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RESEARCH ARTICLE: Measuring the Benefits of Compact Development on Vehicle Miles and Climate Change

Published online by Cambridge University Press:  21 October 2009

Jerry Walters  and
Reid Ewing
Jerry Walters*
Affiliation:
Fehr & Peers, Walnut Creek, California
Reid Ewing
Affiliation:
City and Metropolitan Planning, University of Utah, Salt Lake City, Utah
*
Address correspondence to: Jerry Walters, PE, Principal, Fehr & Peers, One Walnut Creek Center, 100 Pringle Avenue, Suite 600, Walnut Creek, CA. 94596; (phone) 925-930-7100; (fax) 925-933-7090; (email) [email protected]
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Abstract

For climate stabilization, the United States (US) will need to cut its transportation carbon dioxide (CO2) emissions by 60%–80% by 2050. As we cannot accomplish this reduction through vehicle and fuel technology alone, the environmental and transportation communities are focusing on strategies to reduce growth in vehicle miles traveled (VMT), reversing recent trends. The majority of recent VMT growth is due, not to population growth, but to the effects of urban environment, such as increased auto ownership, longer trips, and driving alone. The US Department of Energy forecasts that driving will continue to increase at almost threefold the growth in population. Even under the most stringent vehicle and fuel standards, transportation-related CO2 emissions will be 40% above the target level. In response, climate-change legislation has been passed in California and is pending in other states and in the US Congress that places strict new requirements on mandated environmental impact documentation. One limitation on compliance has been the lack of a unified set of scientific information on the underlying relationships between development form and VMT generation. This article distills and reconciles various forms of prior research on the subject, producing a unified quantitative understanding of the mechanisms that relate urban development forms with VMT and CO2. The findings will help improve the insightfulness and accuracy of the next generation of environmental documents. The article provides results of research and planning studies from throughout the US that indicate the degree to which developments with higher densities, mix of uses, accessible destinations, and interconnected streets reduce vehicle trips and VMT. Sources include regional blueprint studies, as well as project-specific studies such as Atlanta's Atlantic Station development, whose predicted trip reduction was found to be so compelling that the development was deemed a transportation control measure and air quality benefit by the Environmental Protection Agency and Federal Highway Administration. The article identifies the emerging practices in transportation impact analysis for assessing the degree to which the characteristics of development can reduce VMT and carbon impacts. It presents the empirical evidence of how vehicle travel is affected by density, diversity, walkability, regional accessibility, and distance from transit. It also reviews tools that transportation planners and analysts have available to capture these effects, including simple elasticities and new trip generation rates under consideration by the Institute of Transportation Engineers.

Environmental Practice 11:196–208 (2009)

Type
FEATURES
Information
Environmental Practice , Volume 11 , Issue 3 , September 2009 , pp. 196 - 208
Copyright
Copyright © National Association of Environmental Professionals 2009

