Large-Scale Flight Phase Identification from ADS-B Data Using Machine Learning Methods

Conference paper (2016)
Copyright: © 2016 Junzi Sun, Joost Ellerbroek, J.M. Hoekstra
Machine learning Big data Fuzzy logic BlueSky ATM data
More Info
expand_more

Published Date

2016

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

With the increasing availability of ADS-B transponders on commercial aircraft, as well as the rapidly growing deployment of ground stations that provide public access to their data, accessing open aircraft flight data is becoming easier for researchers. Given the large number of operational aircraft, significant amounts of flight data can be decoded from ADSB messages daily. These large amounts of traffic data can be of benefit in a broad range of ATM investigations that rely on operational data and statistics. This paper approaches the challenge of identifying and categorizing these large amounts of data, by proposing various machine learning and fuzzy logic methods. The objective of this paper is to derive a set of methods and reusable open source libraries for handling the large quantity of aircraft flight data.