Vulnerability analysis is the premise of operational risk management and control for the large-scale and complex urban rail transit network (URTN) under the operation interruption of important stations. The temporary operation interruption of one station in an emergency may lead to the cascading failure and the paralysis of the whole URTN due to the load of other stations exceeding the limited capacity. The priority of important stations is proposed by combining its location and function in URTN. In addition, focusing on the analysis of the travel behaviour of passengers and the synergy of public transport networks, a novel cascading failure evolution model is established to simulate the cascading failure process of URTN under different attack scenarios. The vulnerability indicators are constructed to dynamically evaluate the vulnerability of URTN considering cascading failure evolution, which are different from the traditional vulnerability indicators based on complex network theory. Taking the Beijing urban rail transit network as an example, the dynamic simulation results show that the cascading failure of URTN is closely related to the temporal-spatial distribution of passenger flows and malicious attacks are more destructive than random attacks. Compared with the important stations with the largest betweenness or degree, the interrupted stations with largest intensity have a greater impact on the operational stability of URTN. Moreover, increasing the capacity coefficient of the station can reduce the vulnerability of URTN.

As we all known, estimating the proportion of passenger route choice is of great significance in almost every aspect of urban rail transit control system, including passenger allocation, fare clearing and flow control strategies. Existing researches only pay attention to the route choice through travel time, but usually ignore the influence in different periods of the day. Therefore, this paper proposes a novel estimation method for the proportion of passenger route choice in different periods. Firstly, by introducing the normalized value of passenger flow and the standard coefficient of peak passenger flow, the train operation time is divided into peak and flat periods. Secondly, the travel time distribution of each route can be obtained by estimating the expected value and standard deviation of passenger travel time in each different period. The Naïve Bayes algorithm is further employed to realize the identification of the proportion of passenger route choices. Finally, this proposed algorithm is applied to Hangzhou Metro. The result shows that by using the segmented estimation, the error can be reduced by more than 60% compared with the whole-day experiment, which indicates the superiority of the method.