The current industrial environment relies heavily on maritime transportation. Despite the continuous technological advances for the development of innovative safety software and hardware systems, there is a consistent gap in the scientific literature regarding the objective evaluation of the performance of maritime operators. The human factor is profoundly affected by changes in human performance or psychological state. The difficulty lies in the fact that the technology, tools, and protocols for investigating human performance are not fully mature or suitable for experimental investigation. The present research aims to integrate these two concepts by (i) objectively characterizing the psychological state of mariners, i.e., mental workload, stress, and attention, through their electroencephalographic (EEG) signal analysis, and (ii) validating an innovative safety framework countermeasure, defined as Human Risk-Informed Design (HURID), through the aforementioned neurophysiological approach. The proposed study involved 26 mariners within a high-fidelity bridge simulator while encountering collision risk in congested waters with and without the HURID. Subjective, behavioral, and neurophysiological data, i.e., EEG, were collected throughout the experimental activities. The results showed that the participants experienced a statistically significant higher mental workload and stress while performing the maritime activities without the HURID, while their attention level was statistically lower compared to the condition in which they performed the experiments with the HURID (all p < 0.05). Therefore, the presented study confirmed the effectiveness of the HURID during maritime operations in critical scenarios and led the way to extend the neurophysiological evaluation of the HFs of maritime operators during the performance of critical and/or standard shipboard tasks.

The shipping industry constantly strives to achieve efficient use of energy during sea voyages. Previous research that can take advantages of both ethnographic studies and big data analytics to understand factors contributing to fuel consumption and seek solutions to support decision making is rather scarce. This paper first employed ethnographic research regarding the use of a commercially available fuel-monitoring system. This was to contextualize the real challenges on ships and informed the need of taking a big data approach to achieve energy efficiency (EE). Then this study constructed two machine-learning models based on the recorded voyage data of five different ferries over a one-year period. The evaluation showed that the models generalize well on different training data sets and model outputs indicated a potential for better performance than the existing commercial EE system. How this predictive-analytical approach could potentially impact the design of decision support navigational systems and management practices was also discussed. It is hoped that this interdisciplinary research could provide some enlightenment for a richer methodological framework in future maritime energy research.

This study done at the Vessel Traffic Services (VTS) explored how the VTS operators (VTSOs) communicated with ships and other actors in the maritime sociotechnical system and how decisions were made with regard to assisting traffic in maintaining safe passage in port areas, where most vessel movements are seen and accidents occur. The fieldwork was done during four independent visits to a VTS centre under the Swedish Maritime Authority, with a total sample of six VTSOs and one VTS instructor. The qualitative data were sorted and coded using a grounded theory approach. The data pointed at non-technical information processing and communication factors that play a role in decision-making and ultimately in safety. During protocol operations at the VTS, these factors influenced how VTSOs judged the skills of the vessels’ bridge teams, and how they approached them. This is a time where much effort is being put into upgrading technological systems, and these will have the power to change the ways in which the maritime network obtains and processes information, as well as how they can communicate with each other. The further development of technological systems, work protocols and training programmes can benefit from taking the soft aspects of communication and the needs of the operators and their tasks into account for the enhancement of safety.

The Sea Traffic Management (STM) Validation project is a European based initiative which focuses on connecting and updating the maritime world in real time, with efficient information exchange. The purpose of this paper is to evaluate two functions developed during the project: a ship to ship route exchange (S2SREX) function and rendezvous (RDV) information layer, collectively referred to as S2SREX/RDV. S2SREX displays the route segment consisting of the next seven waypoints of the monitored route of a collaborating ship and the RDV layer that predicts a meeting point. S2SREX/RDV provides supplementary information to data acquired by existing navigation systems and is intended to improve situational awareness and safety through a more comprehensive understanding of the surrounding traffic. Chalmers University of Technology and Solent University completed an experiment using twenty-four experienced navigators in bridge simulators. Six traffic scenarios were developed by subject matter experts and tested with and without S2SREX/RDV functionalities. Qualitative data were collected using post-test questionnaires and group debriefs to evaluate the participants’ perceptions of S2SREX/RDV in the various traffic scenarios, and quantitative data were collected to assess the ship distances and behavior in relation to the International Regulations for Preventing Collisions at Sea (COLREGs). The results revealed that participants generally trusted the S2SREX/RDV information, and most used S2SREX/RDV for decision support. The quantitative assessment revealed that the COLREGs were breached more often when S2SREX/RDV was used. Experimental findings are discussed in relation to safety, trust, reliance, situational awareness, and human-automation interaction constructs.

The objective of this study was to determine the effects of muscle contraction intensity, neuromuscular fatigue, and noise on vigilance performance. Dependent variables included simple (reaction time and movement time) and complex (video game: Tetris) vigilance tasks (SVT and CVT respectively) and maximum voluntary contraction (MVC) force and activation. Vigilance tasks and MVC were randomly allocated to 5 minute blocks during a pre-test. Following the pre-test, the tests were again randomly allocated within three, 15 minute testing sessions over 65 minutes, while 1) being exposed to high (95 dB (A)) or low (53 dB (A)) levels of noise, and 2) performing muscle contractions at 20% and 5% of MVC, or no contractions. Ninety-five (95) dB (A) noise increased (p ≤ 0.01) SVT (reaction time and movement time combined) by 11.2% and decreased (p ≤ 0.01) CVT by 20%. Both 20% and 5% MVC impaired SVT and CVT to a similar extent, while no changes were seen with no contractions. Furthermore, neuromuscular fatigue had no apparent effect on vigilance task performance. These findings suggest that the distraction of noise and divided attention between muscle contraction and a vigilance task decreases performance.