With urbanization and the rapid growth in the number of motor vehicles, urban traffic congestion has intensified. The traditional clock signal control system is difficult to meet the dynamic needs of traffic. Effective traffic flow forecasting and signal optimization are critical to alleviating congestion and improving efficiency. Currently, traffic flow forecasting models are vulnerable to complex scenarios, such as sudden accidents and extreme weather, resulting in poor accuracy. Signal optimization algorithms are generally based on static rules, which are difficult to integrate into real-time prediction, resulting in delays in signal adjustment and it is difficult to achieve optimal dynamic allocation of traffic resources. This work begins with building a deep learning prediction model that integrates data from multiple sources (traffic, weather data, and spatio-temporal characteristics). It uses an improved LSTM to capture the spatio-temporal dependence of traffic flow and improve prediction accuracy. Secondly, an algorithm for optimizing dynamic signals is designed based on reinforcement learning with prediction results as input. Establish a reward function to reduce average vehicle delay and improve road efficiency. Finally, the model was formed and verified using real traffic data sets from cities such as Beijing and Shenzhen. Then, a simulation platform is built to simulate the effect of signal control in different scenarios. Experiments showed that during peak hours in the morning, the proposed algorithm reduced the average delay by 43% compared to the fixed timing scheme and by 25% compared to the scoot algorithm. During night rush hours, the proposed algorithm reduces the average delay by 42% compared to the fixed timing scheme and by 24% compared to the scoot algorithm, far exceeding the traditional scheme.
Jianwei Ning (Thu,) studied this question.