ClusterST: Clustering Spatial-Temporal Network for Traffic Forecasting

doi:10.1109/TITS.2022.3215703

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	ClusterST: Clustering Spatial-Temporal Network for Traffic Forecasting
	Luo, Guiyang 5,6; Zhang, Hui 4; Yuan, Quan 5,6; Li, Jinglin 6; Wang, Fei-Yue 1,2,3
刊名	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	2022-11-17
页码	12
关键词	Index Terms- Traffic forecasting graph convolutional network spatial-temporal networks over-smoothing
ISSN号	1524-9050
DOI	10.1109/TITS.2022.3215703
通讯作者	Zhang, Hui()
英文摘要	Traffic forecasting aims to capture complex spatial-temporal dependencies and non-linear dynamics, which plays an indispensable role in intelligent transportation systems and other domains like neuroscience, climate, etc. Most recent works rely on graph convolutional networks (GCN) to model the dependencies and the dynamics. However, the over-smoothing issue of GCN would produce indistinguishable features among nodes, leading to poor expressivity and weak capability of modeling complex dependencies and dynamics. To address this issue, we present a novel clustering spatial-temporal (ClusterST) unit, which incorporates unsupervised learning into GCN for extracting discriminative features. Specifically, we first exploit a neural network to learn a dynamic clustering, i.e., learning to partition the neighbors of each node into clusters at each time step. Two probabilistic losses are proposed to improve the separability of clusters. Then, the extracted features of different clusters can be distinguished. Based on the dynamically formed clusters, a vanilla GCN is applied to aggregate features within each cluster. By purely exploiting such a ClusterST unit, large improvements over the state-of-the-art are achieved. Furthermore, ClusterST units with a different number of clusters can be regarded as basic components to construct an inception-like ClusterST network for going deeper. We evaluate the framework on two real-world large-scale traffic datasets and observe an average improvement of $18.19\%$ and $7.62\%$ over state-of-the-art baselines, respectively. The code and models will be publicly available.
资助项目	National Natural Science Foundation of China[62102041] ; National Natural Science Foundation of China[62203040] ; National Natural Science Foundation of China[61876023] ; National Natural Science Foundation of China[62272053]
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:000890835700001
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50794]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Zhang, Hui
作者单位	1.Macau Univ Sci & Technol, Inst Syst Engn, Taipa, Macao, Peoples R China 2.Qingdao Acad Intelligent Ind, Innovat Ctr Parallel Vis, Qingdao 266000, Peoples R China 3.Chinese Acad Sci, Inst Automation, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100876, Peoples R China 5.Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China 6.Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
推荐引用方式 GB/T 7714	Luo, Guiyang,Zhang, Hui,Yuan, Quan,et al. ClusterST: Clustering Spatial-Temporal Network for Traffic Forecasting[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2022:12.
APA	Luo, Guiyang,Zhang, Hui,Yuan, Quan,Li, Jinglin,&Wang, Fei-Yue.(2022).ClusterST: Clustering Spatial-Temporal Network for Traffic Forecasting.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,12.
MLA	Luo, Guiyang,et al."ClusterST: Clustering Spatial-Temporal Network for Traffic Forecasting".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022):12.