AI-Driven Traffic Monitoring System for Real-Time Congestion Detection and Route Optimization in 6G-Enabled Smart Cities

Urban development and rising vehicular traffic patterns create major issues with congestion together with environmental problems. Activity Management via Deep Learning uses computer vision reinforcement alongside deep learning to study current traffic videos and identify traffic blockages while offering best route alternatives. The system achieves fast and low-latency traffic analysis through YOLO-based vehicle detection and graph-based pathfinding algorithms while taking advantage of 6G network capabilities to perform dynamic rerouting. The system uses MongoDB to store data together with Twilio API notifications and relies on AWS cloud services for ensuring scalability and reliability. Urban mobility reaches enhancement through autonomous traffic management thanks to this solution which outperforms static sensor-based traffic systems and manual control features. This system benefits smart cities by selecting intelligent routes and analyzing future traffic flow data which leads to shorter journeys and reduced carbon emissions and lower fuel costs thereby optimizing transportation networks