Exploration of the Evolution of Airport Ground Delay Programs

  • Author(s) / Creator(s)
  • This study introduces a novel method of merging disparate but complementary datasets and applying machine learning techniques to ground delay program (GDP) data. More specifically, it aims to characterize GDPs with respect to changing weather forecasts, GDP plan parameters, and operational performance. The analysis aims to gain insights into GDP usage patterns (implementation and revisions), with respect to these key dimensions. It also aims to gain insights into how GDP cancelations and revisions correlate with operational efficiency and predictability. The results could be used to help traffic managers and air carriers understand complex patterns in the evolution of GDPs, so that they might, for example, better anticipate or even plan a response to a change in weather conditions. The focus is on GDPs at Newark Liberty International Airport (EWR), from 2010 through 2014. A master dataset was generated by merging several datasets on GDPs, weather forecasts, and individual flight information. Several scenarios of GDP evolution were then identified by reducing the dimensionality of the master GDP dataset, then applying cluster analysis on the lower dimensional data. It was found that GDPs at EWR can be categorized into 10 types based on weather forecasts, realized weather, GDP scope, arrival rates, and duration. The characteristics of these 10 GDP clusters were further explored by examining the relationships between GDP scenarios and their performance. It was found that GDPs under stable, low-severity weather and with large scope may score higher on the efficiency metric than expected. When GDPs called in the same weather conditions have high program rates, medium durations, and narrow scopes, capacity utilization was higher than expected—less affected flights lead to fewer cancelations 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. 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. More specifically, the findings could, for example, be used to support discussion around, or even future guidance regarding, how to set and adjust GDP program rates. In future work additional data could be utilized to provide a more comprehensive operational picture of GDPs, and a wider range of performance metrics could be considered. It is also recommended that the patterns of how GDPs evolve over their lifetimes be further explored using other machine learning techniques that may provide new and useful insights. This work applies machine learning techniques to describe ground delay programs (GDPs) with respect to changing weather forecasts, realized weather, and GDP characteristics and performance. Our purpose is to gain insights into GDPs with respect to these key dimensions, by describing GDP performance in response to these (changing) variables. These insights could be used to start discussions with traffic managers and air carriers that allow all to gain a greater understanding of complex patterns in the evolution and performance of GDPs. Although there has been some work in evaluating GDP performance retrospectively (1), there has been little to no exploration into how GDPs evolve over the course of their lifetimes (typically, a day). This research characterizes GDPs with respect to weather, operational parameters, and performance, focusing on GDPs at Newark Liberty International Airport (EWR) from 2010 through 2014. We created a comprehensive master dataset of GDP initiatives, weather forecasts, and individual flight data, merged from several datasets obtained from various sources; identified GDP evolution scenarios through cluster analysis based on data visualization and the results of data dimensionality reduction; and explored the relationships between GDP scenarios and performance using statistical analysis. A brief introduction to the literature is followed by a section describing the datasets used. Then we describe the machine learning techniques used to classify EWR GDPs into 10 types based on weather forecasts and GDP plan parameters, performance metrics calculated for these 10 GDP types, and how the metrics’ values compare with expectation.

  • Date created
    2019-11-12
  • Subjects / Keywords
  • Type of Item
    Article (Draft / Submitted)
  • DOI
    https://doi.org/10.7939/r3-esnw-p911
  • License
    Attribution-NonCommercial-NoDerivatives 4.0 International