Fine-Grained Vessel Traffic Flow Prediction With a Spatio-Temporal Multigraph Convolutional Network
Liang, Maohan1,2; Liu, Ryan Wen1,2; Zhan, Yang1,2; Li, Huanhuan3; Zhu, Fenghua4; Wang, Fei-Yue4
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN1524-9050
2022-08-26
Pages14
Corresponding AuthorLiu, Ryan Wen(wenliu@whut.edu.cn) ; Zhu, Fenghua(fenghua.zhu@ia.ac.cn)
AbstractThe accurate and robust prediction of vessel traffic flow is gaining importance in maritime intelligent transportation system (ITS), such as vessel traffic services, maritime spatial planning, and traffic safety management, etc. To achieve fine-grained vessel traffic flow prediction, we will first generate the maritime traffic network (which is essentially a graph), and then propose a graph-driven neural network. In particular, to represent various correlations among spatio-temporal vessel traffic flow, we tend to extract the feature points (i.e., starting, way and ending points) by utilizing the knowledge of vessel positioning data. These feature points are essentially related to the geometrical structures of massive vessel trajectories collected from massive automatic identification system (AIS) records, contributing to the generation of maritime traffic network. We then propose a spatio-temporal multi-graph convolutional network (STMGCN)-based vessel traffic flow prediction method by exploiting multiple types of inherent correlations in the generated maritime graph. The proposed STMGCN mainly contains one spatial multi-graph convolutional layer and two temporal gated convolutional layers, beneficial for extracting spatial and temporal traffic flow patterns. The main benefit of our graph-driven prediction method is that it takes full advantage of the maritime graph and multi-graph learning. Comprehensive experiments have been implemented on realistic AIS dataset to compare our method with several state-of-the-art prediction methods. The fine-grained prediction results have demonstrated our superior performance in terms of both accuracy and robustness.
KeywordTraffic flow prediction maritime traffic network multi-graph convolutional network automatic identification system (AIS)
DOI10.1109/TITS.2022.3199160
Indexed BySCI
Language英语
Funding ProjectKey-Area Research and Development Program of Guangdong Province[2020B0909050001] ; National Natural Science Foundation of China[51609195] ; National Natural Science Foundation of China[U1811463]
Funding OrganizationKey-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000849257900001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/50027
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 AuthorLiu, Ryan Wen; Zhu, Fenghua
Affiliation1.Wuhan Univ Technol, Sch Nav, Wuhan 430063, Peoples R China
2.Natl Engn Res Ctr Water Transport Safety, Wuhan 430063, Peoples R China
3.Liverpool John Moores Univ, Sch Engn Technol & Maritime Operat, Liverpool L3 3AF, Merseyside, England
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Liang, Maohan,Liu, Ryan Wen,Zhan, Yang,et al. Fine-Grained Vessel Traffic Flow Prediction With a Spatio-Temporal Multigraph Convolutional Network[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2022:14.
APA Liang, Maohan,Liu, Ryan Wen,Zhan, Yang,Li, Huanhuan,Zhu, Fenghua,&Wang, Fei-Yue.(2022).Fine-Grained Vessel Traffic Flow Prediction With a Spatio-Temporal Multigraph Convolutional Network.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,14.
MLA Liang, Maohan,et al."Fine-Grained Vessel Traffic Flow Prediction With a Spatio-Temporal Multigraph Convolutional Network".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022):14.
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