Distribution Prediction of Strategic Flight Delays via Machine Learning Methods

Ziming Wang (), Chaohao Liao, Xu Hang, Lishuai Li, Daniel Delahaye and Mark Hansen
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Ziming Wang: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Chaohao Liao: Air Traffic Management Bureau of Central-South China, Guangzhou 510422, China
Xu Hang: Air Traffic Management Bureau of Central-South China, Guangzhou 510422, China
Lishuai Li: School of Data Science, City University of Hong Kong, Kowloon, Hong Kong SAR, China
Daniel Delahaye: Department of Civil and Environmental Engineering, UC Berkeley, Berkeley, CA 94720, USA
Mark Hansen: ENAC Lab, Ecole Nationale de L’Aviation Civile, 31400 Toulouse, France

Sustainability, 2022, vol. 14, issue 22, 1-14

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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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