论文标题
基于GNN预测的交通信号灯动态控制算法，具有深厚的加固学习
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN Prediction
论文作者
论文摘要
当今的智能交通灯控制系统基于当前的交通交通条件。但是，这些方法无法事先利用未来的流量信息。在本文中，我们提出了与图神经网络（GNN）集成的深入增强学习（DRL）算法的Gplight，以减轻多交流智能交通控制系统的交通拥堵。在Gplight中，图形神经网络（GNN）首先用于预测交叉点的未来短期交通流。然后，交通流量预测的结果用于交通信号灯控制中，代理将预测的结果与观察到的当前交通状况相结合，以动态控制交叉路口的交通信号灯的相位和持续时间。关于Hangzhou和New-York的合成和两个现实数据集的实验验证了Gplight算法的有效性和合理性。
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.
