This paper proposes a data-driven approach using percolation theory to analyze the resilience of highway networks under rainfall conditions. The proposed approach's main contribution is integrating real-world traffic data with percolation theory to evaluate the impact of rainfall on traffic flow and identify the critical links of highway networks. The resilience indicators, accounting for network topology and functionality, were formulated. To calculate these indicators under various rainfall intensities, the traffic flow fundamental diagrams were established using empirical rainfall and traffic data, and a probabilistic rainfall simulation model was developed. A case study of the East Midlands, UK highway network under a heavy rainfall event on September 27, 2019, validated the approach's feasibility. Furthermore, control experiments showed that the critical links identified by the proposed method enhance highway network resilience more effectively than traditional methods, thus validated the novelty of our approach.

中文翻译：

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使用基于数据驱动的渗滤理论的方法对降雨条件下公路网的弹性进行分析

本文提出了一种利用渗流理论的数据驱动方法来分析降雨条件下公路网的恢复能力。该方法的主要贡献是将现实世界的交通数据与渗流理论相结合，以评估降雨对交通流的影响并确定高速公路网络的关键环节。制定了考虑网络拓扑和功能的弹性指标。为了计算不同降雨强度下的这些指标，利用经验降雨和交通数据建立了交通流基本图，并建立了概率降雨模拟模型。对 2019 年 9 月 27 日强降雨事件下的英国东米德兰兹高速公路网的案例研究验证了该方法的可行性。此外，控制实验表明，该方法识别的关键链路比传统方法更有效地增强了公路网络的弹性，从而验证了我们方法的新颖性。