REAL-TIME AMBULANCE NAVIGATION AND EMERGENCY DECISION SUPPORT SYSTEM USING SOFT COMPUTING

Abstract

Rapid urbanization has significantly increased traffic congestion, leading to critical delays in emergency medical services. Ambulances often experience extended waiting times at intersections, which directly impacts patient survival during life-threatening situations. This study proposes an intelligent ambulance traffic priority system that integrates real-time tracking, machine learning-based route optimization, and dynamic traffic signal control. The system continuously monitors ambulance location using GPS and analyzes traffic conditions to determine optimal routes. A decision-support mechanism enables automated traffic signal adjustments to create a priority path for emergency vehicles. Additionally, a communication module ensures seamless data exchange between ambulances, dispatch centers, and hospitals, allowing pre-arrival medical preparation. Patient information is securely transmitted to healthcare facilities to improve treatment readiness. The proposed model enhances coordination, minimizes response time, and reduces manual intervention errors. Experimental evaluation demonstrates improved efficiency compared to traditional traffic management systems. The system is scalable and can be integrated into smart city infrastructures. This approach contributes to advanced emergency response frameworks by combining transportation intelligence with healthcare support, ensuring faster and more reliable ambulance services in congested urban environments.