On the Statistics and Predictability of Go-Arounds
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Frazzoli_On the statistics.pdf
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487.17 KB
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Checksum (MD5)
3c61a50b21758a0459d1e6cb7b1fce99
Author(s)
Gariel, Maxime
Spieser, Kevin
Frazzoli, Emilio
Date Issued
October 2011
Journal
Proceedings of the 2011 Conference on Intelligent Data Understanding
Citation
Gariel, Maxime, Kevin Spieser, and Emilio Frazzoli. "On the Statistics and Predictability of Go-Arounds." 2011 Conference on Intelligent Data Understanding, October 19-21, 2011.
Version
Original manuscript
Abstract
This paper takes an empirical approach to identify operational factors at busy airports that may predate go-around maneuvers. Using four years of data from San Francisco International Airport, we begin our investigation with a statistical approach to investigate which features of airborne, ground operations (e.g., number of inbound aircraft, number of aircraft taxiing from gate, etc.) or weather are most likely to fluctuate, relative to nominal operations, in the minutes immediately preceding a missed approach. We analyze these findings both in terms of their implication on current airport operations and discuss how the antecedent factors may affect NextGen. Finally, as a means to assist air traffic controllers, we draw upon techniques from the machine learning community to develop a preliminary alert system for go-around prediction.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://c3.nasa.gov/dashlink/static/media/other/CIDU_Proceedings2011.pdf
