Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix

doi:10.3390/app14104104

	Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
	Zhang, Ran 1; Chen, Xiaohui 1; Ye, Lin 1; Yu, Wentao 1,2; Zhang, Bing 1; Liu, Junnan 3
	2024-05-01
发表期刊	APPLIED SCIENCES-BASEL
卷号	14 期号:10 页码:20
摘要	This study proposes a vessel position prediction method using attention spatiotemporal graph convolutional networks, which addresses the issue of low prediction accuracy due to less consideration of inter-feature dependencies in current vessel trajectory prediction methods. First, the method cleans the vessel trajectory data and uses the Time-ratio trajectory compression algorithm to compress the trajectory data, avoiding data redundancy and providing feature points for vessel trajectories. Second, the Spectral Temporal Graph Neural Network (StemGNN) extracts the correlation matrix that describes the relationship between multiple variables as a priori matrix input to the prediction model. Then the vessel trajectory prediction model is constructed, and the attention mechanism is added to the spatial and temporal dimensions of the trajectory data based on the spatio-temporal graph convolutional network at the same time as the above operations are performed on different time scales. Finally, the features extracted from different time scales are fused through the full connectivity layer to predict the future trajectories. Experimental results show that this method achieves higher accuracy and more stable prediction results in trajectory prediction. The attention-based spatio-temporal graph convolutional networks effectively capture the spatio-temporal correlations of the main features in vessel trajectories, and the spatio-temporal attention mechanism and graph convolution have certain interpretability for the prediction results.
关键词	ais trajectory prediction attention mechanism spatio-temporal graph convolution
DOI	10.3390/app14104104
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:001232735100001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40061
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chen, Xiaohui
作者单位	1.Informat Engn Univ, Inst Data & Target Engn, Zhengzhou 450001, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Zhengzhou Univ, Inst Geosci & Technol, Zhengzhou 450001, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Ran,Chen, Xiaohui,Ye, Lin,et al. Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix[J]. APPLIED SCIENCES-BASEL,2024,14(10):20.
APA	Zhang, Ran,Chen, Xiaohui,Ye, Lin,Yu, Wentao,Zhang, Bing,&Liu, Junnan.(2024).Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix.APPLIED SCIENCES-BASEL,14(10),20.
MLA	Zhang, Ran,et al."Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix".APPLIED SCIENCES-BASEL 14.10(2024):20.