraints. Science of Computer Programming, 36, pp. 53-64, 2000. [14] Treharne, H., Turner, E., Paige, R. F., et al., Automatic generation of integrated formal model corresponding to UML model. 47th International Conference, TOOLS EUROPE 2009, pp. 357-367, 2009. [15] Hansen, H., Ketema, J., Luttik, B., et al., Towards model checking executable UML specification in mCRL2. Innovation in Systems and Software Engineering, 6(1-2), pp. 83-90, 2010. [16] Raistrick, C., Francis, P., Wright, J., et al., Model Driven Architecture with Executable UML. Cambridge University Press, Cambridge, 2004. [17] OMG Unified Modeling Language: Superstructure, version 2.0 – final adopted specification, August 2008. http://www.omg.org. [18] Scatergood, B., The semantics and implementation of machine-readable CSP. PhD thesis, University of Oxford, 1998. [19] Bisztray, D., Heckel, R., Ehrig, H., Verification of architectural refactoring rules. Technical report, University of Leicester, 2008.

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A model-based framework for the safety analysis of computer-based railway signalling systems R. Niu & T. Tang State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiao Tong University, China

Abstract Ensuring safety in railway signalling systems is always considered as significant as a guarantee of the safe and efficient operation of the whole railway. In fact, safety analysis of the signalling system with distributed computer technique is becoming extraordinarily difficult, because of the frequent and complex interaction between components and the various backup modes. The dominant approaches are subjective, difficult to reuse and not well structured, thus leaving the safety analysis process time-consuming and error-prone. This paper develops a hierarchical methodology for safety analysis based on the failure propagation model and state-transition model. Unlike traditional safety analyses, the proposed approach demonstrates more accurate representation of practical failure behaviour in a computer-based signalling system. Dynamic properties, system structure and failures at the component level are separately modelled in different layers, and connected with synthesis laws. The analysis can be easily refined as the system design progresses and automatically produces safety-related information to help the engineer in making design decisions. The preliminary design of the Communication Based Train Control (CBTC) system for the Yizhuang Line in Beijing is used to demonstrate this approach. Keywords: signalling system, automatic safety analysis, model-based, FPTN.

1 Introduction Railway systems have a very low tolerance for accidents, because of the potentially large numbers of injuries and deaths, huge financial losses and even worse social effects. Achieving a high degree of safety is one of the most WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100751

828 Computers in Railways XII important objectives of a railway signalling system. While advanced information techniques have been widely used in new generation signalling systems, safety analysis becomes a genuine challenge. Due to the development of automation, networking and to the general increase of train speed, the number of interacting components or subsystems has increased drastically over recent decades. Transplanting the redundant structure and degrade-recovery technique into a digital system makes the signalling system even more complex (Leveson [1]). It is not sufficient to comprehend the system in its minute details just depending on intuition and experience. What is worse, as the functions are much stronger and the techniques are totally changed, the availability of safety data for the new computer-based signalling systems, such as accident or incident statistics, is limited (Vernez and Vuille [2]). To cope with the increasing complexity of signalling systems, CENELEC, IEC and many countries have developed several standards and recommendations. These standards regulate the system development process (lifecycle) of signalling systems to design for safety, and also give out technical requirements, such as SIL. Traditional techniques are recommended in the safety assessment process, including HAZOP, FTA, FMEA/FMECA, etc. These specific inductive or deductive methods of analysis are used to identify hazard, trace causation and evaluate their risk at different stages of the lifecycle, and the results are the main basis for design decisions. This methodology has been used by most railway equipment suppliers over the last 20 years, although they obviously lag behind the state-of-the- art engineering practice. These dominant applied approaches commonly rely on expert opinion. The analysis models explain accidents in terms of multiple events connected by causality relationship. The methods just give out a very simple rule (tree structure or tables) for the description of relationship. There is no limitation for the category of events, and they could be some type of component failure, human error, or energy-related event. However, the selection of these events, the links between the events and even the point of beginning and ending is arbitrary (Khan and Abbasi [3]). In order to reduce the subjectivity, more experts with different academic backgrounds are involved and the results need to be reviewed at least once, which obviously make the safety analysis time-consuming and mentally intensive. Furthermore, the simple rules of most classic safety analysis are not well structured. The forward or backward reasoning is carried out with regard to the hierarchy of failure influences rather than to the architecture of the system (Vaidhyanathan and Venkatasubramanian [4]). So at each stage, if the design of the system has changed, many analyses need to start from the very beginning. Moreover, there are major defects in most traditional safety analysis techniques, so different techniques are chosen at different stages of the lifecycle, and two or more techniques are usually employed at one stage to make up the defects of each other. However, as there is no unifying framework for these techniques, it is very difficult to relate the results of the various safety studies to each other and back to the high level failure analysis. In the past ten years, many researchers have devoted themselves to the solution to these problems of traditional safety analysis with model-based WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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approaches [5–10]. They intend to build precise models for the system architecture and its failure modes, so that computers can help to do the tedious and error-prone hazard sources tracing and probability calculation. One solution of model based safety analysis is extending the system development model with a fault mode. Formal languages are used to describe normal and failure behaviours of the system, and model checking tools or simulation engines are used to do automatic analysis. Some commercial safety analysis software tools/packages based on this idea are available, such as FSAP/NuSMV-SA [5] and SCADE [6]. However, the major portion of this kind of model is still a normal process, rather than a failure process. It is very difficult to plug in detail failure information because of the limitation of model scale from analysis tools. Another solution is to model the failure propagation behaviour directly. The Failure Propagation and Transformation Notation (FPTN) described in [7, 8] is the first component-based failure behaviour model. Kaiser [9] introduced modular concepts for a basic fault tree to analyze complex component-based systems. Based on early researches, Papadopoulos et al. [10] proposed a modelbased semi-automatic safety and reliability analysis technique that uses tabular failure annotations as the basic building block of analysis at the component level, called Hierarchically Performed Hazard Origin and Propagation Studies (HiPHOPS). This tool can automatically synthesise the component failure modes and generate a fault tree. However, the model does not work well in describing the dynamic behaviour of system. The present study proposes an improved failure propagation approach for the safety analysis of a computer based rail signalling system. In order to describe the complex structure and function, the study has developed an output-guided hazard identification method with a scenario hazard table to ensure the correctness of system understanding and the completeness of hazard identification. A kind of simplified state machine model is used to express the dynamic properties of signalling system structure. The study has also developed an iterative algorithm to combine the dynamic model with FPTN components and compute qualitative results automatically. The rest of the paper is organized as follows: Section 2 is a description of the dynamics of a computer-based signalling system. Section 3 introduces the hierarchical dynamic safety analysis framework, including methodology hypothesis, definitions of each layer, and the synthesis algorithms of different layers. The case study of a CBTC system in Section 4 demonstrates the application of this approach. The conclusion is drawn in Section 5.

2

Dynamics of computer-based signalling systems

Computer-based signalling systems generally adopt a distributed structure, including a trackside control centre and onboard vital computer systems, which are connected with a wireless communication network. The trackside equipments collect the parameters of trains within a certain area and related information from other trackside systems (such as ATS, interlocking) to compute a safe unoccupied region for each train. The onboard computer systems are responsible WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

830 Computers in Railways XII for keeping train speed within the upper limit computed with the safe region from the trackside and train parameters from the onboard computer. The European Train Control System (ETCS) and Communication Based Train Control (CBTC) system applied in urban mass transit are the representative computer-based signalling systems. Traditionally, the logic relations of different scenarios are expressed by the combination of the trackside discrete electromechanical components, while the function of each signalling system remains unchanged. In computer-based signalling systems, trackside equipments are cut down, and their functions are integrated into onboard computers. In this way, computers should provide different functions and work with different interfaces under different operation scenarios. This kind of system is called a phased-mission system (Alam and Al-Saggaf [11]), which means that the mission served by the system composes of several distinct phases with different objectives (the phased-mission characteristic is called behavioural dynamics). In each mission phase, the system has different service objectives, and therefore the safety constrains may change from time to time, which make the safety analysis error-prone. For example, safety engineers often make the mistake of generally treating the measured value of train distance as greater than the actual value that is safe. In fact, when a train is moving out of a station or a speed-limit section, see fig. 1, a greater measured value of distance will make the calculated permitted speed larger than the real one, which might cause a derailment or train rollover. Not only the structure of the signalling system, but also the function of the onboard computer is different when the operation level or mode changes. Additionally, some safety measures inherited from the electromechanical system increase the dynamics of the signalling system. In order to apply the powerful and undependable computer technique into a safety critical signalling system, redundant structures are used in almost all of the kernel trackside and onboard processors. Moreover, the control mechanisms and even the whole architectures are designed to be redundant, which are represented in the form of backup modes and system levels. For example, the CBTC system used in the Beijing Yizhuang Line defines three operation levels for the whole system and

Speed restriction Normal train speed curve Train speed curve when measured position is bigger than real one

POSITION

Figure 1:

Speed curves when a train is moving out of a speed restriction region.

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three operation modes for the onboard system. Therefore, the structure of this subsystem will be changed with time, in case any replications are down.

3 Model-based dynamic safety analysis framework 3.1 Framework for the safety analysis of computer-based signalling systems The construction of the hierarchical structure approach is shown in Fig. 3. Hierarchical modelling is used in our framework, as it fits in well with the system design process and reduces the complexity of system analysis. The system is successively split into subsystems until the level of the basic components is reached following a top-down approach. This kind of approach has been successfully used in recent studies proposed by other authors, such as the successive modelling approach used in HHM to address large hierarchical systems [12], and the MFM approach used in the Safe-SADT method [13]. The block at the top in Fig. 2 represents the operation scenarios of the system, which should be defined at the beginning of its lifecycle. For each scenario, the states definition and state transition of the system/subsystem can be described by the state-transition model. For each state, the safe critical functions can be decided and refined by FPTN models, and it becomes more and more specific when moving down along the system structure. The safety analysis process can be divided into the dynamic layer and the failure propagation layer. The dynamic layer, used to structure and describe the dynamic attributes, is combined with the scenario lists and the state transition models. The failure propagation layer is expressed by FPTN language. System Design

Safety Analysis SN Item Description S1 S2

1

System Definition

.

. IL

BL OC

CB TC

FPTN Modules (Subsystem) 2

Subsystem Design * Subsystems operation mode Definition * Subsystem Interfaces

Detail Design * SW Components * HW Structure

Subsystem State Machines 1

FPTN Modules (Components) 2

Fault Tree Generation Algorithm

Subsystem Level

CB TC

Layer Synthesis Algorithm

System Level

Architecture Design * Subsystems Functional Definition * Subsystem Interfaces * Scenario chart

BL OC

Layer Synthesis Algorithm

I L

* Scenario Identification - operation condition - system interface * System operation level Definition

Pec.

1: Dynaic Layer of Safety Analysis Model 2: Failure Propagation Layer of Safety Analysis Model

Figure 2:

Framework of hierarchical safety analysis.

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832 Computers in Railways XII Ref. SN01 …

Scenario Draw up at a station

Operation Mode

System Structure

β

Function Set

Output

Hazards

CBTCAM

VOBC,ZC, DCS

0.12

Door open interlocking

DoorO SDoorO

DoorO:c SDoorO:c

…

…

…

…

…

Figure 3:

Scenario hazard table.

3.1.1 Output-guided hazard identification Just like all other safety analysis methods, hazard identification is the first procedure in our safety analysis framework. Unlike general automatic control systems, traditional hazard identification methods do not work quite so well for computer-based railway signalling systems. Firstly, as computers are widely used nowadays in signalling systems, most vital functions are processed together by computers and the critical information translation between the trackside and onboard computers becomes much more dangerous. Secondly, the computerbased signalling system is large scaled and its control logic and interactions between components are very complex. Traditional brain-storming methods, such as HAZOP, apparently cannot ensure the correctness and completeness of hazard identification. Fortunately, in railway systems, the signalling system does not control the train directly. Instead, it detects the working conditions of the train, and gives out safe guidance or performs emergency action when necessary. In the other words, the safety of trains is dependent on the correct and prompt output of its signalling system. Therefore, in our safety analysis framework, hazards of the signalling system are defined as abnormalities of system output. In our output-guided hazard identification process, it is necessary to identify the abnormal condition of system output in each system state and each operation scenario, because the output of the system and the safe range of the output value vary with the scenarios and system states. Information is recorded in the table shown in fig. 3. Factor β is used to synthesis hazard events under different scenarios in quantitative analysis. This procedure, although a little tedious, makes it much easier to find out the unexpected system output when the system working conditions are specified. The completeness of hazard identification can be ensured as all operation scenarios are analyzed. The synthesized failure propagation models can decide whether a hazard will or will not be in the hazard list. However, if there is change in the operation scenarios or the system state models, the hazard list needs to be regenerated. 3.1.2 State-transition model Fig. 4 illustrates the five primitive elements of the simplified state machine notation. In this model, dynamic behaviour is expressed as a set of different states of the system (operation mode and system level) and a set of transitions between those states (the mode change condition). State transitions occur for two reasons: either the state changes are induced by some other events, or are triggered by the state change in other mode-chart. The mechanism enables a transition in one mode-chart to trigger other transitions at higher or lower layers of the dynamic model, which allows us to represent situations where failures of WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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sub-systems may lead to losses of function at system level. It also allows us to represent situations where a change of function at system level should be followed by a number of necessary functional or structural transformations at lower levels. 3.1.3 FPTN FPTN (Failure Propagation Transformation Notation) modules describe how failure modes of incoming messages, together with internal faults of the components, propagate to failure modes of outgoing messages. The basic entity of the FPTN is a FPTN-module. This FPTN-module contains a set of standardized sections. In the first section (the header section), for each FPTNmodule an identifier (ID), a name and a criticality level (SIL – Safety Integrity Level) is given. The second section specifies the propagation of failures, the transformation of failures, the generation of internal failures and the detection of failures in the component. These failures are denoted as incoming and outgoing of the FPTN-module. This paper gives out a modified failure categorization for

Figure 4:

Notation of the state-transition model.

Table 1: Categories Provision Failure Value Failure

Time Failure Communication Failure

Handle limit

Failure class definition.

Failure Class Commission Omission High

Sign c o h

Low

l

Stuck Delay Early Insertion Masquerade Corruption Repetition Resequence Deletion Limit

s d e is ms cr rp rs dl limit

Explanation Unexpected output No output The value is higher or bigger than the normal range The value is lower or smaller than the normal range. The value is stuck to a certain number. Later than intended. Earlier than intended. Wrong message destination Wrong message source. The data is error with uncertain tendency. Message is send more than once. The sequence of message is changed. Message is lost. Limits of deviation handler.

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ID

Speed Detection

SIL

Rotation Pulse:l

Propagation

Speed:l

Rotation Pulse:h

Speed:h =Rotation Pulse:h & Redundancy:limit Speed:l = (Rotation Pulse:l || Rotation Pulse:o)& Redundancy:limit

Speed:h

Rotation Pulse:o

Handle Rotation Pulse:l by [Redundancy] with [0.9] Rotation Pulse:h by [Redundancy] with [0.9]

Internal

Speed:o

Speed:o Generated by [mechnical failure] with 10E6

Figure 5:

A simplified FPTN-module of the train speed detection component.

computer based railway signalling systems, in order to include the seven kinds of threats (deletion, repetition, resequence, delay, corruption, insertion, masquerade) brought by the general network, see table 1. Fig. 5 provides an example of a FPTN-module of the train speed detection component. The incoming failures are Rotation Pulse:l, Rotation Pulse:h and Rotation Pulse:o, and the outgoing failures are Speed:l, Speed:h and Speed:o. The propagation and transformation of failures is specified inside the module with a set of equations or predicates (e.g. for propagation: Speed:h=Rotation Pulse:h and for transformation Speed:l=Rotation Pulse:l || Rotation Pulse:h). Furthermore, a component can also generate a failure (e.g. Speed:o) or handle an existing failure (e.g. Rotation Pulse:l and Rotation Pulse:h). Consequently, it is necessary to specify a failure cause or a failure handling mechanism and a probability. 3.2 Safety analysis process In order to analyze the cause of each hazard, this study designs an algorithm for automatic fault tree generation. Firstly, the layer synthesis algorithm is used to integrate the FPTN-modules under different modes. Then, a kind of depth first search algorithm is used to draw a fault tree for each hazard. 3.2.1 Layer synthesis algorithm 1. Scenario synthesis The hazard events in different scenarios are generally separated by time and space, which means they occur in different times and different places. In fact, it is not necessary to synthesize these scenarios in qualitative analysis. In quantities analysis, the probability of a hazard event appearing in several scenarios can be calculated by the weighted summing-up of the number of each scenario with factor β of the scenario hazard table as the weight coefficient. 2. Mode synthesis The state-transition model and FPTN-modules are synthesized with the algorithm shown in fig. 6. The E_Transition of the state are added to the FPTNWIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 6:

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Layer synthesis algorithm.

modules as the input failures and the cause of the output failures is the E_Transition AND original Boolean expression. S_Transitions of the state connect this model with other state-transition models. Find out the E_Transition of the state indicated by the S_Transition, and run the above steps again. 3.2.2 Fault tree generation algorithm The synthesis algorithm translates the system (or sub-system) failures to component failures, and translates the failure propagation formula of the FPTN module to the Fault Tree. When a sub-system is encountered during the traversal of the hierarchical model, the causes of its output failure are always traced first at the sub-ordinate hierarchical level of the design, which describes the architecture of the sub-system. A simplified pseudo-code representation of the proposed fault tree synthesis algorithm is presented in Fig. 7.

4 Case study The Yizhuang line of Beijing is composed of a large number of equipments and highly interactive subsystems of various natures (see Fig. 9) (electromechanical, electrical, infrastructure, hard-/software, electromagnetic) and locations (tracks elements, control centre, embarked systems), most of which are still under development. The signalling system of the Beijing Yizhuang Line employs the CBTC system design by the Beijing Jiaotong University. The system consists of a Vehicle On-Board Controller (VOBC), Zone Controller (ZC) and Data Communication System (DCS). The DCS includes a wired backbone network and wireless communication between on-board devices and trackside equipments. The DCS transmits data packages in a manner transparent to the application. Secure Devices (SD) are installed as the safe guard between the safety critical part (e.g. ATP) and the non-safety related part (DCS) of the CBTC.

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SynthesisFPTN(sys, op){ module = FindFPTNModule(sys, op);

//Recursive FPTN synthesiser //Travers the modules within sys and find //the module with output deviation op. PropagatonToFaultTree(module, op); //Transform the propagation bool formula of op to Fault Tree If leafnode is not (a handler limit) or (a internal deviation) or (a deviation of system input) SynthesisFPTN(sys, leafnode); //If the leafnode is not a basic event then call the recursive FPTN //synthesiser

}

FaultTreeGeneration (scenario, failure){ system = Findstructure(scenario); //Travers the scenario hazard table and find the //system module array of the scenario SynthesisFPTN(system, failure ); //Call the recursive FPTN synthesiser }

Figure 7:

Figure 8:

Fault tree generation algorithm.

Analysis results of the “draw up at a station” scenario.

4.1 System modelling The first step in the safety analysis is to identify operation scenarios of a particular application and elaborate a scenario hazard table of the system. The table helps to identify the system functions and interfaces in each working condition. Now 11 scenarios are identified for the whole CBTC system operation process and 21 system level safety related functions, including 15 functions for ensuring traffic safety and 6 functions to protect passengers. The deviations of the system output treated as the hazard events will be used as the top event of the fault tree (see description in Section 3.2.1). WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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The CBTC system of the Beijing Yizhuang Line defines three operation levels for the whole system and three operation modes for the onboard system. The system levels are divided into the CBTC Level (ATP trackside equipments communicate with onboard equipments using WLAN), the BLOCK Level (ATP trackside equipments communicate with onboard equipments by balises) and the IL Level (onboard equipments cannot be controlled by ATP trackside equipments, safe train separation is protected by interlocking). The operation modes of onboard system are RM (Restricted Manual) mode, CM (Controlled Manual) mode and EUM mode (i.e. Bypass mode). The state-transition model of the “Draw up at a station” scenario is shown in Fig. 9 as an example. Starting from the top function for which the system is designed (‘‘trains follow successively their optimal route”), the system is successively broken down into sub functions, individual elements/components, and then the FPTNmodules can be elaborated by analyzing the failure propagation/transformation behaviour of each module. These modules are connected by component interfaces. 4.2 Results For each potential threat, the output deviation of safety related system functions in every scenario, we have tracked down the causes and evaluated the corresponding occurrence probability. The results are expressed as Boolean expressions of component failures as a column of the Hazard Log, and also can be shown as fault tree figures to make them easier to understand. Thirty seven FPTN-modules were built and 183 hazard events have been identified, which is obviously too large to lay out in a single piece. Therefore, this paper only shows the results of the “Draw up at a station” scenario as a demonstration.

5 Conclusion and future work This study has addressed some of the pitfalls pointed out in the literature (lack of system overview, conflicting objectives) and offers some solutions to overcome some of the difficulties. In this study, a hierarchical framework, based on the Failure Propagation Transformation Notation (FPTN), has been developed to perform safety analysis and risk management of large and complex computer based railway signalling systems. This approach is based on the data flow among components rather than the hardware description of the system, which enables failure behaviour modelling in various stages of the design lifecycle. Some further notation developments for FPTN are still needed in this direction to allow for a better expression of the time properties of failure events. Enriching FPTN with Temporal Logic is part of our current research. The Temporal Logic should cover all kinds of sequential relations of failures, and should not make the model too complex to solve. Another interesting research is the more accurate description of the deviation of continuous data. These continuous data are usually affected by several different factors. How to express the influence of each factor in the failure propagation model and how to decide the synthetic variation tendency are the problems that need to be solved urgently. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Acknowledgements This paper is sponsored by the National Natural Science Foundation of the P. R. China under grant No.60634010, with the title "The Theory and Key Technology Research of Train Control System", and is also supported by the Urban Rail Transit Automation and Control Beijing Municipal Government Key Laboratory.

References [1] Leveson, N. G., A New Accident Model for Engineering Safer Systems, Safety Science, vol. 42, pp. 237-270, 2004. [2] Vernez, D. and Vuille, F., Method to assess and optimise dependability of complex macro-systems: Application to a railway signalling system, Safety Science, vol. 47, pp. 382-394, 2009. [3] Khan, F. I., Abbasi, S. A., TOPHAZOP: A knowledge-based software tool for conducting HAZOP in a rapid, efficient yet inexpensive manner, Journal of Loss Prevention in the Process Industries, vol. 10, pp. 333–343, 1997. [4] Vaidhyanathan, R., Venkatasubramanian, V., Diagraph-based models for automated HAZOP analysis, Reliability Engineering and System Safety, vol. 50, pp. 33–49. 1995. [5] Bozzano, M., Cavallo, A., Cifaldi, M., et al, Improving Safety Assessment of Complex Systems : An Industrial Case Study, Proc. of the Formal Methods 2003, Vol. 2805, Springer-Verlag, pp. 208-222, 2003. [6] Abdulla1, P. Deneux1, A., et al, Designing Safe, Reliable Systems Using Scade, Leveraging Applications of Formal Methods. vol. 4313 of LNCS, Springer-Verlag, pp.115-129, 2006. [7] Fenelon P., McDermid J., et al, Towards integrated safety analysis and design, ACM Computing Reviews, pp. 21-32, 1994. [8] Fenelon P., McDermid J., An integrated toolset for software safety analysis. Journal of Systems and Software, 21(3):279–290, 1993. [9] Kaiser, B., Extending the Expressive Power of Fault Trees, Proc. of the 51st Annual Reliability & Maintainability Symposium (RAMS05), 2005. [10] Papadopoulos, Y., Mcdermid, J., et al, Analysis and Synthesis of the Behaviour of Complex Systems in Conditions of Failure. Reliability Engineering & System Safety. Vol 71, pp. 229-247. 2001. [11] Alam, M., Al-Saggaf, U. M., Quantitative Reliability Evaluation of Repairable Phased-Mission Systems Using the Markov Approach, IEEE Transactions on Reliability, R-35:498-503, 1986. [12] Bozzano, M. & Villafiorita, A., Integrating Fault Tree Analysis with Event Ordering Information, Proc. of the ESREL 2003, pp. 247-254, 2003. [13] Kaiser, B., Liggesmeyer, P., Mackel, O., A New component Concept for Fault Trees, Proc. of the 8th Australian Workshop on Safety Critical Systems and Software (SCS’03), Adelaide, 2003.

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A scenario-based safety argumentation for CBTC safety case architecture C. Liu1, X. Sha2, F. Yan3 & T. Tang1 1

State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, China 2 Beijing Traffic Control Technology Co., Ltd, China 3 School of Electronic and Information Engineering, Beijing Jiaotong University, China

Abstract The Communication based Train Control System (CBTC), as a symbol that China has stepped into the stage of rapid urban rail traffic development, is a safety-critical system that guarantees rail traffic safe-operating and high transportation efficiency. The safety case for the CBTC generic product is an essential justification document to prove the system can be accepted as adequately safe. To extract safety requirements implicitly illuminated within the system requirement specification, operational scenarios are widely used to depict the behaviours and interactions of subsystems and components, which becomes a challenge when constructing safety case architecture from the aspect of system function. This paper presents a promising method based on Goal Structuring Notation (GSN) to establish a composition of safety argumentations for managing safety cases. The method introduces the concept of safety argument modules to express rationally encapsulated goal-based safety claim sets that conform to safety requirements, but are deduced in accordance with hazard analysis based on the operational scenarios. An example generic modular safety case architecture for CBTC generic products is presented to illustrate how the whole safety case architecture is structured to be in line with system requirements, and the ease with which module updates and reuse, according to revises for system development, can be performed. Keywords: CBTC, GSN, safety case, safety argument module.

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1

Introduction

1.1 CBTC generic product As key equipment deployed in urban rail traffic systems, the CBTC system is comprised of Automatic Train Supervision (ATS), Automatic Train Protection (ATP), Automatic Train Operation (ATO), Computerized Interlocking (CI) system, and Data Communication System (DCS), and is conducted to guarantee safe operation and improve the traffic capacity of stations and sections, as well as realize automatic railway traffic control and high transportation efficiency. The ATP system is the core of the CBTC system, which dominantly serves to guarantee safe operation. The ATP system consists of the Vehicle On-Board Controller (VOBC) and the Zone Controller (ZC), see Fig 1. The VOBC measures and sends location information to the ZC periodically via both trackside Access Points and waveguides. Combining train location with line occupancy supplied by the CI, Database Storage Unit (DSU) and ATS, as well as other trackside equipment, the ZC calculates movement authority for a specified train and sends information back to the VOBC in the same way, with which the VOBC generates service brake and emergency brake profiles to supervise the train movement. The DCS includes a redundant wired backbone network and wireless communication between on-board devices and trackside equipments, both of which can provide protocol-independent data transmission for the functional application. As a safety-critical system, the CBTC generic product should be certified to meet the requirements in railway standards regarding safety related applications, e.g., the EN5012X series. For specific functional domains, diverse standards are adopted to achieve design targets, for example the LCF-300 CBTC product developed by BJTU, MIL-STD-882C, is applied for semiconductor component

Figure 1:

Configuration of CBTC generic products.

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design, EN50129-2 and IEEE1474.1-1999 are referred for wireless communication application, etc. However, this paper focuses on CBTC product safety case development, which mainly consults with CENELEC standards, namely EN50126, -8, and -9 [1–3]; other norms are outside the scope of this paper. 1.2 Safety argument in a safety case The production of a safety case is an essential part of the safety assessment process for safety-critical system development. The gist is to communicate a clear, comprehensive and defensible argument that a system is acceptably safe to operate in a particular context (Kelly and Weaver [5]).The safety case consists of three principal elements: Requirements, Argument and Evidence, which are composed to convince someone that the system is safe enough (when compared against some definition or notion of tolerable risk). According to the review of some conventional context based safety cases, a common flaw exists, which is that the role of the safety argument is neglected and, instead, many pages of supporting evidence are often presented (e.g. hundreds of pages of fault trees or FMECA tables), but little is done to explain how this evidence relates to the safety objectives. Safety arguments aiming to communicate the reasoning relationship between requirements and evidence are often suggested to be expressed in well-structured texts; such arguments can be efficient to be understood by the involved developers of the safety case, but can be ambiguous and unclear to other engineers who are not familiar with the author’s literary manner. Besides, cross-references are necessarily introduced to argue integrity of evidences, however, multiple cross-references in text can be awkward and can disrupt the flow of the main argument. Without a clear and shared understanding of the argument, safety case management is often an inefficient and ill-defined activity. This paper will introduce a structured technique, Goal Structuring Notation (GSN), to provide an explicit representation of the concepts required to create an argument and to represent the argument inferences linking the requirements to the evidence. 1.3 Incremental safety case for railway applications In order to obtain safety approval for a generic product, safety case need to well organize the overall documentary evidences to be submitted. Historically, the production of safety cases has often been viewed as an activity to be completed to the end of the safety lifecycle. To initiate safety case development at the earliest possible stage and arrange phrasal evidences incrementally collected in step with system development, a common approach to managing the gradual development of the safety case is to submit a safety case at various stages of project development. For instance, the U.K. MoD Defence Standard 00-55 [7] talks of formally issuing at least three versions of the Safety Case:  Preliminary Safety Case – after definition and review of the system requirements specification WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

842 Computers in Railways XII  Interim Safety Case – after initial system design and preliminary validation activities  Operational Safety Case – just prior to in-service use, including complete evidence of having satisfied the systems requirements The EN50129 [3] also recognizes the importance of recording the relationship between partial safety cases and overall safety cases that a section of the recommended safety case structure is reserved for this purpose. As EN50129 talks of safety cases being structure into six parts:  Part One – Definition of the System  Part Two – Quality Management Report  Part Three – Safety Management Report  Part Four – Technical Safety Report  Part Five – Related Safety Cases  Part Six – Conclusions Part Five of the safety case acts a dual role. Firstly, it should be used to record references to the safety cases of any subsystems or equipment on which the main safety case depends. Secondly, it could be used to present an account of the evidence of satisfying safety conditions from other safety cases, which could embrace those partial safety case carried forward into the bases of the main Safety Case. This paper will emphasize the role operational scenarios play during the incremental safety case development, and a method upon the scenarios of establishing the traceability between phrasal safety case and main safety case

2 Goal structure notation

Figure 2:

Principle elements in GSN.

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{

{

{

The Goal Structuring Notation (GSN) (Kelly and Weaver [5]) – a graphical argumentation notation - explicitly represents the individual elements of any safety argument (requirements, claims, evidence and context) and (perhaps more significantly) the relationships that exist between these elements, see Fig 2. The principal purpose of a goal structure is to show how goals are broken down into sub-goals, and eventually supported by evidence (solutions) whilst making clear the strategies adopted (e.g. adopting a quantitative or qualitative approach),the rationale for the approach (assumptions, justifications) and the context in which goals are stated (e.g. the system scope or the assumed operational role).

Evidence presented

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An extension to GSN is an explicit representation of modules themselves. This is required to be able to represent a module as providing the solution for a goal. In order to refer to goals defined within other modules, the concept of ‘away element’ is introduced (e.g. away goal or away solution), which derives a vital feature of modular GSN: argument module interfaces. The argument module interfaces define clearly the visible contents of a argument module including the Objective addressed by the module, public objectives and evidences that support to (or from) other argument modules, assumed context defined within the module together with any dependencies on other cases. Interfaces are specified to provide other argument module developers with sufficient information to allow them use a particular argument module.

3 Safety case architecture Following the definition of software architecture (Bass et al. [8]), Kelly [9] presents a similar terms of Safety case architecture: ‘The high level organisation of the safety case into components of arguments and evidence, the externally visible properties of these components, and the interdependencies that exist between them’. This definition declares equal importance to the dependencies between safety case modules (or ‘components’) as to the components themselves, which means for the incremental safety case development during safety lifecycle, an kind of structures that is able to establish clear and seamless interfaces so that safety case elements can be safely composed, removed and replaced, should be considered from the very beginning stage of constructing safety case. 3.1 High level argumentation Constructing a safety case architecture for CBTC from high-level requirements during the system development lifecycle allows the low-level requirements for evidence to be identified. Thus the need for testing, analysis and other evidence generation approaches can be determined during system design. EN 50129 [3] supports the principles of establishing multiple related safety cases in stating a safety case provides evidence that a generic product is safe in a variety of applications. However, an attempt to enumerate and justify all possible configurations is unfeasibly expensive; to establish the safety case for a specific configuration will nullifies the benefit of flexibility (Kelly [10]). A more promising approach is to attempt to establish a modular, compositional safety case that has a correspondence with the modular structure of the underlying architecture (Kelly and McDermid [11]). However, it is more significant that what aspects of system architecture can be classified as basis of partitioning argument modules. Whilst conceiving a complex safety critical system, designers are prone to scheming safety functionalities that system should achieve rather than constructing system structure, because the structure is just a specific solution of all function requirements. Besides, to discuss how one function relies on another WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

844 Computers in Railways XII and their interface requirements is more practical than make clear the boundaries between subsystem structures just after system requirement has been defined. Hence, constructing modular safety cases in accordance with system requirements will be easy to operate and make the potential modular change minimized. In addition, this style has one advantage over the subsystem decomposition style in that it promises to be more cohesive from a safety perspective. 3.2 Preliminary safety case After specifying the system requirements of CBTC generic product, the Preliminary Hazard Analysis (PHA) can be undertaken in order to identify hazards related to design and operation and ensure that the preliminary design is built-in with safety properties from the beginning of the CBTC system development. Consequently, High level argumentation in Preliminary Safety Case covers the functional requirements, as well as the specification of all external interfaces, performance requirements, Electromagnetic Compatibility (EMC) requirements, and Reliability, Availability, Maintainability and Safety (RAMS) requirements, all of which form a framework that safety case architecture has to conform to, see Fig 3. Next, operational scenarios will introduced to deal with functional division cutting across subsystem boundaries, also help to collect safety goals supported by other modules according to the reference relationships indicated in Fig 3.

