Traffic control using intelligent timing of traffic lights with reinforcement learning technique and real-time processing of surveillance camera images

Optimal management of traffic light timing is one of the most effective factors in reducing urban traffic. In most old systems, fixed timing was used along with human factors to control traffic, which is not very efficient in terms of time and cost. Nowadays, methods in the field of traffic management are based on the use of artificial intelligence. In this method, by using real-time processing of video surveillance camera images along with reinforcement learning, the optimal timing of traffic lights is determined and applied according to several parameters. In the research, deep learning methods were used in vehicle detection using the YOLOv9-C model to estimate the number and other characteristics of vehicles such as speed. Finally, by modeling vehicles in an urban environment simulator at OpenAI Gym using multi-factor reinforcement learning and the DQN Rainbow algorithm, timing is applied to traffic lights at intersections. Additionally, the use of transfer learning along with retraining the model on images of Iranian cars has increased the accuracy of the model. The results of the proposed method show a model that is reasonably accurate in both parts of analyzing surveillance cameras and finding the optimal timing, and it has been observed that it has better accuracy than previous research.