Learning All Dynamics: Traffic Forecasting via Locality-Aware Spatio-Temporal Joint Transformer
文献类型：期刊论文
| 作者 | Fang, Yuchen2; Zhao, Fang2; Qin, Yanjun2; Luo, Haiyong1; Wang, Chenxing2 |
| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
 |
| 出版日期 | 2022-08-16 |
| 页码 | 14 |
| ISSN号 | 1524-9050 |
| 关键词 | Forecasting Correlation Convolution Roads Transformers Predictive models Task analysis Traffic forecasting spatio-temporal joint transformer diffusion convolution network |
| DOI | 10.1109/TITS.2022.3197640 |
| 英文摘要 | Forecasting traffic flow and speed in the urban is important for many applications, ranging from the intelligent navigation of map applications to congestion relief of city management systems. Therefore, mining the complex spatio-temporal correlations in the traffic data to accurately predict traffic is essential for the community. However, previous studies that combined the graph convolution network or self-attention mechanism with deep time series models (e.g., the recurrent neural network) can only capture spatial dependencies in each time slot and temporal dependencies in each sensor, ignoring the spatial and temporal correlations across different time slots and sensors. Besides, the state-of-the-art Transformer architecture used in previous methods is insensitive to local spatio-temporal contexts, which is hard to suit with traffic forecasting. To solve the above two issues, we propose a novel deep learning model for traffic forecasting, named Locality-aware spatio-temporal joint Transformer (Lastjormer), which elaborately designs a spatio-temporal joint attention in the Transformer architecture to capture all dynamic dependencies in the traffic data. Specifically, our model utilizes the dot-product self-attention on sensors across many time slots to extract correlations among them and introduces the linear and convolution self-attention mechanism to reduce the computation needs and incorporate local spatio-temporal information. Experiments on three real-world traffic datasets, England, METR-LA, and PEMS-BAY, demonstrate that our Lastjormer achieves state-of-the-art performances on a variety of challenging traffic forecasting benchmarks. |
| 资助项目 | National Natural Science Foundation of China[61872046] ; Beijing Natural Science Foundation[4212024] ; Science and Technology Plan Project of Inner Mongolia Autonomous Region[2019GG328] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000842745600001 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19450]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zhao, Fang; Luo, Haiyong |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 2.Beijing Univ Posts & Telecommun, Sch Comp Sci, Natl Pilot Software Engn Sch, Beijing 100876, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fang, Yuchen,Zhao, Fang,Qin, Yanjun,et al. Learning All Dynamics: Traffic Forecasting via Locality-Aware Spatio-Temporal Joint Transformer[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2022:14. |
| APA | Fang, Yuchen,Zhao, Fang,Qin, Yanjun,Luo, Haiyong,&Wang, Chenxing.(2022).Learning All Dynamics: Traffic Forecasting via Locality-Aware Spatio-Temporal Joint Transformer.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,14. |
| MLA | Fang, Yuchen,et al."Learning All Dynamics: Traffic Forecasting via Locality-Aware Spatio-Temporal Joint Transformer".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022):14. |
入库方式： OAI收割
来源：计算技术研究所
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