4 Operational scenarios and hazard analysis As panoptic view of functional design, operational scenarios aim to reveal detailed schemes which are constructed by the system designers to fulfil specific TopLevelArg CBTC High Level Argument Module Argument module over EMC requirement

Argument Module over Interfaces requirement

Figure 3:

Argument module over RAMS requirement

Argument module over Functional requirements

Argument module over Performance requirement

Modules in high level argumentation.

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functionalities, also provide legible process charts for assessors to follow when identifying latent sub-system hazards. In order to recognize the potential causes and consequences for each identified hazards, system safety analysts can find clues referring to the pre-and post-conditions of each step, interactions between sub-systems during single-step execution, as well as the input and output data of components which can awake potential chain-reacting fault states in future interactions. Fig 4 shows the operational scenario conceived to implement when train starts up in the depot then departs to operate on the mainline. After system requirements has defined, it is more feasible in reason for the designers to decompose function requirements other than deploy subsystem or component, because it is hard to assign the specific function points to corresponding physical divisions especially when correlativity between primary functions has not been clearly discussed yet. Here operational scenarios offer such materials for both Track

MMI

Driver

On-board equipment

Selfcheck no display

Provide the storage battery

Self check

ZC

ATS

CI

Enter Ready mode

Start the workbenc h

Waiting for the wake up Wake up and enter into RM

MMI display MMI test button

Wireless test and braking trail Confirm the feedback information is correct

MMI input ID

Check the validation and record

Route Setting command Operatio n plan

The signal opens

The driver confirms the signal display

Start the departure request from the depot Start up the train

Speed supervision under RM

Enter “train operation” scenario

Figure 4:

Scenario of the train start-up process.

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Set the route

846 Computers in Railways XII designers and assessors to talk through a particular process: firstly, system designers illustrate their mentalities on the designated function point via each behaviour (the rectangle with circular on the top right corner) on the thread of subsystem objects which they consider as possibly contribution to Hazard log of Train start-up operational Scenario implementation. Secondly, the implementers can negotiate with designers about the function boundaries as criterion to follow with when they later define the subsystem. Most important, all participants will reach an agreement on detailed design, which is of great benefit in case of necessary modification even on a single function point. For example, the assessors can easily trace the related behaviour sequence and evaluate the side effect on the identified hazards, consequently, decide whether new evidences are needed for this change in relevant phrasal safety cases. Take the scenario in Fig 4 for example, which elaborates the three stages of train power-on, wake-up and start-up. For each behaviour the assessor will use HAZOP method to question the designers in form of ‘Object (direct or indirect) +guideword (no, more or less, etc) +parameter (velocity or voltage or data, etc)’. Designers will follow these questions to investigate the potential causes and consequences in case it happened as a hazard. To complete hazard log, designers need to propose mitigation measures on the purpose of bringing down the risk to a tolerable level, which are essential to form the safety goals in the argumentation. Table 1 gives a fragment of Hazard Log of train start-up operational Scenario, as space is limited, only the reference number and potential causes are presented to explain how the hazards are identified from scenarios.

5 Safety goals decomposition based on scenarios As has already been discussed, the mitigation measures against each hazard can be treated as sub safety goals under a top safety function representing the safety requirement corresponded with the operational scenario. Before one measure is taken into account of decomposed safety goals, some reduction strategies below will be adopted to avoid unnecessarily duplicated argument work:  Combine the hazards with same potential causes, which inevitably means identical mitigation measures;  For the similar measures in different scenarios, if the same supported evidences are needed, can be argued as away goal;  Eliminate as agilely as possible the human factor hazards from technical safety argument into safety management argument, which will be of great benefit to function argument reuse, as not under all possible scenarios the same human faults happen.  If one identified hazard serves to be the potential cause of another hazard, then relevant measure could be the sub safe goal of the upper goals derived from that hazard. With these strategies, the measures of all hazards in Table 1 has been simplified into sub goals which finally construct the whole argumentation under the top goal ‘Train safely leaves the depot’, see Fig 5. Inside this argument, the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Table 1: Scen-Func Ref. No. S1-F1\2

847

Hazard log of train start-up operational scenario.

Hazard Description

Potential Cause

Train cannot supply the power to the on-board equipments On-board equipment failed to self-check or check overtime On-board equipment failed to wake-up, or overtime.

1.Driver skills were sufficient 2.Storage battery was depleted/not regularly maintained/float charged. 1. On-board equipment design deficiencies; 2.VOBC functionally failed;

S1-F8

MMI cannot display train-borne information when VOBC powers on

1.MMI powered down; 2.Communication between train-borne and MMI failed

S1-F9\10

On-board equipment failed to detect rear onboard equipments.

1.Communication between ends of vehicle failed; 2.failed to collect data of rear of vehicle when changing the ends

S1-F11

On-board equipment incorrectly passed braking testing.

S1-F12

Driver failed to input IDs.

1. Train-borne collecting board /collecting channels failed; 2. Drivers considered the wrong feedback information is as the normal; 1. On-board equipment failed to query drivers to input ID; 2.Communication between train-borne and MMI fails; 3.Drivers make human errors.

S1-F13

On-board equipment did not check the validity of drivers' IDs On-board equipment failed to enter corrective mode status which is selected On-board equipment cannot link to wireless

S1-F3\4

S1-F6\7

S1-F15

S1-F14

S1-F15

Signal may not be open yet when train left depot.

1.Internal communication failed; 2.Drivers did not choose the head of train;

train-borne software fails

1. Mode switches failed. 2. Drivers make mistakes; 3. Mode switch is incorrectly wired during building process. 1.No wireless signals, DCS fails; 2.Train-borne equipments fail, cannot receive wireless signals; 3. ZC equipments fail, cannot receive wireless signals. 1.ATS did not arrange the operation plan or arrange a wrong operation plan; 2. CI did not arrange routes or arrange wrong routes due to failures; 3.Communication between CI and signal failed.

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848 Computers in Railways XII top safety goal is marked as public goal, as it will be representatively cited to support other argument modules. G2~G11 are sub goals decomposed with the strategy of ‘all hazards have been handled’, namely the safety requirements separated out from mitigation measures using the strategies mentioned above, which can be referred in the context of Hazard Log. For those safety goals need to be supported by specific evidences are presented as undeveloped goals which will be finished in operational safety case. Particularly, G4 is related to the RAMS performance of CBTC system, has to gain testimony from argument module over RAMS requirement, consequently, it is expressed as an away goal. In order to establish the traceability, an incidence matrix including the relationship of safety requirements, safety functions in scenarios, hazard Log and requirement specification is necessary not only for safety argument, but also for the safety case reuse and maintenance. As the safety case architecture was built on operational scenarios, the incidence matrix, also called verification matrix, is designed referring to the operational scenarios likewise. As a fragment of such matrix listed in Table 2, one record of a sub goal needs to contain the full information during its period of validity, that is, how it is generated, what it affects, and where it is stated. In case that change happened, e.g. designer have to modify his thought, or implementer have to update the definition of system boundary, the operational scenarios bear the brunt to recompose synchronously. So it is quite vital to recognize the range of influences for a single safety goal as well as functional interaction that this goal will take with other safety goals. To combat this, verification matrix is created to ensure that function interactions are recorded and considered as a separate ‘interactions’ sub safety case, which is obviously less comprehensible but easier to maintain. For someone wishing to investigate all of the possible issues surrounding the maintenance of a particular safety goal, they will find them largely addressed within such a single sub safety case.

Figure 5:

Train start-up safety function argumentation.

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Table 2:

Traceability between sub safety goals and migrating measures in the verification matrix.

Sub safety goals

Scen-Func Ref. No.

Source

Name of Requirements

G2

S1-F1\2\3\4

HL-029[1]

VOBC Drive Module Design Specification

G3

S1-F6\7\8

HL-031[2] HL-154[2]

VOBC Subsystem Requirement Specification

G4

S1-F8

HL-032[1] HL-053[3]

HW User Manual-INC70.xx hardware manual

7.2Electrical specification

S1-F14

HL-098[2] HL-136[2] HL-149[3] HL-192[3]

ZC subsystem architecture specification

3.2.2.2-Redundancy design principle

S1-F6\7\8

HL-032 [2] HL-053 [4]

VOBC subsystem architecture specification

G5

G6

849

No. Requirements in specification 3.2drvSystemSelfTest description 3.6drvSelfTestResult description 3.3.4-Communication status check among subsystems 9.1.1Information display function

3.1-Subsystem division 5.2.2-Logical Interface between ATP and MMI

6 Conclusion Rather than organizing the safety case architecture in accordance with the existing system structure, another style is to decompose the case according to safety functions. This style has one advantage over the subsystem decomposition style in that it promises to be more cohesive from a safety perspective. This paper constructs a modular safety case architecture following the system requirements, then introduces operational scenarios as skeleton to guide the safety goal decomposition, and records safety argumentation in functionindependent modules with GSN method. With such method, the dependences between argument modules can be explicitly expressed in module interfaces and be directly traced in verification matrix, which will obviously bring the potential benefits of changeability and reusability compared to a monolithic safety case. In future work, we intent to use the extended GSN concept of safety contract to record such traceable cross-references between argument modules to preferably help manage the dependences.

Acknowledgements The bulk of the work reported here was supported by the project of Natural Science Foundation of China (NSFC): the basic theory and key technology research of train operation control and organizations (Ref.60634010). WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

850 Computers in Railways XII We are grateful to Beijing Subway Yizhuang Line Project and Department of safety and quality assurance of Beijing Traffic Control Technology Co., Ltd for the information and explanations which formed the basis of our case study.

References [1] EN 50126 Railway Applications - the Specification and Demonstration of Reliability, Availability, Maintainability and Safety (RAMS) - Part 1: Basic requirements and generic process. European Committee for Electrotechnical Standardisation, 1999. [2] EN 50128 Railway Applications – Software for railway control and protection systems. European Committee for Electrotechnical Standardisation, 2001. [3] EN 50129 Railway Applications – Safety related electronic systems for signalling. European Committee for Electrotechnical Standardisation, 2003. [4] Railtrack: the yellow book: Engineering Safety Management Volume 1 and 2:Fundamentals and Guidance Issue 4, Rail Safety and Standards Board,2007 [5] Kelly, T., Weaver, R. The Goal Structuring Notation – A Safety Argument Notation. Proc. of Dependable Systems and Networks 2004 Workshop on Assurance Cases,2004 [6] MoD Defence Standard 00-56 Safety Management Requirements for Defence Systems, Ministry of Defence.1996 [7] MoD Defence Standard 00-55, Requirements of Safety Related Software in Defence Equipment, Ministry of Defence.1997 [8] Bass, L., Clements, P. and Kazman, R. Software Architecture in Practice,Addison-Wesley,1998 [9] Kelly, T. Using Software Architecture Techniques to Support the Modular Certification of Safety-Critical Systems. Proc. Eleventh Australian Workshop on Safety-Related Programmable Systems (SCS 2006), Melbourne, Australia. CRPIT, 69. Cant, T., Ed. ACS. pp53-65, 2006. [10] Kelly, T. P., Arguing Safety – A Systematic Approach to Safety Case Management, DPhil Thesis YCST99-05, Department of Computer Science, University of York, UK, 1998 [11] Kelly, T.P., McDermid, J.A., A Systematic Approach to Safety Case Maintenance, Reliability Engineering and System Safety vol. 71, Elsevier, pp271-284,2001.

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The cost benefit analysis of level crossing safety measures R. Ben Aoun, E.-M. El Koursi & E. Lemaire University of Lille North of France, INRETS, France

Abstract Even though the general safety level of rail transport is quite satisfactory compared to road transport, a problem still persists, that of level crossings (LCs). The fact is that road users’ behavior plays a large part in accidents where most of them do not occur following a failure of the railway system but are due to individuals’ behavior. Knowing this, the Rail Optimization Safety Analysis (ROSA) project intends to identify several safety measures through a cost benefit analysis (CBA) in order to enhance the safety level at LCs on the French and German railway systems. The choice of leading a CBA is not random. Indeed, it allows comparisons between all the possible alternatives to aid the decision makers to be able to invest in the most profitable safety measure. However, it is very difficult to include all the effects of all the possible safety options. This is why the results have to be interpreted with caution. Keywords: cost benefit analysis, safety measure, railway, level crossing, LC.

1

Introduction

A cost benefit analysis (CBA) aspires to estimate the profitability of a project from the whole community point of view by quantifying the willingness-to-pay or the willingness-to-accept. The willingness-to-pay – or the willingness-toaccept – is the stated amount that an individual is willing to pay – or to accept – in compensation for a loss or a diminution of its utility. For instance, the willingness-to-pay for human life informs society about the importance that the governments grant for human life (e.g. Bellavance et al. [3]). In general, a CBA takes place in four stages: i) the qualitative and quantitative assessment, ii) the identification of all the possible effects for all the foreseen options and from the point of view of different groups of concerned individuals, iii) the monetary WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100771

852 Computers in Railways XII valuation of all these impacts so as to, iv) select the most suited solution for the community. This last stage is based on three crucial selection criteria: the net present value (NPV), which is the difference between the updating benefits and the updating costs; the internal rate of return, which is the rate for which the net present value is equal to zero; and, the benefit to cost ratio, which is the discounted benefits divided by the discounted costs. Within this framework, the actualization is very important in the sense where it reflects the arbitrary choices between the present and the future generations which the community makes. Actually, the future costs and benefits have to be discounted according to a recommended rate (e.g. Rodgers and Leland [23]). To be consistent and to be able to compare several results from a few CBAs (e.g. Treasury Board of Canada Secretariat [30]; National Institute of Health [21]; Ministry of the Equipment, the Transport, the Accommodation, the Tourism and the Sea [20]), several cost benefit analysis guidelines recommend that each CBA must specify the point of view of the analysis, adopt a standardized step to be able to compare all the different alternatives by updating the obtained results, determine the willingness-to-pay or the willingness-to-accept if the market prices are distorted (or refer to the literature for reference values) and endorse the results with a sensitivity analysis. This last stage is very important because the sensitivity analysis indicates if the results are reliable or not. For this, each sensitivity analysis has to take into account the following guidelines (NIH IT projects [21]): A parameter is not considered to be sensitive if it requires a decrease of 50% or an increase of 100% to cause a change in the selected alternative; A parameter is considered to be sensitive if a change between 10% and 50% causes a change in the selected alternative; A parameter is considered to be very sensitive if a change of 10% or less causes a change in the selected alternative. Moreover, to be thorough and strengthen the results, the CBA has to take into account the same parameters for the two different countries. Indeed, one of the stakes of the CBA is to make a harmonization and to compare the results between the two countries so that the definitions of parameters are elementary. Actually, to harmonize the results, the European countries must have the same definitions in terms of fatality, heavy injury and accident in order to draw the correct conclusions. For our case study, the definitions of parameters comply with the Eurostat definitions. Actually, it is supposed that “deaths in road accidents are people who were killed outright or who died within 30 days as a result of the accident” and that a serious injury is “an injury for which a person is detained in hospital as an “in-patient” or any of the following injuries whether or not the injured person is detained in hospital” but do not involve the death within the recording period (Odgaard et al. [22]). It should be noted that the analysis intentionally does not include the net benefits for the avoided slight injuries because of the lack in the Eurostat and French and German databases. The following part is dedicated to the presentation of the cost benefit approach in

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transport. The next one is devoted to the definition of the framework whereas the last parts outline the whole level crossing case study and the results.

2

Economics in transport

2.1 The role of the CBA In transport, a CBA has to emphasize the best alternative to reach the objectives at lower costs. The difficulty rests on the fact that the decision makers have to make a selection under uncertainty because a few effects are unpredictable (Jokung [16]; Laffont [18]). Moreover, a CBA is not always applicable and the means do not always square with the objectives (Rune [24]). Concretely, the CBA has to highlight the different choices by supplying an economic evaluation from a fault tree analysis and clarify all the costs and benefits to focus on the best option in economic terms. To mint these costs and benefits, two types of techniques are elaborated, especially for the goods which do not enter the merchant sphere. The first method is based on a contingent valuation (Terra [29]), which directly infers a willingness-to-pay or a willingness-to-accept regarding answers to questions of investigations according to several scenarios. The second rests on a hedonic method (Gravel et al. [14]), which consists in observing individual decisions on the market of risk to determine an implicit value of goods. Thus, these two different methods allow the monetary valuation of costs and benefits in order to deter-mine if a project is profitable or not. For the majority of projects in transport, benefits mainly concern avoided accidents and saved lives or injuries (Carsten and Tateb [7]). Within this framework, it is important to understand how the value of human life is estimated. 2.2 Monetary valuation of human life Three methods are used to appreciate the cost of life in transport. Indeed, the value of human life can be based on the means invested to compensate the effects of an accident; this is the method of cost compensation. The human capital approach aims at estimating the updated losses of the society following human damage, and, the willingness to pay or to accept principle seeks to evaluate the satisfaction levels for a sample of individuals in order to estimate a mean value. In transport, this approach can allow the valuation of human life by asking individuals the maximum amount they are willing to pay to benefit from a better safety level. For several years, the suggested value of human life to retain for all the European projects of collective transport has been one and a half million euro (Boîteux [6]; Desaigues and Rabil [11]; Odgaard et al. [22]). However, individuals are considered to be more responsible for their own safety level on roads (LievremontArtinian and Bertel [19]; SNCF [28]), that is the reason why the value of human life for road transport only represents 66% of the total cost of life, that is to say one million euro. As the LC case study relies on the French and German railway systems, it is necessary to compare the values of human life for the two countries to see if the European common value can be used in the CBA. Thanks to the values WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

854 Computers in Railways XII expressed in purchasing power parity in the table below, we can see that the cost of human life for France and Germany is very close to the European common value, so it can be used in the CBA. The purchasing power parity unit allows the conversion between economic indicators expressed in a national currency and an artificial common currency in order to compare prices between countries. Table 1:

Estimated values of human damage in France and in Germany.

Country

Fatality (€2002)

Severe injury (€2002)

Fatality (€2002 PPP)

Severe injury (€2002 PPP)

France Germany

1,617,000 1,661,000

225,800 229,400

1,548,000 1,493,000

216,300 206,500

To comprehend the CBA, the part below is devoted to the presentation of the context and the stakes of the French and German Rail Optimization Safety Analysis (ROSA) project (Ben Aoun et al. [5]; Klinge [17]).

3

Background

The ROSA project evolves within the regulatory framework of the Interoperability Directives 96/48/EC (Council Directive 96/48/EC [9]) and 2001/16/EC (Directive 2001/16/EC [10]) through the technical specifications for interoperability (TSI) which claim that “each subsystem or part of a subsystem is covered in order to meet the essential requirements and ensure the interoperability of the trans-European high-speed and conventional rail systems”. In spite of technical and scientific progress, railway competitiveness and the rail safety directive 2004/49/CE [8] do not suffice for LCs. Indeed, heavy and constant safety measures have to be implemented to decrease the number of accidents and fatalities especially at “worrying” LCs, i.e. LCs with a high rate of accidents and/or incidents. Within this framework and in the continuity of the Safer European Level Crossing Appraisal and Technology (SELCAT) project, the Franco-German ROSA project foresees a risk analysis for the two railway systems in order to identify the safety levels of the new railway safety functions and to quickly choose the best safety measures to implement. Therefore, the CBA is essential in the sense that it aims to identify all the possible options and determine the best alternatives in economic terms. Concretely, the ROSA project serves three significant aims: improvement of the understanding of railway safety in Germany and in France, ensuring the profitability of investments, and support the impact assessments for safety target definitions for the European Railway Agency. For doing so, the roles of DB AG (German railway undertaking) and SNCF (French railway undertaking) are vital regarding the definition of safety targets through the preliminary safety analysis of the overall railway systems. Concerning safety, the global French and German railway systems are the object WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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of a risk analysis. The aim is to investigate the consequences of the allotment of railway common safety targets in order to at least maintain or improve the actual safety level thanks to the best safety investments. This risk analysis has to take into account the different safety directive groups at risk and introduce, if possible, a risk aversion factor in the calculations through the value of human life according to the willingness-to-pay or to-accept.

4

The level crossing case study

Before presenting the LC CBA, it is necessary to briefly define the functioning of a LC in France and in Germany. In France, when a train is approaching the LC, the barriers are supposed to be closed and free of any obstacle. As it is not always true, a classification of the “worrying LC” has been done to improve safety at the most dangerous LCs. In Germany it is different: the train is supposed to be able to stop before an unprotected LC. This supposes a constant attention for the train driver and a good visibility on the road when the train is approaching the LC. 4.1 Presentation First of all, it should be noted that this particular case study obliges the CBA to consider all the possible safety measures capable of enhancing safety on road and on rail. For this, it is necessary to identify the main causes of accidents at LCs, to estimate the potential reduction of accidents and to integrate the past tendencies into the calculations in terms of number of accidents, fatalities and heavy injuries to obtain the correct results. The CBA has to clarify the different technical solutions in order to specify the most efficient safety measure in terms of avoided human damage. By doing so, the CBA will compute all the discounted costs and benefits for all the stakeholders to estimate the three selection criteria inherent to the CBA and to implement the best safety option. Nevertheless, it is necessary to determine the current safety level regarding accidents and human damage in order to compare those data with the estimated ones thanks to linear regression straight lines. As previously said, the main profits concern saved human lives. In general (Boîteux [6]), human life is estimated at €1,500,000 for collective transport and at €1,000,000 for road transport. In the same way, a heavy injury is estimated at €225,000 for collective transport and at €150,000 for road transport. As more than 98% of accidents at LCs in France and in Germany are due to road users’ behavior, the value of human life and of heavy injury to be taken into account are consequently those regarding road transport. However, let us recall that the benefits for avoidable slight injuries are not included. 4.2 Data sources and hypotheses The CBA allowed the identification of four different solutions to reach the objectives of enhancing safety at LCs: the half-barrier implementation, WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

856 Computers in Railways XII the LC suppression, radar installations on road, and, safety campaigns. According to a SELCAT study, the potential reduction of accidents for a halfbarrier implementation at unprotected LCs is supposed to be 69% for cross bucks and 45% for LCs with flashing lights and bells. The effectiveness of an LC suppression is supposed to be 100% for all LCs. The French experimentation showed that radar implementations between 2003 and 2004 could reduce the number of accidents by 23% so the effectiveness is supposed to be the same for the two countries (any kind of radar measure is known for Germany). However, the European Railway Agency Guidelines (ERA [13]) claim that the hypotheses have to be checked to maximize the reliability of results. For this reason, regression linear equations for all the parameters are computed in order to integrate the past tendencies into the calculations and estimate the real effects of radars on road. Besides, the CBA is supported to determine the best safety measure for railway so radars are also supposed to be implemented by infrastructure managers in order to benefit from the income of fines in the same time. This amount is not negligible when we know that the mean cut fine is €90 and that the mean surcharged fine is €135 in France. According to the Interdepartmental National Observatory for Road Safety (ONISR) in France, the mean fine observed in 2007 was €65 for 6,983,650 parking tickets. Thus, the CBA takes into account two cases for the radar safety option: road responsibility and rail responsibility. As the apportionment from these fines income is unknown for Germany, the CBA only considers the first case for Germany, that is to say road responsibility. According to the action plan for road safety (20032010), safety campaigns could reduce the number of accidents by 2,35% per year over 7 years. This is the first hypothesis regarding the effectiveness of safety campaigns. Regarding the effectiveness of radar implementations, it is not utopian to think that safety campaigns could also reduce the number of accidents by 23%. This is the second hypothesis. The last hypothesis is based on a report about safety measures (Canadian National Institute for Public Health [21]) which claims that safety campaigns could decrease the number of accidents from 19% to 26% so the CBA also takes into account these two extremities. Thus, the economic analysis takes into account the following measures: Option n°1.1: Implementation of a half-barrier (for a potential reduction of accidents of 69%), Option n°1.2: Implementation of a half-barrier (for a potential reduction of accidents of 45%), Option n°2: LC suppression, Option n°3.1: Radar installations on road (without correction), Option n°3.2: Radar installations on road (with correction), Option n°3.3: Radar installations if costs and a part of benefits come to railways (if the mean fine is 65€), for France,

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Option n°3.4: Radar installations if costs and a part of benefits come to railways (if the mean fine is 90€), for France, Option n°3.5: Radar installations if costs and a part of benefits come to railways (if the mean fine is 135€), for France, Option n°4.1: Safety campaigns (for a potential reduction of accidents of 2,35%), Option n°4.2: Safety campaigns (for a potential reduction of accidents of 19%), Option n°4.3: Safety campaigns (for a potential reduction of accidents of 23%), Option n°4.4: Safety campaigns (for a potential reduction of accidents of 26%). For the calculations of the selection criteria, four updating rates are retained in order to test the reliability of the results: 3%, 4%, 5% and 8%. 3% is the updating rate commonly used in Germany, 8% in France, 4% is the reviewed rate in France (HEATCO [15]) and 5% is the recommended rate for public projects of investments (European Commission [12]). Most of the physical and economic data come from the SNCF for France and from the DB AG for Germany. Tables 2 and 3 give estimations of the profitability of the four safety measures according to the selection criteria in France and Germany

5

Results and discussion

The two tables, 2 and 3 shown overleaf sum up the general results of the CBA according to the three selection criteria. The cells in yellow (shaded) show the best economic results. The error message means that the internal rate of return is negative due to the fact that benefits do not cover costs over time. The “senseless” message indicates that the calculation of the IRR is not necessary because there is no cost for railways if radars are implemented on roads. Thus, any other safety measures could be more profitable for rail transport. As previously said, the updating rate commonly used in CBAs is 8% in France and 3% in Germany. Within this framework, a safety measure is profitable only if the net present values are positive, if the benefit to cost ratio is higher than one and if the internal rate of return is higher than the updating rate to be more gainful than a financial investment on the market (Abraham-Frois [1]). These conditions have to be respected at the same time. At first sight, we could think that the most efficient option in France is safety campaigns, as the net present value is widely higher that of the radar option, but the internal rate of return is far from 8%, which is not the case for radar implementations on road. From the railway point of view, if we consider that the updating rate to return for the internal rate of return is the reviewed rate of 4%, the best safety option is still radar implementations but only if the mean fine is €90 or €135. In the same way, it seems that the best option is the last one in Germany, but the internal rate of return is lower than 3%. In this case, the best safety option seems to be radar installations on roads but, from the railway point of view, investments should be angle towards safety campaigns. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Estimations of the profitability of the four safety measures according to the selection criteria in France. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

Table 2:

Estimations of the profitability of the four safety measures according to the selection criteria in Germany.

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Table 3:

859

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6

Conclusion

This paper presents the results of the CBA for LCs within the framework of the rail optimization safety analysis project. To that purpose, we analyzed the railway systems in France and in Germany in order to identify the possible safety options to implement for enhancing safety at LCs. As more than 98% of accidents at LCs are due the non-respect of the rules of the road, it is not surprising to notice that the best safety measures are without any doubt those which directly act on road users’ behavior, such as radar installations on roads and on rail or safety campaigns. Nevertheless, it is important to recall that the aim of the CBA is to determine the best actions to be taken from the railway point of view. This is the reason why the analysis supposed that radar installations could be undertaken by railways and that they could benefit from the same profits as if they were undertaken for road transport. The economic valuation speaks for itself because one rail radar installation allows a profit estimated at more than two million euro per year in terms of fine incomes. In other words, the fact of implementing only one radar per year allows the saving of one life. However, it is very difficult to include all the effects of a safety option in a CBA (Andrieu [2]). Thus, the results have to be used with caution.

Acknowledgements This study is the result of the work for the Deufrako project financed by the National Agency for Research and was supported in part by the ROSA partners.

Reference [1] Abraham-Frois G., Political Economics, Economica, 2001. [2] Andrieu L., de Palma A., Picard N., Risk in Transport Investments, 2006. [3] Bellavance F., Dionne G., Lebeau M., The Value of a Statistical Life: A Meta-Analysis with a Mixed Effects Regression Model, 2006. [4] Ben Aoun R., El Koursi E.M., Lemaire E., Rafrafi M., Cost-Benefit Analysis approach in railway sector, Rail Optimisation Safety Analysis, Delivrable 3.1., March 2008. [5] Ben Aoun R., El Koursi E.M., Lemaire E., How can risk aversion factor characterize choices of economic agents under uncertainty, Symposium on Formal Methods for Automation and Safety in Railway and Automotive Systems, October 2008. [6] Boîteux M., “Transport: choix des investissements et coût des nuisances”, Commissariat Général du Plan, June 2001. [7] Carsten O.M.J., Tateb F.N., “Intelligent speed adaptation: accident savings and cost-benefit analysis”, Accident analysis and prevention, February 2004. [8] Council Directive 2004/49/EC on Safety on the Community’s Railways and amending Council Directive 95/18/EC on the licensing of railway WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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[9] [10]

[11] [12] [13] [14]

[15]

[16] [17] [18]

[19] [20]

[21] [22] [23]

[24] [25]

[26]

861

undertakings and Directive 2001/14/EC on the allocation of railway infrastructure capacity and the levying of charges for the use of railway infrastructure and safety certification (Railway Safety Directive), Official Journal of the European Union. Council Directive 96/48/EC of 23 July 1996 on the interoperability of the trans-European high-speed rail system. Directive 2001/16/EC of the European Parliament and of the Council of 19 March 2001 on the interoperability of the trans-European conventional rail system. Desaigues B. and Rabl A., Reference values for human life, 1995. European Commission, “Methodological guideline for cost benefit analyses application”, August 2006. European Railway Agency, Economic Evaluation Methodology Guidelines, March 2006. Gravel N., Michelangeli A., Trannoy A., “Measuring the social value of local public goods: an empirical analysis within Paris metropolitan area”, 2006. Developing Harmonized European Approaches for Transport Costing and Project Assessment, 2005. For more information, the web site is http://heatco.ier.uni-stuttgart.de/. Jokung O., Micro economy of the uncertain, Dunod, 2001. Klinge K., Optimization safety analysis for common railway safety indicators, World Congress on Railway Research, May 2008. Laffont J.J, Economie de l’incertain, Vol. 2 “Cours de Théorie Microéconomique”, Collection “Economie et statistiques avancées”, Economica, 1999. Lievremont-Artinian S. and Bertel D., SNCF, “The management of the risk in the railroad transport”, June 1992. Ministry of the Equipment, Transport, the Accommodation, the Tourism and the Sea, The frame-instruction relative to methods of economic evaluation of big projects of transport infrastructure, May 2005. National Institutes of Health IT projects, “Cost-benefit analysis evaluation guide”, August 2000. Odgaard T., Kelly C., Laird J., HEATCO Work Package 3: Current practice in project appraisal in Europe, Delivrable1/Vol.1, January 2005. Rodgers A.B., Leland E.W., “A retrospective benefit-cost analysis of the 1997 stair-fall requirements for baby walkers”, Accident analysis and prevention, April 2007. Rune E., “Cost-benefit analysis of road safety measures: applicability and controversies”, Accident analysis and prevention, November 1999. R. Slovak, E. M. El Koursi, L. Tordai, M. Woods, E. Schneider, SELCAT: Its contribution to European Level Crossing safety, EURAILmag n°. 18, 12p, 2008. E. M. El Koursi, L. Khoudour, N. Lazarevic, L. Tordai, R. Slovak, 2008. Safer European Level Crossing Appraisal and Technology, “appraisal”,

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[27]

[28] [29]

[30]

Workshop 16 May 2007, Actes n°117, ISSN 0769-0266, Collection Actes INRETS E. M. El-Koursi, L. Khoudour, S. Impastato, G. Malavasi, S. Ricci, 2008. Safer European Level Crossing Appraisal and Technology, “Technology”, Workshop 22nd - 23rd November 2007. SNCF, “Value of life and coefficient of aversion”, May 1994. Terra S., Direction of the economical studies and the environmental assessment, Good practices guide for the implementation of the method of contingent assessment, May 2005. Treasury Board of Canada Secretariat, “Benefit-Cost Analysis Guide”, 1998.

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Proposal of the standard-based method for communication safety enhancement in railway signalling systems H.-J. Jo, J.-G. Hwang, B.-H. Kim, K.-M. Lee & Y.-K. Kim Train Control & Communication Research Division, Korea Railroad Research Institute (KRRI), South Korea

Abstract Safety-critical systems related to the railway communications are currently undergoing changes. Mechanical and electro-mechanical devices are being replaced by programmable electronics that are often controlled remotely via communication networks. Therefore designers and operators now not only have to contend with component failures and user errors, but also with the possibility that malicious entities are seeking to disrupt the services provided by their systems. Recognizing the safety-critical nature of the types of communications required in rail control operations, the communications infrastructure will be required to meet a number of safety requirements such as system faults, user errors and the robustness in the presence of malicious attackers who are willing to take determined action to interfere with the correct operation of a system. This paper discusses the safety strategies employed in the railway communications and proposes a security mechanism for the Korean railway communication system. We present the developed communication safety evaluation tool based on the proposed security mechanism and also evaluate its protecting capability against threats of masquerading, eavesdropping, and unauthorized message manipulation. Keywords: railway communication, safety evaluation tool, security mechanism.

