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Ship trajectory prediction via an ensemble graph convolution neural network and recurrent attention mechanism
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Xinqiang CHEN1, Weiping CHEN1, Bing HAN3, Chaofeng LI1, Huafeng WU2, Zongliang ZHU1
Navigation of China | 2025, 48(3) : 41 - 48
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Navigation of China | 2025, 48(3): 41-48
Marine Traffic Safety
Ship trajectory prediction via an ensemble graph convolution neural network and recurrent attention mechanism
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Xinqiang CHEN1, Weiping CHEN1, Bing HAN3, Chaofeng LI1, Huafeng WU2, Zongliang ZHU1
Affiliations
  • 1.Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
  • 2.Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
  • 3.Shanghai Ship and Shipping Research Institute Co., Ltd., Shanghai 200135, China
Published: 2025-09-25 doi: 10.3969/j.issn.1000-4653.2025.03.005
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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.

ship trajectory  /  graph convolutional network  /  recurrent attention unit  /  ship safety  /  trajectory prediction
Xinqiang CHEN, Weiping CHEN, Bing HAN, Chaofeng LI, Huafeng WU, Zongliang ZHU. Ship trajectory prediction via an ensemble graph convolution neural network and recurrent attention mechanism[J]. Navigation of China, 2025 , 48 (3) : 41 -48 . DOI: 10.3969/j.issn.1000-4653.2025.03.005
Year 2025 volume 48 Issue 3
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Article Info
doi: 10.3969/j.issn.1000-4653.2025.03.005
  • Receive Date:2024-03-17
  • Online Date:2026-03-17
  • Published:2025-09-25
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  • Received:2024-03-17
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Affiliations
    1.Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
    2.Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
    3.Shanghai Ship and Shipping Research Institute Co., Ltd., Shanghai 200135, China
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表12种不同金属材料的力学参数

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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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