  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.
