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Modeling urban transportation safety resilience under extreme rainstorms: A catastrophe theory approach

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  • Ma, Fei
  • Zhang, Yujie
  • Liu, Qing
  • Guo, Yaru
  • Yang, Zhijie
  • Zhang, Jiaju

Abstract

Extreme rainstorm disasters severely affect urban transportation safety. To scientifically assess urban transportation safety resilience (UTSR11Urban transportation safety resilience) and its evolutionary process under extreme rainstorm disasters, this study proposes a novel assessment method by modeling the UTSR using the catastrophe theory approach. First, a safety framework for the urban transportation system is constructed, and catastrophe theory is applied to analyze catastrophic effects on the system. Second, factors affecting UTSR are identified, and their relationships are analyzed using a stock and flow model. Finally, the effectiveness of the UTSR dynamic simulation model is analyzed using the case study of an extreme rainstorm event in Xi'an, China. The results reveal that increasing the investment levels of government regulation effort (GRE22Government regulation effort), information synergy degree (ISD33Information synergy degree), and municipal drainage effectiveness (MDE44Municipal drainage effectiveness) leads to modeled increases in UTSR levels by 59.44%, 50.18%, and 16.79%, respectively. The results demonstrate that strengthening GRE and ISD significantly enhances UTSR, while MDE has a relatively minor impact. This study contributes a new theoretical perspective and practical modeling tool for capturing abrupt resilience transitions, offering detailed management strategies for enhancing UTSR when facing extreme rainstorms.

Suggested Citation

  • Ma, Fei & Zhang, Yujie & Liu, Qing & Guo, Yaru & Yang, Zhijie & Zhang, Jiaju, 2025. "Modeling urban transportation safety resilience under extreme rainstorms: A catastrophe theory approach," Reliability Engineering and System Safety, Elsevier, vol. 263(C).
    • Handle: RePEc:eee:reensy:v:263:y:2025:i:c:s0951832025005022
    DOI: 10.1016/j.ress.2025.111301
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    1. Tong, Bingrui & Liu, Hui & Zhu, Junjie & Wang, Yuxuan & Mei, Ting, 2026. "Enhancing urban system safety through urban resilience: A case study of Ningbo, China," Reliability Engineering and System Safety, Elsevier, vol. 267(PA).
    2. Li, Xiaojuan & Chen, Rixin & Ren, Yifei & Jim, C.Y., 2026. "Intrinsic drivers of urban flood disasters from the resilience perspective in China," Reliability Engineering and System Safety, Elsevier, vol. 265(PB).
    3. Kui Xu & Yizhuang Tian & Lingling Bin & Chengguang Lai & Weichao Yang, 2026. "Rapid Prediction of Compound Flood Based on Hydrological-Hydrodynamic Model and Convolution Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 40(4), pages 1-21, March.
    4. Gao, Chong & Jiang, Hui & Guo, Xiaoling, 2026. "Bridging interactions and robustness of inter-community structures in disaster response systems: A hypergraph-based analysis," Reliability Engineering and System Safety, Elsevier, vol. 266(PB).

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