Applying Airspace Capacity Estimation Models to the Airspace of Hungary

Abstract

Estimation of airspace capacity in order to keep air traffic controller workload at an optimal level is essential for the safety of air traffic. In the past, several different methods were developed for airspace capacity estimation with different benefits and drawbacks. In our research we studied the applicability of one of these methods (based on a neural network model) in the airspace of Hungary. This paper presents a possible way of gathering and processing data and validating the results given by the model.