Simulation Tool for Estimation of Air Traffic Distribution

This paper deals with the development of a software tool for visualization of current and predicted future air traffic. The software, implemented in the Python programming language, uses real air traffic data from the OpenSky Network that represents the general air traffic situation for further understanding of the distribution of aircraft over European airspace. The methodology includes clustering of initial air traffic data sets using various clustering algorithms and the Gaussian mixture model to generate or remove aircraft from air traffic that allows users to increase and decrease air traffic based on their needs, for example following the air traffic forecast, and observe the generated air traffic distribution enabled by the visualization. This work contributes to advancements in analyzing and visualizing the evolution of air traffic and enables simulations in different areas of Air Traffic Management where forecasting future air traffic is crucial.