Driving and the Built Environment The Effects of Compact Development on Motorized Travel, Energy Use, and CO2 Emissions -- Special Report 298 (2009) / Chapter Skim
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3 Impacts of Land Use Patterns on Vehicle Miles Traveled: Evidence from the Literature
3 Impacts of Land Use Patterns on Vehicle Miles Traveled: Evidence from the Literature
Pages 50-105
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and earlier reviews of the literature, the main section of the chapter summarizes the results of the most methodologically sound studies that examine the relationship between household travel and the built environment while controlling for socioeconomic variables and other factors (e.g., attitudes, preferences) that influence travel behavior.
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VMT itself is a composite measure -- the product of trip length, trip frequency, and mode choice (Ewing and Cervero 2001)
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that enhance the pedestrian- and bicycle-friendliness of an area. • Destination accessibility: Ease or convenience of trip destinations from point of origin, often measured at the zonal level in terms of distance from the central business district or other major centers.
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, automobile ownership, and travel modes that are also strongly affected by income, age, household size, and other socioeconomic variables (Badoe and Miller 2000)
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. Aggregate analyses such as Newman and Kenworthy's mask real differences in densities within metropolitan areas, as well as in the travel behavior of subpopulations, that vary on the basis of socioeconomic characteristics.
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The authors use traffic zones3 within three metropolitan areas -- Chicago, Los Angeles, and San Francisco -- as the geographic unit of analysis, control for household size and income effects, and draw on odometer readings (as captured by legally mandated smog checks) rather than self-reported diaries to measure VMT.4 They find that both automobile ownership and use decline in a systematic and predictable pattern as a function of increasing residential density.
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, the quality of the data, and questions about their model specification limit the reliability of these results.5 To minimize or eliminate the aggregation issues that cloud the relationship between the built environment and travel behavior, many studies use disaggregate data -- household-level travel data and neighborhood-, census tract–, or zip code–level data on the built environment -- in regression models, controlling for a much richer combination of socioeconomic variables available at the household level. However, these studies are also subject to research design and data issues discussed below, which may help explain the wide range of their results.
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Cross-sectional studies may find a statistically significant correlation between the built environment and VMT. Well-specified analyses that use disaggregate data from metropolitan areas and carefully control for socioeconomic variables and other factors that affect residential location and travel choices are valuable.
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The reason the distinction matters is the need to predict with some degree of accuracy the impact of substantial changes in the built environment on travel behavior. If future policies encourage a dramatic increase in the number of people living in compact, mixed-use areas but the increase is due primarily to policy incentives or to a limited supply of compact developments rather than to an intrinsic desire to live in such areas, the VMT reductions for those responding to such policies will probably not be as great as for those actively preferring to live in such areas.
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The instrumental variables technique treats this problem by purging the endogenous variable (residential location) of its correlation with other variables in the equation for travel behavior.
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Using data from the Puget Sound Transportation Panel, Krizek (2003) examines the travel behavior of a sample of households that moved to neighborhoods with higher local accessibility during 1989–1997.
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Researchers may study trip lengths, trip frequencies, and mode choice, and they may include automobile ownership under a broad definition of travel. Reducing VMT could be achieved by affecting each of these factors: (a)
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For example, by decreasing distances between origins and destinations, higher densities should reduce trip lengths, all else being equal, but could work in the opposite direction for trip frequencies, depending on the time-cost of travel (Crane 2000) .10 Mode choice, particularly the decision to use transit, depends on threshold density levels adequate to support good transit service, as well as on socioeconomic variables (Ewing and Cervero 2001)
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Rather, both should be viewed as complementary land use strategies for reducing VMT and VHT. Generalizability Another issue that affects the findings reported in the literature, particularly studies that use disaggregate data to examine the effects of the built environment on the travel behavior of neighborhood residents, is the applicability of the findings to other settings.
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Comprehensive Reviews of the Literature Over the past two decades, numerous studies have been conducted that have analyzed travel behavior while attempting to control for measures of the built environment and socioeconomic variables that also influence this behavior. Fortunately, noted scholars have conducted five comprehensive reviews of this burgeoning literature (Badoe and Miller 2000; Crane 2000; Ewing and Cervero 2001; Handy 2005; Cao et al.
