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Abstract
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.
Publication Date
1-3-2025
Journal Title
IEEE Access
Publisher
IEEE
Digital Object Identifier (DOI)
Document Type
Article
Recommended Citation
A. Troupiotis-Kapeliaris, C. Kastrisios and D. Zissis, "Vessel Trajectory Data Mining: A Review," in IEEE Access, vol. 13, pp. 4827-4856, 2025, doi: 10.1109/ACCESS.2025.3525952. keywords: {Trajectory;Data mining;Reviews;Feature extraction;Forecasting;Filtering;Visualization;Tracking;Predictive analytics;Planning;Maritime monitoring;data mining;spatio-temporal data mining;trajectory analytics;pattern mining;descriptive analytics;predictive analytics},
Comments
This is an open access article published by IEEE in IEEE Access in 2025, available online: https://dx.doi.org/10.1109/ACCESS.2025.3525952