Modeling aircraft performance parameters with open ADS-B data

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientific

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Abstract

Open access to flight data from ADS-B (Automatic Dependent Surveillance Broadcast) has provided researchers more insights for air traffic management than aircraft tracking alone. With large quantities of trajectory data collected from a wide range of different aircraft types, it is possible to extract accurate aircraft performance parameters. In this paper, a set of more than thirty parameters from seven distinct flight phases are extracted for common commercial aircraft types. It uses various data mining methods, as well as a maximum likelihood estimation approach to generate parametric models for these performance parameters. All parametric models combined can be used to describe a complete flight that includes takeoff, initial climb, climb, cruise, descent, final approach, and landing. Both analytical results and summaries are shown. When available, optimal parameters from these models are also compared with the Base of Aircraft Data and Eurocontrol aircraft performance database. This research not only presents a comprehensive set of methods for extracting different aircraft performance parameters but also provides a first part of open-source parametric performance models that is ready to be used by the ATM community.

Original languageEnglish
Title of host publication12th Seminar Papers
Subtitle of host publication12th USA/Europe Air Traffic Management Research and Development Seminar
Number of pages10
Publication statusPublished - 2017
Event12th USA/Europe Air Traffic Management Research and Development Seminar - Seattle, United States
Duration: 26 Jun 201730 Jun 2017
Conference number: 12
http://www.atmseminarus.org/12th-seminar/papers/

Conference

Conference12th USA/Europe Air Traffic Management Research and Development Seminar
Abbreviated titleATM 2017
Country/TerritoryUnited States
CitySeattle
Period26/06/1730/06/17
Internet address

Keywords

  • ADS-B
  • Aircraft performance
  • Data mining
  • Maximum likelihood estimation

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