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Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

文献类型：期刊论文

作者Shao, Zezhi2,3; Zhang, Zhao2; Wei, Wei4; Wang, Fei2; Xu, Yongjun2; Cao, Xin5; Jensen, Christian S.1
刊名PROCEEDINGS OF THE VLDB ENDOWMENT
出版日期2022-07-01
卷号15期号:11页码:2733-2746
ISSN号2150-8097
DOI10.14778/3551793.3551827
英文摘要We all depend on mobility, and vehicular transportation affects the daily lives of most of us. Thus, the ability to forecast the state of traffic in a road network is an important functionality and a challenging task. Traffic data is often obtained from sensors deployed in a road network. Recent proposals on spatial-temporal graph neural networks have achieved great progress at modeling complex spatial-temporal correlations in traffic data, by modeling traffic data as a diffusion process. However, intuitively, traffic data encompasses two different kinds of hidden time series signals, namely the diffusion signals and inherent signals. Unfortunately, nearly all previous works coarsely consider traffic signals entirely as the outcome of the diffusion, while neglecting the inherent signals, which impacts model performance negatively. To improve modeling performance, we propose a novel Decoupled Spatial-Temporal Framework (DSTF) that separates the diffusion and inherent traffic information in a data-driven manner, which encompasses a unique estimation gate and a residual decomposition mechanism. The separated signals can be handled subsequently by the diffusion and inherent modules separately. Further, we propose an instantiation of DSTF, Decoupled Dynamic Spatial-Temporal Graph Neural Network (D(2)STGNN), that captures spatial-temporal correlations and also features a dynamic graph learning module that targets the learning of the dynamic characteristics of traffic networks. Extensive experiments with four real-world traffic datasets demonstrate that the framework is capable of advancing the state-of-the-art.
资助项目National Natural Science Foundation of China[61902376] ; National Natural Science Foundation of China[61902382] ; National Natural Science Foundation of China[61602197] ; CCF-AFSG Research Fund[RF20210005] ; HUST ; Pingan Property & Casualty Research (HPL) ; China Postdoctoral Science Foundation[2021M703273]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000992390600035
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/21423]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wei, Wei; Wang, Fei
作者单位1.Aalborg Univ, Dept Comp Sci, Aalborg, Denmark
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Wuhan, Peoples R China
5.Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW, Australia
推荐引用方式
GB/T 7714		 Shao, Zezhi,Zhang, Zhao,Wei, Wei,et al. Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting[J]. PROCEEDINGS OF THE VLDB ENDOWMENT,2022,15(11):2733-2746.
APA Shao, Zezhi.,Zhang, Zhao.,Wei, Wei.,Wang, Fei.,Xu, Yongjun.,...&Jensen, Christian S..(2022).Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting.PROCEEDINGS OF THE VLDB ENDOWMENT,15(11),2733-2746.
MLA Shao, Zezhi,et al."Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting".PROCEEDINGS OF THE VLDB ENDOWMENT 15.11(2022):2733-2746.

入库方式： OAI收割

来源：计算技术研究所

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