The overall goal of the dissertation is to contribute to the growing literature on multiple discrete-continuous (MDC) choice models. In MDC choice situations, consumers often encounter two inter-related decisions at a choice instance – which alternative(s) to choose for consumption from a set of available alternatives, and the amount to consume of the chosen alternatives. In the recent literature, there is increasing attention on modeling MDC situations based on a rigorous underlying micro-economic utility maximization framework. Among these models, the multiple-discrete continuous extreme value MDCEV model (Bhat, 2005, 2008) provides a number of advantages over other models. The primary objective of this dissertation is to extend the MDCEV framework to accommodate more realistic decision-making processes from a behavioral standpoint. The dissertation has two secondary objectives. The first is to advance the current operationalization and the econometric modeling of MDC choice situations. The second is to contribute to the transportation literature by estimating MDC models that provide new insights on individuals’ travel decision processes. The proposed extensions of the MDCEV model include: (1) To formulate and estimate a latent choice set generation model within the MDCEV framework, (2) To develop a random utility-based model formulation that extends the MDCEV model to include multiple linear constraints, and (3) To extend the MDCEV model to relax the assumption of an additively separable utility function. The methodologies developed in this dissertation allow the specification and estimation of complex MDC choice models, and may be viewed as a major advance with the potential to lead to significant breakthroughs in the way MDC choices are structured and implemented. These methodologies provide a more realistic representation of the choice process. The proposed extensions are applied to different empirical contexts within the transportation field, including participation in and travel mileage allocated to non-work activities during various time periods of the day for workers, participation in recreational activities and time allocation for workers, and household expenditures in disaggregate transportation categories. The results from these exercises clearly underline the importance of relaxing some of the assumptions made, not only in the MDCEV model, but in MDC models in general. / text

Rapid urbanization is a pressing issue for planners, policymakers, transportation engineers, air quality modelers and others. Due to significant environmental, traffic and other impacts, the process of land development highlights a need for land use models with behavioral foundations. Such models seek to anticipate future settlement and transport patterns, helping ensure effective public and private investment decisions and policymaking, to accommodate growth while mitigating environmental impacts and other concerns. A variety of land use models now exist, but a market-based model with sufficient spatial resolution and defensible behavioral foundations remains elusive. This dissertation addresses this goal by developing and applying such a model. Real estate markets involve numerous interactive agents and real estate with a great level of heterogeneity. In the absence of tractable theory for realistic real estate markets, this research takes a “bottom-up” approach and simulates the behavior of tens of thousands of individual agents based on actual data. Both the supply and demand sides of the market are modeled explicitly, with endogenously determined property prices and land use patterns (including distributions of households and firms). Notions of competition were used to simulate price adjustment, and market-clearing prices were obtained in an iterative fashion. When real estate markets reach equilibrium, each agent is aligned with a single, utility-maximizing location and each allocated location is occupied by the highest bidding agent(s). This approach helps ensure a form of local equilibrium (subject to imperfect information on the part of most agents) along with useroptimal land allocation patterns. The model system was applied to the City of Austin and its extraterritorial jurisdiction. Multiple scenarios reveal the strengths and limitations of the market simulation and available data sets. While equilibrium prices in forecast years are generally lower than observed or expected, the spatial distributions of property values, new development, and individual agents are reasonable. Longer-term forecasts were generated to test the performance the model system. The forecasted households and firm distributions in year 2020 are consistent with expectations, but property prices are forecasted to experience noticeable changes. The model dynamics may be much improved by more appropriate maximum bid prices for each property. More importantly, this work demonstrates that microsimulation of real estate markets and the spatial allocation of households and firms is a viable pursuit. Such approaches herald a new wave of land use forecasting opportunities, for more effective policymaking and planning. / text

Models of land use change fall into two broad categories: pattern based and process based. This thesis focuses on pattern based land use change models, expanding our understanding of these models in three important ways. First, it is demonstrated that some driving variables do not have a smooth impact on the land use transition process. Our example variable is access to water. Land managers with access to piped water do not have any need for surface or groundwater. For variables like this, a model needs to change the way that driving variables are represented. The second important result is that including a variable which captures spatial correlation between land use types significantly increases the explanatory power of the prediction model. A major weakness of pattern based land use models is their inability to model interactions between neighbouring land parcels; the method proposed in this study can be an alternative to account for spatial neighbourhood association. These innovations are applied using the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) system to the Deep Creek watershed in the Southern Interior of British Columbia. The results highlight the challenge of balancing the protection of agricultural land and conserving forest and natural areas when population and economic growth are inevitable. The results also demonstrate the implications of land use change on existing land use policies. The calibrated model was validated using remote sensing data. A series of discriminant functions were estimated for each land use type in the recent period and these functions were then used to classify. The calibrated model was run in reverse, back to the generated land use classification, and results compared. Fit was reasonable with error rates falling below ten percent when radii beyond 2.5 km were considered. Another important contribution is demonstrating the importance of modelling dynamic variables. Some important drivers are changing continuously and others depend on land use change itself. Failure to update these variables will bias model forecasts. Spatial neighbourhood association, an endogenous variable governed by land use change itself, is again used as the example dynamic variable. The study demonstrates the importance of updating all associated information. / Graduate Studies, College of (Okanagan) / Graduate