Shipping Accidents Dataset: Data-Driven Directions for Assessing Accident’s Impact and Improving Safety Onboard

Panagiotis Panagiotidis, Kyriakos Giannakis, Nikolaos Angelopoulos and Angelos Liapis
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Panagiotis Panagiotidis: Konnekt-able Technologies Ltd., X91 W0XW Waterford, Ireland
Kyriakos Giannakis: Konnekt-able Technologies Ltd., X91 W0XW Waterford, Ireland
Nikolaos Angelopoulos: Konnekt-able Technologies Ltd., X91 W0XW Waterford, Ireland
Angelos Liapis: Konnekt-able Technologies Ltd., X91 W0XW Waterford, Ireland

Data, 2021, vol. 6, issue 12, 1-19

Abstract: Recent tragic marine incidents indicate that more efficient safety procedures and emergency management systems are needed. During the 2014–2019 period, 320 accidents cost 496 lives, and 5424 accidents caused 6210 injuries. Ideally, we need historical data from real accident cases of ships to develop data-driven solutions. According to the literature, the most critical factor to the post-incident management phase is human error. However, no structured datasets record the crew’s actions during an incident and the human factors that contributed to its occurrence. To overcome the limitations mentioned above, we decided to utilise the unstructured information from accident reports conducted by governmental organisations to create a new, well-structured dataset of maritime accidents and provide intuitions for its usage. Our dataset contains all the information that the majority of the marine datasets include, such as the place, the date, and the conditions during the post-incident phase, e.g., weather data. Additionally, the proposed dataset contains attributes related to each incident’s environmental/financial impact, as well as a concise description of the post-incident events, highlighting the crew’s actions and the human factors that contributed to the incident. We utilise this dataset to predict the incident’s impact and provide data-driven directions regarding the improvement of the post-incident safety procedures for specific types of ships.

Keywords: marine accidents; dataset; accident reports; human error; incident’s impact; contingency plans; machine learning (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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