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

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.

[1]  P. Deb.  Finite Mixture Models , 2008 .

[2]  Wolfgang Jank,et al.  The EM Algorithm, Its Randomized Implementation and Global Optimization: Some Challenges and Opportunities for Operations Research , 2006 .

[3]  Djamel Bouchaffra,et al.  Genetic-based EM algorithm for learning Gaussian mixture models , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Craig Wanke,et al.  PROBABILISTIC CONGESTION MANAGEMENT , 2005 .

[5]  Michael O. Ball,et al.  ESTIMATING ONE-PARAMETER AIRPORT ARRIVAL CAPACITY DISTRIBUTIONS FOR AIR TRAFFIC FLOW MANAGEMENT , 2004 .

[6]  P.T.R. Wang,et al.  Flight connections and their impacts on delay propagation , 2003, Digital Avionics Systems Conference, 2003. DASC '03. The 22nd.

[7]  G. McLachlan,et al.  On some Variants of the EM Algorithm for the Fitting of Finite Mixture Models , 2003 .

[8]  Gano B. Chatterji,et al.  ANALYSIS OF AIRCRAFT ARRIVAL AND DEPARTURE DELAY CHARACTERISTICS , 2002 .

[9]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[10]  John-Paul Clarke,et al.  Queuing Model for Taxi-Out Time Estimation , 2002 .

[11]  Bala G. Chandran.  Predicting Airspace Congestion using Approximate Queueing Models , 2002 .

[12]  David Millner,et al.  Flight delay propagation analysis with the Detailed Policy Assessment Tool , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[13]  George L. Nemhauser,et al.  SimAir: a stochastic model of airline operations , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[14]  James A. Rome,et al.  Preliminary Evaluation of Flight Delay Propagation through an Airline Schedule , 1999 .

[15]  Jeff A. Bilmes,et al.  A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .

[16]  Robert A. Shumsky.  Real-Time Forecasts of Aircraft Departure Queues , 1997 .

[17]  Jean-Marc Rousseau,et al.  Chapter 5 Models in urban and air transportation , 1994, Operations research and the public sector.

[18]  B. Silverman,et al.  Nonparametric Regression and Generalized Linear Models: A roughness penalty approach , 1993 .

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[20]  R. A. Boyles.  On the Convergence of the EM Algorithm , 1983 .

[21]  C. R. Deboor,et al.  A practical guide to splines , 1978 .

[22]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[23]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[24]  C. Reinsch.  Smoothing by spline functions , 1967 .