Predicting the road traffic speed is a challenging task due to different types of roads, abrupt speed changes, and spatial dependencies between roads, which requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over long input sequences. This paper proposes a novel Spatio-Temporal Graph Attention (STGRAT) that effectively captures the spatio-temporal dynamics in road networks. The features of our approach mainly include spatial attention, temporal attention, and spatial sentinel vectors. The spatial attention takes the graph structure information (e.g., distance between roads) and dynamically adjusts spatial correlation based on road states. The temporal attention is responsible for capturing traffic speed changes, while the sentinel vectors allow the model to retrieve new features from spatially correlated nodes or preserve existing features. The experimental results show that STGRAT outperforms existing models, especially in difficult conditions where traffic speeds rapidly change (e.g., rush hours). We additionally provide a qualitative study to analyze when and where STGRAT mainly attended to make accurate predictions during a rush-hour time.

该论文提出了一种新的模型ST-GRAT，通过空间和时间注意力以及哨兵向量的使用，能够更有效地捕捉道路网络中的时空动态，并且在交通速度快速变化的困难条件下表现出色。

ST-GRAT：一种新颖的时空图注意力网络，用于准确预测动态变化的道路速度