.png){kind=icon}
.png){kind=icon}
Return
Vessel Trajectory Data Mining: A Review
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2025.3525952.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Maritime monitoring
Data mining
spatio-temporal data mining
trajectory analytics
pattern mining
descriptive analytics
predictive analytics
Maritime monitoring
data mining
spatio-temporal data mining
trajectory analytics
pattern mining
descriptive analytics
predictive analytics
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
