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Infrastructure Resilience Conference 2018

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Rail Transit Network Passenger Flow Optimization Under Uncertain Disruptions

Public rail transit is an essential mode of transportation that can effectively enable rapid passenger travel mobility in many highly populated city-states. On the other hand, rail transit systems can be very fragile to even isolated disruption events in the network. In this work, we propose an optimization-based model for a rail network system that may experience uncertain disruptions at station platforms. Our basic model captures the detailed logic of passenger movement from origin to destination, and the minute-by-minute train schedule at the rail platform level. This is then embedded in an uncertain disruption scenario model, which describes the logic of disruption events and their consequences on train capacity. The passenger flow model consists of two variations: a path-based planning formulation minimizing queuing delays for recommending travel patterns under normal conditions, and an arc-and-node formulation for contingency planning under disruptions maximizing passenger outflows. The goal of our study is to recommend, at the system level, the optimal rail transit schedules and passenger travel paths that not only adhere to waiting delay service requirements, but also robust to network disruptions. The large-scale mixed integer optimization model is solved via an efficient iterative cutting plane procedure. We demonstrate the practical significance of the model based on a case study example.

Lei Xu
Future Resilient Systems


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