C. Borst
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79 records found
1
Journal article
(2026)
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Eline S. Bakker, Ferdinand Dijkstra, Clark Borst, Marinus M. van Paassen, Max Mulder
Air traffic growth leads to increasingly complex traffic situations near large airports. To support air traffic controllers in maintaining safety and improving efficiency, new support tools are needed. This paper presents the Inbound Traffic Support System (ITSS), a visual interface prototype developed in collaboration with the Netherlands’ Air Navigation Service Provider, LVNL. The interface visualizes constraints associated with the merging task and was tested for the South Sector of Amsterdam Airport Schiphol. By portraying potential merging solutions, it helps area controllers anticipate the impact of their decisions and supports their role in managing inbound traffic. Professional controllers provided positive feedback during the development phase while also identifying concerns such as display clutter that should be addressed. An experiment with semiprofessional controllers assessed the impact of the interface on operator performance and workload. Participants were instructed to rely on the interface rather than operational experience to ensure consistent results. Findings showed that the interface improved aircraft adherence to altitude restrictions and reduced the number of clearances needed in more complex scenarios. By revealing the solution space, the ITSS encourages earlier planning and reduces the need for late-stage interventions. Future work will include professional controller evaluations, increased simulation fidelity, and extended training.
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Air traffic growth leads to increasingly complex traffic situations near large airports. To support air traffic controllers in maintaining safety and improving efficiency, new support tools are needed. This paper presents the Inbound Traffic Support System (ITSS), a visual interface prototype developed in collaboration with the Netherlands’ Air Navigation Service Provider, LVNL. The interface visualizes constraints associated with the merging task and was tested for the South Sector of Amsterdam Airport Schiphol. By portraying potential merging solutions, it helps area controllers anticipate the impact of their decisions and supports their role in managing inbound traffic. Professional controllers provided positive feedback during the development phase while also identifying concerns such as display clutter that should be addressed. An experiment with semiprofessional controllers assessed the impact of the interface on operator performance and workload. Participants were instructed to rely on the interface rather than operational experience to ensure consistent results. Findings showed that the interface improved aircraft adherence to altitude restrictions and reduced the number of clearances needed in more complex scenarios. By revealing the solution space, the ITSS encourages earlier planning and reduces the need for late-stage interventions. Future work will include professional controller evaluations, increased simulation fidelity, and extended training.
Towards a unified taxonomy for algorithmic transparency
Insights from uncrewed air traffic management
With the rapid advancement of drone technology, their applications have become increasingly widespread. However, the integration of drones into the airspace also poses risks to crewed aircraft, particularly around airports. To address this issue, a highly automated Uncrewed Air Traffic Management (UTM) system is being developed. Since fully safe and reliable automation does not exist yet, UTM still requires human supervision to enhance the overall system safety and reliability. Some form of “seeing-into” transparency may be necessary to help operators better understand the limitations and behavior of the automated UTM system. As UTM is a novel concept, research on transparent UTM is limited. Many efforts have been made in other fields, but there still remains a lack of consensus on what transparency entails, particularly for algorithmic systems. Therefore, this article first presents a unified taxonomy for algorithmic transparency, with operational, domain and engineering transparency introduced as its core concepts. From the taxonomy, twenty UTM transparency elements and their corresponding visual prototypes were then designed, which also showcases how the taxonomy can be applied in practice. A survey-based user study was conducted to collect the opinions of air traffic controllers and drone experts regarding the designed elements and prototypes. Results indicate that transparency is deemed imperative for UTM, especially in scenarios featuring automation failure. It also reveals that operational transparency is generally preferable over engineering transparency in nominal operations. Participants were asked to group the designed elements, and their results closely aligned with the structure of the proposed taxonomy.
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With the rapid advancement of drone technology, their applications have become increasingly widespread. However, the integration of drones into the airspace also poses risks to crewed aircraft, particularly around airports. To address this issue, a highly automated Uncrewed Air Traffic Management (UTM) system is being developed. Since fully safe and reliable automation does not exist yet, UTM still requires human supervision to enhance the overall system safety and reliability. Some form of “seeing-into” transparency may be necessary to help operators better understand the limitations and behavior of the automated UTM system. As UTM is a novel concept, research on transparent UTM is limited. Many efforts have been made in other fields, but there still remains a lack of consensus on what transparency entails, particularly for algorithmic systems. Therefore, this article first presents a unified taxonomy for algorithmic transparency, with operational, domain and engineering transparency introduced as its core concepts. From the taxonomy, twenty UTM transparency elements and their corresponding visual prototypes were then designed, which also showcases how the taxonomy can be applied in practice. A survey-based user study was conducted to collect the opinions of air traffic controllers and drone experts regarding the designed elements and prototypes. Results indicate that transparency is deemed imperative for UTM, especially in scenarios featuring automation failure. It also reveals that operational transparency is generally preferable over engineering transparency in nominal operations. Participants were asked to group the designed elements, and their results closely aligned with the structure of the proposed taxonomy.
