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.

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.

An essential task of an air traffic controller is to manage the traffic flow by predicting future trajectories. Complex traffic patterns are difficult to predict and manage and impose cognitive load on the air traffic controllers. In this work we present an interactive visual analytics interface which facilitates detection and resolution of complex traffic patterns for air traffic controllers. The interface supports air traffic controllers in detecting complex clusters of aircraft and further enables them to visualize and simultaneously compare how different re-routing strategies for each individual aircraft yield reduction of complexity in the entire sector for the next hour. The development of the concepts was supported by the domain-specific feedback we received from six fully licensed and operational air traffic controllers in an iterative design process over a period of 14 months.

Artificial intelligence has the potential to improve air traffic management through the consistent use of machine learning. AI can bring benefits to air traffic controllers in terms of workload, situational awareness, trust, and thus operational efficiency and safety. However, human problem-solving strategies can potentially collide with AI and lead to misunderstandings and a decrease in user acceptance of air traffic control systems. The proposed paper focuses on the design of the ML system, in particular providing insights and guidelines derived from results of recent field studies as they addressed the impacts of conformance and transparency on controller behaviour and survey responses. Several guidelines were distilled based on empirical insights obtained from experiments, feedback from controllers and workshop results. The guidelines are divided into different categories: ML/AI design, Personalization, Transparency, and HCI. The proposed paper also describes a contribution to a different use case to test the generalizability of the guidelines themselves, as well as a recent update in the explainability framework developed by a regulatory authority.

This volume is the proceedings of the 10th biennial Nordes conference, hosted by Linköping University, 12 – 14 June 2023. All contributions relate in different ways to the overall theme, ‘This Space Intentionally Left [Blank]’.

A blank space—a silence, pause, interstice, gap, in-between, opening, punctuation, negative space—can be deliberately or incidentally devoid of content. A space left blank is as much an invitation to notice the void and the structure that surrounds it, as it is a question as to why and how they are perceived. Such absence can remind us to seek out conscious or unconscious intentions hidden in plain sight. Philosophies and worldviews that acknowled- ge the importance of ‘absence’, ‘emptiness’ or ‘nothingness’ consider everything to be relational, fluid, dynamic and ‘in-between’, rather than the binary dualism that linger from Cartesian constructs. Do we pay these blank spaces enough attention, as integrated parts of design practice and design research?

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.

Multi Remote Tower Operations (MRTO), where one ATCO has the responsibility of two airports simultaneously, have become an important means to reduce the cost for air traffic control at small regional airports in Sweden without sacrificing safety or service levels. A challenge in MRTO is to keep normal movements operational on an airport while there is busy traffic on the second airport handled by the same ATCO. Earlier work has described the potential of using a digital tower assistant (DiTA), an automation that handles the communication and monitoring of e.g. a single, simple approach and landing on an airport with an otherwise empty sky, while the ATCO needs to focus their attention on the other airport. In this paper we let two interaction designers analyse the interview material from a recent study with five experienced ATCOs, each performing two scenarios using DiTA, and present the conclusions made from an interaction design perspective.

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 objective of en-route control is to ensure minimum separation between aircraft in the sector under all circumstances. Exploring and understanding the related work patterns of air traffic controllers on how to successfully perform this task is crucial for the future development and implementation of automation solutions. Future automation must match the logic of decision making and the need for information at the right time in identifying and resolving conflicts. The objective of this paper is to identify “decision cues” that are relevant during decision-making and its relation to the controller’s intention. A retrospective think aloud method was applied in which en-route controllers commented their own work behaviour after playing a simple conflict scenario in the simulator. A set of decision cues were identified using 13 controllers and classified using a Conflict Life Cycle-model, dividing the task into four work steps. The result shows clear differences in the compilation of decision cues used between work steps. Large differences were found among controllers, indicating personal preferences in consideration of information, timing, and chosen conflict resolution. The results further show that the “conflict resolution probing” step is the most challenging task because it contains the most decision cues. The high inter-individual variance in the cue composition of this step indicates a high degree of individual skill development on which the adoption and selection of conflict solutions is based. The results support the future hypothesis-driven verification of controllers’ work pattern and intention of decision-making and related automated solutions.

Artificial Intelligence provides both opportunities and considerable challenges to the continued growth of Air Traffic Control (ATC) services. This paper presents a study where a personalized and transparent machine learning decision aid for ATC conflict resolution was built and empirically evaluated with air traffic controllers. Multi-site simulations were conducted with 34 controllers working together with an AI agent to solve conflicts between aircraft in enroute traffic scenarios. Resolution advisories varied in conformance (degree of personalization) and transparency. Main effects of conformance were found on controllers’ resolution performance and response to advisories in terms of acceptance and ratings of agreement and similarity to own solution. The separation distance aimed for by the advised solution was found to be particularly important for the response to optimal advisories. More positive responses were measured for controllers whose separation margin preferences was closer aligned with the advisory. The study provides the aviation community with knowledge on how conformal and transparent AI support systems affect operators’ responses to system-generated resolution advisories.