Changes in the U.S. domestic airline route system have increased competition between international gateways. To assist in understanding this phenomenon, a system of models that predicts airline passenger flows resulting from different airline gateway hubbing strategies has been developed. The calibrated models predict baseline passenger flows quite accurately. The system was then used to project future traffic at a specific airport under alternative hubbing strategies of an airline. The results show that future international traffic at this airport is strongly sensitive to the airline’s choice of strategy.

Infrastructure capacity investment has been traditionally viewed as an important means to mitigate congestion and delay in the air transportation system. Given the huge amount of cost involved, justifying the benefit returns is of critical importance when making investment decisions. This dissertation proposes an equilibrium-based benefit assessment framework for aviation infrastructure capacity investment. This framework takes into consideration the interplays among key system components, including flight delay, passenger demand, flight traffic, airline cost, and airfare, and their responses to infrastructure capacity investment. We explicitly account for the impact of service quantity changes on benefit assessment. Greater service quantity is associated with two positive feedback effects: the so-called Mohring effect and economies of link/segment density. On the other hand, greater service quantity results in diseconomies of density at nodes/airports, because higher traffic density at the airport leads to greater airport delays. The capacity-constrained system equilibrium is derived from those competing forces.

Two approaches are developed to investigate air transport system equilibrium and its shift in response to infrastructure capacity expansion. In Chapter 2, we first view the system equilibrium from the airline competition perspective. We model airlines' gaming behavior for airfare and frequency in duopoly markets, assuming that airlines have the knowledge of individuals' utility structure while making decisions, and that delay negatively affects individuals' utility and increases airline operating cost. The theoretical airline competition model developed in Chapter 2 provides analytical insights into the interactions among various system components. Under a symmetric Nash equilibrium, we find that the presence of flight delay increases passenger generalized cost and discourages air travel. Airlines would not pass delay cost entirely onto passengers through higher fare, but also account for the impact of service degradation on passenger willingness-to-pay and consequently passenger demand. To avoid exorbitant flight delays, airlines would use larger aircraft, meanwhile taking advantage of economies of aircraft size. The resulting unit cost reduction partially offsets operating delay cost increase. The equilibrium shift triggered by capacity expansion reduces both schedule delay and flight delay, leading to lower passenger generalized cost and higher demand, despite slightly increased airfare. Airlines will receive larger profit, and consumer welfare will increase, as a result of the expansion. Although delay reduction is less than expected because of induced demand, the overall benefit, which encompasses reduction in both schedule delay and flight delay, would be much greater than estimated from a purely delay-based standpoint.

The equilibrium analysis can be alternatively approached from a traveler-centric perspective. The premise of an air transport user (i.e. traveler) equilibrium is that each traveler in the air transportation system maximizes his/her utility when making travel decisions. The utility depends upon market supply and performance characteristics, consisting of airfare, flight frequency, and flight delay. The extent of airline competition is implicitly reflected in the determination of airfare and flight frequency. Given the limited empirical evidence of the delay effect on air transportation system supply, two econometric models for airfare and flight frequency are estimated in Chapter 3. We find positive delay effect on fare, which should be interpreted as the net effect of airlines' tendency to pass delay cost to passengers while also compensating for service quality degradation. Higher delay discourages carriers from scheduling more flights on a segment. Both delay effects, however, are relatively small. The estimated fare and frequency models, together with passenger demand and airport delay models presented in Chapter 4, are integrated to formulate the air transport user equilibrium as fixed point and variational inequality problems. We prove that the equilibrium existence is guaranteed; whereas equilibrium uniqueness cannot be guaranteed. We apply the user equilibrium to a fully connected, hypothetical network with the co-existence of direct and connecting air services. Using a simple, heuristic algorithm, we find that the equilibrium is insensitive to initial demand values, suggesting that there may be a single equilibrium for this particular model instance. Hub capacity investment attracts spoke-spoke passengers from non-stop routes, and generates new demand on hub-related routes. At the market level, hub capacity expansion would result in greater total demand and consequently passenger benefits in almost all markets--except for ones where a predominant portion of passengers choose non-stop routes due to extremely high circuity for one-stop travel. In the latter set of markets, after capacity expansion passenger demand and benefits would be both reduced. This counter-intuitive result carries important implications that capacity increase does not necessarily benefit everyone in the system. Similar to the findings from the airline competition model, with changes in flight delay, schedule delay, airfare, and total demand, the user equilibrium model yields much higher passenger benefits from capacity investment than the conventional method; whereas hub delay saving is offset by traffic diversion and induced demand. With continuous capacity investment, the air transportation network will witness substantial changes in service supply and traffic patterns.

