MobiAmbulance: Optimal Scheduling of Emergency Vehicles in Catastrophic Situations

With recent experience in multiple large-scale disasters, it has been widely confirmed that the severity of a disaster is greatly dependent on the effectiveness of ambulance dispatching during disaster phase. However, previous base station (i.e., temporary or permanent hospital) based ambulance redeployment methods and dynamic ambulance scheduling methods cannot handle the ambulance dispatching problem in catastrophic situations. In this paper, we present MobiAmbulance: a human Mobility based Ambulance dispatching system that aims to maximize the total number of fulfilled patient pick-up requests, and minimize the driving delay of the fulfilled requests. We studied a state-scale human mobility dataset and found that the change of vehicle flow rate can be utilized to determine the connection status between road segments, and the distribution of people in catastrophic situations is drastically different from that in normal situations. Then, we develop a method to determine the road network connection status and the set of road segments that can still be driven through by ambulances after disaster. Based on the updated road network graph, we develop an ambulance dispatching method based on weighted driving route to maximize the total number of fulfilled patient pick-up requests, and minimize the driving delays of the fulfilled requests. Our trace-driven experiments demonstrate the superior performance of MobiAmbulance over other comparison methods.