Using Real-time Data to Improve Reliability on High-Frequency Transit Services by David Maltzan B.A., Mathematics and Economics Tufts University (2009) 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 2015 ○c Massachusetts Institute of Technology 2015. All rights reserved. Author............................................................................ Department of Civil and Environmental Engineering May 21, 2015 Certified by. Nigel H.M. Wilson Professor, Department of Civil and Environmental Engineering Thesis Supervisor Certified by. John P. Attanucci Research Associate, Department of Civil and Environmental Engineering Thesis Supervisor Accepted by....................................................................... Heidi M. Nepf Donald and Martha Harleman Professor of Civil and Environmental Engineering Chair, Departmental Committee for Graduate Students 2 Using Real-time Data to Improve Reliability on High-Frequency Transit Services by David Maltzan Submitted to the Department of Civil and Environmental Engineering on May 21, 2015, in partial fulfillment of the requirements for the degree of Master of Science in Transportation Abstract In recent years, automatically-collected data from many transit agencies have been made available to the public in real time. This has dramatically improved the experience of riding transit, by allowing passengers to use detailed information on the current state of service to make more informed travel decisions. The “open data” movement has allowed independent mobile-phone app developers to create a variety of useful tools to improve the passenger experience. However, agencies’ use of real-time data for operational purposes has lagged behind customer-facing app development. This research examines the use of real-time data for the application of operational control strategies on transit services. Two high-frequency bus routes of the Massachusetts Bay Transportation Authority are used as a case study. It begins with the development of an application to download, interpret, and present data on bus service and recommended control actions in a graphical user interface. This application is then used to conduct an experiment with a terminal-based holding strategy on MBTA Route 1. The results of this experiment drive further investigation into the causes of deviations from scheduled or assigned departure times at terminals. To supplement the experimental data, a simulation model of MBTA Routes 1 and 28 is developed. This simulation is used to test additional control strategies, as well as the effect of reducing unexplained operator deviations from assigned departure times. The research finds that real-time data can be used to create significant operational im- provements. In particular, holding strategies at terminals, along with reducing unexplained operator deviations from assigned terminal departure times, have a strong effect. Several specific recommendations are made for a number of strategies that the MBTA can useto improve the precision of terminal departure times on bus services. This research also finds that holding at midpoints and short-turning can provide some additional benefit, but the costs and benefits to passengers of these strategies are more complicated and shouldbe investigated with further research and implemented using optimization schemes rather than the heuristic rules used here. Thesis Supervisor: Nigel H.M. Wilson Title: Professor, Department of Civil and Environmental Engineering Thesis Supervisor: John P. Attanucci Title: Research Associate, Department of Civil and Environmental Engineering 3 4 Acknowledgments Thanks are due first to my advisors, Nigel Wilson, John Attanucci, and David Block- Schachter, who guided this work from start to finish, and from whom I have learned far more than any coursework could teach me. Their high standards and attention to detail were invaluable, and they helped me achieve more than I would have thought possible. Thanks to Haris Koutsopoulos, Fred Salvucci, Mikel Murga, Jinhua Zhao, and Jay Gor- don, whose insightful comments during lab meetings contributed to this work. A huge thanks to Gabriel Sanchez-Martinez, not only for providing well-formatted, well-documented code for me to modify willy-nilly, but for taking time out of his own work for many, many, detailed white-board discussions on transit control strategies. To Dave Carney and the inspectors, operators, and dispatchers of MBTA Bus Opera- tions who cooperated with my experiment, answered many questions, and provided valuable insight into the on-the-ground realities of running an urban bus service. To Dominick Tribone, Dave Barker, Laurel