Author
Oruç, Muhammet Furkan, Altan, Yiğit Can
Publication Date
2023-04-28
Publication Place
-
MDPI
Subject
Anomaly detection, Atomatic identification system (AIS), Clustering analysis, Maritime safety, Multi-dimensional K-means clustering, Strait of Istanbul
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
2077-1312
Record ID
a1d17bac-e066-4d81-9ba8-29d3be667b67
Library Location
Civil Engineering
Date
2023-04-28
Sample Text
The volume of maritime traffic is increasing with the growing global trade demand. The effect of volume growth is especially observed in narrow and congested waterways as an increase in the ship-ship encounters, which can have severe consequences such as collision. This study aims to analyze and validate the patterns of risky encounters and provide a framework for the visualization of model variables to explore patterns. Ship–ship interaction database is developed from the AIS messages, and interactions are analyzed via unsupervised learning algorithms to determine risky encounters using ship domain violation. K-means clustering-based novel methodology is developed to explore patterns among encounters. The methodology is applied to a long-term dataset from the Strait of Istanbul. Findings of the study support that ship length and ship speed can be used as indicators to understand the patterns in risky encounters. Furthermore, results show that site-specific risk thresholds for ship–ship encounters can be determined with additional expert judgment. The mid-clusters indicate that the ship domain violation is a grey zone, which should be treated carefully rather than a bold line. The developed approach can be integrated to narrow and congested waterways as an additional safety measure for maritime authorities to use as a decision support tool.
DOI
10.3390/jmse11050950
Cilt
11
