Risky maritime encounter patterns via clustering

Name: Risky maritime encounter patterns via clustering
Author: Oruç, Muhammet Furkan, Altan, Yiğit Can

İsim	Risky maritime encounter patterns via clustering
Yazar	Oruç, Muhammet Furkan, Altan, Yiğit Can
Basım Tarihi:	2023-04-28
Basım Yeri	- MDPI
Konu	Anomaly detection, Atomatic identification system (AIS), Clustering analysis, Maritime safety, Multi-dimensional K-means clustering, Strait of Istanbul
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2077-1312
Kayıt Numarası	a1d17bac-e066-4d81-9ba8-29d3be667b67
Lokasyon	Civil Engineering
Tarih	2023-04-28
Örnek Metin	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

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Risky maritime encounter patterns via clustering

Yazar Oruç, Muhammet Furkan, Altan, Yiğit Can

Basım Tarihi 2023-04-28

Basım Yeri - MDPI

Konu Anomaly detection, Atomatic identification system (AIS), Clustering analysis, Maritime safety, Multi-dimensional K-means clustering, Strait of Istanbul

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2077-1312

Kayıt Numarası a1d17bac-e066-4d81-9ba8-29d3be667b67

Lokasyon Civil Engineering

Tarih 2023-04-28

Örnek Metin 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