HEURISTIC POLICY ANALYSIS OF REGIONAL LAND USE, TRANSIT, AND TRAVEL PRICING SCENARIOS USING TWO URBAN MODELS

Two different urban models, an advanced travel demand model and an integrated land use and transportation model, are used to simulate land use, transit and auto pricing policies in the Sacramento region. This application highlights the advantages of using multiple models to address the uncertainty in large-scale urban models. First, the intersection of two uncertain models produces more robust results than one grand model. Second, the process of operationalizing the policy sets exemplifies the theoretical and structural differences in the models. Third, a comparison of the results from multiple models illustrates the implications of the respective models\u27 strengths and weaknesses and may provide some insights into heuristic policy strategies. Empirical and modeling literature is reviewed to identify effective policies and optimal combinations of those policies, as well as to provide a comparative context for the results of the simulation. Findings from the analysis are given. This study finds that land use and transit policies may reduce vehicle miles traveled and emissions by about 5-7%, and the addition of modest auto pricing policies may increase the reduction by about 4-6% compared to a future base case scenario for a 20-year time horizon. Results also suggest that parking pricing should not be imposed in areas served by light rail lines and in areas in which increased densities are promoted with land subsidy policies. Finally, results indicate that development taxes and land subsidy policies may not be sufficient to generate effective transit-oriented land uses without strict growth controls elsewhere in the region