Learning-Based Hybrid Exploration Method for Adaptive Traffic Signal Control

The application of deep reinforcement learning techniques to adaptive traffic signal control is effective in reducing traffic congestion, due to its advantages over more traditional experience-based or model-driven methods. Although numerous studies have been conducted on traffic signal control, many of them suffer from issues such as unreasonable phase settings and long model-building times in complex environments. By analyzing the spatial-temporal characteristics of intersection traffic volume, this paper puts forward an automatic phase-switching mechanism to improve the problem of phase scheme mismatch with actual traffic flow. To reduce the training time of the model, this paper proposes to add noise to the parameters of the agent to stabilize the environment exploration process, so that the agent’s decision is more dependent on the observed environment state. Using real-world traffic flow data and simulated data for experimental verification, the experimental results show that the signal control model after optimization can effectively reduce the average travel time and increase network throughput.