Discussion on density-based clustering methods applied for automated identification of airspace flows

Date published

2018-12-10

Free to read from

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

IEEE

Department

Type

Conference paper

ISSN

2155-7209

Format

Citation

Verdonk Gallego CE, Gómez Comendador VF, Sáez Nieto FJ & García Martínez M (2018) Discussion on density-based clustering methods applied for automated identification of airspace flows. In: 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC), London, 23-27 September 2018

Abstract

Air 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.

Description

Software Description

Software Language

Github

Keywords

density-based clustering, air traffic flows, machine learning, air traffic management

DOI

Rights

Attribution-NonCommercial 4.0 International

Relationships

Relationships

Supplements

Funder/s