1

Introduction

As the conventional mechanical and electronic systems used in communication for railway signal control are being replaced by programmable electronic systems that can be remote-controlled through telecommunication networks, WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100781

864 Computers in Railways XII the safety of railway communication networks have been highlighted. Factors that may adversely affect safety in railway communication networks include the faults or failures of system components or software and security issues arising from conversion to an open system from closed one. In other words, the closed system uses only physically dedicated wired networks, requiring safety provisions against failures or faults. However, the open system uses networks based on wireless communication or Internet technologies, which are not physically independent, and require traditional safety measures as well as stricter security provisions against unauthorized access or intentional attacks [1–3]. With regard to the safety of the communication component in the railway control system, the EU developed European railway signalling safety standards (EN 50159-1 (for the closed system) and EN 50159-2 (for the open system), and IEC 62280-1 (for the closed system) and IEC 62280-2 (for the open system)) to provide the requirements for communication safety [4, 5]. In this study to investigate the security issues relating to data transmitted through the railway signalling communication network, the safety evaluation system for wireless communication in the railway control system is analyzed and then, approaches, requirements, and procedures for safety evaluation of wireless open systems are provided. Further, requirements for the validation of communication safety and criteria for the determination of safety are analyzed to suggest potential factors that may pose risks to the communication networks in the railway control system and provide recommendations on a secure data link for the communication networks [6]. Moreover, this study describes the means for safety evaluation of the open system on the basis of basic design derived from analysis and discusses such means and their potential applications. In principle, those means are based on the international standards IEC 62280-2.

2

Safety evaluation system for wireless communication networks in railway control system

The open system has network control and management functions that can set (and dynamically re-set) the message routes according to the program unknown to users, through arbitrary routes consisting of more than one transmission media with the characteristics sensitive to external influences unknown to users at both ends of the system. The open transmission system is not known to the control and protection system designers and may have other users that send unknown amount of data in unknown formats. Further, there may be users that may attempt to access data sent by other users, in order to read or copy data without authorization from system administrators. Moreover, the open system may be affected by additional threats of all kinds that may pose risks to the safety-related data integrity. In addition, the transmission link of the open system consists of all items (H/W, S/W, transmission media, etc.) between more than 2 pieces of safety-related equipment connected through the transmission system. The system reference structure is shown in Figure 1 that uses the open transmission system connecting safety-related and non-safety-related systems WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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with unreliable transmission systems, irrespective of what kinds of internal transmission protection approaches are employed. The safety-related transmission systems relate to the unreliable transmission system and any kind of safety requirements should not be imposed on the open transmission system. This structure is based on safety-related transmission functions and safety-related connection protection functions.

Figure 1:

Structure of safety-related system using a non-trusted open transmission system.

Confirmation of functional and technical safety regarding the safety-related transmission function should follow the provisions described in IEC 62280-2. However, any kinds of safety requirements are not imposed on unreliable transmission systems, but the safety procedures and safety encryption that operate inside the safety-related equipment are employed for the safety aspects. As a result, the safety-related message expression models on the transmission media are obtained as shown in Figure 2. In order to evaluate the safety of open communication networks, the hazard cases encountered in the networks and external environments, relationship with threats, and provisions for defense are summarized in Table 1. Especially, in Table 1, the security factors that have to be considered in the open transmission networks are added to summarizing the hazard cases encountered in the closed transmission networks. In other words, the disrupters and intruders are added as hazard factors to the identification of hazard cases in external environments. Hazard cases that may be caused by disrupters include the taping of WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

866 Computers in Railways XII communication lines, damage to or destruction of hardware, and unauthorized modifications to software. Hazard cases that may be caused by intruders include picking-up of channels and unauthorized transmission of messages. Table 1: Hazardous events HW systematic failure SW systematic failure Cross-talk Wires breaking Antennas misalignment Cabling errors HW random failures HW ageing Use of not calibrated instruments Use of not suited instruments Incorrect HW replacement Fading effects EMI Human mistakes Thermal noise Magnetic storm Fire Earthquake Lightning Overloading of transmission system Wires tapping HW damage or breaking Not authorized SW modifications Transmission of not authorized messages Monitoring of channels3)

Defenses

Hazard cases, threats and defenses based on an open transmission system. Threats Resequencing Corruption

Repetition

Deletion

Insertion

x

x

x

x

x

x

x

x x

x

x

x

Delay

Masquerade

x

x

x1)

x

x

x1)

x x

x

x1)

x x

x

x1)

x

x

x

x1)

x

x

x

x

x1)

x

x

x

x

x

x1)

x

x

x

x

x

x

x1)

x

x

x

x

x

x

x1)

x

x x

x x

x

x

x

x x x x x

x x x x

x

x

x

x

x

x

x

x

x x x

x

x

x x x x x x x

x

x x

x

x x

x

x

-Using sequence # -Time stamp

x

x

x

x

x

x

x

x

-Using sequence # -Source & destination identifiers -Feedback message -Identification procedure

x1)

x2) x2)

x

-Using sequence #

x1)

-Using sequence # -Time stamp

-Using safety code - Using cryptographic techniques

-Time stamp -Timeout

-Feedback message -Identification procedure -Using cryptographic techniques

1) In this case, a correct message is delivered to the wrong receiver due, for instance, to a misrouting; a possible countermeasure is the specification of the sender address. 2) In this case, the message is fraudulent from the beginning; a strong defence is needed, for example the use of a key. 3) It makes sense that there is no threat for the hazardous event “monitoring of channels”; the secrecy, in fact, is a system requirement: it has to do with the particular application

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Figure 2:

Figure 3:

3

867

Model of a safety-related message.

Total structure of testing tool for safety evaluation in the open transmission system.

Means for validation and determination of communication safety of railway control system

As shown in Figure 3, the basic structure for the realization of a means for safety evaluation of the open system consists of two modules. With regard to the WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

868 Computers in Railways XII safety-related transmission function (IEC 62280-2), it consists of a safety test function module for the open system and a logic transmission media module based on the open system replacing the transmission media based on unreliable open transmission system. The safety test function module for the open system consists of a sending component and a reception component and communication is realized through the underlying logic transmission media module based on the open system. Figure 4 shows the safety test function module for the open system. This module (sending component) is the open-type safety function simulation and consists of

Figure 4:

Safety test function module for the open transmission system.

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the following seven functional submodules; “sequence number generation submodule”, “time stamp generation submodule”, “source and destination identifiers generation submodule”, “identifiers generation submodule”, “safety codes generation submodule”, “encryption generation submodule”, and “modified message combination submodule”. The “sequence number generation submodule” generates the sequence numbers to prevent various threats, such as repetition, deletion, insertion, or sequence rearrangement, in the open-type transmission. The “time stamp generation submodule” generates the time stamps for generation of messages to prepare for various threats, such as repetition, sequence rearrangement, and delay. The “source and destination identifiers generation submodule” generates the identifiers for both source and destination to prepare for insertion threats. The “identifiers generation submodule” generates the identifiers for data sources to prevent insertion and falsity threats. The “safety codes generation submodule” generates the safety codes to prevent damage threats. The “encryption generation submodule” generates encryption to prepare for falsity threats. Finally, the “modified message combination submodule” collects data created by the above submodules and combines them to make a modified message. Such combined messages are sent to the underlying logic transmission media module based on the open system.

Table 2:

Program structure of testing tool for safety transmission and validation in the open system(JAVA).

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870 Computers in Railways XII Meanwhile, the safety test function module for the open system (receipt component) is the open-type safety function simulation and consists of the following eight functional submodules; “encryption verification submodule”, “safety codes verification submodule”, “identifiers verification submodule”, “source and destination identifiers verification submodule”, “time stamp verification submodule”, “sequence number verification submodule”, “return message generation submodule”, and “submodule for combination of modified messages to be sent to safety-related equipment”. The program structure of actual testing tool for the safety transmission and verification in the open system is composed of JAVA programming languages like Table 2. Each developed program module is summarized as Table 2, that is, total program structure file & folder, inner file & folder, and functions. The program explanations for simulation tool are as followings. “project2.exe” is executive file and “project2.java” is methods and program involving GUI(Graphic User Interface) related simulations. “datainfo.java” includes methods for structure and structure approach. The specific contents of each program module are like followings. 1) datainfo.java The structure is made up of ‘frame start(1byte)’, ‘data length(1byte)’, ‘message type(1byte)’, ‘source identifier(1byte)’, ‘received identifier(1byte)’, ‘data sequence number(1byte)’, ‘time stamp(1byte)’, ‘data(40byte)’, ‘MD5(16byte)’, ‘error-detection CRC-16(2byte)’ and ‘frame end(1byte)’. MD5 calculates source identifier, received identifier, data sequence number, time stamp and data. CRC-16 calculates the remaining things except frame start and end. 3DES calculates all things including CRC-16. 2) project1.java - crc16Tab[] : crc-16 table for calculating crc-16 - crc16Check : returning crc-16 for incoming byte[], byte number - generate_others : generating the remaining things except data and CRC for structures of transmitted/received part - generate_data_word : generating transmitted 40byte data randomly(the type of random small alphabet) - generate_random_data_error : generating the modifications of random position for data - go : methods for GUI - actionPerformed : methods for GUI - md5Check : generating md5 code for incoming byte[] - generate_3des_key() : generating keys for 3des - Encrypt_3des : 3des encrypt - Decrypt_3des : 3des decrypt - simulation: methods for simulation The total operation of the simulation tool executes firstly inputting the number of simulation running and operating simulation for the number by using ‘for sentence’. And then after making transmitted messages by WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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‘generate_data_word’ and ‘generate_others’, Hazard cases of 25 kinds are decided randomly from mixing various types. We can choose randomly the number from ‘1’ to ‘100’ for each hazard case, the selected hazard case is generated if the number is more than ‘90’. Also the possible threats for hazard cases are able to be selected randomly, and the selected probability is tend to high if some threats occur simultaneously at hazard conditions. We determine randomly the number from ‘1’ to ‘100’ for each threat, and use the number like following contents for each error. - Corruption: Case of the number less than ‘15’ - Delay: Case of non-selecting above mentioned threats & less than ‘20’ - Repetition: Case of non-selecting above mentioned threats & less than ‘20’ - Deletion: Case of non-selecting above mentioned threats & less than ‘20’ - Resequence: Case of non-selecting above mentioned threats & less than ‘20’ - Insertion: Case of non-selecting above mentioned threats & less than ‘50’ - Masquerade: Case of non-selecting above mentioned threats & less than ‘80’ - Normality: Case of non-selecting all threats

Figure 5:

The screen of frequencies at total hazard cases in the validation tool of open transmission systems.

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Figure 6:

The screen for success number of the developed validation tool in open transmission systems.

This developed tool can show statistical data and inspect whether or not it is continuously checking threats for the inputted number. The operating total procedure of the simulating tool is expressed at the screen like Figure 5. As shown in Figure 5, there are generated frequencies by the program execution as hazard cases. The percentage and number of safety transmission and verification for a total hazard frequency can be presented as the screen of Figure 6.

4

Conclusion

Existing railway communication systems are based on the closed communication networks characterized by expensive costs for installation and difficult maintenance and repair. Owing to the lack of alternative communication methods, those systems result in a delayed introduction of flexible railway control systems. However, new communication technologies, such as wireless communication and TCP/IP protocol, are able to provide various railway communication services with lower costs for installation of infrastructures relating to the open-type communication technology. From the economic perspective, these new technologies are promising. However, the open communication systems have safety and security problems. Change into the open-type, remote-controlled railway control system is increasing. Such a trend in transmission or communication-based railway control (CBTC) leads to concerns over the safety and security aspects. It is not possible to determine the costs for such broadcast communication systems from the safety perspectives and use of the open communication networks with CBTC is strongly required for more application efforts. The safety is a combination of the system design and the environment where the system is used. The railway control application environments are considerably different from the current basic structure and operation environments. In the closed communication networks, the safety-critical systems can be designed with the assumption that errors and failures are key risk factors to one-to-one communication links. However, the open communication networks require stable operation even when malicious and intentional attacks are increased. In conclusion, in order to ensure the safety of the railway WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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communication networks from more complex and various risk factors, measures for safety and security are required to prepare for threats from both perspectives of the closed and open communication networks.

References [1] Winther R. and Johnsen O., Gran B. A.(2001), “Security Assessments of Safety Critical Systems Using HAZOPs,” Proceedings of 20th International Conference on Computer Safety, Reliability and Security, SAFECOMP, Lecture Notes in Computer Science, Vol. 2187, pp. 14-24 [2] Knight J. C.(2002), “Safety Critical Systems: Challenges and Directions,” Proceedings of the 24th International Conference on Software Engineering, pp. 547-550 [3] Eames D. P. and Moffett J.(1999), “The Integration of Safety and Security Requirements,” Proceedings of 18th International Conference on Computer Safety, Reliability and Security, SAFECOMP, Lecture Notes in Computer Science, Vol. 1698, pp. 468-481 [4] IEC 62280-1(2002), “Safety-related communication in closed transmission systems” [5] IEC 62280-2(2002), “Safety-related communication in open transmission systems” [6] Jong-Gyu Hwang, Hyun-Jeong Jo, Yong-Ki Yoon, Yong-Kyu Kim(2006), “Safety Characteristics Analysis of Korean Std. Protocol for Railway Signalling according to IEC 62280,” Autumn Conference of Korean Society for Railway 2006, pp. 863-869

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Section 13 Timetable planning

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A heuristic algorithm for the circulation plan of railway electrical motor units J. Miao1, Y. Yu2 & Y. Wang1 1

The State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiao University, China 2 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

Abstract The rolling stock rostering problem with maintenance constraints is NP-hard. In this paper, a two-stage approach is proposed. Relaxing the maintenance constraints, a transition network with minimum train unit is established in the first stage. A heuristic algorithm based on the transitions interchange is devised to search for a feasible maintenance route. The algorithm is verified with the operational timetable of Guangzhou-Shenzhen railway, and the performance is satisfactory. Without consideration of empty movements, the algorithm can just be applied in the situation where the depots are well planned. Keywords: electrical motor units, circulation plan, heuristic algorithm.

1 Introduction Before the year 2007, all the passenger trains on the traditional railway lines in China were built-up with locomotive(s) and cars. The locomotives and passenger car fleets are managed under a different department and operated separately according to different plans. As the passenger trains often run across great distances and take several days, most of the trains are allocated with multiple car fleets. A car fleet is often assigned to execute a given train, except for a few short-distance trains. The composition of fleets often persists over a relatively long time unless there is maintenance. With the train speed upgrade project and the development and operation of passenger dedicated rail lines, Electrical Motor Units (EMU) are employed in the operation of high-speed trains which run at 200-300km/h speed. There are two WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100791

878 Computers in Railways XII component forms of EMU in railway of China: the short form with 8 vehicles as a unit and the long form with 16 vehicles as a unit. Two short units can be coupled together if they are of the same model. The combination and disassembling of EMU are conducted in the depot. Inspections and maintenances on the EMU must be performed based on the regulated operational time and distance [1]. Due to the high cost of EMU, and the fact that most of the high-speed trains are operated in short and middle-range distance, shorter non-commercial time is desired to improve the utilization of EMU. As a result, one EMU is often required to operate for multiple trains. Based on the characteristics of the operation of EMU in railway of China, the optimization algorithm of EMU circulation problem with given train schedule is studied in this paper. The rest of the paper is organized as the following: an elaboration to the circulation of EMU in railway of China is presented in Section 2, the literature review is given in Section 3, the proposed heuristic algorithm is investigated in Section 4, the results of our computational experiments are presented in Section 5, and we conclude in Section 6.

2 The EMU circulation problem in railway of China Several terminologies from [2] are adopted in our discussion of the EMU circulation problem. A route is a path between two given stations. A ride is a train unit on a certain route with distinct departure and arrival time. The connection relationship of two consecutive rides carried out by one EMU is called transition. In China’s railway, the type of EMU and the number of cars are assigned during the line plan stage. Hence, only the transition between the rides carried out by same type of EMU are to be investigated, and no shunting will occur in stations. We define a rotation is the order of rides performed by one EMU from the end of a class one maintenance to the end of the next maintenance. A circulation is the aggregation of all rotations. A feasible circulation in China railway must meet the following constraints: (1) Every train must be assigned with required type and amount of EMU. i.e., every scheduled ride must belong to one single rotation. This is called the ride cover constraint. (2) The transition time should be longer than the necessary turnaround time in stations. (3) To meet the maintenance regulation of EMU. In railway of China, the 1st class and the 2nd class inspection and maintenance can be carried out in any depot. The 1st class inspections are daily inspections with a period of 4000km or 48h for the most types of EMU. The 2nd and higher class inspections or maintenance are generally with long intervals and are time-consuming, and such inspections are arranged by dispatcher according to the short term operation plan, and are not studied in our research.

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Typically, to meet the demand of passenger flow, unless absolute necessary, there will be no empty train movements in China in order to reduce the waste of the infrastructure capacity. If the feasible EMU circulation for a timetable does not exist, the operator will reschedule the timetable with changing the departure time, canceling or adding some trains till the feasible solution emergence. Therefore, the empty movement is not included in our work. Without the empty movements, indexes for evaluating circulation are the amount of EMU, and the number of inspections. (1) Amount of EMU Because the amount of EMU is less till now in China, to use as fewer as possible EMU to carry out scheduled trains is the top objective of the railway operator. For a given timetable, the number of EMU needed can be calculated by: number of EMU 

 travel time  min  transition time timetable ' s time horizon

(1)

(2) Number of inspections Less number of inspections is better as long as the regulations are met. For a given timetable, there are time and mileage constraints to determine the number of inspection, and the lower bound can be calculated by the following respectively: number of inspection by time 

amount of EMU  timetable ' s time horizon timeinterval for inspection

number of inspection by mile 

 travel mileage

(2) (3)

mileage for inspection

Hence, the lower bound of inspection times is determined by: minimum number of inspection 

max(  number of inspection by time  ,  number of inspection by mile  )

(4)

The increase of number of inspection renders higher operation cost and consumes more depot capacity. Based on the above discussion, we can draw a conclusion that EMU circulation problem is a Rolling Stock Rostering problem with time and distance maintenance constraints (RSR-M).

3 Literature review Lots of research has been conducted on the rolling stock circulation. In Anderegg et al. [2], some basic concept in the operation of passenger trains are introduced, Erlebach et al. [3] summarizes the operation of passenger trains into basic models and variable constraints, and he states that the Rolling Stock Rostering with Maintenance constraint is a NP-Hard problem. In the literature, there are different ways in solving such problems with different constraints and characteristics due to the background of the problems and the empirical demands. Arianna et al. [4] studies the circulation planning of multiple types train unit operated on a single line. The objective of their research WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

880 Computers in Railways XII is to minimize the number of train units and the operational kilometers under the constraints that the given amount of passenger being matched with the consideration of the sequence of train units. Mixed Integer Programming is utilized and CPLEX is employed in solving the problem. Fioole et al. [5] and Maróti [6] optimize the rolling stock circulation with the consideration of trains combining and splitting. CPLEX is employed in the solution, and several heuristic methods are designed to improve the efficiency. Marc and Kroon [7] models the rolling stock circulation problem with Integer Programming. The objective of the model is to maximize the satisfaction of passenger’s demand, and the combining and splitting of train units are also taken into consideration. The Column Generation is used in solving the model. Cordeau et al. [8] studies the simultaneous optimal problem of the locomotives and cars. A basic model is built for the assignment of locomotives and cars, and the model is extended with maintenance constraints. Column generation methods are embedded into the Brunch and bound algorithm and this algorithm is used to solve the mentioned problem. Zhao [9] studies the daily circulation of train units, the problem is broken into two parts, one is the train assignment and the other is the maintenance routing. The problem is modeled with the Traveling Salesman Problem (TSP) and a local searching algorithm based on probability theory is designed to find the solution. Our study is different from the previous ones including in two things. First, we hierarchize the multi-objective by constructing the transition network with minimum train unit number and heuristic searching maintenance routes. Second, an interchange method with heuristic is adopted to find the feasible solution.

4 The heuristic algorithm for the EMU circulation The circulation problem is with great complexity due to the ride cover constraint and maintenance constraint, and it is proved to be a NP-Hard problem in [3, 10]. A review on circulation planning algorithms is found in [11], where algorithms like set partition with path generation, TSP heuristic with random choice or Lagrange relax, min-cost flow or bipartite matching with heuristic searching are discussed. In our proposed algorithm, an interchange method similar to [2, 10, 11] is adopted. But with improvement on the network construction and the heuristic rules. A two-stage scheme is employed in our algorithm. The objective of the first stage is to minimize the amount of EMU and interchange method is applied in the construction of the transition network without the maintenance constraints. In the second stage, the interchange method is applied in finding the route that meet the maintenance constraint in the network constructed during the first stage, and the final circulation plan is completed at the end of the second stage. There are two steps in the second stage: (a) Finding of maintenance route, the maintenance route may violate the maintenance constraint. (b) Adjustment of maintenance route, so that it meets the maintenance constraint, and the solution of the EMU circulation is thereby formed. In the following, we will discuss the construction of transition network in section 4.1, and the construction of maintenance route will be introduced in WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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section 4.2, in section 4.3, the process of adjustment of maintenance routes to make it a legal circulation is presented. 4.1 Construction of the transition network For a given timetable, some notions are defined in table 1. Table 1:



symbol

S Sc bs



R ri

ri d

meaning the time horizon of a timetable, it’s one day for China railway the set of the stations where a ride is begin or end the set of the stations which connect the depot directly the minimum station turnaround time the minimum duration for the class one maintenance the set of the rides in the time horizon the ith ride in the set R， i=1,…,n the departure time of ri

ri

a

the arrival time of ri

ri

ds

the departure station of ri

ri

as

the arrival station of ri

ri

m

the travel mile of ri

ri

jt

the travel time of ri

k

Notions definition.

the minimum maintenance times

a station s  S c is called maintenance station. Maintenance is performed only at a maintenance station. A network G (V, E, W), shown in Fig. 1, can be used to model the scheduled rides. Each node that belongs to the set V represents one ride, so the set V=R. The transition eij  E , which indicates the ride j is carried out by the EMU

arrived as the ride i, is an arc in the G. wij  W represents the weight of eij and its value is determined by the equation (5).

rjds  ri as  rjd  ri a  br  rjd  ri a  d a ds as d a wij    rj  ri rj  ri  rj  ri  br  ds as rj  ri   if eij satisfies the following criterion,

ds j

rjds  S c    wij  2

then eij is a maintenance arc noted as eij . WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

ds j

(5)

(6)

882 Computers in Railways XII

1 2

3

1

4

Ride Transition Maintenance Transition

5 6

Figure 1:

Transition network.

In the network, a rotation is a route which starts from a head node of maintenance arc, and ends up with a tail node of maintenance arc. Any node must be covered by only one route. The first step in the construction of the ride network is to establish the connection relations between nodes. Although the EMU of a terminated train can execute any departure train which meets the criterion of (5), if the total transition time cannot be guaranteed to be the minimum, the amount of EMU will be increased. Bipartite graph matching can be used to construct transition network with the objective of minimum total connecting time. The bipartite graph can be established with two sets of departure time and arrival time, and the matching weights from equation (5). To ensure perfect matching of bipartite graph, i.e. there is connection for every ride, for stations where the number of arrival trains and departure trains is not equal, empty movements of EMU must be added as compensation, i.e. to add some extra nodes so that the number of nodes in the two sets are equal. There are many methods for solving such matching problems. In [12], First Arrival First Departure (FAFD) method is presented and it is proved to be able to achieve the optimal resolution, with the computational complexity of O(nlogn). The assignment problem of EMU and the assignment problem of locomotives are identical when there is no coupling and uncoupling of trains, and the FAFD method can be employed to find optimal solution, noted as . A transition network must include as many as possible optimal matching solutions so that to supply a sufficient searching space. As the timetable of trains cycles with period, as indicated in [13], for any station, if the connection relations of eij and ei' j ' meets the criterion in (7), a new matching solution can be obtained by swapping the two connection relations of a given matching solution. This feature is illustrated in Fig. 2. Multiple optimal matching solutions can be derived from an initial solution  according to (7).

rjd  ri'a  bs  rjd'  ri a  bs ,

rjds  ri'as , rjds'  ri as

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(7)

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Figure 2:

883

Transitions interchange.

The following notations are given for the definition of the transition network. For a given solution , k  is the number of maintenance arcs, and

k *  max(k  ) for all . For v  V ,   (v) is the times that node v being the tail node of maintenance arc. The algorithm for the establishment of transition network is depicted in Fig. 3. Proc Establish_Netwok Begin

Employ FAFD method to match rides with the objective of min   wij , n

and the result set  is added to set E, let k : k , *



 (v ) 

n

i 1 j 1

is set

according to . Loop Begin If eij   , ei' j '   meet the criteria in (6), where eij '  E or ei' j  E Then Swap eij and ei' j ' to have  ' , and the new path is added to E;

Update   (v) and k * , and let  :  ' . Else break loop Loop End Proc End

Figure 3:

The establishment process of the transition network.

The rule for updating   (v) is: if k   k and v is the tail node in a

maintenance arc,   (v) will be increased by 1.

If k *  k for the established network, there will be no solution for it.

4.2 Generating the maintenance routes

The objective of this process is to find at most k* routes in G, and these routes covers all nodes in the network, and each node are covered only once. Every route, which starts with the head node (included) of maintenance arc and ends WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

884 Computers in Railways XII with the head node (excluded) of another (or same, if the route is a circuit) maintenance arc, is called a maintenance route. A maintenance route may violate the maintenance constraint and it’s the intermediate process of legal EMU circulation. The duration of the maintenance route p, denoted by p t , is the summation of travel time of all rides and all transition time included in the route, and the distance of the route, presented as p m , is the total travel miles of the rides belonging to the route. Constants  and  are the maximum distance and time constraints for class one maintenance respectively, if  p  max( p t /  , p m /  ) is

noted as the distance factor for route p, a circulation for an EMU is a maintenance route with   1.0 . A matching solution  is consisted of one or more circuits that cover all nodes, the maintenance arcs split it into several routes. Equation (4) gives the lower bound of the optimal number of maintenance in an EMU circulation, i.e. the minimum number of routes. Hence, to get the optimal number of maintenance, k non-mutually exclusive maintenance arcs should be selected as the starting and ending of a circulation. These maintenance arcs are the candidate maintenance arcs, noted as . The first problem in searching for maintenance routes is the choice of . Every combination of maintenance arcs represents a group of . There are cases where there are feasible solutions in the network, but they are not in the selected group of .  must be changed and reselected for these cases. To reduce the number of iterations, maintenance arcs with more matching solutions is more preferable as . The selection process is: first, select  nodes randomly as the candidate tail nodes of maintenance arcs, nodes with bigger   (v) are with high priority,  maintenance arcs can be obtained based on these nodes. To speedup the searching for optimal solution,  is initialized as   k . Secondly, with the  maintenance arcs as known matching, the FAFD method is employed again to search for . If the total weight of  is bigger than the optimal matching weight, the nodes will be reselected, otherwise  is the set of all maintenance routes. 4.3 Adjustment of maintenance routes

The maintenance routes have to be transformed into feasible solutions if they are non-feasible. Swapping connection relations is adopted to transform the solutions, the objective of the swapping is to make 0    1 . If the maintenance routes set is P, for two maintenance routes   P and   P , vu  V is the uth node in  , vl  V is the lth node in  , if there is

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 n

n

 Figure 4:

 v1 v2

vu

vu1

vm

 v1 v2

vl

vl1

vn

885

Swapping the connection to adjust maintenance routes.

v  l 1  N  (vu ) and vua1  N  (vl ) , where N  (vu )  V are predecessor set of vu , then  intersects  at x . Maintenance routes  and  are interchangeable at x to form new maintenance routes n , n , this process is illustrated in Fig. 4. The objective of swapping is to shorten those long maintenance routes to make them feasible rotations. Therefore, the swapping should be performed on the longest illegal rotations in the rotation set, the point of intersection is preferably located in the middle of maintenance route so that to speedup this process. To avoid duplicated swapping at the same intersection point, an “escape-list” is used, and all the intersection points where the swapping is performed are listed in. Points in the list are not reused for swapping except for a better optimal status achieved at the point. Let     p 1, pP ( p  1) be the optimal status of the set P, and is

initialized as    . O is the global escape-list, and its minimum length is E . The algorithm for the adjustment of maintenance routes is given in Fig. 5.

Proc Adjust_MR Begin Loop Begin Search for pˆ with the maximum  in P If  pˆ  1 Then End Proc with P

Search for xˆ in pˆ ; Swap maintenance routes at xˆ , and  ,  are substituted by n , n  ;

compute  ' , if  '   , then  :  ' ; add xˆ to O; Loop End Proc End Figure 5:

Procedure of adjusting the maintenance routes.

There will be no feasible solution in this candidate maintenance arcs set when a no-solution prompt returns from the algorithm. The algorithm in Section 4.2 WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

886 Computers in Railways XII will be applied to update , and readjustment will occur under this condition. 

will be increased by 1 if it is unable to update  with  until   k * . As the transition network is constructed with the shortest transition time, and this guaranteed minimum amount of EMU. The algorithm for the adjustment of maintenance route shortens the length of rotation rapidly. Meanwhile, the selection of maintenance arcs enables the fast finding of high quality solution. Consequently, our algorithm is fast in search of solutions with minimum EMU. 4.4 Computational experiments

The case study of algorithm is base on the actual timetable form GuangzhouShenzhen railway line which is located in the south China. There are 156 trains scheduled and 3 turnaround stations, which are Guangzhou, Guangzhou East and Shenzhen. The maintenance station is Guangzhou East. The total rides kilometrage is 20,368Km and the summation of travel time is 9,455 minutes. In the case study, we set the parameters bs  12 ,   4,000Km,   2,880min and  =120 min. According to equation (1) and (4), the minimum number of EMU utilized and the number of inspection is 13 and 7 respectively. The transition network with E =156, V =388 and k * =8 is established by 170 times interchange. The algorithm runs 10 rounds on the PC (Pentium4 3.0GHz, 512M Ram, Windows XP) and the results is listed in table 1. According to the different candidate maintenance arcs selected, the algorithm has various interchange iterations. Table of results.

Table 2: round 1 2 3 4 5 6 7 8 9 10

Number of Inspections

update times of 

7 7 7 7 7 7 7 7 7 7

1 1 1 1 1 1 1 1 1 1

Total interchange times 7 41 22 5 18 33 3 4 15 22

Runtime (sec.) 0.078 0.125 0.063 0.047 0.078 0.165 0.063 0.062 0.089 0.062

5 Conclusion The RSR problem imposes maintenance constraints on a matching problem, and this makes it a NP-hard problem. In this paper, a network approach for the EMU WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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assignment is proposed, with the aim of minimizing the amount of EMU for operation so that to alleviate the shortage of EMU situation of railway of China. The network is divided into multiple searching spaces based on the possibility of the existence of high-quality solutions, and a heuristic algorithm is devised to search for feasible solutions in the searching spaces with a swapping method. The algorithm is verified with the operational time table of Guang-Shen railway. Our algorithm is based on the assumption that the depots of EMU are wellplanned. There could be no solution when this assumption is not valid, and the algorithm has a low efficiency in the situation of no solution. Hence, to determine the existence of feasible solution in a shorter time and to find a way of dealing with the situation of no solution so as to guarantee the minimum of the amount of EMU is the direction of our future works.

Acknowledgements This paper is based on the work carried out under the railway operation optimizing project (RCS2008ZZ003) which is supported by the state key laboratory of rail traffic control & safety fund.

References [1]

[2]

[3]

[4] [5]

[6] [7]

[8]

[9]

The Ministry of Railway P.R.C., Temporary Rules for Railway Electrical Motor Units Operation & maintenance, China Railway Publishing House, Beijing, pp 2-4, 2007. Anderegg, L., Eidenbenz, S., Gantenbein, M., Stamm, Ch., Taylor, D.S., Weber, B., Widmayer, P., Train Routing Algorithms: Concepts, Design, Choices, and Practical Considerations, Proc. of the5th workshop on algorithm engineering and experiments (ALENEX03), Baltimore, 2003. Erlebach T, Gantenbein M, Hürlimann D, Neyer G, Pagourtzis A, Penna P, et al. On the complexity of train assignment problems. Proc. of ISAAC'01, International Symposium on Algorithms and Computation, Christchurch, 2001. Arianna A., Groot R., Kroon L., Schrihver A., Efficient circulation of railway rolling stock,. Transportation Science, 40(3), pp. 378-391, 2006. Fioole P.J, Kroon L., Maróti G., Schrijver A., A rolling stock circulation model for combining and splitting of passenger trains. European Journal of Operational Research, 174(2), pp. 1281-1297, 2006. Maróti G. Operations research models for railway rolling stock planning, PhD thesis, Technische Universiteit Eindhoven, 2006. Marc P., Kroon L., Circulation of railway rolling stock a branch-and-price approach. Computers & Operational Research, 35(2), pp. 538-556, 2008. Cordeau J.F., Desaulniers G., Lingaya N., Soumis F., Desrosiers J., Simultaneous locomotive and car assignment at VIA Rail Canada. Transportation Research Part B, 35(8), pp. 767-787, 2001. Zhao P, Tomii N, Fukumura PN, Sakaguchi T. An algorithm for train-set scheduling based on probabilistic local search. Computers in Railways WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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[10]

[11]

[12]

[13]

VIII, eds. J. Allan, R.J. Hill, C. A. Brebbia, G. Sciutto, S. Sone, WIT Press: Southampton, pp. 817-826, 2002 Maróti G., Kroon L., Maintenance routing for Train units: The interchange model, Computer & Operations Research, 34(4), pp. 11211140, 2007. Hong S P, Kim K M, Lee K Park B H, A pragmatic algorithm for the train-set routing: The case of Korea high-speed railway, Omega, 37 (3), pp. 637-645, 2009. Xiao L. W., Computerized Planning of the Optimal Locomotive Working Diagram, Journal of Changsha Railway University (In Chinese), 17(1), pp. 52-57, 1999. LI Zhizhong, SUN Yan, Optimize the Locomotive Working Diagram by Computer. Railway Transport and Economy (In Chinese), 5, pp. 12-15, 1988.

