Fire and explosion accidents occur frequently in tankers because they transport large quantities of dangerous cargo. To prevent fire and explosion accidents, it is necessary to analyze factors that cause accidents and their effects. In this study, factors that cause fire and explosion accidents were classified using the 4M disaster analysis method, and each factor’s effect on the accident was analyzed using fault tree analysis (FTA). First, the unsafe tank atmosphere environment was identified as a primary cause of fire and explosion accidents in tankers, and the underlying causes of these accidents were investigated. The probability of underlying causes leading to primary causes was derived using an expert survey. The results showed that management and media factors had a greater impact on the unsafe tank atmosphere environment than human factors. To prevent fire and explosion accidents, it is necessary to ensure sufficient working and resting times for seafarers and compliance with procedures and work guidelines. A generalization of the results of present and future studies will enable the identification of the cause and preventive measures for fire and explosion accidents in tankers. Furthermore, this will reduce accidents in tankers and contribute to future safety management measures of ships and companies.

Maritime education and training (MET) for seafarers who operate ships has struggled to flexibly adapt to technological and environmental changes. In particular, as social demand for online MET arose due to COVID-19, the need for sustainable MET beyond traditional teaching methods grew exponentially. In order to identify the most optimal MET methods among face-to-face and online methods, this study reviewed the concepts and applications of existing MET methods, grouped them using a fuzzy analytic hierarchy process, and supplemented this structure through a designed survey. The results showed that the online methods had the greatest weight, and the “XR (extended reality) within the metaverse” teaching method had the highest priority. This study identified which MET methods should be prepared for the post-COVID era through quantitative analysis. We confirmed the need for attention to XR within the metaverse as a field of online methods in the future. Furthermore, our findings reveal that online education platforms via metaverse-based “expansion” and “connection” are needed, and pave the way for future research to expand empirical studies on MET satisfaction regarding existing International Maritime Organization model courses.

As high vessel traffic in fairways is likely to cause frequent marine accidents, understanding vessel traffic flow characteristics is necessary to prevent marine accidents in fairways. Therefore, this study conducted semi-continuous spatial statistical analysis tests (the normal distribution test, kurtosis test and skewness test) to understand vessel traffic flow characteristics. First, a vessel traffic survey was conducted in a designated area (Busan North Port) for seven days. The data were collected using an automatic identification system and subsequently converted using semi-continuous processing methods. Thereafter, the converted data were used to conduct three methods of spatial statistical analysis. The analysis results revealed the vessel traffic distribution and its characteristics, such as the degree of use and lateral positioning on the fairway based on the size of the vessel. In addition, the generalization of the results of this study along with that of further studies will aid in deriving the traffic characteristics of vessels on the fairway. Moreover, these characteristics will reduce maritime accidents on the fairway, in addition to establishing the foundation for research on autonomous ships.

Ship collisions are a major maritime accident; various systems have been proposed to prevent them. Through investigating and analyzing the causes of maritime accidents, it has been established that ship collisions can either caused by delaying actions or not taking the sufficient actions to avoid them. Recognizing the limitations in providing quantitative numerical values for avoiding ship collisions, this study aimed to use Bayesian regularized artificial neural networks (BRANNs) to suggest the proper time and sufficient actions required for ship collision avoidance consistent with the Convention on the International Regulations for Preventing Collisions at Sea. We prepared the data by calculating the proper times and sufficient actions based on precedent research and used them to train, validate, and assess the BRANNs. Subsequently, an artificial neural network controller was designed and proposed. The data of the proposed neural network controller were verified via simulation, validating the controller. This study is limited in cases such as overtaking a ship in front. However, it is expected that this controller can be improved by establishing the criteria for an appropriate overtaking distance after further examining the closest point of approach (CPA) and time to the CPA (TCPA) for overtaking a ship in front and using the method presented herein.