评估强降雨下的交通韧性：一个集成建模框架与指标
Assessing transport resilience to heavy rainfall: An integrated modelling framework with metrics
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- University of Cambridge（剑桥大学）
- Newcastle University（纽卡斯尔大学）
- ETH Zürich（苏黎世联邦理工学院）
- Arup（奥雅纳）
- Tyndall Centre for Climate Change Research, Newcastle University（纽卡斯尔大学廷德尔气候变化研究中心）
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中文总结 AI 辅助
本研究提出多维性能指标与集成建模框架，结合降雨、洪水和智能体交通模型评估道路客运的洪水韧性，并通过30年一遇降雨事件验证，表明行为适应能减少延误、取消和经济损失。
中文摘要 AI 辅助
道路客运交通面临强降雨引发的暴洪造成的严重中断风险。为捕捉系统复杂性和动态性以评估其洪水韧性，本研究（a）提出了从物理、运营、环境、社会和经济维度量化中断影响的性能指标，并（b）开发了一个集成建模框架，通过结合降雨建模、地表水洪水建模和基于智能体的交通建模来评估所选指标，模拟资产级中断和用户行为响应。该方法通过英格兰东北部泰恩-威尔地区的一场30年一遇降雨事件进行了演示。模拟了三种对降雨的行为响应情景，包括最坏情景、理想响应情景和预警情景。结果突显了行为适应在通过减少出行延误、取消和经济损失来增强道路洪水韧性方面的潜力。该框架和多维性能指标为压力测试降雨情景和评估干预措施提供了一种可转移的方法，支持基于证据的气候韧性交通规划。
英文摘要
Road passenger transport faces acute disruption risks from flash flooding from heavy rainfall. To capture system complexity and dynamics for assessing its flood resilience, this study (a) proposes performance metrics to quantify disruption impacts from physical, operational, environmental, social, and economic dimensions, and (b) develops an integrated modelling framework to assess selected metrics by combining rainfall modelling, surface water flood modelling, and agent-based transport modelling to simulate asset-level disruptions and user behavioural responses. This methodology is demonstrated through a 1-in-30-year rainfall event in Tyne and Wear, North East England. Three behavioural response scenarios to rainfall are simulated, including a worst-case scenario, an ideal-response scenario, and an early-warning scenario. Results highlight the potential of behavioural adaptation in enhancing road flood resilience by reducing journey delays, cancellations, and economic losses. The framework and multidimensional performance metrics offer a transferable approach for stress-testing rainfall scenarios and evaluating interventions, supporting evidence-based climate-resilient transport planning.
