Vessel Trajectory Data Mining: A Review
Recent advancements in sensor and tracking technologies have facilitated the real-time tracking of marine vessels as they traverse the oceans. As a result, there is an increasing demand to analyze these datasets to derive insights into vessel movement patterns and to investigate activities occurring...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10824766/ |
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| author | Alexandros Troupiotis-Kapeliaris Christos Kastrisios Dimitris Zissis |
| author_facet | Alexandros Troupiotis-Kapeliaris Christos Kastrisios Dimitris Zissis |
| author_sort | Alexandros Troupiotis-Kapeliaris |
| collection | DOAJ |
| description | Recent advancements in sensor and tracking technologies have facilitated the real-time tracking of marine vessels as they traverse the oceans. As a result, there is an increasing demand to analyze these datasets to derive insights into vessel movement patterns and to investigate activities occurring within specific spatial and temporal contexts. This survey offers a comprehensive review of contemporary research in trajectory data mining, with a particular focus on maritime applications. The article collects and evaluates state-of-the-art algorithmic approaches and key techniques pertinent to various use case scenarios within this domain. Furthermore, this study provides an in-depth analysis of recent developments in trajectory data mining as applied to the maritime sector, identifying available data sources and conducting a detailed examination of significant applications, including trajectory forecasting, activity recognition, and trajectory clustering. |
| format | Article |
| id | doaj-art-54c6748db668485e8ca119aae84e3df8 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-54c6748db668485e8ca119aae84e3df82025-08-20T02:27:39ZengIEEEIEEE Access2169-35362025-01-01134827485610.1109/ACCESS.2025.352595210824766Vessel Trajectory Data Mining: A ReviewAlexandros Troupiotis-Kapeliaris0https://orcid.org/0000-0001-8726-6693Christos Kastrisios1https://orcid.org/0000-0001-9481-3501Dimitris Zissis2https://orcid.org/0000-0003-2870-2656Department of Product and Systems Design Engineering, Intelligent Transportation Systems Laboratory (Smart MOVE), University of the Aegean, Ermoupoli, GreeceCenter for Coastal and Ocean Mapping/Joint Hydrographic Center, University of New Hampshire, Durham, NH, USADepartment of Product and Systems Design Engineering, Intelligent Transportation Systems Laboratory (Smart MOVE), University of the Aegean, Ermoupoli, GreeceRecent advancements in sensor and tracking technologies have facilitated the real-time tracking of marine vessels as they traverse the oceans. As a result, there is an increasing demand to analyze these datasets to derive insights into vessel movement patterns and to investigate activities occurring within specific spatial and temporal contexts. This survey offers a comprehensive review of contemporary research in trajectory data mining, with a particular focus on maritime applications. The article collects and evaluates state-of-the-art algorithmic approaches and key techniques pertinent to various use case scenarios within this domain. Furthermore, this study provides an in-depth analysis of recent developments in trajectory data mining as applied to the maritime sector, identifying available data sources and conducting a detailed examination of significant applications, including trajectory forecasting, activity recognition, and trajectory clustering.https://ieeexplore.ieee.org/document/10824766/Maritime monitoringdata miningspatio-temporal data miningtrajectory analyticspattern miningdescriptive analytics |
| spellingShingle | Alexandros Troupiotis-Kapeliaris Christos Kastrisios Dimitris Zissis Vessel Trajectory Data Mining: A Review IEEE Access Maritime monitoring data mining spatio-temporal data mining trajectory analytics pattern mining descriptive analytics |
| title | Vessel Trajectory Data Mining: A Review |
| title_full | Vessel Trajectory Data Mining: A Review |
| title_fullStr | Vessel Trajectory Data Mining: A Review |
| title_full_unstemmed | Vessel Trajectory Data Mining: A Review |
| title_short | Vessel Trajectory Data Mining: A Review |
| title_sort | vessel trajectory data mining a review |
| topic | Maritime monitoring data mining spatio-temporal data mining trajectory analytics pattern mining descriptive analytics |
| url | https://ieeexplore.ieee.org/document/10824766/ |
| work_keys_str_mv | AT alexandrostroupiotiskapeliaris vesseltrajectorydataminingareview AT christoskastrisios vesseltrajectorydataminingareview AT dimitriszissis vesseltrajectorydataminingareview |