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<p>Urban rail transit systems (URTS) are very susceptible to extreme weather events such as heavy rainfall and flooding, which can easily create flooding incidents at subway stations, resulting in significant operational disruptions and casualties. This research creates a comprehensive flood risk assessment framework to overcome the uncertainty of objective risk elements and expert subjective experience in the risk assessment process. First, the critical elements contributing to the station's flood risk are identified methodically, and a risk assessment index system (RAIS) is created. Second, for objective-level uncertainty, different asymmetric probability distribution functions are integrated to derive the best fitting distribution of risk factors; for subjective-level uncertainty, we propose a fitted triangular fuzzy number-analytic hierarchy process (FTFN-AHP) method to derive RAIS weights and build the station risk function. Finally, Monte Carlo simulation (MC) and probabilistic methods are used to calculate the flooding risk for each station in Beijing's URTS, and the results are validated using real-world flooding occurrences. The research has found that the flood risk is highest at Nongda South Road, Beijing West Road, Chegongzhuang, Jin'anqiao, and Taoranting stations, with a risk exceeding 10% at a risk threshold of 0.7. High-risk flood stations are characterized by their location in urban centers and a history of flood events. This finding provides support for URTS's flood prevention and disaster relief efforts.</p>

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Flood risk assessment of urban rail transit stations based on uncertainty analysis

  • 期刊论文

  • 发表日期：

    2025年8月27日

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  • Liang Mu,
  • Zixu Yan,
  • Yurui Kang,
  • Guangyu Zhu

摘要

Urban rail transit systems (URTS) are very susceptible to extreme weather events such as heavy rainfall and flooding, which can easily create flooding incidents at subway stations, resulting in significant operational disruptions and casualties. This research creates a comprehensive flood risk assessment framework to overcome the uncertainty of objective risk elements and expert subjective experience in the risk assessment process. First, the critical elements contributing to the station's flood risk are identified methodically, and a risk assessment index system (RAIS) is created. Second, for objective-level uncertainty, different asymmetric probability distribution functions are integrated to derive the best fitting distribution of risk factors; for subjective-level uncertainty, we propose a fitted triangular fuzzy number-analytic hierarchy process (FTFN-AHP) method to derive RAIS weights and build the station risk function. Finally, Monte Carlo simulation (MC) and probabilistic methods are used to calculate the flooding risk for each station in Beijing's URTS, and the results are validated using real-world flooding occurrences. The research has found that the flood risk is highest at Nongda South Road, Beijing West Road, Chegongzhuang, Jin'anqiao, and Taoranting stations, with a risk exceeding 10% at a risk threshold of 0.7. High-risk flood stations are characterized by their location in urban centers and a history of flood events. This finding provides support for URTS's flood prevention and disaster relief efforts.