On the Statistics and Predictability of Go-Arounds
CoRR(2011)
摘要
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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