Distribution Prediction of Strategic Flight Delays via Machine Learning Methods

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.

A hybrid machine learning-based model for predicting flight delay through aviation big data

“…In Ref. 12 , a machine learning-based approach for strategic-level flight delay prediction was presented, distinguishing it from previous works. Strategic-level flight delay prediction covers a period of up to six months before the flight and the models based on it can serve as effective tools for flight schedule adjustments at airports and airline managers.…”