基于时空图注意力神经网络的交通道路拥塞和异常预测

Traffic Road Congestion and Anomaly Prediction Based on Spatio-temporal Graph Attention Neural Networks

为全面捕获交通路网的时空特性,分析路况的复杂多变性,实现道路拥堵和突发情况的高效准确预测,研究提出一种时空图注意力神经网络模型,通过将道路网络建模成一系列随时间变化的图,利用图注意力机制(graph attention network,GAT)关注路网图关键节点的空间特性并捕获动态的全图空间特征,再利用门控循环单元(gated recurrent neural network,GRU)充分捕获相邻路网图的时间相关性并降低模型冗余,提升了对道路拥堵和异常情况的预测准确率。采用PEMSD数据集进行实验。结果表明,所提方法与对比模型相比准确率与召回率均优于现有方法,有效提升了交通异常预测精度。

In order to comprehensively capture the spatio-temporal characteristics of the traffic road network,analyze the complexity and variability of road conditions,and achieve accurate prediction of road congestion and emergencies,a spatio-temporal graph attention neural network model was proposed.By modeling the road network as a series of time-varying graphs,the graph attention network(GAT)mechanism was used to focus on the spatial characteristics of key road network graphs and capture the dynamic full-graph spatial features.Gated recurrent neural network(GRU)was used to fully capture the temporal correlation of adjacent road network graphs and reduce model redundancy.The results of the experiments using the PEMSD dataset show that the proposed method outperforms the existing methods in terms of accuracy and recall compared with the baselines.It is concluded that the proposed model further improves the prediction accuracy of traffic anomalies.