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Exploration of the Evolution of Airport Ground Delay Programs Open Access


Other title
Ground Delay Programs
unsupervised learning
clustering analysis
GDP evolution characterization
multivariate statistical analysis
Type of item
Degree grantor
University of Alberta
Author or creator
Ren, Kexin
Supervisor and department
Kim, Amy (Civil and Environmental Engineering-Transportation)
Examining committee member and department
Kuhn, Kenneth (RAND Corporation, USA)
Deng, Lijun (Civil and Environmental Engineering-Geotechnical Engineering)
Department of Civil and Environmental Engineering
Transportation Engineering
Date accepted
Graduation date
2017-11:Fall 2017
Master of Science
Degree level
A Ground Delay Program (GDP) delays flights at their departure airports on ground to absorb their potential airborne delays, when an imbalance between flight demand and airport capacity is expected at the arrival airport. The existing literature have looked into the issues existing before (e.g. weather forecasts), during (e.g. GDP planning) and after (e.g. GDP evaluation) the initiation of GDPs. However, no one has explored how GDPs evolve over the course of their lifetimes (typically, a day). The thesis introduces a novel method of merging disparate but complementary datasets and applying data mining techniques to gain more insights into GDPs – particularly with respect to their evolving characteristics. More specifically, it aims to characterize GDPs with respect to changing weather forecasts, GDP plan parameters, and operational performance. The purpose of this analysis is to gain some insights into the temporal patterns of GDPs with respect to these several key dimensions, by describing GDP performance in response to key (changing) variables. We focus on GDPs at Newark Liberty International Airport, from 2010 through 2014. We first generated a master dataset by merging several datasets on GDPs, weather forecasts, and individual flight information. We then identified several scenarios of GDP evolution based on the merged master dataset, by first reducing the dimensionality of the master GDP dataset, then applying cluster analysis on the resulting lower-dimensional data. We found that GDPs at EWR can be categorized into 10 types based on (changing) weather forecasts, GDP scope, program rate, and duration. We then further explored the characteristics of these 10 GDP evolution scenario clusters by examining the relationships between GDP scenarios and their performance (using metrics previous developed by Liu and Hansen (2014)) using statistical analysis. We found that GDPs under stable, low-severity weather and with large scope may score higher on the efficiency metric than we would expect. When GDPs called in the same weather conditions have high program rates, medium durations, and narrow scopes, we find that capacity utilization is higher than expected – less flights lead to fewer cancellations and more arrivals (albeit delayed), and therefore, higher capacity utilization. Results also suggest that program rates are set more conservatively than needed for some poor weather conditions that end earlier than expected, with GDP being canceled early as well. GDPs with fewer revisions were associated with a higher predictability score but lower efficiency score. These findings can provide greater insights and knowledge about GDPs for future planning purposes. For future work, we recommend that additional data be utilized to provide a more comprehensive operational picture of GDPs, and that a wider range of performance metrics be considered in the analysis. In addition, it is also recommended that the patterns of how GDPs evolve over their lifetimes be further explored using other novel machine learning techniques that may provide new and useful insights.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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