Energy management is essential in low-energy flight conditions. Changes in fixed-wing aircraft configuration affect performance and energy boundaries, and improved insight therein should allow pilots to better predict potentially dangerous situations, maintain suitable safety margins, and more effectively react to unforeseen events. This paper presents the design and experimental evaluation of a vertical situation display with enhancements portraying changes in the flight performance envelope. Sixteen pilots were tasked to fly approach and go-around scenarios with both a baseline and an enhanced display, with some of the scenarios including unexpected failures in configuration changes. Results show that the new display makes pilots maintain larger margins in velocity, thus spending less time below the advised minimum speed limit in final approach. However, these larger velocity margins also led to larger errors with respect to target velocities. Failures in configuration changes were more quickly discovered with the new display, although these results could not be substantiated due to a lack of statistical significance. However, pilots did report feeling better able to predict dangerous situations. Overall, pilots preferred the novel display; no significant differences in workload were found.
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Energy management is essential in low-energy flight conditions. Changes in fixed-wing aircraft configuration affect performance and energy boundaries, and improved insight therein should allow pilots to better predict potentially dangerous situations, maintain suitable safety margins, and more effectively react to unforeseen events. This paper presents the design and experimental evaluation of a vertical situation display with enhancements portraying changes in the flight performance envelope. Sixteen pilots were tasked to fly approach and go-around scenarios with both a baseline and an enhanced display, with some of the scenarios including unexpected failures in configuration changes. Results show that the new display makes pilots maintain larger margins in velocity, thus spending less time below the advised minimum speed limit in final approach. However, these larger velocity margins also led to larger errors with respect to target velocities. Failures in configuration changes were more quickly discovered with the new display, although these results could not be substantiated due to a lack of statistical significance. However, pilots did report feeling better able to predict dangerous situations. Overall, pilots preferred the novel display; no significant differences in workload were found.
Journal article
(2025)
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Mats Dirkzwager, Ferdinand Dijkstra, Clark Borst, Marinus M. van Paassen, Max Mulder
On final approach, an approach controller is responsible for separating aircraft lining up on the instrument landing system. In an attempt to increase traffic throughput, especially in strong headwind conditions, European regulation advises all European airports to move from distance-based to time-based separation. This effectively changes the controller’s task from a distance-based to a time-based problem. Further complications arise because of the European recategorization of aircraft types initiative, and experts fear that the gains foreseen with time-based separation will not be realized. This paper presents a visual tool integrated into the radar screen to assist controllers in performing time-based separation, the ideal turn-in point (ITIP) display. To assist controllers in selecting optimal approach strategies, starting from the moment aircraft enter the terminal control area, the display shows the possibilities and restrictions in the system rather than giving (restricting) advisories. A proof-of-concept experiment was performed with people knowledgeable in air traffic control (N = 8) and compared the ITIP to a current industry state-of-the-art display designed by U.K.’s National Air Traffic Services in scenarios of varying difficulty. Results show that with the ITIP tool, efficiency improved with similar or higher levels of safety and similar or lower workload. These promising results justify testing the interface with professional air traffic controllers. Future work aims at reducing clutter, increasing simulation fidelity, and increasing the level of support in complex traffic situations.
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On final approach, an approach controller is responsible for separating aircraft lining up on the instrument landing system. In an attempt to increase traffic throughput, especially in strong headwind conditions, European regulation advises all European airports to move from distance-based to time-based separation. This effectively changes the controller’s task from a distance-based to a time-based problem. Further complications arise because of the European recategorization of aircraft types initiative, and experts fear that the gains foreseen with time-based separation will not be realized. This paper presents a visual tool integrated into the radar screen to assist controllers in performing time-based separation, the ideal turn-in point (ITIP) display. To assist controllers in selecting optimal approach strategies, starting from the moment aircraft enter the terminal control area, the display shows the possibilities and restrictions in the system rather than giving (restricting) advisories. A proof-of-concept experiment was performed with people knowledgeable in air traffic control (N = 8) and compared the ITIP to a current industry state-of-the-art display designed by U.K.’s National Air Traffic Services in scenarios of varying difficulty. Results show that with the ITIP tool, efficiency improved with similar or higher levels of safety and similar or lower workload. These promising results justify testing the interface with professional air traffic controllers. Future work aims at reducing clutter, increasing simulation fidelity, and increasing the level of support in complex traffic situations.
Preprint
(2025)
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Milad Leyli-abadi, Ricardo J. Bessa, Antoine Marot, Maroua Meddeb, Manuel Meyer, Viola Schiaffonati, Manuel Schneider, Toni Waefler, Jan Viebahn, Daniel Boos, Clark Borst, Alberto Castagna, Ricardo Chavarriaga, Mohamed Hassouna, Bruno Lemetayer, Giulia Leto
The interaction between humans and AI in safety-critical systems presents a unique set of challenges that remain partially addressed by existing frameworks. These challenges stem from the complex interplay of requirements for transparency, trust, and explainability, coupled with the necessity for robust and safe decision-making. A framework that holistically integrates human and AI capabilities while addressing these concerns is notably required, bridging the critical gaps in designing, deploying, and maintaining safe and effective systems. This paper proposes a holistic conceptual framework for critical infrastructures by adopting an interdisciplinary approach. It integrates traditionally distinct fields such as mathematics, decision theory, computer science, philosophy, psychology, and cognitive engineering and draws on specialized engineering domains, particularly energy, mobility, and aeronautics. Its flexibility is further demonstrated through a case study on power grid management.
