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96937 |
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|a Gariel, Maxime
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|a Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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|a Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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|a Gariel, Maxime
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|a Spieser, Kevin
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|a Frazzoli, Emilio
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|a Spieser, Kevin
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|a Frazzoli, Emilio
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|a On the Statistics and Predictability of Go-Arounds
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|c 2015-05-08T14:36:04Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/96937
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|a 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.
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|a United States. National Aeronautics and Space Administration (Grant NNX08AY52A))
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|a en_US
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|a Article
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|t Proceedings of the 2011 Conference on Intelligent Data Understanding
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