On the Statistics and Predictability of Go-Arounds
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.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[
"026:00"
]
|
| contactPoint |
{
"fn": "Vlad Popescu",
"@type": "vcard:Contact",
"hasEmail": "mailto:vmpopescu@gmail.com"
}
|
| description | 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. |
| identifier | DASHLINK_308 |
| issued | 2011-02-07 |
| keyword |
[
"ames",
"dashlink",
"nasa"
]
|
| landingPage | https://c3.nasa.gov/dashlink/resources/308/ |
| modified | 2025-07-17 |
| programCode |
[
"026:029"
]
|
| publisher |
{
"name": "Dashlink",
"@type": "org:Organization"
}
|
| title | On the Statistics and Predictability of Go-Arounds |
