Distribution Prediction of Strategic Flight Delays via Machine Learning Methods

Wang, Ziming; Liao, Chaohao; Hang, Xu; Li, Lishuai; Delahaye, Daniel; Hansen, Mark

doi:10.3390/su142215180

Open AccessArticle

Distribution Prediction of Strategic Flight Delays via Machine Learning Methods

by

Ziming Wang

^1,*,†,

Chaohao Liao

^2,†,

Xu Hang

²,

Lishuai Li

³,

Daniel Delahaye

⁴

and

Mark Hansen

⁵

¹

College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

²

Air Traffic Management Bureau of Central-South China, Guangzhou 510422, China

³

School of Data Science, City University of Hong Kong, Kowloon, Hong Kong SAR, China

⁴

Department of Civil and Environmental Engineering, UC Berkeley, Berkeley, CA 94720, USA

⁵

ENAC Lab, Ecole Nationale de L’Aviation Civile, 31400 Toulouse, France

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Sustainability 2022, 14(22), 15180; https://doi.org/10.3390/su142215180

Submission received: 27 October 2022 / Revised: 8 November 2022 / Accepted: 12 November 2022 / Published: 16 November 2022

(This article belongs to the Special Issue Air Traffic Management (ATM) for the Sustainability and Environmental Performance of Aviation)

Download

Browse Figures

Versions Notes

Abstract

Predicting flight delays has been a major research topic in the past few decades. Various machine learning algorithms have been used to predict flight delays in short-range horizons (e.g., a few hours or days prior to operation). Airlines have to develop flight schedules several months in advance; thus, predicting flight delays at the strategic stage is critical for airport slot allocation and airlines’ operation. However, less work has been dedicated to predicting flight delays at the strategic phase. This paper proposes machine learning methods to predict the distributions of delays. Three metrics are developed to evaluate the performance of the algorithms. Empirical data from Guangzhou Baiyun International Airport are used to validate the methods. Computational results show that the prediction accuracy of departure delay at the 0.65 confidence level and the arrival delay at the 0.50 confidence level can reach 0.80 without the input of ATFM delay. Our work provides an alternative tool for airports and airlines managers for estimating flight delays at the strategic phase.

Keywords: strategic flight schedule; machine learning; distribution prediction; flight delay

Share and Cite

MDPI and ACS Style

Wang, Z.; Liao, C.; Hang, X.; Li, L.; Delahaye, D.; Hansen, M. Distribution Prediction of Strategic Flight Delays via Machine Learning Methods. Sustainability 2022, 14, 15180. https://doi.org/10.3390/su142215180

AMA Style

Wang Z, Liao C, Hang X, Li L, Delahaye D, Hansen M. Distribution Prediction of Strategic Flight Delays via Machine Learning Methods. Sustainability. 2022; 14(22):15180. https://doi.org/10.3390/su142215180

Chicago/Turabian Style

Wang, Ziming, Chaohao Liao, Xu Hang, Lishuai Li, Daniel Delahaye, and Mark Hansen. 2022. "Distribution Prediction of Strategic Flight Delays via Machine Learning Methods" Sustainability 14, no. 22: 15180. https://doi.org/10.3390/su142215180

APA Style

Wang, Z., Liao, C., Hang, X., Li, L., Delahaye, D., & Hansen, M. (2022). Distribution Prediction of Strategic Flight Delays via Machine Learning Methods. Sustainability, 14(22), 15180. https://doi.org/10.3390/su142215180

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Distribution Prediction of Strategic Flight Delays via Machine Learning Methods

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI