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http://dx.doi.org/10.36498/kbigdt.2022.7.1.89

A Study on the Applicability of Safety Performance Indicators using the Density-Based Ship Domain  

Yeong-Jae Han (부산대학교 산업공학과 산업데이터공학융합전공)
Sunghyun Sim (동의대학교 산업경영빅데이터공학과)
Hyerim Bae (부산대학교 산업공학과 산업데이터공학융합전공)
Publication Information
The Journal of Bigdata / v.7, no.1, 2022 , pp. 89-97 More about this Journal
Abstract
Various efforts are needed to prevent accidents because ship collisions can cause various negative situations such as economic losses and casualties. Therefore, research to prevent accidents is being actively conducted, and in this study, new leading indicators for preventing ship collision accidents is proposed. In previous studies, the risk of collision was expressed in consideration of the distance between ships in a specific sea area, but there is a disadvantage that a new model needs to be developed to apply this to other sea areas. In this study, the density-based ship domain DESD (Density-based Empirical Ship Domain) including the environment and operating characteristics of the sea area was defined using AIS (Automatic Identification System) data, which is ship operation information. Deep clustering is applied to two-dimensional DESDs created for each sea area to cluster the seas with similar operating environments. Through the analysis of the relationship between clustered sea areas and ship collision accidents, it was statistically tested that the occurrence of accidents varies by characteristic of each sea area, and it was proved that DESD can be used as a leading indicator of accidents.
Keywords
Empirical ship domain; AIS; Deep learning; Leading Indicator of an accident;
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Times Cited By KSCI : 1  (Citation Analysis)
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