Traffic Signal Control System Based on Attention Mechanism and Reinforcement Learning

Abstract: Traffic Signal control has a wide range of applications in traffic management, automatic driving and other traffic fields. Now the dominant signal control models are based on reinforcement learning. The state of the model is generally defined as the position and speed of the vehicle in the road grid and the state of the signal light, but this state definition is relatively rough and cannot fully represent the information of traffic flow, so this paper considers the combination of representation learning and reinforcement learning to improve the performance of the traffic signal control system.
First, we use the attention mechanism to enable the agent to automatically select information from the traffic flow and avoid the interference caused by artificially extracted features. Based on this, we construct the state space of the reinforcement learning model. The signal control system is thus modeled as a problem of interaction of the signal lights with the traffic environment.And the action space of the model is produced y a recurrent neural network which makes our model does well in regular traffic.
Through extensive experiments in simulated environments, we demonstrate that our method has significant advantages in reducing traffic congestion and enhancing the safety of traffic systems. In addition, we also analyze the robustness and generalization performance of the model, demonstrating its ability in different scenarios and large-scale applications.
To sum up, the research in this paper introduces new ideas into the field of signal control. By combining the attention mechanism and reinforcement learning, the efficiency and adaptability of the signal control system are improved, which is expected to make a major breakthrough in practical applications. Future work The optimization of the model will be further explored and extended to a wider range of fields to achieve more intelligent, efficient, and sustainable traffic management.