Estimating Flight Departure Delay Distributions—A Statistical Approach With Long-Term Trend and Short-Term Pattern
- 1 March 2008
- journal article
- research article
- Published by Taylor & Francis Ltd in Journal of the American Statistical Association
- Vol. 103 (481), 112-125
- https://doi.org/10.1198/016214507000000257
Abstract
In 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.Keywords
This publication has 5 references indexed in Scilit:
- Ascent EM for fast and global solutions to finite mixtures: An application to curve-clustering of online auctionsComputational Statistics & Data Analysis, 2006
- Genetic-based EM algorithm for learning Gaussian mixture modelsIeee Transactions On Pattern Analysis and Machine Intelligence, 2005
- Model-Based Clustering, Discriminant Analysis, and Density EstimationJournal of the American Statistical Association, 2002
- Queuing Model for Taxi-Out Time EstimationAir Traffic Control Quarterly, 2002
- Real-Time Forecasts of Aircraft Departure QueuesAir Traffic Control Quarterly, 1997