A machine learning method for the recognition of ship behavior using AIS data
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| Titel: | A machine learning method for the recognition of ship behavior using AIS data |
|---|---|
| Autoren: | Ma, Quandang, Lian, Sunrong, Zhang, Dingze, Lang, Xiao, 1992, Rong, Hao, Mao, Wengang, 1980, Zhang, Mingyang |
| Quelle: | Ocean Engineering. 315 |
| Schlagwörter: | AIS data processing, Machine learning, Ship behavior recognition, Clustering algorithm, Maritime traffic safety |
| Beschreibung: | The efficiency of maritime traffic management and the safety of ship navigation have become top priorities. This study introduces a ship behavior recognition method that utilizes the Extreme Gradient Boosting (XGBoost) classification model, in conjunction with the Sparrow Search Algorithm (SSA), to enhance proactive maritime traffic management. The method leverages Automatic Identification System (AIS) data as its primary source and involves several critical steps. Initially, the AIS data is preprocessed, and ship behaviors are encoded. Subsequently, the encoded behaviors are clustered using spectral clustering to create a labeled dataset. Then, this dataset is employed to train and validate the SSA-XGBoost classification algorithm for identifying ship behaviors. Finally, an example analysis is performed in the Yangtze River. The results indicate that the proposed method can accurately and swiftly identify ship behaviors, achieving an accuracy of 97.28%, precision of 96.97%, recall of 97.43%, and an F1 score of 97.19%, surpassing the performance of the existing algorithms. The findings have the potential to aid maritime supervision authorities in promptly assessing ship navigation statuses and provide a valuable reference for developing ship scheduling decisions. |
| Dateibeschreibung: | electronic |
| Zugangs-URL: | https://research.chalmers.se/publication/544075 https://research.chalmers.se/publication/544075/file/544075_Fulltext.pdf |
| Datenbank: | SwePub |
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| Items | – Name: Title Label: Title Group: Ti Data: A machine learning method for the recognition of ship behavior using AIS data – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ma%2C+Quandang%22">Ma, Quandang</searchLink><br /><searchLink fieldCode="AR" term="%22Lian%2C+Sunrong%22">Lian, Sunrong</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Dingze%22">Zhang, Dingze</searchLink><br /><searchLink fieldCode="AR" term="%22Lang%2C+Xiao%22">Lang, Xiao</searchLink>, 1992<br /><searchLink fieldCode="AR" term="%22Rong%2C+Hao%22">Rong, Hao</searchLink><br /><searchLink fieldCode="AR" term="%22Mao%2C+Wengang%22">Mao, Wengang</searchLink>, 1980<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Mingyang%22">Zhang, Mingyang</searchLink> – Name: TitleSource Label: Source Group: Src Data: <i>Ocean Engineering</i>. 315 – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22AIS+data+processing%22">AIS data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Ship+behavior+recognition%22">Ship behavior recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithm%22">Clustering algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Maritime+traffic+safety%22">Maritime traffic safety</searchLink> – Name: Abstract Label: Description Group: Ab Data: The efficiency of maritime traffic management and the safety of ship navigation have become top priorities. This study introduces a ship behavior recognition method that utilizes the Extreme Gradient Boosting (XGBoost) classification model, in conjunction with the Sparrow Search Algorithm (SSA), to enhance proactive maritime traffic management. The method leverages Automatic Identification System (AIS) data as its primary source and involves several critical steps. Initially, the AIS data is preprocessed, and ship behaviors are encoded. Subsequently, the encoded behaviors are clustered using spectral clustering to create a labeled dataset. Then, this dataset is employed to train and validate the SSA-XGBoost classification algorithm for identifying ship behaviors. Finally, an example analysis is performed in the Yangtze River. The results indicate that the proposed method can accurately and swiftly identify ship behaviors, achieving an accuracy of 97.28%, precision of 96.97%, recall of 97.43%, and an F1 score of 97.19%, surpassing the performance of the existing algorithms. The findings have the potential to aid maritime supervision authorities in promptly assessing ship navigation statuses and provide a valuable reference for developing ship scheduling decisions. – Name: Format Label: File Description Group: SrcInfo Data: electronic – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/544075" linkWindow="_blank">https://research.chalmers.se/publication/544075</link><br /><link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/544075/file/544075_Fulltext.pdf" linkWindow="_blank">https://research.chalmers.se/publication/544075/file/544075_Fulltext.pdf</link> |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.oceaneng.2024.119791 Languages: – Text: English Subjects: – SubjectFull: AIS data processing Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Ship behavior recognition Type: general – SubjectFull: Clustering algorithm Type: general – SubjectFull: Maritime traffic safety Type: general Titles: – TitleFull: A machine learning method for the recognition of ship behavior using AIS data Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ma, Quandang – PersonEntity: Name: NameFull: Lian, Sunrong – PersonEntity: Name: NameFull: Zhang, Dingze – PersonEntity: Name: NameFull: Lang, Xiao – PersonEntity: Name: NameFull: Rong, Hao – PersonEntity: Name: NameFull: Mao, Wengang – PersonEntity: Name: NameFull: Zhang, Mingyang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00298018 – Type: issn-locals Value: SWEPUB_FREE – Type: issn-locals Value: CTH_SWEPUB Numbering: – Type: volume Value: 315 Titles: – TitleFull: Ocean Engineering Type: main |
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