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More Great Research Quantifying Smart Growth Benefits

New research can help planners understand how specific decisions will affect transport activity (how and how much people travel), and their ultimate economic, social, and environmental impacts.
Todd Litman | July 14, 2014, 5am PDT
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Much of my current research concerns the relationships between land use and transportation, and methods for quantifying these impacts. A few months ago I discussed some initial findings in my column, New Research On Smart Growth Benefits. I now have more to share.

Overall this research indicates that land use development patterns significantly affect travel activity. For example, the following graph shows the relationship between density and vehicle travel for 58 higher-income cities. This relationship is statistically strong (R2 0.8392) and the largest reductions occur as density increases from low (under 10 residents per hectare) to moderate (25-50 residents per hectare), which suggests that relatively modest land use changes (such as reductions in single-family lot size) can achieve large vehicle travel reductions; smart growth does not require that everybody live in high rise apartments and forego automobile travel.

This figure illustrates the negative relationship between density and per capita vehicle travel in 58 high-income cities. The relationship is statistically strong. The largest reductions occur as density increases from low (under 10 residents per hectare) to moderate (25-50 residents per hectare), which suggests that relatively modest land use changes can achieve large vehicle travel reductions.

Like most older research on this subject, this graph only measures density (people and jobs per hectare or square kilometer), but there are actually several land use factors related to density that tend to affect transport activity including regional accessibility, land use mix, centricity, roadway connectivity, transport system diversity, and parking supply. Most land use impact studies consider these factors in aggregate, which is sometimes called compactness. A few recent studies apply more disaggregated analysis of these factors.

My previous column described Smart Growth America’s recent report Measuring Sprawl 2014, which is a very comprehensive and detailed analysis comparing sprawl and compact development impacts. It assigned a Sprawl Index (although, since it increases with smart growth attributes, it is better to think of it as a Compactness Index) score to 221 U.S. metropolitan areas and 994 counties based on four primary factors: density (people and jobs per square mile), mix (whether neighborhoods had a mix of homes, jobs and services), roadway connectivity (the density of road network connections), and centricity (the portion of jobs in major activity centers). It then incorporated various data sets and used sophisticated statistical methods to tease out various economic, social, and environmental impacts.

Unfortunately, this study is so good – so detailed and sophisticated – that most people interested in these issues will have difficulty understanding the results. At my request the authors, University of Utah professors Reid Ewing and Shima Hamidi, produced the following table to summarize their key findings (thanks!).

Summary of Analysis Outcomes


Relationship to Compactness

Impact of 10% Score Increase

Average household vehicle ownership

Negative and significant

0.6% decline

Vehicle miles traveled


7.8% to 9.5% decline

Walking commute mode share

Positive and significant

3.9% increase

Public transit commute mode share

Positive and significant

11.5% increase

Average journey-to-work drive time

Negative and significant

0.5% decline

Traffic crashes per 100,000 population

Positive and significant

0.4% increase

Injury crash rate per 100,000 population

Positive and significant

0.6% increase

Fatal crash rate per 100,000 population

Negative and significant

13.8% decline

Body mass index

Negative and significant

0.4% decline


Negative and significant

3.6% decline

Any physical activity

Not significant

0.2% increase

Diagnosed high blood pressure

Negative and significant

1.7% decline

Diagnosed heart disease

Negative and significant

3.2% decline

Diagnosed diabetes

Negative and significant

1.7% decline

Average life expectancy

Positive and significant

0.4% increase

Upward mobility (probability a child born to a family in the bottom income quintile reaches the top quintile by age 30)

Positive and significant

4.1% increase

Transportation affordability

Positive and significant

3.5% decrease in transport costs relative to income

Housing affordability

Negative and significant

1.1% increase in housing costs relative to income.

This table summarizes various economic, health and environmental impacts from more compact development.

Using these values it is possible to model these impacts and estimate smart growth benefits. For example, a policy that increases the Index 20% is likely to reduce traffic fatalities by 27.6%, reduce the portion of household budgets devoted to transportation by 7%, and increase life expectancy by 0.8%.

Ewing and Hamidi’s analysis represents major progress in understanding the relationships between land use and transport activity because it disaggregates sprawl into individual components. Disaggregated analysis is useful because it is possible to have dense sprawl (for example, large high-rise developments scattered over an automobile-dependent landscape) and rural smart growth (development concentrated in villages with common services within convenient walking distance of most households, connected to larger urban centers with convenient public transit services). Disaggregated analysis expands the range of policy tools that can be used to encourage more efficient travel activity; for example, even if a city cannot increase development density it may be able to increase mix, road connectivity, and the quality of resource-efficient travel modes (walking, cycling and public transport).