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The interaction between humans and AI in safety-critical systems presents a unique set of challenges that remain partially addressed by existing frameworks. These challenges stem from the complex interplay of requirements for transparency, trust, and explainability, coupled with the necessity for robust and safe decision-making. A framework that holistically integrates human and AI capabilities while addressing these concerns is notably required, bridging the critical gaps in designing, deploying, and maintaining safe and effective systems. This paper proposes a holistic conceptual framework for critical infrastructures by adopting an interdisciplinary approach. It integrates traditionally distinct fields such as mathematics, decision theory, computer science, philosophy, psychology, and cognitive engineering and draws on specialized engineering domains, particularly energy, mobility, and aeronautics. Its flexibility is further demonstrated through a case study on power grid management.
Air traffic control is advancing digitalization by developing advanced decision-support systems, where the way information is presented to operators plays a central role in shaping performance. However, the effects of different visual representations within these systems on human decision-making remain not fully understood. In this study, we compared two Conflict Detection and Resolution (CD&R) tools: the Highly Interactive Problem Solver (HIPS) and the Solution Space Diagram (SSD). Although both systems are grounded in the same control problem, they differ in how they represent the control constraints that define conflict conditions and feasible responses. Through a human-in-the-loop experiment under low-and high-traffic conditions, we analyzed how these differences influence decision-making. Results showed that, particularly in low-density traffic, HIPS enabled quicker responses, fewer commands, and smaller safety margins, whereas SSD, despite receiving more favorable subjective ratings, led to greater variability in actions. These findings suggest that visualization significantly impacts decision-making consistency and efficiency. However, in highly complex environments, overall effectiveness may depend more on operators' ability to shift and adapt decision-making patterns facilitated by the interface than on specific visual elements.
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Air traffic control is advancing digitalization by developing advanced decision-support systems, where the way information is presented to operators plays a central role in shaping performance. However, the effects of different visual representations within these systems on human decision-making remain not fully understood. In this study, we compared two Conflict Detection and Resolution (CD&R) tools: the Highly Interactive Problem Solver (HIPS) and the Solution Space Diagram (SSD). Although both systems are grounded in the same control problem, they differ in how they represent the control constraints that define conflict conditions and feasible responses. Through a human-in-the-loop experiment under low-and high-traffic conditions, we analyzed how these differences influence decision-making. Results showed that, particularly in low-density traffic, HIPS enabled quicker responses, fewer commands, and smaller safety margins, whereas SSD, despite receiving more favorable subjective ratings, led to greater variability in actions. These findings suggest that visualization significantly impacts decision-making consistency and efficiency. However, in highly complex environments, overall effectiveness may depend more on operators' ability to shift and adapt decision-making patterns facilitated by the interface than on specific visual elements.
Gamification in Automated Air Traffic Control
Increasing Vigilance Using Fictional Aircraft
The introduction of more advanced automation in air traffic control seems inevitable. Air traffic controllers will then take the role of automation supervisors, a role which is generally unsuitable for humans. Gamification, the use of game elements in non-gaming contexts, shows promising results in mitigating the effects of boredom in highly automated domains requiring human supervision. An example is luggage screening, where dangerous items are rarely found, through projecting fictional threats on top of x-ray scans. This paper presents and experimentally tests a proposed implementation of gamification within highly automated en-route air traffic control. Fictional flights were superimposed among automatically controlled real traffic, thus creating fictional conflicts that needed resolving. System supervisors were tasked to supervise the behaviour of a fully automated conflict detection and resolution system, while manually routing fictional flights safely and efficiently through the sector, avoiding conflicts with both real and fictional flights. Automation anomalies were simulated, as well as an automation failure event, after which the system supervisor needed to assume manual control over all traffic. The presence of fictional flights increased self-reported concentration levels and reduced boredom. However, some participants reported that fictional flights were distracting. Thus, while the use of fictional flights increases engagement, it might negatively affect other cognitive functions, and with that, compromise safety. Thus, while the implementation of such a tool might provide benefits in terms of skill retention and engagement, further research is recommended involving professional air traffic controllers, improved measurement tools and a longitudinal study that better excites boredom, complacency, and skill erosion in order to understand and mitigate its negative effects.
