Predicting performance of ground delay programs

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

Predicting performance of ground delay programs

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

Research output: Contribution to conference › Paper › peer-review

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 90^th percentile responses for two performance indicators: average arrival delay and the number of cancelled arrivals.

Original language	English (US)
State	Published - 2017
Externally published	Yes
Event	12th USA/Europe Air Traffic Management Research and Development Seminar, ATM 2017 - Seattle, United States Duration: Jun 26 2017 → Jun 30 2017

Other

Other	12th USA/Europe Air Traffic Management Research and Development Seminar, ATM 2017
Country/Territory	United States
City	Seattle
Period	6/26/17 → 6/30/17

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

Find It

Find It at U of M Libraries

Cite this

@conference{ad73032322b946d0942a2e8cf0c98748,

title = "Predicting performance of ground delay programs",

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.",

keywords = "Air traffic management, Delay prediction, Ground delay program",

author = "Alexander Estes and Ball, {Michael O.} and Lovell, {David J.}",

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.; 12th USA/Europe Air Traffic Management Research and Development Seminar, ATM 2017 ; Conference date: 26-06-2017 Through 30-06-2017",

year = "2017",

language = "English (US)",

}

TY - CONF

T1 - Predicting performance of ground delay programs

AU - Estes, Alexander

AU - Ball, Michael O.

AU - Lovell, David J.

N1 - 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.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Air traffic management

KW - Delay prediction

KW - Ground delay program

UR - http://www.scopus.com/inward/record.url?scp=85031038167&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85031038167&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85031038167

T2 - 12th USA/Europe Air Traffic Management Research and Development Seminar, ATM 2017

Y2 - 26 June 2017 through 30 June 2017

ER -

Predicting performance of ground delay programs

Abstract

Other

Bibliographical note

Keywords

Find It

Other files and links

Fingerprint

Cite this