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References

References

Allen, E., and Benfield, F.K.. 2003. Environmental Characteristics of Smart Growth Neighborhoods. Phase II: Two Nashville Neighborhoods. Natural Resources Defense Council, Washington, DC, 18 pp. Available at http://www.nrdc.org/cities/smartgrowth/char/charnash.pdfGoogle Scholar
Bartholomew, K. 2005. Integrating Land Use Issues into Transportation Planning, University of Utah/FHWA, 34 pp.http://faculty.arch.utah.edu/bartholomew/SP_SummaryRTP_Web.pdfGoogle Scholar
Bartholomew, K. 2007. Land Use–Transportation Scenario Planning: Promise & Reality. Transportation 34(4):397–412.CrossRefGoogle Scholar
Bernstein, S., Burwell, D., Winkelman, S.. 2003. Climate Matters: The Case for Addressing Greenhouse Gas Reduction in Federal Transportation Policy. Funders Network for Smart Growth & Livable Communities, Coral Gables, FL, 40 pp. Available at http://www.fundersnetwork.org/info-url_nocat2778/info-url_nocat_show.htm?doc_id=148724Google Scholar
Boarnet, M.G, and Crane, R.. 2001. The Influence of Land Use on Travel Behavior: Specification and Estimation Strategies. Transportation Research Part A 35(9):823–845.Google Scholar
Boarnet, M.G., Nesamani, K.S., and Smith, C.S.. 2003. Comparing the Influence of Land Use on Nonwork Trip Generation and Vehicle Distance Traveled: An Analysis Using Travel Diary Data. UCI-ITS-AS-WP-03-1. Prepared for the 83rd Annual Meeting of the Transportation Research Board, Washington, DC, January 11–15, 2004. Institute of Transportation Studies, University of Irvine, Irvine, CA, 16 pp.Google Scholar
Cervero, R., and Kockelman, K.. 1997. Travel Demand and the 3Ds: Density, Diversity, and Design. Transportation Research Part D 2(3):199–219.CrossRefGoogle Scholar
Environmental Protection Agency (EPA). 1999. Project XL and Atlantic Steel: Supporting Environmental Excellence and Smart Growth. EPA 231-R-99-004. EPA, Washington, DC, 10 pp. Available at http://www.epa.gov/smartgrowth/pdf/atlantic_steel_xl.pdfGoogle Scholar
Environmental Protection Agency (EPA). 2001a. Comparing Methodologies to Assess Transportation and Air Quality Impacts of Brownfields and Infill Development. EPA 231-R-01-001. EPA, Washington, DC, 39 pp. Available at http://www.epa.gov/livability/pdf/comparing_methodologies.pdfGoogle Scholar
Environmental Protection Agency (EPA). 2001b. EPA's Smart Growth INDEX In 20 Pilot Communities: Using GIS Sketch Modeling to Advance Smart Growth. EPA 231-R-03-001. EPA, Washington, DC, 39 pp. Available at http://epa.gov/livablecommunities/pdf/Final-print.pdfGoogle Scholar
Environmental Protection Agency (EPA). 2006. Environmental Benefits of Brownfield Redevelopment: Empirical Results of Previous Studies. Unpublished working paper. Environmental Management Support, Inc., Silver Spring, MD.Google Scholar
Ewing, R., Bartholomew, K., Winkelman, S., Walters, J., and Chen, D.. 2008. Growing Cooler: Evidence on Urban Development and Climate Change. Urban Land Institute, Washington, DC, 170 pp. Available at http://www.smartgrowthamerica.org/documents/growingcoolerCH1.pdfGoogle Scholar
Ewing, R., Pendall, R., and Chen, D.. 2002. Measuring Sprawl and Its Impact. Smart Growth America/US Environmental Protection Agency, Washington, DC, 40 pp. Available at http://www.smartgrowthamerica.org/sprawlindex/MeasuringSprawl.PDFGoogle Scholar
