Climate change is increasingly influencing the aviation industry, motivating novel aircraft concepts such as the Flying V. However, unconventional configurations introduce new challenges in stability, control, and handling qualities. Additionally, aerodynamic model uncertainties and limited research on robust control for the Flying V highlight need for improved flight control systems. This study advances the conceptual maturity of the Flying V by developing a H∞ loop-shaping C* longitudinal controller designed to ensure robust stability and performance under aerodynamic uncertainty and input/output disturbances while targeting Level 1 handling qualities. The Flying V model is implemented in MATLAB/Simulink, before a one-degree-of-freedom (1DOF) controller is designed. This is extended to a two-degree-of-freedom (2DOF) architecture incorporating parametric uncertainty and multi-model synthesis to maintain stability across the flight envelope. The 2DOF controller improves tracking and maintains good disturbance rejection while largely meeting Level 1 handling quality requirements and maintaining robust stability over many test case simulations. ... Read more

Helicopter missions experience various critical safety and efficiency challenges during landing and takeoff on unprepared areas. To address these, automatic takeoff and landing functions are being developed, such as the Airbus ENGEL system. This paper presents the design and evaluation of an interactive 3D exocentric head-down display for supervising the ENGEL system. Due to the dynamic risks of operations on unprepared areas, the human-machine interface includes predictive path preview modalities and proximal tethered orbit perspectives to maintain 360-degree spatial awareness. Using a desktop-based prototype, an exploratory usage-driven analysis is conducted across nominal and non-nominal scenarios for a cohort consisting of one experimental test pilot and three aviation experts (𝑁 = 4). The study aims to understand usage-behavior, perceived workload and situation awareness, and the system’s contribution to effective decision-making for mission efficiency. Results suggest a strong use of predictive modes during low-critical phases to assess anomalies found in high-risk phases such
as landing. Furthermore, despite an increase in workload ratings during non-nominal obstacle events, mission understanding was not compromised. This indicates a successful conversion of physical effort into effective cognitive support. Consequently, the implementation supports timely decision-making, ensuring early planning and optimizing mission efficiency. Future research recommends evaluating a cockpit-integrated system to further validate these findings under a realistic operational context. ... Read more

Currently, a high workload is experienced by executive controllers operating in Area Control Center (ACC) Sector 3 of Dutch airspace. Due to a lack of predictive information prior to departure, the planner controller is unable to manipulate regional outbound traffic with the aim of reducing the experienced workload of the executive controller. The main contribution of this paper is the introduction of a decision support tool (DST), that supports the planner controller in manipulating regional outbound flights. The DST incorporates an adapted version of the Luchtverkeersleiding Nederland (LVNL) Workload Model (WLM), which is currently used by ACC supervisors for long-term workload management for executive controllers, to compute workload scores that support the planner controller’s decisions on the timing of take-off clearances for regional outbound flights. Within this research, only the postponement of take-off clearances was considered, as departures can be delayed when necessary, whereas advancing a departure time is often operationally infeasible. An experiment was conducted involving one planner controller and five executive controllers to explore the effect of DST use by the planner controller on the workload experienced by the executive controllers. Overall, the use of the DST demonstrates potential to reduce the workload experienced by executive controllers; however, further adaptations to the WLM, such as incorporating aircraft performance characteristics into the workload calculation, may enhance the effectiveness of the DST for medium-term planning in multi-airport environments. ... Read more

The continued growth of air traffic demand is placing increasing pressure on current air traffic control (ATC) systems, prompting the need for alternative ATC strategies. A promising approach is a shared ATC environment between a human controller and an automated controller, where basic, low-complexity traffic is delegated to automation while complex traffic remains under human control. This concept requires a reliable method for predicting the operational complexity of individual flights. This research presents the design and evaluation of a complexity-based flight allocation algorithm for an en-route shared human–automation ATC environment. The allocator classifies incoming aircraft based primarily on the predicted number of interactions along their trajectories, using a flight-filtering mechanism derived from existing models. Additional allocation metrics include the expected number of interactions between human and automation-directed flights and a minimum number of flights controlled by each controller. The allocator was evaluated using offline simulations with real traffic data, followed by a human-in-the-loop experiment with two professional air traffic controllers. Results show that the allocator can consistently assign more complex flights to the human controller while maintaining a balanced workload distribution. The human-in-the-loop experiment saw substantial manual re-allocation and revealed low trust in both the allocator and the automation, indicating the need for further refinement and closer integration with automation capabilities. ... Read more

