Vessel Trajectory Data Mining: A Review
Alexandros Troupiotis-Kapeliaris, Christos Kastrisios, Dimitris Zissis
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.