International Journal of Scientific Research and Engineering Development

The rapid urbanization and exponential growth of vehicles on roads have intensified traffic congestion in modern cities. Traditional traffic monitoring systems based on fixed signals and manual supervision are inadequate for dynamically changing traffic conditions. This paper presents an AI Based Smart Traffic Monitoring and Congestion Prediction System that leverages Artificial Intelligence, Machine Learning, and Computer Vision to continuously monitor traffic flow and predict congestion in advance. The system collects real-time traffic data from CCTV cameras, traffic sensors, GPS devices, and historical records. Computer Vision techniques are applied to detect, classify, and count vehicles from live video streams, while Machine Learning models analyze traffic patterns to forecast congestion levels. Experimental results demonstrate approximately 95% accuracy in vehicle detection and 92% accuracy in congestion prediction.The system enables proactive traffic management through dynamic signal optimization, alternate route suggestions, and real-time alerts, significantly improving traffic flow, reducing delays, and enhancing road safety in urban environments.

Mr.R.Ramakrishnan, A.Arun,"AI Based Smart Traffic Monitoring and Congestion Prediction System" International Journal of Scientific Research and Engineering Development, V9(3): Page(2184-2189) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.