Article(id=1240655134695412009, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655131507740845, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2025.03.005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1710604800000, receivedDateStr=2024-03-17, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773724834368, onlineDateStr=2026-03-17, pubDate=1758729600000, pubDateStr=2025-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773724834368, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773724834368, creator=13701087609, updateTime=1773724834368, updator=13701087609, issue=Issue{id=1240655131507740845, tenantId=1146029695717560320, journalId=1240618002186551303, year='2025', volume='48', issue='3', pageStart='1', pageEnd='184', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773724833608, creator=13701087609, updateTime=1773725188865, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240656621609742880, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655131507740845, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240656621613937185, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655131507740845, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=41, endPage=48, ext={EN=ArticleExt(id=1240655136083726648, articleId=1240655134695412009, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Ship trajectory prediction via an ensemble graph convolution neural network and recurrent attention mechanism, columnId=1240655017900823355, journalTitle=Navigation of China, columnName=Marine Traffic Safety, runingTitle=null, highlight=null, articleAbstract=

Ship trajectory prediction has become increasingly important in marine traffic management, shipping safety, and related fields. Current methods for ship time-series prediction exhibit certain limitations when handling multi-feature data inputs in water traffic scenarios, as they fail to adequately capture the correlations among features or focus on the critical information within time-series data. To address these shortcomings and further improve the accuracy of ship trajectory prediction, this study proposes a method named GCAU, which integrates an improved Graph Convolutional Network (GCN) with a Recurrent Attention Unit (RAU). First, Graph Convolutional Networks are employed to capture interdependencies between features, thereby enhancing the model's capability to extract feature correlations. Second, an attention gate is incorporated into the Recurrent Attention Unit (RAU), enabling selective emphasis on time-level features. Finally, the study evaluates four different ship time-series prediction methods across three distinct scenarios. The results demonstrate that GCAU outperforms the other methods in all tested scenarios, achieving lower values in Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The proposed method can effectively enhance the accuracy and stability of ship trajectory prediction, thereby providing more reliable decision support for maritime traffic management and other related applications.

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船舶轨迹预测在海洋交通管理、航运安全等领域的重要性日益凸显。当前的船舶时序预测方法在处理水上交通多特征数据输入时存在一些限制，未能充分捕捉特征之间的相关性以及关注时序数据中的重要信息。针对这些不足，并进一步提高船舶轨迹预测的精度，提出融合改进的图卷积网络（GCN）与循环注意力单元（RAU）的船舶轨迹预测方法（GCAU）。利用图卷积网络来捕捉特征之间的相关性，从而提升模型对特征之间相关性的提取能力。该方法还引入RAU中的注意力门，能有选择性地获取时间层面的特征。对4种不同的船舶时序预测方法进行评估，并且在3个不同的场景下进行测试。结果表明:GCAU在所有测试场景中均表现最佳，其具有更低的均方误差（MSE）和均方根误差（RMSE）、平均绝对百分比误差（MAPE） 和平均绝对误差（MAE）指标，能有效地提高船舶航迹预测的准确性和稳定性，为海事交通管理等领域提供更可靠的决策支持。

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陈信强（1987—），男，副教授，研究方向为海事大数据挖掘、交通图像处理。E-mail: xqchen@shmtu.edu.cn

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陈信强（1987—），男，副教授，研究方向为海事大数据挖掘、交通图像处理。E-mail: xqchen@shmtu.edu.cn

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