Another important study I recently discovered is the Japan International Cooperation Agency’s Research on Practical Approach for Urban Transport Planning, which summarize extensive research on the factors that affect public transit demand and system efficiency and, therefore, the type of transit system most suited to various types of cities. It includes detailed analysis of the relationships between factors including city size and growth rates, population density, income or GDP, vehicle ownership, mode share, transit service type (metro rail, Bus Rapid Transit, and conventional bus), and types of urban transportation problem (traffic congestion, high accident rates, pollution, lack of public transit service, crowded transit, and social inequity), based on comprehensive data from 398 major cities around the world, including 65 cities where JICA has helped develop urban transport master plans. This information is used to help provide guidelines to determine, for example, what cities should develop metro rail or BRT systems and other urban transportation improvement strategies.

One important way that compact, multi-modal urban development reduces motor vehicle travel is by reducing vehicle ownership. Described differently, in automobile-dependent areas, where private automobile travel is necessary for a significant portion of trips, households will tend to purchase one vehicle per driver, and, because automobiles have high fixed costs and low variable costs, once a driver owns a vehicle they will use it for a major portion of trips, including  marginal value automobile travel (vehicle-kilometers that provide small net user benefits). As a result, reducing vehicle ownership tends to significantly reduce total vehicle travel. To reduce vehicle ownership (and therefore leverage reductions in these marginal-value vehicle-kilometers) by higher-income households a neighborhood must include the combination mobility services that provide a high level of accessibility without requiring private automobile travel. This includes:

  • Commonly-used services (shops, schools, parks, healthcare, etc.) located within convenient walking distances.
  • Good walking and cycling conditions, and good public transit and taxi services (including safety and comfort). These need to be integrated, so for example, it is easy to walk and bike to transit stops and stations, which have secure bicycle parking.
  • Convenient vehicle rental services (including carsharing).
  • Social acceptability of non-automobile modes. As more community residents rely on walking, cycling, and public transit, the social acceptability of these modes increases.

The figures below from the Japan International Cooperation Agency’s study illustrate the relationships between density and vehicle ownership. The study found much weaker relationships between density and transit mode share and between incomes and transit mode share, which probably reflect the large variations in transit service quality: if transit service quality is very poor, even residents of dense, congested, low-income cities will continue to rely on automobile travel, but if transit service is good, affluent residents will use it.

Urban Density Versus Vehicle Ownership

These three figures illustrate the relationships between population density and vehicle ownership, taking into account city size, per capita gross domestic product (GDP), and world region. The high R2 values indicate statistically strong relationships. This indicates that even in affluent cities, increased density reduces per capita vehicle ownership, which in turn leverages reductions in per capita vehicle travel.

This research indicates that vehicle travel reductions do not require high urban densities; relatively modest increases can significantly reduce vehicle travel if implemented with complementary smart growth policies that increase accessibility and transport system diversity. Such policies can be implemented in various geographic scales; they can be tailored to urban, suburban and rural conditions. 

Land Use Impacts on Travel - Summary



Travel Impacts

Regional accessibility

Location of development relative to regional urban center.

Reduces per capita vehicle mileage. More central area residents typically drive 10-40% less than at the urban fringe



People or jobs per unit of land area (acre or hectare).

Reduces vehicle ownership and travel, and increases use of alternative modes. A 10% increase typically reduces VMT 0.5-1% as an isolated factor, and 1-4% including associated factors (regional accessibility, mix, etc.).


Proximity between different land uses (housing, commercial, institutional)

Tends to reduce vehicle travel and increase use of alternative modes, particularly walking. Mixed-use areas typically have 5-15% less vehicle travel.

Centeredness (centricity)

Portion of jobs and other activities in central activity centers (e.g., downtowns)

Increases use of alternative modes. Typically 30-60% of commuters to major commercial centers use alternative modes compared with 5-15% at dispersed locations

Network Connectivity

Degree that walkways and roads are connected

Increased roadway connectivity can reduce vehicle travel and improved walkway connectivity increases non-motorized travel

Roadway design

Scale, design and management of streets

Multi-modal streets increase use of alternative modes. Traffic calming reduces VMT and increases non-motorized travel

Active transport (walking and cycling) conditions

Quantity, quality and security of sidewalks, crosswalks, paths, and bike lanes.

Improved walking and cycling conditions tends to increase nonmotorized travel and reduce automobile travel. Residents of more walkable communities typically walk 2-4 times more and drive 5-15% less than in more automobile-dependent areas.

Transit quality and accessibility

Quality of transit service and access from transit to destinations

Increases ridership and reduces automobile trips. Residents of transit oriented neighborhoods tend to own 10-30% fewer vehicles, drive 10-30% fewer miles, and use alternative modes 2-10 times more than in automobile-oriented areas.

Parking supply and management

Number of parking spaces per building unit or acre, and how parking is managed and priced

Tends to reduce vehicle ownership and use, and increase use of alternative modes. Cost-recovery pricing (users finance parking facilities) typically reduces automobile trips 10-30%.

Site design

Whether oriented for auto or multi-modal accessibility

More multi-modal site design can reduce automobile trips, particularly if implemented with improvements to other modes.

Mobility management

Strategies that encourage more efficient travel activity

Tends to reduce vehicle ownership and use, and increase use of alternative modes. Impacts vary depending on specific factors.

Integrated smart growth programs

Travel impacts of integrated programs that include a variety of land use management strategies

Reduces vehicle ownership and use, and increases alternative mode use. Smart growth community residents typically own 10-30% fewer vehicles, drive 20-40% less, and use alternative mode 2-10 times more than in automobile-dependent locations, and even larger reductions are possible if integrated with regional transit improvements and pricing reforms.

This table describes various land use factors that can affect travel behavior and population health.

Critics often argue that smart growth policies conflict with consumer demands, but there is now good evidence that an increasing portion of households prefer to live in compact, multi-modal neighborhoods. This means that smart growth programs can work with land markets to optimize development patterns. Recent research identifies practical ways to create more resource-efficient communities in ways that are responsive to consumer demands, including analysis by Shlomo "Solly" AngelBenoit Lefèvre, and the NYU Stern Urbanization Project. These researchers argue that regulations, such as minimum lot or unit size and minimum parking requirements tend to reduce densities, but regulations are not generally effective at increasing densities; with a few exceptions, such as when building for a captive clientele like public housing, developers will only build dense buildings if consumers demand it. This suggests that smart growth should focus mainly on removing regulations that limit density and mix, and provide positive incentives that make compact, multi-modal neighborhoods more livable and attractive, such as improving walking, cycling, and public transit; improving urban parks and public schools; streetscaping; supporting development of more affordable and diverse housing types (including housing for families with children, as well as seniors and young adults) in walkable and transit-oriented neighborhoods; plus efficient parking management and transportation demand management.  

This is interesting and important research which helps us understand how specific planning decisions will affect transport activity (how and how much people travel), and their ultimate economic, social and environmental impacts. 


For More Information

Robert W. Burchell and Sahan Mukherji (2003), “Conventional Development Versus Managed Growth: The Costs of Sprawl,”American Journal of Public Health, Vol. 93, No. 9, Sept., pp. 1534-1540.

Reid Ewing and Shima Hamidi (2014), Measuring Urban Sprawl and Validating Sprawl Measures, Metropolitan Research Center, University of Utah, for the National Cancer Institute, the Brookings Institution and Smart Growth America.

Eben Fodor (2011), Cost of Infrastructure to Serve New Residential Development in Austin, Texas, Fodor and Associates.

JICA (2011), The Research on Practical Approach for Urban Transport Planning, Japan International Cooperation Agency.

Benoit Lefèvre (2009), “Urban Transport Energy Consumption: Determinants and Strategies for its Reduction: An Analysis of the Literature,”SAPIENS (Surveys and Perspectives Integrating Environment & Society); Vol. 2, No. 3.

Todd Litman (2010), Where We Want To Be: Home Location Preferences And Their Implications For Smart Growth, Victoria Transport Policy Institute.

Todd Litman (2011), “Can Smart Growth Policies Conserve Energy and Reduce Emissions?” Portland State University’sCenter for Real Estate Quarterly, Vol. 5, No. 2, Spring, pp. 21-30.

Todd Litman (2011), “Why and How to Reduce the Amount of Land Paved for Roads and Parking Facilities,”Environmental Practice, Journal of the National Association of Environmental Professionals,      Vol. 13, No. 1, March, pp. 38-46.

Todd Litman (2013), Land Use Impacts On Transport: How Land Use Factors Affect Travel Behavior, Victoria Transport Policy Institute.

NYU Stern Urbanization Project identifies efficient urbanization policies.

Caroline Rodier, John E. Abraham, Brenda N. Dix and John D. Hunt (2010), Equity Analysis of Land Use and Transport Plans Using an Integrated Spatial Model, Report 09-08, Mineta Transportation Institute.

RPA (2014), “Access To Jobs,”Fragile Success, Regional Plan Association.

Stantec (2013), Quantifying The Costs And Benefits To HRM, Residents And The Environment Of Alternate Growth Scenarios, Halifax Regional Municipality.

USEPA (2013), Our Built and Natural Environments: A Technical Review of the Interactions Among Land Use, Transportation, and Environmental Quality, U.S. Environmental Protection Agency. 

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