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Working out an incomplete cyclic train timetable for high-speed railways by computer D. Yang, L. Nie, Y. Tan, Z. He & Y. Zhang School of Traffic and Transportation, Beijing Jiaotong University, China

Abstract Cyclic train timetables as a popular mode of train operation have been successfully applied for many years in European railways, especially in highspeed railways (hereinafter referred to as ‘HSR’). However, in China, most studies on the train timetables of HSR still follow the traditional mode. By analyzing the characteristics of Chinese HSR, this paper proposes an incomplete cyclic timetable mode that will be more suitable for the Chinese situation. The characteristics and process of working out incomplete a cyclic train timetable are discussed. There are four key issues involved in developing this kind of timetable: (1) Adaptability analyzing. This paper analyzes the proportion of trains that can be operated cyclically in terms of the technical condition and passenger flow of each HSR in order to determine the structure of the timetable. (2) Model developing. By analyzing the condition of the Chinese HSR, the existing model is improved to solve the problem more precisely and practically. (3) Non-cyclic train path insertion. According to the travel demand of passengers, the principles and technologies of inserting non-cyclic train paths into cyclic train paths is developed. (4) Seasonal expanding. The seasonal fluctuation of passenger flow makes more non-cyclic train paths. The ways to balance the disaccord in different periods are discussed to keep the operation efficient. Furthermore, a system using VC++ is designed with consideration of the four issues in its functions and working process, based on inputting the solution of the model. Finally, a successful case of the Beijing-Shanghai HSR shows the feasibility of the incomplete cyclic timetable and the practical value of the system.

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890 Computers in Railways XII Keywords: incomplete cyclic train timetable, Beijing-Shanghai high-speed railway, computer system.

1 Introduction HSR has become a world tendency. As a key work of the HSR organization and management, how to work out a reasonable and scientific train timetable is an important subject to experts and scholars. The cyclic train timetable is an advanced mode of train operation that has been successfully applied for many years in the world. Scholars have adopted a variety of mathematical methods to solve cyclic timetable problems gradually with the introduction of train timetables worked out by computer. In 1980, Assad [1] first utilized a mathematical model to work out a solution to a transport problem. Subsequently, many experts began to utilize mathematical model to research train timetables and designed a series of numerations. Serafini and Ukovich [2] promoted the Period Event Scheduling Problem (PESP) in 1989, which is very similar to the cyclic railway timetable problem. Currently, most scholars tend to research modes and algorithms based on PESP. In 1996, Odijk [3] utilized PESP design numeration to work out a series of train timetables and compared the similarities and differences of the train timetable to supply a detailed illustration of the expansion scheme of stations. In 2002, Giesemann [4] utilized numeration to establish a simple mathematical model and get a train timetable suitable for a small station. In 1996, Nachtigall and Voget [5] worked out a train timetable with passengers’ minimum latency as the objective. In 2000, based on Serafini and Ukovich’s idea, Linder [6] introduced the branch and bound method into train timetable design and rolling stock turnover programming, with the objective of a minimum train fleet. Graph theory knowledge is used by literature [7] to connect circles of a constrain graph and train timetable with the change train timetable problem to a mixed integer programming problem. The results of other studies, [8, 9], showed some methods to control the traffic. In recent years, Chinese researchers have done a great many of researches on the train timetable of HSR: the cyclic timetable has been suggested to be applied in China’s HSR [10, 11], and a model and algorithm of working out the cyclic timetable have been discussed [12–14]; in addition, computers are also being applied to draw train timetables [15]. Generally speaking, scholars has made some research on working out the HSR cyclic timetable and have achieved some results; however, most of the researches is mainly on the complete cyclic mode and has not taken the combination of cycle and non-cycle into consideration. Moreover, the research on how to utilize a computer to work out the incomplete cyclic mode is comparatively rare. Although China has many HSR, which are long and wide spread, train timetables still follow the traditional mode. So a suitable timetable mode and methods of working out timetables need to be developed. This paper researches four key problems that shall be solved in its working out method and working out course, and then proposes an incomplete cyclic timetable mode.

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Meanwhile, the paper also designs and develops a computer system to work out a train timetable of the Beijing-Shanghai high speed railway.

2 Incomplete cyclic timetable mode Theoretically, a cyclic timetable means train paths are repeated in each cycle T (e.g. 1 hour) in a basic train timetable, that is to say the departure/arrival time, the departure/arrival sequences and the dwell time of trains are the same in every cycle. 2.1 Introduction to the incomplete cyclic timetable China has a large high speed railway network, and too many passenger flow ODs with a complex passenger flow structure. The timetable structure has its own features as follows: (1) Only partial railway lines have the condition to adopt the cyclic timetable, other railway lines can only adopt the timetable with the combination mode of cyclic and non-cyclic, because long lines can be affected easily by night trains and over-line trains with different OD. (2) The mixed operation of high speed and medium speed lines adopt the model based on PESP to solve the timetable structure, but it is difficult to guarantee the robustness of the timetable as there is too much overtaking and waiting time. (3) No solution or low quality solution appears easily when the PESP-based model is adopted for lines with long distance and heavy density passenger flow, as there are many variables of the model, and it brings increasing difficulties in solving the model. Considering the above situation, the complete cyclic timetable and current model cannot fully meet the demand of China’s situation. This paper puts forward a modified timetable mode, named the incomplete cyclic timetable mode, in order to suit the Chinese HSR, which means that the complete cyclic timetable is recommended for lines that are characterized by short or mediumlong distance, high train frequency and large passenger flow. For those long distance lines, only partial sections with enough passenger flow can adopt the cyclic mode timetable. In other situations, the combination mode of the cyclic and non-cyclic timetable should be adopted. To work out an incomplete timetable, some non-cyclic paths have to be added into the cyclic timetable. 2.2 Flowchart of incomplete cyclic timetable design Drawing a timetable is a difficult task, which needs to use some mathematical method and computer programming. The process of working out an incomplete cyclic timetable is shown in Figure 1. Figure 1 shows that an incomplete cyclic timetable takes the cyclic train service plan as a precondition. Firstly, a peak hour timetable needs to be designed through solving a mathematical model, and then expanded to a 24-hour timetable on the basis of passenger flow. In practice, train paths are removed and WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

892 Computers in Railways XII train paths are inserted, considering different passenger is demanded in each hour, and index statistics are necessary in order to feed back useful information on the timetable, so it can help the operator to find the problem in a timely manner and solve it quickly. Moreover, adding or removing non-cyclic train paths properly helps to expand the timetable to a weekly timetable and a seasonal timetable according to actual passenger flow changes. 2.3 Key issues for an incomplete cyclic train timetable There are four key issues for working out a cyclic train timetable by mathematical model and computer system. 2.3.1 Adaptability analysis The cyclic timetable is suitable for HSR with high train frequency. If the running cycle is one hour and excludes six hours overnight, the passenger flow in some sections shall reach at least 18*800=14400 persons everyday to satisfy the running condition of cyclic trains. For the lower passenger flow density, a two hour cycle can be suggested with the addition of some non-cyclic paths. Six high-speed railway lines have been opened to traffic and their statistics are shown in Table 1. Table 1 shows that the passenger flow of most of the railway lines has largely exceeded the least passenger flow required by cyclic running trains. Certainly, some sections may not meet the conditions for opening a cyclic timetable. So adopting the methods of train path removal and train path insertion based on cyclic lines to make an adjustment is required. Finally, a timetable in the incomplete cyclic timetable mode is formed.

Figure 1:

A flowchart of working out an incomplete cyclic timetable.

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Table 1:

893

Statistics of China’s opened high-speed railway lines.

High-speed railway Beijing-Tianjin ShijiazhuangTaiyuan Coastal Railway Hefei-Wuhan Wuhan-Guangzhou Zhengzhou-Xi'an

137

Average number of passengers (ten thousand per month) 162

Average number of passengers (ten thousand per day) 5.4

189

104

3.5

557 356 1069 484

123 50 143 23

4.1 1.7 4.8 0.8

Length of lines (km)

2.3.2 Model development Some of China’s HSR have long distance and heavy flow density. In order to control the problem scale and get a more practical solution, improvements can be made based on the existing model [2, 13]. When solving the model, the cyclic constrain variable P can be defined as a 01 variable, as layover time can be reduced by improving station operation. If properly managed, the P value is not equal 2P in all situations. In this way, the solution requiring scale can be further compressed and the solving difficulty can be lowered. Moreover, some mathematical software can be used to solve model, such as CPLEX and LINGO. 2.3.3 Non-cyclic train path insertion The non-cyclic train path inserting is a challenge of integrating timetable design and timetable adjustment. The necessary headway shall be guaranteed in inserting a train path for safety operation; moreover, the necessary linkage among trains for transfer shall also be considered. In addition, we shall try our best to keep the cyclic operation. The solution for model is generally adopted to execute inserting. Based on considering influence on original cyclic graph and its own travel time, the objective of the model is: (1) min z    m    n where m is the influence of new-adding non-cyclic train paths on original timetable, so periodicity is the minimum; n is the travel time of new-adding non-cyclic trains is the least;  is the weight of cyclic timetable influence factor;  is weighing for travel time factor of inserted trains. The following several aspects are mainly considered for constraint condition: constrain for arrival and departure time section; constraint for interval time of adjacent lines; constraint for dwell time; constraint for sectional running time.

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894 Computers in Railways XII 2.3.4 Seasonal expansion The National Day, Spring Festival and summer holiday are peak period of passenger flow in China. The timetable can be properly adjusted to form particular seasonal timetable on the basis of prediction passenger flow fluctuation. In the seasonal timetable, cyclic trains are suitable for large passenger flow while non-cyclic trains are suitable for low frequency train. Hence, in the seasonal expansion course, the most important thing is to estimate which lines are suitable for cyclic lines to insert or for non-cyclic lines. The following two kinds of expansion modes shall be adopted flexibly with the fluctuation of passenger flow. (1) Non-cyclic expansion: when the number of cycle affected by inserted non-cyclic train paths is small, the adjustment for the several cycles of inserted non-cyclic train paths can be executed. Moreover, the number of removing lines is determined on the basis of passenger flow. (2) Cyclic expansion: when the number of cycle affected by inserted noncyclic train paths is large, the proper adjustment for each cycle can be executed in order to guarantee periodicity of timetable. In addition, removing paths is needed to meet the demand of passenger flow.

3 Computer system for an incomplete cyclic timetable The above key issues show that incomplete cyclic timetable design is a very complicated task, so computer system is introduced to work out the timetable based on the key issues. 3.1 System objective and framework The objective of system mainly include well embodiment of periodicity of timetable, friendly interface design, accurate data reading, favourable input and output functions, rapid information display, the human-computer interaction provided. However, how to solve the four key issues in the process of working out incomplete timetable is the most important thing for this computer system. So, some corresponding function should be designed to meet the demand of operator. This system is developed on VC++ platform and its framework mainly consists of three mutual nested classes including TimeTable, ParentTimeTableLine and SubTimeTableLine. The main functions of the three classes are introduced as follows: TimeTable is used for forming the base map of timetable and describing all of sectional stations and train running time of timetable; ParentTimeTableLine is used for describing some special train in timetable; and SubTimeTableLine is used for describing the running of all of the detailed trains in all of the sections. 3.2 System functions This system designs four functional modules such as display, search, index statistics and human-computer interaction adjustment aiming for solving four key issues in incomplete cyclic timetable design, as is shown in Figure 2. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 2 shows that the system functions can not only work out a cyclic timetable quickly but also expand it to be an incomplete one easily. Obviously, four key issues have been solved in this system, and specific functions are described as below. (1) Display function. The system accomplishes data input work through inputting the solution of model, and automatically forms timetable. So, the timetable can embody periodicity well. (2) Search function. Three kinds of search functions are provided to supply convenience to users to obtain timetable relevant information. Search stations and time: click at any place in timetable and the time section and running section of this place can be obtained; Search train paths: show the relevant information of some certain train path; Search trains: input the serial number of trains through dialog box and the relevant information about this train can be displayed. (3) Index statistics function. The system can form the relevant indexes of this timetable through calculation with respect to the formed timetable automatically, which supplies the feedback information. It’s useful for the adjustment of timetable in anaphase. (4) Human-computer interaction adjustment function. The functions of train path removing and train path inserting are designed in this system. Users can add, delete the non-cyclic train paths and modify the formed cyclic train paths as per passenger flow rule to adjust the cyclic timetable to ‘cyclic + non-cyclic’ mode timetable to supply convenience to users.

Figure 2:

System function design.

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4 The case study Beijing-Shanghai HSR being opened to traffic in 2012 will be the longest, fastest lines in china, and lots of scholars focus on the research of this line. 4.1 Cyclic train service plan Referring to the preliminary service plan of Beijing-shanghai HSR designed by the 3rd China Railway Survey & Design Institute, and in order to make the plan periodic, combination methods is adopted to complete the data processing. The final cyclic train service plan of Beijing-Shanghai HSR is shown in Figure 3. The dwell plan will be formed by referring to the preliminary dwell plan designed by the 3rd Railway Survey & Design Institute. The 21 stations can be classified into three grades. Trains of whose speed is less than 300km/h will stop at all of the stations. The final dwell plan is got through analyzing and amalgamating ODs based on passenger flow, as shown in Figure 4. 4.2 Train departure and arrival order plan This paper uses improved fixed order model, the departure and arrival order of trains is required to be worked out. Figure 5 shows the departure and arrival order of trains. 4.3 Solution of the cyclic timetable model Figure 6 shows that the global optimal solution of model has been found.

Figure 3:

The cyclic train service plan of Beijing-Shanghai HSR.

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Figure 4:

Figure 5:

897

The dwell plan of Beijing-Shanghai HSR.

The departure and arrival order of trains on the Beijing-Shanghai HSR.

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Figure 6:

Figure 7:

The LINGO solver status.

Train path search.

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4.4 Timetable display The system reads the model solution and draws Beijing-shanghai HSR cyclic timetable, as is shown in Figure 7. Click any train path in the timetable and the corresponding train path will become bold line; click right key of the mouse and then, the relevant information of this path can be obtained Moreover, in order to solve four issues, this system also has some functions such as human-computer interaction adjustment, zoom and index statistics. These functions will not be introduced detailed for its easy operation.

5 Conclusion Developing cyclic timetable for HSR plays an important role in improving the operation efficiency and quality. In order to designing an economic, convenient, efficient and regular train timetable, the incomplete cyclic timetable mode which may be more suitable for china’s HSR is presented in this paper. Through analyzing the work flow of this mode, four key issues in developing the timetable are discussed to provide the theoretical basis and technical support for the construction and operation of this important HSR. Simultaneously, computer system is suggested to develop for railway department to meet the demand of management. Bases on the four key issues, this paper adopted the improved mathematical model and existing mathematical software to design and develop incomplete cyclic timetable mode computer system. The system solves four key issues in working out of incomplete cyclic timetable mode and realizes a series of practical functions such as model solution result input, timetable display, line information searching and human-computer interaction running adjustment. Through the case study of Beijing-Shanghai HSR, the system has been proved to be very practical and valuable. Moreover, the solution of arrival and departure order of trains shall be worked out when utilizing fixed order model. How to sort out the feasible solution depends on many solutions of train arrival and departure order requires considering many factors. The solution adopted by this paper is manual working out. If the model can be applied for automatically creating train arrival and departure order, the efficiency will be largely improved. Further research is needed in order to create a model to work out the train departure and arrival order model which is meaningful for finding an optimization solution rapidly and improving efficiency.

Acknowledgements This research was supported in part by the National Fund of Natural Science (60870012), the Ministry of Railway (2008X027-A, 2009BAG12A10 jointed support of the Ministry of Science and Technology) and the State Key Laboratory of Rail Traffic Control and Safety (RCS2009 ZT008).

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References [1] Arjang Assad. Models for rail transportation [J]. Transportation Research, Vol.14, No.3, 1980.6 [2] Paolo Serafini, Walter Ukovich. A mathematical model for the fixed-time traffic control problem [J]. European Journal of Operational Research, 1989, 42(2): 152-165. [3] Michiel A Odijk. A constraint generation algorithm for the construction of periodic railway timetables [J]. Transportation Research, 1996, 30(6): 455464. [4] Carole Giesemann. Seminar on Algorithms and Models for Railway Optimization. University of Constance, 2002 [5] Karl Nachtigall, Stefan Voget. A genetic algorithm approach to periodic railway Synchronization [J]. Computers and Operations Research, Vol.23, No.5, 1996.5 [6] Thomas Lindner. Train schedule optimization in public rail transportation. Ph.D. thesis Technical University Braunschweig, Braunschweig, Germany, 2000 [7] Christian Liebchen, Leon Peeters. On Cyclic Timetabling and Cycles in Graphs. Technische Universitat Berlin, No. 761, 2002 [8] Paolo S, Walter U. A mathematical model for the fixed-time traffic control problem［J］. European Journal of Operational Research, 1989, 42 2）: 152-165. [9] Domschke, W. Schedule synchronization for public transit networks [J]. OR Spektrum, 1989, 11(1): 17-24. [10] Shi Hao. Analysis on rational Applied Modes of High-Speed Train Diagram in Our Country [J]. Journal of the China Railway Society, 2000, 22(1): 92–97. [11] Jia Yong-gang, Du Xu-sheng, Problems of Working out Train Timetable for Chinese Passenger Dedicated Line [J]. Railway Transportation and Economy, 2005, 28 5）: 76-78. [12] Wang Bo, Yang Hao, Zhang Zhi-hua. The Research on the Train Operation Plan of the Beijing-Tianjin Inter-city Railway Based on Periodic Train Diagrams [J]. Journal of the China Railway Society, 2007, 29: 8–13. [13] Xie Mei-quan, Nie Lei. The Model of Working out Cyclic Train Timetable[J], Journal of the China Railway Society, 2009, 4: 7-13. [14] Xie Mei-quan, Su Mei, Mao Bao-hua, Gao Li-ping, Liang Xiao. Problem of Cyclic Railway Timetable Based on High-Speed Network[C]. 2009 China control and decision-making conference papers (2), 2009. [15] Chen Yong, Xie An-liang, Sun Quan-xin, Hu Si-ji. Research on Drawing System of Train Running Diagram of High Speed Railway [J]. Railway Computer Application. Vol.9, No.4, 2000:4-7.

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A novel research on the relation between the number of passengers and the braking distance of a metro L. Wang1, Y. Li1 & X. Hei1,2 1

School of Computer Science and Engineering, Xi’an University of Technology, China 2 State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University, China

Abstract Due to heavy traffic at peak times, it is necessary to ensure that trains have an absolute safety braking distance. For this problem, this paper not only analyzes various factors that influence a metro’s stopping time, but also analyzes a time model of a metro’s stopping in the station. Secondly, based on the model and combined with the physical process of a metro’s approach, this paper calculates the braking distance of oncoming trains. Finally, a novel relation between the number of passengers and the braking distance of an oncoming metro is established. Theoretical analysis and simulation experiments indicate that the braking distance of an oncoming metro can be effectively calculated according to the number of passengers on the platform. Keywords: braking distance, oncoming metro, peak time.

1 Introduction With the rapid development of the economy, the pressure of urban public transport is increasing, and the metro plays an more important role in the whole city traffic. Stations are junctions of park and shift, but are also the bottleneck of passenger transport. The phenomenon that oncoming trains stop-start frequently often occurs. Meanwhile, passengers are not able to leave punctually because of

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902 Computers in Railways XII the delay of trains. Worse, the problem often arises that trains get into the station behind schedule. Finally, it is difficult to increase the efficiency of transport. This paper aims to find the correspondence between the number of passengers and the braking distance of trains. Based on the factors that influence the braking distance of oncoming trains, this paper firstly points out the key factor, namely the number of passengers on the platform. Then, it analyzes a model of metro’s stopping time. Based on the model and according to actual data, the paper establishes the braking distance of oncoming metro.

2 Decomposition of the entire research process In order to meet the needs of practical problems, the whole process of research is divided into two parts, including analyzing a model of metro’s stopping time in the station, and model making of the braking distance of trains which are drawing up at the station. 2.1 Distribution graph of metro trains As shown in figure 1, there are metro trains stopping at the station, and passengers are getting on and off the trains, while the oncoming train calculates the braking distance according to the time spent by passengers getting on and off the trains as well as relevant influence coefficients. By means of the distance, the oncoming trains are guided to draw up at the station steadily, accurately, and safely. And the delaying time of arriving trains is also efficiently decreased, leading to a high frequency. In Figure 1, the 1st train and 2nd train are respectively stopping on the 1st and 2nd platform while the 3rd train is ready to draw up at the station. What this paper aims to study is to calculate the braking distance of the 3rd train according to the number of passengers getting on and off the 1st and 2nd train which is stopping in the station. 2.2 Stopping time of trains that are in the station In this paper, the model of stopping time derives from the model which is made by Zhuge Cheng-xiang and Gao Jian who are in Beijing Jiaotong University. The

Figure 1:

Distribution map.

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stopping time of trains which are in the station are influenced by many factors, including the total time of passengers’ getting on and off train, the time of opening doors(t1), the time of closing doors(t2), the delaying time of trains’ stopping(t0). Therefore, the stopping time [7] is expressed as follows:

t  t 0  t1  t 2 + t '

(1)

In this formula: t1 and t2 are primarily determined by the type of trains; t0 is set for the purpose of the trains’ safety. Because of a certain distance between both adjacent trains, they must remain a certain distance. t ' is set at the maximum value of time spent by passengers in getting on and off trains. Because the number of passengers getting on and off every door is different, t ' is expressed by:

t '  max{t1' , t 2 ' , t 3' ,.., t n '}

(2) ’

In the above formula, n is the number of carriages, and ti is the time spent by passengers in getting on and off the ith carriage of the train stopping in the station. 2.3 The amount of time to get on and off the train The following part is to analyze a model of time spent by passengers getting on and off the ith door of the train. The following precondition is given: 1. The number of passengers getting on the ith carriage is Ni and the number of passengers getting off the ith carriage is Mi. 2. The amount of time for a passenger to get on the train is represented by the letters a, and the amount of time to get off the train is represented by the letter b. Then the time for the ith door to keep open is expressed [7]: (3) ti  N i  a  M i  b The above formula holds only if the influence between passengers is not considered. However, the size of the crowdedness in the carriage and the availability of vacant seats actually have influences on the efficiency of getting on or off trains. We can define the crowdedness coefficient as K, and also define the crowding level of the ith carriage as Ki to quantify such influence. Table 1 [7] shows the concrete value of Ki: Table 1: Degree of crowdedness

Degree of crowdedness inside a carriage. A

B

C

D

influence coefficient 0.9 1 1.18 1.2 Note: A—vacant seats available; B—sufficient standing space; C—not much standing space; D—crowded with standing passengers.

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904 Computers in Railways XII Table 2:

Values of influence coefficient. a

Degree of influence

b

c

d

e

1 1.05 1.1 1.15 1.25 Note: a—no influence; b—a little influence; c—general influence; d—severe influence; e—more severe influence influence coefficient

Table 3: Degree of influence influence coefficient

Values of influence coefficient. A

B

C

D

E

1.1

1.0

0.9

0.8

0.7

Note: A—more severe influence; B—severe influence; C—general influence; D—a little influence; E—no influence

The next part refers the influence of passengers being on and off the train. Such influence will increase the amount of time to get on or off train. An influence coefficient S is defined and Si is the influence coefficient of the ith door. The table 2 lists the values of different influence coefficients and the concrete value of S. Concerning the passengers’ luggage, the size and amount of luggage will also affect the efficiency of getting on and off trains. We define a luggage influence coefficient as J and Ji is the influence coefficient of the ith door. The table 3 lists the values of different influence coefficients and the concrete value of J. After the definition of coefficients Ki, Si and Ji，the amount of time passengers to get on and off the ith is given by:

t i  S i  K i  J i  ( N i  a  M i  b) '

(4)

As for the whole train, the total amount of time to get on and off train is the maximum of ti', which is given by:

t '  max{( S1  K1  J1  ( N1a  M 1b)), ( S 2  K 2  J 2

( N 2 a  M 2b)),....., ( Sn K n  J n ( N n a  M n b))}

(5)

2.4 The amount of actual time for trains to stop at the station Theoretically the dwell time of the train could be retrieved from the mentioned formula. By use of linear regression methods, we can find the relationship between the theoretical result and the empirical one, which is expressed [7] as follows:

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11.497 e 0.0235 x Peak time  f ( x)    10.775e 0.0252 x Usual time 

905

(6)

where x is the theoretical result and f(x) is the actual dwell time. 2.5 The distribution of passengers in peak time It is supposed that the average passengers’ number of every train in each station is d. According to the relevant data, the crowdedness coefficient of carriage is usually 1, and the inference coefficient of passengers’ getting on and off trains is usually 1.05, and the influencing coefficient of cargo is 1, and the velocity of passengers’ getting on the train is 1.05, and the velocity of passengers’ getting off the train is 1, and the time of opening each door is 2.4s, and the time of closing each door is 2.4s. In case of reasonable case, the delay time of metro is 0s. Except for the upper concrete parameters, the conditions of passengers’ getting on and off trains are unknown. Due to great randomness of passengers’ getting off the train, the number of passengers getting off the train is set to be b. The number of passengers getting on the train has certain regularity. Because of great randomness of passengers’ getting off trains, we suppose that the number of passengers getting off train is b. By contrast to the number of passengers getting off the train, the distribution of passengers getting on trains on the platform has certain regularity, which is mainly influenced by passengers’ behavioural features. According to the relevant information, the distribution of passengers in peak time is shown in Figure 2. It is shown in Figure 2 that the number of passengers is much more in the middle of platform while less at the extremes. The number of passengers in the ith carriage is expressed as follows [7]:

Pi  d  

Figure 2:

Distribution of passengers getting on the train in peak time.

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906 Computers in Railways XII In the upper formula, d is the passenger flow volume of the station while β is the percent of passengers getting on the ith door. It is shown in Figure 2 that the maximum of β is 7.0%. It is supposed that if the total number of passengers is n, the time of passengers’ getting on train should be the time spending in getting on train through the door in front of which stand the most of passengers. Therefore, the time [7] of passengers’ getting on the train in peak time should be:

t ''  n max a

(8) In the upper formula, the letter a is the time for every passenger to get on train, and the letter n is the total number of passengers getting on train, the letter β is the percent of the most passengers through each door. 2.6 The parameter value of a train’s stopping time

t  t '  t 0  t1  t 2

The stopping time of trains is expressed as follows: (9)

The parameter value of the upper formula is summed up as follows: t0 is the delaying time which is set to be 0s; t1 is the door opening time, which is set to be 2.4s； t2 is the door closing time, which is set to be 2.4s； t ' is the total time of passengers’ getting on and off trains, which is expressed in formula (5); S11 is the influencing coefficient of the eleventh carriage, which is set to be 1.05; m is the number of passengers who are on the platform, and the letter m is a variable; K11 is the crowdedness coefficient of the eleventh carriage, and is set to be 1. J11 is the influencing coefficient of goods of the eleventh carriage, and is set to be 1. a is the time of each passenger’s getting on trains, and is set to be 1.05; b is the time of each passenger’s getting off trains, and is set to be 1; In formula (9), t ' , t0, t1, t2 are respectively set to be concrete values as follows:

t '  S11K11J11   N11 *a  M11 * b 

 1.05*1*1*  n *7% *1.05  n *7% *1  1.05*0.1435* n

(10)

 0.150675* n

According to the upper formulas, the actual stopping time of trains is calculated as follows:

t ''  10.775e0.0252( t’  t0  t1  t2 ) " According to the upper formulas, t is calculated as follows:

t ''  10.775e0.00379701*n0.12096 

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(12)

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3 Model building of the metro’s braking instance 3.1 The premises of the model of the metro’s braking distance i ii

The frequency of metro is high; In the premise of safety braking distance, the successive trains’ distance should be short greatly. Once a train has stopped in the station, the sequential train decides its braking distance according to the stopping time of the train in the station. the velocity of the metro drawing up at the station is uniform. the train is at a steady speed before drawing up at the station. the braking time of the sequential train is equal to the stopping time of the train in the station.

iii iv v vi

3.2 The braking distance of an oncoming train Under the premise of ensuring the fore mentioned assumptions, the braking time and distance of the trains are related with acceleration and initial velocity of the metro train, and can be expressed as follows:

1 S  v0t  at 2 2

(13) The parameters of the formula are listed as follows: v0: initial velocity of the train that is drawing up at the station; t: time taken to brake; a: acceleration of the train that is drawing up at the station； With regard to the equation (11), its precondition is in the process from start of braking to stopping of oncoming train, speed is uniformly reduced. Braking distance of train can be calculated by the following formula:

S  V * t

(14) is the average speed of the braking train. As the oncoming train’s braking is uniformly decelerational and the terminational speed is 0m/s, (V  0) (15) V  0 2 From the equation (12) (13), we can see: V

S  0.5 * V0 * t 

(16) When the initial braking velocity of the train is constant, the braking distance of the train merely has a relation with time. The braking time is connected with the stopping time of the fore train and the transmission time of the signal. We can calculate the stopping time t " of fore train by use of the formula (10). Suppose the transmission delay time of signal is t5, the braking time of the oncoming train meets the following relationship:

t  t"  t5

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908 Computers in Railways XII S  0.5 * V 0 * ( t "  t 5 )

From the formula (14) (15), we can see:

S  0.5*V0 *(t5  10.775e0.00379701*n0.1296 )

(18)

From the equation (10) (16), we can see:

(19)

Parameters’ value of the above formula： V0 : initial brake velocity of the oncoming train, for instance, V0 = 40m/s; t5: transmission delay time of the signal, which is assumed to be 0.5s; By calculating, S is expressed as follows:

S  10  215.5e(0.00379701*n  0.1296)

(20)

Experiment simulation is shown in the following section.

4 Analysis of the simulation result As can be shown in figure 3, when the train’s delay time was respectively 0.1s, 0.2s, 0.3s, 0.4s, 0.5s, and the number of passengers getting on and off is certain, the braking distance of the oncoming trains is almost the same. This shows that if the delay time of trains is within a certain extent, it has little influence on the braking distance. The figure 4 indicates the relation between the braking distance and the delay time of trains. In figure 4, various curves reflect that trains’ braking distance is different in case of different number of passengers. Meanwhile, different delay time brings about different braking distances of the follow-up trains. However, within a certain time extent, the change of oncoming train’s braking distance is not large. As can be shown in figure 5, corresponding to different initial braking velocity V0, there are three curves, including V0=80m/s, V0=60m/s and V0=40m/s.

Figure 3:

Curves of trains’ braking distances in the case of different delay time.

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Figure 4:

Curves of metro’ braking distances in the case of a different number of passengers.

Figure 5:

The curve about the braking distance in the case of different initial velocity.

Figure 5 shows that when the initial velocity of a train is certain, its braking distance increases when the number of passengers at the fore station increases; When the number of passengers on platform is certain, the braking distance of an oncoming trains increases with the increase of the initial velocity of trains that are drawing up at the station.

5 Conclusion This paper does research on the relation between the number of passengers on the platform and the braking distance of an oncoming train. Firstly, this paper analyzes a model of metro’s stopping time. Secondly, based on the established model and operation conditions, the braking distance of oncoming trains is calculated. In the next stage, it is important to make the expression of braking distance more accurate in order to reduce the error between the actual value and WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

910 Computers in Railways XII the expected one. In addition, the braking distance of an oncoming train will be tested to access whether or not the braking distance is really reasonable.

Acknowledgement This work is supported by the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RCS2008K008）and Natural Science Basic Research Plan of Shaanxi Province (2009JQ8010).

References [1] Xinhong Hei: Improving Reliability of Railway Interlocking System with Component-based Technology, Journal of Reliability Engineering Association of Japan, Vol.28, No.8, pp. 557-568, 2006.12. [2] Xinhong Hei: Distributed Interlocking System and Its Safety Verification, the 6th IEEE World Congress on Intelligent Control and Automation, vol.10, pp. 8612-8615, Dalian, China, 2006.6. (EI, ISTP) [3] Xinhong Hei: Toward Developing a Decentralized Railway Signalling System Using Petri Nets, 2008 IEEE Conference on Robotics, Automation and Mechatronics, pp.851-855. EI） [4] Xinhong Hei: Modelling and Analyzing Component-based Distributed Railway Interlocking System with Petri Nets, IEEJ Trans. Sec. D, Vol. 129 , No. 5. [5] Xinhong Hei: Modeling and Performance Analysis of Distributed Railway Interlocking System, Proceedings of the Third International Conference on Railway Traction Systems, pp.98-103, Tokyo, Japan, 2007.11 [6] Xinhong Hei: Modeling and Evaluation of Component-based Distributed Railway Interlocking System Using Petri Nets, Special Issue of Nihon University College of Science and Technology, No.1, pp.43-46. [7] Chengxiang ZhuGe, Jian Gao, XiMeng Wang, LiWei Chen: The Modelling and application of metro’s dwell time in the station, http://trans.bjtu.edu.cn/news/documents/jiaoxueke/20090928/upfile/2009928 9229_1.doc

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Computation and evaluation of scheduled waiting time for railway networks A. Landex Department of Transport, Technical University of Denmark, Denmark

Abstract Timetables are affected by scheduled waiting time (SWT), which prolongs the travel times for trains and thereby passengers. SWT occurs when a train prevents another train from running at the necessary speed. The SWT affects both the trains and the passengers in the trains. The passengers may be further affected due to longer transfer times to other trains. SWT can be estimated analytically for a given timetable or by simulation of timetables and/or plans of operation. The simulation of SWT has the benefit of making it is possible to examine the entire network. This makes it possible to improve the future timetable by analyzing different timetables and/or plans of operation. This article presents methods to examine SWT by simulation for both trains and passengers in entire railway networks. Keywords: scheduled waiting time, timetable, passenger delay, simulation, railway network.

