LIFELINEAI: INTELLIGENT DEEP EMBEDDED CLUSTERING FOR OPTIMAL EMERGENCY AMBULANCE PLACEMENT

Abstract

Timely medical assistance during road accidents can be the difference between life and death. Traditional ambulance positioning systems rely on static deployment strategies, which often fail to adapt to dynamic traffic conditions and changing accident hotspots. This study introduces LifeLineAI, an intelligent framework that employs Deep Embedded Clustering (DEC) and machine learning techniques for optimal ambulance positioning in real time. The proposed system analyzes historical accident data, traffic flow, population density, and road network structures to identify high-risk zones and determine optimal standby locations for ambulances. By integrating deep feature extraction with unsupervised clustering, LifeLineAI dynamically groups accident-prone areas and continuously updates ambulance deployment strategies based on live data. Experimental results indicate that this approach significantly reduces average response times and improves resource utilization compared to conventional heuristic methods. The framework demonstrates the potential of AI-powered optimization in saving lives through intelligent emergency response management.