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.