DEEP EMBEDDED CLUSTERING FOR OPTIMAL AMBULANCE POSITIONING IN RESPONSE TO ROAD ACCIDENTS

Abstract

Efficient ambulance positioning is crucial for minimizing response times and improving emergency medical services (EMS) in road accident scenarios. Traditional ambulance deployment strategies often rely on static stationing models or historical data-based heuristics, which may not effectively adapt to dynamic accident patterns. This paper proposes a Deep Embedded Clustering (DEC)-based approach for optimal ambulance positioning, leveraging real-time accident data, traffic conditions, and geographic information. The model integrates deep learning-based feature extraction with unsupervised clustering techniques to dynamically identify high-risk accident zones and determine optimal ambulance locations. By embedding accident-related features into a lower-dimensional latent space, the DEC model enhances the clustering process, improving both spatial efficiency and response accuracy. Experimental evaluations using real-world accident datasets demonstrate that the proposed approach outperforms traditional k-means and density-based clustering models in terms of cluster stability, ambulance coverage efficiency, and response time reduction. This research highlights the potential of deep learning-driven optimization for EMS deployment, contributing to enhanced public safety and emergency response management.