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

In this paper, we develop a model for estimating flight departure delay distributions required by air traffic congestion prediction models. We identify and study major factors influencing 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. In order 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 2000/01 to train and validate our model.

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