Smart Route Optimization for Emergency Vehicles

Emergency vehicle response time is one of the most decisive factors in determining survival outcomes across cardiac arrest, fire, and trauma scenarios. In India alone, an estimated 10,000 lives are lost annually due to preventable ambulance delays in urban areas. Yet despite the proliferation of GPS and connected vehicle technologies, most emergency routing systems continue to rely on static, pre-computed paths that fail entirely when confronted with real-time traffic disruption. A review of more than 20 peer-reviewed studies published between 2012 and 2025 reveals a field that has generated genuine progress across individual sub-domains — reinforcement learning-based routing, IoT signal preemption, VANET-based tracking, and GIS-driven planning — but has consistently failed to unify these capabilities into a single, coherent system. No reviewed study simultaneously addresses the full problem: every approach treats either the route or the signal or the prediction as the primary optimization target and handles the others as secondary concerns. This paper presents a structured thematic review of the existing body of knowledge, a rigorous comparative analysis of 20 studies and their limitations, and the design of the Adaptive Emergency Routing Intelligence System (AERIS) — a novel framework integrating deep reinforcement learning with a graph neural network traffic encoder, IoT-enabled multi-intersection signal coordination, and a predictive traffic modeling layer. Projected performance improvements of 25–35% over conventional routing, combined with a city-agnostic modular design, position AERIS as a meaningful step forward for emergency transportation systems.