OPTIMAL AMBULANCE POSITIONING FOR ROAD ACCIDENTS WITH DEEP EMBEDDED CLUSTERING

Abstract

Road accidents are a leading cause of mortality and morbidity worldwide, and rapid medical response plays a critical role in reducing fatalities. Efficient ambulance deployment is essential to ensure timely medical assistance, especially in high-risk zones. This study proposes an Optimal Ambulance Positioning framework using Deep Embedded Clustering (DEC) to identify strategic locations for ambulance stations based on historical accident data, traffic patterns, and geographic information. The DEC model integrates feature learning and clustering into a unified framework, allowing the system to capture complex spatial-temporal patterns in accident occurrences. By clustering accident hotspots and optimizing ambulance station placement within these clusters, the approach minimizes response times and improves emergency service coverage. Simulation results demonstrate that the proposed system significantly reduces average ambulance response times compared to conventional location-allocation methods. This methodology offers a data-driven solution for emergency medical services, potentially saving lives and enhancing public safety on road networks.