The increasing demand for Uncrewed Aircraft Systems (UAS) will begin to strain the capacity of Air Traffic Control (ATC) in the coming years. In particular, the use of UAS to support urban delivery and infrastructure inspection missions poses the greatest opportunity for growth in the UAS sector, but also the highest risk to low-flying crewed aviation in the vicinity of towered aerodromes. Achieving a safe and orderly integration of UAS flights into existing controlled airspace structures will be crucial to prevent collisions between crewed and uncrewed aircraft. Yet, the path to achieve this is anything but straight forward. Recent disruptions and airspace closures caused by reported UAS sightings near major European airports have shown how little-prepared the current air traffic management ecosystem is to integrate UAS flights. Assuming that human Air Traffic Controllers (ATCOs) will not be able to manage the complexities of UAS missions, the aviation industry and regulators are considering the implementation of separate UAS Traffic Management (UTM) systems to guide and manage the flow of UAS and prevent collisions. Their reliance on high levels of automation and limited human intervention presents a challenge in an airspace requiring both Air Traffic Management (ATM) and UTM supervision, such as in the controlled traffic region around towered aerodromes.

The work in this thesis explored how an ecologically-inspired design of a collaborative ATC-UTM interface for tower controllers could assist them in supervising UTM decisions on UAS and achieve a safe and expeditious flow of air traffic within the control zone. The concept relies on the segregation of ATC and UTM areas of responsibility to avoid the issue of having multiple agents (human tower controller vs. automated UTM) manage different traffic in the same airspace. However, dynamic changes in crewed and uncrewed airspace demands may occur, making it necessary to provide flexible airspace management mechanisms. By using tools that automated UTM systems can interpret (geofences and UASspecific commands) human controllers can temporarily turn static airspace segregation into active separation management of individual vehicles to maintain safety...
... Read more

Airside operations at major airports are under increasing pressure as traffic demand grows, environmental constraints tighten, and expectations for safety remain uncompromised. On the airport surface, increasing congestion, uncertain taxiing behaviour, and rising operational complexity place additional demands on Air Traffic Control Officers (ATCOs), intensifying the risk of inefficiencies, elevated workload, and human error. In parallel, the transition towards more sustainable ground operations, such as the emergence of engine-off taxiing techniques, require a fundamental rethinking of the existing airport operations.

Against this backdrop, regulators, researchers, and practitioners increasingly view AI-enabled decision-support and automation as key enablers for future improvements. Yet, the literature lacks a modelling framework that simultaneously captures the interrelated operational processes, heterogeneous actors, and fidelity requirements necessary to evaluate next-generation concepts for airport surface movement operations (ASM Ops) in a realistic and systematic manner. Many existing models remain limited to isolated subsystems, single concepts, or strong simplifying assumptions, which restrict their ability to compare alternative future approaches at a detailed level. To address this gap, the objective of this thesis is to design models, methods, and tools to investigate next-generation concepts of operations (ConOps) for automated and sustainable ASM Ops.

The thesis is structured around eight research questions (RQs) and proceeds in three phases. The development phase (Chapters 2 to 5) first derives modelling requirements from the operational structure of ASM Ops and from the goal to represent surface movements at high fidelity while remaining computationally tractable (RQ 1). ASM Ops encompasses a hierarchy of tasks, ranging from strategic planning (long-term and high-level decisions) to scheduling (tactical allocation of resources), routing (tactical planning of movements), guidance (operational planning of movements), and finally movement execution (controlling aircraft and ground vehicles). This hierarchical decomposition, combined with the need for coordinated oversight to maintain the strict safety standards in aviation, motivates a hierarchical–distributed modelling approach.

To provide the required modularity, adaptability, and expressiveness in both fast-time and real-time simulations, this thesis adopts the multi-agent systems (MAS) paradigm. A generalised MAS architecture is proposed, comprising environmental objects and four agent categories aligned with the hierarchy of ASM Ops tasks (RQ 2). The architecture supports different allocations of responsibilities and different coordination mechanisms, enabling the instantiation of tailored model instances for specific ConOps while retaining a reusable simulation environment and component structure.

To support trajectory-based automation studies at airport scale (RQ 3), the thesis then outlines the Multi-Agent Motion Planning on Airport Surfaces (AS-MAMP) algorithm as the decision-logic for centralised path planning in a fully-automated operational setting, i.e. on EASA’s AI Level 3. AS-MAMP is a two-level solver that builds on Priority-Based Search (PBS) and its variants for high-level conflict resolution. Because existing low-level planners were insufficient to compute realistic 4D ground trajectories under operational constraints, the thesis introduces the novel Safe Interval Motion Planning (SIMP) algorithm. SIMP plans continuous-time trajectories in accordance with the operational processes in ASM Ops (e.g. pushback and engine-start, tug coupling/decoupling, and holding), motions based on finite acceleration, and conflict avoidance in continuous space and time.

