A Direction-Preserved Vessel Trajectory Compression Algorithm Based on Open Window

Ship trajectory data can be used in most marine-related research, and most ship trajectory data come from AIS. The large number of ships and the short reporting period of AIS have resulted in a huge amount of ship trajectory data, which has caused a certain amount of pressure on the relevant researc...

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Published in:	Journal of marine science and engineering Vol. 11; no. 12; p. 2362
Main Authors:	Ma, Lin, Shi, Guoyou, Li, Weifeng, Jiang, Dapeng
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.12.2023
Subjects:	Adaptability Algorithms Coastal waters Compression Direction direction-preserved Low speed open window Pattern recognition Position errors radial distance Redundancy Sea vessels Traffic flow vessel trajectory
ISSN:	2077-1312, 2077-1312
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Abstract	Ship trajectory data can be used in most marine-related research, and most ship trajectory data come from AIS. The large number of ships and the short reporting period of AIS have resulted in a huge amount of ship trajectory data, which has caused a certain amount of pressure on the relevant research. This paper proposes a direction-preserved vessel trajectory compression method based on Open Window, which can effectively retain the direction change feature points while ensuring the position error. In addition, the method can work in both offline and online modes. Also, the velocity threshold and low-speed redundancy points problems are considered. In order to verify the effect of the proposed method and the adaptability of the method to different feature data, a parallel experiment was performed on port water and coastal water datasets. The results show that our method can compress vessel trajectories while retaining the feature points. Meanwhile, by comparing it with the compression method that is based on distance thresholds, the vessel trajectory compress time is drastically reduced by up to 87.3% in the port water data. The research in this paper provides a new method through which to compress vessel trajectories for research in marine-related fields on vessel trajectory.
AbstractList	Ship trajectory data can be used in most marine-related research, and most ship trajectory data come from AIS. The large number of ships and the short reporting period of AIS have resulted in a huge amount of ship trajectory data, which has caused a certain amount of pressure on the relevant research. This paper proposes a direction-preserved vessel trajectory compression method based on Open Window, which can effectively retain the direction change feature points while ensuring the position error. In addition, the method can work in both offline and online modes. Also, the velocity threshold and low-speed redundancy points problems are considered. In order to verify the effect of the proposed method and the adaptability of the method to different feature data, a parallel experiment was performed on port water and coastal water datasets. The results show that our method can compress vessel trajectories while retaining the feature points. Meanwhile, by comparing it with the compression method that is based on distance thresholds, the vessel trajectory compress time is drastically reduced by up to 87.3% in the port water data. The research in this paper provides a new method through which to compress vessel trajectories for research in marine-related fields on vessel trajectory.
Author	Ma, Lin Jiang, Dapeng Shi, Guoyou Li, Weifeng
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Cites_doi	10.1109/TITS.2019.2908191 10.1016/j.oceaneng.2022.113036 10.1016/j.oceaneng.2020.108086 10.1017/S0373463316000850 10.1017/S037346331800067X 10.1145/3147.3165 10.1109/TITS.2017.2681810 10.1109/ACCESS.2020.3001934 10.3390/jmse11040880 10.1016/j.jss.2017.01.003 10.14778/2536206.2536221 10.1016/j.ocecoaman.2023.106622 10.1109/ICBDA55095.2022.9760355 10.1007/978-3-540-24741-8_44 10.3390/s19051216 10.1016/j.future.2016.09.019 10.1017/S0373463318000188 10.1017/S0373463315000831 10.3390/jmse11102005 10.1109/ACCESS.2021.3078642
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Copyright_xml	– notice: 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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SubjectTerms	Adaptability Algorithms Coastal waters Compression Direction direction-preserved Low speed open window Pattern recognition Position errors radial distance Redundancy Sea vessels Traffic flow vessel trajectory
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Title	A Direction-Preserved Vessel Trajectory Compression Algorithm Based on Open Window
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