Scenario inference model of urban metro system cascading failure under extreme rainfall conditions

Zhen Yang, Xiaobin Dong and Li Guo

Reliability Engineering and System Safety, 2023, vol. 229, issue C

Abstract: Metro systems have become high-vulnerability entities due to the increasing frequency and severity of urban flooding. Flood events may cause cascading failure to metro systems; therefore, exploring the cascading failure risk of the metro system is a prerequisite for urban flooding prevention and risk management. This study presented a Rank-Order Centroid (ROC) based CIA-ISM (Cross-Impact Analysis, and Interpretive Structural Modeling) method to accurately assess the reliability of emergency management in metro systems under extreme rainfall conditions. We applied this approach to a metro flooding case in Zhengzhou on July 20, 2021. The reliability results show that efficient rescue and timely shutdown notification are the most critical causal events in the cascading failure scenarios. The events of system vulnerability that have the most significant impact on casualties, property losses, and social panic are, respectively, timely notification of the shutdown, humanitarian aid, and public opinion guidance. In forecast scenarios with emergency management measures in effect, the probability of casualties, property losses, and social panic decrease by 96.3%, 58.58%, and 64.28%, respectively. Moreover, a comparison with Bayesian Network (BN) model verified the effectiveness of the ROC-based CIA-ISM approach. Based on the study, we suggest the metro companies release a timely notification of the shutdown. This study can provide scientific data for decision-makers to reasonably develop emergency strategies, significantly reducing flood losses and promoting cities’ sustainable development.

Keywords: Extreme rainfall; Metro system; Cascading failure; Interpretive structural modeling; Scenario inference (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022005051

DOI: 10.1016/j.ress.2022.108888

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