Although the airline industry has drastically changed since its deregulation in 1978, publically available sources of data have remained nearly the same. In the U.S., most researchers and decision-makers rely on government data that contains highly aggregated price information (e.g., average quarterly prices). However, aggregate data can hide important market behavior. With the emergence of online distribution channels, there is a new opportunity to model air travel demand using detailed price information. This dissertation uses online prices and seat maps to build a dataset of daily prices and bookings at the flight-level. Several research contributions are made, all related to leveraging online data to better understand airline pricing and product strategies, and how these strategies impact customers, as well as the industry in general. One major contribution is the finding that the recent product debundling trend in the U.S. airline industry has diluted revenues to the U.S. Airport and Airways Trust Fund by at least five percent. Additionally, several new behavioral insights are found for one debundling trend that has been widely adopted by U.S. airlines: seat reservation fees. Customers are found to be between 2 and 3.3 times more likely to purchase premium coach seats (with extra legroom and early boarding privileges) when there are no regular coach window or aisle seats that can be reserved for free, suggesting that the ability of airlines to charge seat fees is strongly tied to load factors. Model results are used to explore optimal seat fees and find that an optimal static fee could increase revenues by 8 percent, whereas optimal dynamic fees could increase revenues by 10.2 percent. Another major contribution is in modeling daily bookings and estimating price elasticities using ordinary least squares (OLS) regression without correcting for price endogeneity and two-stage least squares (2SLS) regression, which corrects for endogeneity. Results highlight the importance of correcting for price endogeneity (which is not often done in air travel applications). In particular, models that do not correct for endogeneity find inelastic demand estimates whereas models that do correct for endogeneity find elastic demand estimates. This is important, as pricing recommendations differ for inelastic and elastic models. A set of instrumental variables are found to pass validity tests and can be used to correct for price endogeneity in future models of daily flight-level demand.

A methodology for modeling general aviation transportation systems from a system of systems perspective is presented. The completed framework aids the conceptual design process by providing capability-based metrics to the design engineer, as opposed to the traditional performance and cost, system-level metrics. The methodology is applied to two example problems representing promising future general aviation aircraft: the general aviation piston (GAP) and jet (GAJ). Results are presented in an array of formats, and the decision-making strategies that are now apparent in light of the capability-based metrics are described. The findings suggest that the system of systems framework might act as an analytical surrogate to the conventional problem definition process, providing indications of market preferences when that information is not immediately available through the conventional means. Implementation of this methodology can afford engineers a more autonomous perspective in the concept exploration process, providing dynamic feedback about a design's potential success in specific market segments. The method also has potential to strengthen the connection between design and business departments, as well as between manufacturers, service providers, and infrastructure planners - bringing information about how the respective systems interact, and what might be done to improve synergism of systems.

The air transportation demand at large hubs in the U.S. is anticipated to double in the near future. Current runway construction plans at selected airports can relieve some capacity and delay problems, but many are doubtful that this solution is sufficient to accommodate the anticipated demand growth in the National Airspace System (NAS). With the worsening congestion problem, it is imperative to seek alternative solutions other than costly runway constructions. In this respect, many researchers and organizations have been building models and performing analyses of the NAS. However, the complexity and size of the problem results in an overwhelming task for transportation system modelers. This research seeks to compose an active design algorithm for an evolutionary airline network model so as to include network specific control properties. An airline network designer, referred to as a network architect, can use this tool to assess the possibilities of gaining more capacity by changing the network configuration. Since the Airline Deregulation Act of 1978, the airline service network has evolved from a point-to-point into a distinct hub-and-spoke network. Enplanement demand on the H&S network is the sum of Origin-Destination (O-D) demand and transfer demand. Even though the flight or enplanement demand is a function of O-D demand and passenger routings on the airline network, the distinction between enplanement and O-D demand is not often made. Instead, many demand forecast practices in current days are based on scale-ups from the enplanements, which include the demand to and from transferring network hubs. Based on this research, it was found that the current demand prediction practice can be improved by dissecting enplanements further into smaller pieces of information. As a result, enplanement demand is decomposed into intrinsic and variable parts. The proposed intrinsic demand model is based on the concept of 'true' origin-destination demand which includes the direction of each round trip travel. The result from using true O-D concept reveals the socioeconomic functional roles of airports on the network. Linear trends are observed for both the produced and attracted demand from the data. Therefore, this approach is expected to provide more accurate prediction capability. With the intrinsic demand model in place, the variable part of the demand is modeled on an air transportation network model, which is built with accelerated evolution scheme. The accelerated evolution scheme was introduced to view the air transportation network as an evolutionary one instead of a parametric one. The network model takes in intrinsic demand data before undergoing an evolution path to generate a target network. The results from the network model suggests that air transportation networks can be modeled using evolutionary structure and it was possible to generate the emulated NAS. A dehubbing scenario study of Lambert-St. Louis International Airport demonstrated the prediction capability of the proposed network model. The overall process from intrinsic demand modeling and evolutionary network modeling is a unique and it is highly beneficial for simulating active control of the transportation networks.

