Traffic forecasting is a critical task to extract values from cyber-physical infrastructures, which is the backbone of smart transportation. However owing to external contexts, the dynamics at each sensor are unique. For example, the afternoon peaks at sensors near schools are more likely to occur earlier than those near residential areas. In this paper, we first analyze real-world traffic data to show that each sensor has a unique dynamic. Further analysis also shows that each pair of sensors also has a unique dynamic. Then, we explore how node embedding learns the unique dynamics at every sensor location. Next, we propose a novel module called Spatial Graph Transformers (SGT) where we use node embedding to leverage the self-attention mechanism to ensure that the information flow between two sensors is adaptive with respect to the unique dynamic of each pair. Finally, we present Graph Self-attention WaveNet (G-SWaN) to address the complex, non-linear spatiotemporal traffic dynamics. Through empirical experiments on four real-world, open datasets, we show that the proposed method achieves superior performance on both traffic speed and flow forecasting. Code is available at: https://github.com/aprbw/G-SWaN

翻译：交通预测是从信息物理基础设施中提取价值的关键任务，也是智能交通的基石。然而，由于外部背景的影响，每个传感器的动态特性都是独特的。例如，学校附近传感器的下午高峰时间往往比住宅区附近更早出现。本文首先分析了真实交通数据，以证明每个传感器具有独特的动态性。进一步分析还表明，每对传感器之间也具有独特的动态性。接着，我们探讨了节点嵌入如何学习每个传感器位置的独特动态性。随后，我们提出了一种称为空间图变换器（Spatial Graph Transformers, SGT）的新型模块，利用节点嵌入来增强自注意力机制，确保两个传感器之间的信息流能够根据每对传感器的独特动态性自适应调整。最后，我们提出了图自注意力波网（Graph Self-attention WaveNet, G-SWaN），以处理复杂非线性的时空交通动态。通过对四个真实世界开放数据集的实证实验，我们证明该方法在交通速度和流量预测方面均实现了优越性能。代码见：https://github.com/aprbw/G-SWaN