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Journal of Undergraduate Research

Keywords

large zone economic module, LZEM, SE3M model, land use, transportation

College

Family, Home, and Social Sciences

Department

Geography

Abstract

Integrated land use and transportation forecasting models are used to assist decision makers in the policy analysis and infrastructure capital improvements selection process (1, 2, and 3). These models are typically given precise, point-estimate inputs that are mathematically linked, through a series of submodels, to forecasted model outputs (4 and 5). These point-estimate inputs represent an unrealistic level of precision and a growing body of research is focusing on statistical techniques to model uncertainty in model inputs and parameters and tracking the effects of this uncertainty through the various submodels to the model outputs (see 4, 6, and 7 and literature cited therein for examples). Modeling uncertainty provides at least two benefits. First, it gives the model user a better understanding of how the modeling framework functions in uncertain conditions—thereby testing the robustness of the model as a whole—and second, it allows the outputs of the model to be presented within a set of confidence intervals—thereby determining whether or not the model is able to statistically differentiate between two policy or infrastructure investment alternatives.

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