Identifying Critical Stations Affecting Vulnerability of a Metro Network Considering Passenger Flow and Cascading Failure: Case of Xi'an Metro in China

Analyzing the importance grade of each metro station is the premise and foundation to ensure the safe and efficient operation of the metro network. This paper explores the identification method of critical stations in the metro network on the basis of considering the influence of passenger flow. Eight indicators were selected from both the static and dynamic aspects to construct the critical station identification index system considering the impact of passenger flow and cascading failure. Then, the triangular fuzzy analytic hierarchy process (TFAHP) method and critical importance through intercriteria correlation (CRITIC) method were used to calculate subjective and objective weight of all indicators, and the combined weighting method based on game theory was applied to calculate the comprehensive weight. The improved technique for order of preference by similarity to ideal solution (TOPSIS) method was developed to calculate the station importance and rank all stations according to their importance. Finally, the Xi'an metro network by mid-2021 was taken as the case study. The results show that there are 21 critical stations in the Xi'an metro network in three time periods (i.e., morning rush hours, evening rush hours, and nonrush hours) on weekdays and weekends, respectively, and those critical stations are divided into three levels. High centrality, high passenger flow intensity, and strong destructive ability are three important characteristics of critical stations in the metro network, and different critical stations have different importance characteristics. The Xi'an metro operation management authority needs to conduct a classification management scheme for those critical stations. The conclusions can provide the basis for the metro operation management authority to formulate accurate station management scheme, so as to increase the robustness of the metro network. (c) 2023 American Society of Civil Engineers.