提出新型图注意力网络，提升交通流量预测的时空关联捕捉能力
A Global-Local Graph Attention Network for Traffic Forecasting
- 设计全局-局部注意力机制，兼顾整体与节点特异性
- 在两个真实数据集上优于现有主流方法，表现稳定
- 适合交通流预测、智能交通系统研究者参考
交通预测是智能交通系统的重要组成部分。其核心挑战在于挖掘时空相关性。近年来，图卷积网络和图注意力网络已逐步取代传统统计模型用于交通预测。然而，这两类方法在处理节点特征差异较大时仍存在困难。为此，本文提出全局-局部图注意力网络（GLGAT），引入成对编码和基于事件的邻接矩阵。该模型为整个图设置全局注意力矩阵，同时为每个节点分配局部注意力矩阵集合。在两个真实世界交通数据集上的实验表明，GLGAT能有效捕捉时空相关性，在多个基准测试中表现优异，具有竞争力。
原文摘要 · Abstract (English)
Traffic forecasting is a significant part of intelligent transportation systems. One of the critical challenges of traffic forecasting is to find spatio-temporal correlations. In recent years, graph convolutional networks and graph attention networks have replaced traditional statistical models to predict future traffic. However, it is complicated for both of them to allow vertices to have far different characters. To address this, we propose the Global-Local Graph Attention Network (GLGAT) with pairwise encoding and the event-based adjacency matrix. The GLGAT allows vertices to have a global attention matrix set for the whole graph and assigns local attention matrix sets to each vertex. Experiments on two real-world traffic datasets show that GLGAT can effectively capture spatio-temporal correlations and has competitive performance against other state-of-the-art baselines.
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