Fine-Grained Vessel Traffic Flow Prediction With a Spatio-Temporal Multigraph Convolutional Network
文献类型：期刊论文
| 作者 | Liang, Maohan1,4; Liu, Ryan Wen1,4; Zhan, Yang1,4; Li, Huanhuan2; Zhu, Fenghua3 ; Wang, Fei-Yue3
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| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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| 出版日期 | 2022-08-26 |
| 页码 | 14 |
| 关键词 | Traffic flow prediction maritime traffic network multi-graph convolutional network automatic identification system (AIS) |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2022.3199160 |
| 通讯作者 | Liu, Ryan Wen(wenliu@whut.edu.cn) ; Zhu, Fenghua(fenghua.zhu@ia.ac.cn) |
| 英文摘要 | 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. |
| 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] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000849257900001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Key-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50026]  |
| 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 | Liu, Ryan Wen; Zhu, Fenghua |
| 作者单位 | 1.Wuhan Univ Technol, Sch Nav, Wuhan 430063, Peoples R China 2.Liverpool John Moores Univ, Sch Engn Technol & Maritime Operat, Liverpool L3 3AF, Merseyside, England 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Natl Engn Res Ctr Water Transport Safety, Wuhan 430063, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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