Complex spatial dependencies in transportation networks make traffic prediction extremely challenging. Much existing work is devoted to learning dynamic graph structures among sensors, and the strategy of mining spatial dependencies from traffic data, known as data-driven, tends to be an intuitive and effective approach. However, Time-Shift of traffic patterns and noise induced by random factors hinder data-driven spatial dependence modeling. In this paper, we propose a novel dynamic frequency domain graph convolution network (DFDGCN) to capture spatial dependencies. Specifically, we mitigate the effects of time-shift by Fourier transform, and introduce the identity embedding of sensors and time embedding when capturing data for graph learning since traffic data with noise is not entirely reliable. The graph is combined with static predefined and self-adaptive graphs during graph convolution to predict future traffic data through classical causal convolutions. Extensive experiments on four real-world datasets demonstrate that our model is effective and outperforms the baselines.

翻译：交通网络中复杂的空间依赖关系使交通预测极具挑战性。现有大量工作致力于学习传感器间的动态图结构，这种从交通数据中挖掘空间依赖关系的策略（即数据驱动）是一种直观且有效的方法。然而，交通模式的时间偏移以及随机因素导致的噪声阻碍了数据驱动的空间依赖建模。本文提出一种新型动态频域图卷积网络（DFDGCN）来捕获空间依赖关系。具体而言，我们通过傅里叶变换减轻时间偏移的影响，并在捕获数据进行图学习时引入传感器的身份嵌入和时间嵌入——因为含噪声的交通数据并非完全可靠。在图卷积过程中，该图与静态预定义图及自适应图相结合，通过经典因果卷积预测未来交通数据。在四个真实数据集上的大量实验表明，我们的模型有效且优于基线方法。