Publication Detail

NCST White Paper: The Effect of Land Use Policies and Infrastructure Investments on How Much We Drive: A Practitioner’s Guide to the Literature

UCD-ITS-RR-15-37

Research Report

National Center for Sustainable Transportation

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Suggested Citation:
Salon, Deborah (2015) NCST White Paper: The Effect of Land Use Policies and Infrastructure Investments on How Much We Drive: A Practitioner’s Guide to the Literature. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-15-37

Policymakers aiming to reduce vehicle miles traveled (VMT) want to know what they should do to reduce the amount people drive, and what evidence suggests that this is the best course of action. The relationships between built environment characteristics and driving have generally been shown to be consistent with expectations. As alternatives to solo driving become available, people drive less. As driving becomes more expensive and less convenient, people drive less. As trip destinations and origins move closer together, people drive less. Based on this evidence, policymakers should not hesitate to enact policies and make prudent investments that encourage less driving.

However, despite an extensive academic literature on this subject, the specific answer to the policymaker’s question is not straightforward. There is a wide variety of possible policy actions, and the action(s) that will be most effective in a particular situation depend critically on context: who is driving, where they are going, and what alternative modes and destinations are available. Existing research results can provide guidance but cannot dictate a universally applicable recipe.

This white paper provides a guide for practitioners on how to read, understand, and use results reported in this especially challenging area of the literature: the relationship between the built environment, the transportation system, and driving. It identifies theoretical relationships, highlights the challenges inherent in exploring these relationships using real-world data, and discusses three prominent studies in detail to illustrate how to interpret the results.