Optimized identification for critical metro stations from urban resilience perspective

Abstract

Abstract Urban rail transit systems and metro-led underground space contribute to urban resilience. The critical level of metro-led underground space in a metro network demands different resilience requirements, thereby rendering the identification of critical metro stations before the evaluation of the resilience contribution essential. This study optimized the identification of critical metro stations in terms of network model construction and evaluation methods to suit urban resilience perspective. The optimized metro network model considered the influence of transfers, difference between above-ground and underground routes, and cost between adjacent stations, to construct an undirected weighted graph of a metro network. Three evaluation metrics: node betweenness, network efficiency loss, and route redundancy, were adopted for a comprehensive identification method of critical stations. The optimized identification method was applied to the Shanghai Metro network (402 stations and 481 links by 2023). The betweenness identified the downtown stations to be more critical, whereas the network efficiency loss and route redundancy identified the critical stations at downtown-suburban connections. Moreover, the vulnerability and distribution of above-ground stations influenced the identification result of critical stations. The identification results are expected to facilitate the resilience evaluation of metro-led underground space, to examine whether the contribution is balanced with the demand.