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Research on ship trajectory prediction model based on GRU-Attention-BiLSTM
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Zhitao YUAN1, 2, Zewei LI1, Kezhong LIU1, 2, Mozi CHEN1, 2, Hang YUAN1
Navigation of China | 2025, 48(4) : 132 - 140
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Navigation of China | 2025, 48(4): 132-140
• Intelligent Shipping •
Research on ship trajectory prediction model based on GRU-Attention-BiLSTM
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Zhitao YUAN1, 2, Zewei LI1, Kezhong LIU1, 2, Mozi CHEN1, 2, Hang YUAN1
Affiliations
  • 1.School of Navigation, Wuhan University of Technology, Wuhan 430063, China
  • 2.Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China
Published: 2025-12-25 doi: 10.3969/j.issn.1000-4653.2025.04.015
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To address the issue of low accuracy in ship trajectory prediction in complex navigable waters, this paper proposes a GRU-Attention-BiLSTM model for ship trajectory prediction. In the encoder part, the Gated Recurrent Unit (GRU) is employed to capture temporal features in trajectory sequences. The decoder adopts a Bidirectional Long Short-Term Memory Network (BiLSTM) integrated with an Attention mechanism to adjust the weights of data features. The model input is based on the longitude, latitude, speed and heading of the ship at the historical moment, and the ship density in the water area after median filtering smoothing is introduced as an additional feature. Using Automatic Identification System (AIS) data from the core port area of Ningbo-Zhoushan Port in March 2024, the model was trained and validated. Quantitative and qualitative comparisons with GRU, LSTM, Seq2Seq-LSTM, Attention-BiLSTM, and Transformer models demonstrate that the proposed model achieves superior prediction performance across different prediction durations and navigation scenarios.

complex navigable waters  /  ship trajectory prediction  /  attention mechanism
Zhitao YUAN, Zewei LI, Kezhong LIU, Mozi CHEN, Hang YUAN. Research on ship trajectory prediction model based on GRU-Attention-BiLSTM[J]. Navigation of China, 2025 , 48 (4) : 132 -140 . DOI: 10.3969/j.issn.1000-4653.2025.04.015
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Year 2025 volume 48 Issue 4
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Article Info
doi: 10.3969/j.issn.1000-4653.2025.04.015
  • Receive Date：2025-01-02
  • Online Date：2026-03-17
  • Published：2025-12-25
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  • Received：2025-01-02
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Affiliations
    1.School of Navigation, Wuhan University of Technology, Wuhan 430063, China
    2.Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
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Genus		 种数
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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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