Real-Time Traffic Monitoring and Adaptive Control with YOLOv11 for Emergency Vehicles

Abstract

Urban traffic management remains an important concern as high-density traffic causes congestion, delays, and emergency-related challenges. This paper involves an automated traffic monitoring system that uses computer vision and deep learning for vehicle detection and classification while giving priority to emergency vehicles for real-time applications. Continuous transmission of traffic data from the webcam is processed with the YOLOv11 model, which is indeed the choice of this proposal for its speed and accuracy on dynamic environments such as roads and streets, therefore enabling vehicle detection and counts to be quality ensured in various lanes. The system classifies vehicles in real time and distinguishes emergency vehicles such as ambulances from general traffic, thus enabling optimum management of traffic flow during an emergency. Embedded Deep Reinforcement Learning is the core of this programming system; it connects variable lanes to dynamic timing of lights via intelligent lane allocation mechanism that has congestive reduction aimed at maximizing response time for emergency vehicles. Thus, the DRL agent is self-trained, using historical records and real-time feedback, to improve general flow and prioritization for emergency vehicles with Advances in Traffic Light Synchronization using Computer Vision. When it comes to managing congestion, the real-time information when provided comprises standard automotive parameters like whole lane counts, average speed, and traffic flow information which can be useful to the operators. Once any lane is recognized as an emergency lane, they are immediately tagged as having a "high priority" status that enables immediate clearance for ambulances and similar vehicles through coordination of signals from traffic lights or that of close hanging traffic systems. Through the effective means of emergency venue priority introduction and urban traffic efficiency improvement, such a system faces modern-day traffic problems effectively.