Travel demand Management (TDM) focuses on improving the efficiency of the transportation system through changing traveler's travel behavior rather than expanding the infrastructure. An employer based integrated TDM program generally includes strategies designed to change the commuter's travel behavior in terms of mode choice, time choice and travel frequency. Research on TDM has focused on the evaluation of the effectiveness of TDM program to report progress and find effective strategies. Another research area, identified as high-priority research need by TRB TDM innovation and research symposium 1994 [Transportation Research Circular, 1994], is to develop tools to predict the impact of TDM strategies in the future. These tools are necessary for integrating TDM into the transportation planning process and developing realistic expectations. Most previous research on TDM impact evaluation was worksite-based, retrospective, and focused on only one or more aspects of TDM strategies. That research is generally based on survey data with small sample size due to lack of detailed information on TDM programs and promotions and commuter travel behavior patterns, which cast doubts on its findings because of potential small sample bias and self-selection bias. Additionally, the worksite-based approach has several limitations that affect the accuracy and application of analysis results. Based on the Washington State Commute Trip Reduction (CTR) dataset, this dissertation focuses on analyzing the participation rates of compressed work week schedules and telecommuting for the CTR affected employees, modeling the determinants of commuter's compressed work week schedules and telecommuting choices, and analyzing the quantitative impacts of an integrated TDM program on individual commuter's mode choice. The major findings of this dissertation may have important policy implications and help TDM practitioners better understand the effectiveness of the TDM strategies in terms of person trip and vehicle trip reduction. The models developed in this dissertation may be used to evaluate the impacts of an existing TDM program. More importantly, they may be incorporated into the regional transportation model to reflect the TDM impacts in the transportation planning process.

Since the beginning of the 21st Century, the world has seen the rapid development of the so-called "sharing economy" or collaborative consumption (Botsman, 2010). One of the first areas affected by the shared economy is vehicle ownership. With the emergence of several new providers of mobility services, such as Uber and car2go, there has been the promise of changes to the traditional way of owning and using a vehicle (Wong, Hensher, & Mulley, 2017). One potential consequence of shared mobility services is the reduction in vehicle ownership. At the same time, cities are trying to anticipate these changes by reducing the amount of space dedicated to parking, including parking requirements for residential developments. This thesis aims to assess the extent to which new shared mobility services (specifically, carsharing, bikesharing, and ridehailing) and travel demand management strategies (especially parking requirements and transit pass availability) relate to vehicle ownership among residents of multifamily dwellings. To do this, we use a web-based survey targeted to residents of multifamily apartments from Portland, Oregon. With these data, we built a multinomial logistic of the number of the vehicles owned as a function of socio-demographics, built environment, parking supply, transit passes, and three forms of shared mobility services. Results suggest that there is a strong association between shared mobility use and car ownership. However, it is not as significant as the effects of income, household size, distance to work, transit pass ownership, or even parking availability. Carshare use was negatively associated with the number of household vehicles, suggesting that it may be a useful tool in reducing car ownership. For respondents with higher education and income levels, increased carshare use was associated with fewer cars. Ridehail use, however, was not as clearly associated with reducing vehicle ownership and the effect was much smaller than that of carsharing. Parking availability in the building also has a significant and positive association with vehicle ownership. In sites with no parking available, there is an increased chance of the household owning less than two or more vehicles. However, this effect seems to disappear with the increased use of shared mobility. For all income levels, monthly use of ridehail and carshare between two and three times may decrease the odds of owning two or more vehicles. The use of both options, relaxing parking requirements and shared mobility availability, seems the best strategy to reduce vehicle ownership. In the short term, it is an alternative to those residents that decide to get rid of one or all cars but still are not ready to give up using cars. For the long term, a new relationship with vehicle ownership can be built now for the younger generation.

Thesis (Ph.D. (Town and Regional Planning))--North-West University, Potchefstroom Campus, 2008.