The resulting planning approach is evaluated through structured benchmarking on a synthetic airport layout, where the high-level coordination component is compared against PBS variants and SIMP is benchmarked against SIPP and kinodynamic A*. The evaluation is complemented by experiments on the real-world layout of Amsterdam Airport Schiphol to assess scalability and operational relevance under realistic airport complexity. Taken together, these results establish the MAS architecture and AS-MAMP planning capability as an enabling foundation for subsequent operational analyses, while also identifying limitations and implementation challenges that arise when moving towards real-world deployment.

The validation phase (Chapters 6 to 8) applies the developed MAS framework in operational studies and subsequently strengthens its realism. Two far-term analyses investigate fully-automated ASM Ops under multi-engine taxiing (RQ 4) and under engine-off taxiing (RQ 5), examining system-level implications under dense traffic assumptions. Building on feedback from ATCOs and other operational experts gathered throughout the thesis, historical aircraft ground tracks are then analysed to improve the calibration of key movement parameters (RQ 6) so that the model is able to more accurately reproduce the historical operations. The MAS model is further extended to represent aircraft towing movements and to assess their impact on regular traffic (RQ 7). Based on these modifications, multiple operational model variations are compared to clarify the sensitivity of results to modelling assumptions and to discuss implications for next-generation surface operations.

Fully-automated operations pose significant implementation challenges in real-world settings, so human involvement remains required for the foreseeable future. Therefore, as a final exploration phase, Chapter 9 provides an outlook towards EASA’s AI Level 2 concepts by examining how human–automation interaction mechanisms can be embedded into the MAS model to enable operator involvement (RQ 8). These demonstrations focus on technical feasibility – illustrating how interfaces and information exchange could be embedded into the MAS model – rather than validated evidence from human-in-the-loop experiments. Chapter 10 concludes by synthesising the findings across all research questions, reflecting on limitations and implications for future ASM Ops, and outlining directions for continued development and socio-technical validation. ... Read more

Current approach control at Schiphol is mainly based on radar vectors, which offer high flexibility to the air traffic controller. New technologies such as Required Navigation Performance - Authorization Required enable fixed approach routes with curved segments to be flown with high precision. While fixed approach routes are desirable to reduce track miles, to enable continuous descents, and to avoid noise-sensitive areas, decision support is currently only available on the final approach leg whereas earlier support is needed. The shift to fixed routes requires a decision support tool to merge different approach types without increasing the workload. In this paper, the design of the Final Merge Tool (FMT) is presented, which combines projections of downwind traffic with separation markers on the final approach leg. It uses a time-based prediction algorithm and integrates the projections in the existing support tool. The FMT interface is evaluated in a first exploratory real-time simulation with four professional air traffic controllers from the Netherlands Air Traffic Control, comparing different mixes of traffic on fixed routes versus vectored traffic. Results from this evaluation show that the number of commands issued decreased with the tool and that the subjective workload was lower. Controllers were able to use their own strategies with the tool and generally found the support helpful for determining the sequence on final approach. There were no indications that the tool decreased safety, but further research is needed to confirm this with more certainty. ... Read more

Handling high workload is a key concern when implementing Reduced-Crew Operations (RCO). Research has shown that both checklist completion time and decisionmaking performance suffer when reducing the crew complement from two to one. Although automation has historically been used to address workload issues, it has introduced its own set of challenges. Therefore, allocating more tasks to automation with the aim to lower workload may amplify adverse side effects instead of solving any. Instead, automation should be designed to increase the performance of the human-machine system as a whole.

RCO presents an opportunity to critically reassess automation on the flight deck by redefining the role of the pilot. Many researchers agree that the pilot remains the ultimate decision-maker and is responsible for ensuring the safety and success of the flight operation. The pilot’s role will encompass flight planning, communication, and surveillance, while system management tasks are considered suitable candidates for automation. However, automating system management may lead to diminished system state awareness, potentially compromising flight plan management performance. Consequently, additional support is needed to keep the pilot actively engaged with flight plan management tasks.

