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.

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. 

The analysis of operators work patterns in safety-critical domains is increasingly assisted by eye-tracking technologies. A reason is a growing demand for empirically justified assurance to ascertain an equal level of safety after the implementation of new techniques and higher levels of automation. In the field and simulation studies, head-mounted eye-tracking devices are a preferred choice because of the easy, reliable and fast startup. The downside is time-consuming work to map gaze points to the world/workplace coordinates which makes head-mounted devices applicable to a small number of episodes to analyze. This paper presents a solution called AoI Mapping and Analysis Tool (AMAT) that relies on visual features provided by the integrated scene video camera. AoI-templates can be defined from the video recording and are constantly matched for an AoI-analysis. AMAT was evaluated with a field study example from Air Traffic Control (ATC) in the tower at Linkoping City airport and a training simulator study example from Vessel Traffic Service (VTS) in Gothenburg. Example situations demonstrate the capabilities of AMAT to define AoIs, evaluate the validity of the chosen set of AoIs and export to an example analysis program Eloquence for the visualization of results. The AoI-sequence of 4 vessels encounter in the Gothenburg archipelago was shown as well as the comparison of four tower controllers sequences during the final approach. The final discussion highlights the capabilities and limitations of the implemented techniques that rely on the AKAZE point feature detection and linear homographic transformations. A major source of disturbance lies in the varying light conditions evoking under-exposure of the video camera and fast head movements.

Artificial intelligence is considered a key enabler for realizing a more efficient future air traffic management system. As the automation designed to support us grows more sophisticated and complex, our understanding of it tends to suffer. Recent research has addressed this issue in two ways: either through increased automation transparency or increased personalization. This paper overviews recent work in these two areas of strategic conformance (i.e., personalization) and automation transparency (e.g., explainable artificial intelligence and machine learning interpretability). We discuss how to achieve and how to balance conformance and transparency in the context of a machine learning system for conflict detection and resolution in air traffic control. In the MAHALO project, we aim to build, and empirically evaluate, a personalized and transparent decision support system by combining supervised and reinforcement learning approaches. We believe that such a system could strive for optimal performance while accommodating individual differences. By knowing the individuals preferences, the system would be able to afford transparency by explaining both why it suggests another solution (that deviates from the individuals), and why this solution is considered to be better.

A bottleneck of (future) multiple remote tower operations (MRTO) is for controllers to monitor traffic movements at two airports (or more) in real-time, at the same time. To address this, we propose an autonomous agent (digital colleague), the DiTA Digital Tower Assistant. The key issue addressed by the DiTA concept is simultaneous movements on the two airports. We present results from the first stages of concept design and evaluation of DiTA. We conducted two workshops with three fully licensed air traffic controllers. During the workshops the controllers evaluated the DiTA concept of operations through scenario walk-throughs using printed airspace maps. The outcome of the workshop series was a tentative concept of DiTA operations and key concerns for one MRTO scenario. We present and discuss the emerging picture of common concerns and views on acceptable concept of operations as the workshops progressed. We conclude that it is is vital that operators can place the right level of trust in autonomous system competence, especially when they manage safety critical movements. To achieve this, DiTA competence is a key concern. The build-up of operator trust and competence to assess DiTA comptence (re-skilling) could both rely on automation transparency and a stepwise introduction of the concept in operations.

Air traffic control is a safety critical high-risk environment where operators need to analyse and interpret traffic dynamics of spatio-temporal data in real-time. To support the air traffic controller in safely separating traffic, earlier research has applied real-time visualisation techniques that explore the constraints and solution spaces of separation problems. Traditionally, situation displays for conflict detection and resolution have used visualisations that convey information about the relative horizontal position between aircraft. Although vertical solutions for solving conflicts are common, and often a preferred among controllers, visualisations typically provide limited information about the vertical relationship between aircraft. This paper presents a design study of an interactive conflict detection and resolution support tool and explores techniques for real-time visualisation of spatio-temporal data. The design evolution has incorporated several activities, including an initial work domain analysis, iterative rounds of programming, design, and evaluations with a domain expert, and an evaluation with eight active controllers. The heading-time-altitude visualisation system is developed based on formulating and solving aircraft movements in a relative coordinate system. A polar-graph visualisation technique is used to construct a view of conflicting aircraft vertical solution spaces in the temporal domain. Using composite glyphs, the final heading-time-altitude visualisation provides a graphical representation of both horizontal and vertical solution spaces for the traffic situation.

Advanced automation has been highlighted as contributory to several accidents involving modern bridge support systems and automation aiding maritime pilots for maneuvering and navigation. This paper argues for reskilling for automation collaboration, that operators need training that provides an understanding of what data the automation uses and how, and to transfer this skill to their working environment and be able to make full use of the automation even under influence of inaccurate data. As a case, this paper explores the predictor automation, which is an advanced navigation aid that visualizes an estimation of the ships future trajectory on an electronic chart display. Field studies and a literature review of maritime accidents were carried out to determine difficulties maritime pilots have with understanding the predictor. This research provides valuable guidance for how automation transparency can be an important part of reskilling and how to achieve it. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.

Although where to look, when, and in what order is crucial for situation awareness and task performance in tower control, instructors are lacking support systems that can help them understand operators’ visual scan behaviours. As a way forward, this paper investigates the existence and characteristics of visual scan patterns in tower control and explores a novel support tool that can help instructors in searching for and exploring these patterns. First, eye-tracking data from two controllers were collected in a high-fidelity tower simulator. Second, a workshop was conducted with three instructors to discuss specific scan patterns that can be expected in relation to the approach scenarios used in the eye-tracking data collection. Six template visual scan patterns were identified during the workshop. Finally, an interactive visual sequence mining tool was used to identify and explore instances of the template scan patterns in the recorded eye-tracking data. Four of these could be detected using the tool: runway scans, landing clearance, touchdown and landing roll, and phases of visual focus. The identification of template scan patterns provides additional insight for formalising controllers’ visual work in tower control. The ability to detect and explore visual scan patterns in the proposed tool shows promise for improving instructors’ understanding of controllers’ visual scan behaviours, and for improving training effectiveness.

Cognitive Work Analysis (CWA) is an appropriate approach in design for high-stakes domains, such as air traffic management (ATM) since it focuses on human expert performance in regular and contingency situations. However, CWA is not suitable for the design of a first-of-a-kind system since there is nothing to analyse before the start of the design process. In 2017, unmanned air traffic management (UTM) for intense drone traffic in cities was such a system. Making things worse, the UTM system has to be in place before the traffic, since it provides basic safety. In this research-through-design study, we present conceptual designing as a bootstrapping approach to CWA in the design of a first-of-a-kind UTM system. In a series of co-design workshops, we identified future services, traffic patterns, and regulations that framed the design of UTM system concepts. They were based on combinations of four basic building blocks: points, lines, planes, and volumes. Concepts of point-based control, airport geofences, grid squares, layers, and tubes were discussed. Throughout the conceptual designing, results were documented in an evolving Work Domain Analysis (WDA), which is a cornerstone of CWA. This approach allowed us to bootstrap the CWA for a first-of-a-kind-system.