United States Department of Transportation
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Evaluation of sketch-level VMT quantification tools : strategic growth council grant programs evaluation support project.
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2017-08-01
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[PDF-1.77 MB]
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Abstract:The State of California has enacted ambitious policies that aim to reduce the state’s greenhouse gas (GHG) emissions. Some of these policies focus on reducing the amount of driving throughout the state, measured in vehicle miles traveled (VMT), given that transportation, primarily automobile use, is the largest single source of California’s GHG emissions.1 To encourage local plans and projects that reduce VMT, California has established several grant programs to which local jurisdictions may apply. These grant programs have generated a need for methods to estimate the potential VMT – and thus GHG – impacts of proposed planning efforts, land development projects, and transportation projects. A range of VMT estimation methods are available for use by funding applicants. Regional travel demand models, for example, are used to estimate the VMT and GHG implications of alternative scenarios in the development of federally-required regional transportation plans and state-required sustainable communities strategies. These models are resource intensive, however, requiring modeling expertise and sometimes many days to complete a single analysis. To fill the need for less resource-intensive methods more appropriate for localized plans and individual projects, upwards of a dozen “sketch” tools have been developed. These sketch tools vary in their approach and appropriateness for the breadth of development projects and project locations in the state. Practitioners are often unsure as to which method to use for a particular project and have little information to guide their choice. In this report we compare and evaluate VMT estimation tools across a sample of land use projects. We compare the results from different tools for each project, consider the applicability of methods in particular contexts and for different types of projects, and assess data needs, relative ease of use, and other practical considerations.
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