The objective of this paper is to model and characterize the percolation dynamics in road networks during a major fluvial flooding event. First, a road system is modelled as planar graph, then, using the level of co-location interdependency with flood control infrastructure as a proxy to the flood vulnerability of the road networks, it estimated the extent of disruptions each neighborhood road network experienced during a flooding event. Second, percolation mechanism in the road network during the flood is captured by assigning different removal probabilities to nodes in road network according to a Bayesian rule. Finally, temporal changes in road network robustness were obtained for random and weighted-adjusted node-removal scenarios. The proposed method was applied to road flooding in a super neighborhood in Houston during hurricane Harvey. The result shows that, network percolation due to fluvial flooding, which is modelled with the proposed Bayes rule based node-removal scheme, causes the decrease in the road network connectivity at varying rate.
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