A quantitative approach to density-based clustering of flight trajectories for efficient air traffic noise simulations

Abstract

The continuous growth of air traffic and increased availability of open source data has enabled the application of data-driven approaches for the prediction of noise contours around airports. Aiming at efficient noise simulations, this contribution proposes a framework for the probabilistic description of the air traffic around an airport. The methodology is based on using the density-based clustering algorithm OPTICS to cluster flight trajectories. The clustered trajectories serve as a basis for the creation of backbone and dispersion tracks, which, together with a prescribed number of flight operations per aircraft type, provide a probabilistic description of the air traffic to the noise simulations. A major focus is given to quantitatively assess the sensitivity of the OPTICS algorithm to different hyper-parameters to reduce the dimensionality of the problem. This framework is demonstrated utilizing a dataset of ADS-B trajectory data associated with flights approaching Hannover airport. Noise simulations based on the ECAC Doc. 29 best-practice method are conducted using SoundPLAN. A good agreement between noise contours is obtained when comparing simulations performed using the proposed framework and the full dataset while the computational time required decreased. Furthermore, this approach identifies most of the trajectory patterns with the least amount of outliers.