Aircraft trajectory generation is a high stakes problem with a wide scope of applications, including collision risk estimation, capacity management and airspace design. Most generation methods focus on optimizing a criterion under constraints to find an optimal path, or on predicting aircraft trajectories. Nevertheless, little in the way of contribution has been made in the field of the artificial generation of random sets of trajectories. This work proposes a new approach to model two-dimensional flows in order to build realistic artificial flight paths. The method has the advantage of being highly intuitive and explainable. Experiments were conducted on go-arounds at Zurich Airport, and the quality of the generated trajectories was evaluated with respect their shape and statistical distribution. The last part of the study explores strategies to extend the work to non-regularly shaped trajectories.

The analysis and prediction of go-arounds, also referred to as missed approaches, is an active field of research due to the go-around’s impact on safety and the disruption of the traffic flow at airports. The advent of open-source aircraft trajectories available to researchers has increased the level of interest in the field. This paper introduces a publicly available dataset containing metadata of almost 9 million landings and 33,000 go-arounds. The dataset is based on observations from the OpenSky Network and includes data from 176 airports in 44 countries observed in the year 2019. After downloading the data, a go-around classification was performed and the quality was assessed. The usefulness of the dataset is illustrated with two novel example applications. The first example shows how the go-around rate for a runway can be modeled by using a quasi-binomial generalized linear model, while the second example compares the go-around rates for a number of airport–airline pairs. The introduced dataset is significantly larger than the data used so far in the analysis of go-arounds and provides the opportunity to develop novel use cases. This dataset frees researchers from having to collect and process large amounts of data and instead lets them focus on the analysis. The authors are convinced that this large dataset will stoke the creativity of the research community and facilitate interesting and novel applications.

Reliable short-term aircraft trajectory prediction is essential for safety and efficiency in Air Traffic Management (ATM). This work introduces a generative framework for probabilistic 4D trajectory forecasting based on Conditional Flow Matching (CFM), a recent deep generative modeling approach that combines stable likelihood-based training with efficient sampling. The model is trained on historical ADS–B data from the OpenSky Network to predict aircraft motion over a 60 s horizon, conditioned on the preceding 60 s of observations. The model generates ensembles of realistic future trajectories that capture the inherent uncertainty of aircraft motion and enable probabilistic assessment of potential conflicts. As an application, we estimate the probability of mid-air collision during a loss-of-separation event using Monte Carlo simulation over the generated trajectories, providing a quantitative risk measure. The results demonstrate that flow-based generative modeling offers a principled foundation for uncertainty-aware trajectory prediction and safety analysis in ATM.

The reaction time to a deconfliction situation refers to the interval between detecting a potential loss of separation and taking corrective action. For air traffic controllers, it represents the lead time between identifying a conflict and issuing a deconfliction instruction to a pilot. For pilots, it corresponds to the delay between receiving an ATC clearance and initiating the associated maneuver. Because both processes are influenced by human factors, these response times constitute a significant source of uncertainty in Air Traffic Management. While the controller’s reaction time is particularly difficult to estimate since the exact moment at which a conflict is cognitively detected cannot be directly inferred from operational data, this paper focuses on quantifying pilot response times. To this end, we propose a methodology that combines Natural Language Processing techniques with surveillance and flight plan data. ATC–pilot voice communications are transcribed using a fine-tuned Automatic Speech Recognition model, and aircraft callsigns are identified through Named Entity Recognition. The transcriptions are then matched with corresponding flights in ADS-B surveillance data. Using flight plans, we identify lateral deconfliction maneuvers and align them temporally with the preceding ATC clearances to estimate the elapsed time between instruction and execution. Because the approach depends on a sequence of emerging algorithms whose robustness is still evolving, the study focuses on identifying the conditions under which the methodology performs reliably, highlighting its current limitations and associated data challenges, and proposing ways to overcome them.