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

Junzi Sun; Joost Ellerbroek; Jacco Hoekstra

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

Junzi Sun, Joost Ellerbroek, Jacco Hoekstra

Control & Simulation

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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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 language	English
Title of host publication	7th International Conference on Research in Air Transportation
Subtitle of host publication	Philadelphia, USA
Editors	D. Lovell, H. Fricke
Number of pages	7
Publication status	Published - 2016
Event	7th International Conference on Research in Air Transportation - Drexel University, Philadelphia, United States Duration: 20 Jun 2016 → 24 Jun 2016 Conference number: 7 http://www.icrat.org/icrat/index.cfm http://www.icrat.org/icrat/7th-international-conference/

Conference

Conference	7th International Conference on Research in Air Transportation
Abbreviated title	ICRAT 2016
Country/Territory	United States
City	Philadelphia
Period	20/06/16 → 24/06/16
Internet address	http://www.icrat.org/icrat/index.cfm http://www.icrat.org/icrat/7th-international-conference/

Keywords

machine learning
ATM data
big data
fuzzy logic
BlueSky

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Sun, Ellerbroek, Hoekstra - 2016 - Large-Scale Flight Phase Identification from ADS-B Data Using Machine Learning MethodsAccepted author manuscript, 977 KB

Cite this

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title = "Large-Scale Flight Phase Identification from ADS-B Data Using Machine Learning Methods",

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. ",

keywords = "machine learning, ATM data, big data, fuzzy logic, BlueSky",

author = "Junzi Sun and Joost Ellerbroek and Jacco Hoekstra",

year = "2016",

language = "English",

editor = "{ Lovell}, D. and H. Fricke",

booktitle = "7th International Conference on Research in Air Transportation",

note = "7th International Conference on Research in Air Transportation, ICRAT 2016 ; Conference date: 20-06-2016 Through 24-06-2016",

url = "http://www.icrat.org/icrat/index.cfm, http://www.icrat.org/icrat/7th-international-conference/",

}

Large-Scale Flight Phase Identification from ADS-B Data Using Machine Learning Methods. / Sun, Junzi ; Ellerbroek, Joost ; Hoekstra, Jacco.
7th International Conference on Research in Air Transportation: Philadelphia, USA. ed. / D. Lovell; H. Fricke. 2016.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Sun, Junzi

AU - Ellerbroek, Joost

AU - Hoekstra, Jacco

N1 - Conference code: 7

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - machine learning

KW - ATM data

KW - big data

KW - fuzzy logic

KW - BlueSky

UR - http://resolver.tudelft.nl/uuid:af67a6bd-d812-474d-a304-7a594991390b

M3 - Conference contribution

BT - 7th International Conference on Research in Air Transportation

A2 - Lovell, D.

A2 - Fricke, H.

T2 - 7th International Conference on Research in Air Transportation

Y2 - 20 June 2016 through 24 June 2016

ER -

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

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