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The introduction of more advanced automation in air traffic control seems inevitable. Air traffic controllers will then take the role of automation supervisors, a role which is generally unsuitable for humans. Gamification, the use of game elements in non-gaming contexts, shows promising results in mitigating the effects of boredom in highly automated domains requiring human supervision. An example is luggage screening, where dangerous items are rarely found, through projecting fictional threats on top of x-ray scans. This paper presents and experimentally tests a proposed implementation of gamification within highly automated en-route air traffic control. Fictional flights were superimposed among automatically controlled real traffic, thus creating fictional conflicts that needed resolving. System supervisors were tasked to supervise the behaviour of a fully automated conflict detection and resolution system, while manually routing fictional flights safely and efficiently through the sector, avoiding conflicts with both real and fictional flights. Automation anomalies were simulated, as well as an automation failure event, after which the system supervisor needed to assume manual control over all traffic. The presence of fictional flights increased self-reported concentration levels and reduced boredom. However, some participants reported that fictional flights were distracting. Thus, while the use of fictional flights increases engagement, it might negatively affect other cognitive functions, and with that, compromise safety. Thus, while the implementation of such a tool might provide benefits in terms of skill retention and engagement, further research is recommended involving professional air traffic controllers, improved measurement tools and a longitudinal study that better excites boredom, complacency, and skill erosion in order to understand and mitigate its negative effects.
Review
(2025)
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Marco Mussi, Alberto Maria Metelli, Marcello Restelli, Gianvito Losapio, Ricardo J. Bessa, Daniel Boos, Clark Borst, Giulia Leto, Alberto Castagna, More authors...
Artificial Intelligence (AI) is transforming every aspect of modern society. It demonstrates a high potential to contribute to more flexible operations of safety-critical network infrastructures under deep transformation to tackle global challenges, such as climate change, energy transition, efficiency, and digital transformation, including increasing infrastructure resilience to natural and human-made hazards. The widespread adoption of AI creates the conditions for a new and inevitable interaction between humans and AI-based decision systems. In such a scenario, creating an ecosystem in which humans and AI interact healthily, where the roles and positions of both actors are well-defined, is a critical challenge for research and industry in the coming years. This perspective article outlines the challenges and requirements for effective human-AI interaction by taking an interdisciplinary point of view that merges computer science, decision-making sciences, psychological constructs, and industrial practices. The work focuses on three emblematic safety-critical scenarios from two different domains: energy (power grids) and mobility (railway networks and air traffic management).
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Artificial Intelligence (AI) is transforming every aspect of modern society. It demonstrates a high potential to contribute to more flexible operations of safety-critical network infrastructures under deep transformation to tackle global challenges, such as climate change, energy transition, efficiency, and digital transformation, including increasing infrastructure resilience to natural and human-made hazards. The widespread adoption of AI creates the conditions for a new and inevitable interaction between humans and AI-based decision systems. In such a scenario, creating an ecosystem in which humans and AI interact healthily, where the roles and positions of both actors are well-defined, is a critical challenge for research and industry in the coming years. This perspective article outlines the challenges and requirements for effective human-AI interaction by taking an interdisciplinary point of view that merges computer science, decision-making sciences, psychological constructs, and industrial practices. The work focuses on three emblematic safety-critical scenarios from two different domains: energy (power grids) and mobility (railway networks and air traffic management).
Algorithmic transparency in path planning
A visual approach to enhancing human understanding
Computer algorithms facilitate increased automation in various human-centered work areas to improve operational safety and efficiency. Algorithmic transparency is considered essential for human operators, policy makers and system developers, as it allows them to understand the capabilities and limitations of an algorithm. In this research, we focus on path-planning algorithms and propose a purely visual approach to achieve their transparency. This approach extracts and portrays information directly from the algorithms, aiming to visually reveal their inner workings. Benchmark tests indicate that extracting information from path-planning algorithms may significantly slow them down. For time-constrained operations, it is recommended to store only the necessary data during the pathfinding process and perform information extraction afterwards. Based on theories from cognitive engineering, six transparency levels were designed to chunk meaningful information pertaining path-planning algorithms. A user study among non-experts (N=40) was then conducted to evaluate the impact of visual algorithmic transparency on human understanding. The results suggest that increased transparency levels allow non-experts to more correctly and confidently understand the details of a path-planning algorithm. However, it is also found that certain transparency levels can lead to confusion, especially when the algorithm behaves in a way contrary to human expectations. This study further reveals that, given the same level of transparency, sampling-based algorithms may be easier to comprehend than graph-based algorithms. This research can serve as a reference for how to achieve transparency in path-planning-related applications and how to hierarchically portray and organize transparency information.