In addition to addressing the potential adverse effects of automating system tasks, the current support for flight plan management requires already a significant improvement. A key challenge in handling non-normals lies in assessing and integrating disturbances into the flight plan. Pilots must gather, combine, and analyze environmental and system information. This information is often fragmented across multiple sources and requires decryption to become actionable. This process heavily relies on the pilot’s initiative and experience, increasing the risk of unconsidered impacts.

This study examined the impact of elevating the Level of Automation (LOA) for system and flight plan management functions. A proposed concept elevated the LOA of the system management support, specifically the action execution stage from a stepby- step action support to a system that autonomously performs a sequence of actions after human activation. In flight plan management, the information acquisition and analysis stages were highly automated, with the goal of reducing workload while enhancing decision-making performance…
... Read more

With the rapid advancement of technology, drones are being actively deployed across various domains. To manage their growing presence in the airspace, Uncrewed Air Traffic Management (UTM) has been proposed and is currently under development. Given the expected high volume of drone traffic, UTM will rely heavily on high levels of automation, as it is impractical to control each drone manually in the manner of traditional Air Traffic Control (ATC). However, this reliance on automation presents potential risks, particularly in airspace around airports where crewed aircraft are taking off and landing. Since completely reliable automation has not yet been achieved, anomalies or failures in UTM systems could increase the risk of collisions between drones and crewed aircraft. Therefore, UTM still warrants human supervision to ensure the safety of drone operations.

As automation becomes more advanced and complex, it also becomes increasingly difficult for humans to supervise, thereby hindering their trust and acceptance. Previous research suggests that some form of “seeing-into” transparency may be required to address this issue and support effective human supervision of automated systems. In this dissertation, “seeing-into” transparency is categorised into operational transparency and engineering transparency. Operational transparency offers (real-time) insights into the automation’s states, actions, goals, and environmental impact, helping operational users maintain situation awareness and respond effectively to changing conditions. Engineering transparency, in contrast, discloses the inner workings of automation, enabling users to develop a deeper understanding of automation behaviour. This research adopts a bottom-up approach, beginning with engineering transparency and progressing towards operational transparency.

This dissertation focuses on achieving transparent path planning in UTM routing. To this end, a visual approach was first proposed to reveal the internal processes of path-planning algorithms, with a focus on graph- and sampling-based ones, as shown in Chapter 2. To demonstrate the effectiveness of the approach, a novel web-based pathfinding visualiser was developed that incorporates various classic and advanced path-planning algorithms, such as A*, Theta*, Anya, Polyanya, Rapidly-exploring Random Tree (RRT), RRT*, Informed RRT* and Batch Informed Tree (BIT*). To evaluate the impact of the proposed approach on algorithm runtime, extensive benchmark tests were performed on public datasets. Results show that extracting all search trees during the search process may significantly slow down the original algorithms. For large-scale, real-time operations, it is recommended to record only necessary data during the search and perform search tree extraction afterwards for visualisation.

To further investigate the effectiveness of algorithmic transparency, a user study was conducted to evaluate its impact on human understanding, as presented in Chapter 3. Considering that directly presenting the search process may overwhelm users, particularly in operational contexts, the path-planning transparency was structured into six distinct levels. Results indicate that as the transparency level increases, so does human understanding. However, the relationship between transparency and understanding is not a linear one. When the algorithm behaves contrary to human expectations and increased transparency fails to provide a clear explanation, users may become even more confused than without the additional transparency. For non-expert users unfamiliar with the algorithm, full transparency is often critical for meaningful understanding. The user study suggests that sampling-based algorithms may be easier to comprehend than graph-based algorithms. While the randomness inherent in sampling-based algorithms makes their behaviour difficult to predict, their overall rationale and underlying principles are meaningful and intuitive to humans.

As the ultimate goal of this dissertation is to achieve transparent path planning for UTM, the focus was then shifted from path-planning algorithms to UTM routing, broadening the concept of algorithmic transparency from purely engineering concerns to encompass operational dimensions, as shown in Chapter 4. A unified transparency taxonomy was developed, integrating diverse aspects of algorithmic transparency. Based on the proposed taxonomy, twenty transparency elements and their corresponding visual prototypes were devised for UTM routing. A survey study was then conducted to investigate the needs and preferences of Air Traffic Controllers (ATCos) and drone experts regarding these elements and prototypes. Results show that operational transparency is deemed more useful than engineering transparency in nominal UTM scenarios, whereas engineering transparency becomes more valuable when UTM routing fails. In the survey, operators were also asked to group the transparency elements, and their groupings aligned with the proposed transparency taxonomy.