The Federal Aviation Administration uses Air Traffic Management Initiatives (TMIs) to mitigate the consequences of aviation system capacity shortfalls, for example by delaying aircraft on the ground at their origin airports. In order to make more efficient use of National Airspace (NAS) resources, reduce delay costs, and increase the flexibility of NAS users to meet their operational needs, tremendous efforts have been made to design TMIs in a manner to encourage cooperation between the FAA and airlines. Airlines are offered opportunities to make choices such as cancelling flights and increasing delays on some flights while decreasing delays on others. However, there has been little study of airlines' resulting behavior. In this dissertation, we analyze choices made by airlines in response to TMIs and attempt to infer from these key features of airlines' preference structures. Two econometric models are specified and estimated. The first model focuses on airlines' flight cancellation decisions, and the second model examines airline requests to simultaneously re-assign arrival slots and cancel flights using Slot Credit Substitution (SCS) messages.

The cancellation model captures how airlines value delays of the subject flight itself and potential delay savings of other flights in making a flight cancellation decision. Aircraft size, along with segment frequency and load factor, are all significant factors in cancellation decisions; larger, fuller, and less frequent flights are less likely to be cancelled. Somewhat surprisingly, a higher average fare is found to increase cancellation probability. Hub-bound flights are found more likely to be cancelled than spoke-bound flights. The model also confirms airlines' hedging behavior in response to TMIs by preferentially cancelling short-haul flights. In addition, a piece wise linear specification of the utility function confirms that the delay impact is non-linear. Individual airline model reveals some consistent behavior as well as some differences in how different factors enter into cancellation decisions.

The SCS model captures airlines' tradeoff behavior in dealing with flight cancellations and delays. It confirms that cancelling flights decreases airlines' utility while reducing delays increases the utility. Moreover, airlines are sensitive to the aircraft size and average fare of flights in performing these actions. In this model, however, average fare has the expected sign. The model estimates that airlines are willing to cancel a flight if the cancellation can reduce around 100 minutes of delays on their other flights that are in the ground delay program.

Airport arrival capacity, referred to here as the airport acceptance rate (AAR), is strongly influenced by the weather in the vicinity of the airport and thus AAR prediction necessitates an airport-specific weather forecast. Weather forecasts, however, are seldom accurate in predicting the actual weather conditions. Strategic decisions, for example arrival rates in a ground delay program (GDP), must be made ahead of time, usually more than two hours, when there is an uncertainty about the future capacity. This research uses probabilistic capacity scenarios to represent the uncertainty in the future arrival capacity. A probabilistic capacity scenario is defined as a time series of AAR values with which a certain probability of realization is associated. A set of probabilistic capacity scenarios may be used to represent the uncertainty in arrival capacity at an airport over the course of the day.

There has been considerable research in developing GDP models that determine efficient ground delay decisions and require probabilistic capacity scenarios as inputs. It is assumed that the capacity scenarios can be developed from weather forecasts or can be obtained from the expertise of the air traffic managers. There is, however, considerably less literature on the development of specific day-of-operation probabilistic capacity scenarios from weather forecasts. This limits the use of these GDP models in real- world application. This thesis fills that gap and presents methodologies to generate probabilistic capacity scenarios from weather forecasts.

In this thesis we develop methodologies for generating probabilistic capacity scenarios using a widely available airport-specific weather forecast called the Terminal Aerodrome Forecast (TAF). These methodologies require the issued TAF forecast and the realized capacity for days in the past. We apply and assess the performance of these methodologies on four US airports: San Francisco International Airport, Boston Logan International Airport, Chicago O'Hare International Airport and Los Angeles International Airports. Though we have focused on these airports as case studies, the TAF-based scenario generation techniques can be applied to any airport.