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Computer algorithms facilitate increased automation in various human-centered work areas to improve operational safety and efficiency. Algorithmic transparency is considered essential for human operators, policy makers and system developers, as it allows them to understand the capabilities and limitations of an algorithm. In this research, we focus on path-planning algorithms and propose a purely visual approach to achieve their transparency. This approach extracts and portrays information directly from the algorithms, aiming to visually reveal their inner workings. Benchmark tests indicate that extracting information from path-planning algorithms may significantly slow them down. For time-constrained operations, it is recommended to store only the necessary data during the pathfinding process and perform information extraction afterwards. Based on theories from cognitive engineering, six transparency levels were designed to chunk meaningful information pertaining path-planning algorithms. A user study among non-experts (N=40) was then conducted to evaluate the impact of visual algorithmic transparency on human understanding. The results suggest that increased transparency levels allow non-experts to more correctly and confidently understand the details of a path-planning algorithm. However, it is also found that certain transparency levels can lead to confusion, especially when the algorithm behaves in a way contrary to human expectations. This study further reveals that, given the same level of transparency, sampling-based algorithms may be easier to comprehend than graph-based algorithms. This research can serve as a reference for how to achieve transparency in path-planning-related applications and how to hierarchically portray and organize transparency information.
As concepts for incorporating uncrewed aerial vehicles (UAS) into controlled airspace are being developed, the need for automated UAS traffic management (UTM) systems to guide UAS and maintain safety is becoming more apparent. A major point of concern for the implementation of UTM is how such systems could coexist alongside the human-centric air traffic management system that is already in place. The European Union’s U-space concept proposes the use of dynamic segregation of airspace reserved for UAS within the control zone. We conducted a simulation experiment with ten air traffic control officer (ATCO) volunteers to gather insights into the feasibility of tower controllers performing the dynamic segregation task. An interface prototype that supports dynamic geofencing and low-level UAS control was developed for this purpose. We found that our proposed interface design helped ATCOs detect potential conflicts between UAS and crewed aircraft. However, they were not always able to adequately resolve them, which resulted in several loss of separation events. It appears that the limitations of the dynamic segregation concept do not fit well with typical air traffic control strategies used by ATCOs. To substantiate our findings, we propose future research to investigate how to overcome the limitations of dynamic segregation to resolve tactical conflicts by revising ATCO control strategies, reevaluating their role in dynamic segregation, as well as considering the definition of flight rules and separation minima for UAS.
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As concepts for incorporating uncrewed aerial vehicles (UAS) into controlled airspace are being developed, the need for automated UAS traffic management (UTM) systems to guide UAS and maintain safety is becoming more apparent. A major point of concern for the implementation of UTM is how such systems could coexist alongside the human-centric air traffic management system that is already in place. The European Union’s U-space concept proposes the use of dynamic segregation of airspace reserved for UAS within the control zone. We conducted a simulation experiment with ten air traffic control officer (ATCO) volunteers to gather insights into the feasibility of tower controllers performing the dynamic segregation task. An interface prototype that supports dynamic geofencing and low-level UAS control was developed for this purpose. We found that our proposed interface design helped ATCOs detect potential conflicts between UAS and crewed aircraft. However, they were not always able to adequately resolve them, which resulted in several loss of separation events. It appears that the limitations of the dynamic segregation concept do not fit well with typical air traffic control strategies used by ATCOs. To substantiate our findings, we propose future research to investigate how to overcome the limitations of dynamic segregation to resolve tactical conflicts by revising ATCO control strategies, reevaluating their role in dynamic segregation, as well as considering the definition of flight rules and separation minima for UAS.
It is widely recognized that airspace capacity must increase over the coming years. It is also commonly accepted that meeting this challenge while balancing concerns around safety, efficiency, and workforce issues will drive greater reliance on automation. However, if automation is not properly developed and deployed, it represents something of a double-edged sword, and has been linked to several human–machine system performance issues. In this article, we argue that human–automation function and task allocation may not be the way forward, as it invokes serialized interactions that ultimately push the human into a problematic supervisory role. In contrast, we propose a flight-based allocation strategy in which a human controller and digital colleague each have full control authority over different flights in the airspace, thereby creating a parallel system. In an exploratory human-in-the-loop simulation exercise involving six operational en route controllers, it was found that the proposed system was considered acceptable after the users gained experience with it during simulation trials. However, almost all controllers did not follow the initial flight allocations, suggesting that allocation schemes need to remain flexible and/or be based on criteria capturing interactions between flights. In addition, the limited capability of and feedback from the automation contributed to this result. To advance this concept, future work should focus on substantiating flight-centric complexity in driving flight allocation schemes, increasing automation capabilities, and facilitating common ground between humans and automation.
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It is widely recognized that airspace capacity must increase over the coming years. It is also commonly accepted that meeting this challenge while balancing concerns around safety, efficiency, and workforce issues will drive greater reliance on automation. However, if automation is not properly developed and deployed, it represents something of a double-edged sword, and has been linked to several human–machine system performance issues. In this article, we argue that human–automation function and task allocation may not be the way forward, as it invokes serialized interactions that ultimately push the human into a problematic supervisory role. In contrast, we propose a flight-based allocation strategy in which a human controller and digital colleague each have full control authority over different flights in the airspace, thereby creating a parallel system. In an exploratory human-in-the-loop simulation exercise involving six operational en route controllers, it was found that the proposed system was considered acceptable after the users gained experience with it during simulation trials. However, almost all controllers did not follow the initial flight allocations, suggesting that allocation schemes need to remain flexible and/or be based on criteria capturing interactions between flights. In addition, the limited capability of and feedback from the automation contributed to this result. To advance this concept, future work should focus on substantiating flight-centric complexity in driving flight allocation schemes, increasing automation capabilities, and facilitating common ground between humans and automation.