The survey study captures only the initial opinions of operators, shaped by their prior knowledge and experience. To gain more insights, a human-in-the-loop experiment was performed to further examine the actual usage of various transparency elements in dynamic scenarios, where time pressure is a key concern, as shown in Chapter 5. Results show that information regarding the Closest Point of Approach (CPA) between drones and crewed aircraft is the most useful element for supporting tactical UTM supervision. When UTM routing fails, operators typically seek more information, such as constraint changes and details about the algorithm’s inner workings, to understand the failure and to gather clues that inform their intervention strategies. Similar to the user study presented in Chapter 3, the experiment in Chapter 5 also suggests that in UTM contexts, sampling-based algorithms might be more suitable for supervision than graph-based algorithms. This is likely because the search tree visualisation of sampling-based algorithms could more clearly convey the algorithms’ exploration efforts, offering useful cues for human intervention, such as indicating regions that are likely to be conflict-free.

In conclusion, this research achieves algorithmic transparency in path planning and demonstrates its application within UTM contexts. It contributes further empirical evidence to the growing body of research underscoring the importance and benefits of algorithmic transparency. The findings suggest that algorithmic transparency can enhance human understanding, but its utility in operational settings may be limited by situations, time pressure, and workload. As operators develop trust or expertise, their need for transparency may diminish. Overall, transparency is essential to facilitating trustworthy automation, especially when it is not yet fully reliable. ... Read more

Air traffic control (ATC) is transitioning towards a more automated system where human air traffic control officers (ATCOs) are increasingly supported by systems working at a high(er) level of automation (LOA). Made possible by advancements in computing power, artificial intelligence and a more data-driven air traffic management (ATM) system, automation is expected to address major issues, such as a global staff shortage, growing air traffic demand and environmental concerns.

On this shift towards greater reliance on automation, two main strategies can be identified that each have a distinct impact on the system's operators (i.e., ATCOs). Chapter 2 details how these differ between a traditional function-based strategy, where all flights are controlled at a gradually increasing LOA, and a constraint-based strategy, where a subset of flights is operated at a higher LOA than other flights. The former strategy brings many human-automation issues that have been widely demonstrated through empirical research, such as 'out-of-the-loop' situation awareness, transient workload peaks, skill erosion, boredom and reduced job satisfaction. The latter strategy has the advantage of avoiding mixed authority over individual flights by creating a more parallel system than the function-based serial system. The resulting human-autonomy team (HAT) accelerates the introduction of higher LOA in operational environments, fostering innovation.

The HAT perspective has only recently appeared on the radar of the ATC community, and practical examples of its potential and implications are scarce. An interesting example is found at Maastricht Upper Area Control Centre (MUAC), an air navigation service provider (ANSP) responsible for air traffic above 24,500 ft over Belgium, Luxembourg, the Netherlands, and part of Germany. MUAC is currently employing a constraint-based strategy in the development of a future shared airspace where ATC services for low-complexity routine flights are fully automated while complex flights stay with the ATCO. A key challenge for such an ATC system is to determine which flights should be allocated to either the human ATCO or the automation.

This research set out to broaden the knowledge about constraint-based automation in ATC and the desired allocation of flights in particular. Each chapter addresses a subquestion, often through empirical research with professional MUAC ATCOs. The research had three phases, starting with a first exploration, followed by an impact analysis of flight allocation on ATCO workflows and the role of flight complexity in this. The thesis concludes with a validation exercise consolidating all insights from the preceding chapters.

To test several preconditions and general ATCO acceptance of this novel concept, Chapter 3 begins with an exploratory simulator experiment. The participating ATCOs had full control over which flights they would delegate to the automation. Although pre-defined suggestions were presented, the ATCOs mostly ignored these. This experiment demonstrated the potential for allocating selected flights to either human or automation in a single airspace, but also stressed the importance of using a clever algorithm to determine this allocation. Geographic sector-based allocation, with automation handling all traffic in one sector and the ATCO all traffic in another sector, was rejected by the majority of participating ATCOs. They preferred an interaction-based allocation, hinting at the need to establish a complexity-score for each single flight.

