Ambulance Routing and Traffic Signal Preemption Using Sea Lion Optimization and Haar Cascade Classifier

Abstract

Emergency Medical Services (EMS) require rapid response and efficient routing to ensure timely patient care. However, urban traffic congestion and static routing methods often delay ambulance arrivals. To addressthis, this paper proposes an intelligent ambulance routing and traffic-signal preemption framework, termed SLnO-CC, integrating Sea Lion Optimization (SLnO) for optimal route planning and a Haar Cascade Classifier (CC) for real-time emergency vehicle detection and signal control. The proposed model wasevaluated across eight real-world traffic scenarios within a 15 km urban area, benchmarking against A*, Advanced A* with Dispersion Index, Ant Colony Optimization (ACO), and standalone SLnO. Experimental results demonstrate that SLnO-CC achieved the lowest average response time (9.06 min) and travel time(5.36 min), outperforming A* (9.70 min, 12.20 min) and ACO (9.44 min, 11.47 min) by 6.6% and 13.2%, respectively. In terms of total routing efficiency, SLnO-CC reduced the overall distance and time by 17.8% and 19.6%, respectively, compared with existing baselines. The Haar Cascade–based preemption module achieved 96.8% detection accuracy under varying illumination and occlusion. Overall, the SLnO-CC framework enhances routing adaptability, congestion awareness, and emergency responsiveness—ensuring total response time remains within 10 minutes over a 15 km operational range with high detection reliability.