Automation errors may result in human performance issues that are often difficult to grasp. Skraaning and Jamieson (2023) proposed a taxonomy for classifying automation errors into categories based on the visible symptoms of design problems, so as to benefit the design of training scenarios. In this paper, we propose a complementary classification that is based on the mechanisms of human-automation interaction guided by Rasmussen’s Skill, Rule and Knowledge (SRK) taxonomy. We identified four main failure classes and expect that this classification can support automation designers.
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Automation errors may result in human performance issues that are often difficult to grasp. Skraaning and Jamieson (2023) proposed a taxonomy for classifying automation errors into categories based on the visible symptoms of design problems, so as to benefit the design of training scenarios. In this paper, we propose a complementary classification that is based on the mechanisms of human-automation interaction guided by Rasmussen’s Skill, Rule and Knowledge (SRK) taxonomy. We identified four main failure classes and expect that this classification can support automation designers.
Air Traffic Controllers (ATCOs) ensure safe and efficient operations by scanning radar displays to identify flights needing clearances. They then compare flight parameters to assess the impact of potential actions on sector safety. With global air traffic expected to rise, comparing flight labels will become more time-consuming, increasing workload and response delays. To ease this cognitive burden, a flight filtering mechanism is introduced, focusing on flights with spatio-temporal proximities to a selected flight of interest. Based on data from a previous study involving five professional controllers, filter parameters and their thresholds have been selected and tuned. Results indicate that filtering by consolidated state- and intent-based interaction parameters yield the best match to controllers’ judgements about relevant flights relative to a flight of interest. It is anticipated that the filter, outputting a list of relevant flights, can serve as an operational support tool by fading non-relevant flights, reducing cognitive effort in visual searches, and could aid Flight-Centric Air Traffic Control (ATC) allocation models that are based on predicting flight-centric complexity.
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Air Traffic Controllers (ATCOs) ensure safe and efficient operations by scanning radar displays to identify flights needing clearances. They then compare flight parameters to assess the impact of potential actions on sector safety. With global air traffic expected to rise, comparing flight labels will become more time-consuming, increasing workload and response delays. To ease this cognitive burden, a flight filtering mechanism is introduced, focusing on flights with spatio-temporal proximities to a selected flight of interest. Based on data from a previous study involving five professional controllers, filter parameters and their thresholds have been selected and tuned. Results indicate that filtering by consolidated state- and intent-based interaction parameters yield the best match to controllers’ judgements about relevant flights relative to a flight of interest. It is anticipated that the filter, outputting a list of relevant flights, can serve as an operational support tool by fading non-relevant flights, reducing cognitive effort in visual searches, and could aid Flight-Centric Air Traffic Control (ATC) allocation models that are based on predicting flight-centric complexity.
Any-angle path planning is an extension of traditional path-planning algorithms that aims to generate smoother and shorter paths in graphs by allowing any-angle moves between vertices, rather than being restricted by edges. Many any-angle path-planning algorithms have been proposed, such as Theta*, Block A* and Anya, but most of them are designed only for static environments, which is not applicable when dynamic obstacles are present. Time-Optimal Any-Angle Safe-Interval Path Planning (TO-AA-SIPP) was developed to fill this gap, which can find an optimal collision-free any-angle path that minimizes the traversal time. However, as indicated by its authors, TO-AA-SIPP may not be efficient enough to be used in multi-agent pathfinding (MAPF). Therefore, this paper presents a new algorithm Zeta*-SIPP to improve TO-AA-SIPP by means of 1) reducing useless search nodes that have no contribution to finding optimal solutions, and 2) introducing Field of View (FoV) instead of Line of Sight (LoS) to speed up visibility checks with static obstacles. Benchmark experiments showed that Zeta*-SIPP reduced the computation time of TO-AA-SIPP by around 70%-90% on average.
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Any-angle path planning is an extension of traditional path-planning algorithms that aims to generate smoother and shorter paths in graphs by allowing any-angle moves between vertices, rather than being restricted by edges. Many any-angle path-planning algorithms have been proposed, such as Theta*, Block A* and Anya, but most of them are designed only for static environments, which is not applicable when dynamic obstacles are present. Time-Optimal Any-Angle Safe-Interval Path Planning (TO-AA-SIPP) was developed to fill this gap, which can find an optimal collision-free any-angle path that minimizes the traversal time. However, as indicated by its authors, TO-AA-SIPP may not be efficient enough to be used in multi-agent pathfinding (MAPF). Therefore, this paper presents a new algorithm Zeta*-SIPP to improve TO-AA-SIPP by means of 1) reducing useless search nodes that have no contribution to finding optimal solutions, and 2) introducing Field of View (FoV) instead of Line of Sight (LoS) to speed up visibility checks with static obstacles. Benchmark experiments showed that Zeta*-SIPP reduced the computation time of TO-AA-SIPP by around 70%-90% on average.
