Modeling Weather Impact on Ground Delay Programs

Scheduled arriving aircraft demand may exceed airport arrival capacity when there is abnormal weather at an airport. In such situations, Federal Aviation Administration (FAA) institutes ground-delay programs (GDP) to delay flights before they depart from their originating airports. Efficient GDP planning depends on the accuracy of prediction of airport capacity and demand in the presence of uncertainties in weather forecast. This paper presents a study of the impact of dynamic airport surface weather on GDPs. Using the National Traffic Management Log, effect of weather conditions on the characteristics of GDP events at selected busy airports is investigated. Two machine learning methods are used to generate models that map the airport operational conditions and weather information to issued GDP parameters and results of validation tests are described.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[3]  Fabio Roli,et al.  Multiple Classifier Systems, 9th International Workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010. Proceedings , 2010, MCS.

[4]  Lara Cook,et al.  A Model for Determining Ground Delay Program Parameters Using a Probabilistic Forecast of Stratus Clearing , 2010 .

[5]  David A. Clark.  INVESTIGATING A NEW GROUND DELAY PROGRAM STRATEGY FOR COPING WITH SFO STRATUS * , 2009 .

[6]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[7]  Raymond J. Mooney,et al.  Experiments on Ensembles with Missing and Noisy Data , 2004, Multiple Classifier Systems.

[8]  Alexander Klein,et al.  Weather Forecast Accuracy: Study of Impact on Airport Capacity and Estimation of Avoidable Costs , 2009 .

[9]  Martin T. Hagan,et al.  Gauss-Newton approximation to Bayesian learning , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[10]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[11]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[12]  A. Klein,et al.  Airspace capacity estimation using flows and Weather-Impacted Traffic Index , 2008, 2008 Integrated Communications, Navigation and Surveillance Conference.

[13]  Joseph Rios.  Aggregate Statistics of National Traffic Management Initiatives , 2010 .

[14]  Oliver D. Anderson,et al.  Accuracy and Precision , 1992 .