YOLOv8 vs. Sensor-Based Traffic Control: Kalman Filter Integration for Hybrid Management - Koirala, Anuj; Aryal, Abikal; Dahal, Pradip; Shakya, Sahaj (International Journal on Engineering Technology, 2026)
Modern traffic management systems of kamislot an urban environment presuppose adaptive systems of response to changing conditions of pedestrian and vehicle presence. Research is a comparative analysis of sensor-based and YOLOv8 traffic control systems, where the detection response time, the spatial gap analysis, environmental sensitivity, and accuracy are evaluated. Experimental validation indicates unique operational benefits since sensor-based systems are 92% accurate in high pedestrian environments, whilst the YOLOv8 is 84% effective in vehicle recognition. A novel hybrid architecture is proposed, implementing strategic technology assignment where sensors manage pedestrian monitoring and YOLOv8 handles vehicle detection, integrated through Kalman filter decision engines. Sophisticated 15-day simulation with Poisson distribution modeling shows 35% reduction in mean delay time, 84% accuracy of vehicle identification, and 78% accuracy of pedestrian identification in crowded scenarios. Kalman filter integration reaches 78% diminution of noise and enables predictive traffic control with 91% accuracy for a 10 minute horizon. The hybrid model overcomes the specific system constraints with a maximization of complementary benefits to intelligent urban traffic management.