ESTNet: Embedded Spatial-Temporal Network for Modeling Traffic Flow Dynamics

doi:10.1109/TITS.2022.3167019

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

	ESTNet: Embedded Spatial-Temporal Network for Modeling Traffic Flow Dynamics
	Luo, Guiyang 7; Zhang, Hui 5,6; Yuan, Quan 7; Li, Jinglin 3,4; Wang, Fei-Yue 1,2,5
刊名	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	2022-05-13
页码	12
关键词	Roads Correlation Feature extraction Sensors Deep learning Convolution Sensor phenomena and characterization Traffic forecasting graph convolutional network spatial-temporal networks
ISSN号	1524-9050
DOI	10.1109/TITS.2022.3167019
通讯作者	Li, Jinglin(jlli@bupt.eud.cn)
英文摘要	Accurate spatial-temporal prediction is a fundamental building block of many real-world applications such as traffic scheduling and management, environment policy making, and public safety. This problem is still challenging due to nonlinear, complicated, and dynamic spatial-temporal dependencies. To address these challenges, we propose a novel embedded spatial-temporal network (ESTNet), which extracts efficient features to model the dynamic correlations and then exploits three-dimension convolution to synchronously model the spatial-temporal dependencies. Specifically, we propose multi-range graph convolution networks for extracting multi-scale static features from the fine-grained road network. Meanwhile, dynamic features are extracted from real-time traffic using a gated recurrent unit network. These features can be applied to identify the dynamic and flexible correlations among sensors and make it possible to exploit a three-dimension convolution unit (3DCon) to simultaneously model the spatial-temporal dependencies. Furthermore, we propose a residual network by stacking multiple 3DCon to capture the nonlinear and complicated dependencies. The effectiveness and superiority of ESTNet are verified on two real-world datasets, and experiments show ESTNet outperforms the state-of-the-art with a significant margin. 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[61876023]
WOS关键词	PREDICTION ; DEEP
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:000799568700001
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/173211/49530]
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	Li, Jinglin
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.State Key Lab Networking & Switching Technol, Shijiahznang 050081, Hebei, Peoples R China 4.Beijing Univ Posts & Telecommun, Sci & Technol Commun Networks Lab, Beijing 100088, Peoples R China 5.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 6.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China 7.Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100088, Peoples R China
推荐引用方式 GB/T 7714	Luo, Guiyang,Zhang, Hui,Yuan, Quan,et al. ESTNet: Embedded Spatial-Temporal Network for Modeling Traffic Flow Dynamics[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2022:12.
APA	Luo, Guiyang,Zhang, Hui,Yuan, Quan,Li, Jinglin,&Wang, Fei-Yue.(2022).ESTNet: Embedded Spatial-Temporal Network for Modeling Traffic Flow Dynamics.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,12.
MLA	Luo, Guiyang,et al."ESTNet: Embedded Spatial-Temporal Network for Modeling Traffic Flow Dynamics".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022):12.