Abstract:

In response to the existing reinforcement learning-based traffic signal control methods that do not consider the changing trends in traffic flow, leading to congestion and inability to adapt to complex and variable road conditions, we propose a traffic signal timing optimization reinforcement learning method based on flow prediction. A phase timing amplitude control model is introduced. This model analyzes the spatiotemporal characteristics of historical traffic data to predict the flow for the next time slot and calculates a reasonable range for phase timing based on the prediction results. The H-PPO algorithm is employed to control the signal phase while simultaneously increasing its timing control. We design a pressure valve reward function to avoid frequent phase changes in controlling signals, thereby affecting the driving experience of motorists. The simulation results demonstrate that the proposed method performs well in enhancing the intersection traffic efficiency and reducing the frequency of phase switching, outperforming the comparison methods.

Key words: traffic signal control, intelligent transportation, reinforcement learning, ConvGRU