Bus Bridging Decision-Support Toolkit: Optimization Framework and Policy Analysis by Alaa Itani A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Department of Civil and Mineral Engineering University of Toronto © Copyright by Alaa Itani 2019 Bus Bridging Decision-Support Toolkit: Optimization Framework and Policy Analysis Alaa Itani Master of Applied Science Department of Civil and Mineral Engineering University of Toronto 2019 Abstract Bus Bridging is the strategy most commonly applied in responding to rail service interruptions in North America and Europe. In determining the required number of buses and source routes, most transit agencies rely on ad-hoc approaches based on operational experience and constraints, which can lead to extensive delays and queue build-ups at affected stations. This thesis developed an optimization model, to determine the optimal number of shuttle buses and route allocation which minimize the overall subway and bus riders delay. The generated optimal solutions are sensitive to bus bay capacity constraints along the shuttle service corridor. The optimization model is integrated with a previously developed simulation tool that tracks the evolution of system queues and delays throughout the bus bridging process. A set of bus bridging policy guidelines were developed based on further analysis of the optimization model outputs using a Classification and Regression Tree (CART) model. ii Acknowledgments First, I would like to thank my parents and for their continuous support and trust in my abilities. Although they were thousands of miles away, they were always supportive, I couldn’t have made it here without their presence. Secondly, I would like to thank Professor Amer Shalaby, my MASc, supervisor for pushing me to the limits and challenging my abilities. Professor Shalaby was supportive in my ups and downs, his feedback was always helpful, innovative, and challenging. I wouldn’t have made it here without his trust in my abilities and patience in my progress. Thirdly, I would like to thank my flatmates, Toka Sabry, and Asma Nsiri, they were like my family and were always supportive and giving me the best advice, thank you for keeping me company and baring my nagging patterns. Fourthly, I would like to thank my sisters whom I always rely on, although they are thousands of miles away and I barely see them twice a year, their presence in my life is the most essential and most important. I would like also to thank my colleagues in the Transportation Lab, especially the amazing mother Lina El Morshedy who was my sister in Toronto away from my actual family. Her advice, support, help in course work and her coffee breaks were heartwarming. I would like to thank other colleagues, Sami, Sanjana, Albert, Patric, Zahra, Wenting, Graham, Marc, and Daniel for keeping always fun and comfortable environment for work and research. They were always there for answering my questions, hear me nagging, and they were the best lunch/dinner, planners. I would also thank the research associate and project manager Siva for his continuous help, especially in coding, his help was essential and critical. Lastly, I would love to acknowledge Trapeze Group for funding support, provincial funding provided by the Ontario Centres of Excellence and Ontario Research Fund, Canadian federal funding provided by the Natural Sciences and Engineering Research Council, and SOSCIP. iii Table of Contents Acknowledgments.......................................................................................................................... iii Table of Contents ........................................................................................................................... iv List of Tables ................................................................................................................................. vi List of Figures ............................................................................................................................... vii List of Appendices ...........................................................................................................................x Chapter 1 Introduction .....................................................................................................................1 Managing Unplanned Rail Disruptions .......................................................................................1 1.1 Implemented Strategies and Applications ...........................................................................1 1.1.1 Current Practices ......................................................................................................2 1.1.2 Mathematical Models in Research ...........................................................................3 1.2 Scope of Work .....................................................................................................................4 Chapter 2 Bus Bridging Assessment Tool .......................................................................................6 A User Delay Modelling Tool ....................................................................................................6 2.1 Model Data: Input and Output .............................................................................................6 2.1.1 Shuttle Buses Scenario and Input Data ....................................................................6 2.1.2 Model Output .........................................................................................................11 2.2 Case Study: Toronto Transit Commission .........................................................................12 2.2.1 Validating the User Delay Modelling Tool ...........................................................13 2.2.2 Shuttle Bus Data and Scenario Selection ...............................................................15 2.3 Policy Analysis and Implications.......................................................................................17 2.3.1 Shuttle Bus Initial Dispatch Direction ...................................................................17 2.3.2 Dispatch Time ........................................................................................................23 2.3.3 Uncertainty in Predicting Incident Duration ..........................................................24 2.3.4 Demand Reduction.................................................................................................28 2.3.5 Summary of Observations and Policy Guidelines .................................................29 iv 2.4 Web User Interface ............................................................................................................31 Chapter 3 Bus Bridging Optimization Tool ...................................................................................36 Bus Bridging Optimization Model ............................................................................................36 3.1 Problem Description ..........................................................................................................36 3.2 Optimization Model ...........................................................................................................39 3.2.1 Mathematical Formulation .....................................................................................39 3.2.2 Bus-Bay Capacity ..................................................................................................41 3.3 Solution Approach: Genetic Algorithm .............................................................................44 3.3.1 Addressing constraints in GA ................................................................................47 3.4 Case Study .........................................................................................................................49 3.4.1 Description of Incidents .........................................................................................49 3.4.2 Results and Discussion ..........................................................................................50 Chapter 4 Classification and Analysis of Rail Disruptions ...........................................................58 Clustering Analysis and Regression Tree Models ....................................................................58 4.1 Subway Disruption Data ....................................................................................................58 4.2 Clustering Analysis ............................................................................................................59 4.2.1 Major Clusters and Sample Incidents ....................................................................62 4.3 Classification and Regression Trees (CART) ....................................................................64 4.3.1 Classification based on Total User Delay (TUD) ..................................................66 4.3.2 Classification based on Number of Shuttle Buses .................................................67 4.4 Rail Disruption Severity Scale ...........................................................................................69 4.4.1 Time Variation and Severity Scale ........................................................................70 Chapter 5 Conclusion .....................................................................................................................72 Lessons Learned and Future Work ...........................................................................................72