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This article describes a data-driven approach to flood risk exposure evaluation and route delineation during heavy rainfall events. We cross-referenced diverse geospatial and drainage infrastructure datasets with the street network of Hoboken to uncover the factors that increase flood risk. Elevation, slope, precipitation level, imperviousness, and distance to the drainage system’s outlets were the most significant predictors to link flooding. We used the link flood risk patterns found in the data to train a reinforcement learning model that generates routes that avoid flood-prone areas. We benchmarked the route assistance model with shortest path and most reliable path algorithms, demonstrating our model has balanced path length and path reliability. We provided the flood risk model outputs at the link-level, which city authorities can use to plan road closures ahead of heavy precipitation events. The route assistance model can be used by drivers to better navigate flood-prone environments by detouring around riskier areas or canceling trips altogether.

This article addresses the impacts of flood events on urban street networks. Macro-traffic simulation techniques were used on disrupted and undisrupted scenarios to assess the increase on the network’s mobility and accessibility. Local topographical aspects of the terrain were analyzed to identify portions of the network more prone to disruption. Flood maps were used to systematically remove links from the network, generating its disrupted state for different scenarios. The traffic assignment model generated routes using k-shortest path methods with link impedance penalty functions, selecting them based on user equilibrium assumption. Simulation results indicated the viability of the method to analyze the impacts of flood events of different severity and duration. The successful validation of this method indicated its viability as a tool for benefit cost analysis of urban improvement projects including resilience plans for high risk cities. The analysis was validated using the City of Hoboken, New Jersey’s transportation network and flood models. Results can be applied to cities with a high chance of flooding and should help authorities to effectively review their infrastructure strategic plans as well as their short and long-term urban mobility plans.