Title:
Application of Machine Learning to the Analysis and Prediction of the Coincidence of Ground Delay Programs and Ground Stop

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dc.contributor.author Mangortey, Eugene
dc.contributor.author Bleu-Laine, Marc-Henri
dc.contributor.author Puranik, Tejas G.
dc.contributor.author Pinon, Olivia J.
dc.contributor.author Mavris, Dimitri N.
dc.contributor.corporatename Georgia Institute of Technology. Aerospace Systems Design Laboratory en_US
dc.date.accessioned 2020-01-15T11:57:55Z
dc.date.available 2020-01-15T11:57:55Z
dc.date.issued 2020-01
dc.description.abstract Traffic Management Initiatives such as Ground Delay Programs and Ground Stops are implemented by traffic management personnel to control air traffic volume to constrained airports when traffic demand is projected to exceed the airports’ acceptance rate due to conditions such as inclement weather, volume constraints, etc. Ground Delay Programs are issued for lengthy periods of time and aircraft are assigned departure times later than scheduled. Ground Stops on the other hand, are issued for short periods of time and aircraft are not permitted to land at the constrained airport. Occasionally, Ground Stops are issued during an ongoing Ground Delay Program, and vice versa, which hinders the efficient planning and implementation of these Traffic Management Initiatives. This research proposes a methodology to help stakeholders better capture the impact of the coincidence of weather related Ground Delay Programs and Ground Stops, and potentially help reduce the number and duration of such coincidences. This is achieved by leveraging Machine Learning techniques to predict their coincidence at a given hour, predict which Traffic Management Initiative would precede the other during their coincidence, and identify key predictors that cause their coincidence. The Random Forests Machine Learning algorithm was identified as the best suited algorithm for predicting the coincidence of weather-related Ground Delay Programs and Ground Stops, as well as the Traffic Management Initiative that would precede the other during their coincidence. en_US
dc.identifier.citation Mangortey, Eugene, et al. “Application of Machine Learning to the Analysis and Prediction of the Coincidence of Ground Delay Programs and Ground Stops.” AIAA Scitech 2020 Forum, January 2020, doi:10.2514/6.2020-1683. en_US
dc.identifier.doi 10.2514/6.2020-1683. en_US
dc.identifier.uri http://hdl.handle.net/1853/62371
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.relation.ispartofseries ASDL; en_US
dc.subject Machine learning en_US
dc.subject Ground delay en_US
dc.subject Ground stop en_US
dc.title Application of Machine Learning to the Analysis and Prediction of the Coincidence of Ground Delay Programs and Ground Stop en_US
dc.type Text
dc.type.genre Paper
dspace.entity.type Publication
local.contributor.author Mavris, Dimitri N.
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.contributor.corporatename Aerospace Systems Design Laboratory (ASDL)
local.contributor.corporatename College of Engineering
relation.isAuthorOfPublication d355c865-c3df-4bfe-8328-24541ea04f62
relation.isOrgUnitOfPublication a348b767-ea7e-4789-af1f-1f1d5925fb65
relation.isOrgUnitOfPublication a8736075-ffb0-4c28-aa40-2160181ead8c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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