Diving deeper into the impact that flight allocation might have on the workflow of an ATCO, Chapter 4 focuses on the core ATCO tasks: conflict detection and resolution (CD&R). Following a literature study and on-the-job ATCO observations, cognition flowcharts were constructed for these two tasks. Through an experiment with simplified static traffic scenarios, in which ATCOs had to detect and resolve conflicts, the most cognitively demanding types of traffic situations were searched for, as a means to quantify the various cognitive paths that can be traversed in the flowcharts. This turned out to be challenging, as ATCOs, like other experts, make frequent use of shortcuts and parallel processing. The constructed flowcharts can, however, serve as a starting point for the design of more human-like CD&R algorithms, such as used in this thesis' experiments. Automation that performs tasks in similar fashion as an ATCO might increase operator acceptance. This chapter's results stressed the importance of understanding flight-centric complexity before the impact of flight allocation on workflows can be determined.

To increase this understanding, the experiment in Chapter 5 used actual traffic snapshots overlaid with a single flight of interest for which the ATCOs had to indicate their perceived complexity. This individual flight complexity was a unique approach, compared to existing literature that mainly considers sector-wide complexity. Despite individual differences, flights on either end of the complexity scale were reliably identified. These results indicate that a flight allocation scheme may not need to be fine-tuned towards individual ATCO preferences. In general, a flight's complexity appears to be mostly driven by (potential) spatiotemporal interactions with other flights.

Consolidating the insights from preceding chapters, Chapter 6 discusses the most realistic and extensive experiment of this thesis. It replicates the experiment from Chapter 3 while addressing many of that experiment's shortcomings. Lessons learned in the preceding chapters led to several improvements, such as an increase in automation capabilities and communication, and more informed allocation schemes than the pragmatic schemes from the first experiment. In a direct comparison between two distinct allocation schemes, it was found that an interaction-based scheme is subjectively preferred by ATCOs and shows small efficiency benefits over a simpler flow-based allocation. In addition, it was concluded that automation should be sufficiently equipped to issue the same instructions as ATCOs, and should have the same notion of constraints from letters of agreement, to create a common ground and reduce mixed conflicts.

In conclusion, this thesis has brought forward the knowledge about flight allocation in an airspace that is shared between a human ATCO and a computer system. It can serve as a starting point for future research and development of highly automated ATC systems. Fully autonomous ATC will not become a reality in the short-term, but results show promising effects and a general feasibility of higher LOA applied to a constrained environment (i.e., a subset of flights). Researchers and ANSPs are encouraged to step beyond purely function-based visions on automation allocation and embrace a constraint-based automation strategy. This thesis has shown that a combination of these two strategies may lead to desired human-automation teamwork. ... Read more

This study explores the application of cognitive work analysis (CWA) and ecological interface design (EID) in the development of a novel display system for a Dash 8 Q300 aircraft retrofitted with a hydrogen-electric fuel system. By leveraging CWA and EID, this research aimed to address the challenges of managing cognitive complexity in next-generation aviation systems, focusing on designing interfaces that enhance pilot decision-making and situational awareness. These analytical frameworks informed the design process, ensuring that the displays were tailored to the cognitive demands of the pilots. To validate the effectiveness of the proposed designs, interviews were conducted with a regional commercial pilot, an airworthiness engineer, and a test pilot. These interviews provided qualitative insights that confirmed the applicability of the CWA/EID-based designs, particularly emphasizing the need for simplicity and clarity in time-constrained regional operations. The study highlights the importance of focusing display content on critical and abnormal conditions to reduce cognitive load, aligning with rule-based behavior (RBB) frequently employed by pilots. Future work should involve controlled human-in-the-loop experiments with a larger participant pool to empirically test the proposed display designs. ... Read more

The introduction of Trajectory Based Operations (TBO) is set to change the operation on all levels of Air Traffic Control (ATC), including aerodrome control. Here, adherence to a planned four-dimensional trajectory is important to achieve a stable operation. At the same time, this concept provides possibilities for support tools to be used in ATC, by making use of new data and information. This research presents the Take-off Timing Support Tool (TTST) for aerodrome control, and more specifically runway control, at Amsterdam Airport Schiphol. It is incorporated into the already existing Electronic Flight Strip System and features a time axis on which flight strips can be placed. By dragging departures along the time axis a planning and sequence can be constructed, while taking into account the provided solution space to prevent conflicts. An experiment conducted with the TTST showed that professional air traffic controllers used the information from the tool in their decision-making, leading to a decrease in conflict count, and it was demonstrated that a stable planning could be established. Therefore, the TTST is a suitable platform to support TBO in aerodrome control and assists air traffic controllers with time-based control. No significant difference in departure interval was present. The workload did increase moderately. ... Read more

