Emergency medical response systems play a critical role in reducing mortality during life-threatening situations where rapid transportation and timely intervention are essential. Existing ambulance routing and dispatch systems primarily rely on static distance-based navigation approaches that fail to account for dynamic urban challenges such as traffic congestion, infrastructure damage, signal interference, and unstable network connectivity. Several studies have explored GPS-enabled dispatching, GIS-based traffic systems, swarm intelligence algorithms, and Tele-EMS integration; however, most existing frameworks remain fragmented and lack real-time synchronization with smart-city infrastructure.This paper presents a comprehensive review of current emergency vehicle routing methodologies and identifies major research gaps in dynamic traffic integration, disaster-aware routing, network-aware navigation, and scalable urban routing architectures. Based on these gaps, a novel intelligent routing framework is proposed that combines GIS-based traffic synchronization, meta-heuristic optimization algorithms, predictive congestion analysis, and network-aware path planning. The proposed framework aims to improve ambulance response efficiency, reduce navigation delays, maintain stable Tele-EMS connectivity, and support adaptive routing during urban emergencies and disaster scenarios. The study contributes a structured review of existing approaches, identifies limitations in current systems, and proposes a scalable smart emergency transportation model for future urban healthcare infrastructure.
Wilson et al. (Tue,) studied this question.