This study provides a bibliometric review of 279 studies on the applications of big data and artificial intelligence (AI) in the maritime industry, published in 214 academic outlets, authored by 842 scholars. We extracted bibliographical data from the Web of Science database and analysed it using the Bibliometrix tool in R software. Based on citation analysis metrics, we revealed the most influential articles, journals, authors and institutions. Using the bibliographic coupling methodology, we identified four underlying research clusters: (1) digital transformation in maritime industry, (2) applications of big data from AIS, (3) energy efficiency and (4) predictive analytics. We analysed these clusters in detail and extracted future research questions. Besides, we present research collaboration networks on the institution and author level.

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As a key measure for safety and environmental protection during offshore well operations, drill rigs are equipped with Emergency Quick Disconnect (EQD) systems. However, an EQD operation is in itself considered a risky operation with a major economic impact. For this reason, it is of great importance to aid the operators in their assessment of the situation at all times, and help them make the best decisions. However, despite the availability of such systems, accidents do happen. This demonstrates the vulnerability of our human decision-making capabilities in extremely stressful situations. One way of improving the overall human-system performance with respect to EQD is to increase the level and quality of the automation and decision support systems. Although there is plenty of evidence that automated systems have weaknesses, there is also evidence that advanced software systems outperform humans in complex decision-making. The major challenge is to make sure that EQD is performed when necessary, but there is also a need to decrease the number of false EQDs. This paper applies an existing framework for levels of automation in order to explore the critical decision process leading to an EQD. We provide an overview of the benefits and drawbacks of existing automation and decision support systems vs. manual human decision-making. Data are collected from interviews of offshore users, suppliers, and oil companies, as well as from formal operational procedures. Findings are discussed using an established framework for the level of automation. Our conclusion is that there is an appropriate level of automation in critical situations related to the loss of the position of the drill rig, and that there is the promising potential to increase the autonomy level in a mid-and long-term situation assessment.

Standards IEC 62288:2014 and MSC.191(79) require information on the displays of shipboard navigation systems to be logically grouped, but only provide limited specification for this ‘logical’ criterion. Meanwhile, complex interfaces and information overload remain as major design issues, being connected to several maritime accidents. To address this matter, a three-phase study was conducted to develop a pattern to organise essential information on Radar and Electronic Chart Display and Information System (ECDIS) displays and their equivalent modules on integrated navigation systems and integrated bridge systems. The first phase involved identifying the information most essential for safe navigation using cognitive task analyses, equipment performance standards and frequency of use. The second phase involved a card-sorting experiment with seafarers (n = 63) to develop an initial grouping pattern for the identified essential information. The third phase involved validating the initial grouping pattern with a new sample of seafarers (n = 35). The result is a pattern to group 48 types of information on shipboard navigation displays into 13 groups. The paper details the selected methods and the findings, and provides implications for future research.