Paget-Seekins, and many other MBTA staff members who provided thoughtful comments and ideas for research, and hosted me over the summer at 10 Park Plaza. To my fellow students on the MBTA project: Mike, Catherine, Raph, William, Katie, and Chris. Thanks for working together to make the most out of our massive datasets, to get all our PowerPoint presentations coordinated, and to make sure we all showed up at 45 High Street on time. To Emily, Lauren, Dan, Nat, Zach, Anne, Becca, and the rest of the M.S.T. class of 2015. I couldn’t have asked for a better group to go on this journey with. Thanks for being there for all the fun times, and all the tough times. To MIT Medical’s Mental Health and Counseling service - they don’t get thanked enough, but they provide amazing support for students and staff in a place where it is all-too- frequently needed. To Michael and Michelle, for being such kind and understanding roommates, even as I came and went at all hours, forgot to wash the dishes, and committed other roommate sins. To Nick, for being my gym buddy and listening to my complaints, even when you had your own (far more stressful) problems to deal with. To Ross, for listening to me from near and far, and lending your hitherto-unknown editing talents. To the Garimella family, for giving me a second home. Sometimes a home-cooked dinner can be a lifesaver. To my Dad, who rode the 62 to Alewife every day. To my brother Pete, who inspires me with the work and commitment he puts in at his start-up every day. To my Mum, without whom I would neither have gotten into, nor out of, MIT. Thanks for always checking in on me. Finally, to Swini. Thanks for listening to me when I needed to talk, and letting me be silent when I needed not to talk. Thanks for organizing our trips to the BSO, and other little things that gave me something to look forward to along the way. You’re the best. 5 6 Contents 1 Introduction 17 1.1 Motivation . 17 1.1.1 The importance of reliability . 17 1.1.2 Technological progress . 18 1.2 Objectives . 18 1.3 Approach . 18 1.3.1 Automated tool development . 18 1.3.2 Experimental approach . 19 1.3.3 Simulation modeling . 19 1.3.4 MBTA application context . 19 1.4 Outline of Thesis . 20 2 Literature Review 23 2.1 Transit service reliability . 23 2.1.1 Bus supervision . 24 2.1.2 Measuring unreliability and its causes . 24 2.2 Automatically-collected data for transit service improvement . 25 2.3 Simulation models of bus service . 26 2.4 Heuristic strategies for transit control . 26 2.4.1 Holding . 26 2.4.2 Skip-stop strategies . 27 2.5 Rolling-horizon optimization . 28 2.6 Holding experiments . 28 2.7 Summary of literature review . 30 7 3 Automated tools for transit performance improvement 33 3.1 Automated decision-support tools . 34 3.2 Real-time control strategies . 36 3.2.1 Holding . 36 3.2.2 Deadheading and expressing . 38 3.2.3 Short-turning . 39 3.3 Performance analysis and service planning . 39 3.3.1 On-time performance . 40 3.3.2 Running-time analysis . 40 3.4 Decision-support tool for real-time control of high-frequency bus routes . 41 3.4.1 Data sources . 42 3.4.2 Prediction interpreter . 43 3.4.3 Decision engine . 43 3.4.4 User interface . 44 3.4.5 Archived data . 45 3.5 Implementation for MBTA experiment . 45 3.5.1 Data sources . 45 3.5.2 Prediction interpreter . 46 3.5.3 Decision engine . 48 3.5.4 User interface . 48 3.5.5 Archived data . 49 3.6 Summary . 50 4 Experiment 51 4.1 Description of experiment . 51 4.1.1 Route 1 . 51 4.1.2 Strategy . 52 4.1.3 Personnel . 53 4.2 AVL data analysis for three typical weeks . 53 4.2.1 Recovery time and half-cycle time . 54 4.2.2 Headway variance along the route . 56 4.2.3 Deviation from scheduled departure time . 56 8 4.3 Summary of holding instructions given and deviation from instructions . 58 4.3.1 Holding instructions . 58 4.3.2 Deviations from suggested departure times . 59 4.4 Impact of the experiment on headway regularity . 62 4.4.1 Coefficient of variation of headway . 62 4.4.2 Average passenger wait time . 64 4.5 Factors contributing to unreliability at Dudley . 66 4.5.1 Terminal departure discipline . 66 4.5.2 Boarding times . 70 4.5.3 Dudley station layout and operations . 71 4.6 Deadheading and expressing strategies . 77 4.6.1 Types of strategies . 77 4.6.2 Evaluation of possible strategies . 77 4.6.3 Low-visibility strategies . 80 4.7 Recommendations . 81 4.7.1 Departure discipline . 81 4.7.2 Operations planning . 81 4.7.3 Back-door boarding . 82 4.7.4 Layovers . 82 4.7.5 Guidelines for special control strategies . 82 5 Simulation 83 5.1 General framework . 84 5.1.1 Route . 84 5.1.2 Locations . 84 5.1.3 Vehicles . 85 5.1.4 Events . 85 5.1.5 Terminal.