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Measuring Journey Time Reliability in London Using Automated Data Measuring Journey Time Reliability in London Using Automated Data Collection Systems by Micka¨elSchil Ing´enieurDipl^om´ede l'Ecole Sp´ecialedes Travaux Publics (ESTP) Paris, France (2011) Submitted to the Department of Civil and Environmental Engineering in partial fulfillment of the requirements for the degree of Master of Science in Transportation at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2012 c Massachusetts Institute of Technology 2012. All rights reserved. Author............................................................................ Department of Civil and Environmental Engineering May 21, 2012 Certified by........................................................................ Nigel H.M. Wilson Professor of Civil and Environmental Engineering Thesis Supervisor Certified by........................................................................ Harilaos N. Koutsopoulos Visiting Professor of Civil and Environmental Engineering Thesis Supervisor Accepted by....................................................................... Heidi M. Nepf Chair, Departmental Committee for Graduate Students Measuring Journey Time Reliability in London Using Automated Data Collection Systems by Micka¨elSchil Submitted to the Department of Civil and Environmental Engineering on May 21, 2012, in partial fulfillment of the requirements for the degree of Master of Science in Transportation Abstract Service reliability is critical for both users and operators of transit systems. The rapid spread of Automated Data Collection Systems, such as Automated Fare Collection (AFC) and Automatic Vehicle Location (AVL), provides new sources of information that can be used to measure and assess service reliability. The main objective of this thesis is to develop a set of simple, customer-driven metrics of journey time reliability, that could be useful and meaningful for both customers and operators. The set of metrics are consistent across transit modes (rail and bus networks). The proposed methodology, common to rail and bus systems, consists of (1) an analysis of the journey time distributions at the finest spatial and temporal resolution, the origin- destination pair (O-D) and time period level (customer perspective), (2) the aggregation of the reliability metrics at the line (route) level (operator perspective), and (3) the definition of journey time reliability standards at the O-D pair and time period level, by the identi- fication of a representative \good" journey time distribution (both customer and operator perspective). For fully gated transit systems, like the London Underground, AFC data provides direct travel time measures for every journey from the fare gate at the entry station to the fare gate at the exit station. For non-gated systems, such as many bus networks, no information is available on passengers' arrival times at the origin bus stop. A method that combines AVL and AFC data is proposed to estimate waiting times at stops so that they can be included in the journey time reliability calculation. Furthermore, the method accounts for the multiple overlapping routes that sometimes serve the same O-D pairs. The proposed methodology is tested using the London public transport system as an illustration. The use of the reliability metrics for operators and customers is also discussed, with proposed modifications of the information provided by journey planners. Thesis Supervisor: Nigel H.M. Wilson Title: Professor of Civil and Environmental Engineering Thesis Supervisor: Harilaos N. Koutsopoulos Title: Visiting Professor of Civil and Environmental Engineering Acknowledgments I would like to express my deepest gratitude to my advisors, Nigel Wilson, Haris Kout- sopoulos, and John Attanucci for their guidance, support and encouragement. Thank you for challenging me and believing in me. Thank you to Fred Salvucci, Mikel Murga and Juan Carlos Mu~noz. Your comments and advice have always been helpful. Thank you to Ginny and all the CEE staff for your dedication to the students, and for keeping things running in the department and the MST program. I would like to thank the people at Transport for London for making this research pos- sible. I want to especially thank Duncan Horne, who provided valuable help, suggestions and feedback during and after my internship. Thank you as well to David Glazebrook, Ken Farnhill and Andrew Gaitskell for your help and support over the summer. Thank you to Gabriel, for your help, advice, support, and for always taking the time to answer my questions and give suggestions. Thanks also for bearing with me as a roommate for this past year. Jay, thank you for your program and the help you provided for using it. Thank you to Alex and Kevin for your help, advice, and above all for these endless conversations