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Human and organizational factors are the contributing factors for collision accidents from the historical data. To discover the key influencing factor, a human factor analysis and classification system based Bayesian Network model is proposed in this paper. The kernel of this proposed model is first to derive the unsafe acts from the perspective of perception, decision-making, and execution failures using the collision avoidance scheme, to classify the human factors into five categories using the modified human-factor analysis and classification system, and to transform the influencing factors of HOFs in the modified HFACS into the graphical structure of the Bayesian network. The results are verified from historical collision accidents data in the Yangtze River, and sensitivity analysis is carried out to validate the axioms of the Bayesian network. From further analysis, the causation factor and global causation chain of ship collision accidents can be derived. Consequently, the results are beneficial for the prevention and control of ship collision accidents in the Yangtze River by reducing human and organization factors. [ABSTRACT FROM AUTHOR]

Navigational accidents (collisions and groundings) account for approximately 85% of mari-time accidents, and consequence estimation for such accidents is essential for both emergency resource allocation when such accidents occur and for risk management in the framework of a formal safety assessment. As the traditional Bayesian network requires expert judgement to develop the graphical structure, this paper proposes a mutual information-based Bayesian network method to reduce the requirement for expert judgements. The central premise of the proposed Bayesian network method involves calculating mutual information to obtain the quantitative element among multiple influencing factors. Seven-hundred and ninety-seven historical navigational accident records from 2006 to 2013 were used to validate the methodology. It is anticipated the model will provide a practical and reasonable method for consequence estimation of navigational accidents. [ABSTRACT FROM AUTHOR]

This paper proposes a power-driven ship collision alert model considering geometric encounter situations in sight situations by collecting the natural environment, ship static and dynamitic information. The kernel of the proposed method is to introduce a ship geometric encounter model to determine the ship encounter situation and to identify the influencing factors, including ship position, ship speed and ship heading, by considering ship-ship collision situations. These factors, together with ship static parameters and natural environment, are treated as input variables to derive the collision risk through a fuzzy inference system. The merit of developed model is that the relative bearing, collision distances and relative course are intuitively described by graphical user interface, which can assist the crew well understand the decision of machine and can be applied to human-machine collaboration for intelligent ship (e.g. remotely control ship). Moreover, from further analysis, the nearest point and the actions taken at the nearest point provide a useful alert for collision risk. From these analyses, the proposed model can be used to improve ship handling and reduce the probability of collision during ship encounters. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates port traffic risk issues by discussing historic accidents in Hong Kong port. A total of 2012 marine accidents with 94 deaths were reported in Hong Kong waters in year 2001–2005, while 660,427 oceanic ships visited the port. A negative binomial regression model is used to analyze the record of dataset. It is found that port traffic risks are of certain pattern and collision accidents are the most popular incidents when port traffic is heavy. Passenger-type vessels have higher potential for injuries during accidents. The findings have identified statistically significant factors for improvements in managing port traffic risks. [Copyright &y& Elsevier]

Collision accident accounts for the largest proportion among all types of maritime accidents, emergency decision-making is essential to reduce the consequence of such accidents. This paper proposes a novel Bayesian Network based emergency decision-making model for consequence reduction of individual ship-ship collision in the Yangtze River. The kernel of this method is to propose a three-layer decision-making framework, to develop the graphical structure for describing the accident process and to establish the conditional probability tables for the quantitative relationships. The merits of the proposed method include the intuitive representation of accident development, easy to implement, ability to deal with incomplete information and updated information. This proposed method is applied to a typical collision accident in the Yangtze River. Consequently, this paper provides a practical and novel decision-making method for collision accidents. • Bayesian Network based emergency decision-making model for consequence reduction of individual ship-ship collision. • Intuitive representation, easy to implement, ability to deal with incomplete and updated information. • Practical and novel decision-making method for collision accidents. [ABSTRACT FROM AUTHOR]