Investigating maritime accidents that involve dangerous goods using hierarchical clustering

Abstract

Although the important progress in terms of safety and technological advances, maritime accidents remain a critical issue in merchant shipping. A high number of accidents continue to occur every year, with negative consequences both in economic and environmental terms (with often disastrous and lasting environmental impacts for marine ecosystems) and in the loss of human life. Understanding the maritime accidents phenomenon is expedient to giving shipping practitioners a focus for tailored interventions aimed at enhancing maritime safety. Using hierarchical clustering methods, this paper analyses historical data relating to maritime accidents to highlight the potential causal relationships that can describe homogeneous groups of accidents. The study explores a database consisting of 1,079 marine accidents that occurred worldwide in the 2009-2019 decade. Accident data is taken from the International Maritime Organization (IMO) database. After illustrating a description of the data set, a non-supervised hierarchical clustering analysis is applied to identify accident patterns, thus helping to better describe the phenomenon and identify potential causal relations that repeat in various accidents. A significant distinction emerges between the accidents that occur for technical reasons and those where human factors (stress, fatigue, situation awareness, decision-making, communication, etc.) play a prevalent role. Afterwards, the clustering analysis is applied to a sub-set of accidents (153 accidents) involving ships carrying dangerous goods (gases, oils, explosives, etc.). The results of the analysis point out the role of the human factor as the prevalent (or contributing) cause of the marine accidents related to work operations. Conversely, fires and explosions, which are by far the most frequent accidents involving ships carrying dangerous goods, are mainly caused by technical problems.