Traffic Control System using YOLO Algorithm

Traffic congestion is becoming a serious problem with a large number of cars on the roads. Vehicles queue length waiting to be processed at the intersection is rising sharply with the increase of the traffic flow, and the traditional traffic lights cannot efficiently schedule it. A real-time traffic light control algorithm based on traffic flow is proposed in this paper. In fact, we use computer vision and machine learning to have the characteristics of the competing traffic flows at the signalized road intersection. This is done by a state-of-the-art, real-time object detection based on a deep Convolutional Neural Network called You Only Look Once (YOLO). Then traffic signal phases are optimized according to collected data, mainly queue density and waiting time per vehicle, to enable as many as more vehicles to pass safely with the minimum waiting time. YOLO can be implemented on embedded controllers using the Transfer Learning technique, which makes it possible to perform Deep Neural Networks on limited hardware resources.