Analysing seafarer competencies in a dynamic human-machine system

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

Towards objective human performance measurement for maritime safety: A new psychophysiological data-driven machine learning method

Human errors significantly contribute to transport accidents. Human performance measurement (HPM) is crucial to ensure human reliability and reduce human errors. However, how to address and reduce the subjective bias introduced by assessors in HPM and seafarer certification remains a key research challenge. This paper aims to develop a new psychophysiological data-driven machine learning method to realize the effective HPM in the maritime sector. It conducts experiments using a functional Near-Infrared Spectroscopy (fNIRS) technology and compares the performance of two groups in a maritime case (i.e. experienced and inexperienced seafarers in terms of different qualifications by certificates), via an Artificial Neural Network (ANN) model. The results have generated insightful implications and new contributions, including (1) the introduction of an objective criterion for assessors to monitor, assess, and support seafarer training and certification for maritime authorities; (2) the quantification of human response under specific missions, which serves as an index for a shipping company to evaluate seafarer reliability; (3) a supportive tool to evaluate human performance in complex emerging systems (e.g. Maritime Autonomous Surface Ship (MASS)) design for ship manufactures and shipbuilders

Incorporation of seafarer psychological factors into maritime safety assessment

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

HUMAN FACTORS IN MARITIME TRANSPORTATION AND MENTAL WORKLOAD ANALYSES FOR SEAFARERS IN BRIDGE SIMULATION

Since the United States Coast Guard (USCG) reported in 1993 that human factors had essentially caused approximately 80% of maritime accidents and near misses, there has been an overwhelming understanding that human factors play a significant role in a considerable number of incidents or catastrophes by triggering chain events. The work has initially documented a literature review underlining human factors in maritime accidents, mental workload study and functional Near-Infrared Spectroscopy (fNIRS) technique to imply how it can be studied for human factors in maritime transportation. It investigates how different risk factors generate an impact on different types of human-related maritime transportation accidents using a data-driven approach, and how mental workload influences neurophysiological activation and decision- making of seafarers by conducting an experimental study in bridge simulation. The results of the developed models formalise the causal interdependencies between the risk factors with human factors perspectives and highlight the implications through scenario analyses. On the other hand, the findings of the fNIRS experimental study revealed the role of the prefrontal cortex and functional connectivity in watchkeeping and collision avoidance during maritime operations. It is concluded that the understanding of risk factors contributing to human errors will help reduce the risk level or eliminate the potential hazards of ships, and provide the clue for accident investigation and generate insights for accident prevention. Also, the experimental study supports fNIRS as a valuable neuroimaging technique in realistic situations. It examines the mental workload and functional connectivity of seafarers, which helps generate insights for human performance and seafarers’ training. Finally, the inclusion of a broader range of human factors and experimental methods shows promise by associating neurophysiological experiment in the maritime section