HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting

Understand

Traffic forecasting, which aims to predict traffic conditions based on historical observations, has been an enduring research topic and is widely recognized as an essential component of intelligent transportation.

• Recent proposals on Spatial-Temporal Graph Neural Networks~(STGNNs) have made significant progress by combining sequential models with graph convolution networks.
• However, due to high complexity issues, STGNNs only focus on short-term traffic forecasting (e.g., 1-h ahead), while ignoring more practical long-term forecasting.
• In this paper, we make the first attempt to explore long-term traffic forecasting (e.g., 1-day ahead).