Traffic forecasting is the foundation for intelligent transportation systems. Spatiotemporal graph neural networks have demonstrated state-of-the-art performance in traffic forecasting. However, these methods do not explicitly model some of the natural characteristics in traffic data, such as the multiscale structure that encompasses spatial and temporal variations at different levels of granularity or scale. To that end, we propose a Wavelet-Inspired Graph Convolutional Recurrent Network (WavGCRN) which combines multiscale analysis (MSA)-based method with Deep Learning (DL)-based method. In WavGCRN, the traffic data is decomposed into time-frequency components with Discrete Wavelet Transformation (DWT), constructing a multi-stream input structure; then Graph Convolutional Recurrent networks (GCRNs) are employed as encoders for each stream, extracting spatiotemporal features in different scales; and finally the learnable Inversed DWT and GCRN are combined as the decoder, fusing the information from all streams for traffic metrics reconstruction and prediction. Furthermore, road-network-informed graphs and data-driven graph learning are combined to accurately capture spatial correlation. The proposed method can offer well-defined interpretability, powerful learning capability, and competitive forecasting performance on real-world traffic data sets.

翻译：交通预测是智能交通系统的基础。时空图神经网络在交通预测中已展现出最先进的性能。然而，这些方法并未显式建模交通数据中的某些自然特征，例如涵盖不同粒度或尺度下时空变化的多尺度结构。为此，我们提出一种受小波启发的图卷积循环网络（WavGCRN），它将基于多尺度分析（MSA）的方法与基于深度学习（DL）的方法相结合。在WavGCRN中，交通数据通过离散小波变换（DWT）分解为时频分量，构建多流输入结构；随后，对每个流采用图卷积循环网络（GCRNs）作为编码器，提取不同尺度下的时空特征；最后，将可学习的逆DWT与GCRN结合作为解码器，融合所有流的信息以进行交通指标重构与预测。此外，道路网络信息图与数据驱动图学习相结合，以准确捕获空间相关性。所提出方法在真实交通数据集上提供了清晰的可解释性、强大的学习能力以及具有竞争力的预测性能。