For a human reliability assessment in the maritime domain, the main question is how we correctly understand the human factors in the maritime situation in a practical manner. This paper introduces a new approach based on Cognitive Reliability and Error Analysis Method (CREAM). The key to the method is to provide a framework for evaluating specific scenarios associated with maritime human errors and for conducting an assessment of the context, in which human actions take place. The output of the context assessment is, then, to be applied for the procedure assessment as model inputs for reflection of the context effect. The proposed approach can be divided into two parts: processing context assessment and modelling human error quantification. Fuzzy multiple attributive group decision-making method, Bayesian networks and evidential reasoning are employed for enhancing the reliability of human error quantification. Fuzzy conclusion of the context assessment is utilised by the model input in CREAM basic method and weighting factors in CREAM extended method respectively for considering human failure probability which varies depending on external conditions. This paper is expected to contribute to the improvement of safety by identifying frequently occurred human errors during the maritime operating for minimising of human failures.

No abstract

No abstract

No abstract

Human factors (HF) in aviation and maritime safety occurrences are not always systematically analysed and reported in a way that makes the extraction of trends and comparisons possible in support of effective safety management and feedback for design. As a way forward, a taxonomy and data repository were designed for the systematic collection and assessment of human factors in aviation and maritime incidents and accidents, called SHIELD (Safety Human Incident and Error Learning Database). The HF taxonomy uses four layers: The top layer addresses the sharp end where acts of human operators contribute to a safety occurrence; the next layer concerns preconditions that affect human performance; the third layer describes decisions or policies of operations leaders that affect the practices or conditions of operations; and the bottom layer concerns influences from decisions, policies or methods adopted at an organisational level. The paper presents the full details, guidance and examples for the effective use of the HF taxonomy. The taxonomy has been effectively used by maritime and aviation stakeholders, as follows from questionnaire evaluation scores and feedback. It was found to offer an intuitive and well-documented framework to classify HF in safety occurrences.