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This research presents designs and evaluations of various mass transportation alternatives for reducing the problem of the ground access congestion in the Orlando International Airport. The alternatives considered in this research are conventional bus service, minibus service, express bus service, light rail transit service, rail rapid transit service, and monorail service. Details of the origin-destination studies for the present traffic are given. A discussion of the various mass transportation alternatives is provided, with provisions for future systems expansion. Three economic evaluation methods were used in this research for evaluation of various alternatives. According to the results of the economic analysis, the modified bus service is recommended.

The major problem facing air transportation for the next decade is aircraft noise. The noise level due to the operation of large jet aircraft has created a very serious annoyance problem to the people living near of adjacent to jet airports. The noise problem has developed both for take-off and landing operations of these aircraft with take -off noise causing the greatest annoyance factor. A technique called Noise Exposure Forecast (NEF) has been developed to identify the annoyance factor of these noises to people and activities on the ground. With these NEF ratings or numbers, planners can better determine the type of buildings and activities to locate in the vicinity of airports. This paper presents a computer method for determining NEF areas or contours which eliminate the necessity of performing laborious hand calculations and iterations normally required to determine a given NEF locus about an airport. A land use compatibility table showing land use versus NEF numbers is given on page 3. A sample computer program is given on pages 21 through 25 of the appendix and a sample computer output page is given on page 26. The computer technique developed for this paper can be used for any airport.

A weakness with the Swedish Transport Administration’s model for long-distance trips is that it onlyallows for one mode of transportation per trip. A long-distance trip usually consists of several modesof transportation, while the current model does not allow modelling of, for example, a flight with aconnecting car, bus or train transfer. This is problematic as a significant part of the generalized costof travel can be derived from an access trip.The master thesis has been devoted to studying how a model for choice of access mode anddeparture airport can be designed. The work has been limited to study long-distance trips starting inStockholm county, carried out by residents of the county. The airport choice has been limited toArlanda and Bromma Airport.In a literature review, the underlying mathematical theories which the project is based on arepresented. The theory originates from the work on discrete choice models by Daniel McFadden,which later was further developed and summarized by Kenneth Train. By studying previous researchdealing with modelling of access mode and airport choice, it can be concluded that there is noobvious approach to model design, apart from the fact that the model should be based on the logittheory.The observations on which the model is estimated are retrieved from travel surveys conductedduring 2011-2016. A cost estimate for the modes of travel included in the model has been donemanually. Travel times for all modes have been generated using the Swedish TransportAdministration’s regional base model for Stockholm County dated 2014, using the macro simulationsoftware Emme.Out of all the estimated models, one model is presented as the best and final one. It is a multinomiallogit model divided into two segments, one for business travelers and one for private travelers. Themodel is analyzed by calculating value of time, cost- and time elasticity for each of the alternatives inthe choice set. For some of the alternatives, the value of time is relatively high, which is addressed inthe concluding discussion.

In recent decades, government officials and social scientists have increased their study of American airports and their relationship to security and national defense. Despite the growing attention, airports remain interpreted primarily as homogenized, transient spaces deprived of any culturally unique qualities. This thesis will study American airports as historical artifacts with significant layers of meaning. If contextualized and situated within a broader historical framework, then airports expose larger trends throughout American history including resistance to multiculturalism and diversity. The stress and anxiety often associated with airports reflect a prolonged struggle to embrace the democratization of public places. If studied with an historical approach from multiple perspectives, then the airport provides historians with a tangible, familiar object to engage popular audiences about complicated issues such as surveillance, xenophobia, and urban renewal. This thesis proposes a conceptual framework for historians to assess the significance of airport space and offers suggestions to better engage the national conversations surrounding these complicated spaces. / History

The air transportation system has grown significantly during the past few decades. The demand for air travel has increased tremendously as compared to the increase in the supply. The air transportation system can be divided into four subsystems: airports, airlines, air traffic control, and passengers, each of them having different interests. These subsystems interact in a very complex way resulting in various phenomena. On the airport side, there is excessive flight demand during the peak hours that frequently exceeds the airport capacity resulting in serious flight delays. These delays incur costs to the airport, passengers, and airlines. The air traffic pattern is also affected by the characteristics of the air transportation network. The current network structure of most major airlines in United States is a hub-and-spoke network. The airports are interested in reducing congestion, especially during the peak time. The airlines act as direct demand to the airport and as the supplier to the passengers. They sometimes compete with other airlines on certain routes and sometimes they collaborate to maximize revenue. The flight schedule of airlines directly affects the travel demand. The flight schedule that minimizes the schedule delay of passengers in directed and connected flights will attract more passengers. The important factors affecting the airline revenue include ticket price, departure times, frequency, and aircraft type operated on each route. The revenue generated from airline depends also on the behavior of competing airlines, and their flight schedules. The passengers choose their flight based on preferred departure times, offered ticket prices, and willingness of airlines to minimize delay and cost. Hence, all subsystems of air transportation system are inter-connected to each other, meaning, strategy of each subsystem directly affects the performance of other subsystems. This interaction between the subsystems makes it more difficult to analyze the air transportation system. Traditionally, analytical top-down approach has been used to analyze the air transportation problem. In top-down approach, a set of objectives is defined and each subsystem is fixed in the overall scheme. On the other hand, in a bottom-up approach, many issues are addressed simultaneously and each individual system has greater autonomy to make decisions, communicate and to interact with one another to achieve their goals when considering complex air transportation system. Therefore, it seems more appropriate to approach the complex air traffic congestion and airline competition problems using a bottom-up approach. In this research, an agent-based model for the air transportation system has been developed. The developed model considers each subsystem as an independent type of agent that acts based on its local knowledge and its interaction with other agents. The focus of this research is to analyze air traffic congestion and airline competition in a hub-and-spoke network. The simulation model developed is based on evolutionary computation. It seems that the only way for analyzing emergent phenomenon (such as air traffic congestion) is through the development of simulation models that can simulate the behavior of each agent. In the agent-based model developed in this research, agents that represent airports can increase capacity or significantly change landing fee policy, while the agents that represent airlines learn all the time, change their markets, fare structure, flight frequencies, and flight schedules. Such a bottom-up approach facilitates a better understanding of the complex nature of congestion and gains more insights into the competition in air transportation, hence making it easier to understand, predict and control the overall performance of the complex air transportation system. / Ph. D.

This dissertation looks at the relationship between airport size (e.g. small, medium, large) and air service continuity and operational performance. It consists of three studies, each written in journal format. The first study analyzes the markets served pre- and post-recession while focusing on the operational strategies adopted by the top Major Carriers and Low-Cost Carriers (LCCs) in the United States. Findings show that LCCs have outpaced major carriers in terms of expanding their network and the number of markets served. During the same time, major carriers have gained a greater flight share in the markets they already serve. Post-recession, LCCs have shown preference to competing with major carriers over other LCCs. The second study investigates the declining service levels at small airports compared to large-hub airports, which continue to benefit from higher levels of service and increased airline presence. Using a fixed-effects conditional logistic regression, this study looked at factors contributing to service loss in region-to-region markets serving small communities between 2007 and 2013. Results show that 1) markets affected by a merger are indeed at a higher risk of losing service; 2) markets that are operated by a fuel-intensive, small-aircraft fleet have a higher chance to be discontinued and 3) an increased number of competitors greatly reduces potential market service loss. The third and final study proposes a new methodology to calculate original delay and propagated delays using combined aviation operational datasets that provide detailed flight information and causal factors behind delays. In addition to calculating original and propagated delay for the month of July of 2018, this study differentiated between original delays that occur during the turnaround phase, taxiing phase and en-route and incorporates causal factor information to identify the true source behind propagated delay. Two fixed-effects linear regression models were introduced that predict Total Propagated Delay and the share of propagated delay given an airport's ability to absorb upstream delay during the turnaround phase. Results show that most delay propagation chains originate at large-hub airports and are mostly concentrated at airports within the same geographical area. However, delays originating at large-hub airports were found to be the quickest to recover (i.e. least number of downstream flight legs affected) and large-hub airports have a higher ability to absorb delay at the turnaround phase compared to smaller airports given the significantly higher schedule buffer time airlines plan at large-hub airports. / Doctor of Philosophy / The changing nature of the air service industry is dependent on several key factors, including but not limited to the major and low-cost airlines, the frequency of service at different sized-airports and the operational performance of the airports in the system. Each airport can be classified by size based on the annual number of enplanements. This dissertation looks at the relationship between airport size (e.g. small, medium, large), service continuity and operational performance. It consists of three studies, each written in journal format. Over the past two decades, the U.S. air transportation network witnessed several economic downturns forcing airlines to shift their operational strategies, cease service or merge with an airline counterpart. The first study analyzes routes served before and after the recession by exploring the presence of major and low-cost carriers in these markets to understand how several economic downturns have influenced the operating strategy of airlines in the US. While Low-cost carriers focused on expanding their network and offering service in an increased number of new routes, major carriers increased their presence in the markets in which they already serve. Furthermore, after the recession, low-cost carriers chose to increasingly compete with major carriers over their low-cost counterparts. The second study explored the factors that can potentially contribute to the loss of service in routes serving small communities. While airlines continue to compete on the most profitable routes, small airports recently suffered from reduced service levels and in some instance service discontinuity. Results show that 1) routes that were once served by two airlines that merged are at a higher risk of losing service; 2) routes that are operated by a fuel-intensive small aircraft fleet have a higher chance to be discontinued and 3) an increased presence of airlines competing in a route greatly reduces potential service loss. In addition to evaluating service continuity, the third and final study looks at flight delays across the US and dives into the effect of airport size on propagated delay. Delays on a flight can be caused by inefficiencies and capacity restrictions at airports and may also be the result of delay that happen earlier in the day and that propagates to multiple flights downstream that share the same resources. That is, a delay can affect multiple flights whenever these flights are all operated by the same aircraft equipment. Costing the air transportation network billions of dollars annually, the third study examines the original and propagated delays at US airports by collecting data from multiple sources to incorporate the original source and cause of delay. Results show that most delay originates at large-hub airports and are mostly concentrated at airports within the same geographical area. However, delays originating at large-hub airports were found to be the quickest to recover and large-hub airports have a higher ability to absorb delay at the turn compared to smaller airports as airlines allocate additional minutes of schedule padding at large-hub airports.

An acoustic pattern recognition system has been developed to identify aerospace acoustic sources. The system is capable of classifying five different types of air and ground sources: jets, propeller planes, helicopters, trains, and wind turbines. The system consists of one microphone for data acquisition, a preprocessor, a feature selector, and a classifier. This thesis presents two new classifiers, one based on an associative memory and one on artificial neural networks, and compares their performance to that of the original classifier developed at VPI&SU (1,2). The acoustic patterns are classified using features that have been calculated from the time and frequency domains. Each of the classifiers undergoes a training period during which a set of known patterns is used to teach the classifier to classify unknown patterns correctly. Once training was completed each classifier is tested using a new set of unknown data. Two different classifier structures were tested, a single level structure and a tree structure. Results show that the single level associative memory and artificial neural network classifiers each identified 90.6 percent of the acoustic sources correctly. The original linear discriminant function single level classifier (1,2) identified 86.7 percent of the sources. The tree structure classifiers classified respectively 90.6 percent, 91.8 percent, and 90.1 percent of the sources correctly. / Master of Science

Effective and efficient air transportation systems are crucial to a nation's economy and connectedness. These systems involve capital-intensive facilities and equipment and move millions of people and tonnes of freight every day. As air traffic has continued to increase, the systems necessary to ensure safe and efficient operation will continue to grow more and more complex. Hence, it is imperative that air transport analysts are equipped with the best tools to properly predict and respond to expected air transportation operations. This dissertation aims to improve on those tools currently available to air transportation analysts, while offering new ones. Specifically, this thesis will offer the following: 1) A model for predicting arrival runway occupancy times (AROT); 2) a model for predicting departure runway occupancy times (DROT); and 3) a flight planning model. This thesis will also offer an exploration of the uses of unmanned aerial vehicles for providing wireless communications services. For the predictive models of AROT and DROT, we fit hierarchical Bayesian regression models to the data, grouped by aircraft type using airport physical and aircraft operational parameters as the regressors. Recognizing that many existing air transportation models require distributions of AROT and DROT, Bayesian methods are preferred since their output are distributions that can be directly inputted into air transportation modeling programs. Additionally, we exhibit how analysts will be able to decouple AROT and DROT predictions from the traditional 4 or 5 groupings of aircraft currently in use. Lastly, for the flight planning model, we present a 2-D model using presently available wind data that provides wind-optimal flight routings. We improve over current models by allowing free-flight unconnected to pre-existing airways and by offering finer resolutions over the current 2.5 degree norm. / Ph. D.