Vessel Trajectory Data Mining: A Review
Title | Vessel Trajectory Data Mining: A Review |
Publication Type | Journal Article |
Year | 2025 |
Authors | Troupiotis-Kapeliaris, A, Kastrisios, C, Zissis, D |
Journal | IEEE Access |
Volume | 13 |
Pages | 4827-4856 |
Date Published | 03 January |
Publisher | IEEE |
Place Published | IEEE Access |
Keywords | data mining, descriptive analytics, maritime monitoring, pattern mining, predictive analytics, spatio-temporal data mining, trajectory analytics |
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. | |
DOI | 10.1109/ACCESS.2025.3525952 |
Refereed Designation | Refereed |