Resilience assessment for bus-metro multimodal networks considering various attacking scenarios

• A novel framework is proposed to evaluate the resilience of multimodal transportation networks, integrating structural and functional characteristics and using integral calculations to capture dynamic evolution of resilience throughout the process. • By categorizing attack scenarios based on node degradation, failure types, and temporal variations, the study proposes 90 distinct scenarios, enabling a thorough evaluation of network resilience. • Analysis of Beijing's swipe card data shows weaker resilience when passengers transfer to both bus and metro stations after a metro station attack in certain cases, and significant off-peak impacts due to reduced scheduling frequency. Multimodal transportation systems significantly enhance travel convenience but also introduce vulnerabilities. Disruptions in one segment can cascade across the network, compromising overall network performance. These disruptions, often stemming from diverse attack scenarios, highlight the critical need to study and enhance resilience in transportation networks. This paper introduces a resilience assessment model that considers the characteristics of abrupt events in passenger networks. A series of attack scenarios are set up, categorized by the extent of node capability degradation, the number and types of nodes subjected to attack, and the duration of the attack. Focusing on Beijing's bus-metro multimodal network, the results show that in certain scenarios, recovery performance is worse when passengers transfer to both nearby bus and subway stations after a subway station attack, compared to transferring only to bus stations. This is due to longer walking transfer times and higher passenger volumes at subway stations, which increase flow delays and risk cascading failures. Furthermore, off-peak node failures also worsen network performance due to reduced scheduling frequency. Consequently, the entire transportation system requires an extended recovery period. These insights are critical for informing targeted emergency recovery strategies in the aftermath of public transportation disruptions.