Accurate and real-time traffic state prediction is of great practical importance for urban traffic control and web mapping services. With the support of massive data, deep learning methods have shown their powerful capability in capturing the complex spatialtemporal patterns of traffic networks. However, existing approaches use pre-defined graphs and a simple set of spatial-temporal components, making it difficult to model multi-scale spatial-temporal dependencies. In this paper, we propose a novel dynamic graph convolution network with attention fusion to tackle this gap. The method first enhances the interaction of temporal feature dimensions, and then it combines a dynamic graph learner with GRU to jointly model synchronous spatial-temporal correlations. We also incorporate spatial-temporal attention modules to effectively capture longrange, multifaceted domain spatial-temporal patterns. We conduct extensive experiments in four real-world traffic datasets to demonstrate that our method surpasses state-of-the-art performance compared to 18 baseline methods.

翻译：准确且实时的交通状态预测对城市交通控制和网络地图服务具有重大实际意义。在大数据支撑下，深度学习方法在捕捉交通网络的复杂时空模式方面展现出强大能力。然而，现有方法使用预定义图结构及简单的时空组件集合，难以建模多尺度时空依赖关系。本文提出一种新颖的基于注意力融合的动态图卷积网络以解决这一不足。该方法首先增强时序特征维度的交互，随后将动态图学习器与GRU结合，共同建模同步时空相关性。此外，我们融合时空注意力模块以有效捕捉长距离、多层面的领域时空模式。在四个真实交通数据集上开展的大量实验表明，相较于18种基线方法，本方法取得了超越现有最优的性能。