Real-Time Ambulance Redeployment: A Data-Driven Approach

Abstract

Emergency Medical Services (EMS) are of great importance to saving people's lives from emergent accidents and diseases by efficiently picking up patients using ambulances. The transporting capability of an EMS system (e.g., defined as the average pickup time of patients) significantly depends on the real-time redeployment strategy of ambulances. That is, which station should an ambulance be redeployed to, after it becomes available (after it transports a patient to a hospital or after it finishes the in-site treatment for a patient)? However, it is a challenging task concerning with the multiple data D1-D5 as detailed in Introduction. To this end, in this paper, we propose a data-driven real-time ambulance redeployment approach that redeploys an ambulance to a proper station after it becomes available, so as to optimize the transporting capability of an EMS system, considering the aforementioned multiple data D1-D5. Specifically, the proposed approach is comprised of two stages to well consider the D1-D5. First, we propose a method (a safety time-based urgency index) to incorporate D1, D2, and D3 into each ambulance station's urgency degree (D*). Second, we propose an optimal matching algorithm to combine D*, D4, and D5 into the redeployment of the current available ambulance. Experimental results using data collected in real world demonstrate the significant advantages of our approach over many baselines. Comparing with baselines, our approach can save $\sim$ ∼ 4 minutes ( $\sim$ ∼ 35 percent) of the average pickup time for each patient, improve the ratio of patients picked up within 10 minutes from 0.684 and 0.803 ( $\sim$ ∼ 17 percent), and largely enhance the survival rate of patients ( $\sim$ ∼ 12 percent for patients in category A1 and $\sim$ ∼ 17 percent for patients in A2).