1 Introduction When planning timetables for trains, it is often desirable to have more and faster trains along the same line, providing it is a good business case. However, in the timetabling process it is often not possible to fulfil the planning objectives due to capacity constraints. Instead, it is often necessary to reduce the number of trains and/or homogenize the operation by reducing the speed of some trains (planned delays). This creates a conflict between the different planning objectives. For instance, it might not be possible to operate as many fast trains and/or the fast trains as fast as wanted, because fast trains catch up with slower freight/regional trains. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100821

912 Computers in Railways XII If the market demand for fast trains is very high compared to that of freight/ regional trains, it might be decided to give the fastest trains a higher priority than the freight/regional trains by running fewer freight/regional trains and/or allowing the freight/regional trains to be overtaken by the faster trains. The reduced speed due to fast trains catching up with slower trains and additional waiting/dwell time at stations due to overtakings is denoted scheduled waiting time (SWT). The amount of SWT can according to [1] be used as a measure for quality. This is because SWT gives an indication of the extent of conflicts between the planning objectives and the means of action. SWT depends on the given infrastructure and timetable. It results in longer travel times for trains and passengers. Passengers can be further affected if the needed transfers to/from other trains have long (scheduled) waiting times due to too many interdependencies in the timetable and/or infrastructure. The article shows how SWT can be considered for trains and passengers respectively. First, SWT for trains (section 2) and for passengers (section 3) is presented. Then it is explained how to calculate SWT for trains (section 4) and passengers (section 5). Section 6 discusses how calculation of SWT can be used to improve the timetables and operation before section 7 draws up the conclusions.

2 Scheduled waiting time for trains When the railway operation results in high capacity consumption, the speed of fast trains must adapt to that of the slower trains, cf. figure 1 (left). This will increase the running time (SWT) for these trains that could run at higher speeds if they were not hindered by other trains. Alternatively, it might be possible to adapt the slower (regional) trains to the faster (intercity) trains by e.g. omitting stops or in the longer term changing the existing trains for trains with better acceleration.

Figure 1: Scheduled waiting time for trains. Based on [2–4].

If the SWT is high (on double track lines), it might be decided to use this time, or part of it, to include extra stops for the fastest train services, cf. figure 1 (middle). In this way the planned timetable has trains with more stops than desired in the wanted timetable. However, it is difficult to evaluate SWT when it, or some of it, has already been converted into additional stops as it is difficult/impossible to identify the stops that have been added to the timetable. For single track lines, the location of the crossing stations can result in additional dwell time (i.e. SWT), cf. figure 1 (right). WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Pachl [5] states that SWT for trains can be added to the dwell time at stations and to the running time. In Norway, travel time is extended by scheduled waiting time at the railway lines in the suburbs [6]. In The Netherlands, the stations at The Hague (The Hague Holland Spoor and The Hague Central Station) are examples of stations where the dwell time has been extended due to conflicts with other trains [7]. If the traffic demand at the intermediate stops is low, passengers who do not use the stops will experience prolonged travel time due to additional stops. In addition, as a result of the longer travel time, train operating companies might need more trains, and hence more crew, to obtain the same train frequency. Ultimately, the slower travel time can result in a lower frequency of the trains which then reduce the SWT.

3 Scheduled waiting time for passengers SWT for passengers occurs when the travel time is prolonged compared to that in the originally wanted timetable. Therefore, SWT for the trains affects the passengers too. This is because the interdependencies in the railway network prolong the travel time (in the train) and reduce the degrees of freedom in the timetable, which potentially reduces the frequency. However, SWT for passengers also includes transfers. Not all transfers in (larger) public transport networks are well-planned transfers, as improving one transfer due to network effects might worsen others. Timetable alternatives for a simple railway network for the situations with and without a planned transfer at ‘Stop B’ can be seen in table 1. Although the running times of the individual trains are unchanged, the travel time for passengers between ‘Stop D’ and ‘Stop C’ varies depending on the transfer time at ‘Stop B’. For passengers travelling from ‘Stop D’ to ‘Stop C’, timetable scenario 1 in table 1 results in a journey time of 16 minutes, of which 8 minutes is transfer time. However, if timetable scenario 2 in table 1 is used, the journey time would be 26

Table 1: Timetable scenarios between ‘Stop D’ and ‘Stop C’ (– indicates trains not serving the station and gray cells indicate the route of the passengers). Time of departure ց

Scenario 1

Scenario 2

Scenario 3

Train → A1 B1

B2

A1

B1

B2

Stop D Stop A

2 –

– 8

– 28

12 –

– 8

– 28

8 –

– 8

– 28

Stop B

6

14

34

16

14

34

12

14

34

Stop C

–

18

38

–

18

38

–

18

38

Total time D → C

16 minutes

26 minutes

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A1 B1

B2

10 minutes

914 Computers in Railways XII minutes, of which 18 minutes is transfer time, as the corresponding train leaves ‘Stop B’ only 2 minutes before the train from ‘Stop’ D arrives at the station. It is possible to reduce the transfer time in both scenario 1 and 2 and thereby reduce the journey time for passengers travelling from ‘Stop D’ to ‘Stop C’. By reducing the transfer time, the train from ‘Stop A’ will depart from ‘Stop B’ 2 minutes after the train from ‘Stop D’ has arrived. This will ensure sufficient transfer time. This results in a travel time from ‘Stop D’ to ‘Stop C’ of 10 minutes (Scenario 3 in table 1). The extra travel time in scenario 1 and 2 (6 minutes and 16 minutes) is SWT for the passengers. The example in table 1 is straightforward to overview, but according to [8], the reduction of transfer times becomes more complex for more complex networks. Figure 2 shows a journey with two transfers. In the beginning and in the end of the journey there are train services with 20-minute frequency but in between there is a 5-minute frequency train service. By examining the transfers independently, it can be seen that there are short transfers at both stops, but the passengers in the example on the left in figure 2 will not have short transfer time at the second station due to the long waiting time, whereas there is a short transfer time in the example on the right in figure 2.

Figure 2: Journey with two transfers: long transfer time (left) and well-planned transfer time (right). Based on [4]. Due to the dependency on the characteristics of the infrastructure and the timetables, the SWT for the passengers can be estimated as the (additional) time the passengers have to spend in the system. This measurement for the SWT for passengers is similar to the SWT measurement for the trains but includes the passengers’ waiting time at the station(s). In the literature, there are various studies where scheduled transfer time, as a part of the SWT for passengers, is attempted to be minimized by changing the time schedules. For instance, [9] minimize the transfer waiting time in a railway system and [10] minimize the transfer waiting time for the bus-train relations for the entire public transport network of Copenhagen. However, these models consider only one transfer, and not the passenger’s total journey, why the total SWT for passengers is underestimated. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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4 Calculation of scheduled waiting time for trains SWT can according to [11, 12] be estimated analytically for a given timetable but it can also be estimated by simulating plans of operation. The Danish developed SCAN model (Strategic Capacity Analysis of Network) is a strategic tool to calculate capacity in a railway network [13,14]. The tool simulates random regular interval timetables (see [15,16] for classification of timetables) and calculates their SWT. For this, SCAN uses the infrastructure (on the meso level – see [17] for the aggregation levels of infrastructure data), the plan of operation (i.e., the number of trains within each category and their stop pattern) and the main dynamics of the rolling stock. The workflow of calculating SWT using SCAN is [14]: 1. Prepare the model (build up infrastructure, key in dynamics of rolling stock, and enter a plan of operation). 2. Calculate minimum running time and kilometers of operation. 3. Generate regular interval timetables by random departure times for the first departure for each train system (at the first station). The following departure times for each train system are determined by the frequency. In this way a number of different timetables are generated. In this stage there may be conflicts between trains. 4. Synchronic simulation of each timetable by a discrete simulation model where the priority of the trains determines which trains run first. The result of the simulation is a conflict-free timetable for how trains can be operated. 5. Calculate running time and SWT (difference between simulated running time and minimum running time) for each timetable. The flow of calculating the SWT can be seen in figure 3.

Figure 3: Calculation of SWT in the SCAN model [4].

Examining a large number of different timetables based on the same plan of operation will result in different SWTs. These different SWTs can then be sorted according to the SWT as shown in figure 4. It is then possible to see the span in SWT and choose the timetable that has the lowest scheduled waiting time and still fulfils other potential requirements of the timetable, e.g., possible transfers between trains. Consequently, the final (chosen) timetable is not necessarily the timetable with the lowest SWT. Therefore, [14] suggests using the 25% percentile to describe a satisfactory quality of operation, and thereby the expected SWT of the plan of operation. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

916 Computers in Railways XII A problem with the SCAN model is that timetable supplements are not included. Therefore, the model can be used only to evaluate the plan of operation at the strategic level. Alternatively, the North American Train Performance Calculator (TPC) can be used to generate a large number of timetables, which can be investigated. The workflow in the TPC model is (based on [18]): 1. Prepare the model (build up infrastructure, key in dynamics of rolling stock and train services). 2. Generate timetables randomly by choosing random departure times for all trains. 3. Simulation of each timetable to generate conflict-free timetables. Based on the results of the TPC-model, it is possible to calculate the actual running times of the timetables. To examine SWT it is necessary to calculate the minimum running time of the train services too. The timetables can then be ranked by SWT as in figure 4.

Figure 4: Sorting the timetables according to the SWT. Based on [3].

The major differences between the TPC and SCAN models are that the SCAN model examines randomly generated regular interval timetables, while TPC examines timetables where all the trains are operated randomly; furthermore, SCAN calculates the SWT itself, whereas the calculation has to be done manually in the TPC model. In North America, the trains are (as described in [5, 19]) operated according to a more or less improvised timetable, which is why the TPC model is well suited there. This random operation is possible because only few corridors are dominated by passenger trains. However, in Denmark (and Europe) the operation is mostly based on regular interval timetables; accordingly, the TPC model is less suited to simulate the operation here. The SCAN model simulates regular interval timetables but does not include timetable supplements. To have a better simulation model well suited for analyses in the Danish/European context, the SCAN model should be developed to include timetable supplements, and/or the TPC model should be adapted to examine regular interval timetables. Generally, simulation models based on future plans of operation are well suited for strategic network analyses, but simulation models has difficulties examining where the capacity problems and SWT, are most severe. Consequently, it is also difficult to examine where the infrastructure should be improved and what effects the improvement will have. Combining microscopic and macroscopic models can, WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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however, help in this kind of analysis. Railnet Austria has according to [20] combined microscopic and macroscopic models in the infrastructure planning. Here, future timetables have been developed and evaluated (in this case by the UIC 406 method – [21] describes the UIC 406 methodology) by converting data between the macroscopic and microscopic level; the methodology could be the basis when evaluating SWT for different timetable alternatives.

5 Calculation of scheduled waiting time for passengers SWT for passengers is the delay of the passengers compared with that of the ‘optimal’ timetable. This definition is very similar to the SWT for trains. However, cases with a small amount of SWT for trains do not necessarily result in a small amount of SWT for passengers, and vice versa. This is because just little SWT for a train might result in a lost transfer for the passengers, but only if the transfer time is tight, otherwise the total travel time remains unchanged. The SCAN model used to calculate SWT for trains can also be used to calculate SWT for passengers. This is because the output timetables from SCAN can be used as a basis to calculate passenger delays as the difference between the times used in the actual analyzed timetable and the best-analyzed timetable. Using simulation models, such as SCAN, the risk of delays in the operation, and thus the risk of missing a transfer, is omitted, which makes it difficult to analyze SWT in real and contingency operation. Hence a high risk of missing a connecting train will increase the travel time. Therefore, a timetable without planned transfer(s) might be better than the timetable with planned transfer(s) if the risk of delays is high, as the travel time for the passenger will most likely remain the same. To reflect the actual operation and take the punctuality of the railway system – and thereby the risk of missing a connecting train – into account when calculating SWT for the passengers, it is necessary to simulate the (candidate) timetables. This can be done by ‘traditional’ simulation where the infrastructure and timetables are built up before simulating the operation with initial delays, cf. figure 5. Passenger delay models can based on the output of ‘traditional’ simulation estimate how much time the passengers spend in the railway system. Therefore, passenger delay models can be used to deduce SWT for passengers. [4] defines different types of passenger delay models of which only the newest generations can be used to estimate SWT. The different combinations of infrastructures and timetable variants will result in a different amount of SWT in the system. This type of traditional simulation project is time consuming, but by combining microscopic and macroscopic models, the workload of generating and simulating timetables can according to [23,24] be reduced.

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Figure 5: Simulation of railway traffic with passenger delays. Based on [22].

6 Discussion Timetables for railway networks can be improved by examining SWT in the planning process. By examining SWT for different future (candidate) timetables, it is possible to examine different timetable strategies, for example additional overtaking. In the examination, it is possible to evaluate both the time gain for the passengers in the fast train and the time loss for the passengers in the train that is overtaken. The examination of SWT can be done either locally for a single railway line or for the entire system including transfers to/from other trains. Improving the timetables without taking the risk of train delays during the operation into account can result in an over-optimized timetable for passengers. This is because even small train delays will result in lost transfers for the passengers – or even degenerated schedules. To take common train delays into account, it is recommended to simulate of the timetables with a typical delay distribution. The additional SWT for the passengers can then be calculated based on the simulated timetables. This makes it possible to plan timetables with less SWT for normal operation. Optimizing SWT is not possible in all types of timetables, for example, in an integrated fixed interval timetable (see [15, 16] for classification of timetables) where all trains meet at the same time at stations/hubs throughout the network. In integrated fixed interval timetables the structure of the timetable is fixed when the stations/hubs have been selected. Additionally, SWT has virtually been determined by the chosen stations/hubs as all the trains have to meet at the station/hub and a train is not permitted to leave before the last train has arrived (and the passengers have had time to make a transfer). Therefore, SWT cannot be optimized in integrated fixed interval timetables, but the amount of SWT can be used to WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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describe how well the infrastructure can handle the chosen integrated fixed interval timetable. This can be used to examine which improvements in the infrastructure that reduce SWT the most. In the longer term the simulation approach can also be used by the dispatch centers to decide if a train should wait for a delayed train to obtain the planned transfer. Thus, the simulation of the traffic combined with calculating SWT for trains and passengers can be used to evaluate the consequences of different scenarios. In this way it is possible to improve the operation. The short-term operation can be improved too by including evaluation of SWT in the planning process. Timetables can be simulated and SWT for both trains and passengers can be calculated so that the best possible timetable is chosen. This approach can also be used when planning timetables for contingency operation, so the best timetable can be used in cases of disrupted operation.

7 Conclusion Railway operation is often affected by scheduled waiting time (SWT) because fast trains (due to infrastructure restrictions) cannot overtake slower trains. This means that additional time – SWT – has to be implemented in the timetable. The article shows how SWT affects both the trains and the passengers in the trains. The article also demonstrates that passengers are further affected by SWT in the case of transfers. The article illustrates that SWT for trains can be calculated by simulation models such as the Danish SCAN model and the North American TPC model. Based on SWT for trains and passenger delay models the article presents a method to calculate SWT for passengers. The article also demonstrates how it is possible to estimate SWT in the case of delays. Calculating SWT for candidate timetables makes it possible to test different timetable strategies and choose the best strategy for the final timetable. This can improve the timetables for both the operator(s) and the passengers.

References [1] Handstanger, A.C.T., Scheduled waiting time from crossing on single track railway lines. Ph.D. thesis, Norwegian University of Science and Technology, 2009. [2] Salling, K.B. & Landex, A., Computer based ex-ante evaluation of the planned railway line between Copenhagen and Ringsted by use of a new Decision Support System named COSIMADSS. CompRail X, p. 65, 2006. [3] Landex, A. & Nielsen, O.A., Network effects in railway systems. European Transport Conference, 2007. [4] Landex, A., Methods to estimate railway capacity and passenger delays. Ph.D. thesis, Technical University of Denmark, 2008. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

920 Computers in Railways XII [5] Pachl, J., Railway Timetable & Traffic, Eurail Press, chapter Timetable Design Principles, pp. 9–42, 2008. [6] Skartsæterhagen, S., Capacity of railway lines (Kapacitet p˚a jernbanestrekninger). Technical report, Institute for Energy Technology, Norway, 1993. [7] Nie, L. & Hansen, I.A., System analysis of train operations and track occupancy at railway stations. EJTIR, 5(1), pp. 31–54, 2005. [8] Klemenz, M. & Radtke, A., Method and software tool for an optimized passenger orientated connection management. Computers in railways XI, WITpress, p. 3, 2008. [9] Wong, R.C.W. & Leung, J.M.Y., Timetable synchronization for mass transit railway. International Conference on Computer-Aided Scheduling of Public Transport (CASPT), 2004. [10] Pedersen, M.B., Nielsen, O.A. & Jansen, L.N., Minimizing passenger transfer times in public transport timetables. Conference of Hong Kong Society for Transport Studies: Transportation in the information age, Hong Kong Society for Transport Studies, p. 229, 2002. [11] Wendler, E., The scheduled waiting time on railway lines. Transportation Research Part B, 41(2), pp. 148–158, 2007. [12] Wendler, E., Railway Timetable & Traffic, Eurail Press, chapter Queuing, pp. 106–117. 1st edition, 2008. [13] Kaas, A.H., Development and practical use of a capacity model for railway networks. Conference on Structural Integrity and Passenger Safety, WITpress, p. 73, 1998. [14] Kaas, A.H., Methods to calculate capacity of railways (Metoder til beregning af jernbanekapacitet). Ph.D. thesis, Technical University of Denmark, 1998. [15] Liebchen, C., Periodic Timetable Optimization in Public Transport. Ph.D. thesis, Technical University of Berlin, 2006. [16] Schittenhelm, B., Identification of timetable attractiveness parameters by an international literature review. Annual Transport Conference at Aalborg University (Trafikdage), 2008. [17] Gille, A., Klemenz, M. & Siefer, T., Applying multiscaling analysis to detect capacity resources in railway networks. Computers in railways XI, WITpress, p. 595, 2008. [18] White, T.A., The development and use of dynamic traffic management simulation in north america. International Seminar on Railway Operations Research, IAROR, 2007. [19] White, T.A., North american experience with timetable-free railway operation. International Seminar on Railway Operations Modelling and Analysis, IAROR, 2005. [20] Sewcyk, B., Radtke, A. & Wilfinger, G., Combining microscopic and macroscopic infrastructure planning models. International Seminar on Railway Operations Modelling and Analysis, IAROR, 2007. [21] International Union of Railways (UIC), Capacity (UIC code 406), 2004.

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[22] Landex, A. & Nielsen, O.A., Modelling expected train passenger delays on large scale railway networks. World Congress on Railway Research, 2006. [23] Kettner, M. & Sewcyk, B., A model for transportation planning and railway network evaluation. World Congress on Intelligent Transport Systems, 2002. [24] Kettner, M., Sewcyk, B. & Eickmann, C., Integrating microscopic and macroscopic models for railway network evaluation. European Transport Conference, 2003.

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Computation of a suburban night train timetable based on key performance indicators B. Schittenhelm1,2 & A. Landex1 1 2

Department of Transport, Technical University of Denmark, Denmark Traffic Planning, Rail Net Denmark, Denmark

Abstract Timetable evaluation can be based on a set of key performance indicators. This article presents six essential key performance indicators: fixed interval service frequency, direct connections, transfer waiting time, use of dedicated rolling stock, dedicated train personnel, dedicated tracks and travel time. A short description and specific calculation method is given for each of these. The article recommends three different approaches for dividing the railway network into sections of analysis in regards to the key performance indicators. Three timetable variants for suburban night trains in Copenhagen are evaluated. Each timetable variant was created with a different performance focus. Values for each of the six key performance indicators are calculated and an average value is found for all timetable variants. It can be concluded that the actual implemented timetable receives the highest scores, but a clear picture of which timetable variant is best is not achieved. To get a clearer picture, the introduction of weights is recommended both for the indicators as a whole and in the specific calculation methods. A prioritization of the selected key performance indicators is essential and weights in form of, for example passenger numbers, are needed in the specific calculations. Keywords: timetable, railway timetable, timetable evaluation, key performance indicators, sections of analyses.

1 Introduction On Friday, November 20, 2009, a timetable for suburban night trains was introduced in Copenhagen, Denmark. This timetable was mainly based on input from the train operating company (TOC) DSB S-tog; this is the only TOC WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100831

924 Computers in Railways XII operating trains on this part of the network owned by infrastructure manager (IM) Rail Net Denmark. The starting point for DSB S-tog was the existing contingency timetable which is used in case of large disruptions in traffic. The timetable operates with 4 service lines stopping at all stations and running with a service frequency of 20 minutes. It was decided to reuse the train service lines, arrival and departure times from this timetable – but with a frequency of 1 train per hour. In a normal daytime service, situation line structures are more complicated. Each suburban railway line is serviced with a slow stopping line servicing the inner part of the railway line and a faster line servicing the outer part. See figure 1. This results in shorter travelling times for passengers. Each line has a service frequency of 10 minutes. New timetable proposals should be evaluated and then either rejected, altered or implemented. To perform a fast and efficient evaluation of a given timetable a series of key performance indicators (KPI) have been developed and suggested [1]: Timetable structure, timetable complexity, travel time, transfers and punctuality and reliability. Each indicator consists of up to several quantitative indexes. These give a good first insight into a timetable’s strengths and weaknesses. From this group of indexes, 6 have been chosen for the timetable evaluation in this paper. Three suburban night train timetable variants have been proposed. In section 2 the proposed timetable variants are presented. Section 3 examines how the network should be divided into sections for analyses in regard to timetable

Figure 1:

DSB S-tog timetables left: day (10 minute frequency per line); right: night (1 hour frequency per line).

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evaluation KPI. The KPI are described and calculated in section 4. A discussion of results and perspectives of these can be found in section 5. Finally, conclusions are drawn up in section 6.

2 Timetable variants To avoid inconvenience for the many daily passengers, most of the maintenance works takes place during night time. Therefore, the main concern of Rail Net Denmark regarding timetables for running night trains on the suburban railway network was that maintenance work could continue to take place unhindered during nights. This resulted in two essential requirements: 1.

Planned traffic must be able to be handled by only one track through the central part of the network

2.

Running times on the outer part of the network should allow for trains to run with reduced speed on sections of the network – thereby making it possible to run traffic on one track only between 2 crossovers.

2.1 Proposed timetable from DSB S-tog DSB S-tog proposed a timetable and this was implemented. It consists of 4 lines stopping at all stations. Lines A, B and C have a frequency of 1 train per hour and the independent “half circle” line F has a 30 minute frequency. Lines A, B and C each need minimum 3 train sets and line F 2 train sets to carry out this timetable variant. See table 1. This timetable does not establish a fixed service interval of 20minutes between trains on the shared line section of lines A, B and C, but creates nearly a 30minute service interval. To improve this condition two further timetable variants have been developed. Both ensure a fixed interval service frequency of 20minutes on the shared line section. In the first alternative, the arrival and departure times of line B and C have simply been translated. In timetable variant 2 the philosophy of having fixed train service lines has been abandoned and a flexible approach been taken. Travel times between stations on the outer part of the network have been changed to follow timetable planning rules. This gives slightly shorter travelling times. Table 1: Arr Dep 46½ 32 33 09

↓

Line A

↑

Køge København H Farum 3 trains needed

Arr Dep 39½ 54 53 16

Timetable variant DSB S-tog. Arr Dep 31 56 57 44½

↓

Line B

↑

Høje Taastrup København H Hillerød 3 trains needed

Arr Dep 15½ 50 49 02

Arr Dep 38 28 29 49

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↓

Line C

↑

Frederikssund København H Klampenborg 3 trains needed

Arr Dep 08½ 18 17 56

926 Computers in Railways XII 2.2 Alternative timetable variant 1 Timetable variant 1 focuses on regular intervals between trains through Copenhagen. See table 2. Trains from the south arrive in minute 12 32 and 52 and from the north in 13 33 53 at Copenhagen central station (København H in Danish). Trains in the two driving directions pass each other here to ensure that traffic can be handled using only one track between København H and the next set of crossovers at Østerport station. The travel time between København H and Østerport is 6 minutes – this allows for reduced speed for trains travelling in the secondary driving direction in case of single track operation. It takes 11 trains to run timetable variant 1. In timetable variant 1, the arrival and departure times for line A are the same as in the timetable proposed by DSB S-tog. Times had to be changed for line B and C to achieve fixed interval frequency of 20 minutes. The possible positive effect of recognizable and easy to remember arrival and departure times in the DSB S-tog timetable cannot be complete since customers still must find out/remember which one of the 3 possible departure times is relevant for them. Timetable variant 1 maintains line F as proposed by DSB S-tog in the timetable for night trains. Line F requires 2 trains to be operated. Therefore 11 trains are needed to operate timetable variant 1. 2.3 Alternative timetable variant 2 Disregarding fixed line structures in timetables, a second variant has been developed. See table 3. A train follows the line structure indicated with numbers 1 to 4 in figure 2. Trains between Høje Taastrup and Klampenborg are fixed to this line. Timetable variant 2 maintains line F as it is planned in the DSB S-tog timetable for night trains. Line F requires 2 trains to be run. All 3 investigated timetable variants need 11 trains to be implemented.

3 Railway sections of analysis In the following section a series of KPI are calculated for the 3 presented timetable variants. Since characteristics of timetable variants depend on which part of the network is being investigated, it is important to divide the network in reasonable sections of analysis. How to choose these sections is highly dependent on the specific KPI and how it is calculated. Table 2: Arr Dep 46½ 32 33 09

↓

Line A

↑

Køge København H Farum 3 trains needed

Arr Dep 39½ 54 53 17

Arr Dep 27 52 53 40½

Timetable variant 1. ↓

Line B

↑

Høje Taastrup København H Hillerød 3 trains needed

Arr Dep 59 34 33 45½

Arr Dep 22 12 13 33

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↓

Line C

↑

Frederikssund København H Klampenborg 3 trains needed

Arr Dep 04 14 13 53

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Table 3:

927

Timetable variant 2.

From To Departure Arrival København H Høje Taastrup 26 50 Høje Taastrup København H 01 25 København H Klampenborg 26 45½ Klampenborg København H 05½ 25 København H Køge 06 48 Køge København H 23 05 København H Hillerød 06 51 Hillerød København 00 45 København H Frederikssund 46 34½ Frederikssund København H 56½ 45 København H Farum 46 22 Farum København H 29 05 9 trains needed: 2 trains Høje Taastrup ↔ Klampenborg + 7 trains for flexible line

Figure 2:

Flexible line structure in timetable variant 2.

When calculating KPI, for example service frequency and travelling time, inspiration can be taken from the UIC capacity consumption calculation method [2]. Following the Danish adaption of the recommendations the network is divided into analysis sections at line end stations/terminus and junctions [3]:          

Køge (terminus) – Dybølsbro (junction) Høje Taastrup (terminus) – Valby (junction) Frederikssund (terminus) – Valby (junction) Valby (junction) – Dybølsbro (junction) Dybølsbro (junction) – Svanemøllen (junction) Svanemøllen (junction) – Farum (terminus) Svanemøllen (junction) – Hellerup (junction) Hellerup (junction) – Hillerød (terminus) Hellerup (junction) – Klampenborg (terminus) Hellerup (terminus) – Ny Ellebjerg (terminus)

Between these sections the number of trains per hour change and thereby potentially also service frequencies. This can have influence on the running time as a higher number of trains can cause a higher level of scheduled waiting time in the timetable. When passing a junction a Train service line can go from having dedicated tracks to shared tracks with other lines.

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928 Computers in Railways XII Transfers take place at stations. Only stations with transfer possibilities are of interest when calculating KPI for transfers. These stations are: Ny Ellebjerg, Danshøj, Flintholm, Ryparken, Hellerup, Svanemøllen, Dybølsbro and Valby. Looking at KPI for dedicated rolling stock or crew for a train service, a detailed approach in regards to analysis sections makes no sense. A more overall look on the network is needed. A ratio of departures or train runs becomes more important.

4 Computation of timetable key performance indicators In the following six sections a selected series of KPI will be shortly described and their method of calculation shown. These KPI reveal the main differences between the 3 suggested timetable variants. 4.1 Fixed interval frequency The used clock faced index in England [4] is not able to evaluate the regularity of service frequency of trains in a given analysis section. Therefore, the following index for regularity in frequency is proposed. See equation (1).

I regular frequency 

H H12 H 23   ...  nm H av H av H av

(1)

Iregular frequency = Index for regularity of frequency Hnm = Timetable headway time between trains n and m Hav = Average headway time if regular frequency A perfect regular frequency will give an index value equal to 1. A highly irregular frequency will give a value close to 0. From a customer point of view, a regular frequency is in general preferable to an irregular [4]. 4.2 Transfers There are 84 stations on the suburban network. In the timetable proposed by DSB S-tog train service line A has 35 stations, line B has 28 (7 are shared with other lines), line C has 31 (10 are shared with other lines) and line F has 12 stations (5 are shared with other lines). The layout of the network in combination with service line structure ensures that from a given starting station you can reach any other station with maximum one transfer. It may though be faster for passengers to choose a route with two transfers instead of one – e.g. from Farum to Høje Taastrup with transfers at Ryparken and Danshøj. Transfers are mostly avoided by passengers – if possible. An index for direct connections can be calculated as shown in equation (2).

I Direct connections 

 connections without transfer  connections

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(2)

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This index is calculated for each station and then summed up to get a timetable dependent index. For example, a station only served by line A: 34 stations can be reached without transfer out of 83. This gives an IDirect connections = 34/83 = 0.41. There are 26 of these stations. For København H you have: 76 stations can be reached without transfer out of 83. IDirect connections = 76/83 = 0.92. There are 7 stations with this characteristic. To measure the quality of transfers, one key parameter is prolongation of the travel time caused by transfers. An index for this is suggested in equation (3). If travel time is not prolonged an index value of 1 is achieved. The index goes down towards 0 with increasing waiting time for transfers.

I transfer waiting time 

 Minimum waiting time  Waiting time with transfer

(3)

Figure 3 gives an overview of transfer stations in the network and the arrival and departure times from the DSB S-tog timetable variant. It is impossible to exchange making a transfer with a direct train at Ny Ellebjerg, Danshøj, Flintholm and Ryparken stations. Minimum transfer time is set to 4 minutes at these stations. For other stations the following rule is used: If a transfer can be made at the same platform, minimum transfer time is set to 2 minutes, if not 4 minutes. I transfer waiting time is calculated for each transfer station, but only for relevant transfers. The station indexes are then summed up and averaged to get an overall index for the given timetable variant. For example, at Ny Ellebjerg there are 6 transfer possibilities: Each arrival from line F (00 and 30) can transfer to line A

Figure 3:

Overview of selected transfer stations and times.

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930 Computers in Railways XII (02 and 24). The arrivals from line A (02 and 24) will chose the next departure on line F (16 and 46). Transfer times are: (24+2+32+54+14+22)min / (6×4)min = 0.17. 4.3 Dedicated rolling stock The risk of consecutive delays is reduced if the same rolling stock is used on one line of service only – line dedicated rolling stock. This is because a cancellation of a train or a break down on one line not necessarily will spread to other lines. The proposed KPI to evaluate use of dedicated rolling stock looks at the rate between sums of all train runs and runs with dedicated rolling stock. See equation (4) [3, 5].

I dedicated rolling stock 

 Train departures with dedicated rolling stock  Train departures

(4)

If all rolling stock is dedicated to one train service line, the index value will be 1. The opposite situation gives a value equal to 0. 4.4 Dedicated train personnel As with rolling stock, train personnel can be dedicated to one train service line. This reduces the risk of consecutive delays because delayed train personnel from one service line can bring the delay with them to other potentially unaffected service lines – e.g. a train driver arriving delayed will not result in another train not being able to move [1, 5]. Below the developed KPI looks at the ratio between sums of all train runs and runs with shared train personnel.

I dedicated train personnel 

 Train departures with dedicated train personnel  Train departures

(5)

Using only dedicated personnel gives an index value of 1. The opposite gives an index equal to 0. This KPI has the same value for all 3 timetable variants. Rules for train personnel rostering are based on agreements between TOC and railway unions. All suburban trains change train personnel at København H. Train drivers start/ end their shift or change to other service lines when passing this station. Train drivers for line F have to travel between København H and Hellerup. 4.5 Dedicated tracks Only one infrastructure variant is available to the TOC and it is used in the same manner by all 3 timetable variants. Service lines have dedicated tracks on the outer part of the network and have to share tracks on the central part. A KPI for dedicated tracks is suggested in equation (6).

