HUTFormer: Hierarchical U-Net transformer for long-term traffic forecasting
文献类型：期刊论文
| 作者 | Shao, Zezhi2; Wang, Fei2,3; Sun, Tao2; Yu, Chengqing2,3; Fang, Yuchen4; Jin, Guangyin1; An, Zhulin2; Liu, Yang5; Qu, Xiaobo5; Xu, Yongjun2,3 |
| 刊名 | COMMUNICATIONS IN TRANSPORTATION RESEARCH
 |
| 出版日期 | 2025-12-01 |
| 卷号 | 5页码:15 |
| 关键词 | Traffic condition forecasting Long-term time series forecasting Multivariate time series forecasting |
| ISSN号 | 2772-4247 |
| DOI | 10.1016/j.commtr.2025.100218 |
| 英文摘要 | 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). To this end, we first reveal its unique challenges in exploiting multi-scale representations. Then, we propose a novel Hierarchical Unet 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. |
| 资助项目 | National Natural Science Foundation of China[62502505] ; National Natural Science Foundation of China[62372430] ; Youth Innovation Promotion Association, Chinese Academy of Sciences[2023112] |
| WOS研究方向 | Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:001619741900001 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/43069]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Wang, Fei |
| 作者单位 | 1.Sapienza Univ Rome, Dept Planning Design & Technol Architecture, I-00196 Rome, Italy 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 4.Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610054, Peoples R China 5.Tsinghua Univ, Sch Vehicle & Mobil, Beijing 100084, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shao, Zezhi,Wang, Fei,Sun, Tao,et al. HUTFormer: Hierarchical U-Net transformer for long-term traffic forecasting[J]. COMMUNICATIONS IN TRANSPORTATION RESEARCH,2025,5:15. |
| APA | Shao, Zezhi.,Wang, Fei.,Sun, Tao.,Yu, Chengqing.,Fang, Yuchen.,...&Xu, Yongjun.(2025).HUTFormer: Hierarchical U-Net transformer for long-term traffic forecasting.COMMUNICATIONS IN TRANSPORTATION RESEARCH,5,15. |
| MLA | Shao, Zezhi,et al."HUTFormer: Hierarchical U-Net transformer for long-term traffic forecasting".COMMUNICATIONS IN TRANSPORTATION RESEARCH 5(2025):15. |
入库方式： OAI收割
来源：计算技术研究所
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