Review of techniques and challenges of human and organizational factors analysis in maritime transportation

• A detailed review and analysis of techniques for HOFs analysis in maritime transportation during 2000–2020. • Developed HOFs analysis methods are analysed and compared in accident investigation, risk analysis and emergency situations. • Study also reviewed potential challenges for HOFs analysis in maritime transportation. • Study provides deep insight into use of alternative techniques for HOFs analysis. This paper summarises the advanced techniques adopted for the analysis of human and organizational factors, which are the predominant factors in maritime accidents, and the various attempts that have been made to reduce human errors by identifying the existing challenges. Advanced techniques for human and organizational factor modelling, including human error identification in accident investigation, human error probability quantification in risk analysis, and human and organizational factor analysis for emergency situations, are comprehensively analysed and discussed. The most widely used modelling technique for human error identification is the Human Factors Analysis and Classification System (HFACS), and preconditions and unsafe acts exert the most important impacts on maritime accidents in previous studies. Moreover, Cognitive Reliability Error Analysis (CREAM) is the most widely used technique for human error probability quantification, and fuzzy, evidential reasoning and Bayesian networks are often incorporated for common performance condition (CPC) quantification and synthesis processes. In the future, other techniques should be introduced and developed for modelling HOFs for maritime transportation. Moreover, the challenges for human and organizational factors, including data collection, individual factors, and autonomous shipping, are identified for future studies. Consequently, this paper provides insight into human and organizational factors for maritime transportation, including quantification modelling, solutions to data collection and future research directions.