Development of an Aircraft Landing Database and Models to Estimate Aircraft Runway Occupancy Times
This dissertation represents the methodologies used to develop an aircraft landing database and predictive models for estimating arrival flight runway occupancy times. In the second chapter, all the algorithms developed for analyzing the airport surface radar data are explained, and detailed statistical information about various airports in the United States in terms of landing behavior is studied. In the third chapter a novel data-driven approach for modeling aircraft landing behavior is represented. The outputs of the developed approach are runway occupancy time distributions and runway exit utilizations. The represented hybrid approach in the third chapter is a combination of machine learning and Monte Carlo simulation methods. This novel approach was calibrated based on two years of airport radar data. The study's output is a computer application, which is currently being used by the Federal Aviation Administration and various airport consulting firms for analyzing and designing optimum runway exits to optimize runway occupancy times at airports. In the fourth chapter, four real-world case scenarios were analyzed to show the power of the developed model in solving real-world challenges in airport capacity. In the fifth chapter, pilot motivational behaviors were introduced, and three methodologies were used to replicate motivated pilot behaviors on the runway. Finally, in the sixth chapter, a neural network approach was used as an alternative model for estimating runway occupancy time distributions.