DMGSTCN: Dynamic Multigraph Spatio-Temporal Convolution Network for Traffic Forecasting
文献类型：期刊论文
| 作者 | Qin, Yanjun1; Tao, Xiaoming1; Fang, Yuchen2; Luo, Haiyong3; Zhao, Fang2; Wang, Chenxing2 |
| 刊名 | IEEE INTERNET OF THINGS JOURNAL
 |
| 出版日期 | 2024-06-15 |
| 卷号 | 11期号:12页码:22208-22219 |
| 关键词 | Time series analysis Convolution Forecasting Correlation Task analysis Roads Predictive models Graph convolution network (GCN) spatial-temporal data traffic forecasting |
| ISSN号 | 2327-4662 |
| DOI | 10.1109/JIOT.2024.3380746 |
| 英文摘要 | Traffic forecasting belongs to intelligent transportation systems and is helpful for public property and life safety. Therefore, to forecast traffic accurately, researchers pay great attention to dealing with complex problems by mining intricate spatial and temporal dependencies of the traffic. However, some challenges still hold back traffic forecasting: 1) Most studies mainly focus on modeling correlations of traffic time series of close distances on the road network and ignore correlations of remote but similar traffic time series; 2) Previous static graph-based methods failed to reflect the dynamic changed spatial relations of multiple time series in the evolving traffic system. To tackle the above issues, we design a new dynamic multigraph spatio-temporal convolution network (DMGSTCN) in this article, which utilizes the gated causal convolution with the dynamic multigraph convolution network (DMGCN) to simultaneously extract spatial and temporal information. Specifically, DMGCN uses not only distance-based graphs but also structure-based graphs to obtain spatial information from nearby and remote but similar traffic time series, respectively. Moreover, to dynamically model spatial correlations, DMGCN first splits neighbors of each traffic time series into different regions according to relative position relationships. Then DMGCN assigns different weights to different regions at different time slices. Empirical evaluations on four traffic forecasting benchmarks reveal that DMGSTCN outperforms existing methods. |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:001242362600105 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39910]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Tao, Xiaoming |
| 作者单位 | 1.Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China 2.Beijing Univ Posts & Telecommun, Sch Comp Sci, Natl Pilot Software Engn Sch, Beijing 100876, Peoples R China 3.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qin, Yanjun,Tao, Xiaoming,Fang, Yuchen,et al. DMGSTCN: Dynamic Multigraph Spatio-Temporal Convolution Network for Traffic Forecasting[J]. IEEE INTERNET OF THINGS JOURNAL,2024,11(12):22208-22219. |
| APA | Qin, Yanjun,Tao, Xiaoming,Fang, Yuchen,Luo, Haiyong,Zhao, Fang,&Wang, Chenxing.(2024).DMGSTCN: Dynamic Multigraph Spatio-Temporal Convolution Network for Traffic Forecasting.IEEE INTERNET OF THINGS JOURNAL,11(12),22208-22219. |
| MLA | Qin, Yanjun,et al."DMGSTCN: Dynamic Multigraph Spatio-Temporal Convolution Network for Traffic Forecasting".IEEE INTERNET OF THINGS JOURNAL 11.12(2024):22208-22219. |
入库方式： OAI收割
来源：计算技术研究所
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