The Identification of Ship Trajectories Using Multi-Attribute Compression and Similarity Metrics Article Swipe
Chang Liu
,
Shize Zhang
,
Lufang Cao
,
Bin Lin
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/jmse11102005
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/jmse11102005
Automatic identification system (AIS) data record a ship’s position, speed over ground (SOG), course over ground (COG), and other behavioral attributes at specific time intervals during a ship’s voyage. At present, there are few studies in the literature on ship trajectory classification, especially the clustering of trajectory segments, to measure the multi-dimensional information of trajectories. Therefore, it is necessary to fully utilize the multi-dimensional information from AIS data when utilizing ship trajectory classification methods. Here, we propose a ship trajectory classification method based on multi-attribute trajectory similarity metrics which utilizes the following steps: (1) Improve the Douglas–Peucker (DP) algorithm by considering the SOG and COG; (2) use a multi-attribute symmetric segmentation path distance (MSSPD) for the similarity metric between trajectories; (3) cluster the segmented sub-trajectories based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm; (4) adaptively determinate the optimal input parameters based on the proposed comprehensive clustering performance metrics. The proposed method was tested on real AIS data from Bohai Sea waters, and the experimental results show that the algorithm can accurately cluster the ship trajectory groups and extract traffic distributions in key waters.
Related Topics
Concepts
Trajectory
Cluster analysis
Computer science
DBSCAN
Similarity (geometry)
Data mining
Position (finance)
Identification (biology)
Segmentation
Pattern recognition (psychology)
Artificial intelligence
Fuzzy clustering
Image (mathematics)
CURE data clustering algorithm
Biology
Astronomy
Botany
Physics
Economics
Finance
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jmse11102005
- https://www.mdpi.com/2077-1312/11/10/2005/pdf?version=1697623576
- OA Status
- gold
- Cited By
- 10
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387743902
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387743902Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/jmse11102005Digital Object Identifier
- Title
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The Identification of Ship Trajectories Using Multi-Attribute Compression and Similarity MetricsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-18Full publication date if available
- Authors
-
Chang Liu, Shize Zhang, Lufang Cao, Bin LinList of authors in order
- Landing page
-
https://doi.org/10.3390/jmse11102005Publisher landing page
- PDF URL
-
https://www.mdpi.com/2077-1312/11/10/2005/pdf?version=1697623576Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2077-1312/11/10/2005/pdf?version=1697623576Direct OA link when available
- Concepts
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Trajectory, Cluster analysis, Computer science, DBSCAN, Similarity (geometry), Data mining, Position (finance), Identification (biology), Segmentation, Pattern recognition (psychology), Artificial intelligence, Fuzzy clustering, Image (mathematics), CURE data clustering algorithm, Biology, Astronomy, Botany, Physics, Economics, FinanceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 4, 2023: 2Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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