ClusterST: Clustering Spatial-Temporal Network for Traffic Forecasting

doi:10.1109/TITS.2022.3215703

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	ClusterST: Clustering Spatial-Temporal Network for Traffic Forecasting
	Luo, Guiyang 1,2; Zhang, Hui3 ; Yuan, Quan 1,2; Li, Jinglin 1; Wang, Fei-Yue4,5,6
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
	2022-11-17
页码	12
通讯作者	Zhang, Hui()
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.
关键词	Index Terms- Traffic forecasting graph convolutional network spatial-temporal networks over-smoothing
DOI	10.1109/TITS.2022.3215703
收录类别	SCI
语种	英语
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]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000890835700001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50795
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Zhang, Hui
作者单位	1.Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China 2.Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China 3.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100876, Peoples R China 4.Chinese Acad Sci, Inst Automation, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 5.Qingdao Acad Intelligent Ind, Innovat Ctr Parallel Vis, Qingdao 266000, Peoples R China 6.Macau Univ Sci & Technol, Inst Syst Engn, Taipa, Macao, 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.