Dynamic penalty-based dispatching decision-making for improved emergency response in urban environments: a heuristic approach

Emergency medical services (EMS) are a crucial component of urban safety and responsiveness, and optimizing their operations aligns with the broader goals of creating safe, resilient cities. This study focuses on improving the EMS dispatching process by leveraging urban mobility data collected by connected vehicles and simulation. EMS dispatching is inherently sequential and dynamic, where each decision impacts future resource availability. Traditional greedy approaches, which dispatch the nearest available unit without considering supply-demand dynamics in the surrounding area, can lead to suboptimal outcomes. This study introduces a penalty metric that quantifies supply-demand levels within each ambulance’s catchment zone—defined by isochrones that delineate the area the ambulance can reach within an allowable time—prior to dispatch. This metric forms the foundation of a dynamic penalty-based dispatching strategy that penalizes dispatches from high-demand, low-coverage areas for low-priority calls, ultimately conserving resources for high-priority emergencies. The heuristic method was tested simulating EMS operations in Manhattan, New York. Simulation results showed that 90% of episodes with the heuristic policy had a mean response time of less than 6 min for high-priority calls, compared to only 75% with the conventional greedy approach. This paper presents a proof-of-concept study that introduces a novel ambulance dispatching policy and contributes to the optimization of emergency response systems in urban environments. Additionally, this study demonstrates how smart technologies and large-scale mobility data can enhance decision-making support tools, improving EMS efficiency and resource utilization and aligning with sustainability goals.