Identifying and assessing the likelihood and consequences of maritime accidents has been a key focus of research within the maritime industry. However, conventional methods utilised for maritime risk assessment have been dominated by a few methodologies each of which have recognised weaknesses. Given the growing attention that supervised machine learning and big data applications for safety assessments have been receiving in other disciplines, a comprehensive review of the academic literature on this topic in the maritime domain has been conducted. The review encapsulates the prediction of accident occurrence, accident severity, ship detentions and ship collision risk. In particular, the purpose, methods, datasets and features of such studies are compared to better understand how such an approach can be applied in practice and its relative merits. Several key challenges within these themes are also identified, such as the availability and representativeness of the datasets and methodological challenges associated with transparency, model development and results evaluation. Whilst focused within the maritime domain, many of these findings are equally relevant to other transportation topics. This work, therefore, highlights both novel applications for applying these techniques to maritime safety and key challenges that warrant further research in order to strengthen this methodological approach.

Autonomous vehicles (AV) offer promising benefits to society in terms of safety, environmental impact and increased mobility. However, acute challenges persist with any novel technology, inlcuding the perceived risks and trust underlying public acceptance. While research examining the current state of AV public perceptions and future challenges related to both societal and individual barriers to trust and risk perceptions is emerging, it is highly fragmented across disciplines. To address this research gap, by using the Web of Science database, our study undertakes a bibliometric and performance analysis to identify the conceptual and intellectual structures of trust and risk narratives within the AV research field by investigating engineering, social sciences, marketing, and business and infrastructure domains to offer an interdisciplinary approach. Our analysis provides an overview of the key research area across the search categories of ‘trust’ and ‘risk’. Our results show three main clusters with regard to trust and risk, namely, behavioural aspects of AV interaction; uptake and acceptance; and modelling human–automation interaction. The synthesis of the literature allows a better understanding of the public perception of AV and its historical conception and development. It further offers a robust model of public perception in AV, outlining the key themes found in the literature and, in turn, offers critical directions for future research.

Antarctic subglacial lakes are thought to be extreme habitats for microbial life and may contain important records of ice sheet history and climate change within their lake floor sediments. To find whether or not this is true, and to answer the science questions that would follow, direct measurement and sampling of these environments are required. Ever since the water depth of Vostok Subglacial Lake was shown to be >500 m, attention has been given to how these unique, ancient, and pristine environments may be entered without contamination and adverse disturbance. Several organizations have offered guidelines on the desirable cleanliness and sterility requirements for direct sampling experiments, including the U.S. National Academy of Sciences and the Scientific Committee on Antarctic Research. Here we summarize the scientific protocols and methods being developed for the exploration of Ellsworth Subglacial Lake in West Antarctica, planned for 2012–2013, which we offer as a guide to future subglacial environment research missions. The proposed exploration involves accessing the lake using a hot‐water drill and deploying a sampling probe and sediment corer to allow sample collection. We focus here on how this can be undertaken with minimal environmental impact while maximizing scientific return without compromising the environment for future experiments.

It has been 20 years since the concept of the Autonomous Ocean Sampling Network (AOSN) was first introduced. This vision has been brought closer to reality with the introduction of underwater gliders. While in terms of functionality the underwater glider has shown to be capable of meeting the AOSN vision, in terms of reliability there is no communitywide hard evidence on whether persistent presence is currently being achieved. This paper studies the reliability of underwater gliders in order to assess the feasibility of using these platforms for future AOSN. The data used are taken from nonunderwater glider developers, which consisted of 205 glider deployments by 12 European laboratories between 2008 and 2012. Risk profiles were calculated for two makes of deep underwater gliders; there is no statistically significant difference between them. Regardless of the make, the probability of a deep underwater glider surviving a 90-day mission without a premature mission end is approximately 0.5. The probability of a shallow underwater glider surviving a 30-day mission without a premature mission end is 0.59. This implies that to date factors other than the energy available are preventing underwater gliders from achieving their maximum capability. This reliability information was used to quantify the likelihood of two reported underwater glider surveys meeting the observation needs for a period of 6 months and to quantify the level of redundancy needed in order to increase the likelihood of meeting the observation needs.