## 1 Introduction  
Metro emergencies such as power system breakdown, signal system failure, train malfunction and accidents caused by human factors (passengers and operators), are the main causes of the delays in the metro system. In most cases, the delays can easily lead to congestions in stations or sections, which could spread from one station (section) to another and congest the lines or even the entire metro network. Understanding the spontaneous congestion propagation process without intervention measures under metro emergency-caused delays, can be beneficial to obtain the influencing range and duration of the emergencies and provide metro operation department valuable references and decision-making basis in making passenger control measures. In other words, only if we understand how the congestion spread in the metro network under metro emergencies without passenger control measures, can we know when, where and how to reduce the impact of metro emergency-caused delays.  
Generally, the congestion propagation process in the metro network can be regarded as the aggregation of passengers' spontaneous route choice results, and their heterogeneities on route choices are vital factors influencing passenger flow distribution under metro emergencies in the entire metro network. In addition, due to the uncertainty and anxiety caused by the metro service disruptions, passengers' route choice behaviors are dynamically changing and significantly different from the normal conditions, the state transfer process of all the stations and links in the metro network is also a dynamic process. However, the metro operators and relevant researchers are more accustomed to predicting metro passenger flow distribution just using the aggregated ridership data and ignoring passengers' heterogeneity in route choices between metro emergency and normal conditions.  
Therefore, this paper aims to simulate the congestion propagation process without intervention measures under metro emergency-caused delays, where the microscopic passengers' route choice behaviors are integrated in order to quantify the congestion propagation rate of each metro station. In detail, based on a stated preference survey data and the random regret minimization (RRM) theory, the route choice model is developed to describe passengers' preferences under the metro emergency-caused delays. Meanwhile, in order to get better understandings of the congestion propagation process, a dynamical simulation model is proposed, which is implemented with graph cellular automata (graph-CA) model and ASEIR (advanced SEIR, susceptible-exposed-infectious-recovered) model on Guangzhou metro (GZM) network. Furthermore, a quantified method for congestion propagation rate based on passengers' routing preferences and ridership distributions is proposed and the simulation on a subnetwork of the GZM network is carried out as the empirical study.  
The remaining part of this paper is organized as follows. Section 2 gives the literature review of relevant researches. Section 3 describes the route choice models with data, variables, methodology, and estimation results. Section 4 shows the congestion propagation process on the metro network under a graph-CA environment with the ASEIR model, where the simulation on the GZM network is made, then, the passengers' routing preferences are integrated into the congestion propagation rate of the ASEIR model. Section 5 analyzes the congestion propagation process with an empirical study, and Section 6 presents the conclusions.