Towards Smart Traffic Lights based on Deep Learning and Traffic Flow Information

Traffic congestion is a significant cause hindering development and adversely affecting socio-economic life; mean-while, traditional traffic light systems have become obsolete. Therefore, the application of machine learning to enhance the effectiveness of these systems has received much attention from the research community. However, their practical application is limited because of the lack of training datasets and high computational requirements. In this paper, we propose a lightweight approach that can dynamically control traffic lights at intersections based on current traffic situation. To do this, we design a deep learning model based on the Bidirectional LSTM architecture to estimate the appropriate duration of traffic lights by learning traffic flow information. Our model achieves high accuracy and is lightweight enough to deploy resource-constrained IoT devices. In addition, we introduce an algorithm to generate data about traffic flow information from a well-known traffic simulation framework. The evaluation results show that the model could accurately estimate the duration of the traffic light with a low mean square error and outperformed other machine learning models.