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Transit Fare Policy: Use of Automated Data to Improve Incremental Decision Making

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Transit Fare Policy: Use of Automated Data to Improve Incremental Decision Making Transit Fare Policy: Use of Automated Data to Improve Incremental Decision Making By Andrew W. Stuntz S.B. in Economics Massachusetts Institute of Technology (2013) Submitted to the Department of Civil and Environmental Engineering and the Department of Urban Studies and Planning in partial fulfillment of the requirements for the degrees of Master of Science in Transportation and Master in City Planning at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2018 © 2018 Massachusetts Institute of Technology. All rights reserved. Author ……………………………………………………………………………………………………… Department of Civil and Environmental Engineering Department of Urban Studies and Planning May 11, 2018 Certified by…………………………………………………………………………………………………. John P. Attanucci Research Associate, Center for Transportation and Logistics Thesis Supervisor Certified by…………………………………………………………………………………………………. Frederick P. Salvucci Senior Lecturer, Center for Transportation and Logistics Thesis Supervisor Accepted by………………………………………………………………………………………………… Professor of the Practice, Ceasar McDowell Chair, MCP Committee Department of Urban Studies and Planning Accepted by...………………………………………………………………………………………………. Jesse Kroll Professor of Civil and Environmental Engineering Chair, Graduate Program Committee 2 Transit Fare Policy: Use of Automated Data to Improve Incremental Decision Making by Andrew W. Stuntz Submitted to the Department of Civil and Environmental Engineering on May 11, 2018 in Partial Fulfillment of the Requirements for the Degrees of Master of Science in Transportation and Master in City Planning Abstract Incremental changes in fare policy can have substantial and long-term impacts on transit ridership and revenue, but they are often driven by near-term revenue needs and determined within short time frames with limited analysis. This thesis proposes a procedural framework to organize analysis of incremental fare changes, linking exploration of current pricing strategies to estimation of behavioral parameters and modeling of fare change scenarios. Within this framework, empirical case studies are presented at two of the five largest transit agencies in the U.S. – the Massachusetts Bay Transportation Authority (MBTA) and the Chicago Transit Authority (CTA). These agencies have increased the price of passes relative to pay-per-use fares in recent years, motivating three particular applications that make extensive use of automated fare collection (AFC) data: 1) differentiating employer-based, pre-tax, automatically-renewing pass sales from other pass sales, 2) estimating cost sensitivity of both ridership frequency and fare product choice using only recent experience at a single agency, and 3) incorporating fare product choice in a traditional elasticity spreadsheet model to predict impacts of fare change scenarios. Passes sold through employer programs and online are found to have lower use than other passes, contributing substantially to revenue while increasing ridership; expanding these programs or extending tax benefits to all transit commuters could further increase revenue and ridership. Individual-level AFC data are used to estimate fare-related behavioral parameters: resulting MBTA elasticity estimates of -0.7 for pay-per-use and -0.5 for employer-based passes are higher than current agency assumptions of -0.25 and -0.15, use of a CTA 30-day or 7-day pass appears to boost a customer’s ridership by up to 11% or 21% (respectively), and a CTA product choice model is estimated without reliance on stated preference data. A CTA fare model combining product choice and elasticities predicts substantial switching between fare products when pass multiples are changed, and a simplified model illustrates that passes should be priced below revenue maximization to capture low-cost gains in ridership. The procedural framework in this thesis applies to all transit agencies, and the empirical applications are relevant to agencies that collect AFC data and offer multiple payment structures. Thesis Supervisor: John P. Attanucci Title: Research Associate, Center for Transportation and Logistics Thesis Supervisor: Frederick P. Salvucci Title: Senior Lecturer, Center for Transportation and Logistics 3 4 Acknowledgments I am grateful to many people for supporting this work and supporting me over the past three years. I would like to thank the CTA and the MBTA for providing the financial, administrative, and supervisory support that made this research possible. At the CTA, Maulik Vaishnav provided invaluable guidance and essential context for my work. Thank you for taking an interest in this project and giving me a window into the CTA (even from afar). I am grateful to many others as well: to Jeremy Fine, Scott Wainwright, Alex Cui, Michael Connelly, Sonali Tandon, and others who provided lively discussion and insightful feedback during my visits to Chicago; to Tom McKone, Carole Morey, Laura De Castro, Silvia Garcia, and President Carter for supporting and facilitating this research; and to Marge Keller, Garrett Vandendries, and Bryan Post for always making sure I had what I needed. At the MBTA, I would not have gotten far without Laurel Paget-Seekins and Ian Thistle. I am grateful for your extensive knowledge, dedication to improving the status quo, and good humor. The same goes for Anna Gartsman and the rest of OPMI, Evan Rowe, Brendan Fogarty, Steven Andrews, and Annette Demchur – thank you all for your support and advice, and for valuing the ideas of a lowly grad student. A special thanks to Jim D'Arcangelo, Bill Haberlin, Will Kingkade, Lynne O’Neill, and Prateek Agarwal for helping me access and understand different fare-related data. At MIT, my research advisors, John Attanucci and Fred Salvucci, provided valuable direction and practical wisdom throughout my program, urging me every week toward a mix of idealism about what urban transit could look like and pragmatism about how to get there. Thank you for the confidence you placed in me, and for always making clear that my well-being was even more important than my research. The other faculty in the MIT Transit Lab — Jinhua Zhao, Nigel Wilson, and Haris Koutsopoulos — created a wonderful environment to share and develop ideas; I have learned a great deal from Friday seminars and will miss attending. Gabriel Sanchez-Martinez and Jay Gordon made my work possible by building the data infrastructure for AFC-based research at the MBTA, and they helped me many times to use it more efficiently and effectively. I am grateful to my MST cohort (especially Josh, David, Malivai, Adam, Scott, Sid, and Alex), my MCP cohort (especially my Intro to HCED and Brockton finance / life sciences buddies), fellow MBTA and CTA RAs (Chris, Catherine, Josh, David, Mihir, Katya, Gabe, Eli, Apaar, Ari, Ru, and Jintai), and the rest of the Transit Lab for being kind friends and generous colleagues. I thank my DUSP instructors and academic advisor Karl Seidman for helping me to see and imagine the world differently. I am also very thankful for my church family at Christ the King Presbyterian Church and my immediate family. This master’s program was a gift, but you have been with me through both sorrows and joys. Most of all, I am grateful for Emily. Thank you for knowing me and still loving me, and for being my companion in grad school and for life. 5 6 Table of Contents 1 Introduction .............................................................................................................................................. 13 1.1 Motivation ......................................................................................................................................... 13 1.2 Research Scope and Questions ......................................................................................................... 15 1.3 Methodology ..................................................................................................................................... 19 1.4 Thesis Organization .......................................................................................................................... 19 2 Case Study Background ........................................................................................................................... 21 2.1 MBTA ............................................................................................................................................... 21 2.2 CTA .................................................................................................................................................. 34 2.3 Conclusions ....................................................................................................................................... 45 3 Literature Review ..................................................................................................................................... 46 3.1 Fare Policy Elements ........................................................................................................................ 46 3.2 Pricing Theory and Strategy ............................................................................................................. 53 3.3 Exploratory Analysis of Fare-Related Behavior Using Automated Transit Data ............................. 59 3.4 Demand Modeling and Fare Scenario Prediction ............................................................................. 63 4 A Framework for Incremental Transit Fare Policy Analysis ................................................................... 87 4.1 Step
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