Enroute Air Traffic Controllers (ATCOs) determine the appropriate course of action by scanning radar displays to identify and select a flight that requires clearances to ensure safe and efficient operations within their airspace sector. After selecting a flight of interest, ATCOs visually compare the flight parameter displayed in the flight labels to assess the impact of potential flight control actions on the sector safety. Expected surge in global air traffic will make it difficult for ATCOs to compare flight parameters, leading to delayed responses and increased workload. This research proposes a flight filtering mechanism based on interacting flight trajectories with spatio-temporal proximities to guide ATCOs’ attention toward potential interaction flights after selecting a flight of interest. ATCOs will find potential interaction flights more saliently as non-interacting flights fade once the flight of interest is selected. This approach targets ATCO’s pre-attentive phase of visual processing, alleviating additional perceptual and cognitive efforts in finding and processing information during conflict detection and previewing clearances. Experiment results with eight subject matter experts as participants showed no significant differences in the safety, operator performance, and perceived workload while controlling traffic due to demanding task requirements and strategy variation among participants. Nonetheless, participants expressed positive feedback regarding the assistance offered by filtering in fading non-interacting flights. No significant differences in objective measures coupled with filtering’s positive perceptions indicate filtering did not negatively affect the performance, suggesting further research with optimal balance between task requirements and participant expertise to evaluate the flight filtering concept. ... Read more

An important bottleneck in managing inbound traffic capacity is the limited size of the Terminal Manoeuvring Area, which should not become crowded by aircraft arriving from the Control Area. This is done by arrival metering, a process entailing sending aircraft through the Initial Approach Fix (IAF) at pre-established Expected Approach Times (EAT). Currently, area controllers are required to deliver aircraft at the IAF at their EAT ± 120 seconds. Reducing this window in the near future would allow for less buffer times between aircraft to increase capacity, potentially allowing implementation of time-based separation at the IAF as an initial step towards trajectory based operations. This research aims to produce a visual decision support tool to aid the aircraft sequencing process and achieving EAT adherence at the IAF, that is compatible with the currently in use or upcoming systems used in the Amsterdam Area Control Center. To avoid significant changes to the modus operandi of controllers, the aim is to show the user the operational constraints and focus on expediting traffic. An experiment was conducted with eight professional controllers to compare the newly developed tool with a rendition of the current interface. The results are positive, indicating less deviation from the EATs in all participants and overall lower participant workload as a result of using the proposed decision support tool. ... Read more

The joint management of airspace by human controllers and automated agents is gaining prevalence in nextgeneration Air Traffic Control (ATC). In such settings, human controllers are challenged with maintaining situation awareness while dealing with intricate and often opaque automated technologies. This study probed the potential of a concept interface, which augments traditional radar displays, to facilitate human controller-automation teamwork. With visual explanations and interactive elements, it intended to make the control behavior of automated agents transparent and comprehensible to human operators. The primary objective was to explore whether features from a non-conventional design could offer insights for designing interfaces for highly automated ATC operations. The concept was tested in a prototype-based user experience evaluation with static traffic scenarios, involving 15 active air traffic controllers from the Maastricht Upper Area Control Center. Results indicated that the visual explanations of the interface enabled controllers to comprehend the plans and reasoning underlying automation’s behavior. A significant correlation between controllers’ evaluations and their attitudes toward novel technologies underlines the influence of pre-existing beliefs about automation on the acceptance of new interface tools. Future research is warranted to streamline parts of the design and further investigate the interface’s capacity for explaining automation’s actions in dynamic, real-time scenarios. ... Read more

The Dutch Air Traffic Control has implemented fixed approach trajectories within approach control during night operations when traffic density is low. During the day, when traffic density is higher, vector-based operations are used. The Dutch Air Traffic Control aims to implement fixed approach trajectories when traffic density is high. This shift from the current vector-based operations will allow aircraft to perform a continuous descent and fly around inhabited areas, reducing the emissions and noise. To improve the capacity of the landing runway in strong headwind conditions, time-based separation is envisioned. Separating aircraft on time will result in a constant runway capacity in all wind conditions. However, to make these changes possible, additional decision support tools are required to assist controllers and reduce their workload. This paper discusses the use of a new toolset, centered around a time-space diagram which shows the expected arrival time and distance-to-go to a selected reference waypoint. Results of an experiment with eight professional air traffic controllers show that the added tools allowed future conflicts to be solved sooner and with fewer instructions. Aircraft followed the fixed approach trajectories better in the latter stages of the approach when compared to current vector-based operations. Although a slight increase in runway capacity was observed with the new toolset, it was not statistically significant. The workload of the controllers did not show a difference when using the new toolset. In conclusion, the added tools enabled the controllers to combine fixed approach trajectories with time-based separation. However, to reduce perceived workload, future designs should aim to integrate the time-space diagram on the main radar screen such that controllers do not need to switch attention between screens. ... Read more

