Decision support tool for predicting aircraft arrival rates from weather forecasts

Abstract

The principle bottlenecks of the air traffic control system are the major commercial airports. Atlanta, Detroit, St. Louis, Minneapolis, Newark, Philadelphia, and LaGuardia all expect to be at least 98% capacity by 2012. Due to their cost and the environmental and noise issues associated with construction, it is unlikely that any new airports will be built in the near future. Therefore to make the National Airspace System run more efficiently, techniques to more effectively use the limited airport capacity must be developed Air Traffic Management has always been a tactical exercise, with decisions being made to counter near term problems. Since decisions are made quickly, limited time is available to plan out alternate options that may better alleviate arrival flow problems at airports. Extra time means nothing when there is no way to anticipate future operations, therefore predictive tools are required to provide advance notice of future air traffic delays. This research describes how to use Support Vector Machines (SVM) to predict future airport capacity. The Terminal Aerodrome Forecast (TAF) is used as an independent variable within the SVM to predict Aircraft Arrival Rates (AAR) which depict airport capacity. Within a decision support tool, the AAR can be derived to determine Ground Delay Program (GDP) program rate and duration and passenger delay. Real world examples are included to highlight the usefulness of this research to airlines, air traffic managers, and the flying consumer. New strategies to minimize the effect of weather on arrival flow are developed and current techniques are discussed and integrated into the process. The introduction of this decision support tool will expand the amount of time available to make decisions and move resources to implement plans.