Maritime safety is a critical concern for the transport sector and remains a key challenge for the international shipping industry. Recognizing that maritime accidents pose significant risks to both safety and operational efficiency, this study explores the application of big data analysis techniques to understand the factors influencing maritime transport accidents (MTA). Specifically, using extensive datasets derived from vessel performance measurements, environmental conditions, and accident reports, it seeks to identify the key intrinsic and extrinsic factors contributing to maritime accidents. The research examines more than 90 thousand incidents for the period 2014–2022. Leveraging big data analytics and advanced statistical techniques, the findings reveal significant correlations between vessel size, speed, and specific environmental factors. Furthermore, the study highlights the potential of big data analytics in enhancing predictive modeling, real-time risk assessment, and decision-making processes for maritime traffic management. The integration of big data with intelligent transportation systems (ITSs) can optimize safety strategies, improve accident prevention mechanisms, and enhance the resilience of ocean-going transportation systems. By bridging the gap between big data applications and maritime safety research, this work contributes to the literature by emphasizing the importance of examining both intrinsic and extrinsic factors in predicting maritime accident risks. Additionally, it underscores the transformative role of big data in shaping safer and more efficient waterway transportation systems.

The main objective of this study is to highlight the internal risk factors associated with maritime transportation accidents and the important role of presenting them in the dataset at the time of the incident. Since the study period involves a pre- and post-pandemic timeline, we refer to COVID-19, although it is not part of our analysis. The issue at hand is the appropriate statistical analysis and investigation of the possible correlations between the cause of the incident and internal factors/indicators that may affect the safety of crews on sea routes. We developed a comprehensive study based on advanced econometric modeling, utilizing multifactorial models of robust regression, structural equation modeling (SEM), and Gaussian/mixed-Markov graphical models (GGMs, MGMs) and applying them to a newly compiled dataset covering the 2014–2022 period. Our results bring to the fore important factors that can determine the causes of various accidents and injuries suffered by workers, ranging from work location to work activity and even the rank of the seafarers on board. We do not consider the external factors associated with a maritime transportation accident, as the risk of an accident in this sector due to external factors (i.e., weather conditions, defaults, failures, etc.) is limited. Reducing the number of injuries to seafarers will result not only in better seafarer health but also a reduction in the operating costs of shipping companies due to the reduced insurance premiums they will have to pay. It will also lead to a reduction in the amounts disbursed by Protection and Indemnity (P&I) Clubs to compensate seafarers. In future research, we will use external factors to determine seafaring risks related to, for example, weather conditions, the quality of ships, technology, safety measures, regulations, and more.

Maritime safety is a critical concern for the transport sector and remains a key challenge for the international shipping industry. Recognizing that maritime accidents pose significant risks to both safety and operational efficiency; this study explores the application of Big Data analysis techniques to understand the factors influencing Maritime Transport Accidents (MTA). Specifically, using extensive datasets derived from vessel performance measurements, environmental conditions, and accident reports, it seeks to identify the key intrinsic and extrinsic factors contributing to maritime accidents. The research examines more than 90 thousand incidents for the period 2014–2022.

The main objective of this article is to determine the internal factors of maritime transportation accidents using a comprehensive approach through robust regression and Gaussian/mixed-Markov graphical models. Globally, this could be a strong incentive for the employees to negotiate higher compensation and for the insurance companies to impose higher premiums to cover the risk for these kinds of accidents. The article uses a dataset consisting of 166 real cases (human injuries) in the period 2014–2022 in different ships owned by a shipping company indexed in the New York Stock Exchange. The results of the study support the hypotheses as have been set in the article, connecting the internal factors with the injuries of any type. The practical implementation of the study is its ability to be used by policy makers in shipping to compensate employees depending on the risk of their work on board and at the same time to calculate the insurance premiums in a more accurate way. The originality of the research lies in the fact that this is a unique study in maritime transportation related to human accidents and not on ship or cargo casualties. The idea came from the results of another study conducted on a bibliometric analysis of the factors related to maritime transportation accidents. The findings of the current study can provide valuable insights to stakeholders and shipping planners in formulating effective policies for better wage packages and insurance premiums.