• Journal of Navigation and Port Research
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• v.42 no.3
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• pp.159-166
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• 2018

The identification of behavioral errors by seafarers that have caused marine accidents may provide important clues for the reduction or prevention of marine accidents. The purpose of this study is to identify the behavioral errors of seafarers by the type of marine accident using the theory of Skill-, Rule-, and Knowledge-Based Behavior (SRKBB). In order to identify behavioral errors, we collected the information related to 1,744 cases of maritime accidents over a 9 year period (2008 ~ 2016). The behavior errors of the seafarers who caused the marine accidents were classified as SBBE (Skill-Based Behavioral Error), RBBE (Rule-Based Behavioral Error), and KBBE (Knowledge-Based Behavioral Error). After analyzing the frequency of behavioral errors according to the type of marine accident, results showed SBBE had the highest frequency of errors, followed by RBBE. Additionally, the frequency of occurrence of accidents such as stranding, overturning, and sinking was high in KBBE. This study showed it is possible to identify behavioral errors of seafarers according to the type of marine accidents.

• Journal of Navigation and Port Research
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• v.43 no.3
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• pp.160-165
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• 2019

The conduct of seafarer is major cause of marine accidents. This study models the behavior of the seafarer based on the Hidden Markov Model (HMM). Additionally, through the path analysis of the behavior estimated by the model, the kind of situations, procedures and errors that may have caused the marine accidents were interpreted. To successfully implement the model, the seafarer behaviors were observed by means of the summarized verdict reports issued by the Korean Maritime Safety Tribunal, and the observed results converted into behavior data suitable for HMM learning through the behavior classification framework based on the SRKBB (Skill-, Rule-, and Knowledge-Based Behavior). As a result of modeling the seafarer behaviors by the type of vessels, it was established that there was a difference between the models, and the possibility of identifying the preferred path of the seafarer behaviors. Through these results, it is expected that the model implementation technique proposed in this study can be applied to the prediction of the behavior of the seafarer as well as contribute to the prioritization of the behavior correction among seafarers, which is necessary for the prevention of marine accidents.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.11a
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• pp.19-21
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• 2018

선원의 행동오류는 해양사고를 야기하는 하나의 직접적인 원인이기 때문에 이를 이해하는 것은 해양사고 예방에 근본이 된다. 선원의 행동오류를 이해하기 위해서는 행동오류를 추정하고 예측할 수 있어야 한다. 본 연구에서는 은닉 마르코브 모델(Hidden Markov Model, HMM)을 이용하여 선원들의 행동오류를 추정하고 예측하였다. 아울러 5가지 선박의 종류 각각에 나타나는 선원들의 행동오류를 서로 비교 분석하였다. 모델에 사용한 데이터는 해양안전심판원의 해양사고 보고서에 기록된 내용을 SRKBB(Skill-, Rule- and Knowledge-Based Behavior) 모델을 기반으로 분류하고 관측 수열을 생성하며 라벨링 작업을 통해서 구축하였다. 구축한 데이터를 적용하여 HMM을 보정하고 파라미터를 획득하여 선원들의 행동오류에 관한 모델을 구축하였다. 실험 결과, 선박 종류별로 선원들의 행동오류의 패턴은 서로 다르고, 이를 통해서 선박종류별 해기사들의 행동오류의 추정과 예측이 가능함을 일차적으로 확인할 수 있었다. 추후 본 연구를 지속 전개하여 해양사고 예방을 위한 인적오류의 저감에 기여할 수 있는 방안을 모색할 에정이다.