Predicting and reconstructing clearances from air traffic data using a supervised learning approach

Abstract 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.