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

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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.
Original languageEnglish
Title of host publication7th International Conference on Research in Air Transportation
Subtitle of host publicationPhiladelphia, USA
EditorsD. Lovell, H. Fricke
Number of pages7
Publication statusPublished - 2016
Event7th International Conference on Research in Air Transportation - Drexel University, Philadelphia, United States
Duration: 20 Jun 201624 Jun 2016
Conference number: 7
http://www.icrat.org/icrat/index.cfm
http://www.icrat.org/icrat/7th-international-conference/

Conference

Conference7th International Conference on Research in Air Transportation
Abbreviated titleICRAT 2016
CountryUnited States
CityPhiladelphia
Period20/06/1624/06/16
Internet address

Keywords

  • machine learning
  • ATM data
  • big data
  • fuzzy logic
  • BlueSky

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