Dynamic Lane Traffic Signal Control with Group Attention and Multi-Timescale Reinforcement Learning

Traffic signal control has achieved significant success with the development of reinforcement learning. However, existing works mainly focus on intersections with normal lanes with fixed outgoing directions. It is noticed that some intersections actually implement dynamic lanes, in addition to normal lanes, to adjust the outgoing directions dynamically. Existing methods fail to coordinate the control of traffic signal and that of dynamic lanes effectively. In addition, they lack proper structures and learning algorithms to make full use of traffic flow prediction, which is essential to set the proper directions for dynamic lanes. Motivated by the ineffectiveness of existing approaches when controlling the traffic signal and dynamic lanes simultaneously, we propose a new method, namely MT-GAD, in this paper. It uses a group attention structure to reduce the number of required parameters and to achieve a better generalizability, and uses multi-timescale model training to learn proper strategy that could best control both the traffic signal and the dynamic lanes. The experiments on real datasets demonstrate that MT-GAD outperforms existing approaches significantly.