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I dedicated tracks 

 Analysis sections with dedicated tracks  Analysis sections

931

(6)

For the examined timetable variants, 7 out of 10 analysis sections have dedicated tracks to train services. This giving an I Dedicated tracks = 7/10 = 0.70. 4.6 Travel time Passenger want as short travel times as possible – still arriving on time – while the TOC and IM want a robust and timetable where it is possible to absorb smaller delays. Therefore, running time supplements are included in a timetable. A KPI that describes the ratio between planned travel time in a given timetable and minimum travel time according to planning rules is suggested. This is calculated for each analysis section and each train service line. See equation 7.

I Travel time 

Travel time Analysis section, minimum

(7)

Travel time Analysis section, timetable

A train from Køge to Dybølsbro uses 42 minutes and 50 seconds according to timetable variant 1, while the minimum travel time is 40 minutes and 5 seconds. This gives an I Travel time = 0.94. 4.7 Calculated KPI Table 4 gives an overview of the calculated KPI for the 3 investigated timetable variants.

5 Discussion and perspective Three different approaches were necessary to define sections for analyses for timetable evaluation that can be applied to all used KPI. For KPI travel time, dedicated tracks and fixed interval frequency a similar division of the network as suggested in the UIC-406 method is used [2, 3]. KPI for transfers are calculated on station level. A ratio of departures is used for calculating KPI for dedicated trains and personnel. Using different analysis sections has not weakened the KPI approach for timetable evaluation and comparison. Looking at the average KPI score achieved by the 3 timetable variants, it becomes evident that weighting of the KPI is needed. The importance of each KPI needs to be defined and weights based on this created. These weights of importance can, for example, be found by holding a decision conference where all timetable stakeholders take part and come to an agreement. This will be very difficult but is an important input for timetable evaluation. Weights should also be applied within the calculation methods for the KPI – for example, a transfer waiting time should be multiplied with the number of passengers making the given transfer. In this way more unambiguous evaluation results of timetable variants can be produced and create a better basis for

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932 Computers in Railways XII Table 4: KPI

Calculated KPI for timetables variants.

Køge – Dybølsbro Høje Taastrup – Valby Frederikssund – Valby Valby – Dybølsbro Dybølsbro – Svanemøllen Svanemøllen – Farum Svanemøllen – Hellerup Hellerup – Hillerød Hellerup – Klampenborg I Direct connections Timetable I transfer waiting time Ny Ellebjerg I transfer waiting time Danshøj I transfer waiting time Flintholm I transfer waiting time Ryparken I transfer waiting time Hellerup I transfer waiting time Svanemøllen I transfer waiting time Dybølsbro I transfer waiting time Valby

Timetable DSB S-tog 1.00 1.00 1.00 0.99 0.38 1.00 0.99 1.00 1.00 0.58 0.16 0.16 0.16 0.20 0.14 0.08 0.09 0.06

I Dedicated rolling stock timetable

1.00

1.00

0.33

I Dedicated personnel timetable

0.00

0.00

0.00

I Dedicated tracks timetable

0.70

0.70

0.70

0.94 0.98 0.98 1.04 0.92 0.97 0.95 0.95 0.97 0.71

0.94 0.98 0.98 1.04 0.92 0.97 0.95 0.95 0.97 0.69

1.00 1.00 1.00 1.00 0.92 1.00 1.00 1.00 1.00 0.68

Analysis section / station

Regularity of service frequency

Transfers

Dedicated rolling stock Dedicated personnel Dedicated tracks

Køge – Dybølsbro Høje Taastrup – Valby Frederikssund – Valby Valby – Dybølsbro Dybølsbro – Svanemøllen Travel time Svanemøllen – Farum Svanemøllen – Hellerup Hellerup – Hillerød Hellerup – Klampenborg Average KPI value

Timetable 1

Timetable 2

1.00 1.00 1.00 0.89 1.00 1.00 0.89 1.00 1.00 0.58 0.16 0.15 0.18 0.17 0.13 0.09 0.09 0.08

1.00 1.00 1.00 0.89 1.00 1.00 0.89 1.00 1.00 0.56 0.15 0.15 0.18 0.15 0.13 0.08 0.08 0.08

deciding which timetable variant to implement. Passenger numbers were not available before the timetable was implemented since there had not been run night trains before. The importance of a fixed interval frequency KPI depends on what timetable philosophy is preferred: Specific demand oriented – or fixed interval? Passenger demands for a night train timetable probably focus on transporting people between suburbs and the city centre. Since the service frequency is only 1 train per hour for each train service line – 3 trains per hour through Copenhagen – this KPI loses some importance. This gives the possibility to look into a different timetable category where all trains meet at København H, for example on the hour – inspired by the Swiss Bahn 2000 timetable concept. This requires that 2 different trains can make use WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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of the same plat form track at the same time, which technically is possible. Reduced functionality in the existing signalling system reduces the headway between two trains running in the secondary direction on a given track to approximate 5 minutes. The essential requirements from Rail Net Denmark – the possibility to unhindered carrying out maintenance work at night – make this timetable category unfeasible. This timetable category could be looked into when an improved signalling system – for example a CBTC system – is available. The KPI for transfer waiting time identifies good and bad transfer possibilities at stations but does not indicate how many customers are affected by these. Weighting each transfer relation with the number of passengers making use of it – looking at passenger transfer minutes instead of simply transfer minutes – would give a more correct picture. Transfer stations have been chosen based on an unbiased approach to the train service line patterns. If the same transfer can be achieved at a series of stations, it has not been investigated if one station offers a more comfortable transfer than the others e.g. because of a station canopy, and therefore would be chosen by transferring passengers. One transfer aspect has not been covered by the chosen transfer KPI: In the DSB S-tog timetable variant it is possible to make a transfer from the train leaving Svanemøllen towards København H at minute 43 to the train leaving Svanemøllen towards København H at minute 39, by using the F line from Ryparken to Ny Ellebjerg. Unfortunately this is not possible for passengers in the opposite travelling direction between trains leaving Dybølsbro at minute 25 and 29. The DSB S-tog timetable variant gives better opportunities to make use of line F in regards to transfer possibilities but is not given any reward for this. To deal with this, developing an existing KPI or adding an additional KPI is needed. Travel time is a very important KPI. The suggested KPI indicates if planned travelling times, within a given analysis section, are close to the shortest possible. This should be weighted with the number of passengers affected by this to see how many passenger scheduled waiting time minutes are generated. A new timetable can attract new passengers to an existing railway system. To calculate weighted KPI for a number of new timetable variants, input from a traffic model estimating future passenger numbers is needed. Having this available improves the evaluation of future timetables. To get an insight into the influence of dedicated rolling stock, personnel and tracks to a timetable variant’s vulnerability to primary delays and delay transfers from train to train, a simulation of the timetable can be helpful. The punctuality data from a simulation can be multiplied with passenger numbers using a passenger delay model, giving an estimate of passenger delay minutes [1, 5]. In this timetable variant evaluation and comparison, changes only occur on parts of the network served by train service lines A, B and C. The timetable for Line F is kept constant in all 3 variants. Creating a fixed service frequency of 20 minutes on parts of the network could give the idea to do the same on line F. This can potentially improve the transfer KPI for stations on line F but increases the need for trains from 2 to 3. The arrival and departure times for line F could also simply be translated to potentially achieve better transfer waiting times. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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6 Conclusion Six key performance indicators (KPI) have been selected for use in an evaluation of 3 timetable variants for suburban night trains in Copenhagen. These KPIs are: fixed interval frequency, transfers, dedicated rolling stock, dedicated personnel, dedicated tracks and travel time. Calculation of these KPI demanded three different ways to divide the railway network into sections of analyses. An approach as suggested in the UIC-406 method has been adapted, relevant transfer stations identified and ratios of train departures been recommended. Evaluation of different alternatives for suburban night trains using selected KPI scores show that the implemented DSB S-tog suburban night train timetable variant is the best. It achieved a KPI score of 0.71 whereas timetable variants 1 and 2 got 0.69 and 0.68 respectively. This indicates that DSB S-tog has taken the topics covered by the proposed KPI into account in their timetable development process. The differences in achieved scores are minimal and therefore give a weak basis for making a decision on which timetable variant to implement. To see the differences between timetables more clearly it is necessary that each KPI must be weighted with its importance and weights – for example estimated or registered passenger numbers – also have to be part of the specific calculations for each KPI.

References [1] Schittenhelm, B. & Landex, A., Quantitative Methods to Evaluate Timetable Attractiveness, Proc. of the 3rd Int. Seminar on Railway Operations Modelling and Analysis, Zürich, 2009 [2] UIC 406 leaflet 406, Capacity, 1st edition, UIC International Union of Railways, France, 2004 [3] Landex, A., Methods to estimate railway capacity and passenger delays, PhD Thesis, Technical University of Denmark, 2008 [4] Wardman, M. & Shires, J. & Lythgoe, W. & Tyler, J., Consumer benefits and demand impacts of regular train timetables, International Journal of Transport Management, (2), 2004 [5] Landex, A. & Nielsen, O.A., Modelling expected train passenger delays on large scale railway networks, Proc. of the 7th World Congress on Railway Research, 2006 [6] Landex, A. & Nielsen O.A., Timetable Planning & Information Quality, WIT Press, (chapter) Simulation of disturbances and modelling of expected train passenger delays, eds. I.A. Hansen pp. 85-94, 2010

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A cooperative strategy framework of train rescheduling for portal junctions leading into bottleneck sections L. Chen1,2, F. Schmid2, B. Ning1, C. Roberts3 & T. Tang1 1

State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, China 2 School of Civil Engineering, University of Birmingham, UK 3 School of Electronic, Electrical and Computer Engineering, University of Birmingham, UK

Abstract On main line railways, bottleneck sections in urban area usually have high intensity traffic flows because of trains converging from different origins through portal junctions. As a result, a small delay to one train can cause long knock-on delays to following trains because of the limit margin time and recovery time in the nominal timetable in bottleneck sections. This paper proposes a cooperative strategy framework for train rescheduling of portal junctions leading into bottleneck sections to decrease the overall delay and recovery from the unpredictable event of disturbances. The strategy is mainly based on an improved Differential Evolution algorithm for the Junction Rescheduling Model (DE-JRM), which is proved to be suitable for solving train rescheduling problems for both individual fly-over junctions and flat junctions. Keywords: train rescheduling, differential evolution, bottleneck sections.

1 Introduction In practical railway operations, most train delays occur in junction areas, where trains from different origins converge. Because of the conflict at the junction point, a delay to one train can cause unplanned stops and consequential delays for the trains on other converging routes. A typical example is shown in Figure 1. Train 1 and train 2 approach the station ahead from different routes, via WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100841

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Figure 1:

Example of train rescheduling.

the same junction point. The nominal train trajectories for the two trains are shown as curve 1 and curve 2 in Figure 1, respectively. For instance, if train 1 is delayed from curve 1 to curve 3 because of some disturbance, it will cause conflicts with train 2 at the junction point. Without timely traffic management, train 2 has to take an unplanned stop before the junction point, as shown with curve 5. This consumes more time and increases energy consumption. If the conflict can be detected and train 2 can acquire a new train rescheduling decision from the traffic management system in advance, the driver of train 2 can slow down the train when approaching the junction point, as shown with curve 4, and the unplanned stop caused by the delayed train 1 can be avoided. This will reduce train delay and energy consumption in the event of disturbances. Considering all approaching trains to the junction point in a time window, the rescheduling problem refers to the optimisation of route setting sequences and train arrival time at junction points. On many railways, sections of the infrastructure with junctions at the portals are described as bottlenecks. These usually have the highest traffic flows in railway networks. A typical urban railway configuration, with a bottleneck section and the associated approach tracks, is shown in Figure 2. Generally, bottleneck sections are located at the heart of networks, between portal junctions where many trains converge from a range of origins and diverge to a variety of destinations. In this scenario, a relatively short delay to one train may cause long consequential delays for following trains, because of resource conflicts at junctions and dense traffic flow in bottleneck sections. Conventional train service management approaches cannot achieve reliably a level of timetable adherence that permits accurate presentation of trains at portals. A great deal of effort has been devoted to the train rescheduling in these areas, to ensure optimal use of the available capacity and to minimise the disruption to services from some unpredictable incidents [1-3]. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 2:

937

Layout of generic bottleneck sections.

The prediction of approaching train movement and the detection of potential conflicts are essential for train rescheduling in junction areas. The prediction mainly depends on the rescheduling decisions in the adjacent junctions. So the rescheduling decisions in adjacent junctions could have influence on each other. The decision making for individual junctions needs to know the decisions made in the adjacent junctions in advance. That means if there are no any cooperative mechanism applied into the decision making process for junctions, the local optimal decisions generated by each junction may not be optimal solutions to other junctions, even cause conflicts between each other and eventually have feedback to the initial rescheduling decisions. It could make the local optimal decisions infeasible. Because of the limit of recovery and margin time in bottleneck sections, the cooperative rescheduling of approaching trains for portal junctions of bottleneck sections is an efficient approach to maintain high service quality and gain better associated cost expressed in different aspects like monetary terms, weighted delay minutes and energy consumption etc., as well as the particular definition of passenger satisfaction (Tomii et al. [4]). Relevant papers have been published on different aspects of railway traffic management and control with different modelling methods (Alternative Graph, D'Ariano et al. [1], Discrete Event Modelling, Dorfman and Medanic [3], Object-oriented Modelling, Goodman and Takagi [5], Description Language for rescheduling patterns, Hirai et al. [6], etc), solution algorithms (Intelligent Search [1, 2], Dynamic Programming Ho et al. [7] etc), and also collaborative rescheduling for distributed railway traffic control based on a heuristic search for optimisation of train sequences (Chou et al. [8]). Earlier studies on optimisation of rescheduling decisions mostly focused on solving combinatorial optimisation problems like train sequences change, trains connections combination, trains re-routing, while disregarding the train running time optimisation issues together. The rescheduling strategy in this paper is focused on the retiming and re-sequencing of perturbed trains approaching portal junctions of bottleneck sections. A cooperative strategy framework for train rescheduling of portal junctions leading into bottleneck sections is proposed. The strategy is mainly based on an improved Differential Evolution algorithm for Junction Rescheduling Model (DE-JRM) which is proved to be suitable for solving train rescheduling problems for both individual fly-over junctions and flat junctions, based on a quantitative statistical evaluation method. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Figure 3:

Sketch map of junction rescheduling decision making.

This paper is organised as follows. In section 2, an individual junction rescheduling methodology is briefly introduced. Section 3 describes the quantitative statistical evaluation of DE_JRM for both flyover junctions and flat junctions. Finally, a cooperative strategy framework for train rescheduling of portal junctions leading into bottleneck sections is proposed.

2 Train rescheduling for individual junctions 2.1 Junction rescheduling model (JRM) The basic JRM principle can be represented as shown in Figure 3. Binary Decision Trees can be used for the graph based modelling of the process of rescheduling trains through a two tracks junction. For a fly-over junction, the route 1 and route 2 shown in Figure 3 are grade separated by bridges or tunnels. There will be one potential conflict point caused by the trains on approaching route 0 and 1. The rescheduling decision making process can be graphically modelled with the decision tree shown in the bottom left. Every branch of the decision tree(s) denotes a route setting for the trains on different routes approaching the junction. The train arrival time can be denoted with the length of branches. For a flat junction, two potential conflict points are created by approaching trains on three different routes (Route 0 and Route 1, Route 2 and Route 1), so that two decision trees with a common branch (Route setting 1) are used for the graph based modelling. The optimisation objective is to find the optimal decision tree branch routes with the optimal duration (train arrival time) complying the constraints of operation and signalling systems. The objective function in this paper is defined as Weighted Average Delay, which reflects the deviation of rescheduled timetable with nominal timetable and the effects on the passengers on board. The details of the mathematic formulation of JRM were presented in Chen et al. [9]. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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The presented optimisation problem for train rescheduling in junction areas is a typical NP-hard problem, as well as a hybrid optimisation problem. It is unlikely to find a classic optimisation algorithm that solves such a problem in a polynomial time. However, it is possible to find near optimal or acceptable solutions in a reasonable time using an efficient algorithm. 2.2 Differential evolution algorithms for JRM To solve the presented hybrid optimisation problem including continuous variables (train arrival time) and discrete variables (route setting decisions), an improved Differential Evolution (DE) algorithm is proposed to optimise the continuous train arrival time, taking discrete route setting decisions as constraints for the algorithm. DE algorithms are proposed to be simple and efficient evolutionary approaches for handling continuous variable optimisation problems by Storn and Price [10]. The improved Differential Evolution algorithm for Junction Rescheduling Model (DE-JRM) presented here is based on the DE algorithm “JADE” presented by Zhang and Sanderson [11]. An additional operation “Modification” is added in the process of DE_JRM, compared with traditional DE algorithms. The pseudo-code of DE-JRM is shown in Figure 4. The main function of Modification is to adapt invalid solution individuals generated by stochastic Mutation and Crossover operations based on the Greedy Rules so that they become valid in terms of the constraint rules of JRM because of the train operation and control constraints like train headway control, train running time limit etc. The details of algorithms DE_JRM can be seen in Chen et al. [9]. On the basis of large numbers of valid individuals in every generation, DE-JRM can evolve improved solutions from generation to generation and converge after numbers of generations.

Figure 4:

Pseudo-code of DE_JRM.

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3 Evaluation of DE_JRM for both fly-over junction and flat junction To validate the efficiency of DE_JRM for both fly-over junction and flat junction, a method based on Monte-Carlo simulation methodology is used to evaluate the performance of the algorithm DE-JRM quantitatively, in terms of a Statistical WAD (SWAD). The First-Come-First-Served (FCFS) strategy, which has been widely used for junction control in British railways, was chosen as the bench mark for performance comparison. A sketch map of the layout for the case study with two types of scenarios is shown in Figure 5. The left graph shows the configuration with a typical fly-over junction and the right one shows the configuration with a typical flat junction. These two main types of scenarios were studied for the evaluation of the proposed algorithm for train rescheduling. In each scenario, DE_JRM and FCFS rescheduling strategies will be applied and the performance will be compared for both fly-over junction scenarios and flat junction scenarios where 24 trains from different origins approach.

Figure 5:

Figure 6:

Sketch map of two types of scenarios for case study.

Comparison of SWAD for fly-over junction and flat junction.

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As required by the Monte-Carlo simulation methodology, large numbers of perturbed scenarios are generated for simulation experiments based on the train delay probability distribution of boundary arrival time, and Statistical WAD (SWAD) can be gained from simulation results of 10000 independent experiments for DE-JRM and FCFS. SWAD represents the overall performance value, and the comparison is shown in Figure 6. SWAD is expected to be smaller when better train rescheduling algorithms or strategies applied. It can be seen that, for both fly-over junction and flat junction, the WAD can be significantly decreased by rescheduling with DE_JRM compared with FCFS.

4 Framework of cooperative strategy for portal junctions of bottleneck sections The presented rescheduling methodology can be used for train rescheduling of individual junctions. For bottleneck sections, there are usually two junctions located at the portals where many trains converge from different origins. As shown in the Figure 7, the output train flow of one portal junction will be the input train flow of another portal junction. If the two portal junctions are located far away from each other that the train running time between two portal junctions is much longer than the rescheduling time window. That means the rescheduling decisions making in one portal junction do not need to know the rescheduling decisions making in another portal junctions in advance because the prediction of trains’ movement from another portal junction in one rescheduling time window will not be affected by the new rescheduling decisions of another portal junction. If the two portal junctions are located not far away from each other, the rescheduling decisions made in one portal junction will depend on the rescheduling decisions made in another one and have influence to each other. In addition, if there is no cooperative mechanism between two portal junctions, it is unlikely to get optimal decisions for both two portal junctions and could generate

Figure 7:

Coordinator for train rescheduling of portal junctions.

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Figure 8:

Flow chart of the cooperative strategy.

conflicts in the rescheduling decisions each other. To avoid the possible conflict decisions and try to get optimal decisions for both two portal junctions, a ‘coordinator’ is introduced as shown in Figure 7. The main task of the coordinator is to check the conflicts of the rescheduling decisions from two portal junctions and modify the decisions if necessary in the process of decisions making. The aim of modification operation in the coordinator is to adapt the invalid solutions to be valid in terms of signalling and operation constraints in bottleneck sections. The flow chart of the cooperative strategy is shown in Figure 8. Based on the DE_JRM algorithm for individual junction rescheduling, the modification operations in rescheduling process of two portal junctions are integrated into the coordinator. All of the generated decision solutions will be sent into the coordinator for conflicts check and modification. As well, the total cost of the decisions for two portal junctions will be calculated in the coordinator. The updated decision solutions without conflicts and the total cost of the decision solutions will be sent back to the rescheduling decision units of two portal junctions. The current best solution is the best solution for all trains approaching the bottleneck sections combining the best solutions from two portal junctions in current generation. Based on the proposed cooperative strategy framework for the train rescheduling of portal junctions, the train rescheduling problem for bottleneck sections can be divided into distributed individual junction rescheduling problems with cooperative mechanism between each other. This framework WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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gives a parallel rescheduling decision making approach for two portal junctions of bottleneck sections. Compared with centralised rescheduling decision making for bottleneck sections, this framework can decrease the dimension of rescheduling problem by half and also ensure that the rescheduling decisions have no conflicts. The data transmission between the coordinator and rescheduling decision units of two portal junctions will not take long time by local area networks as the decision data amount is not large and can be transmitted within only several data frames in one generation of the algorithm.

5 Conclusions Since both the margin time and the recovery time in the timetable for trains in bottleneck sections are limited, train rescheduling on the converging routes is a useful approach to achieving recovery from disturbance in railway operation in junction areas. A cooperative strategy framework for train rescheduling of portal junctions leading into bottleneck sections is proposed in this paper based on an improved Differential Evolution algorithm for Junction Rescheduling Model (DE-JRM) which has been proved to be suitable for solving train rescheduling problems for both individual fly-over junctions and flat junctions. The ongoing research is focused on the validation of the proposed cooperative strategy in terms of computation time, goodness of rescheduling solutions etc.

Acknowledgements This paper was supported by National Natural Science Foundation of P. R. China (No.60634010), and funded by Network Rail UK.

References [1] D'Ariano, A., Pranzo, M. & Hansen, I. A., Conflict Resolution and Train Speed Coordination for Solving Real-Time Timetable Perturbations. Intelligent Transportation Systems, IEEE Transactions on, vol.8, no.2, pp.208-222, 2007. [2] Sahin, I., Railway traffic control and train scheduling based on inter-train conflict management. Transp. Res.—Part B, vol. 33, no. 7, pp. 511–534, 1999. [3] Dorfman, M. J. & Medanic, J., Scheduling trains on a railway network using a discrete event model of railway traffic. Transp. Res.—Part B, vol. 38, no. 1, pp. 81–98, 2004. [4] Tomii, N., Tashiro, Y., Tanabe, N., Hirai, C. & Muraki, K., Train operation rescheduling algorithm based on passenger satisfaction. Quarterly Report of RTR, Vol. 46, No. 3, pp.167-172, 2005. [5] Goodman, C.J. & Takagi, R., Dynamic re-scheduling of trains after disruption. COMPRAIL 2004, pp.765-774, 2004.

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944 Computers in Railways XII [6] Hirai, C., Tomii, N., Tashiro, Y., Kondou, S. & Fujimori A., An algorithm for train rescheduling using rescheduling pattern description language R. COMPRAIL 2006, pp. 551-561, 2006. [7] Ho, T. K., Norton, J. P. & Goodman, C. J., Optimal traffic control at railway junctions. IEE Proc., Electric. Power Appl., vol. 144, no. 2, pp. 140–148, 1997. [8] Chou, Y. H., Weston, P. F. & Roberts, C., Collaborative Rescheduling in a Distributed Railway Control System. Proceedings of 3rd International Seminar on Railway Operations Modelling and Analysis (CD-Rom), pp. 117, 2009. [9] Chen, L., Schmid, F., Dasigi, M., Ning, B., Roberts, C. & Tang, T., Realtime train rescheduling in junction areas. Proc. IMechE, Part F: J. Rail and Rapid Transit, Accepted, 2010. [10] Storn, R. & Price, K., Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, vol. 11, pp. 341-359, 1997. [11] Zhang, J. & Sanderson, A. C., JADE: Self-Adaptive Differential Evolution with Fast and Reliable Convergence Performance. Evolutionary Computation, 2007 IEEE Congress on, pp.2251-2258, 2007.

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Circle rail transit line timetable scheduling using Rail TPM J. Zhibin, G. Jia & X. Ruihua School of Transportation Engineering, Tongji University, China

Abstract Timetable scheduling is an important and difficult task in operation. Rail TPM is a user-friendly rail transit timetable scheduling tool, it can be easily scheduled for simple-path, share-path or circle-path trains. This paper emphasizes the definition of circle line topology structure, time-space structure, calculation and application of rolling stocks, rolling stocks assignment, train storage management, transfer schemes and so on. Finally, a case study of Line 4 in Shanghai illustrates the practical value of the Rail TPM program. Keywords: rail transit, time-space diagram, circle routing, computing, Rail TPM.

1 Introduction The growing mobility in the big cities of China puts pressure on both the road and the rail transit network. High quality rail transit services are needed to facilitate the increasing numbers of passengers. The rail transit system plays a key role in mobility in urban cities. With the rapid development of rail transit lines in China, more and more types of transit lines are in operation, such as “Y” type and circle type lines. Rail transit has the characteristics of simple track, small train interval, flexible rolling stock turn-back, and high peak-time passenger flow and so on. Timetable scheduling is an important and difficult task in operation. The circle line is very important in the rail transit network. The flow characteristics, traffic organization and passenger organization of the circle line are different from other rail line types. In particular, the train diagram compilation in a circle line needs to consider the line structure, flow characteristics, customer service and rolling stock assignment. References [4] and [5] studied the key issues of time-space WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100851

946 Computers in Railways XII scheduling in a single routing line in detail. This paper mainly researches the key issues of timetable scheduling in a circle line with the computer tool Rail TPM, focuses on the line topology structure, time-space structure, rolling stock assignment, rolling stock storage management and transfer scheme of the circle line.

2 Circle line and ring routing in urban mass transit 2.1 The characteristics of ring routing If the rail transit network has a circle line, the ring routing needs to be considered. In the circle routing, the trains always run in a certain direction (clockwise or counterclockwise); train turn-back operations are not needed. In some complicated rail transit networks, there are some special ring routings constituted by different lines, which are illustrated in fig. 1. 2.1.1 Single ring line The single ring line is commonly found in urban centers, showed in fig.1-a. It has the characteristics of small intervals, short distance stations, high peak-time passenger flow and high transfer passenger flow. This line type is common in developed rail transit networks, such as the Beijing metro line 2 and the Moscow circle metro line. 2.1.2 Ring line plus straight line The straight line plus ring line is a special line kind, showed in fig.1-b and fig.1-c. The ring line and branch line can be connected with each other. The operational schemes of this line kind are complicated, and the time-space scheduling is more difficult. This line type is more common in a developed urban mass transit network, such as the Shanghai No. 3 line and No. 4 line (ring in the middle, there are nine shared stations), the Tokyo Yamanote Line (ring at the end) and Seoul Subway No. 2 line (one ring plus two branch lines). 2.2 The basic characteristics of train operation in a circle line The train operation of a circle line has some significant features.

a. Single ring Line Figure 1:

b. Ring in the middle

c. Ring in the end

Circle line structure in a rail transit network.

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2.2.1 The turnaround of rolling stock is simple The ring line is linked together from beginning to end, the trains always run in a certain direction (clockwise or counterclockwise). The turn-back of rolling stock is very simple. There are no any fixed beginning stations and terminal stations, and there also are no train turnaround operations; in addition, the train operations can be out of or in depot. 2.2.2 The operations of the inner ring and outer ring are independent, which is suitable for the design of an asymmetric time-space diagram The inner ring and outer ring are independent, so their operations will not interference each other, and the running intervals of the inner ring and outer ring could be different. These characteristics are beneficial for timetable scheduling, and the train operation adjustment is easier. For example, if the trains of one direction were delayed, the opposite direction’s trains will not be influenced. That means the anti-interference capability of the circle line is more stable than the straight line. 2.2.3 Different direction trains can reach the same destinations, and passengers’ choices are flexible In the circle line, the trains in both directions could reach passengers’ destinations, although the travel times are different. Regardless of the travel time, passengers could choose the comfortable direction to travel. In this way, the carrying capacity of both lines could be utilized fully to reduce the unbalanced degrees in different directions.

3 Key issues of timetable scheduling in a circle transit line 3.1 Topology structure definition of a circle line With the strong connectivity, the topology structure of ring line should be defined firstly. For describing the characteristics of train operations in ring line, a station should be virtualized, then the ring line be virtualized to be a straight line. For example, the station “a” in fig. 2 was virtualized to be the stations “a” and “a′”.

d

d

c

c a

a

b

Figure 2:

a a'

d

b

Topology structure of a circle line.

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c

b

a'

948 Computers in Railways XII

d

c

a(a’)

b

Figure 3:

b

a d c b a'

Time-space diagram structure of a circle line.

a d c b a'

Figure 4:

Time-space diagram of a circle line.

3.2 The definition of the time-space diagram map structure Because of the complication of base map structure, the time-space diagram map structure of total line should be illustrated firstly for describing the train running. Reasonable base map structure is benefit to the rolling stock circulation. Fig. 3 is the example of time-space diagram structure. 3.3 Display method of a running line In order to express train running process more clearly and directly, the display method of running line must be simply, directly and friendly, such as that showed in fig. 4. 3.4 Calculation and application of rolling stocks In circle line, if the virtual original or terminal station only has double track, the trains could not stay in these stations too long. If no such, the follow-up trains will be affected. When scheduling in circle rail transit line, the running intervals and total running time should be considered synthetically. The trains in inner ring and outer ring could run independently, so the number of rolling stocks needs to be calculated separately. The total number of rolling WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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stocks is decided by the train interval and total running time. The computational formulas are as follows: (1) N  N inner  N outer  nreserve inner  N inner  T inner / tinterval

outer  N outer  T outer / tinterval

T inner   rninner   tninner n 1

n

j 1

j 1

T outer   rnouter   tnouter n 1 j 1

where N is the number of rolling stocks;

(2) (3) (4)

n

j 1

(5)

N inner is the number of rolling stocks in

N outer is the number of rolling stocks in outer circle ring inner is the cycle (unit); nreserve is the number of reserve rolling stocks (unit); T

inner circle ring (unit);

time in inner ring (s); T

outer

is the cycle time in outer ring (s); tinterval is the

average interval; n is the total number of stations; is the stopping time (s).

r is the running time (s); t

3.5 Rolling stock storage management If the ring lines have many depots, there will be various scheme of rolling stock storage management. The rolling stock operation of depot should consider the flexibility and economy firstly. 3.6 Schedule of first and last trains The management of first and last trains is an important factor of scheduling. Circle line has many transfer nodes, so the running operation of first and last trains in other lines should consider the schedule of circle line. 3.7 Connection of trains in the transfer station The connection of trains in transfer station should to be considered emphatically for shortening the transfer time of passengers.

4 Timetable scheduling process in a circle routing line When computing the time-space diagram in circle routing, the line topology structure and base map structure should be constructed firstly. Based on the passenger flow and running intervals, the total running cycle time in inner ring and outer ring should be calculated separately. After matching the running WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

950 Computers in Railways XII intervals and running cycles, the number of rolling stocks could be calculated to compile the time-space diagram. Then the rolling stock storage management and transfer scheme of circle line could be made out. At last, the time-space graph and relevant indexes can be exported. The total process is illustrated in fig. 5.

5 Case application The above designing thought has been applied to the Rail Transit Train Plan Maker System (Rail TPM V4.8). Rail TPM is a user-friendly rail transit timetable scheduling tool, it can be easily scheduled the simple-path, share-path or circle-path trains. Using this software, the time-space diagrams of Shanghai Metro Line 4 and Beijing Metro Line 2 had been compiled successfully. With this tool, the efficiency and speed of computing time-space diagrams could be raised.

Start Topology structure building Base map building Running intervals inputting Running cycle adjusting

N

Whether the cycle and intervals matches? Y Rolling stocks calculating

Whether the rolling stocks matches?

N

Y Running routings drawing Storage lines drawing Transfer optimizing Indexes exporting End

Figure 5:

Flowchart of time-space diagram computing in circle routing.

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Computers in Railways XII

Figure 6:

Figure 7:

951

Metro line 4 in Shanghai.

409# time-space diagram in Shanghai Metro line 4.

The Shanghai Metro Line 4 is about 34 kilometers, with 26 stations, and has nine shared stations with metro Line 3 (showed in fig. 6). Fig. 7 is the time-space diagram examples of Metro Line 4.

6 Conclusion To computing the time-space diagram in circle lines, the line topology structure, time-space structure, rolling stock assignment, rolling stock storage management and transfer scheme should be considered comprehensively. With the frequently WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

952 Computers in Railways XII change of passenger flow characteristics in time and space, the time-space diagrams in circle line could be computed with Rail TPM timely. The computing efficiency and passenger service level could be raised.