As transportation policies are changing to encourage alternative modes of transportation to reduce congestion problems and air quality impacts, more planning organizations are considering or implementing activity-based travel demand models to forecast future travel patterns. The proclivity towards operating activity-based models is the capability to model disaggregate travel data to better understand the model results that are generated with respect to the latest transportation policy implementations. This thesis first examines the differences between the two major modeling techniques used in the United States and then describes the assessment tool that was developed to recommend whether a region should convert to the advanced modeling procedures. This tool consists of parameters that were decided upon based on their known linkages to the advantages of activity-based models.

This research develops a framework to estimate the effective sample size of Global Positioning System (GPS) based panel surveys in urban travel behavior studies for a variety of planning purposes. Recent advances in GPS monitoring technologies have made it possible to implement panel surveys with lengths of weeks, months or even years. The many advantageous features of GPS-based panel surveys make such surveys attractive for travel behavior studies, but the higher cost of such surveys compared to conventional one-day or two-day paper diary surveys requires scrutiny at the sample size planning stage to ensure cost-effectiveness. The sample size analysis in this dissertation focuses on three major aspects in travel behavior studies: 1) to obtain reliable means for key travel behavior variables, 2) to conduct regression analysis on key travel behavior variables against explanatory variables such as demographic characteristics and seasonal factors, and 3) to examine impacts of a policy measure on travel behavior through before-and-after studies. The sample size analyses in this dissertation are based on the GPS data collected in the multi-year Commute Atlanta study. The sample size analysis with regard to obtaining reliable means for key travel behavior variables utilizes Monte Carlo re-sampling techniques to assess the trend of means against various sample size and survey length combinations. The basis for the framework and methods of sample size estimation related to regression analysis and before-and-after studies are derived from various sample size procedures based on the generalized estimating equation (GEE) method. These sample size procedures have been proposed for longitudinal studies in biomedical research. This dissertation adapts these procedures to the design of panel surveys for urban travel behavior studies with the information made available from the Commute Atlanta study. The findings from this research indicate that the required sample sizes should be much larger than the sample sizes in existing GPS-based panel surveys. This research recommends a desired range of sample sizes based on the objectives and survey lengths of urban travel behavior studies.

Urban traffic congestion is recognised as a major problem by most people in world cities. However, the implementation of congestion reducing measures on a wide scale eludes most world cities suffering from traffic congestion, as many oppose the notion of road pricing and despite economists and transportation professionals having advocated its benefits for a number of decades. The effects of road pricing have attracted considerable attention from researchers examining its effects, as it is thought to hold the key in understanding and overcoming some inherent obstacles to implementation. Unfortunately, many of the attempts consider the effects in isolation and with hypothetical, idealised and analytical tools, sometimes loosing sight of the complexities of the problem. This research empirically investigates the effects of road pricing in London, and identifies factors, which may prove to sustain it as a traffic reduction instrument. The results indicate that an integrated approach has to be developed and implemented, based upon the recognition of local perceptions, concerns, aspirations and locally acceptable solutions, if the acceptance of road pricing is to be improved. The key to dealing with the effects of road pricing, is to encourage a concerted effort by various stakeholders developing strategies considering a range of differing initiatives, coordinating and managing them in the realm of the political-economic context in which they exist. / Thesis (M.Art. et Scien. (Town and Regional Planning))--North-West University, Potchefstroom Campus, 2005.

Urban traffic congestion is recognised as a major problem by most people in world cities. However, the implementation of congestion reducing measures on a wide scale eludes most world cities suffering from traffic congestion, as many oppose the notion of road pricing and despite economists and transportation professionals having advocated its benefits for a number of decades. The effects of road pricing have attracted considerable attention from researchers examining its effects, as it is thought to hold the key in understanding and overcoming some inherent obstacles to implementation. Unfortunately, many of the attempts consider the effects in isolation and with hypothetical, idealised and analytical tools, sometimes loosing sight of the complexities of the problem. This research empirically investigates the effects of road pricing in London, and identifies factors, which may prove to sustain it as a traffic reduction instrument. The results indicate that an integrated approach has to be developed and implemented, based upon the recognition of local perceptions, concerns, aspirations and locally acceptable solutions, if the acceptance of road pricing is to be improved. The key to dealing with the effects of road pricing, is to encourage a concerted effort by various stakeholders developing strategies considering a range of differing initiatives, coordinating and managing them in the realm of the political-economic context in which they exist. / Thesis (M.Art. et Scien. (Town and Regional Planning))--North-West University, Potchefstroom Campus, 2005.