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Despite the efforts of maritime authorities to enhance seafarer competencies through the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW), human error remains a leading cause of maritime accidents. To thoroughly investigate the impact of various human errors among seafarers on accidents, this paper aims to examine the relationships between seafarer competencies and maritime accidents using a data-driven approach from the perspective of bridge resource management (BRM). Through analysis of historical maritime accident reports, the dataset of seafarer competencies associated with maritime accidents is established. The least absolute shrinkage and selection operator (LASSO) method is employed to identify the critical seafarer competencies for accident prevention. Then, a data-driven Bayesian Network (BN) model, based on a Tree Augmented Naive Bayes (TAN) method, is constructed to reveal the relationship between seafarer competencies and accident types, which are validated by sensitivity analysis and case study. The results indicate that the key seafarer competencies for all maritime accidents are 'Maneuvers', 'Amend/maintain ship course', 'Decision making', 'Cognitive capacity', 'Information', 'Procedure operations', 'Situational awareness' and 'Communication'. Moreover, the study underscores the importance of leveraging lessons learned from past accidents to mitigate risks and ensure safe maritime operations. The findings contribute to a deeper understanding of the dynamics between seafarer competencies and accident types, unveiling the joint impact of different seafarer competencies on maritime accidents. This perspective offers valuable insights for maritime authorities in strengthening maritime safety regulations. • Develop a new data-driven BN model to investigate the impact of seafarer competencies on maritime accidents. • Identification of critical seafarer competencies in restricted waters using the LASSO algorithm. • Unveiling the joint impact of different seafarer competencies on maritime accidents. • Provision of rational strategies to reduce maritime accidents in restricted waters. [ABSTRACT FROM AUTHOR]

• Utilisation of historical records and machine learning to generate a human fatigue model. • Identification of significant factors from an objective accident/incident occurrence perspective. • Combination of LASSO and BN to propose a data-driven model to investigate human fatigue. • Providing solution to fatigue research with limited psychological data. In maritime transport, fatigue conditions can impair seafarer performance, pose a high risk of maritime incidents, and affect safety at sea. However, investigating human fatigue and its impact on maritime safety is challenging due to limited objective measures and little interaction with other risk influential factors (RIFs). This study aims to develop a novel model enabling accident data-driven fatigue investigation and RIF analysis using machine learning. It makes new methodological contributions, such as 1) the development of a human fatigue investigation model to identify significant RIFs leading to human fatigue based on historical accident and incident data; 2) the combination of least absolute shrinkage and selection operator (LASSO) and bayesian network (BN) to formulate a new machine learning model to rationalise the investigation of human fatigue in maritime accidents and incidents; 3) provision of insightful implications to guide the survey of fatigue's contribution to maritime accidents and incidents without the support of psychological data. The results show the importance of RIFs and their interdependencies for human fatigue in maritime accidents. It takes advantage of available knowledge and machine learning to open a new direction for fatigue management, which will benefit the maritime fatigue investigation and provide insights into other high-risk sectors suffering from human fatigue (e.g. nuclear and offshore). [ABSTRACT FROM AUTHOR]

Human factors have been deemed to affect a variety of unsafe acts and hazardous conditions, with no exceptions in the maritime sector. With increasing applications of automation techniques in shipping, seafarers' roles are changing, and their competencies require to be assessed and assured for safety at sea accordingly. The studies on seafarer competencies have therefore been tightly bound with a human-machine system which consists of the interaction of seafarers and ship operational systems and sub-systems. To evaluate the seafarer competencies that fit automation systems in shipping, this paper aims to develop a new dynamic human-machine model in shipping that can be used to analyse human factors in a closed-loop system. Based on Crew Resource Management and the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers, it reflects the input, process, and output phases of the human system and its interactions with machine sub-systems. A new tool to analyse seafarer competencies is proposed to rationalise human factor evaluation in the maritime closed-loop system and reflect the dynamic human-machine cooperation process. Two case studies have been conducted to illustrate the feasibility of the new model and in the meantime to investigate seafarer competencies in the dynamic human-machine system. It produces a new human factor analysis tool to investigate maritime accidents. The results and policy implications help explore the adjustment of maritime training to support ship automation and provide guidance on risk management for traditional and autonomous ships. [ABSTRACT FROM AUTHOR]

Psychological factors have been a critical cause of human errors in sectors such as health and aviation. However, there is little relevant research in the maritime industry, even though human errors significantly contribute to shipping accidents. It becomes even more worrisome given that seafarers are changing their roles onboard ships due to the growth of automation techniques in the sector. This research pioneers a conceptual framework for assessing seafarer psychological factors using neurophysiological analysis. It quantitatively enables the psychological factor assessment and hence can be used to test, verify, and train seafarers' behaviours for ship safety at sea and along coasts. A case study on ship collision avoidance in coastal waters demonstrates its feasibility using ship bridge simulation. An experimental framework incorporating neurophysiological data can be utilised to effectively evaluate the contribution of psychological factors to human behaviours and operational risks. Hence, it opens a new paradigm for human reliability analysis in a maritime setting. This framework provides insights for reforming and evaluating operators' behaviours on traditionally crewed ships and in remote-controlled centres within the context of autonomous ships. As a result, it will significantly improve maritime safety and prevention of catastrophic accidents that endanger oceans and coasts. [ABSTRACT FROM AUTHOR]

Human factors contribute to majority of maritime accidents. This study proposes an advanced methodology for maritime accident prevention strategy formulation from a human factor perspective. It is conducted by incorporating Bayesian network (BN) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in a multi-criteria decision-making system. In order to develop rational accident prevention strategies, this work integrates Multiple Correspondence Analysis (MCA), Hierarchical Clustering (HC) and Classification Tree (CT) to generate strategies and describes accident types as criteria for a new multi-criteria risk-based decision-making system. Specifically, MCA is performed to detect patterns of contributory factors explaining maritime accident types. It is complemented by HC and a CT, aiming at creating different classes of vessels. Next, a Bayesian-based TOPSIS model is built to illustrate the features of multiple criteria and the relations among alternatives (i.e. strategies), so as to select the best-fit strategies for accident prevention. The results show that the information, clear order, and safety culture are the three most effective recommendations for maritime accident prevention considering human errors, which presents new insights for accident prevention practice for maritime authorities. • Accident prevention strategies are generated from a human factor perspective. • Methodology for accident prevention strategy formulation using BN and TOPSIS. • The model illustrates features of multiple criteria and relations among strategies. • The best-fit strategies are proposed for maritime accident prevention. [ABSTRACT FROM AUTHOR]