Abstract
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.
Abstract (translated)
准确的交通预测对于有效的城市规划和交通管理至关重要。尽管深度学习（DL）方法在交通预测方面取得了巨大的成功，但仍然存在捕捉交通动态复杂性的挑战。在本文中，我们通过强调空间特征是动态的并且会随着时间的推移而变化，来识别和解决这一挑战。我们引入了一种新的特征表示，称为动态时空（ Dyn-ST）特征，其中包含了跨不同时间的空间特征。此外，我们提出了一个动态时空网Transformer网络（DST-GTN），通过捕捉 Dyn-ST特征和其他路口的动态邻接关系，来捕捉交通信号的动态 ST 关系。DST-GTN 可以准确地建模节点之间的动态 ST 关系，并通过低通和全通滤波器的自适应权重来优化全局和局部 ST特性的表示，从而从交通时间序列数据中提取 Dyn-ST特征。通过公开数据集的数值实验，DST-GTN 在各种交通预测任务上实现了最先进的性能，并展示了增强的稳定性。
URL
https://arxiv.org/abs/2404.11996
