MobiRescue: Optimal Dispatching of Rescue Teams Under Flooding Disasters

Effective dispatching of rescue teams under flooding disasters is crucial. However, previous methods are either incapable of handling flooding disaster situations, or cannot accurately estimate the distribution of rescue requests and accordingly adjust the search of the rescue teams. We propose MobiRescue, a human Mobility based Rescue team dispatching system, which aims to maximize the total number of rescued people, minimize the rescue delay and the number of serving rescue teams. We studied a city-scale human mobility dataset collected under the Hurricane Florence, and observed that several natural and demographic factors are closely related to impact severity, and road segment passability must be considered. Accordingly, we first propose a Support Vector Machine based method to predict the distribution of rescue requests considering the disaster-related factors. Then, we design an Euler path based method to determine the search paths for rescue team dispatching. Subsequently, we develop a Reinforcement Learning based method to guide the search of the rescue teams. Finally, we design a multi-objective optimization problem based method to adapt to the changed road segment passability. Our experiments demonstrate that compared with the other methods, MobiRescue increases the total number of timely served rescue requests by 43.4% in average.