This thesis presents two aspects of air transportation systems, capacity and efficiency. The first study improved a runway capacity simulation model for estimating the capacity of airports under various conditions. The main contribution of this study was to develop a simulation model which is able to analyze different airports with individual aircraft types. Many air traffic regulations were added to the simulation model to give more realistic results to the potential users of the model. Analysis of different separation systems between aircraft pairs show that the capacity of airports can improve by using more efficient separation systems specially under tough weather conditions. One of the outputs of the study, is an upgraded user interface which can be used by airport authorities for estimating their facilities' available capacity under different scenarios. The second study represents a new method for estimating unimpeded taxi times for taxing airplanes at airports. This study focused specifically on 6 large airports in the United States and the real ground radar data for all of the movements at those facilities. By tracking the real trajectories of every operation, the taxing behavior of each flight was analyzed. A standalone application was designed to summarize the flights information at each airport and represent the taxing behavior. The results show that using the ground radar data at airports can represent the taxing behavior in the highest fidelity. / Master of Science / The federal aviation administration predicts an ongoing growth in aviation industry over the following 20 years. The number of passengers using air transportation systems will increase annually in the next 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. This will lead to an efficient aviation system which does not suffer severely from long delays. The contribution of the first study in this thesis was to help the process of predating airports’ capacities under various conditions by utilizing a computer model. As a result, a user interface was designed to help airport planners choosing their desired airports and estimating the maximum capacity under different operational conditions. An accurate prediction of airport capacity, can be useful for future designs and improvement plans. The second part of this thesis, represents a new methodology for analyzing the behavior of airplanes while moving at airports. Every flight operation starts its journey at the origin airport and finishes its journey at the destination airport. It is very important to analyze the behavior of each airplane while it is moving on the ground at the origin or destination airport. Therefore, parameters like taxing duration, waiting duration, average taxi speed can be used to evaluate the taxing performance of the airplanes. This study extracts the critical parameters of airplanes’ behavior at airports to help the planners evaluating the ground performance of each airport.

<p dir="ltr">At airports, aircraft taxi time may have effect on congestion, engine pollutants, and aircraft fuel consumption. An End-Around Taxiway (EAT) improves airport runway efficiencies and safety by providing a path for aircraft to move from one side of the runway to the other side without crossing that runway (FAA, 2022). The EAT has been implemented in four airports in the U.S.: Dallas/Fort Worth International Airport (KDFW), Hartsfield-Jackson International Airport (KATL), Detroit Metro Airport (KDTW), and Miami International Airport (KMIA) (Le, 2014). Currently, all the EATs are implemented at parallel runways. Previous research have shown that EAT on parallel runways has the potential to improve airport capacity and reduce fuel consumption (Fala et al., 2014; Feng & Johnson, 2021). There was no published application or research found about implementing EAT on crossing runways. This research is an explanatory study that focuses on analyzing the effect of EATs on airports with crossing runways. This research uses dynamic discrete event stochastic simulation software to build simulation models to analyze the effects of implementing EAT at crossing runways. Using a fictional airport loosely based on existing commercial service airports, the effect of EATs on a crossing runway airport was studied. The research has three experiments to measure the effects of the EAT in terms of taxi-in time, taxi-out time, and number of operations completed.</p><p dir="ltr">The major findings of the research are: 1) using EAT for taxi-in operations significantly reduces the taxi-in time and taxi-out time at the fictional airport with crossing runways; 2) using EAT for taxi-out operation significantly increases taxi-in time at the fictional airport with crossing runways; 3) using EAT for taxi-out operations significantly reduces taxi-out times at the fictional airport with crossing runways; 4) there is no statistical significance found when implementing EAT at the fictional airport with crossing runways in terms of number of operations completed per day. The configuration of the airport, the number of operations, the weather, and other factors may affect the transfer of these results to other airports with crossing runways.</p><p dir="ltr">Current EATs are only implemented and proposed at parallel runway airports. As aviation demand grows, this research may provide insights about a novel usage and operation strategy of EATs. The simulation model in this research is subject to assumptions and limitations. Future research is needed to improve the simulation model and further explore the effect of EATs on crossing runways.</p>