Heuristic policy analysis of regional land use, transit, and travel pricing scenarios using two urban models

摘要

To address some of the uncertainties inherent in large-scale models, two very different urban models, an advanced travel demand model and an integrated land use and transportation model, are applied to evaluate land use, transit, and auto pricing policies in the Sacramento, CA(US), region. The empirical and modeling literature is reviewed to identify effective land use, transit, and pricing policies and optimal combinations of those policies and to provide a comparative context for the results of the simulation. The study illustrates several advantages of this approach for addressing uncertainty in large-scale models.