The Federal Aviation Administration (FAA) is continuously assessing the operational performance of the National Airspace System (NAS), where they analyze trends in the aviation industry to help develop strategies for a more efficient air transportation system. To measure the performance of various elements of the aviation system, the FAA and the International Civil Aviation Organization (ICAO) developed nineteen key performance indicators (KPIs). This dissertation contains three research studies, each written in journal format, addressing select KPIs. These studies aim at answering questions that help understand and improve different aspects of airport operational efficiency. In the first study, we model the flight times within the terminal airspace and compare our results with the baseline methodology that the FAA uses for benchmarking. In the second study, we analyze the efficiency of deicing operations at Chicago O'Hare (ORD) by developing an algorithm that analyzes radar data. We also use a simulation model to calculate potential improvements in the deicing operations. Lastly, we present our results of a clustering analysis surrounding the response of airports to demand and capacity changes during the COVID-19 pandemic. The findings of these studies add to literature by providing a methodology that predicts travel times within the last 100 nautical miles with greater accuracy, by providing deicing times per aircraft type, and by providing insight into factors related to airport response to shock events. These findings will be useful for air traffic management decision makers in addition to other researchers in related future studies and airport simulations. / Doctor of Philosophy / The Federal Aviation Administration (FAA) is the transportation agency that regulates all aspects of civil aviation in the United States. The FAA is continuously analyzing trends in the aviation industry to help develop a more efficient air transportation system. They measure the performance of various elements of the aviation system. For example, there are indicators focused on the departure phase of flights measuring departure punctuality and additional time in taxi-out. On the arrivals side, there are indicators that measure the additional time spent in the last 100 nautical miles of flight. Additionally, there are indicators that measure the performance of the airport as a whole such as the peak capacity and the peak throughput. This dissertation contains three research studies, each one aims at answering questions that help understand and improve a different aspect of airport operational efficiency. The first study is focused on arrivals where we model the flight times within the last 100 nautical miles of flight. Our model incorporated factors such as wind and weather conditions to predict flight times within the last 100 nautical miles with greater accuracy than the baseline methodology that the FAA currently uses. The resulting more accurate benchmarks are important in helping decision makers, such as airport managers, understand the factors causing arrival delays. In the second study, we analyze the efficiency of deicing operations which can be a major source of departure delays during winter weather. We use radar data at Chicago O'Hare airport to analyze real life operations. We developed a simulation model that allowed us to recreate actual scenarios and run what-if scenarios to estimate potential improvements in the process. Our results showed potential savings of 25% in time spent in the deicing system if the airport changed their queueing style towards a first come first served rather than leaving it for the airlines to have their separate areas. Lastly, we present an analysis of the response of airports to demand and capacity changes during the COVID-19 pandemic. In this last study, we group airports by the changes in their throughput and capacity during two time periods. The first part of the study compares airports operations during 2019 to the pandemic during the "shock event" in 2020. The second part compares the changes in airports operations during 2020 with the "recovery" time period using data from 2021. This analysis showed which airports reacted similarly during the shock and recovery. It also showed the relationship between airport response and factors such as what kind of airlines use the airport, airport hub size, being located in a multi-airport city, percentage of cargo operations. The results of this study can help in understanding airport resilience based on known airport characteristics, this is particularly useful for predicting airport response to future disruptive events.

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.

This research aims to improve the design and placement of runway exits at airports through analysis and modeling of the effects that exogenous causal factors have on pilots' landing behavior and exit selections. Incorporating these factors into modeling software will strengthen the software's utility by providing project teams the ability to specify which pilot motivational causal factors apply to a new or existing runway. The main findings suggest pilots' exit selections are deterministic but dependent on the presence (or absence) of six (6) causal factors. A model and two (2) case studies are presented and compared against predictions generated by existing modeling software. The results support a finding that the causal factor model improves motivation-based predictions over current modeling techniques, which are drawn from stochastic distributions. The accuracy of this model enables designers to optimize runway exit placement and geometry to maximize runway capacity. / Master of Science / Airport design engineers currently plan the locations and geometric characteristics of runway exits by balancing the expected fleet mix of aircraft on that runway with the capacity and delay effects that the number and placement of these exits might cause. This technique originated from research beginning in the early 1970s, which found that pilots' exit motivations primarily resulted from the capabilities and limitations of their aircraft. Since pilots tend to "fly by the numbers" (i.e., exhibit predictable approach airspeeds, power levels, wing flaps, touchdown locations, landing speeds, and braking efforts), engineers thus employed design principles in which the numbers, locations and geometries of exits were primarily functions of the physical and performance-based characteristics of the specific types of aircraft expected to utilize the runway. However, in studying more than 4 million landings by a single aircraft type (the Boeing 737-800) at 42 U.S. airports, the evidence in this thesis shows that pilots' exit selections are behaviorally motivated by more than the physics of motion. This thesis aims to refine previous research and engineering methods by showing evidence that pilots' exit selections have as much to do with the presence (or absence) of certain environmental factors within the landing system. These factors (described in detailed within) are unique to each airport's overall physical network of interconnected runways, exits, taxiways, terminals and other features. Within this network, a pilot's landing behavior and exit selection depends on the locational and relational interactions that each exit choice will have on the time and distance to their apron (gate) assignment. These "interactions" are referred to as causal factors – defined as physical features within a landing environment that pilots have little-to-no control over – but which nevertheless influence their specific exit selections. Two (2) runway case studies provided in this thesis evidence a finding that a causal factor model reliably predicts pilots' exit selections better than current modeling techniques, which are drawn from probability-based statistical distributions. The stability and accuracy of the new model enables engineering design and project teams to optimize runway exit placement and geometry to maximize runway capacity, and can be adopted for use in both existing and future runways.