MART: Ship Trajectory Prediction Model Based on Multi-Dimensional Attribute Association of Trajectory Points Article Swipe
S. J. Zhao
,
Wei Guo
,
Yi Liu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi14090345
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi14090345
Ship trajectory prediction plays an important role in numerous maritime applications and services. With the development of deep learning technology, the deep learning prediction method based on Automatic Identification System (AIS) data has become one of the hot topics in current maritime traffic research. However, as current models always concatenate dynamic information with distinct meanings (such as position, ship speed, and heading) into a single integrated input when processing trajectory point information as input, it becomes difficult for the models to grasp the correlations between different types of dynamic information of trajectory points and the specific information contained in each type of dynamic information itself. Aiming at the problem of insufficient modeling of the relationships among dynamic information in ship trajectory prediction, we propose the Multi-dimensional Attribute Relationship Transformer (MART) model. This model introduces a simulated trajectory training strategy to obtain the Association Loss (AssLoss) for learning the associations among different types of dynamic information; and it uses the Distance Loss (DisLoss) to integrate the relative distance information of the attribute embedding encoding to assist the model in understanding the relationships among different values in the dynamic information. We test the model on two AIS datasets, and the experiments show this model outperforms existing models. In the 15 h long-term prediction task, compared with other models, the MART model improves the prediction accuracy by 9.5% on the Danish Waters Dataset and by 15.4% on the Northern European Dataset. This study reveals the importance of the relationship between attributes and the relative distance of attribute values in spatiotemporal sequence modeling.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijgi14090345
- https://www.mdpi.com/2220-9964/14/9/345/pdf?version=1757325864
- OA Status
- gold
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4414066114
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https://openalex.org/W4414066114Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/ijgi14090345Digital Object Identifier
- Title
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MART: Ship Trajectory Prediction Model Based on Multi-Dimensional Attribute Association of Trajectory PointsWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-09-07Full publication date if available
- Authors
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S. J. Zhao, Wei Guo, Yi LiuList of authors in order
- Landing page
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https://doi.org/10.3390/ijgi14090345Publisher landing page
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https://www.mdpi.com/2220-9964/14/9/345/pdf?version=1757325864Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2220-9964/14/9/345/pdf?version=1757325864Direct OA link when available
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0Total citation count in OpenAlex
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26Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.integrated | 65 |
| abstract_inverted_index.introduces | 133 |
| abstract_inverted_index.prediction | 2, 23, 210, 221 |
| abstract_inverted_index.processing | 68 |
| abstract_inverted_index.trajectory | 1, 69, 91, 120, 136 |
| abstract_inverted_index.Association | 142 |
| abstract_inverted_index.Transformer | 128 |
| abstract_inverted_index.concatenate | 49 |
| abstract_inverted_index.development | 15 |
| abstract_inverted_index.experiments | 198 |
| abstract_inverted_index.information | 51, 71, 89, 96, 103, 117, 167 |
| abstract_inverted_index.outperforms | 202 |
| abstract_inverted_index.prediction, | 121 |
| abstract_inverted_index.technology, | 19 |
| abstract_inverted_index.Relationship | 127 |
| abstract_inverted_index.applications | 10 |
| abstract_inverted_index.associations | 148 |
| abstract_inverted_index.correlations | 83 |
| abstract_inverted_index.information. | 187 |
| abstract_inverted_index.information; | 154 |
| abstract_inverted_index.insufficient | 110 |
| abstract_inverted_index.relationship | 245 |
| abstract_inverted_index.relationships | 114, 180 |
| abstract_inverted_index.understanding | 178 |
| abstract_inverted_index.Identification | 28 |
| abstract_inverted_index.spatiotemporal | 256 |
| abstract_inverted_index.Multi-dimensional | 125 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100648538 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I37461747, https://openalex.org/I4210118728 |
| citation_normalized_percentile.value | 0.501351 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |