Network-based Assessment of Metro Infrastructure with a Spatial–temporal Resilience Cycle Framework

Zizhen Xu and Shauhrat S. Chopra

Reliability Engineering and System Safety, 2022, vol. 223, issue C

Abstract: Urban public transportation systems tend to cripple when faced with challenges, such as natural hazards and social unrest. It is imperative to engineer resilient public transportation systems to provide urban commuters with a reliable alternative to private vehicles. Current network-based approaches for resilience quantification focused on the network topology but seldom considered the impacts of temporal variation of flow pattern and system’s spatial distribution, which provide unique people-centric insights into resilience. This paper applies a resilience cycle framework consisting of four life-cycle stages associated with any disruptive event – preparedness, robustness, recoverability, and adaptation. The proposed flow-weighted and spatial analysis captures the resilience of both the system and users. Additionally, the temporal trends are compared for different resilience indicators associated with the topology and flow patterns. A case study of the Hong Kong metro system shows the utility of the framework. The study found that the average travel distance of flows has a strong negative effect on the network’s robustness to random failures. The vulnerability of the network to random failures can also be explained by the node homogeneity results from the preparedness stage. In the recovery stage, densely-built metro stations are found to provide significant benefit in response to disruptions, provided that the shared risks for the nearby stations are minimal. The resilience cycle framework provides actionable insights for all the relevant stakeholders.

Keywords: Hong Kong metro system; Urban infrastructure; Resilience; Network analysis; Public transportation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:223:y:2022:i:c:s0951832022000990

DOI: 10.1016/j.ress.2022.108434

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