Title:
Predicting the occurrence of ground delay programs and their impact on airport and flight operations

dc.contributor.advisor Mavris, Dimitri N.
dc.contributor.author Mangortey, Eugene
dc.contributor.committeeMember Pinon-Fischer, Olivia
dc.contributor.committeeMember Puranik, Tejas
dc.contributor.committeeMember Paglione, Mike
dc.contributor.committeeMember Tessitore, Tom
dc.contributor.department Aerospace Engineering
dc.date.accessioned 2019-05-29T14:04:20Z
dc.date.available 2019-05-29T14:04:20Z
dc.date.created 2019-05
dc.date.issued 2019-04-25
dc.date.submitted May 2019
dc.date.updated 2019-05-29T14:04:20Z
dc.description.abstract A flight is delayed when it arrives 15 or more minutes later than scheduled. Delays attributed to the National Airspace System are one of the most common delays and can be caused by the initiation of Traffic Management Initiatives (TMI) such as Ground Delay Programs (GDP). A Ground Delay Program is implemented to control air traffic volume to an airport over a lengthy period when traffic demand is projected to exceed the airport's acceptance rate due to conditions such as inclement weather, volume constraints, closed runways or equipment failures. Ground Delay Programs cause flight delays which affect airlines, passengers, and airport operations. Consequently, various efforts have been made to reduce the impacts of Ground Delay Programs by predicting their occurrence or the optimal time for initiating Ground Delay Programs. However, a few research gaps exist. First, most of the previous efforts have focused on only weather-related Ground Delay Programs, ignoring other causes such as volume constraints and runway-related incidents. Second, there has been limited benchmarking of Machine Learning techniques to predict the occurrence of Ground Delay Programs. Finally, little to no work has been conducted to predict the impact of Ground Delay Programs on flight and airport operations such as their duration, flight delay times, and taxi-in time delays. This research addresses these gaps by 1) fusing data from a variety of datasets (Traffic Flow Management System (TFMS), Aviation System Performance Metrics (ASPM), and Automated Surface Observing Systems (ASOS)) and 2) leveraging and benchmarking Machine Learning techniques to develop prediction models aimed at reducing the impacts of Ground Delay Programs on flight and airport operations. These models predict 1) flight delay times due to a Ground Delay Program, 2) the duration of a Ground Delay Program, 3) the impact of a Ground Delay Program on taxi-in time delays, and 4) the occurrence of Ground Delay Programs. Evaluation metrics such as Mean Absolute Error, Root mean Squared Error, Correlation, and R-square revealed that Random Forests was the optimal Machine Learning technique for predicting flight delay times due to Ground Delay Programs, the duration of Ground Delay Programs, and taxi-in time delays during a Ground Delay Program. On the other hand, the Kappa Statistic revealed that Boosting Ensemble was the optimal Machine learning technique for predicting the occurrence of Ground Delay Programs. The aforementioned prediction models may help airlines, passengers, and air traffic controllers to make more informed decisions which may lead to a reduction in Ground Delay Program related-delays and their impacts on airport and flight operations.
dc.description.degree M.S.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/61288
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Flight delays
dc.subject Ground delay programs
dc.subject Machine learning
dc.subject Aviation big data
dc.title Predicting the occurrence of ground delay programs and their impact on airport and flight operations
dc.type Text
dc.type.genre Thesis
dspace.entity.type Publication
local.contributor.advisor Mavris, Dimitri N.
local.contributor.corporatename College of Engineering
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.relation.ispartofseries Master of Science in Aerospace Engineering
local.relation.ispartofseries Master of Science in Aerospace Engineering
relation.isAdvisorOfPublication d355c865-c3df-4bfe-8328-24541ea04f62
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
relation.isOrgUnitOfPublication a348b767-ea7e-4789-af1f-1f1d5925fb65
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relation.isSeriesOfPublication 09844fbb-b7d9-45e2-95de-849e434a6abc
thesis.degree.level Masters
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