Identification of key stations and vulnerability analysis of urban railway networks based on the improved KBKNR algorithm

To investigate the characteristics and vulnerability of urban railway networks to unexpected events, and to improve operational efficiency, this paper develops a topology model of an urban railway network using the Space L method. A comprehensive station degree indicator is proposed, incorporating both network topology and ridership attraction, as reflected by POI data around the stations. This indicator is integrated into the KBKNR algorithm to calculate the comprehensive entropy of urban railway stations, identifying key stations. The effect of random and intentional attacks on network vulnerability is then analyzed using three key station identification methods: degree value priority, betweenness centrality priority, and station comprehensive entropy priority. An empirical analysis using the 2023 Xi'an urban railway network verifies the model's practicality and accuracy. Results show that random attacks have a minor effect on network vulnerability, while intentional attacks significantly affect it. When intentional attacks target key stations identified by the improved KBKNR algorithm, the network's performance degradation rate increases rapidly, highlighting the method's accuracy. Finally, suggestions for mitigating network vulnerability due to key station failures are proposed, offering a solid scientific foundation for managing urban rail transportation safety.