Estimating Flight Departure Delay Distributions—A Statistical Approach With Long-Term Trend and Short-Term Pattern

TLDR

The study develops a strategic departure‑delay prediction model to estimate flight departure delay distributions for air‑traffic congestion forecasting, identifying key influencing factors. The model uses nonparametric techniques to capture daily and seasonal trends, a mixture distribution for residual errors, and a global‑optimization expectation–maximization algorithm inspired by genetic algorithms, trained on United Airlines and Denver International Airport data from 2000–2001. The model achieves reasonable goodness of fit, robustness to parameter choices, and strong predictive performance.

Abstract

AbstractIn this article we develop a model for estimating flight departure delay distributions required by air traffic congestion prediction models. We identify and study major factors that influence flight departure delays, and develop a strategic departure delay prediction model. This model employs nonparametric methods for daily and seasonal trends. In addition, the model uses a mixture distribution to estimate the residual errors. To overcome problems with local optima in the mixture distribution, we develop a global optimization version of the expectation–maximization algorithm, borrowing ideas from genetic algorithms. The model demonstrates reasonable goodness of fit, robustness to the choice of the model parameters, and good predictive capabilities. We use flight data from United Airlines and Denver International Airport from the years 2000/2001 to train and validate our model.KEY WORDS: Airline delayAirspace congestionDelay distributionExpectation–maximizationGenetic algorithmMixture modelSmoothing spline