In the first methodology, TAF Clustering, the scenarios are representative capacity profiles for days having similar TAFs. Groups of similar TAFs are found using K-means clustering and the number is verified using Silhouette value. In the second methodology, Dynamic Time Warping (DTW) Scenarios, the scenarios are the actual realized capacity profiles for days that have similar TAFs. The similarity between TAFs is determined using a statistical technique for comparing multidimensional time series called DTW.

DTW Scenarios uses three airport specific input parameters. These parameters control the numbers and the probabilities of the scenarios. We determine the values of the parameters through optimization to maximize the performance of the scenarios through minimizing average delay costs. The optimal values are determined through a specialized algorithm designed for situations where evaluating the objective function is computationally expensive.

For San Francisco International Airport we also use another forecast: the San Francisco Marine Initiative forecast (STRATUS) to develop the scenarios. In this methodology called, Fog burn-off clustering, the scenarios are representative capacity profiles for days that have the fog burn-off time in the same quarter hour.

We measure the efficacy of the various scenario generation methodologies in a real world setting based on 45 historic days for each of the four case-study airports. For each day, the generated scenarios are provided as inputs to a static stochastic ground delay model (SSGDM) that determines the series of planned arrival rates that minimize the sum of ground delay costs and expected air delay costs, assuming that the plan is not adjusted to evolving information. The ground delay is determined directly from the SSGDM whereas the realized air delay is determined from a queuing diagram based on the planned arrival rate and the realized arrival capacity. The realized delay costs are averaged over 45 days for each airport, and is the metric used to compare the different scenario generation methodologies. Employing this approach, we compare the different methods for capacity scenario generation against each other and against two other reference cases. Under the first reference case, Naïve Clustering, the scenarios are developed from historical capacity data without the use of the weather forecast. Groups of similar arrival profiles are determined though K-means clustering. In the second reference case, Perfect Information, we assume that the GDP is planned based on perfect information about the future arrival capacity.

Our results show that, on average, scenarios generated using the TAF-based DTW method results in the lowest delay cost amongst all scenario based methodologies. It is shown that capacity scenarios generated using day-of-operation weather forecasts can reduce the cost of delays by 5%-30% compared to scenarios that do not make use of weather forecast. The benefit of the TAF based approach is more pronounced on days that have a greater capacity-demand imbalance when compared to Naïve Clustering.

This paper presents a case study of how to empirically estimate the impact of a NAS investment on system performance. We employ multivariate statistical analysis to estimate the impacts of a major capacity expansion at DFW airport.

On September 5, 1989, the Federal Aviation Administration announced its intention to impose a major new security requirement on U.S. airlines. In accordance with an implementation timetable to be developed at a later date, airlines would be required to screen all checked baggage on all international flights with an FAA-approved explosive detection system (EDS).

This report presents statistical information on passenger and cargo traffic, aircraft activity and delays at California airports. Five main topics are considered: passenger enplanements, air travel origin and destination (O-D) patterns, air cargo activity, aircraft operations, and airport delays. In most cases, we present data for the state as a whole, major regions within the state, and major airports within each region. Most of the data are compiled on an annual basis for the years from 1980 to 2000. The report concludes with a discussion of the changes in activity in the California aviation system since the events of September 11, 2001, and some of the implications of these changes.

This report documents the findings of research sponsored by the Los Angeles World Airports to examine the influence of airport capacity constraints on airline fleet mix and to explore the potential effects of policy options to influence airlines to use larger aircraft types and thereby accommodate growth in passenger or cargo demand without a corresponding increase in the number of aircraft operations. This issue is of growing importance at many major airports in the United States and indeed around the world, as a steadily increasing demand for air transportation has resulted in volumes of air traffic that are approaching the capacity of the existing airport infrastructure, resulting in the prospect of significantly greater levels of aircraft delay in the future. At the same time, environmental and other concerns are limiting the ability of airports to construct additional runways to increase their airside capacity.