Abstract

When the air traffic demand is expected to exceed the available airport's capacity for a short period of time, Ground Stop (GS) operations are implemented by Federal Aviation Administration (FAA) Traffic Flow Management (TFM). The GS requires departing aircraft meeting specific criteria to remain on the ground to achieve reduced demands at the constrained destination airport until the end of the GS. This paper provides a high-level overview of the statistical distributions as well as causal factors for the GSs at the major airports in the United States. The GS's character, the weather impact on GSs, GS variations with delays, and the interaction between GSs and Ground Delay Programs (GDPs) at Newark Liberty International Airport (EWR) are investigated. The machine learning methods are used to generate classification models that map the historical airport weather forecast, schedule traffic, and other airport conditions to implemented GS/GDP operations and the models are evaluated using the cross-validations. This modeling approach produced promising results as it yielded an 85% overall classification accuracy to distinguish the implemented GS days from the normal days without GS and GDP operations and a 71% accuracy to differentiate the GS and GDP implemented days from the GDP only days.


Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.1109/dasc.2014.6979510 under the license cc0
https://repository.exst.jaxa.jp/dspace/handle/a-is/61893,
https://academic.microsoft.com/#/detail/2014785326
Back to Top

Document information

Published on 01/01/2014

Volume 2014, 2014
DOI: 10.1109/dasc.2014.6979510
Licence: Other

Document Score

0

Views 0
Recommendations 0

Share this document

Keywords

claim authorship

Are you one of the authors of this document?