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Real-world infrastructures modeled as complex networks have been popular topics in recent decades. As these infrastructures develop, becoming critical to support millions of people, their resilience in the face of disasters and attacks becomes an increasing concern to the engineers and stakeholders. Using a graph theory and complex network approach, we conducted an empirical study on the resilience of five metro, or subway systems selected globally. A multi-phase resilience framework was implemented from the resilience cycle concept, including preparedness, robustness, recovery, and adaptation. It also incorporated the historical flow data into the resilience analysis to account for user behavior impacts. The paper demonstrates a systematic comparison of five metro networks and explores trends in each resilience phase. Generally, diverse performances were observed among the five metro networks, with the Singapore metro showing the best resilience. It is robust to both random disruption and targeted attacks. Analysis suggests its advantage may come from both its moderate heterogeneous topology and the flow pattern, exhibiting the smallest average travel distance. Besides, the result highlights the significance of commuters’ relocation behavior between neighboring stations in the recovery stage. For the adaptation from a long-term perspective, we developed a screening tool to investigate the impact of the network expansion on the existing part of network by mapping its relationship with the characteristics of the newly connected nodes. In general, the observation of the networks’ different behaviors in flow-weighted analysis symbolizes that flow is necessary for consideration in the resilience design of real-world transportation systems.
Xu et al. (Thu,) studied this question.