Knowledge Commons of Institute of Automation,CAS
TVGCN: Time-variant graph convolutional network for traffic forecasting | |
Wang, Yuhu1,2; Fang, Shen1,2; Zhang, Chunxia3; Xiang, Shiming1,2; Pan, Chunhong1 | |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
2022-01-30 | |
卷号 | 471页码:118-129 |
摘要 | Traffic forecasting is a very challenging task due to the complicated and dynamic spatial-temporal correlations between traffic nodes. Most existing methods measure the spatial correlations by defining physical or virtual graphs with distance or similarity measurement, which is constructed with stable edge connections by some prior knowledge. However, the use of such graphs with stable edge connections limits the variations of spatial correlations between traffic nodes at different times, which can not capture the hidden dynamic patterns of traffic graphs. This paper proposes a Time-Variant Graph Convolutional Network (TVGCN) to overcome this limitation. Architecturally, a time-variant spatial convolutional module (TV-SCM) is developed on two graphs without any prior knowledge. One graph is learned to capture the stable spatial correlations of the traffic graph, while the other graph is evolved to model dynamic spatial correlations at different times. Such two graphs are combined hierarchically together under the framework of graph convolutional network (GCN). Moreover, a gated multi-scale temporal convolutional module (GMS-TCM) is designed to extract long-range temporal dependencies within traffic nodes, which are further supplied to the TV-SCM to mutually explore the spatial correlations between traffic nodes. Extensive experiments conducted on three real-world traffic datasets indicate the effectiveness and superiority of our proposed approach. (c) 2021 Elsevier B.V. All rights reserved. |
关键词 | Spatial-temporal correlation Graph convolutional network Traffic forecasting |
DOI | 10.1016/j.neucom.2021.11.006 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2020AAA0104903] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[62072039] ; National Natural Science Foundation of China[62076242] ; National Natural Science Foundation of China[61802407] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000761907400002 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 机器学习 |
国重实验室规划方向分类 | 智能计算与学习 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48051 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
通讯作者 | Wang, Yuhu |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 3.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Wang, Yuhu,Fang, Shen,Zhang, Chunxia,et al. TVGCN: Time-variant graph convolutional network for traffic forecasting[J]. NEUROCOMPUTING,2022,471:118-129. |
APA | Wang, Yuhu,Fang, Shen,Zhang, Chunxia,Xiang, Shiming,&Pan, Chunhong.(2022).TVGCN: Time-variant graph convolutional network for traffic forecasting.NEUROCOMPUTING,471,118-129. |
MLA | Wang, Yuhu,et al."TVGCN: Time-variant graph convolutional network for traffic forecasting".NEUROCOMPUTING 471(2022):118-129. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
TVGCN.pdf(2050KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论