Fehr & Peers. 2007a. Traffic Counts at Pleasant Hill and Colma BART Stations for MacArthur BART Study. Unpublished draft. Fehr & Peers Transportation Consultants, Walnut Creek, CA.Google Scholar
Fehr & Peers. 2007b. Trip Generation Surveys in the Sacramento and San Francisco Bay Area. Unpublished draft. Fehr & Peers Transportation Consultants, Walnut Creek, CA.Google Scholar
Hagler Bailly, Inc. 1998. Transportation and Environmental Analysis of the Atlantic Steel Development Proposal. EPA, Washington, DC.Google Scholar
Hagler Bailly, Inc., and Criterion Planners/Engineers. 1999. The Transportation and Environmental Impacts of Infill versus Greenfield Development: A Comparative Case Study Analysis. EPA 231-R-99-005. EPA, Washington, DC, 31 pp. Available at http://www.epa.gov/dced/pdf/infill_greenfield.pdfGoogle Scholar
Handy, S., Cao, X., and Mokhtarian, P.. 2005. Correlation or Causality between the Built Environment And Travel Behavior? Evidence from Northern California. Transportation Research Part D 10(6):427–444.CrossRefGoogle Scholar
IBI Group, Canada Mortgage and Housing Corporation and Natural Resources Canada. 2000. Greenhouse Gas Emissions for Urban Travel: Tool for Evaluating Neighbourhood Sustainability, Toronto, Canada 50 pp.Google Scholar
Lee, R, and Walters, G.. 2003. Summary of Delphi Survey Results on 4D Elasticities with Respect to Vehicle Trips and Vehicle Miles Traveled. Unpublished draft. Walnut Creek, CA.Google Scholar
McCann, B., and Ewing, R.. 2003. Measuring the Health Effects of Sprawl: A National Analysis of Physical Activity, Obesity, and Chronic Disease. Smart Growth America/Robert Wood Johnson Foundation, Washington, DC, 40 pp. Available at http://www.smartgrowthamerica.org/report/HealthSprawl8.03.pdfGoogle Scholar
Mokhtarian, P.L., and Cao, X.. 2007. Examining the Impacts of Residential Self-Selection on Travel Behavior: A Focus on Methodologies. Transportation Research Part B 43(3):204–222.Google Scholar
Natural Resources Defense Council (NRDC). 2000. Environmental Characteristics of Smart Growth Neighborhoods: An Exploratory Case Study. NRDC, Washington, DC. Available at http://www.nrdc.org/cities/smartgrowth/char/exesum.aspGoogle Scholar
Nelson, AC. 2006. Leadership in the New Era: Comment on “Planning Leadership in a New Era.” Journal of the American Planning Association 72(4):393–409.CrossRefGoogle Scholar
Transit Cooperative Research Program (TCRP). 2008. Report 128: Effects of TOD on Housing, Parking, and Travel. Transportation Research Board, Washington, DC, 126 pp.Google Scholar
Victoria Transport Policy Institute. 2008. Land Use Density and Clustering. In TDM Encyclopedia. Victoria Transport Policy Institute, Victoria, Canada. 30 pp. Available at http://www.vtpi.org/tdm/tdm81.htmGoogle Scholar
Walters, J., Ewing, R., and Schroeer, W.. 2000. Adjusting Computer Modeling Tools to Capture Effects of Smart Growth. Transportation Research Record 1722:17–26.CrossRefGoogle Scholar
Winkelman, S., Hargrave, T., and Vanderlan, C.. 2000. Transportation and Domestic Greenhouse Gas Emissions Trading. Center for Clean Air Policy, Washington, DC.35 pp.Google Scholar

