With increasing air traffic, the workload of air traffic controllers (ATCOs) and their limited number is again a restricting factor for the evolution of airspace management. Currently, possibilities to apply artificial intelligence for improving the support of ATCOs are widely discussed. By introducing a digital ATCO as a team partner for a human ATCO, we can expand capabilities. It can be trained to manage traffic across various airspace sectors without the limitations imposed by required licenses. This way, shortages of human ATCOs may be absorbed, and flexible assignment to sectors is facilitated with a digital ATCO partner. To be effective, the digital ATCO needs an understanding of current and future traffic situations to share the situation awareness of the human ATCO. The goal is to equip the digital ATCO with a comparable understanding—referred as a “mental model”—of the traffic situation and human actions, thereby improving decision-making and build up adequate trust with humans. In this work, decisive factors of traffic and management for the creation of digital situation awareness are identified and examined for their relevance and applicability for digital ATCOs. Within this study, a data-driven process of building up digital situation awareness including the influencing factors are suggested, and the usability of factors like the airspace complexity for indicating digital situation awareness are proposed. Finally, an example is presented and discussed to showcase our approach with focus on the integration of digital and human ATCOs through shared situation awareness.

With increasing automation of Air Traffic Management, new digital systems need to adapt to uncertainties caused by human behavior. One such uncertainty is the pilot response time after an air traffic control clearance issued by the Air Traffic Controller (ATCO). This study investigates factors influencing pilot response times using the Swedish Civil Air Traffic Control (SCAT) dataset by analysing roughly 830,000 clearances collected over 13 weeks. We find that pilot reactions vary significantly depending on altitude, time of day, and the maneuver delta requested by the ATCO. Lower altitudes (0–100 FL) exhibit shorter maneuver initiation delays compared to higher altitudes (300+ FL). The maneuver completion time for heading and level changes strongly correlates with the magnitude of change, whereas this correlation is weaker for speed adjustments. Although slight variations exist between airlines and aircraft types, these factors have less influence on the pilot response time. Based on these findings, we investigate the predictability of both initiation delay and maneuver completion delay. This is done by training and evaluating the performance of a machine learning model. Our model shows that maneuver completion times are highly predictable ( $$\mathbb {R}^ 2> 0.73$$ R 2 > 0.73 ) across all clearance types, while initiation delays prove more challenging to forecast accurately.

The current air traffic system is challenged by the growing volume of air traffic and a shortage of air traffic controllers (ATCOs). A potential solution is to increase the level of automation in air traffic control (ATC) by introducing a digital ATCO that is capable of working as a team partner with human ATCOs e.g., helping to resolve conflicts or optimize trajectories. Given the success of artificial intelligence (AI) in solving such complex, dynamic decision-making processes, it is a promising technology for developing these digital ATCOs. To enable the best possible cooperation between human and digital ATCOs, the latter could be fine-tuned to the decision-making patterns of human ATCOs, as human-conformal decisions tend to be more accepted by human ATCOs. One way to draw conclusions about the behavior of human ATCOs is to analyze Automatic Dependent Surveillance-Broadcast (ADS-B) data and the corresponding ATC-issued clearances and instructions. This data can be used as examples of the behavior of human ATCOs to train or adapt a digital ATCO. However, there are few publicly available datasets that provide both ADS-B and the associated clearances and instructions. Additionally, those that do exist are limited to specific regions and, therefore, may not be sufficient, as human ATCOs may have regional decision patterns or strategies that are not transferable to other areas. Therefore, to address this issue, this paper proposes an approach that uses XGBoost models trained on the Swedish Civil Air Traffic Control (SCAT) dataset, which includes both ATC-issued clearances and ADS-B data, to detect maneuvers that are likely to result from ATC instructions. Accordingly, this research is a step toward labeling any ADS-B data from publicly available sources with synthetic instructions, providing a method for generating training data to fine-tune digital ATCOs to human behavior. Using the proposed setup it was possible to achieve 95% accuracy on the detection of instructions. Also it was possible to achieve accuracies of over 90% percent when using normalized coordinates independent from the Swedish airspace, indicating that the models could be used with data from other regions. Additionally, it is shown that the flight path extracted from the ADS-B data can be used to reconstruct the type and value of the instruction that was likely issued.