Operators in air traffic control facing time- and safety-critical situations call for efficient, reliable and robust real-time processing and interpretation of complex data. Automation support tools aid controllers in these processes to prevent separation losses between aircraft. Issues of current support tools include limited ‘what-if’ and ‘what-else’ probe functionalities in relation to vertical solutions. This work presents the design and evaluation of two visual analytics interfaces that promote contextual awareness and support ‘what-if’ and ‘what-else’ probes in the spatio-temporal domain aiming to improve information integration and support controllers in prioritising conflict resolution. Both interfaces visualize vertical solution spaces against a time-altitude graph. The main contributions of this paper are: (a) the presentation of two interfaces for supporting conflict solving; (b) the novel representation of how vertical information and aircraft rate of climb and descent affect conflicts and (c) an evaluation and comparison of the interfaces with a traditional air traffic control support system. The evaluation study was performed with domain experts to compare the effects of visualization concepts on operator engagement in processing solutions suggested by the tools. Results show that the visualizations support operators' ability to understand and resolve conflicts. Based on the results, general design guidelines for time-critical domains are proposed.

As the levels of automation and reliance on modern artificial intelligence (AI) approaches increase across multiple industries, the importance of the human-centered perspective becomes more evident. Various actors in such industrial applications, including equipment operators and decision makers, have their needs and preferences that often do not align with the decisions produced by black-box models, potentially leading to mistrust and wasted productivity gain opportunities. In this paper, we examine these issues through the lenses of visual analytics and, more broadly, interactive visualization, and we argue that the methods and techniques from these fields can lead to advances in both academic research and industrial innovations concerning the explainability of AI models. To address the existing gap within and across the research and application fields, we propose a conceptual framework for visual analytics design and evaluation for such scenarios, followed by a preliminary roadmap and call to action for the respective communities.

Operational demands in safety‐critical systems impose a risk of failure to the operators especially during urgent situations. Operators of safety‐critical systems learn to make decisions effectively throughout extensive training programs and many years of experience. In the domain of air traffic control, expensive training with high dropout rates calls for research to enhance novices' ability to detect and resolve conflicts in the airspace. While previous researchers have mostly focused on redesigning training instructions and programs, the current paper explores possible benefits of novel visual representations to improve novices' understanding of the situations as well as their decision‐making process. We conduct an experimental evaluation study testing two ecological visual analytics interfaces, developed in a previous study, as support systems to facilitate novice decision‐making. The main contribution of this paper is threefold. First, we describe the application of an ecological interface design approach to the development of two visual analytics interfaces. Second, we perform a human‐in‐the‐loop experiment with forty‐five novices within a simplified air traffic control simulation environment. Third, by performing an expert‐novice comparison we investigate the extent to which effects of the proposed interfaces can be attributed to the subjects' expertise. The results show that the proposed ecological visual analytics interfaces improved novices' understanding of the information about conflicts as well as their problem‐solving performance. Further, the results show that the beneficial effects of the proposed interfaces were more attributable to the visual representations than the users' expertise.

Technological advances such as electronic charts and course prediction systems provide invaluable support to navigation officers and maritime pilots in navigating confined waters. However, recent maritime accidents have been attributed to operators lacking a clear understanding of how the automation works and how to use it, leading to both misuse and disuse. Two concerns emerge: inadequate training and poor automation design, making it difficult and complex to use. To investigate challenges related to understanding, use, and trust in automation, we surveyed Swedish maritime pilots to investigate their experiences with the course predictor automation tool on their portable pilot units. This technology predicts ship trajectories and is commonly used in modern bridge systems. This paper contributes empirical evidence on how maritime pilots trust the predictor currently used, providing insight into their perceptions and experiences of training, level of understanding, and patterns of usage. The results of 69 respondents revealed limited formal training in the predictor, with knowledge acquired primarily from self-learning and practical experience. Although pilots value the predictor and use it frequently, they struggle with sensor error detection and understanding how it works. The trust in the predictor was inversely correlated with age and experience, with lower age and experience associated with higher trust, more frequent use, greater perceived importance, better understanding, and fewer unexplained behaviours encountered. Based on these findings, recommendations are proposed to improve predictor training and improve its transparency through design.