Simulation of Metro Congestion Propagation Based on Route Choice Behaviors Under Emergency-Caused Delays

Abstract

Generally, metro emergencies could lead to delays and seriously affect passengers’ trips. The dynamic congestion propagation process under metro emergency-caused delays could be regarded as the aggregation of passengers’ individual travel choices. This paper aims to simulate the congestion propagation process without intervention measures under the metro emergency-caused delays, which is integrated with passengers’ route choice behaviors. First, using a stated preference survey data collected from Guangzhou Metro (GZM) passengers, route choice models are developed based on random regret minimization (RRM) theory under metro emergency conditions. Then, a simulation environment is established using graph cellular automata (graph-CA) with augmented GZM network structure, where an ASEIR (advanced susceptible-exposed-infectious-recovered) model with time delay is proposed as the evolution rule in graph-CA. Furthermore, considering passengers’ routing preferences, a quantified method for the congestion propagation rate is proposed, and the congestion propagation process on a subnetwork of the GZM network is simulated. The simulation results show that metro congestion during peak periods has a secondary increase after the end of the emergency-caused delays, while the congestion during nonpeak hours has a shorter duration and a smaller influence range. The proposed simulation model could clearly reflect the dynamic process of congestion propagation under metro emergencies.

Modeling the Traveler’s Route Choice Behavior under Unexpected Accidents

“…Ye et al [37] constructed a biattribute regret-minimizing user equilibrium (BRminUE) model with monetary cost and travel time, in which the traveler's goal is to minimize the regret value. Wang et al [38] combined the route choice behavior of urban rail transit passengers based on RT to simulate the nonintervention passenger fow redistribution process.…”