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Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks

文献类型：期刊论文

作者Li, Yong1; Li, Zhaoxuan1; Mei, Qiang4,5; Wang, Peng3,5; Hu, Wenlong2; Wang, Zhishan1; Xie, Wenxin1; Yang, Yang4; Chen, Yuhaoran1
刊名JOURNAL OF MARINE SCIENCE AND ENGINEERING
出版日期2023-07-01
卷号11期号:7页码:18
关键词spatiotemporal graph neural network traffic flow prediction ship big data AIS port traffic prediction
DOI10.3390/jmse11071379
英文摘要The intelligent maritime transportation system has emerged as a pivotal component in port management, owing to the rapid advancements in artificial intelligence and big data technology. Its essence lies in the application of digital modeling techniques, which leverage extensive ship data to facilitate efficient operations. In this regard, effective modeling and accurate prediction of the fluctuation patterns of ship traffic in multiple port regions will provide data support for trade analysis, port construction planning, and traffic safety management. In order to better express the potential interdependencies between ports, inspired by graph neural networks, this paper proposes a data-driven approach to construct a multi-port network and designs a spatiotemporal graph neural network model. The model incorporates graph attention networks and a dilated causal convolutional architecture to capture the temporal and spatial dimensions of traffic variation patterns. It also employs a gated-mechanism-based spatiotemporal bi-dimensional feature fusion strategy to handle the potential unequal relationships between the two dimensions of features. Compared to existing methods for port traffic prediction, this model fully considers the network characteristics of the overall port and fills the research gap in multi-port scenarios. In the experiments, real port ship traffic datasets were constructed using data from the Automatic Identification System (AIS) and port geographical information data for model validation. The results demonstrate that the model exhibits outstanding robustness and performs well in predicting traffic in multiple sub-regional port clusters.
资助项目National Natural Science Foundation of China[71804059] ; Natural Science Foundation of Fujian Province[2021J01821] ; Shanghai Science and Technology Committee[18DZ1206300] ; National Key Research and Development Program of China[2018YFC1407400]
WOS研究方向Engineering ; Oceanography
语种英语
WOS记录号WOS:001036083900001
出版者MDPI
源URL[http://119.78.100.204/handle/2XEOYT63/21283]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Mei, Qiang; Wang, Peng
作者单位1.Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
2.Univ Auckland, Sch Comp Sci, Auckland 1010, New Zealand
3.Chinese Acad Sci, Inst Comp Technol, Beijing 100086, Peoples R China
4.Jimei Univ, Nav Coll, Xiamen 361021, Peoples R China
5.Shanghai Maritime Univ, Merchant Marine Coll, Shanghai 201306, Peoples R China
推荐引用方式
GB/T 7714		 Li, Yong,Li, Zhaoxuan,Mei, Qiang,et al. Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks[J]. JOURNAL OF MARINE SCIENCE AND ENGINEERING,2023,11(7):18.
APA Li, Yong.,Li, Zhaoxuan.,Mei, Qiang.,Wang, Peng.,Hu, Wenlong.,...&Chen, Yuhaoran.(2023).Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks.JOURNAL OF MARINE SCIENCE AND ENGINEERING,11(7),18.
MLA Li, Yong,et al."Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks".JOURNAL OF MARINE SCIENCE AND ENGINEERING 11.7(2023):18.

入库方式： OAI收割

来源：计算技术研究所

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