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.

翻译：准确且实时的交通状态预测对城市交通控制及网络地图服务（如Google Maps）具有重要实际意义。在海量数据的支持下，深度学习方法在捕捉道路网络的复杂时空模式方面展现出强大的能力。然而，现有方法采用独立组件分别建模时间依赖性和空间依赖性，从而忽略了交通流随时间与空间变化的异质特性。本文提出一种融合时空注意力机制的新型动态图卷积网络。该方法不仅能捕捉随时间变化的局部时空信息，还基于时空注意力融合机制综合建模长距离与多尺度时空模式。这一设计思想显著提升了模型的时空感知能力。我们在4个真实世界数据集上开展大量实验，结果表明与22个基线模型相比，本模型达到了最先进的性能水平。