Key station identification of urban rail transit in multi scenario under station-city integration

To enhance the resilience of urban rail transit networks against security risks, a method for identifying key stations in urban rail transit networks under multiple scenarios is proposed, rooted in proactive prevention and in-process control, and incorporating the station-city integration and the improved CRITIC-TOPSIS (ICT) method. First, the model of urban rail transit networks was established based on complex network theory and the Space-L method. Second, considering the network structural characteristics and station-city integration, degree centrality, betweenness centrality, closeness centrality, and points of interest (POI) were selected to construct an index system for evaluating station importance. ICT is utilized to establish a comprehensive “ICT index” to assess station importance. Subsequently, key station identification methods are developed for both static and dynamic scenarios aimed at proactive prevention and mid-event control. Final, taking the Beijing urban rail transit network in 2023 as a case study, key station sequences were identified under static and dynamic scenarios. Furthermore, the identification results were validated through an analysis of changes in network robustness parameters following the failure of key stations. Research results indicate that top 20 static and dynamic key stations mainly locate in Lines 10, 2, and 14, with most being high-value “hub-type” stations (such as Shilihe and Xizhimen stations). Dynamic key stations also include “special stations” (such as Niujie and Mudanyuan stations) with small values of degree but large value of other indicators, surrounded by numerous urban service elements. The significant decrease in network robustness after the failure of key stations validates their important impact on the network’ s resilience against security risks. Through comparative analysis, the rationality of considering station-city integration and employing ICT methods for key station identification in multiple scenarios has been demonstrated. The research conclusions can provide methodological references for ensuring the safe operation of urban rail transit systems. © 2024, Central South University Press. All rights reserved.