A New Classification Method for Ship Trajectories Based on AIS Data
Automatic identification systems (AIS) can record a large amount of navigation information about ships, including abnormal or illegal ship movement information, which plays an important role in ship supervision. To distinguish the trajectories of ships and analyze the behavior of ships, this paper a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/9/1646 |
| _version_ | 1797579390197956608 |
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| author | Dan Luo Peng Chen Jingsong Yang Xiunan Li Yizhi Zhao |
| author_facet | Dan Luo Peng Chen Jingsong Yang Xiunan Li Yizhi Zhao |
| author_sort | Dan Luo |
| collection | DOAJ |
| description | Automatic identification systems (AIS) can record a large amount of navigation information about ships, including abnormal or illegal ship movement information, which plays an important role in ship supervision. To distinguish the trajectories of ships and analyze the behavior of ships, this paper adopts the method of supervised learning to classify the trajectories of ships. First, the AIS data for the ships were marked and divided into five types of ship tracks. The Tsfresh module was then used to extract various ship trajectory features, and a new ensemble classifier based on traditional classification using a machine learning algorithm was proposed for modeling and learning. Moreover, ten-fold cross validation was used to compare the ship trajectory classification results. The classification performance of the ensemble classifier was better than that of the other single classifiers. The average F1 score was 0.817. The results show that the newly proposed method and the new ensemble classifier have good classification effects on ship trajectories. |
| first_indexed | 2024-03-10T22:35:24Z |
| format | Article |
| id | doaj.art-aa839c39a3b84c108dea87f4995bbf3e |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T22:35:24Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-aa839c39a3b84c108dea87f4995bbf3e2023-11-19T11:25:27ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-08-01119164610.3390/jmse11091646A New Classification Method for Ship Trajectories Based on AIS DataDan Luo0Peng Chen1Jingsong Yang2Xiunan Li3Yizhi Zhao4Ocean College, Zhejiang University, Zhoushan 316021, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaOcean College, Zhejiang University, Zhoushan 316021, ChinaOcean College, Zhejiang University, Zhoushan 316021, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaAutomatic identification systems (AIS) can record a large amount of navigation information about ships, including abnormal or illegal ship movement information, which plays an important role in ship supervision. To distinguish the trajectories of ships and analyze the behavior of ships, this paper adopts the method of supervised learning to classify the trajectories of ships. First, the AIS data for the ships were marked and divided into five types of ship tracks. The Tsfresh module was then used to extract various ship trajectory features, and a new ensemble classifier based on traditional classification using a machine learning algorithm was proposed for modeling and learning. Moreover, ten-fold cross validation was used to compare the ship trajectory classification results. The classification performance of the ensemble classifier was better than that of the other single classifiers. The average F1 score was 0.817. The results show that the newly proposed method and the new ensemble classifier have good classification effects on ship trajectories.https://www.mdpi.com/2077-1312/11/9/1646Tsfreshtrajectory classificationmachine learningAIS |
| spellingShingle | Dan Luo Peng Chen Jingsong Yang Xiunan Li Yizhi Zhao A New Classification Method for Ship Trajectories Based on AIS Data Journal of Marine Science and Engineering Tsfresh trajectory classification machine learning AIS |
| title | A New Classification Method for Ship Trajectories Based on AIS Data |
| title_full | A New Classification Method for Ship Trajectories Based on AIS Data |
| title_fullStr | A New Classification Method for Ship Trajectories Based on AIS Data |
| title_full_unstemmed | A New Classification Method for Ship Trajectories Based on AIS Data |
| title_short | A New Classification Method for Ship Trajectories Based on AIS Data |
| title_sort | new classification method for ship trajectories based on ais data |
| topic | Tsfresh trajectory classification machine learning AIS |
| url | https://www.mdpi.com/2077-1312/11/9/1646 |
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