A Methodology for Predicting Ground Delay Program Incidence through Machine Learning

Xiangning Dong (), Xuhao Zhu, Minghua Hu and Jie Bao
Additional contact information
Xiangning Dong: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, No. 29 General Avenue, Nanjing 211106, China
Xuhao Zhu: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, No. 29 General Avenue, Nanjing 211106, China
Minghua Hu: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, No. 29 General Avenue, Nanjing 211106, China
Jie Bao: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, No. 29 General Avenue, Nanjing 211106, China

Sustainability, 2023, vol. 15, issue 8, 1-19

Abstract: Effective ground delay programs (GDP) are needed to intervene when there are bad weather or airport capacity issues. This paper proposes a new methodology for predicting the incidence of effective ground delay programs by utilizing machine learning techniques, which can improve the safety and economic benefits of flights. We use the combination of local weather and flight operation data along with the ATM airport performance (ATMAP) algorithm to quantify the weather and to generate an ATMAP score. We then compared the accuracy of three machine learning models, Support Vector Machine, Random Forest, and XGBoost, to estimate the probability of GDPs. The results of the weather analysis, performed by the ATMAP algorithm, indicated that the ceiling was the most critical weather factor. Lastly, we used two linear regression models (ridge and LASSO) and a non-linear regression model (decision tree) to predict departure flight delays during GDP. The predictive accuracy of the regression models was enhanced by an increase in ATMAP scores, with the decision tree model outperforming the other models, resulting in an improvement of 8.8% in its correlation coefficient ( R 2 ).

Keywords: ground delay programs; machine learning; ATMAP algorithm; feature importance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/8/6883/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/8/6883/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:8:p:6883-:d:1127545

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().