## 2 Literature Review  
Along with continuously increasing travel demands, the scale of metro networks is also rapidly expanding. Hundreds of metro stations and sections inside the metro system, as well as the imbalanced distribution of passengers, have made metro emergencies occur more frequently than ever before. According to the statistics, 334 emergencies have been recorded in the Beijing subway system from 2014 to 2016 [1]. That is to say, a metro emergency occurs every three days on average, which will lead to serious impacts on passengers' daily trips. Several studies related to metro emergencies have been carried out in recent years. The capacity of evacuating subway stations under emergencies is estimated by using a bi-level programming model, and the performance of a subway station has been calculated [2]. Big data methods are also used in analyzing the resilience and vulnerability of transit networks and provide measures and methodologies in planning and preparing for emergencies [3]. Additionally, a combined cause consequence analysis with the fuzzy causal model is proposed to analyze human factors (passengers and operators) in metro accidents, and an emergency of \"passenger clamped by train doors\" which is influenced by operator fatigue is found to happen at certain periods during the day [4]. Obviously, it is a clear signal that human factors' influences in metro emergencies are taking as important elements in making intervention measures for metro operators. However, the existing researches are still just using the aggregated ridership data to predicate the passenger flow distribution and ignoring passengers' heterogeneity in route choice preferences between metro emergency and normal conditions. Therefore, the mutual feedback mechanism between passengers' route choices and the dynamic process of the congestion propagation in the entire metro network needs to be investigated, in order to provide much detailed information to the metro operators. Fully understanding metro passengers' route choice behaviors is the key to predict, simulate and control the massive ridership distributed in the metro network. The decision-making process has been investigated with the introduction of random utility maximization (RUM) theory since the 1970s [5; 6], where the theory assumes that the rational decision-makers can always capable of perceiving the utilities of all the alternatives in the choice set and choosing the alternative with the highest utility. The RUM theory with its conciseness and achievable properties has been widely applied in researches of behavior analysis, where its linear-additive implementation (multinomial logit model, i.e., MNL model) is the most used tool for analyzing travel behaviors. However, despite its many advantages, the disadvantage of the MNL model is also distinguished that it is not sensitive to the changes in the choice set when the random term of the model function follows the identical and independent distribution, and it is called independence of irrelevant alternatives (IIA). Due to the IIA property in the MNL model, either the identical alternatives in the choice set will be treated as different options, or the changes in alternatives or attributes will not be reflected in the comparison between two alternatives, which will lead to failures in model calibration. While, this defect can be remedied with mathematical methods such as nested logit (NL) model, mixed logit model and latent class model (LCM). NL model performs like the multi-level MNL model, where the alternatives are divided into several nests and the IIA property is eliminated between the nests. As the RUM based NL model is preferable in most situations [7], [PERSON] et al. [8] explored route choice behaviors of drivers driving battery-electric vehicles. The mixed logit model is defined as an MNL model with random coefficients drawn from a cumulative distribution function [9], which considers the unobserved heterogeneity by allowing parameters to be distinct from each individual.