Vulnerability Assessment and Critical Station Identification in Wuhan Metro Network

To enhance the resilience of mega-city rail transit systems, this study takes the Wuhan Metro Network as the research object, integrating complex network theory and dynamic passenger flow data to assess network vulnerability and identify critical stations. A topological model was constructed based on the L-space method, combining static and weighted metrics. The entropy weight TOPSIS method was applied to quantify the comprehensive importance of stations, while network performance degradation mechanisms were analyzed through random attacks, targeted attacks, and cascade failure simulations. Findings reveal that the Wuhan Metro Network exhibits significant scale-free characteristics with high centralization of hub nodes. Dynamic weighted metrics identify critical vulnerable stations under passenger flow pressure. Targeted attacks degrade network efficiency more severely than random attacks, with weighted-betweenness-based attacks causing the strongest destruction. Cascade failure simulations demonstrate that the failure of the top 3 critical nodes triggers an efficiency cliff, confirming systemic risks from passenger flow redistribution.