Intelligent Ambulance Position Optimization for Vehicle Collisions Using Deep Embedded Clustering

Rapid urbanization and growing vehicular density have contributed to a rise in road traffic accidents, demanding more efficient emergency medical services. Timely ambulance deployment is a critical factor that significantly affects patient survival and recovery. However, traditional ambulance positioning systems often fall short due to static or reactive planning models. This paper proposes an optimized ambulance positioning framework utilizing Deep Embedded Clustering (DEC) to dynamically predict and respond to accident-prone zones. By integrating historical accident data, real-time traffic conditions, and geographical factors, the DEC model learns high-level representations of spatial-temporal accident patterns. These embeddings are then clustered to identify optimal standby locations for ambulances. The methodology outperforms conventional models by offering greater flexibility, predictive power, and operational efficiency. Experimental results on real-world datasets demonstrate improved response times and better resource allocation. This approach provides a scalable and intelligent solution that aligns with the objectives of smart city planning and public health safety.