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:
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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CitedBy_id crossref_primary_10_1016_j_oceaneng_2024_119329
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ContentType Journal Article
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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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