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Feb 2 8 2007 Archives The Value of Automated Fare Collection Data for Transit Planning: An Example of Rail Transit OD Matrix Estimation By Fabio Gordillo Bachelor of Science in Industrial Engineering, Universidad de los Andes (1999) Submitted to the Engineering Systems Division and the Department of Civil and Environmental Engineering in Partial Fulfillment of the Requirements for the degrees of Master of Science in Technology and Policy and Master of Science in Transportation at the Massachusetts Institute of Technology September 2006 © 2006 Massachusetts Institute of Technology All Rights Reserved Signature of Author .......... ................ ......... Technology and Policy Program, fngineering Systems Division And Civil and Environmental Engineering July 21, 2006 Certified by ............................... .. ..... .................. Nigel H.M. Wilson Professor of Civil and Environmental Engineering Chairman, Master of Science in Transportation Thesis Supervisor 1-0. .1 Accepted by................................................. ... ... •. t Ie e-V Chairman, Departminlal Committee for Graduate Students A ccepted by ............................................ ......... 1.-'..... F n ...... ........ Dava J. Newman Professor of Aeronautics and Astrona itcs and Engineering Systems MASSACHUSE• TS INSTmTE Director, Te hnology and Policy Program OF TECHNOLOGY FEB 2 8 2007 ARCHIVES LISRARIES The Value of Automated Fare Collection Data for Transit Planning: An Example of Rail Transit OD Matrix Estimation By Fabio Gordillo Submitted to the Engineering Systems Division and the Department of Civil and Environmental Engineering on July 21, 2006 in Partial Fulfillment of the Requirements for the degrees of Master of Science in Technology and Policy and Master of Science in Transportation at the Massachusetts Institute of Technology. Abstract: Traditionally, transit agencies across the world have relied on traveler surveys and manual counts to inform many of their service and operations planning decisions. Today, many agencies can add to their existing planning toolbox the data obtained from new Automated Fare Collection (AFC) technologies. By adding this dataset, transit agencies can boost their analytical capabilities and deal with some planning questions that they previously could not easily address. In fact, while with surveys and manual counts transit agencies were able to form a reasonable snapshot of existing demand on their transit system, with accurate AFC data, planners should be able to get a detailed, continuous and accurate vision of the travel behavior of their customers, at a fraction of the prior cost. Nevertheless, there are some technical and operational issues that can affect the quality of AFC data that must be addressed before the new dataset can be fully integrated into the planning process of transit agencies. This research begins to explore these issues in general as well as in the context of the transit system serving London in the United Kingdom. In particular, it identifies bias in the AFC entry and exit data and develops a methodology for building an unbiased estimate of existing travel patterns on the London Underground. The outcome of the research is a methodology to build unbiased estimates of existing travel patterns. The use of this methodology presents two main advantages over the existing survey methods: (i) the resulting estimate corrects the bias in the Oyster dataset and better reflects existing travel patterns than the traditional survey-based methodology and (ii) the methodology should be easy to replicate, offering planners the capability to build origin - destination matrices specific to different time periods, days of week and seasons of the year. The availability of this large set of origin - destination matrices should enable planners to keep track of changes in travel patterns and tackle many planning questions that they could not easily address before. Thesis Supervisor: Nigel H.M. Wilson Title: Professor of Civil and Environmental Engineering Acknowledgement Nigel Wilson with the collaboration of John Attanucci, provided all that I came looking for at MIT. Working with them has been an intellectual and personal challenge that made these two years of my life an intense and rich learning experience. I am very grateful for their dedicated and savvy advice. I would also like to thank many people at Transport for London who provided data and advice to make this research possible. In particular, I would like to thank Geoffrey Maunder, Peter Svensson, Mike Collop, Will Judge, Lauren Sager Weinstein and all the people working at Prestige and at the London Underground. Finally, thanks to Catalina, my family and my friends. They have provided the unconditional love, which gives me strength and motivation to be persistent and work hard to give something back to the world. In praise of all that they represent, I would like to dedicate this thesis to the future generation, to my dear nephew, Antonio. Contents LIST O F FIGURES .......................................................................................... ............................................. LIST O F TA BLES ................................................................................................. ....................................... 7 CHAPTER ONE: INTRODUCTION ......................................................... ............................................. 8 1.1 OVERVIEW OF AFC SYSTEMS ...... .............. .... ...................................... 8 1.2 RIDERSHIP ESTIMATES ...................................................... 10.............10 1.3 RESEARCH O BJEC'TIVES ................................................................... ....................... 11 1.4 VALUE OF IMPROVED ORIGIN-DESTINATION LEVEL ESTIMATIONS......................... .......................... 12 1.4.1. SERVICE PLANNING ................................................................ 13 1.4.2. FARE POLICY ................................................................................ 16 1.4.3. SYSTEM PERFORMANCE ............................................................................. 17 1.5. DIFFERENT SYSTEMS, DIFFERENT APPROACHES .............................................. 18 1.6 INSTITUTIONAL ORGANIZATION OF TRANSPORT IN LONDON ................................................20 1.7 THESIS ORGANIZATION ................................................................... 21 CHAPTER TWO: BACKGROUND INFORMATION: AFC SYSTEM AND SURVEY DATA IN THE LONDON UNDERGROUND .................................................................................................................22 2.1 O VERVIEW OF TRANSIT IN LONDON .................................................................. 22 2.2 OVERVIEW OF LONDON AFC SYSTEM ...................................................................... 24 2.2.1 FARE COLLECTION ON BUSES ................................................................................................. 26 2.2.2 FARE COLLECTION ON THE LONDON UNDERGROUND ......................................................... 27 2.2.3 FARE COLLECTION ON NATIONAL RAIL ........ ....................................... 29 2.2.4 FA RE C OLLECTION ON THE D L R ....................................................................................................... 3 1 2.2.5 FARE COLLECTION ON TRAMLINK........................... ............................... ........................ 32 2.2.6 TRANSACTIONAL DATA SUMMARY .............................................................. 32 2.2.7 PERSONAL DATA ............................................................................ 33 2.2.8 CENTRAL DATA SYSTEM ......................................... ........................................... 34 2.3 OVERVIEW OF OD ESTIMATION AT THE LONDON UNDERGROUND ........................................36 2.3.1 DESCRIPTION OF THE RODS PROCESS ................. ................................ 38 2.3.2 ASSESSMENTOF RODS ............................................................ ...........44 CHAPTER THREE: INTEGRATING AFC DATA AND SURVEY DATA: ESTIMATING THE ORIGIN-DESTINATION MATRIX FOR THE UNDERGROUND ..................................... .... 47 3.1 OVERVIEW OF OYSTER ORIGIN-DESTINATION DATA ............................................................ 47 3.2 METHODOLOGY FOR ESTIMATING THE OD MATRIX ............................. ............................. 50 3.2.1 ESTIMATING STATION ENTRIES AND EXITS ......................................................... ........................ 51 3.2.2 ESTIMATION OF EXPANSION FACTORS ................................................ 60 3.2.3 Row-COLUMN BALANCING .............................................................. 68 3.3 ANALYSI OF RESULTS ................................ .................................................................. 69 CHAPTER FOUR: CONCLUSIONS ......................................................................................................... 73 4.1 OVERVIEW OF RESEARCH FINDINGS ..............................................................73 4.2 LIMITATIONS AND CHALLENGES.......................................................75 4.3 FUTURE R ESEA RCH D IRECTIO NS .......................................................................................................... 76 BIBLIO G R A PHY .............................................................................................................................................. 79 APPENDIX A - CLASSIFICATION OF UNDERGROUND STATIONS .............................................
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