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.

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.

Haptic cues on the side stick are a promising method to reduce loss of control in-flight incidents. They can be intuitively interpreted and provide immediate support, leading to a shared control system. However, haptic interfaces are limited in providing information, and the reason for cues may not always be clear to pilots. This study presents the results of the conceptual development of visual display symbology that supports haptic feedback on the side stick in communicating flight envelope boundaries to pilots. Novel indications for the limits of airspeed, load factor, angle of attack, and angle of bank, which for the first time simultaneously indicate magnitude and direction of the haptic cues, were integrated in an Airbus primary flight display. The symbology was tested in a pilot-in-the-loop experiment with professional Airbus pilots (N=16) flying several approaches in alternate law with haptic feedback. Objective results do not show clear improvements, although the time spent outside the flight envelope is slightly reduced. Subjective results indicate a preference, however, for the new display and an increased understanding of the haptic feedback. Further research is recommended to improve the interface design, remove unused indications, and test a bank scenario using current operational bank limits.

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.

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.

In the quest for more efficient air traffic management, a common approach is to allocate an increasing amount of functionality to higher levels of automation, with a supervisory role for humans. This potentially leads to forthcoming issues such as skill degradation and out-of-the-loop phenomenon. If the traffic in an airspace is instead shared between a human operator and an automated system, with specific flights fully delegated to automation, operators can maintain their skills and stay actively involved in controlling the rest of the traffic. This does, however, lead to new forms of mixed conflicts, where two flights are controlled by different agents. A smart flight allocation strategy, starting with the delegation of basic flights requiring little monitoring or cognitive effort, is expected to improve combined human-automation performance. In this paper, we present flowcharts to model en-route air traffic controller cognitive think and action processes in two core tasks: conflict detection and resolution. We qualitatively describe the impact of delegating flights to automation and the associated introduction of mixed conflicts. Once empirically validated and quantified in follow-up research, these models can be used to design flight allocation strategies for future human-automation teams.

Allocation is a challenge for higher levels of automation in air traffic control, where flights can be dynamically assigned to either a human or an automated agent. Through an exploratory experiment with six professional air traffic controllers, insight was gained into the possibilities and challenges of human-automation teamwork in an en-route environment. Participants showed high levels of automation trust, but mostly ignored automation-suggested allocations, preferring a highly automated sector instead. Most flights were delegated to automation, after they were given a direct and conflict-free path. Flights handled manually were those requiring level changes or non-standard routing. Future research should focus on establishing specifically which flights can be automated.

This paper describes the design and evaluation of a visual display in supplementing haptic feedback on the side stick as a way to communicate flight envelope boundaries to pilots. The design adds indications for the limits in airspeed, load factor, angle of attack and angle of bank to a standard Airbus primary flight display (PFD). The indications not only show the limits of the flight envelope, but also indicate magnitude and direction of the haptic cues. Fifteen professional Airbus pilots and one Airbus sim instructor participated in an experiment in the SIMONA Research Simulator at Delft University of Technology. Several approaches in three different scenarios were flown in alternate law with the old and new PFD, while haptic feedback was always enabled. Objective results do not show clear improvements with the new display, although the time spent outside the flight envelope is slightly reduced. Subjective results indicate a preference, however, for the new display and an increased understanding of the haptic feedback. Further research is recommended to focus on improving the design by removing unused indications and setting up an experiment with a bank scenario that allows the use of operational bank limits rather than artificially reduced limits.