Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
文献类型：期刊论文
| 作者 | Zhang, Ran1; Chen, Xiaohui1; Ye, Lin1; Yu, Wentao1,2; Zhang, Bing1; Liu, Junnan3 |
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 14期号:10页码:20 |
| 关键词 | ais trajectory prediction attention mechanism spatio-temporal graph convolution |
| DOI | 10.3390/app14104104 |
| 英文摘要 | 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. |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Chemistry ; Engineering ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001232735100001 |
| 出版者 | MDPI |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/40060]  |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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