Many cities are reconsidering their reliance on ITE’s Trip Generation Manual, now in its 10th edition. Kelly Clifton and co-investigator Kristina Currans of the University of Arizona examine the advantages and limitations of ITE’s land use taxonomy for multifamily residences. They find that the land use categories aiming to capture intensity of development for residential land uses (high-rise apartments, for example) do not appear to capture any more variation in the vehicle or person trip rates than can be achieved by measure of the built environment. Using inflexible land use codes instead of built environment or socio-economic characteristics is an approach that has limited usefulness over time. Clifton and Currans demonstrate that such land use codes would best be replaced by actual information about the urban, demographic, and economic context; such as density, mixed use development, transit access and median incomes. A more concerted effort to examine the usefulness of the various land use data will be critical as we head into the future. With the introduction of transportation network companies like Lyft and Uber, urban goods delivery, and automated vehicles, vehicle trips may not have the same qualities as previous passenger trips in automobiles. Specifically, this study examines the vehicle and person trip generation rates associated with the land use taxonomies in the ITE Trip Generation Handbook to differentiate between various kinds of residential housing. The primary research questions were:
1. Does the built environment vary across the various ITE Land Use Codes for multifamily housing?
2. How do vehicle trip rates and newly established person trip rates vary across urban locations?
3. How well does ITE’s recommended practice of converting their vehicle trip rate data to person trip rates perform?
To answer these questions, researchers conducted a national study of multifamily housing sites that makes use of archived transportation counts and intercept surveys collected on site. The study leveraged several concurrent or recent trip generation studies in Portland, OR; San Francisco, CA; Los Angeles, CA; and Washington, DC. The data collected from these sites were analyzed using multivariate statistical techniques. The report concludes with discussion of the implication of these findings for multimodal transportation impact analysis of new development and policies that aim for better coordination between urban land use change and transportation investments.
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