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.
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