A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN Prediction
This addresses traffic congestion for urban traffic management systems, representing an incremental improvement by combining existing methods (DRL and GNN) for better prediction-based control.
The paper tackles traffic congestion in multi-intersection control by proposing GPlight, a deep reinforcement learning algorithm integrated with graph neural networks to predict future traffic flow and dynamically adjust traffic lights, achieving verified effectiveness on synthetic and real-world datasets from Hangzhou and New York.
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.