Self-learning traffic signal control method of isolated intersection combining Q-learning and fuzzy logic
结合Q学习和模糊逻辑的单路口交通信号自学习控制方法*

To address the dynamics and uncertainty in unban transportation system, this paper proposed a traffic signal control system based on reinforcement learning, which was suitable for real-time control in isolated intersection. The proposed method was capable of online learning through a combination of BP neural network and Q-learning algorithm. Furthermore, due to the multi-objective property in traffic signal control, this paper developed a reward design method for Q-learning based on fuzzy logic. Conducted simulated experiments in three traffic scenarios, using the Paramics microscopic traffic simulation software. Experimental results show that the proposed method has high control efficiency in different traffic scenarios, and is significantly better than fixed timing control method.