Lukas Beller

• Technical University of Munich

This paper presents a method to assess a novel procedure for Air Traffic Controllers, enabled by predictions of a machine learning-based go-around prediction model, regarding the operational risk of separation infringements and traffic alarms. In a previous work, potentially novel procedures were elaborated in human-in-the-loop simulations with Air...

Machine learning-based go-around predictions have been discussed in the research community for some years. Much work has been done developing algorithms and testing their accuracy, motivated by the assumption that time-in-advance information on the go-around likelihood of arrival aircraft will benefit air traffic controllers. The question of how to...

Flight dynamic-based physical models are employed to assess the risk of runway overruns, predicting risk based on the metric given by the distance to a controllable speed of 80 knots after landing. The analysis incorporates Quick Access Recorder (QAR) data from 711 flights. Despite using observed flight data from flights without accidents as inputs...

Wind has a significant influence on the operational flight safety. To quantify the influence of the wind characteristics, a wind series generator is required in simulations. This paper presents a method to model the stochastic wind based on operational flight data using the Karhunen–Loève expansion. The proposed wind model allows us to generate new...

To improve operational flight safety, a model-based predictive analysis algorithm to quantify the occurrence probabilities of airlines' incidents or accidents has been developed at the Institute of Flight System Dynamics. An incident model based on physics is built to map the relationship between the incident metric and corresponding contributing f...