India, with a population of 1.3 billion and almost 300 million vehicles, is one of the biggest contributors to traffic jams, vehicle-specific pollution, and chronic lung diseases. To manage the footfall of these gigantic vehicles, continuous effort and research is taking place in the direction of both active and passive traffic management. This research paper aims towards showcasing a dynamic, fully autonomous deep learning model that uses real-time feeds from existing traffic junctions/ intersection cameras, process them and provide an intensity score based on the density of traffic in each adjoining lane.The proposed CNN model which is based on YOLO framework uses 10 seconds wait analysis time. The proposed system manages traffic, based on the intensity scores which assign traffic a Go time to each lane using an optimal traffic time in the range of 10-50 seconds.The model also scans for emergency vehicles in each lane, to provide a priority pass to such vehicles. Evaluation of model performance Mean Average Precision (mAP) is used.

A country with a population of 1.3 billion and almost 300 million vehicles, India is one of the biggest contributors to traffic jams, vehicle- specific pollution, and chronic lung diseases. To manage the footfall of this gigantic urban population, and the vehicles, our country has blindly poured in resources towards both active and passive traffic management, investing in measures such as smart traffic management systems and deploying enormous armies of traffic police to handle intersections and exits. The paper aims, towards showcasing a dynamic, fully autonomous model, that uses real-time feeds from existing traffic junctions/ intersection cameras, process them and provide an intensity score based on the density of traffic in each adjoining lane. The system, based upon the intensity scores, provides suitable traffic go time to each lane. The model also scans for emergency vehicles in each lane, to provide a priority pass to such vehicles.