Dynamic Disruption Simulation in Large-Scale Urban Rail Transit Systems

Abstract

We present a simulation-based approach to capture the interactions between train operations and passenger behavior during disruptions in urban rail transit systems. The simulation models the full disruption and recovery cycle. It is based on a discrete-event simulation framework to model the network vehicles movement. It is paired with an agent-based model to replicate passenger route choices and decisions during both the undisrupted and disrupted state of the system. We demonstrate that optimizing and flexibly changing the train dispatch schedules on specific routes reduces the impact of disruptions. Moreover, we show that demand uncertainty considerably changes the measures of performance during the disruption. However, the optimized schedule still outperforms the non-optimized schedule even under demand uncertainty. This work ties into our ongoing project to find flexible strategies to enhance the system resilience by explicitly incorporating uncertainties into the design of rail system architectures and operational strategies.