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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.

The paper is concerned with the impact of market research prior to integration, on the structures of noncompetitive industries in integrated economy. The analysis focuses on separated, single commodity, monopolistic markets with stochastic demand. Monopolistic firms are considered in dynamic multiperiod model, where intertemporal links are determined by expenditures on market research in a present period and benefits from this activity (i.e., smaller variance of the prediction error) in the future. Assuming that each firm maximizes its total discounted expected utility from profit in indefinite time, we show that the optimal market research strategy is stationary and depends on market size. Consequently, in the period following integration firms operating prior to integration in small markets (such as Slovenia, Czech Republic, Hungary or Estonia) are expected to have much less information about the integrated market than their competitors operating before integration on European market. This informational asymmetry may affect the structure of the industry in integrated economy. In the extreme case, the firm operating before integration in the small market can be ruled out from the integrated market. (authors' abstract)

Electricity production from renewable energy resources such as wind energy and photovoltaics is variable. Integration of these intermittent resources into the electricity system leads to new challenges in how to manage imbalance between supply and demand on the grid. One way to meet these challenges is to develop so-called smart grid solutions. One idea, called demand response, is to adjust the amount or timing of energy consumption, e.g. by control of household appliances, to provide flexibility that could be used to balance the grid. In aggregate, when applied to many units across the system, large volumes of energy could be made available when needed and this grid flexibility can be used as a product on the electricity regulation market. Despite the potential benefits, the number of demand response bids is currently low. The aim of this thesis is to identify barriers in the Swedish regulation market, and togive Sweden's transmission system operator, Svenska kraftnät, recommendations on how to facilitate implementations of the technique. This has been done throughliterature studies and a wide range of interviews with people within the electricity market sector. The results indicate that a combination of several elements in the complex energy system impede the introduction of demand response. The main issues are related to market regulations and profitability difficulties. The authors recommend that Svenska kraftnät lowers the minimum bid size in all of the Swedish bidding areas, and adjusts the balance responsibility agreement and the system of balancing settlement.

Syfte: Vi vill ta reda på hur teknikutvecklingen relaterad till VoD påverkar beteendet hos unga vuxna och hur detta beteende förändrar tv-tittandet i denna målgrupp. Teori: Denna studie använder huvudsakligen två teorier för att undersöka problemet. Den första teori som tas upp är Diffusion of innovation, Rogers (2003) som diskuterar innovationers spridning och påverkan i samhället. För att vidare undersöka beteendet hos unga vuxna används Jakob Bjurs avhandling Transforming Audiences (2009) som undersöker individualiseringens mönster inom tv-tittande. Metod: En kvalitativ empirisk fallstudie baserad på intervjuer av fokusgrupper samt personintervju. Detta stöds av en kvantitativ undersökning i syfte att skapa en triangulering för att minimera risken för en felaktig tolkning av resultatet. Resultat: Unga vuxna vill inte längre anpassa sig till tv-tablåer. Generationen blir allt mer individualiserad och vill därför välja helt på egen hand var och när de skall titta på exempelvis ett tv-program. Medieföretagen är medvetna om detta skifte från linjär-tv till VoD och således finns ett fokus på att utveckla ett mer individ-anpassat tittande. Trots individualiseringen och de förbättrade VoD-tjänsterna vill många se bland annat nyheter, större sportevenemang samt melodifestivalen via linjär-tv. Slutsats: Den största anledningen till att många gör denna övergång från linjär-tv till VoD är att de söker en flexibilitet som linjär-tv inte kan erbjuda. Flexibiliteten har gjorts möjlig genom den teknikutveckling som skett inom datorer, smarttelefoner och surfplattor. Det som lockar många VoD-användare till att fortfarande titta på linjär-tv är de sändningar som är viktiga att se i realtid. Tack vare dessa kommer förmodligen tv-tittandet finnas kvar en tid framöver, dock ser vi sjunkande tittarsiffror i de yngre generationerna vilket gör framtiden för linjär-tv oviss.

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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 overarching research objective is to address the tightly-coupled interactions between the demand-side and supply-side components of the United States Commercial Air Transportation System (CATS) in a time-variant environment. A system-of-system perspective is adopted, where the scope is extended beyond the National Airspace System (NAS) level to the National Transportation System (NTS) level to capture the intermodal and multimodal relationships between NTS stakeholders. The Agent-Based Modeling and Simulation technique is employed where the NTS/NAS is treated as an integrated Multi-Agent System comprising of consumer and service provider agents representing the demand-side and supply-side components respectively. Successful calibration and validation of both model components against the observable real world data provided a CATS simulation tool where the aviation demand is estimated from socioeconomic and demographic properties of the population instead of merely based on enplanement growth multipliers. This valuable achievement enabled a 20-year outlook simulation study to investigate the implications of a global fuel price hike on the airline industry and the U.S. CATS at large. Simulation outcomes revealed insights into the airline competitive behaviors and the subsequent responses from transportation consumers.

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.

The general aim of this research is to investigate approaches to: •improve small area market demand (i.e. SAMD) prediction accuracy for the purchase of automobiles at the level of each Census Collection District (i.e. CCD); and •enhance understanding of meso-level marketing phenomena (i.e. geographically aggregated phenomena) relating to SAMD. Given the importance of SAMD prediction, and the limitations posed by current methods, four research questions are addressed: •What are the key challenges in meso-level SAMD prediction? •What variables affect SAMD prediction? •What techniques can be used to improve SAMD prediction? •What is the value of integrating these techniques to improve SAMD prediction? To answer these questions, possible solutions from two broad areas are examined: spatial analysis and data mining. The research is divided into two main studies. In the first study, a seven-step modelling process is developed for SAMD prediction. Several sets of models are analysed to examine the modelling techniques’ effectiveness in improving the accuracy of SAMD prediction. The second study involves two cases to: 1) explore the integration of these techniques and their advantages in SAMD prediction; and 2) gain insights into spatial marketing issues. The case study of Peugeot in the Sydney metropolitan area shows that urbanisation and geo-marketing factors can have a more important role in SAMD prediction than socio-demographic factors. Furthermore, results show that modelling spatial effects is the most important aspect of this prediction exercise. The value of the integration of techniques is in compensating for the weaknesses of conventional techniques, and in providing complementary and supplementary information for meso-level marketing analyses. Substantively, significant spatial variation and continuous patterns are found with the influence of key studied variables. The substantive implications of these findings have a bearing on both academic and managerial understanding. Also, the innovative methods (e.g. the SAMD modelling process and the model cube based technique comparison) developed from this research make significant contributions to marketing research methodology.