Predicting performance of ground delay programs

Alexander Estes, Michael O. Ball, David J. Lovell

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations

Abstract

Models are proposed to estimate the performance of Ground Delay Programs as air traffic management initiatives. We apply Random Forest and Gradient-Boosted Forest regression techniques within the context of Geographically Weighted Regression. We estimate both the mean and 90th percentile responses for two performance indicators: average arrival delay and the number of cancelled arrivals.

Original languageEnglish (US)
StatePublished - 2017
Externally publishedYes
Event12th USA/Europe Air Traffic Management Research and Development Seminar, ATM 2017 - Seattle, United States
Duration: Jun 26 2017Jun 30 2017

Other

Other12th USA/Europe Air Traffic Management Research and Development Seminar, ATM 2017
CountryUnited States
CitySeattle
Period6/26/176/30/17

Bibliographical note

Funding Information:
ACKNOWLEDGMENT The data for this study were processed and graciously provided to us by Sreeta Gorripaty, Yulin Liu, Mark Hansen, and Alexei Pozdnukhov of the University of California at Berkeley and Kennis Chan and John Schade of ATAC, Inc. This work was funded under NASA grant no. NNX14AJ79A.

Keywords

  • Air traffic management
  • Delay prediction
  • Ground delay program

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