MobiRescue: Reinforcement Learning based Rescue Team Dispatching in a Flooding Disaster

Abstract

The effectiveness of dispatching rescue teams under a flooding disaster is crucial. However, previous emergency vehicle dispatching methods cannot handle flooding disaster situations, and previous rescue team dispatching methods cannot accurately estimate the positions of potential rescue requests or dispatch the rescue teams according to the real-time distribution of rescue requests. In this paper, we propose MobiRescue, a human Mobility based Rescue team dispatching system, that aims to maximize the total number of fulfilled rescue requests, minimize the rescue teams’ driving delay to the rescue requests’ positions and also the number of dispatched rescue teams. We studied a city-scale human mobility dataset for the Hurricane Florence, and found that the disaster impact severities are quite different in different regions, and people’s movement was significantly affected by the disaster, which means that the rescue teams’ driving routes should be adaptively adjusted. Then, we propose a Support Vector Machine (SVM) based method to predict the distribution of potential rescue requests on each road segment. Based on the predicted distribution, we develop a Reinforcement Learning (RL) based rescue team dispatching method to achieve the aforementioned goals. Our trace-driven experiments demonstrate the superior performance of MobiRescue over other comparison methods.