References [1] Wong Rachel, C. W., Yuen Tony, W. Y., Fung Kwok Wah et al. Optimizing timetable synchronization for rail mass transit. Transportation Science, 42(1), pp.57-69, 2008. [2] Peeters Marc & Kroon Leo. Circulation of railway rolling stock: a branchand-price approach. Computers & Operations Research, 35(2), pp.538-556, 2008. [3] Kroon Leo, Maroti Gabor, Helmrich Mathijn Retel et al. Stochastic improvement of cyclic railway timetables. Transportation Research Part B: Methodological, 42(6), pp.553-570, 2008. [4] Xu Ruihua, Jiang Zhibin. Key problems of designing train timetable in urban mass transit system with computer. Urban mass transit research, 8(5), pp.31-35, 2005. [5] Jiang Zhibin, Xu Ruihua, Designing multi-interval train working diagram in urban mass transit system with computer. International Doctoral Student Innovation Forum in Traffic and Transportation Engineering, Zhu Zhaohong, China Communications Press: Beijing, pp.75-82, 2005.

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A simulation analysis of train rescheduling strategies on Chinese passenger dedicated lines Z. He1, L. Meng2, H. Li2 & L. Nie2 1

State Key Lab. of Rail Traffic Control and Safety, Beijing Jiaotong University, China 2 School of Traffic and Transportation, Beijing Jiaotong University, China

Abstract Train rescheduling plays a vital role in railway operation. Many factors should be considered in the train rescheduling process and train class, delayed time, remaining distance and current position are the most common ones. There are usually different measures aiming at corresponding factors and combinations of the measures with different priorities compose various train rescheduling strategies. Furthermore, different strategies lead diverse results and they are applicable to various scenarios respectively. This paper researched the effects of different train rescheduling strategies in the background of Wuguang passenger dedicated line by means of simulation experiments. It analyzed class-based strategies first and achieved the suitable strategy and its related parameters. Then it analyzed the influence of different combinations of high-speed strategies and middle-speed strategies under different perturbation scenarios. Keywords: train rescheduling strategy; passenger dedicated line; simulation.

1 Introduction Train rescheduling is the key work in railway operation. Its basic idea is to determine a new order of the trains when some perturbations make current schedule disordered and infeasible. Many factors should be considered together and corresponding measures will be taken in this process. The measures and their various combinations compose different train rescheduling strategies which will probably lead diverse results and are applicable to various scenarios respectively. Because of high density and high speed of trains, train rescheduling work in Chinese Passenger Dedicated Lines (PDL) is more difficult than that in WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line) doi:10.2495/CR100861

954 Computers in Railways XII traditional lines. In addition, the mix-speed operation mode that high-speed trains and middle-speed trains run on the line simultaneously will be applied in many of these lines and will last for many years, which will increase the difficulty of train rescheduling much further. Due to too many factors involved in the process of train rescheduling, effectiveness of each strategy can hardly be calculated by pure mathematical ways. Therefore, computer simulation becomes the common way for analysis of train rescheduling strategies. Nie [1] and Zhang [2] analyzed impact factors in train rescheduling by simulation and gave some good ideas. Jin [3] also analyzed the dispatchers’ preferences which would be helpful to the research of the train rescheduling strategies. This paper will analyze the influence of different strategies on train rescheduling in Chinese PDL by means of computer simulation. The analysis of simulation data under different strategies will provide foundations for reasonable and effective train rescheduling strategies in Chinese PDL.

2

Analysis of train rescheduling strategies

When perturbations occur, some conflicts will generally take place which will make current schedule infeasible. To resolve the conflicts and get new optimized schedules composed the main work of train dispatchers. The common approach is to resolve each conflict from the earliest one and rearrange order of trains pair by pair, as shown in Figure 1. Some researchers had developed the algorithms in accordance to this idea [4-6].

Detect Conflicts

Has conflicts?

N

Y Select earliest conflict

Rearrange orders of 2 trains involved

Drive trains

End

Figure 1:

Process of train rescheduling.

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In this process, new order will be assigned to involved trains according to their characteristics and, in which train class, delayed time and remaining journey are the most common ones. Some strategies are hereby created and applied to determine which train will depart firstly. Train class is usually a primary factor in train rescheduling, which makes the class-based rescheduling strategies- severe strategy and flexible strategy- become the basic strategies in train rescheduling. Severe strategy (Strategy P0) means that higher-class trains will undoubtedly depart first. Flexible strategy (Strategy P1) means that lower-class trains can depart first in some cases. Then various strategies are designed aiming at delayed time and remaining journey respectively in order to study the influence of respective strategies on the trains with different classes in PDL. Under the precondition of class-based rescheduling (strategy P0 or P1), the strategies will be designed as following: If the trains involved in the current conflict have different class, strategy P0 or P1 will be applied to determine which train has higher priority, otherwise, one of the following 5 strategies may be used: 1) Strategy 1: The trains will depart in accordance with their current order and all the other factors will be ignored. 2) If both of the trains involved in the current conflict are delayed, they will depart in accordance with their current order. However, if one of the trains is delayed and another is punctual, then the strategy will be: Strategy 2: The punctual train will always depart first. Strategy 3: The delayed train can depart first only if delayed train run ahead of punctual train currently and the time difference between the earliest possible departure time of the delayed train and punctual train is less than I/2 (where I is the standard headway interval time). In any other cases, the punctual train will depart first. 3) If the difference of remaining journey between the two trains is less than M minutes where M is the running time of about 2-3 sections, they will depart according to current order. Else, the following strategies will be applied: Strategy 4: The train with longer remaining journey has the higher priority and will depart first. Strategy 5: The train with shorter remaining journey has the higher priority and will depart first.

3 Simulation analysis of class-based strategies The framework of the simulation is shown as figure 2. Simulation control module controls occurrence of various perturbations, simulation iterations, kinds of data analyzed and some other parameters. The strategies control module will determine which strategy should be applied in current scenario. The train advancing module is in charge of movement of trains and train primary delay will come into being in this process. The rescheduling module will detect and resolve each conflict in turn in the process of train advancing according to different strategies loaded by strategy control module. Finally, the data statistics and analysis module will collect and analyze simulation results when simulations WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

956 Computers in Railways XII

Perturbance Configuration

Simulation Control

Strategies Control

Timetables

Trains advancing

Rescheduling

Data statistics and analysis Analysis Result

Figure 2:

Framework of the simulation.

aiming at each strategy have been accomplished. The detailed approach of train advancing and rescheduling can be found in reference [6] and [7]. In this study, Wuguang PDL (from Wuhan to Guangzhou) was taken as example. A timetable including 115 pairs of high-speed trains (1st class, 300-350 km/h) and 30 pairs of middle-speed trains (2nd class, 200-250 km/h) was made for the simulation. High-speed trains are distributed into the timetable perioduniformly which means the quantity of the trains per hour is different in different period (morning/ evening rush hours, normal time, etc.) but same within a period. The middle-speed trains are assigned in the timetable also uniformly with the density of 1-2 trains per hour except in rush hours (7:00-9:00 and 17:0018:00). Considering the relative abundant capacity of this line, the minimum interval between two trains in the timetable is set to I+1 in order to ensure enough buffer for train rescheduling. In addition, the allowance of the running time is set to 6%-7%. As mentioned above, there are 2 types of class-based strategies. The difference between them is that whether the lower-class trains are permitted to influence higher-class trains. In fact, the two strategies can be concluded into one strategy as: when a conflict occurs between a higher-class train and a lower-class train, the higher-class train can be moved only if it ran behind of the lower-class train currently and the moved time is no more than N minutes. If N = 0, the strategy is severe strategy, or it's the flexible one. Obviously, N is the key parameter of class-based strategies. First, N is set to 0 and the initial delay time of middle-speed trains which are created by simulation control module is fitted to normal distribution with expected value of 5, 10, 15, 20 (minutes) respectively. Meanwhile, there is no delay with high-speed trains. In the process of train rescheduling, class-based strategy is applied, and then the strategies 1-5 are applied to the middle-speed trains respectively. The results are shown in table 1.

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Computers in Railways XII

Table 1:

AIDT/ QIDT*

Results of middle-speed train rescheduling with different initial delay time. Strategy

1

QFDT 5:00/1.9

10:25/2

15:08/2

19:32/2

957

2 0.9

3 0.8

4 1

5 0.9

0.9

AFDT

27:55

40:24

34:57

29:53

32:09

TFDT

24:59

32:57

33:35

26:21

28:56

QFDT

1.8

1.5

1.6

1.8

1.8

AFDT

39:58

52:10

48:13

40:22

41:45

TFDT

1:10:53

1:17:01

1:15:46

1:12:33

1:13:34

QFDT

2

1.7

1.8

2.1

2

AFDT

43:56

58:54

54:24

43:48

45:30

TFDT

1:29:28

1:37:29

1:36:30

1:30:45

1:31:32

QFDT

2.4

1.9

2

2.5

2.4

AFDT

43:12

58:33

54:35

42:45

45:29

TFDT

1:44:55

1:51:15

1:50:28

1:45:52

1:47:14

*AIDT: Average Initial Delay Time; QIDT: Quantity of Initial delayed trains; TFDT: Total Final Delay Time

QFDT: Quantity of Finally Delayed Trains AFDT: Average Final Delay Time

As we can see from table 1, when AIDT is greater than 10 minutes, the result turns worse remarkably. The reason is that the middle-speed trains will generally enter the time slots of high-speed trains and can hardly recover their delay time. So ten minutes is an important critical value and will be used in the following research. Since then, the study will continue to simulate the cases in which 5% of middle-speed trains will delay ten minutes averagely and N = 0,1,3,5,7 minutes. In the process, strategy 1 will be applied in both middle-speed and high-speed train rescheduling. The result is shown in table 2. Table 2: N Middlespeed trains Highspeed trains

QFDT

Influence of N on rescheduling results. 0

1 1.8

3 1.1

5 0.6

7 0.5

0.5

AFDT

39:58

26:10

19:58

21:17

18:46

TFDT

1:10:53

28:40

12:36

11:39

09:49

QFDT

0

0.3

0.4

0.5

1.1

AFDT

0

02:13

02:23

02:38

03:36

TFDT

0

00:36

01:03

01:26

03:49

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958 Computers in Railways XII The data above show that all the indices of middle-speed trains, especially QFDT and TFDT, have been improved to a quite large extent with the increase of N. When the value of N changes from 0 to 1 and 1 to 3 minutes, TFDT of middle-speed trains increases obviously. However, when the value of N continues to grow up, the increase of TFDT slows down. When the value of N changes from 5 to 7 minutes, the result of high-speed trains turns worse evidently. It indicates that there's a reasonable range of value for N. Considering the change of indices of high-speed and middle-speed trains synthetically, 3 to 5 minutes is a suitable value to the strategy in this study. In addition, the real interval between trains in the timetable of current study is about 4.5 minutes (I+1). The analysis above shows that the value of N should be a little less than the real interval between high-speed trains. If N is more than real interval between high-speed trains in the timetable, a high-speed train is possibly moved behind the other one, which will result that the train can hardly recover from delay. The quality of railway operation will turn worse severely along with it. Since then, the value of 3 minutes is selected as the value of N in the following research.

4 Simulation analysis on different combinations of high-speed strategies and middle-speed strategies under different perturbation scenarios Twelve scenarios of perturbations (train delay) are made after analyzing the current operation of Chinese railway and shown as table 3. Table 3:

Scenarios of perturbations.

High-speed trains No

Middle-speed trains

Probability/%

Delay Time (min)

Probability/%

Delay Time (min)

1

3

5

0

0

2

0

0

5

5

3

0

0

5

10

4

0

0

5

15

5

3

5

5

5

6

3

5

5

15

7

3

5

10

5

8

3

5

10

10

9

5

5

10

15

10

5

5

20

15

11

5

10

20

15

12

10

15

20

15

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Computers in Railways XII

4.5 4 S1 S2 S3 S4 S5

QFDT

3 2.5 2 1.5

TFDT

3.5

1 0.5 0 1

2

3

4

28:48 25:55 23:02 20:10 17:17 14:24 11:31 08:38 05:46 02:53 00:00

S1 S2 S3 S4 S5

1

5

959

2

3

4

5

High-speed Strategies

High-speed strategies

(a) High-speed trains 3.5

54:43

2.5

TFDT

QFDT

47:31 S1 S2 S3 S4 S5

S1 S2 S3 S4 S5

40:19 33:07 25:55 18:43

1.5

1 1

2

3

4

2

3

4

5

5

Middle-speed strategies Middle-speed strategies

(b) Middle-speed trains Figure 3:

Change of QFDT and TFDT.

After a number of simulation experiments, we got many useful data and found some interesting facts. As to each scenario, different combinations of high-speed and middle-speed train rescheduling strategies will be applied with the precondition of strategy P1 (N = 3 minutes). It means that there are 25 strategy combinations applied to each scenario since there are 5 types of strategies for high-speed and middle-speed trains respectively. 1) The interference between a middle-speed strategy and a high-speed strategy is very little in any scenario. We take the result of scenario 8 as example. In this scenario, the QIDT and AIDT of high-speed are 3.6 and 5:20 and them of middle-speed trains are 3.4 and 10:00. The change of QFDT and TFDT of high-speed trains are shown in figure 3(a) in which S1-S5 represent current middle-speed train rescheduling strategies 1-5 and the number of x-axis is current high-speed strategy. As we can see from the figure 3(a), as to the same high-speed train rescheduling strategy, when the middle strategy is changed, the value of QFDT and TFDT almost keep invariable. (The maximum change of QFDT and TFDT are 0.2 trains and 1:33) Now we continue to research the influence of high-speed strategies on middle-speed strategies. The example is also scenario 8. The change of QFDT and TFDT of middle-speed trains are shown in figure 3(b). WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

960 Computers in Railways XII The change of QFDT and TFDT of middle-speed trains is also very little when high-speed strategy is changed, only 0.2 trains and 1:26 respectively. Moreover, the characteristics about interference in other scenarios are very similar to scenario 8. Therefore, the interference between middle-speed strategies and high-speed strategies is little in any scenario. So we can generally pay no attention to another type of strategies when we focus on high-speed or middlespeed train rescheduling strategies. 2) In the view of TFDT of high-speed trains, strategy 1 has the best result according to the data in table 4, which is also right in other scenarios. It means that strategy 1 can make better use of time allowance arranged in timetable than other strategies. Therefore, strategy 1 should be recommended in high-speed train rescheduling. Table 4:

Result of high-speed trains rescheduling under several scenarios.

Scenarios

QIDT/ AIDT

1

3.6/ 5:20

High-speed Strategies QFDT

5

8

12

3.6/ 5:20

3.6/ 5:20

12.3/ 15:04

1

2 1.8

3 1.1

4 2.2

5 1.9

2.2

AFDT

05:01

17:25

07:41

06:32

05:23

TFDT

08:52

19:38

16:51

12:39

12:01

QFDT

2.1

1.6

2.8

2.2

2.8

AFDT

05:14

13:30

06:57

06:50

05:12

TFDT

11:00

21:42

19:14

15:20

14:41

QFDT

3.1

2.3

3.9

3.3

4.1

AFDT

04:42

11:05

06:13

05:49

04:49

TFDT

14:32

25:46

24:14

19:00

19:46

QFDT

18.9

12.6

17.8

19.6

20.1

AFDT

09:20

20:51

11:46

10:07

09:25

TFDT

2:56:41

4:22:22

3:29:17

3:18:56

3:09:19

3) The DFDT of high-speed trains of strategy 2 is the least one. So strategy 2 can be applied if the objective of train rescheduling is to minimize the quantity of delayed trains. However, when we turn our eyes to TFDT, the result of strategy 2 is the worst one. The following work is to research the influence of different strategies on middle-speed train rescheduling. Table 5 shows the result of middle-speed train rescheduling in scenario 5, 8 and 12. 1) Comparing each AIDT with its corresponding AFDT, it will be found that when middle-speed trains are delayed, their delay time will increase obviously regardless of the strategies applied. 2) The difference of the results is very little when various strategies are applied to the same scenario. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

Computers in Railways XII

Table 5: Scenarios

Result of middle-speed trains rescheduling under several scenarios. QIDT/ AIDT

Middlespeed Strategies QFDT

5

8

12

961

1.7/4:57

3.4/10:0 0

6.7/15:3 2

1

2

3

4

5

1.2

1.2

1.2

1.2

1.3

AFDT

08:45

07:55

08:16

08:48

08:55

TFDT

10:31

09:50

10:02

10:19

11:16

QFDT

3.1

3

3.1

3.2

3.1

AFDT

13:11

15:39

14:08

13:34

13:55

TFDT

41:26

46:50

44:09

43:20

42:28

QFDT

9.9

9.5

9.8

9.9

9.8

AFDT

16:38

18:45

17:21

16:46

16:57

TFDT

2:44:22

2:57:25

2:50:30

2:46:44

2:45:50

The two facts above are also right in the other scenarios. They mean that the rescheduling of middle-speed trains is still limited severely although strategy P1 is applied. If we want to achieve better results, the value of N must increase further. However, it will lead to worsen high-speed train rescheduling results remarkably.

5 Conclusions The paper researched the influence of different train rescheduling strategies designed by train classes, delayed time and the remaining journey on train operation in passenger dedicated line under the operation mode with both highspeed and middle-speed trains by means of simulation. The simulation results showed that: 1) If the middle-speed trains are permitted to move the high-speed trains, the TFDT will decrease evidently, in other words, their anti-disturbance ability will increase remarkably. The compelling moving time of high-speed trains should be a little less than the real interval between high-speed trains in timetable in order to improve the results of middle-speed trains rescheduling and keep the influence on high-speed trains quit little at the same time. 2) The interference between middle-speed strategy and high-speed strategy in the same process of train rescheduling is little in any scenario. So we can generally pay no attention to another type of strategies when we focus on highspeed or middle-speed train rescheduling strategies. 3) In the view of finally delay time of high-speed trains, strategy 1 has the best rescheduling result and should be recommended in high-speed train rescheduling. The DFDT of high-speed trains of strategy 2 becomes the least among all strategies, but the overall final delay time of strategy 2 is the worst.

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962 Computers in Railways XII 4) The rescheduling of middle-speed trains is still limited severely by highspeed trains although strategy P1 is applied. So the difference of the result is very little when various strategies are applied to same scenario. The rescheduling strategy involves lots of factors and the relations among them are very complicated. Besides the factors researched in this paper, the strategy has also close relations with many other factors, especially the parameters of timetable. Hence, the influence of the mode of timetable designing and the values of all kinds of parameters of timetable on rescheduling strategy will be the important work for the following research.

Acknowledgement This study was sponsored by State Key Lab of Rail Traffic Control & Safety, Beijing Jiaotong University (No.RCS2009ZT008). The authors deeply appreciate the support.

References [1] Lei Nie, etc. (2001), Study on strategy of train operation adjustment on high speed railway. Journal of the China railway society, 23(4), pp. 1-6 [2] Xingchen Zhang (1998), A simulation analysis of middle speed train delay influence under the operation mode with high and middle speed train in Jinhu high speed railway, Journal of the China railway society, 20(5), pp. 1-6 [3] Fucai Jin (2004), Study on Theory and Methods of Multi-Objective Optimization of Train Operation Adjustment, Doctor Dissertation, Beijing Jiaotong Unitversity. [4] J. Medanic, M.J. Dorfman (2004), Scheduling trains on a railway network using a discrete event model of railway traffic, Transportation Research Part B, 38, pp. 81-98 [5] Ismail Sahin (1999), Railway traffic control and train scheduling based on inter-train conflict management, Transportation Research Part B, 33, pp. 511-534 [6] Zhenhuan He (2005), Study on adjustment method of operation diagram with computer, railway computer application, 14(10), pp. 4-6 [7] Zhenhuan He, etc (2009), Research on Greedy Train Rescheduling Algorithm, Proceedings of the 9th ICCTP, ASCE，Harbin: Harbin Institute of Technology.

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An efficient MIP model for locomotive routing and scheduling M. Aronsson1 , P. Kreuger1 & J. Gjerdrum2 1 Swedish 2 Green

Institute of Computer Science, Sweden Cargo AB, Sweden

Abstract This paper presents a MIP model for a locomotive routing and scheduling problem from the domain of freight railways. Innovative features of the model include the use of binary variables to separate the integer and continuous parts of the problem to maintain the flow character of the integer part of the problem. The model has been developed with, and has found practical Green Cargo, the largest rail freight operator in Sweden. Keywords: vehicle routing and scheduling, rail traffic resource management.

1 Introduction The increasing competition within the railway transportation sector requires effective resource utilisation methods for companies such as Green Cargo, the largest rail freight operator in Sweden. In many countries in Europe, railroads have traditionally been state-owned organisations with diverse interests in e.g. passenger traffic, freight traffic, infrastructure and real estate investments. The Swedish state railway was deregulated in all these areas around the millennium, creating separate companies with dedicated resources. Before the deregulation, locomotives were used for passenger traffic in the daytime and freight traffic at night. Today, locomotives are dedicated to either cargo or passenger traffic, which has brought about utilisation patterns such as in figure 1.

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964 Computers in Railways XII

Figure 1: Locomotive utilisation pattern without optimisation for typical week.

1.1 Timetabling Railway operators on deregulated markets have to adhere to timetables partly designed by the rail infrastructure managers. Operators apply for timed infrastructure allocation (timetable slots) based on information about traffic patterns, customer requirements, and operator resource consideration. If no slot conflicts arise, the operators normally receive their slots, but if not, they either have to accept alternative slots proposed by the infrastructure manager, or negotiate to influence an arbitration process. In this negotiation process, arguments involving customer demands and resource limitation are seriously considered by the infrastructure manager. This paper addresses the problem of generating schedules and corresponding turnaround plans for locomotives, that have to satisfy both customer requirements and limits on operator resource utilisation costs. Resource conflicts on infrastructure resources are handled by the infrastructure manager and are out of the control of the individual traffic operator and not addressed in this paper. Similarly, vehicle maintenance requirements are handled in the fleet assignment process that uses the proposed turnaround plan as input. 1.2 Locomotive optimisation The locomotive optimisation process determines the turnaround plan for all locomotives. In this process, a sequence of timetable slots are assigned to each vehicle such that all transports are covered by the appropriate type and number of locomotives. Transferring a locomotive from one transport to another is called a “turn”, and the set of all turns is called a turnaround plan. Traditionally, the timetable slots for the transports are considered as given in this process. However, if the slots can be shifted in time, many turns that would otherwise be considered infeasible become possible, which can lead to a reduction in the number of locomotives required to perform the same number of transports. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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2 Problem description Minimum cost network flow models are extensively used (see e.g. [1]) to compute an optimal assignment of vehicles to scheduled transports (train movements). Transports are represented as nodes in a network. The fact that a vehicle used by one transport can also be used by another one, is represented as a (directed) arc between their corresponding nodes. Classical network flow models of this kind usually have set partitioning structure and binary flow variables so that each transport is assigned to a unique vehicle. A straightforward generalisation of this type of flow model for cyclic schedules allows (small) integer values for the flows, and has been used for engine routing in rail transportation (see e.g. [2]). In such models, additional integer variables are associated with each node to encode how many vehicles travel with each transport. Flow is conserved on each node, giving cyclic schedules for each vehicle. Lower and upper bounds on the node variables capture the minimum and maximum number of vehicles required and usable by each transport. Lower bounds on the node variables in the cases considered here vary from 0 on (potential) vehicle relocations to 2 for heavy freight transports. Upper bounds larger than the corresponding lower ones encode the possibility to relocate additional accompanying locomotives with a planned transport that is already served with the required number of vehicles. With a cost function penalising the total number of vehicles needed, we get a straightforward and practical model that has seen several years of practical use in e.g. the Swedish rail industry. Normally, the network is statically generated using temporal non-overlap and distance conditions on the transports. It would be of great practical value if this kind of model could be generalised to allow for rescheduling of transports in cases where this would significantly reduce the cost of vehicle usage. However, using time windows for the departure times of the transports and an initial network with connections between any two transports that arrive and depart from the same location, breaks the locality (and hence, the network structure) of the model. Problems of this general type are variants of the “multiple Travelling Salesman Problem” (m-TSP). The case with time windows is normally referred to as a “multiple Travelling Salesman Problem with Time Windows” m-TSPTW [1, 3–5]. This problem is normally (e.g. [6]) considered as a special case of the extensively studied class “Vehicle Routing Problems” (VRPs) [7]. The current paper presents a practical MIP model of this problem that can be used to efficiently and exactly solve practical problems up to the size of those occurring in real life transportation planning, for moderate sizes of departure time windows (< 3 hours), using a state-of-the-art commercial solver. The model and its implementation for the solution of a large scale practical case is presented. The transports in this case form a set of train transports with a fixed schedule whose departure times are relaxed from ±15 up to ±90 minutes, and the vehicles considered are the locomotives used to pull the trains. Performance results for solving several versions of the practical problem using CPLEX 9 [8] on a PC-type workstation are also reported. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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3 Model parameters The model is parametrised by a number of constants and variables with associated bounds that will be summarised here. The constraints and objective function will be presented in section 5. Note that we have chosen to present the variable bounds in connection with their respective variables. Note also that the problem is periodic, i.e., the transport schedule is repeated after a fixed period CT , typically a week. The individual vehicle schedules may, on the other hand, span several such periods.

3.1 Constants CT ti pi loi , ldi rij

Cycle time (period after which the transport schedule is repeated). Travel time for transport i. We require each ti to be positive and strictly smaller than CT . Penalty per vehicle accompanying transport i above that of its vehicle requirement. Origin and destination locations of transport i. Setup time (turn time) for the exchange of one or more vehicles between transports i and j. We require each rij to be positive and fulfil the inequality ti + rij < CT .

3.2 Decision variables (discrete) Xij

′ Cij , Cij

Yij , Yij′ Si

Ei

Integer variable, determining how many vehicles are turned (transferred) from transport i to transport j. In the practical cases considered below, the lower bound, Xi,j is normally 0, and the upper bound Xi,j is either 1 or 2. Boolean variables, used to determine if a turn from transport i to transport j crosses the cycle time border CT . Integer variables, which for any optimal solution will have the values ′ Yij = Cij Xij and Yij′ = Cij Xij respectively. Integer variable used to represent the number of vehicles assigned to transport i. A lower bound Si on this variable encodes the minimal vehicle requirement of the transport while an upper bound Si limits the number of vehicles usable by it. Integer variable used to encode the number of vehicles accompanying a transport in addition to the number Si required by the transport itself.

3.3 Time point variables (continuous) di

Continuous variable denoting the departure time of transport i. The departure time window is represented by the bounds di and di of di . WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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This formulation does not guarantee that the arrival times di + ti will always be smaller than CT , which influences the formulation of the constraints relating the arrival and departure events of the transports. The next section gives a case analysis of the situations that can occur, and motivates the constraint formulation given in the section following it.

4 Turning over the cycle time border The cases are illustrated by figures where coloured bars represent the transports. The vertical extension of a coloured bar is the travel time of the transport (the interval between scheduled departure and arrival time). The surrounding transparent bar illustrates the departure time window of the transport so that the coloured bar may be placed anywhere within the transparent one. There are four main cases for a turn from transport i to transport j to consider, each one described below. Ao The turn, if chosen, will never cross the cycle time border, i.e.: di + ti + rij ≤ dj i j

A1 The turn, if chosen, is certain to cross the cycle time border exactly once, i.e.:    di + ti + rij > dj ∧ di + ti + rij ≤ dj + CT i j

A2 A more rare case, which nevertheless has to be taken into account, is when the turn, if chosen, is certain to cross the cycle time border twice. Note that in this case (as well as sometimes in A1 ), two instances of the transport that crosses the border have to be considered, one leaving the period and one entering the period, i.e.: di + ti + rij > dj + CT i

i j

A turn like this is hardly ever desirable, at least not if the period time is long in comparison with the longest travel time. A more complex case occurs when the time windows overlap so that the turn may or may not cross the cycle time border one or two times, but the exact number depends on the assignment of the departure time variables. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

968 Computers in Railways XII In the general case, it is possible to distinguish the following subcases: B1 The turn may cross the cycle time border once, or not at all, i.e.:       di + ti + rij ≤ dj ∧ di + ti + rij > dj ∧ di + ti + rij ≤ dj + CT i j

B2 The turn may cross the cycle time border twice, but maybe only once, i.e.: 

 

 

di + ti + rij > dj ∧ di + ti + rij ≤ dj + CT ∧ di + ti + rij > dj + CT

i



i j

B3 The turn may cross the cycle time border twice, once, or not at all, i.e.:     di + ti + rij ≤ dj ∧ di + ti + rij > dj + CT i j

In the model below, we penalise the case where a turn crosses the cycle time border two times twice as hard as the case where it does so only once, which means that turns of this type are almost never found in an optimal solution.

5 Model constraints and objective The cases labelled A0 through A2 above are all, if used as turns in a solution, determined to cross the cycle time limit either once, twice, or not at all. The cases labelled Bi , on the other hand, are indeterminate, and will be collectively encoded ′ using the two boolean decision variables Cij and Cij . To be able to treat the A WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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and B cases separately, we will define four mutually exclusive subsets of possible turns. We will need to introduce binary decision variables only for the Bi cases. Let A0 = {i, j | (0 < i, j ≤ n) ∧ di + ti + rij ≤ dj }



 



A1 = {i, j | (0 < i, j ≤ n) ∧ di + ti + rij > dj ∧ di + ti + rij ≤ dj + CT }





A2 = {i, j | (0 < i, j ≤ n) ∧ di + ti + rij > dj + CT }

and B = {i, j | 0 < i, j ≤ n} \ (A0 ∪ A1 ∪ A2 ) Since the main objective of the model is to minimise the number of vehicles used by a solution, and this corresponds exactly to the number of vehicles turned over the cycle time limit, the objective function will treat each of these cases (except A0 which can never contribute to the cost) separately. We also introduce a term in the cost function that penalises the use of additional vehicles for transports that do not need them. Such relocations are in most cases necessary to balance the flow of the model, but should otherwise be avoided. This penalty is weighted by the (temporal) length ti of the transport, and a factor pi specific to each transport. Minimise   Σi,j∈A1 Xij + Σi,j∈A2 2Xij + Σi,j∈B Yij + Yij′ + Σ0 ti + rij ∀i, j (i, j ∈ B) Xij − Yij + M Cij ≤ M ⎪ ⎩ ′ ≤M Xij − Yij′ + M Cij and Si − Ei = Si

∀i

′ ′ (a) Cij , Cij boolean, Cij ≤ Cij (b) Si , Xij (implicitly) integer (c) Variable bounds di ≤ di ≤ di , Si ≤ Si ≤ Si , Xij ≤ Xij ≤ Xij for ∀ij

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970 Computers in Railways XII 5.1 Constraint notes The flow (conservation) constraints (1) ensure that each transport is supplied with as many vehicles as it needs and that the flow is balanced. To ensure that this is always possible, we need to introduce vehicle relocations. This is done by adding “passive” transports with a vehicle requirement of zero, that need not be performed unless they are assigned at least one vehicle. These are penalised more severely than additional locomotives assigned to “real” transports. The turn time constraints (2) and their use of the boolean variables (2a) are the ′ core of the model. Note that, for any optimal solution, Cij = Cij = 0 if and only if ′ di + ti + rij ≤ dj , that Cij = 1 > Cij if and only if dj < di + ti + rij ≤ dj + CT , ′ and finally that Cij = Cij = 1 if and only if di +ti +rij > dj +CT , corresponding exactly to the three A-cases above. This follows from the fact that unnecessarily assigning 1 to Cij while Xij > 0, will be penalised by forcing Yij to become equal ′ to Xij , and similarly for Cij and Yij′ . A key feature of the model, and the main reason that it scales relatively well in practise, is that the integrality constraints on Si , and Xij (2b) need not be enforced by the solver. In each leaf in the search tree branching on the boolean variables ′ Cij and Cij , the part of the coefficient matrix involving these variables will be a pure minimal cost flow. The same obviously does not apply to the part involving the departure time variables di , but since these variables are related to the decision ′ variables Si and Xij only through the booleans (Cij , Cij ), each assignment of the di variables that is consistent with a complete (integral) assignment of the booleans, will also be consistent with the optimal assignment of the decision variables Si and Xij . This means that the optimal solution to the problem obtained ′ by relaxing the integrality constraints on Si and Xij (but not on Cij and Cij ) will also be an optimal solution to the original problem.

