This study aims to characterize the vulnerability of road networks to fluvial flooding using a network diffusion-based method. Various network diffusion models have been applied widely for modeling the spreading of contagious diseases or capturing opinion dynamics in social networks. By comparison, their application in the context of physical infrastructure networks has just started to gain some momentum, although physical infrastructure networks also exhibit diffusion-like phenomena under certain stressors. This study applies a susceptible-impacted-susceptible (SIS) diffusion model to capture the impact of flooding on the road network connectivity. To that end, this paper undertook the following four steps. First, the road network was modeled as primal graphs and nodes that were flood-prone (or the origins of the fluvial flood) were identified. Second, temporal changes in the flood depth within the road network during a flooding event were obtained using a data-driven geospatial model. Third, based on the relationship between vehicle speed and flood depth on road networks, at each time step, the nodes in the road network were divided into two discrete categories, namely functional and closed, standing for Susceptible and Impacted in the SIS diffusion model, respectively. Then, two parameters of the SIS model, average transition probabilities between states, were estimated using the results of the hydraulic simulation. Fourth, the robustness of the road network under various SIS diffusion scenarios was estimated, which was used to test the statistical significance of the difference between the robustness of the road network against diffusions started from the randomly chosen nodes and nodes with different high centrality measures. The methodology was demonstrated using the road network in the Memorial super neighborhood in Houston. The results show that diffusive disruptions that start from nodes with high centrality values do not necessarily cause a more significant loss to the connectivity of the road network. The proposed method has important implications for applying link predictions on road networks, and it casts significant insights into the mechanism by which cascading disruptions spread from flood control infrastructure to road networks, as well as the diffusion process in the road networks.

中文翻译：

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用SIS网络扩散模型表征道路网络对洪水的脆弱性。

这项研究旨在利用基于网络扩散的方法来表征道路网络对河流洪水的脆弱性。各种网络扩散模型已被广泛应用于对传染性疾病的传播进行建模或捕获社交网络中的舆论动态。相比之下，尽管物理基础架构网络在某些压力下也表现出类似扩散的现象，但它们在物理基础架构网络中的应用才刚刚开始获得动力。这项研究应用了易受影响的易感扩散模型来捕获洪水对道路网络连通性的影响。为此，本文采取了以下四个步骤。首先，将道路网络建模为原始图，并确定容易发生洪灾的节点（或河流洪灾的起源）。第二，使用数据驱动的地理空间模型获得洪水事件期间路网内洪水深度的时间变化。第三，根据车速与路网洪水深度之间的关系，在每个时间步长，将路网中的节点分为两个离散类别，即功能性和封闭性，代表SIS扩散模型中的“易感”和“影响”；分别。然后，使用水力模拟的结果估算了SIS模型的两个参数，即状态之间的平均过渡概率。第四，估算了在各种SIS扩散情景下路网的稳健性，该方法用于检验从随机选择的节点和具有不同高中心度度量的节点开始的路网对扩散的鲁棒性之间的差异的统计显着性。休斯顿纪念超级街区的道路网络演示了该方法。结果表明，从具有较高中心度值的节点开始的扩散性破坏并不一定会对道路网络的连通性造成更大的损失。所提出的方法对于在道路网络上应用链接预测具有重要的意义，它为从防洪基础设施到道路网络的级联破坏以及道路网络的扩散过程提供了重要的见解。休斯顿纪念超级街区的道路网络演示了该方法。结果表明，从具有较高中心度值的节点开始的扩散性破坏并不一定会对道路网络的连通性造成更大的损失。所提出的方法对于在道路网络上应用链接预测具有重要的意义，并且它为从防洪基础设施到道路网络的级联破坏以及道路网络中的扩散过程的机理提供了重要的见识。休斯顿纪念超级街区的道路网络演示了该方法。结果表明，从具有较高中心度值的节点开始的扩散性破坏并不一定会对道路网络的连通性造成更大的损失。所提出的方法对于在道路网络上应用链接预测具有重要的意义，并且它为从防洪基础设施到道路网络的级联破坏以及道路网络中的扩散过程的机理提供了重要的见识。