This thesis builds upon Jin and Kockelman’s (2004) work, by extending a Random Utility Based Multiregional Input Output (RUBMRIO) model and using it to evaluate Texas trade, production, travel, and location patterns, under a variety of scenarios, including the construction of the Trans-Texas Corridor system. Three main enhancements are accomplished: incorporation of domestic demands from other U.S. states (to help drive the model); generation of personal and freight vehicle trips based on economic trade flows (and loading of these trips onto a congestible network); and the explicit consideration of labor and floorspace markets (with price response). The incorporation of domestic demands, based on Commodity Flow Survey (CFS) data (BEA 1997), improves the representation of the sate’s economy, bringing model predictions of total production to within 5% of CFS values (across key industry sectors). Freight trips are generated using factors derived from CFS and Vehicle Inventory and Use Survey (VIUS) data. These convert inter-county monetary trade flows to freight tons, and tons to trucks. Modeling results indicate that Chemical and Allied Products, Mining, and Manufacturing sectors generate most of the state’s truck trips, following the same trends noted in the VIUS data. Work trips (a result of the labor market equilibration procedure) and shop trips (obtained assuming average expenditures per household shop trip) exhibit adequate distribution patterns. The model predicts 73% of commute trips to be intra-county (the same percentage as in the 2000 Census) and 95% of shop trips to be intra-county, which is consistent with the Austin Travel Survey data. The incorporation of markets for land and labor, as key factors of production, is based on supply-demand equilibration principles, via appropriate wages and rent calculations. At this stage, supply assumptions for labor and floorspace rely on a simplified approach, where floorspace supply is considered fixed and labor supply is determined by local average wage and cost of living. Results suggest that the new approach captures many of the relationships that define production and population patterns. More accurate estimates can be expected if the corresponding supply models are refined appropriately. As expected, the model is sensitive to variations in final demands and production technologies. Application results highlight the importance of the Agriculture, Mining and Fabricated Metal Products sectors for the state’s economy, as well as the state’s relative dependence on demands of nearby states - such as Florida, Georgia and Mississippi - or states that exert greater demands - such as Maryland or New York. Enhancements in production technologies are predicted to reduce the need for intermediate trading, and have a larger positive impact if applied in appropriate sectors - such as Industrial Machinery and Equipment or Electronic and Electric Equipment - and counties – Northwest counties in particular. Several simulations involving incorporation of the Trans Texas Corridor (TTC) within Texas’ highway and railway networks suggest that the model responds appropriately to changes in the transportation system. It predicts a slight redistribution of economic activities, increasing the supremacy of counties located closer to export zones, while smoothing production patterns among the others. It also suggests a greater diversification of economic activity/production and moderate changes in the distribution of wages, floorspace rents and population, following the production trends. The simulation indicates more noticeable effects in counties traversed by the TTC, especially in those previously inadequately connected to the state’s transportation network. It also suggests that the TTC will reduce traffic volumes on existing highways by 8% and the state’s (dollar-weighted) average highway travel distances by 30%. The extensions and applications of the RUBMRIO model set a functional framework for the analysis of spatial economic interactions, accounting for the transportation system performance, and the distribution and availability of factors of production. The model is useful as a tool for the qualitative evaluation of policy impacts, and provides a flexible starting point for the incorporation of further realistic assumptions, that could eventually lead to a more powerful quantitative impact analysis model