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Individual Path Recommendation Under Public Transit Service Disruptions Considering Behavior Uncertainty and Equity
Friday, July 8, 2022
3:00PM ET
Hybrid Event
Location: 6 MetroTech Center, Brooklyn, NY 11201 RH460
 
During a public transit service disruption, passengers usually need path recommendations to find alternative routes. This study proposes a mixed-integer programming (MIP) formulation to model the individual-based path (IPR) recommendation problem during PT service disruptions with the objective of minimizing system travel time and respecting passengers' path choice preferences. Passengers' behavior uncertainty in path choices given recommendations and their travel time equity are also considered. We model the behavior uncertainty based on passenger's prior preferences and posterior path choice probability distribution with two new concepts: epsilon-feasibility and gamma-concentration, which control the mean and variance of path flows in the optimization problem. The IPR problem with behavior uncertainty is solved efficiently with Benders decomposition. A post-adjustment heuristic is used to address the equity requirement. The proposed approach is implemented in the Chicago Transit Authority (CTA) system with a real-world urban rail disruption as the case study. Results show that the proposed IPR model significantly reduces the average travel times compared to the status quo and outperforms the capacity-based benchmark path recommendation strategy. 
Baichuan Mo is a Ph.D. candidate in the Transportation program at MIT. He completed his dual Master's degree in Transportation and Computer Science at MIT in 2020. His main research interests lie in public transit systems and their intersection with behavior and demand modeling, autonomous vehicles, and policy evaluation. His work utilizes various mathematical techniques including optimization (robust/integer/non-linear), probability and statistics, machine learning (deep/reinforcement learning), econometrics, and game theory. He has published 13 peer-reviewed scientific papers (11 first-authored) in leading transportation journals (TR-A, B, C, E, IEEE ITS, etc.) and was awarded the 2021 MIT UPS Ph.D. Fellowship (top Ph.D. student in transportation/logistics).
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C2SMART Center is a USDOT Tier 1 University Transportation Center taking on some of today’s most pressing urban mobility challenges. Using cities as living laboratories, the center examines transportation problems and field tests novel solutions that draw on recent advances in communication and smart technologies. Our consortium includes New York University, Rutgers University, University of Texas at El Paso, University of Washington, and City College of New York.







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