Conference paper
(2023)
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G. de Rooij, A. Stienstra, C. Borst, A. B. Tisza, M.M. van Paassen, Max Mulder
To alleviate the workload of air traffic controllers, part of the air traffic may be handled by a future automated system. When deciding which flights to delegate, a distinction can be made between basic and non-basic flights, with the former being prime candidates for delegation. The human controller can then focus on the non-basic flights, where human competencies are most valuable and more difficult to automate. The classification of flights is preferably based on objective measures relating to the traffic situation. Existing complexity models are, however, often used for capacity predictions or airspace restructuring and primarily to assess the complexity of a sector as a whole. In this paper we use empirically collected flight complexity ratings from 15 professional en-route air traffic controllers. They indicated which other flights contributed to their complexity assessment of a single flight of interest. This exploratory study was able to build a machine-learning model which adequately classifies these flights, based on a qualified majority of controllers. By analyzing the interactions between the included flights, we discuss whether a classification model can differentiate between basic and non-basic flights, and which traffic features play the largest role. Once this can be done reliably and an appropriate complexity threshold has been chosen, a model can be developed as a starting point for an automatic allocation algorithm that distributes flights between a human controller and the computer.
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To alleviate the workload of air traffic controllers, part of the air traffic may be handled by a future automated system. When deciding which flights to delegate, a distinction can be made between basic and non-basic flights, with the former being prime candidates for delegation. The human controller can then focus on the non-basic flights, where human competencies are most valuable and more difficult to automate. The classification of flights is preferably based on objective measures relating to the traffic situation. Existing complexity models are, however, often used for capacity predictions or airspace restructuring and primarily to assess the complexity of a sector as a whole. In this paper we use empirically collected flight complexity ratings from 15 professional en-route air traffic controllers. They indicated which other flights contributed to their complexity assessment of a single flight of interest. This exploratory study was able to build a machine-learning model which adequately classifies these flights, based on a qualified majority of controllers. By analyzing the interactions between the included flights, we discuss whether a classification model can differentiate between basic and non-basic flights, and which traffic features play the largest role. Once this can be done reliably and an appropriate complexity threshold has been chosen, a model can be developed as a starting point for an automatic allocation algorithm that distributes flights between a human controller and the computer.
To facilitate a smooth integration of drones into the current Air Traffic Management (ATM) system, Unmanned Air Traffic Management (UTM) systems, services and protocols are currently under development. Unlike current ATM, UTM will rely on high levels of automation. This is potentially problematic, because 100% safe and reliable automation under all circumstances cannot be guaranteed. UTM therefore warrants human supervision and interaction, especially at small airports near urban areas where drone traffic may cross the arrival and departure routes of manned air traffic. Supervision, however, requires some form of transparency for humans to understand the limitations and the behavior of an automated system (e.g., what is it currently doing, what it is planning to do next, and why?). Previous research underlined the importance of UTM transparency, but also indicated that it remains unclear what type of human operator will eventually supervise the UTM system. The background, training and expertise of a human operator may impact the transparency needs and what information needs to be communicated and when. In this paper, the results of a questionnaire-based user study are presented in which information needs were collected from twelve operational Air Traffic Controllers and twelve drone operators and engineers. Results indicate that transparency is deemed imperative for UTM and that information elements categorized as ‘operational transparency’ are typically preferable over ‘engineering transparency’ elements, regardless of operator group. Surprisingly, we found no significant difference in transparency needs between controllers and drone operators, suggesting that a ‘one-size-fits-all’ transparency solution for UTM would be possible.
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To facilitate a smooth integration of drones into the current Air Traffic Management (ATM) system, Unmanned Air Traffic Management (UTM) systems, services and protocols are currently under development. Unlike current ATM, UTM will rely on high levels of automation. This is potentially problematic, because 100% safe and reliable automation under all circumstances cannot be guaranteed. UTM therefore warrants human supervision and interaction, especially at small airports near urban areas where drone traffic may cross the arrival and departure routes of manned air traffic. Supervision, however, requires some form of transparency for humans to understand the limitations and the behavior of an automated system (e.g., what is it currently doing, what it is planning to do next, and why?). Previous research underlined the importance of UTM transparency, but also indicated that it remains unclear what type of human operator will eventually supervise the UTM system. The background, training and expertise of a human operator may impact the transparency needs and what information needs to be communicated and when. In this paper, the results of a questionnaire-based user study are presented in which information needs were collected from twelve operational Air Traffic Controllers and twelve drone operators and engineers. Results indicate that transparency is deemed imperative for UTM and that information elements categorized as ‘operational transparency’ are typically preferable over ‘engineering transparency’ elements, regardless of operator group. Surprisingly, we found no significant difference in transparency needs between controllers and drone operators, suggesting that a ‘one-size-fits-all’ transparency solution for UTM would be possible.
