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The version presented here may differ from the published version or from the version of the record. Please see the repository URL above for details on accessing the published version and note that access may require a subscription.

A Bayesian network–based risk analysis approach is proposed to analyse the risk factors influencing maritime transport accidents. Comparing with previous studies in the relevant literature, it reveals new features including (1) new primary data directly derived from maritime accident records by two major databanks Marine Accident Investigation Branch and Transportation Safety Board of Canada from 2012 to 2017, (2) rational classification of the factors with respect to each of the major types of maritime accidents for effective prevention, and (3) quantification of the extent to which different combinations of the factors influence each accident type. The network modelling the interdependency among the risk factors is constructed by using a naïve Bayesian network and validated by sensitivity analysis. The results reveal that the common risk factors among different types of accidents are ship operation, voyage segment, ship type, gross tonnage, hull type, and information. Scenario analysis is conducted to predict the occurrence likelihood of different types of accidents under various situations. The findings provide transport authorities and ship owners with useful insights for maritime accident prevention.

75-96% of maritime accidents are caused by human and organisational factors. Seafarers' emotion may degrade the effectivity of human behaviour when tasks in onboard environment are complex and demanding. This study was concerned with the relationship between seafarers' emotion and occurring events in navigation. The Electroencephalogram (EEG) and Self-Assessment Manikin (SAM) scale rating are used to investigate the occurrence and impact of seafarers' emotions on their performance using a bridge simulator. The study was conducted and described in two sections: emotion calibration and test recognition. In the first section, two types of emotions are induced by the sound clips of the International Affective Digitized Sounds (IADS), developed by the National Institute of Mental Health Center for the Study of Human Emotions. In the second section, emotion is recognised by the Support Vector Machine (SVM) classifier, as well as self-rated after the crew-qualified test in a bridge simulator. The results indicate that SVM can identify the emotions by EEG feature extraction, with an accuracy of 77.55%. The results concerning officers' emotion in a bridge simulator test reveal that seafarers' emotion in maritime operations, relating to events exposure, affects their behaviour and decision-making. In addition, negative emotion has a higher likelihood of contributing to human errors than positive emotion. Less negative emotion is the most dangerous emotion state during navigation, followed by extreme positive emotion.

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Introduction Watchkeeping is a significant activity during maritime operations, and failures of sustained attention and decision‐making can increase the likelihood of a collision. Methods A study was conducted in a ship bridge simulator where 40 participants (20 experienced/20 inexperienced) performed: (1) a 20‐min period of sustained attention to locate a target vessel and (2) a 10‐min period of decision‐making/action selection to perform an evasive maneuver. Half of the participants also performed an additional task of verbally reporting the position of their vessel. Activation of the prefrontal cortex (PFC) was captured via a 15‐channel functional near‐infrared spectroscopy (fNIRS) montage, and measures of functional connectivity were calculated frontal using graph‐theoretic measures. Results Neurovascular activation of right lateral area of the PFC decreased during sustained attention and increased during decision‐making. The graph‐theoretic analysis revealed that density declined during decision‐making in comparison with the previous period of sustained attention, while local clustering declined during sustained attention and increased when participants prepared their evasive maneuver. A regression analysis revealed an association between network measures and behavioral outcomes, with respect to spotting the target vessel and making an evasive maneuver. Conclusions The right lateral area of the PFC is sensitive to watchkeeping and decision‐making during operational performance. Graph‐theoretic measures allow us to quantify patterns of functional connectivity and were predictive of safety‐critical performance.