Abstract
The increasing impact of urban floods, driven by global climate change and the growing frequency of extreme weather events, poses significant threats to public safety, disrupts infrastructure, and hampers economic development. This paper presents a two-stage model for shortest path planning and dynamic dispatching of rescue forces (firefighters and fire engines) in response to urban floods caused by extreme rainfall. In the first stage, a path selection model for rescue vehicles is developed, supported by an efficient customized A* algorithm to determine worst-case travel times from fire stations to flood sites. A preference-based version of the algorithm is also introduced, incorporating driver preferences into path selection. In the second stage, rescue forces are dynamically allocated based on demand at flooded locations, which is estimated using population density and real-time flood depth data. The travel times derived in the first stage serve as inputs to a bi-objective dynamic dispatch model that utilizes real-time flood data to optimize emergency response. By integrating path planning with rescue force dispatching, this study provides essential support for effective flood response operations.