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Transportation systems can be significantly affected by flooding, leading to physical damage and subsequent adverse impacts such as increased travel distance to essential services. Even though flooding is a frequently recurring phenomenon that can affect thousands of people per event, there are limited accessible online tools available for analyzing and visualizing flood risk for supporting decisions on routing and emergency planning, and response. Existing tools are generally based on complicated models and are not easily accessible to non-expert users. Therefore, it is critical to have efficient real-time communication and decision tools for analyzing flood impacts on transportation networks for various stakeholders, including the public, to minimize the adverse impacts on those groups. This paper presents a web application that uses graph network methods and the latest web technologies and standards to assist in describing flood events in terms of operational constraints and provide analytical methods to support mobility and mitigation decisions during these events. The framework is designed to be user-friendly, enabling non-expert users to access information about road status, shortest paths to critical amenities, location-allocation, and service coverage. The study area includes the following two communities in the State of Iowa, Cedar Rapids and Charles City, which were used to test the application's functionality and explore the outcomes. Our research demonstrates that flooding can significantly affect bridge operation, routing from locations to critical amenities, arbitrary point-to-point routing, planning for emergency facility placement, and service area accessibility. The introduced framework is capable of solving complex flood-related analytical decision tasks and providing an understandable representation of transportation vulnerability, enhancing mitigation strategies. Therefore, this web application provides a valuable tool for stakeholders to make informed decisions on transportation networks during flood events.
Alabbad et al. (Thu,) studied this question.