about transportation, politics, summer trip plans and other random stuff, that have provided a welcome distraction from work. Naomi, thank you for everything. Thank you to all the folks in the MST program, especially my officemates in the tran- sit lab. These two years were a lot of fun. Merci `atous mes amis Parisiens qui, depuis Paris, Nottingham ou Sydney, m'ont soutenu, motiv´eet remont´ele moral. Merci `ala French team du MIT pour m'avoir sorti de mon bureau au moins une fois par semaine. To Ann, Henry, Shaina and Willa, thank you for taking care of me and entertaining me during these two years at MIT. Finally, I would like to thank my parents who have always pushed me to give the best of myself, and who gave me this opportunity to come to MIT, for two of the hardest, but also two of the best years of my life. 5 6 Contents 1 Introduction 15 1.1 Motivation . 16 1.1.1 Existing Reliability Metrics . 16 1.1.2 Passenger and Operator's Perspectives . 17 1.1.3 Automated Data Collection Systems . 18 1.2 Introduction to Transport for London . 20 1.3 Research Objectives . 21 1.4 Research Approach . 22 1.5 Thesis Structure . 23 2 Literature Review 25 2.1 Operator's View of Reliability . 27 2.2 Passenger's Definition of Reliability . 28 2.3 Previous Work on Journey Time Reliability Using Automated Data Collec- tion Systems . 31 2.3.1 Journey Time Reliability in the London Underground . 31 2.3.2 Reliability in the Bus Network . 35 2.4 Origin-Destination Inference and Trip Linking . 36 2.5 Conclusion . 37 3 Current Reliability Metrics used by Transport for London 39 3.1 London Underground . 39 3.1.1 Journey Time Metric . 42 3.1.2 Lost Customer Hours . 48 3.2 London Buses . 49 7 3.2.1 Excess Wait Time . 51 3.2.2 Percentage Chance of Waiting More Than a Given Threshold . 53 3.2.3 Publication of the Metrics . 53 3.3 London Streets . 54 3.4 Conclusions on the Current Reliability Metrics . 56 4 Measuring Journey Time Reliability in the London Underground 57 4.1 Requirements for the Journey Time Reliability Metrics . 62 4.2 Reliability Measurement Framework for a Single Underground Line . 63 4.2.1 Data Available . 63 4.2.2 Origin-Destination Pair and Timeband Analysis . 64 4.2.3 Aggregation at the Line Level . 67 4.2.4 Definition of the Journey Time Reliability Standards . 72 4.3 Journey Time Reliability Measurement for Comparison Across Lines . 77 4.4 Applications . 79 4.4.1 Data Processing . 79 4.4.2 Journey Time Reliability Metrics at the Origin-Destination Pair Level 80 4.4.3 Journey Time Reliability Metrics at the Line Level . 85 4.4.4 Journey Time Reliability Standards . 87 4.4.5 Comparison of the Daily Performance with a Rolling Average Median Journey Time . 94 4.4.6 Metric for Comparison of Underground Lines . 96 4.5 Use of the Reliability Metrics . 98 4.5.1 Presentation for Internal Use at Transport for London . 98 4.5.2 Presentation to the Customers: Addition to the Journey Planner . 103 4.6 Conclusion - Assessment of the Proposed Metrics . 106 5 Measuring Journey Time Reliability for High Frequency Bus Services 109 5.1 Assumptions . 110 5.2 Methodology at the Origin-Destination Pair Level . 113 5.2.1 Data . 113 5.2.2 Waiting Time Estimation . 114 5.2.3 In-Vehicle Travel Time . 118 8 5.2.4 Total Journey Time . 118 5.2.5 Waiting Time and Journey Time Reliability Standards . 121 5.3 Aggregation at the Route Level . 122 5.4 Applications . 124 5.4.1 Data Processing . 125 5.4.2 Results at the Origin-Destination Pair Level . 126 5.4.3 Results at the Route Level . 137 5.5 Use of the Reliability Metric . 138 5.5.1 Internal Use at London Buses . 138 5.5.2 Journey Planner Modification . 138 5.6 Conclusion - Assessment of the Proposed Metrics . 141 6 Conclusion 145 6.1 Summary . 145 6.2 Findings and Recommendations . 146 6.3 Future Research . 148 6.3.1 Fully Gated Systems . 148 6.3.2 Bus Networks . 149 6.3.3 Extension of the Metrics to Multimodal Journeys . 150 A Tube Map 153 B London Underground Reporting Periods 155 C London Underground Daily Dashboard 157 D Route 141 Northbound 159 9 10 List of Figures 2-1 The likelihood of delays as a function of the RBT upper percentile value . 32 2-2 Classification into recurrent and incident-related performance . 33 2-3 Definition of recurrent performance . 34 3-1 Contribution of each journey component to Total Journey Time . 45 3-2 Weighed Excess Journey Time Results - Period 9, 2011/12 . 46 3-3 Lost Customer Hours - Period 9, 2011/12 . 49 3-4 Route 141 - Map and QSI points . 50 3-5 Excess Wait Time for high frequency bus services - Long term trend . 54 3-6 Example of determining which journey times are acceptable / unacceptable 55 3-7 London Streets Journey Time Reliability - Quarter 3, 2011/12 . 56 4-1 Map of the 3 origin-destination pairs . 58 4-2 Journey planner - Canary Wharf to Bond Street . 59 4-3 Journey time distributions for 3 O-D pairs - PM peak .
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