  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.