In academic air traffic control research, traffic scenarios are often repeated to increase the sample size and enable paired-sample comparisons, e.g., between different display variants. This comes with the risk that participants recognize scenarios and consequently recall the desired response. In this paper we provide an overview of mitigation techniques found in literature and conclude that rotating scenario geometries is most frequently used. The potential impact of these transformations on participant behavior, as described in this paper, is however not sufficiently addressed in most studies. As an example we, therefore, analyze previously collected eye tracking data from ten professional air traffic controllers, each presented with three repetitions in various rotations of several distinct scenarios. Results imply that researchers wishing to repeat scenarios should more carefully consider whether mitigation techniques might have an impact on their results.
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In academic air traffic control research, traffic scenarios are often repeated to increase the sample size and enable paired-sample comparisons, e.g., between different display variants. This comes with the risk that participants recognize scenarios and consequently recall the desired response. In this paper we provide an overview of mitigation techniques found in literature and conclude that rotating scenario geometries is most frequently used. The potential impact of these transformations on participant behavior, as described in this paper, is however not sufficiently addressed in most studies. As an example we, therefore, analyze previously collected eye tracking data from ten professional air traffic controllers, each presented with three repetitions in various rotations of several distinct scenarios. Results imply that researchers wishing to repeat scenarios should more carefully consider whether mitigation techniques might have an impact on their results.
Expert participants may not always be available for evaluation of new displays or support systems, and in some cases, it might be better to use novice participants, particularly when the display or support significantly changes existing work practices. To provide tools and arguments for selecting the expertise level of participants, we propose the use of Rasmussen’s decision ladder to analyze where and how a new visualization or a support tool changes the task, and identify steps where a novice participant may learn to perform the task to an acceptable level. A comparison to the support with the current operational interfaces then shows where an expert might have difficulty in stepping away from learned practice. This analysis is applied to the domain of air traffic control, and a selected set of relevant past research with both expert and novice participants is reviewed, revisiting the decision for a participant level in the study.
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Expert participants may not always be available for evaluation of new displays or support systems, and in some cases, it might be better to use novice participants, particularly when the display or support significantly changes existing work practices. To provide tools and arguments for selecting the expertise level of participants, we propose the use of Rasmussen’s decision ladder to analyze where and how a new visualization or a support tool changes the task, and identify steps where a novice participant may learn to perform the task to an acceptable level. A comparison to the support with the current operational interfaces then shows where an expert might have difficulty in stepping away from learned practice. This analysis is applied to the domain of air traffic control, and a selected set of relevant past research with both expert and novice participants is reviewed, revisiting the decision for a participant level in the study.
Conference paper
(2023)
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V.G. Vasilopoulos, C. Borst, M.M. van Paassen, A.C. in 't Veld, Max Mulder
The Continuous Descent Approach (CDA) offers reduced aircraft noise emissions and fuel consumption, but the obstacle limiting it to reduced traffic density conditions, is the low predictability of the trajectory and Estimated Time of Arrival (ETA). The solution proposed by this research is to develop a pilot support interface to facilitate the execution of a fixed flight path angle CDA, where thrust is not limited to idle and a velocity profile can be tracked, which will lead to a selected ETA. Initially, the CDA trajectory was investigated through an aerodynamic model, while the solution space of the ETA and the calculation of the stepwise velocity profile were defined. Following the analysis of the pilot’s role, a two-fold support interface was designed based on Ecological Interface Design (EID), with a Vertical Situation Display (VSD) playing a central role for planning and execution. The interface was tested in a MATLAB®/Simulink® setup and five pilots were recruited to execute simulations over different wind conditions. Their on-time performance was satisfactory, while they worked with the provided cues and suggested some interface changes to offer more flexibility. A more robust application of the proposed approach can lead to a wider adoption of the CDA, as a validated procedure.
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The Continuous Descent Approach (CDA) offers reduced aircraft noise emissions and fuel consumption, but the obstacle limiting it to reduced traffic density conditions, is the low predictability of the trajectory and Estimated Time of Arrival (ETA). The solution proposed by this research is to develop a pilot support interface to facilitate the execution of a fixed flight path angle CDA, where thrust is not limited to idle and a velocity profile can be tracked, which will lead to a selected ETA. Initially, the CDA trajectory was investigated through an aerodynamic model, while the solution space of the ETA and the calculation of the stepwise velocity profile were defined. Following the analysis of the pilot’s role, a two-fold support interface was designed based on Ecological Interface Design (EID), with a Vertical Situation Display (VSD) playing a central role for planning and execution. The interface was tested in a MATLAB®/Simulink® setup and five pilots were recruited to execute simulations over different wind conditions. Their on-time performance was satisfactory, while they worked with the provided cues and suggested some interface changes to offer more flexibility. A more robust application of the proposed approach can lead to a wider adoption of the CDA, as a validated procedure.