Using LSTM with Trajectory Point Correlation and Temporal Pattern Attention for Ship Trajectory Prediction Article Swipe
Yi Zhou
,
Haitao Guo
,
Jun Lu
,
Zhihui Gong
,
Donghang Yu
,
Lei Ding
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13234705
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13234705
Accurate ship trajectory prediction is crucial for real-time vessel position tracking and maritime safety management. However, existing methods for ship trajectory prediction encounter significant challenges. They struggle to effectively extract long-term and complex spatial–temporal features hidden within the data. Moreover, they often overlook correlations among multivariate dynamic features such as longitude (LON), latitude (LAT), speed over ground (SOG), and course over ground (COG), which are essential for precise trajectory forecasting. To address these pressing issues and fulfill the need for more accurate and comprehensive ship trajectory prediction, we propose a novel and integrated approach. Firstly, a Trajectory Point Correlation Attention (TPCA) mechanism is devised to establish spatial connections between trajectory points, thereby uncovering the local trends of trajectory point changes. Subsequently, a Temporal Pattern Attention (TPA) mechanism is introduced to handle the associations between multiple variables across different time steps and capture the dynamic feature correlations among trajectory attributes. Finally, a Great Circle Route Loss Function (GCRLoss) is constructed, leveraging the perception of the Earth’s curvature to deepen the understanding of spatial relationships and geographic information. Experimental results demonstrate that our proposed method outperforms existing ship trajectory prediction techniques, showing enhanced reliability in multi-step predictions.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics13234705
- OA Status
- gold
- Cited By
- 5
- References
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4404792772Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/electronics13234705Digital Object Identifier
- Title
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Using LSTM with Trajectory Point Correlation and Temporal Pattern Attention for Ship Trajectory PredictionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-28Full publication date if available
- Authors
-
Yi Zhou, Haitao Guo, Jun Lu, Zhihui Gong, Donghang Yu, Lei DingList of authors in order
- Landing page
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https://doi.org/10.3390/electronics13234705Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/electronics13234705Direct OA link when available
- Concepts
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Trajectory, Computer science, Artificial intelligence, Correlation, Point (geometry), Mathematics, Physics, Geometry, AstronomyTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 5Per-year citation counts (last 5 years)
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76Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4379618302, https://openalex.org/W4288734793, https://openalex.org/W4380854512, https://openalex.org/W2802228104, https://openalex.org/W4220837725, https://openalex.org/W3209412755, https://openalex.org/W2912934741, https://openalex.org/W4386147356, https://openalex.org/W2802606519, https://openalex.org/W3094009967, https://openalex.org/W4293428450, https://openalex.org/W2998069198, https://openalex.org/W4206209964, https://openalex.org/W4288064704, https://openalex.org/W2944791333, https://openalex.org/W3014780258, https://openalex.org/W4366607783, https://openalex.org/W2890403672, https://openalex.org/W6801381213, https://openalex.org/W2966812659, https://openalex.org/W3207181232, https://openalex.org/W4396578912, https://openalex.org/W4312709753, https://openalex.org/W3207160263, https://openalex.org/W4206706469, https://openalex.org/W3184448832, https://openalex.org/W4395699745, https://openalex.org/W3094215927, https://openalex.org/W3156622039, https://openalex.org/W4365514227, https://openalex.org/W4400917205, https://openalex.org/W4392808188, https://openalex.org/W4396505922, https://openalex.org/W4390576778, https://openalex.org/W4366771311, https://openalex.org/W4295926276, https://openalex.org/W4387086512, https://openalex.org/W6770387040, https://openalex.org/W4386434910, https://openalex.org/W2972599895, https://openalex.org/W3151990440, https://openalex.org/W4376870225, https://openalex.org/W4386410620, https://openalex.org/W4401505458, https://openalex.org/W3163016362, https://openalex.org/W4384298251, https://openalex.org/W4386416306, https://openalex.org/W4362465944, https://openalex.org/W4388602068, https://openalex.org/W4392596920, https://openalex.org/W4312495202, https://openalex.org/W6861636890, https://openalex.org/W4403684479, https://openalex.org/W4403585109, https://openalex.org/W4396605070, https://openalex.org/W4400905263, https://openalex.org/W4379519605, https://openalex.org/W4324032808, https://openalex.org/W4362519594, https://openalex.org/W4389687067, https://openalex.org/W6839798893, https://openalex.org/W3084233918, https://openalex.org/W3080330126, https://openalex.org/W2890096158, https://openalex.org/W4296295802, https://openalex.org/W6840031512, https://openalex.org/W4385288942, https://openalex.org/W4214612842, https://openalex.org/W3128634608, https://openalex.org/W4324118713, https://openalex.org/W4396897179, https://openalex.org/W4391566518, https://openalex.org/W4285589513, https://openalex.org/W4285740942, https://openalex.org/W3200862155, https://openalex.org/W2989897953 |
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