## 6 Conclusions  
In order to get a clear understanding of the dynamical congestion propagation process derived from the metro emergency-caused delays, a simulation model without considering passenger control measures in the entire metro network is investigated. Based on passengers' spontaneous route choice behaviors under metro emergencies on the microscope level, route choice models based on the RRM principle is develop with passengers' perceptions on route attributes, network topological factors and delay specific variable. With different route choice models during peak (M2) and nonpeak (M3) periods under metro emergency-caused delays, metro passengers' heterogeneity is analyzed with SP data collected from GZM passengers. Due to the significant influences of metro emergency-caused delays, passengers behave differently from normal conditions, where they may ignore the normally negative effects of transfers and detour routes to avoid further losses caused by the delays. Moreover, the period when the metro delay occurs is vitally important for its congestion propagation process. Compare with nonpeak periods, a larger proportion of metro passengers travel in peak periods will abandon metro and switch to ground traffic modes like bus and taxi to continue their trip.  
Furthermore, a simulation model of congestion propagation using the ASEIR method is proposed in the graph-CA environment with the augmented metro network. In addition, the congestion propagation rate is quantified by integrating passengers' heterogeneous preferences in route choices under metro emergencies. The simulation result of the ASEIR model considering incubation time reveals the differences in metro emergency-caused delays between peak periods and nonpeak periods. Metro emergency that occurs at peak hours may cause a secondary propagation of congestion in the metro network, while emergencies occur at nonpeak hours has only one peak and has a short duration and smaller influencing range. Therefore, during peak hours, the metro operators not only should take measures to control passenger volumes at the first time when the emergency occurs, but also should pay more attention to the second increase of the congested stations in the secondary congestion propagation process and take targeted measures to reduce the effects of the congestions' regrowth.  
In conclusion, correctly understanding the dynamical congestion propagation process under metro emergencies without intervention measures is the foundation to develop emergency intervention measures and properly control the crowded passenger flow in the metro network. The simulation of metro congestion propagation can provide metro operators a more precise perspective to quantify the influence of the metro emergency-caused delays, and it is a useful method to evaluate the efficiency of the passenger control measures. Additionally, further researches concerning metro passenger control measures and train schedule rearrangements could be tested with the help of these simulation methods, so that the influences and the losses caused by the metro emergencies could be minimized.  
Conceptualization, X.W. and E.Y.; methodology, X.W. and E.Y.; software, X.W.; validation, E.Y.; formal analysis, X.W. and E.Y.; writing--original draft preparation, X.W.; writing--review and editing, X.W., E.Y., S.L.  
This work was supported by the Beijing Natural Science Foundation, grant number: 8171003.  
The authors declare no conflict of interest.