6 Empirical results The performance results have all been produced using data from the largest Swedish rail freight company Green Cargo. The case consists of 1304 transports and contains almost all transports handled by their most common vehicle, the electrical RC locomotive, for one full week. The problems solved below were generated by introducing a fixed amount of slack for each departure time in the production plan. In the solutions reported below, accompanying locomotives have been freely introduced and moved around between transports that allow them. Passive transports, on the other hand, are eliminated wherever this leads to an improved objective. Note that introducing slack uniformly is not completely realistic. In reality, customer requirements or limits on infrastructure capacity may not allow free rescheduling of transports within their time windows. To some extent, this can be improved by introducing individual slack for each transport, and weighted binary relations between arrival and departure events that encode e.g. transfers of cars WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Table 1: Without additional passive transports. Slack

Booleans

Vehicles

Accompanying

Deviation

Run time

±0

-

117

minutes

minutes

h:mm:ss

50835

-

0:00:05

±15

1027

116

50206

5107

0:00:07

±30

1995

112

51107

13763

0:00:54

±45

2836

105

51177

20841

0:01:19

±60

3913

99

49402

35651

0:08:22

±75

4930

97

49411

48486

1:53:40

±90

5876

90

50385

69067

23:22:43

minutes

and cargo. In the performance results reported here, no such additional constraints were used. Nevertheless, a production version of the software used to generate these problems is currently in use at Green Cargo in their planning of locomotives. Table 1 reports, for each slack size (in minutes), the number of booleans needed to encode the turn time constraints, which should give a rough indication of the MIP size. It also reports properties of the optimal solution found in terms of the number of vehicles, the total amount of accompanying locomotives, and (additional) “passive” time in minutes. Performance results in terms of run time in seconds for each slack size are also included. More specifically, the run times are those reported by CPLEX 9 on an 2.4 GHz Pentium 4 processor using about 2 GB of main memory. For the larger cases, caching the node tree to disk was done whenever it became larger than the main memory. The strategy used was the default heuristic of CPLEX 9 [8]. Once the optimal solution for the locomotive turns has been found, a new timetable is generated minimising the sum of deviations from the original timetable. This problem is linear and no performance results of these runs are given. The resulting deviation (in minutes) is given in the table to give an indication of how much the original timetable had to be changed to achieve the corresponding improvement of the main objective. As can be seen from the tables, the number of booleans increase more or less linearly with increased time window size, which is reasonable since the booleans correspond to temporal overlaps between transports potentially served by the same vehicle. The number of vehicles used by the optimal solutions to the relaxed timetable is also substantially reduced for increased time window sizes. Going e.g. from 117 locomotives to 90 represents a reduction of vehicle usage by 23%, which would be sensational were it not for the fact that the current model does not take track slot availability into account. Still, these figures do show the potential of taking locomotive fleet costs into account when planning the timetable. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

972 Computers in Railways XII Run times increase fairly rapidly with time window size, but none of the cases above are completely unrealistic for a practical work flow. The model is not particularly sensitive to different timetables, but the potential gain obviously varies.

7 Conclusions We show how rescheduling transports can reduce locomotive usage in a train transportation problem. An optimisation model for transport departure time windows varying around an initial target time is formulated and it is shown that substantial reduction of locomotive usage (up to 23%) can be achieved using a standard commercial IP-solver. Innovative features of the model include the use of boolean variables to separate the integer and continuous parts of the problem to maintain the flow character of the integer part of the problem for each complete assignment of the booleans. Application of the model produces a modified train schedule that accommodates the requirements for an efficient locomotive turnaround plan. The practical usefulness of the model and its scalability is demonstrated on a set of problems derived from a real case in the Swedish rail freight industry. Significant savings can be realised for a uniform fleet of locomotives, in terms of locomotives planned, by utilising the presented method.

References [1] Desrosiers, J., Dumas, Y., Solomon, M. & Soumis, F., Network Routing, North-Holland, volume 8 of Handbooks in Operations Research and Management Science, chapter Time Constrained Routing and Scheduling, pp. 35–139, 1995. [2] Drott, J., Hasselberg, E., Kohl, N. & Kremer, M., A planning system for locomotive scheduling. Technical report, Swedish State Railways, Stab Tågplanering, Stockholm, Sweden, and Carmen Systems AB, 1997. [3] Solomon, M. & Desrosiers, J., Time window constrained routing and scheduling problems. Transportation Science, 22(1), pp. 1–13, 1988. [4] Zwaneveld, P., Kroon, L., Romeijn, H., Salomon, M., Dauzère-Pérès, S., van Hoesel, S. & Ambergen, H., Routing trains through railway stations: Model formulation and algorithms. Transportation Science, 30(3), pp. 181– 194, 1996. [5] Bektas, T., The multiple traveling salesman problem: an overview of formulations and solution procedures. Omega, 34, pp. 209–219, 2006. [6] Cordeau, J.F., Desaulniers, G., Desrosiers, J., Solomon, M.M. & Soumis, F., The Vehicle Routing Problem [9], SIAM Monographs on Discrete Mathematics and Applications,SIAM, Philadelpia, Pa., chapter 7: The VRP with Time Windows, 2002.

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[7] Ball, M., Magnanti, T., Monma, C. & Nemhauser, G., (eds.), Network Routing, volume 8, North-Holland, 1995. [8] ILOG, ILOG CPLEX Callable Library 9.0 Reference Manual. ILOG, 2003. [9] Toth, P. & Vigo, D., (eds.) The Vehicle Routing Problem. SIAM Monographs on Discrete Mathematics and Applications, SIAM, 2002.

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Timetable attractiveness parameters B. Schittenhelm Department of Transport, Technical University of Denmark, Denmark Department of Traffic Planning, Rail Net Denmark, Denmark

Abstract Timetable attractiveness is influenced by a set of key parameters that are described in this article. Regarding the superior structure of the timetable, the trend in Europe goes towards periodic regular interval timetables. Regular departures and focus on optimal transfer possibilities make these timetables attractive. The travel time in the timetable depends on the characteristics of the infrastructure and rolling stock, the heterogeneity of the planned train traffic and the necessary number of transfers on the passenger’s journey. Planned interdependencies between trains, such as transfers and heterogeneous traffic, add complexity to the timetable. The risk of spreading initial delays to other trains and parts of the network increases with the level of timetable complexity. Keywords: timetable, railway timetable, timetable attractiveness, timetable structure, timetable complexity, travel time, transfers, punctuality and reliability.

1 Introduction This article summarizes some of the European research on how to create better timetables. This is done by identifying and examining some of the most important parameters that make timetables attractive towards the customers of the railway sector. If a person wants to travel from one place to another, the journey will be made in the most attractive way according to the person. Most attractive meaning the “cheapest” way in respect to journey costs e.g. travel time and number of necessary transfers. The attractiveness of the railway depends on the given valid railway timetable and the reputation of the topical train operating company (TOC).

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976 Computers in Railways XII Looking at a timetable it is possible to examine:  The scheduled travel time when using the railway for the journey or a part hereof  Number of needed transfers to make the railway journey  Planned transfer time at a given transfer station and if the transfer time ensures that the interchange between trains can be made in the most common operational situations  Number of departures per hour and thereby the amount of planned hidden waiting time in the timetable  Departures in regular intervals such as each 10, 15, 20, 30 or 60 minutes  Trains available when needed e.g. late evening or early morning. The reputation of the TOC will depend on:  The punctuality – how many trains arrive on time after a given on time criteria e.g. less than 5 minutes delayed at arrival  The reliability – how many trains are cancelled during a given period of time  The seating capacity of the trains  The level of comfort in the trains. All these parameters come together in the railway timetable. The level of achievable timetable attractiveness depends on several conditions. This is because the railway system consists of infrastructure (e.g. number of tracks, stations and interlocking systems giving the headway times) and traffic (e.g. intercity, local and freight trains) using the infrastructure. Combining the potential of the infrastructure with the capability of the rolling stock (driving characteristics and size of fleet), possibilities with the train staff (number of employees and flexibility) and the demand for traffic gives the frame work for, and thereby also the complexity of the timetable. If the goal is to run as many trains as possible on the infrastructure, the train traffic has to be 100% homogenous and be running at the optimal speed [1, 2]. The attractiveness parameters mentioned above will be examined in section 2. Section 2.1 examines the factors that describe the superior timetable structure and the advantages/disadvantages of a periodic regular interval timetable. This is followed by the timetable complexity in section 2.2 that describes the interdependencies in the timetable and lists advantages/disadvantages of a complex timetable. Possible travel time and the benchmarking hereof are described in section 2.3. Factors that influence the punctuality and reliability of a railway service are dealt with in section 2.4. This section also describes the existing philosophies to improve punctuality and reliability. In Section 2.5 transfers between trains at stations are examined. This includes the optimal transfer time and the factors that prolong the transfer time. Finally, section 3 draws up the conclusion and identifies subjects for further research.

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2 Timetable attractiveness parameters A timetable is a compromise between the interests of different TOCS and the railway infrastructure manager (IM). The TOCS have a range of services, ranging from high speed trains to freight trains and local trains that they want to operate on the infrastructure. On the other side, the IM wants to sell as much infrastructure capacity as possible but also needs to reserve capacity for maintenance activities and buffer times between trains. In this section, the earlier mentioned attractiveness parameters will be grouped into themes and described further. The themes are timetable structure, timetable complexity, travel time, transfers and punctuality. 2.1 Timetable structure The superior structure of the timetable can be described by 4 factors [3]:  Periodicity/regularity – The entire timetable, or a part of it, is a repeating pattern over a period of time e.g. 1 hour. Also called a regular interval timetable  Symmetry – the pattern applies for all driving directions for a given train service  Constraints on line sections – the heterogeneity of the train traffic on a given line section is an important parameter for the capacity consumption, travel time, number of needed transfers and traffic punctuality  Constraints in stations – at stations transfer possibilities between trains have to be taken into account. The same goes for train crew and rolling stock scheduling aspects. In Europe the periodicity/regularity parameter has been given much attention since it has been proven that this is one of the most important parameters regarding timetable attractiveness towards the customers. Therefore, most countries strive to generate a 100% periodic timetable, also called an ITFtimetable (Integrierter Takt Fahrplan) [4]. One of the best examples is the Swiss “Bahn 2000” timetable. The word “Integrierter” refers to the special focus on minimal time loss connected with train interchanges in the timetable. This is done by selecting a number of transfer nodes where trains from all connecting railway lines meet at the same time and thereby create optimal transfer possibilities which again ensures the optimal travel time [1]. Although more and more countries tend towards implementing periodic timetables there are both advantages and disadvantages associated with this type of timetable structure. In table 1 the most important advantages and disadvantages associated with a periodic timetable are listed. Experience shows that a regular interval timetable gains most advantage when there are 2 or preferable more departures per hour on a given train service. The most attractive departure times at important stations are minute x0 and x5 e.g. 00, 05, 10, 15 etc. These numbers are easy to remember and make it easy to use a

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978 Computers in Railways XII Table 1: 1. 2. 3. 4.

5.

6.

7.

8.

Advantages and disadvantages of a periodic timetable [1, 3, 5, 6].

Advantages Logical and coherent timetable for the entire network Well defined hierarchy of services Focus on short transfer times at selected junctions/stations Regular service intervals reduces the risk for passengers concerning train interchanges Regular intervals minimize waiting time for randomly arriving customers at train stations Best use of capacity because of systematic planning and regularity Repeating patterns are easy to market and memorize for customers. Thereby reducing customers effort of finding departure times of trains and planning the train journey Symmetric services in all driving directions

1.

2.

3.

4.

Disadvantages Regular interval timetables can be difficult to plan for a railway network with the ongoing liberalisation of the railway sector. All involved TOCS have to be interested in achieving this kind of timetable Difficult to fit the number of departures to time sensitive markets or groups of customers. The basic structure of the timetable will not always give the possibility to run extra trains during specific hours of the day Achieving absolute periodicity can create a high level of rigidity in the timetable thereby causing loss of business Transfers are often needed to get through the network resulting in longer travel times

given train service. Having a high frequency of trains gives the opportunity for passengers to show up at a station randomly. Less planning is needed before starting the journey [5]. In Germany investigations have been made regarding the improvement of regional railway attractiveness. They conclude that in a long term perspective the potential increase of passengers is at least proportional to the increase in service e.g. train km and/or number of departures per hour [6]. 2.2 Timetable complexity Timetables are an agreement and a compromise between several actors and therefore, complicated to work out. In the railway business it is an agreement between TOCS and the IM about how many trains of which type are running and at what time. The TOCS had to make compromises with each other via the IM to get a conflict-free and valid timetable. This has possibly let to the situation where some TOCS did not get all their primary wishes regarding their train services fulfilled.

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Table 2: 1.

2.

3.

979

Interdependencies in the timetable [6, 8, 9].

Open line Used capacity per time unit e.g. 1 hour. This depends on the valid timetable, rolling stock and infrastructure Heterogeneity of the railway operation (the traffic mix on the given line section). A metro like service with frequent trains stopping at all stations is homogenous while a line used by both slow regional trains, fast intercity trains and freight trains has a heterogeneous traffic pattern Overtaking of trains is part of heterogeneous operation. It increases the heterogeneity of the operation and thereby contributes to more interdependencies – and thereby a more complex timetable

1.

2.

3.

4.

Station Layover times for rolling stock and train crew. Scheduled layover times can sometimes be close to the minimum time needed for any needed shunting movements for the rolling stock and for the train crew to get ready for departure in the opposite direction Rolling stock utilization. The rolling stock can be used on several different train services during the day. If this is the case, a delay can be spread to a big part of a given railway network following the rolling stock Train crew utilization. A given train crew can work on different train services during their shift. In this case, a delay on one line can spread to another via a train crew Train connections. The valid timetable can hold several planned connections between the topical train service and other train services. If trains have to wait for each other at connection points, delays can be transferred from one train to another and thereby spread to a larger part of the railway network

The complexity of a timetable is characterized by the interdependencies in the timetable. Interdependencies can be found on an open line or a railway station [1, 3, 6]. Table 2 gives an overview of some of these interdependencies. Table 3 shows some of the advantages and disadvantages for timetables with a high level of complexity in them. The most important advantage in complex timetables, as listed in table 3, is the possibility to have both slow and fast trains on a railway line and thereby being able to offer attractive travel times to the majority of train passengers. The structure of a timetable does not necessarily give rise to complexity. Planned interdependencies in the timetable that do cause complexity are based on the timetable structure [3]. WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

980 Computers in Railways XII Table 3: 1.

2. 3.

4.

5.

Advantages and disadvantages of complex timetables [3, 6, 10].

Advantages Adapted product types for different passenger segments e.g. stop trains and fast intercity trains Focussing on conditions for the largest passenger flows Optimizing the need for rolling stock to fulfil given service demands Make full use of the driving characteristics of the rolling stock Reduced costs for breaking and accelerating trains because the number of stops is optimized towards the market segments

1.

2. 3.

4. 5.

Disadvantages Difficult to find unused attractive train paths in the existing timetable The timetable becomes rigid and inflexible Difficult for train dispatchers to react on disturbances in the planned operation A complex timetable is more sensitive towards disturbances The more complex the timetable, the less efficiently the capacity is used

2.3 Travel time The travel time of a given journey is an important attractiveness parameter. A potential customer will, in the decision process before the journey, amongst other things compare the travel time by train with other competitive means of transport e.g. airplane, bus or car. The scheduled travel time depends on the characteristics of the infrastructure and rolling stock, on the agreed running time supplements between IM and TOCS, heterogeneity of the operation on the relevant railway lines and on the number of needed transfers to make the journey. Several countries have developed their own benchmarking methods for the journey time by train. As an example, the English generalised journey time methodology is used [5]. GJT = T + aH + bI (1) GJT = Generalised Journey Time T = Station to Station Journey Time H = Service Headway (frequency) I = Number of Interchanges Needed a = frequency penalty factor b = interchange penalty factor The penalty factors “a” and “b” are needed to convert service headway and number of transfers into equivalent amount of time. Scheduled travel times are a compromise between the railways being competitive compared with other modes of transport but on the other side also insuring a conflict free timetable and a high level of punctuality. The SBB works with the following motto: “So rasch wie nötig, nicht so schnell wie möglich” (as

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fast as needed, not as fast as possible) [1]. This gives a good understanding of the necessary compromise [1, 10]. 2.4 Punctuality and reliability Timetables should be able to absorb minor disturbances that can occur in the most common operational situations. Common situations are e.g. dwell time delays, reduced speed on part of lines because of poor infrastructure conditions or reduced traction effort on rolling stock. It is necessary to be able to keep planned train interchanges with only minimal transfer time and thereby also the expected timetable travel time [10, 15]. The scheduled travelling time may differ considerably from the experienced travel time if a customer misses a connection caused by a minor delay. A missed transfer can prolong the travel time with up to the frequency of the connecting train service which can be more than 100% of the planned journey time [10]. Punctuality is not only important for passenger traffic. A competitiveness parameter on the freight market is guaranteed arrival times for freight trains. This is an important factor for companies with production lines that need raw materials or the recipients of the company’s products [17]. The following factors have a great influence on the punctuality of a train service [9, 13, 15, 17]:  Capacity consumption. A high level of capacity consumption causes higher risk of consecutive delays  Heterogeneity of traffic mix. The more heterogeneous the railway operation the higher the risk of consecutive delays  Allocation of time supplements. There are several opinions on how to allocate time supplements. The trend goes away from distributing the running time reserves equally on the whole network. One opinion is to add the time supplements to the dwell times at stations. The train will under normal conditions arrive too early. This ensures the availability of the entire time supplement of an open line section to the train before the arrival at the next station. Another opinion puts the majority of reserves between capacity bottlenecks which often are larger stations/junctions.  Train capacity. If not the TOC has enough train units and/or locomotives and carriages available, the trains get crowded. This can cause dwell time extensions as it takes longer time to board and alight the trains  Station dwell times. The number of alighting and boarding passengers has to match with the planned dwell time. If a dwell time delay arises this can delay the next train planned to use the same platform track The railway sector has, over time, applied two philosophies to ensure and improve punctuality [17]:  Slack – This philosophy is based on the use of time supplements in the timetable. Both for running and dwell times. This gives a certain degree of slack that can be used to catch up with small delays. Experience has

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982 Computers in Railways XII shown that punctuality not necessarily increases linear with more slack. Giving more time to a task can make the task take longer time  Precision – Here focus is on high availability of the infrastructure and rolling stock together with keeping departure times. The latter is done by teaching passengers how to alight and board trains in an effective manner and creating commitment towards punctuality among the employees of the TOCS and IM [17]. Reliability of a train service can be measured in the number of carried through departures out of the number of scheduled departures. The total or partly cancellation of a train can mainly be caused by 3 reasons:  Rolling stock break down / massive infrastructure failure – this can be caused by external factors or by lack of maintenance and demand cancellation of one or more train runs  Train staff failing to turn op at scheduled place and time  Part of a strategy for restoring normal traffic after a disturbance in the train service – trains can, e.g., be turned before reaching their destination and use their planned time slot for the reverse train run, or train runs can be cancelled completely so the rolling stock can be used elsewhere or wait until the start of the next timetable cycle. 2.5 Transfers The needed number of transfers is an important attractiveness parameter. For passengers with heavy luggage it is not convenient to change trains on their journey. Each transfer can have the risk of extending the travel time compared to a direct train service. In most cases, the passengers will experience a scheduled waiting time in connection with transfers. In the best case scenario, the interchange time p is [1, 5, 10]: p=h+d (2) h = the necessary infrastructure dependent headway between the two trains entering the station d = the planned dwell time of trains The minimum interchange time depends on the transfer conditions. If the connecting train uses the same track or platform, the planned transfer time can be down to a few minutes. If the transferring passengers have to get to a different platform or to a different section of the same platform, then the transfer time depends on the station’s platform and platform track layout. Assigning a platform track to a train can depend on different things [1]:  The same platform is used by connecting train services  The TOC always uses the same track or platform  The track is close to ticket sale facilities, station entrances, parking lot, shops or other public transport modes  The train can be catered when using the given track.

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When using the regular interval timetable (ITF) concept with focus on selected stations as transfer nodes e.g. Swiss Bahn 2000 timetable, all connecting trains meet once or several times per hour at the same time and station. This gives optimal transfer possibilities but an unbalanced use of the station capacity [4]. The station is either almost empty or full of trains. Numerous simultaneous interdependencies at one station add to the complexity of the timetable drastically because the risk of transferring delays increases.

3 Conclusion and further research This article has described approaches to increase the timetable attractiveness. There are several parameters that have influence on the experienced timetable attractiveness. Parameters such as travel time, availability and punctuality decide whether the railway is a competitive means of transport. These parameters are dependent on the timetable structure. Periodic regular interval timetables (ITFFahrplan) are being adopted by more and more European railway companies. This kind of timetable has proven its attractiveness towards the railway customers. Regularity and focus on optimal transfers make these timetables popular. Optimal transfer conditions are created by declaring selected stations as transfer nodes and having all connecting trains meet there at the same time. Transfer times depend on the actual track allocation to trains and the layout of the transfer stations. Scheduled journey time is affected by the infrastructure, rolling stock, running time supplements and timetable structure. In the end the journey time is a compromise between the railways being competitive compared to other means of transport and achieving a high level of punctuality. Every timetable contains more or less interdependencies between different trains, and trains and passengers/train crew. Interdependencies can be planned transfer possibilities, a high level of heterogeneity in the operation, and scheduling aspects for rolling stock and staff. The more interdependencies there exist in a timetable the higher is the level of complexity in the timetable. A high level of complexity increases the risk of delays spreading to other trains and thereby to larger parts of the railway network. This has a negative effect on the achievable punctuality with a given timetable. Further research can focus on developing a benchmarking/index methodology for timetable attractiveness and/or complexity. In this way, it will be possible to compare different timetable alternatives regarding timetable attractiveness. The methodology should take the following aspects into account: possible running time compared to scheduled running time, number of interdependencies attached to a given train run, and the heterogeneity of the train traffic. Another topic for examination is what level of disturbances a new timetable should be able to absorb. First step would be to develop a set of formulas that can identify the socioeconomic optimum when looking at the scheduled travel time, including time supplements, and the derived punctuality.

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984 Computers in Railways XII

References [1] Tyler, J., The philosophy and practice of Taktfahrplan: a case study of the East Coast Main Line, University of Leeds, Institute for Transport Studies Working Paper 579, November 2003 [2] Landex, A & Kaas, A., Planning the most suitable speed for high frequency railway lines, proc. of Computers in Railways, 2006 [3] Weits, E.A.G., Railway Capacity and Timetable Complexity, proc. of EURO Working Group on Project Management and Scheduling, 2000 [4] Borza, V. & Vincze, B. & Kormányos, L., Periodic Timetable-map for the Hungarian Railway System by the Adaptation of the European Structure [5] Wardman, M. & Shires, J. & Lythgoe, W. & Tyler, J., Consumer benefits and demand impacts of regular train timetables, International Journal of Transport Management, (2), 2004 [6] Bosserhoff, D., Making Regional Railroads More Attractive – Research Studies in Germany and Patronage Characteristics, Journal of Public Transportation, Vol. 10, No. 1, 2007 [7] Brünger, O., Rail Traffic and Optimization A Contradiction or a Promising Combination?, proc. of EURO Working Group on Project Management and Scheduling, 2000 [8] UIC leaflet 406, Capacity, 1st edition, UIC International Union of Railways, France, 2004 [9] Vromanns, M. J.C.M. & Dekker, R. & Kroon, L.G., Reliability and heterogeneity of railway services, European Journal of Operational Research 2006 (page 647-665) [10] Engelhardt-Funke, O. & Kolonko, M., Optimal Timetables: Modelling Stochastic Perturbations, proc. of EURO Working Group on Project Management and Scheduling, 2000 [11] UIC leaflet 451-1, Timetable recovery margins to guarantee timekeeping – Recovery margins, 4th edition, UIC International Union of Railways, France, 2000 [12] Haldemann, L., Automatische Analyse von IST-Fahrplänen, Master Thesis, Institut für Verkehrsplanung und Transportsysteme der ETH Zürich, Switzerland 2003 (in German) [13] Rudolph, R. & Radtke, A., Optimisation of Allowances in Railway Scheduling, proc. of World Congress on Railway Research, 2006 [14] Wüst, R., Dynamic rescheduling based on predefined track slots, proc. of World Congress on Railway Research, 2006 [15] Vansteenwegen, P. & Van Oudheusden, D., Decreasing the passenger waiting time for an intercity rail network, Transportation Research Part B 41 2007 (page 478-492) [16] Goverde, R.M.P. & Odijk, M.A., Performance evaluation of network timetables using PETER, proc. of Computers in Railways, 2002 [17] Olsson, N.O.E. & Hauglund, H., Influencing factors on train punctuality – results from some Norwegian studies, Transport Policy 11 2004 (page 387397) WIT Transactions on The Built Environment, Vol 114, © 2010 WIT Press www.witpress.com, ISSN 1743-3509 (on-line)

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Author Index Adey R. .................................... 615 Andersen J. L. E....................... 337 Andersson A. W. ........................441 Andreiouk T. .............................. 71 Aronsson M. ............................ 963 Ballini F. .................................. 537 Bao Y. ...................................... 453 Baynham J. .............................. 615 Bažant L. .......................... 711, 735 Ben Aoun R. ............................ 851 Blanquer J. ............................... 509 Bohlin M. ................................. 349 Bozas F. ................................... 561 Bozzo R. .................................. 537 Braslasu D................................ 561 Cai B. ......................................... 59 Cao F.................................. 89, 663 Carlson E. ................................ 245 Chang E.-F. .............................. 419 Chang H. .................................. 641 Chen D. ...................................... 35 Chen J. ..................................... 221 Chen J.-Q. .................................. 45 Chen L. .................................... 935 Chen L.-Y. ................................. 45 Chen R. .................................... 497 Corman F. ................................ 629 Cucala A. P. ............................. 509 Cucala P. .................................. 549 D’Ariano A. ............................. 629 Daadbin A. ............................... 573 Dascalu A. ............................... 561 Dessagne G. ............................. 193 Doganay K. .............................. 349 Domínguez M. ......................... 509 Du W.......................................... 13 El Koursi E.-M......................... 851 Emery D. .................................. 283 Endresen J. ............................... 245

Fernández A..................... 509, 549 Filip A.............................. 711, 735 Friman B. ................................... 71 Gao C. .......................................... 3 Ge X. ....................................... 815 Geisler M. ................................ 771 Gély L. ..................................... 193 Genova R. ................................ 537 Gheorghe S. ............................. 561 Gjerdrum J. .............................. 963 Gómez-Rey I. .......................... 485 Gu Q. ....................................... 663 Guo B. ....................................... 13 Guo B. Y. ................................. 213 Guo J................................ 497, 805 Guo L.-N.................................... 45 Han B. ...................................... 257 Hansen I. A. ............................. 629 Haugen Ø. ................................ 245 Hayakawa K. ........................... 783 He Z. ................................ 889, 953 Hei X. ................................ 81, 901 Himeno Y. ............................... 155 Hu H.-L.................................... 169 Hwang J.-G. ............................. 863 Ichikura T. ............................... 677 Igata R. .................................... 155 Isaksson-Lutteman G. ................ 441 Ishikawa R. .............................. 183 Ishima R................................... 783 Jaeger B. .................................. 383 Jia G. ........................................ 945 Jia X. .......................................... 35 Jiménez J. A............................. 549 Jo H.-J. ..................................... 863 Jong J.-C. ......................... 169, 419 Jurtz S. ..................................... 359 Kauppi A.................................... 441

986 Computers in Railways XII Kim B.-H. ................................ 863 Kim Y.-K. ................................ 863 Kinoshita N. ............................. 155 Koshino D. ............................... 183 Kosonen T................................ 319 Kovalev R. ............................... 593 Kreuger P. ................................ 963 Krugovova E. ........................... 593 Kufver B. ......................... 581, 605 Lackhove C. ............................. 383 Landex A. ................ 337, 911, 923 Lee C.-K. ................................. 169 Lee K.-M. ................................ 863 Lemaire E. ............................... 851 Lemmer K. ............................... 383 Li B. ........................................... 45 Li D. ......................................... 257 Li F. ......................................... 529 Li H. ......................................... 953 Li K. ................................. 271, 467 Li M. .......................................... 45 Li Q. ......................................... 641 Li S. ......................................... 233 Li W. ........................................ 475 Li Y. ......................................... 901 Lieske U. .................................. 205 Lin T.-H. .................................. 169 Lindfeldt O. ............................. 407 Liu C. ....................................... 839 Liu J. .......................................... 59 Liu L. ....................................... 805 Liu R. ....................................... 641 Liu Y. ....................................... 271 Liu Z. ....................................... 521 Lu F. ........................................ 233 Lundberg A.-I. ......................... 407 Luo R. ........................................ 89 Lupu V. .................................... 561 Ma W. ........................................ 81 Makkinga F. ............................. 327 Mao Y. J. ................................... 13 Martins J. P. ............................. 371 Matsuda H................................ 701

Matsumoto T. .......................... 147 Mehta F...................................... 99 Mellings S. ............................... 615 Meng L. ................................... 953 Mera J. M................................. 485 Miao J. ..................................... 877 Middelkoop A. D. .................... 431 Mikheev G. .............................. 593 Mitroi G. .................................. 561 Miura T. ................................... 783 Mocek H. ......................... 711, 735 Mochizuki H. ........................... 183 Møller-Pedersen B. .................. 245 Montigel M. ............................... 99 Morgado E. .............................. 371 Moritaka H............................... 147 Moroianu L. ............................. 561 Mu R. ....................................... 723 Müeller J. R. .................... 467, 795 Nakamura H............................. 183 Nanmoku T. ............................. 701 Narita H. .................................. 677 Nie L. ............................... 889, 953 Ning B. .............3, 23, 89, 759, 935 Nishida S. ................................ 183 Niu R. ...................................... 827 Obata N.................................... 677 Ouyang N................................... 81 Pei L. ....................................... 663 Persson R. ........................ 581, 605 Pesneau P. ................................ 193 Quiroga L. M. .......................... 687 Radtke A. ................................. 295 Radulescu V............................. 561 Ribera I. ................................... 549 Richter T. ................................. 651 Roberts C. ................................ 935 Rodrigo E................................. 485 Rosinski J................................. 573 Rößiger C................................... 99

Computers in Railways XII

Ruihua X. ................................. 945 Sandblad B..................................441 Sano M..................................... 183 Santos O. M. ............................ 815 Schittenhelm B................. 923, 975 Schmid F. ................................. 935 Schnieder E. ..................... 687, 795 Schuette J. ................................ 359 Schütte J................................... 771 Serrano A. ................................ 549 Sha X. ...................................... 839 ShangGuan Wei ................... 45, 59 Sicre C. .................................... 549 Sone S. ..................................... 133 Soutome H. .............................. 783 Spönemann J. ........................... 395 Strainescu I. ............................. 561 Sturm B. ................................... 327 Sun S. ....................................... 475 Svendsen A. ............................. 245 Takagi R. ................................. 141 Takahashi S.............................. 183 Takikawa M. ............................ 701 Tan Y. ...................................... 889 Tanaka H.................................. 677 Tang T............. 3, 23, 59, 213, 271, ......... 663, 723, 747, 827, 839, 935 Tang W. ................................... 759 Tanuma H. ............................... 783 Tomii N.................................... 111 Tron D...................................... 283 Tudor E. ................................... 561 Tzieropoulos P. ........................ 283 van de Weijenberg D. .............. 307 Vanderbeck F. .......................... 193

987

Wang H...................................... 35 Wang J. ...................................... 59 Wang L. ............................. 81, 901 Wang M. .................................. 521 Wang W. .................................. 521 Wang Y.............................. 89, 877 Weits E. A. G........................... 307 Wendler E. ............................... 395 Wenzel B. ................................ 359 Wingren J................................. 605 Woodcock J. ............................ 815 Xu L. ........................................ 233 Xu R. ....................................... 529 Xu T. ................................ 759, 815 Xun J...................................... 3, 23 Yan F. ...................................... 839 Yang D..................................... 889 Yang Z. .................................... 233 Yazawa E. ................................ 701 Yoshida Y. ............................... 783 Yoshino Y. ............................... 121 Yu Y. ....................................... 877 Yu Z. ........................................ 123 Yuan L. .................................... 271 Zhang L. .................................. 615 Zhang Y. ...........723, 747, 805, 889 Zhao H. .................................... 475 Zhao L. .................................... 759 Zhao X. Q. ............................... 747 Zhao X. .................................... 723 Zheng G. .................................. 221 Zheng W. ..........467, 723, 747, 795 Zhibin J. ................................... 945 Zhongping Y. ........................... 133 Zhu W. ..................................... 529

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Urban Transport XVI Urban Transport and the Environment in the 21st Century Edited by: A. PRATELLI, University of Pisa, Italy and C.A. BREBBIA, Wessex Institute of Technology, UK

This International Conference on Urban Transport and the Environment has been successfully reconvened annually for the last fifteen years. Transportation in cities, with related environmental and social concerns, is a topic of the utmost importance for urban authorities and central governments around the world. Urban Transport systems require considerable studies to safeguard their operational use, maintenance and safety. They produce significant environmental impacts and can enhance or degrade the quality of life in urban centres. The emphasis is to seek transportation systems that minimize any ecological and environmental impact, are sustainable and help to improve the socio-economic fabric of the city. Another area of concern addressed by the conference is that of public safety and security, seeking ways to protect passengers while retaining the efficiency of the systems. The sixteenth conference topics are: Transport Modelling and Simulation; Transport Security and Safety; Transport Technology; Land Use and Transport Integration; Intelligent Transport Systems; Public Transport Systems; Road Pricing; Inter-Model Transport Systems; Transport Automation; Traffic Management; Urban Transport Strategies; Urban Transport Management; Environmental Impact, Including Air Pollution and Noise; Information Techniques and Communications; Mobility Behaviour; Policy Frameworks; Environmentally Friendly Vehicles; Transport Sustainability; Safety of Users in Road Evacuation. WIT Transactions on The Built Environment, Vol 111 ISBN: 978-1-84564-456-7 eISBN: 978-1-84564-457-4 2010 368pp £139.00

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In latter years, energy efficiency has become a crucial concern for every transportation mode, but it is in electrified railways where energy savings have shown a bigger potential due to (i) regenerative braking, that allows converting kinetic energy into electric power, and (ii) vehicle interconnection, that allows other trains to use regenerated power. Power supply and energy management will continue to develop in the future. This book gathers under a single cover several papers published in the Computer on Railways series (IX, X and XI) and focuses on power supply and energy management. Some of the discussed themes are: modelling, simulation and optimisation of AC and DC infrastructure, analysis of rolling stock consumption, and innovative approaches in power supply operation. This book will be invaluable to management consultants, engineers, planners, designers, manufacturers, operators and IT specialists who need to keep abreast of the latest developments in the field. ISBN: 978-1-84564-498-7 2010 208pp £85.00

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