A meta-analysis published recently in the Journal of the American Planning Association finds the most important single factor in minimizing driving is to develop in existing areas of high destination accessibility – like city centers. Going back in time (or back to the future), that would be villages.

Other factors like mixed-use, street and intersection design, and block size prove to be less important than destination accessibility. Still, these factors are more important than mere density. Density is less important than land-use mix and having shops, schools, and workplaces near to where people live.

Not surprisingly, driving is found to have energy and climate implications:

The transportation outcomes . . .  vehicle miles traveled (VMT) and vehicle trips (VT), are critically linked to traffic safety, air quality, energy consumption, climate change, and other social costs of automobile use.

Figuring out a way to drive less – much, much less – is key to coming to grips with peak oil and to arresting global warming before we reach a tipping point beyond which Earth’s climate will spin out of control, resulting in an Eaarth we no longer recognize and which is no longer fit for human habitation.

The free-access analysis, Travel and the Built Environment, was authored by Reid Ewingab of the University of Utah’s Urban Land Institute; and Robert Cerverocde, University of California (Berkeley) Transportation Center, Institute of Urban and Regional Development.

Here’s the abstract:

Problem: Localities and states are turning to land planning and urban design for help in reducing automobile use and related social and environmental costs. The effects of such strategies on travel demand have not been generalized in recent years from the multitude of available studies.

Purpose: We conducted a meta-analysis of the built environment-travel literature existing at the end of 2009 in order to draw generalizable conclusions for practice. We aimed to quantify effect sizes, update earlier work, include additional outcome measures, and address the methodological issue of self-selection.

Methods: We computed elasticities for individual studies and pooled them to produce weighted averages.

Results and conclusions: Travel variables are generally inelastic with respect to change in measures of the built environment. Of the environmental variables considered here, none has a weighted average travel elasticity of absolute magnitude greater than 0.39, and most are much less. Still, the combined effect of several such variables on travel could be quite large. Consistent with prior work, we find that vehicle miles traveled (VMT) is most strongly related to measures of accessibility to destinations and secondarily to street network design variables. Walking is most strongly related to measures of land use diversity, intersection density, and the number of destinations within walking distance. Bus and train use are equally related to proximity to transit and street network design variables, with land use diversity a secondary factor. Surprisingly, we find population and job densities to be only weakly associated with travel behavior once these other variables are controlled.

Takeaway for practice: The elasticities we derived in this meta-analysis may be used to adjust outputs of travel or activity models that are otherwise insensitive to variation in the built environment, or be used in sketch planning applications ranging from climate action plans to health impact assessments. However, because sample sizes are small, and very few studies control for residential preferences and attitudes, we cannot say that planners should generalize broadly from our results. While these elasticities are as accurate as currently possible, they should be understood to contain unknown error and have unknown confidence intervals. They provide a base, and as more built-environment/travel studies appear in the planning literature, these elasticities should be updated and refined.