Current advancements in machine learning have provided new architectures, such as encoder-decoder transformers, for automatic speech recognition. For generic speech recognition, very high accuracies are already achievable. However, in air traffic control, automatic speech recognition models traditionally rely on domain-specific models constructed from limited training data. This study introduces this newly developed transformer model for air traffic control and provides a set of fully open automatic speech recognition models with high accuracies. This paper demonstrates how a large-scale, weakly supervised automatic speech recognition model, Whisper, is fine-tuned with various air traffic control datasets to improve model performance. We also evaluated the performance of different sizes of Whisper models. In the end, it was possible to achieve word error rates of 13.5% on the ATCO2 dataset and 1.17% on the ATCOSIM dataset with a random split (or 3.88% with speaker split). The study also reveals that finetuning with region-specific data can enhance performance by up to 60% in real-world scenarios. Finally, we have open-sourced the code base and the models for future research.

The Metropolis II project aimed to study the impact of centralised separation management for urban aerial mobility. Three concepts were developed in this study: a fully centralised, strategically separated concept, a hybrid concept featuring cen- tralised strategic separation and distributed tactical separation, and a fully distributed tactical concept. A comparative simu- lation study was performed, using traffic scenarios based on predicted demand in an urban airspace in the city of Vienna. Simulations were performed with varying traffic densities and situations. Results show that the purely strategic and purely tactical strategies perform comparably in terms of safety, and that further improvements can be achieved with a combination of those strategies.