Risky Maritime Encounter Patterns via Clustering
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 val...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/5/950 |
| _version_ | 1797599629522501632 |
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| author | M. Furkan Oruc Yigit C. Altan |
| author_facet | M. Furkan Oruc Yigit C. Altan |
| author_sort | M. Furkan Oruc |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:36:58Z |
| format | Article |
| id | doaj.art-ead944e1bdff49ea9f8fec7932c5b724 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T03:36:58Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-ead944e1bdff49ea9f8fec7932c5b7242023-11-18T01:58:58ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-04-0111595010.3390/jmse11050950Risky Maritime Encounter Patterns via ClusteringM. Furkan Oruc0Yigit C. Altan1Department of Civil Engineering, Ozyegin University, Istanbul 34794, TurkeyDepartment of Civil Engineering, Ozyegin University, Istanbul 34794, TurkeyThe 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.https://www.mdpi.com/2077-1312/11/5/950maritime safetyatomatic identification system (AIS)clustering analysisanomaly detectionstrait of Istanbulmulti-dimensional K-means clustering |
| spellingShingle | M. Furkan Oruc Yigit C. Altan Risky Maritime Encounter Patterns via Clustering Journal of Marine Science and Engineering maritime safety atomatic identification system (AIS) clustering analysis anomaly detection strait of Istanbul multi-dimensional K-means clustering |
| title | Risky Maritime Encounter Patterns via Clustering |
| title_full | Risky Maritime Encounter Patterns via Clustering |
| title_fullStr | Risky Maritime Encounter Patterns via Clustering |
| title_full_unstemmed | Risky Maritime Encounter Patterns via Clustering |
| title_short | Risky Maritime Encounter Patterns via Clustering |
| title_sort | risky maritime encounter patterns via clustering |
| topic | maritime safety atomatic identification system (AIS) clustering analysis anomaly detection strait of Istanbul multi-dimensional K-means clustering |
| url | https://www.mdpi.com/2077-1312/11/5/950 |
| work_keys_str_mv | AT mfurkanoruc riskymaritimeencounterpatternsviaclustering AT yigitcaltan riskymaritimeencounterpatternsviaclustering |