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Research on short-term ship traffic flow prediction in port waters based on disaggregated model and long short-term memory (LSTM) model
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Jiacheng CAI, Feng LIAN, Zhongzhen YANG
Navigation of China | 2025, 48(1) : 77 - 83
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Navigation of China | 2025, 48(1): 77-83
• Marine Traffic Safety •
Research on short-term ship traffic flow prediction in port waters based on disaggregated model and long short-term memory (LSTM) model
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Jiacheng CAI, Feng LIAN, Zhongzhen YANG
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  • Marine College, Ningbo University, Ningbo 315832, China
Published: 2025-03-25 doi: 10.3969/j.issn.1000-4653.2025.01.010
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With the continuous increase in seaborne trade volume, ship traffic density in port areas is rising, and navigation conditions in port waters are becoming more complex. Short-term ship traffic prediction in port waters is playing an increasingly critical role in ship traffic control and navigation safety management. To address the limitation of low accuracy in aggregate models, this paper, based on ship Automatic Identification System (AIS) data, employs a disaggregate method to construct a hybrid prediction model. This model combines the Long Short-Term Memory (LSTM) network with ships' historical trajectories to calculate short-term ship trajectories in port waters. The counts of ships' trajectories intersecting with an approach channel section are used to predict the short-term ship flow across the section. A numerical example from Ningbo-Zhoushan Port during June to December 2020 demonstrates that the forecasting accuracy of the proposed model reaches up to 80%, significantly higher than that of traditional aggregate models. The model developed here provides a technical foundation for ports to implement ship traffic control methods and improve channel utilization rates.

disaggregate model  /  ship traffic  /  AIS  /  LSTM  /  port channel  /  short term trajectory
Jiacheng CAI, Feng LIAN, Zhongzhen YANG. Research on short-term ship traffic flow prediction in port waters based on disaggregated model and long short-term memory (LSTM) model[J]. Navigation of China, 2025 , 48 (1) : 77 -83 . DOI: 10.3969/j.issn.1000-4653.2025.01.010
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Year 2025 volume 48 Issue 1
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Article Info
doi: 10.3969/j.issn.1000-4653.2025.01.010
  • Receive Date：2023-04-22
  • Online Date：2026-03-17
  • Published：2025-03-25
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  • Received：2023-04-22
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    Marine College, Ningbo University, Ningbo 315832, China
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表12种不同金属材料的力学参数
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Number of
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Number of
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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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