Accurate traffic forecasting is essential for effective urban planning and congestion management. Deep learning (DL) approaches have gained colossal success in traffic forecasting but still face challenges in capturing the intricacies of traffic dynamics. In this paper, we identify and address this challenges by emphasizing that spatial features are inherently dynamic and change over time. A novel in-depth feature representation, called Dynamic Spatio-Temporal (Dyn-ST) features, is introduced, which encapsulates spatial characteristics across varying times. Moreover, a Dynamic Spatio-Temporal Graph Transformer Network (DST-GTN) is proposed by capturing Dyn-ST features and other dynamic adjacency relations between intersections. The DST-GTN can model dynamic ST relationships between nodes accurately and refine the representation of global and local ST characteristics by adopting adaptive weights in low-pass and all-pass filters, enabling the extraction of Dyn-ST features from traffic time-series data. Through numerical experiments on public datasets, the DST-GTN achieves state-of-the-art performance for a range of traffic forecasting tasks and demonstrates enhanced stability.

翻译：精确的交通预测对于有效的城市规划和拥堵管理至关重要。深度学习（DL）方法在交通预测中取得了巨大成功，但在捕捉交通动态的复杂性方面仍面临挑战。本文通过强调空间特征本质上是动态的且随时间变化，识别并解决了这些挑战。我们引入了一种新颖的深度特征表示，称为动态时空（Dyn-ST）特征，该特征封装了不同时间下的空间特性。此外，提出了一种动态时空图变换网络（DST-GTN），通过捕捉Dyn-ST特征和交叉口之间的其他动态邻接关系进行建模。DST-GTN能够精确建模节点间的动态时空关系，并通过在低通和全通滤波器中采用自适应权重，优化全局与局部时空特性的表示，从而从交通时序数据中提取Dyn-ST特征。基于公开数据集的数值实验表明，DST-GTN在一系列交通预测任务中达到了最先进的性能，并展现了增强的稳定性。