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

doi:10.1109/TITS.2022.3167019

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

	ESTNet: Embedded Spatial-Temporal Network for Modeling Traffic Flow Dynamics
	Luo, Guiyang 1; Zhang, Hui2,3 ; Yuan, Quan 1; Li, Jinglin 4,5; Wang, Fei-Yue3,6,7
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
	2022-05-13
页码	12
通讯作者	Li, Jinglin(jlli@bupt.eud.cn)
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.
关键词	Roads Correlation Feature extraction Sensors Deep learning Convolution Sensor phenomena and characterization Traffic forecasting graph convolutional network spatial-temporal networks
DOI	10.1109/TITS.2022.3167019
关键词[WOS]	PREDICTION ; DEEP
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[61876023]
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:000799568700001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：43[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/173211/49531
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	Li, Jinglin
作者单位	1.Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100088, Peoples R China 2.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China 3.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 4.Beijing Univ Posts & Telecommun, Sci & Technol Commun Networks Lab, Beijing 100088, Peoples R China 5.State Key Lab Networking & Switching Technol, Shijiahznang 050081, Hebei, Peoples R China 6.Qingdao Acad Intelligent Ind, Innovat Ctr Parallel Vis, Qingdao 266000, Peoples R China 7.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. 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.