Accurate and real-time traffic state prediction is of great practical
importance for urban traffic control and web mapping services (e.g. Google
Maps). With the support of massive data, deep learning methods have shown their
powerful capability in capturing the complex spatio-temporal patterns of road
networks. However, existing approaches use independent components to model
temporal and spatial dependencies and thus ignore the heterogeneous
characteristics of traffic flow that vary with time and space. In this paper,
we propose a novel dynamic graph convolution network with spatio-temporal
attention fusion. The method not only captures local spatio-temporal
information that changes over time, but also comprehensively models
long-distance and multi-scale spatio-temporal patterns based on the fusion
mechanism of temporal and spatial attention. This design idea can greatly
improve the spatio-temporal perception of the model. We conduct extensive
experiments in 4 real-world datasets to demonstrate that our model achieves
state-of-the-art performance compared to 22 baseline models.

本文提出了一种新颖的动态图卷积神经网络，采用时空关注融合的方法，全面地模拟了长距离和多尺度的时空模式，取得了 22 种基线模型之外的最先进表现。