Fine-Grained Vessel Traffic Flow Prediction With a Spatio-Temporal Multigraph Convolutional Network

doi:10.1109/TITS.2022.3199160

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

	Fine-Grained Vessel Traffic Flow Prediction With a Spatio-Temporal Multigraph Convolutional Network
	Liang, Maohan 1,2; Liu, Ryan Wen 1,2; Zhan, Yang 1,2; Li, Huanhuan 3; Zhu, Fenghua4 ; Wang, Fei-Yue4
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
	2022-08-26
页码	14
通讯作者	Liu, Ryan Wen(wenliu@whut.edu.cn) ; Zhu, Fenghua(fenghua.zhu@ia.ac.cn)
摘要	The 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.
关键词	Traffic flow prediction maritime traffic network multi-graph convolutional network automatic identification system (AIS)
DOI	10.1109/TITS.2022.3199160
收录类别	SCI
语种	英语
资助项目	Key-Area Research and Development Program of Guangdong Province[2020B0909050001] ; National Natural Science Foundation of China[51609195] ; National Natural Science Foundation of China[U1811463]
项目资助者	Key-Area Research and Development Program of Guangdong Province ; 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:000849257900001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：30[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/173211/50027
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	Liu, Ryan Wen; Zhu, Fenghua
作者单位	1.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
通讯作者单位	中国科学院自动化研究所
推荐引用方式 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.