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-hour forecasting, 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 forecasting. To this end, we
first reveal its unique challenges in exploiting multi-scale representations.
Then, we propose a novel Hierarchical U-net TransFormer (HUTFormer) to address
the issues of long-term traffic forecasting. HUTFormer consists of a
hierarchical encoder and decoder to jointly generate and utilize multi-scale
representations of traffic data. Specifically, for the encoder, we propose
window self-attention and segment merging to extract multi-scale
representations from long-term traffic data. For the decoder, we design a
cross-scale attention mechanism to effectively incorporate multi-scale
representations. In addition, HUTFormer employs an efficient input embedding
strategy to address the complexity issues. Extensive experiments on four
traffic datasets show that the proposed HUTFormer significantly outperforms
state-of-the-art traffic forecasting and long time series forecasting
baselines.

我们首次尝试研究长期交通预测的问题，并提出了一种名为 HUTFormer 的新型模型，通过多尺度表示来有效改善长期交通预测的挑战，并在多个交通数据集上证明其在预测性能上显著优于现有的基准模型。