Critical Station Identification of Metro Networks Based on the Integrated Topological-Functional Algorithm: A Case Study of Chengdu

Abstract As a key mode of transportation, urban metro networks have significantly enhanced urban traffic environments and travel efficiency, making the identification of critical stations within these networks increasingly essential. This study presents a novel Integrated Topological-Functional (ITF) algorithm for identifying critical nodes, combining topological metrics such as K-shell decomposition, node information entropy, and neighbor overlapping interaction with the functional attributes of passenger flow operations, while also considering the coupling effects between metro and bus networks. Using the Chengdu metro network as a case study, the effectiveness of the algorithm is validated under different conditions. The results indicate significant differences in passenger flow patterns between working and non-working days, leading to varying sets of critical nodes across these scenarios. Moreover, the ITF algorithm demonstrates a marked improvement in the accuracy of critical node identification compared to existing methods. This conclusion is supported by the analysis of changes in overall network structure and relative global operational efficiency following targeted attacks on the identified critical nodes. The findings provide valuable insights for urban transportation planning, offering theoretical and practical guidance for improving metro network safety and resilience.