Additional References

Association of Bay Area Governments (ABAG), Kimley-Horn and Associates, and Economic & Planning Systems. 2008. Trip-Generation Rates for Urban Infill Land Uses in California: Phase 1: Data Collection Methodology and Pilot Application. Final report. Caltrans, Sacramento, CA, 254 pp. Available at http://itvendors.dot.ca.gov/research/researchreports/reports/2008/trip_generation_rates_urban_infill.pdfGoogle Scholar
Badoe, D.A., and Miller, E.J.. 2000. Transportation–Land-Use Interaction: Empirical Findings in North America, and Their Implications for Modeling. Transportation Research Part D 5(4):235–263.CrossRefGoogle Scholar
Bartholomew, K. 2005. Integrating Land Use Issues into Transportation Planning: Scenario Planning—Summary Report. Funded by the Federal Highway Administration. Publication no. FHWA-HEP-05-032. US Department of Transportation, Washington, DC, 34 pp. Available at http://faculty.arch.utah.edu/bartholomew/SP_SummaryRpt_Web.pdfGoogle Scholar
California Department of Transportation. 2007. Blueprint Planning Program. Sacramento, CA. http://www.dot.ca.gov/hq/tpp/offices/orip/index.htmlGoogle Scholar
Dill, J. 2004. Travel Behavior and Attitudes: New Urbanist vs. Traditional Suburban Neighborhoods. Presented at the 83rd Annual Meeting of the Transportation Research Board, Washington, DC, January 11–15.Google Scholar
DKS Associates and University of California–Irvine. 2007. Assessment of Local Models and Tools for Analyzing Smart-Growth Strategies. California Department of Transportation (Caltrans), Sacramento, CA, 196 pp. Available at http://www.dot.ca.gov/hq/research/researchreports/reports/2007/local_models_tools.pdfGoogle Scholar
Energy Information Administration (EIA). Annual Energy Outlook 2007: With Projections to 2030. DOE/EIA-0383(2007). US Department of Energy, Washington, DC, 229 pp. Available at http://tonto.eia.doe.gov/ftproot/forecasting/0383(2007).pdfGoogle Scholar
Ewing, R. 1994. Characteristics, Causes, and Effects of Sprawl: A Literature Review. Environmental and Urban Issues 21(2):1–15.Google Scholar
Ewing, R. 1995. Beyond Density, Mode Choice, and Single-Purpose Trips. Transportation Quarterly 49(4):15–24.Google Scholar
Ewing, R. 2007. Research You Can Use: Regional Scenario Plans and Meta-Analysis. Planning, March, p. 38.Google Scholar
Ewing, R., Brownson, R., and Berrigan, D.. 2006. Relationship between Urban Sprawl and Weight of U.S. Youth. American Journal of Preventive Medicine 31(6):464–474.CrossRefGoogle ScholarPubMed
Ewing, R., and Burchell, R.. 2007. Calculating the Transportation Cost Impacts of New Development: Literature Review Related to Procedures. National Cooperative Highway Research Program Project 08-59. August 17, 83–104. Transportation Research Board, Washington, DC.Google Scholar
Ewing, R., and Cervero, R.. 2001. Travel and the Built Environment. Transportation Research Record 1780:87–114.CrossRefGoogle Scholar
Ewing, R., DeAnna, M., and Li, S.. 1996. Land Use Impacts on Trip Generation Rates. Transportation Research Record 1518:1–6.CrossRefGoogle Scholar
Ewing, R., Haliyur, P., and Page, G.W.. 1994. Getting Around a Traditional City, a Suburban PUD, and Everything In-between. Transportation Research Record 1466:53–62.Google Scholar
Ewing, R., Pendall, R., and Chen, D.. 2003. Measuring Sprawl and Its Transportation Impacts. Journal of the Transportation Research Board 1832:175–183.CrossRefGoogle Scholar
Ewing, R., Schmid, T., Killingsworth, R., Zlot, A., and Raudenbush, S.. 2003. Relationship between Urban Sprawl and Physical Activity, Obesity, and Morbidity. American Journal of Health Promotion 18(1):47–57.CrossRefGoogle ScholarPubMed
Federal Highway Administration (FHWA). 2005. Vehicle Registrations, Fuel Consumption, and Vehicle Miles of Travel as Indices. In Highway Statistics 2005. US Department of Transportation, Washington, DC. Available at http://www.fhwa.dot.gov/policy/ohim/hs05/htm/mvfvm.htmGoogle Scholar
Holtzclaw, J., Clear, R., Dittmar, H., Goldstein, D., and Haas, P.. 2002. Location Efficiency: Neighborhoods and Socioeconomic Characteristics Determine Automobile Use. Transportation Planning and Technology 25:1–27.CrossRefGoogle Scholar
Kessler, J., and Schroeer, W.. 1995. Meeting Mobility and Air Quality Goals: Strategies That Work. Transportation 22(3):241–272.CrossRefGoogle Scholar
Lund, H.M., Cervero, R., and Willson, R.W.. 2004. Travel Characteristics of Transit-Oriented Development in California. Sacramento, CA, Caltrans, 132 pp. Available at http://www.csupomona.edu/~rwwillson/tod/Pictures/TOD2.pdfGoogle Scholar
Polzin, S.E. 2006. The Case for Moderate Growth in Vehicle Miles of Travel: A Critical Juncture in U.S. Travel Behavior Trends. US Department of Transportation, Washington, DC, 49 pp.Google Scholar
Sacramento Area Council of Governments (SACOG) and Valley Vision. n.d.Sacramento Region Blueprint. Sacramento, CA, SACOG. Available at http://www.sacregionblueprint.org/sacregionblueprint/home.cfmGoogle Scholar
Smart Growth Network. 2006. This Is Smart Growth. International City/County Management Association (ICMA) and the U.S. Environmental Protection Agency (EPA), Washington, DC, 32 pp. Available at http://www.smartgrowth.org/library/articles.asp?art=2367Google Scholar