Quantifying Passenger Impact of Disruptions on Metro Lines by Mark Perelmuter Bachelor of Engineering in Civil Engineering The Cooper Union for the Advancement of Science and Art (2018) Submitted to the Department of Urban Studies and Planning in partial fulfillment of the requirements for the degree of Master of Science in Transportation at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY May 2020 © Massachusetts Institute of Technology 2020. All rights reserved. Author…………………………………………………………………………………………….... Department of Urban Studies and Planning May 20, 2020 Certified by……………………………………………………………………………………….... Nigel H. M. Wilson Professor Emeritus of Civil and Environmental Engineering Thesis Supervisor Certified by……………………………………………………………………………………….... Haris N. Koutsopoulos Professor of Civil and Environmental Engineering, Northeastern University Thesis Supervisor Accepted by………………………………………………………………………………………... P. Christopher Zegras Professor Chair, Program Committee Quantifying Passenger Impact of Disruptions on Metro Lines by Mark Perelmuter Submitted to the Department of Urban Studies and Planning on May 20, 2020 in partial fulfillment of the requirements for the degree of Master of Science in Transportation Abstract Disruptions occur frequently in urban rail transit systems. Whether due to asset failure, passenger action, weather, or other causes, disruptions often force passengers to change their preferred route or mode, defer their travel to a later time, or avoid making the trip altogether. Researchers and transit network operators have devoted significant time to understanding how passengers respond to disruptions, and modeling the impact that these responses have on the network state. They have also explored many avenues for mitigating the impact of disruptions once they occur. The goal of this research is to understand the effect that infrastructure investments, specifically in track layout, have on mitigating the impact of major disruptions, and to model the effect of these disruptions by developing a simplified passenger assignment model, which aims to accurately represent the impact of a major disruption, such as a partial line suspension, on a transit network while having a sufficiently short computation time to be useful for sketch planning and similar first-order alternatives analysis. Both parts of the work are applied to the London Underground, specifically the Piccadilly line, as a case study. The track layout analysis framework outlines a method to determine optimal locations for new track crossovers and compute the benefit that they have on reducing the impacts of unplanned partial line suspensions, or planned closures, by allowing trains to operate over a greater portion of the affected line. This benefit is then used as an input to a business case, which finds that, for the categories of benefit considered, investment in track layout enhancements on the Piccadilly Line is not justified. The simplified assignment model strikes a balance between accurately representing the behavior of passengers during unplanned disruptions, specifically the difference between expected and experienced network state that is characteristic of these disruptions, and keeping the scope of the model sufficiently small to ensure quick computation time. Despite the model’s simplifications, its results show promise in accurately representing the state of the network during a disruption, and identifying the most overcrowded links on a network, while having a computation time shorter than that of existing models. Thesis Supervisor: Nigel H. M. Wilson Title: Professor Emeritus of Civil and Environmental Engineering Thesis Supervisor: Haris N. Koutsopoulos Title: Professor of Civil and Environmental Engineering, Northeastern University 2 Acknowledgements This thesis would not have been possible without the mentorship, advice, and support of my advisors, Nigel Wilson and Haris Koutsopoulos. I am grateful to them for their guidance throughout this process, which has definitely made me a better student and researcher. Others at MIT, among them Jinhua Zhao, Fred Salvucci, and Mei-Chun Yiu, were also important in helping this work come to fruition. I would like to thank Transport for London for sponsoring this research, and the many people there with whom I worked. Kelvin Blackie, Menno Yap, Liam McGrath, Chris Baitup, Sandra Weddell, Noori Sharma, Chris Locke, and others welcomed me to TfL, offered valuable insight, answered my many questions in great detail, and taught me a lot about the operations of the Underground. My experience would have been significantly diminished without their assistance and advice. I cannot forget to thank my parents, who have offered me love and support throughout this entire process. Special thanks to my friends at the Transit Lab, at Sidney-Pacific, and at MIT as a whole have made these two years in Boston outstanding and unforgettable. Although our time together ended somewhat abruptly, I will always look back upon it fondly. 3 Table of Contents 1 Introduction ................................................................................................................................ 8 1.1 Motivation ............................................................................................................................. 8 1.2 Research Goals.................................................................................................................... 10 1.3 Research Framework .......................................................................................................... 11 1.4 London Underground .......................................................................................................... 14 1.4.1 The Piccadilly Line ...................................................................................................... 16 1.4.2 Overview of Data Sources ........................................................................................... 18 1.4.3 Disruptions on the Underground .................................................................................. 18 1.5 Thesis Organization ............................................................................................................ 20 2 Disruption Modeling in the Literature and in Practice ........................................................ 22 2.1 Literature Review................................................................................................................ 22 2.1.1 Schedule vs. Frequency Based Modeling .................................................................... 22 2.1.2 Graph Theory Approaches ........................................................................................... 24 2.1.3 Static Assignment Models ........................................................................................... 25 2.1.4 Dynamic Assignment Models ...................................................................................... 26 2.1.5 Simulation .................................................................................................................... 27 2.2 Current Practice at London Underground ........................................................................... 27 2.2.1 Underground Ridership data: RODS, Oyster, and Wifi .............................................. 28 2.2.2 Journey Time Metric .................................................................................................... 29 2.2.3 Pre-modeling (LCH) Approach ................................................................................... 30 2.2.4 Retrospective (EJT) Approach ..................................................................................... 38 3 Track Layout Analysis ............................................................................................................ 40 3.1 Literature Review................................................................................................................ 40 3.2 Current Practice .................................................................................................................. 43 3.3 Analysis Procedure and Results .......................................................................................... 45 3.3.1 Assessment of Existing Track Layout ......................................................................... 45 3.3.2 Terminal Capacity Analysis ......................................................................................... 49 3.3.3 Selection of Potential Crossover Locations ................................................................. 55 4 3.3.4 Generation of Service Patterns..................................................................................... 57 3.3.5 Modeling ...................................................................................................................... 59 3.4 Results ................................................................................................................................. 61 3.4.1 Business Case Analysis................................................................................................ 68 3.5 Conclusions ......................................................................................................................... 73 4 Simplified Assignment Model ................................................................................................. 75 4.1 Goals ..................................................................................................................................