Latest revision as of 23:24, 28 January 2021

Abstract

Traffic Management systems generate a huge amount of track data daily. Flight trajectories can be clustered to extract main air traffic flows by means of unsupervised machine learning techniques. A well-known methodology for unsupervised extraction of air traffic flows conducts a two-step process. The first step reduces the dimensionality of the track data, whereas the second step clusters the data based on a density-based algorithm, DBSCAN. This paper explores advancements in density-based clustering such as OPTICS or HDBSCAN*. This assessment is based on quantitative and qualitative evaluations of the clustering solutions offered by these algorithms. In addition, the paper proposes a hierarchical clustering algorithm for handling noise in this methodology. This algorithm is based on a recursive application of DBSCAN* (RDBSCAN*). The paper demonstrates the sensitivity of these algorithms to different hyper-parameters, recommending a specific setting for the main one, which is common for all methods. RDBSCAN* outperforms the other algorithms in terms of the density-based internal validity metric. Finally, the outcome of the clustering shows that the algorithm extracts main clusters of the dataset effectively, connecting outliers to these main clusters.

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The different versions of the original document can be found in:

• http://dspace.lib.cranfield.ac.uk/bitstream/1826/13932/1/automated_identification_of_airspace_flows-2019.pdf under the license https://creativecommons.org/licenses/by-nc

• http://dx.doi.org/10.1109/dasc.2018.8569219 under the license cc-by-nc

• http://xplorestaging.ieee.org/ielx7/8540787/8569218/08569219.pdf?arnumber=8569219,

http://dx.doi.org/10.1109/dasc.2018.8569219

• https://doi.org/10.1109/DASC.2018.8569219,

http://dspace.lib.cranfield.ac.uk/handle/1826/13932

• https://dspace.lib.cranfield.ac.uk/handle/1826/13932,

https://academic.microsoft.com/#/detail/2905463778 under the license http://creativecommons.org/licenses/by-nc/4.0/