In the future, Air Traffic Controllers are expected to work together with more advanced computer-based automation that can automatically take action. The main challenge is then how to design computer-based tools such that they foster acceptance among air traffic controllers. One possible approach to foster acceptance is by matching the automated decisions and actions to individual human problem-solving styles, the so-called strategic conformance. Another approach is by making the automated tool more transparent and thus interpretable. Previous research aimed to combine these two approaches by making use of the Solution Space Diagram, a decision-support tool for Conflict Detection and Resolution, as a visual feature for a supervised machine learning method that aimed to generate individual human prediction models. Results were promising, but prediction accuracy could be significantly improved. In this study, the impact of feature engineering and a revised machine learning architecture on prediction accuracy will be investigated. This is done by evaluating different feature engineering and architecture options using data generated by a simulation in which Conflict Detection and Resolution is performed. It was found that a Convolutional Neural Network can accurately predict exact resolutions using regression and a more optimized architecture is introduced which significantly increases predictive performance. Furthermore, it is concluded that a larger solution space results in a slight increase in predictive performance whereas the use of a color scheme with more colors does not necessarily result in a higher predictive performance. ... Read more

Future ATM systems will rely on automation to make operations more efficient. Creating insight into the inner-workings of automation, also known as agent transparency, is expected to play an important role for effective human-machine collaboration. This research proposes an ecological approach to increase agent transparency in automated rerouting for en-route traffic. For the purpose of this study, an ecological interface for the rerouting task, developed in a previous study, was visually augmented with the constraints guiding the behavior of an experimental path-planning algorithm. This was done in two different ways: a top-down and bottom-up approach. The top-down approach starts at the goal of the system and subsequently adds information related to the physical implications, while the bottom-up approach has the reversed order. The design was tested in a human-in-the-loop experiment with ten participants. Results show that higher levels of transparency significantly increased actual and perceived understanding of the agent’s decisions. Furthermore, the top-down approach performed significantly better in questions related to the strategy of automation, while the bottom-up approach was found more useful for making predictions about the agent’s rationale for making certain decisions. Future research should investigate how agent and domain transparency could be combined and should test situation awareness in addition to understanding of automation. Additionally, because only static situations were investigated in this study, the effects of a dynamic work domain featuring various time-critical situations should be analyzed in future research. ... Read more

The Continuous Descent Approach (CDA) offers reduced noise emissions and fuel consumption, but its main complication is the lack of predictability regarding the Estimated Time of Arrival (ETA). A suggested solution is to develop a pilot support interface for the execution of a fixed flight path angle CDA for a selected ETA, with thrust not being bounded to idle. Initially, the trajectory calculation method of CDA and the boundary cases were defined. Then, a two-fold interface was designed for planning and execution, based on Ecological Interface Design (EID), with a Vertical Situation Display (VSD) playing a central role, and it was tested by five pilots over different wind conditions. The pilots' on-time performance was satisfactory, while they worked with the cues and suggested enhancements to accommodate their strategy. The validation of this approach can lead to applying the CDA in real flight conditions, without limiting itself to low traffic times. ... Read more

The current ATC system is seen as the most significant limitation to coping with an increased air traffic density. Transitioning towards an ATC system with a high degree of automation is essential to cope with future traffic demand of the airspace. In recent studies, reinforcement learning has shown promising results automating Conflict Detection and Resolution (CD&R) in Air Traffic Control. The acceptance of automation by Air Traffic Controllers (ATCos) remains a critical limiting factor to its implementation. This work explores how automation can be developed using Deep Q-Learning from Demonstrations (DQfD), which aims to be transparent and conforms with strategies applied by ATCos to increase acceptance of automation. Reward decomposition (RDX) is used to monitor the learning and to understand what the agent has learned. This study focuses on two-aircraft conflicts, in which the state of the controlled and observed aircraft is represented by raw pixel data of the Solution Space Diagram. It was concluded that pre-training on demonstrations speeds up learning and can increase strategic conformance between the solutions provided by the RL agent and the demonstrator. Next to increasing conformance, results also show that DQfD can improve its policy with respect to the suboptimal demonstrations used during training. Finally, RDX has allowed the designer to examine the policy learned by the RL agent in more detail. ... Read more