ESTNet: Embedded Spatial-Temporal Network for Modeling Traffic Flow Dynamics
Luo, Guiyang1; Zhang, Hui2,3; Yuan, Quan1; Li, Jinglin4,5; Wang, Fei-Yue3,6,7
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN1524-9050
2022-05-13
Pages12
Corresponding AuthorLi, Jinglin(jlli@bupt.eud.cn)
AbstractAccurate 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.
KeywordRoads Correlation Feature extraction Sensors Deep learning Convolution Sensor phenomena and characterization Traffic forecasting graph convolutional network spatial-temporal networks
DOI10.1109/TITS.2022.3167019
WOS KeywordPREDICTION ; DEEP
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[62102041] ; National Natural Science Foundation of China[61876023]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000799568700001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49531
Collection复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
Corresponding AuthorLi, Jinglin
Affiliation1.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
Recommended Citation
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.
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