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. / Doctor of Philosophy / The federal aviation administration predicts ongoing growth in the aviation industry over the following 20 years. Therefore, the airports will be more crowded, and a higher number of operations will occur at those facilities. An accurate prediction of airports' capacities can help the authorities to improve the airports appropriately. Due to significant reductions in in-trail aircraft separations, runway occupancy times will become more significant in airport arrival procedures. In this study, a landing event database was developed to represent the accurate distributions of runway occupancy times. Also, it is essential to have computer applications capable of replicating runway occupancy time distributions. In this dissertation, a novel approach was developed to replicate aircraft runway occupancy times. A massive amount of airport surface radar data was utilized to create all the mentioned computer applications. The results of the final products were validated against real data. Real-world case scenarios were discussed as part of this study to showcase the strengths of the final developed product in solving challenging problems related to airport capacity. Finally, extreme cases of motivated landing behavior from airline pilots were studied, and multiple methodologies were introduced to replicate pilot motivational behavior while landing on runway.