Chenguang Zhao

Xiaorong Hu, Chenguang Zhao, Gang Wang

Today's intelligent traffic light control system is based on the current road traffic conditions for traffic regulation. However, these approaches cannot exploit the future traffic information in advance. In this paper, we propose GPlight, a deep reinforcement learning (DRL) algorithm integrated with graph neural network (GNN) , to relieve the traffic congestion for multi-intersection intelligent traffic control system. In GPlight, the graph neural network (GNN) is first used to predict the future short-term traffic flow at the intersections. Then, the results of traffic flow prediction are used in traffic light control, and the agent combines the predicted results with the observed current traffic conditions to dynamically control the phase and duration of the traffic lights at the intersection. Experiments on both synthetic and two real-world data-sets of Hangzhou and New-York verify the effectiveness and rationality of the GPlight algorithm.

Chenguang Zhao, Xiaorong Hu, Gang Wang

Existing ineffective and inflexible traffic light control at urban intersections can often lead to congestion in traffic flows and cause numerous problems, such as long delay and waste of energy. How to find the optimal signal timing strategy is a significant challenge in urban traffic management. In this paper, we propose PDlight, a deep reinforcement learning (DRL) traffic light control algorithm with a novel reward as PRCOL (Pressure with Remaining Capacity of Outgoing Lane). Serving as an improvement over the pressure used in traffic control algorithms, PRCOL considers not only the number of vehicles on the incoming lane but also the remaining capacity of the outgoing lane. Simulation results using both synthetic and real-world data-sets show that the proposed PDlight yields lower average travel time compared with several state-of-the-art algorithms, PressLight and Colight, under both fixed and dynamic green light duration.