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summarizes evidence for the proposition that new urbanism design strategies will reduce automobile use.12 She comments on how well studies have sorted out the relative importance of socioeconomic characteristics and characteristics of the built environment in explaining travel behavior and addresses issues of causality, including self-selection. The review of Cao et al.
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The impact of residential density is more ambiguous, particularly when socioeconomic characteristics and automobile ownership are controlled for. Ewing and Cervero note as an unresolved issue whether the impact of density on travel patterns is due to density itself or to other unobserved variables with which it is correlated, including attitudes.
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Magnitude of Effects The authors of the literature surveys reviewed above uncovered few studies that estimate the magnitude of the effect of the built environment on travel behavior, even when the effect is statistically significant. Ewing and Cervero (2001)
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-- each variable alone Population centrality alone Regional 114 U.S. MSAs 100 15 (without New York)
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(2008) , who review 28 studies that control for self-selection, find that virtually all the studies report a statistically significant remaining influence of the built environment on travel behavior.20 However, none of the studies quantify the relative importance of the two factors (residential self-selection and the built environment)
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Measures of urban form -- city shape, spatial distribution of population or population centrality, jobs–housing balance22 -- and the supply of public transit are combined with data on the socioeconomic characteristics23 and automobile ownership and travel patterns (i.e., annual miles driven) of households drawn from the 1990 Nationwide Personal Transportation Survey (NPTS)
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Brownstone and Golob (2009) also use a rich set of socioeconomic variables to help control for self-selection and model the relationship among residential density, vehicle use, and fuel consumption for California households.
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Brownstone and Golob draw on the California subsample of the 2001 National Household Travel Survey for data on vehicle ownership and fuel usage, land use densities, and socioeconomic characteristics of California households, thus providing a narrower geographic perspective than the national focus of Bento et al. Brownstone and Golob find that, after controlling for socioeconomic differences, a 40 percent increase in residential density is associated with about 5 percent less annual VMT (see Table 3-1)
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32 on travel patterns and socioeconomic characteristics, the authors select only those households that made a home-based work tour on the survey day.33 The focus on a tour or trip chain, rather than 30 Of the socioeconomic variables, income, education, and race have a statistically significant effect on the probability that a commuter will take transit or walk to work. Higher-income workers are more likely to drive to work, as are white workers.
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Bhat and Guo (2007) jointly model residential location and automobile ownership decisions by using data for Alameda County from the 2000 San Francisco Bay Area Travel Survey and other related sources.
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Implementing the policies necessary to bring about changes of such magnitude, however, presents a considerable challenge, a topic addressed in a subsequent section. 36 In fact, when the local transportation network measures are removed, the researchers find a negative and strongly significant effect of household and employment density on propensity for automobile ownership.
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2008) point to the importance of transit supply and good access to transit in conjunction with land use as critical variables affecting mode choice and hence VMT.
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. The literature review also examines the effect of land use and design features -- mixed land uses, traffic calming, short blocks, street furniture -- on travel patterns, transit ridership, and the decision to locate in a TOD.
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Studies of Compact Development and Urban Truck Travel Most of the studies reviewed in this chapter focus on personal travel. The committee also commissioned a paper to examine how compact 40 Respondents in the Los Angeles region were more likely to choose to live in a TOD for highway than for transit access (21.2 percent and 19.3 percent, respectively)
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. No studies were found that directly address the topic of compact development and urban truck travel, but an analysis by Bronzini of a data set on truck traffic in the 100 largest U.S.
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. As job density increases, VMT-based carbon emissions per dollar of economic activity decline.45 However, there is a good deal of variability at specific density levels, indicating the importance of other factors affecting truck carbon emissions.
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Other Modeling Approaches to Estimating Effects of Compact Development A number of different types of models can provide insight into the relationship between land development patterns and travel. So far, the committee has focused mainly on elasticities derived from disaggregate analyses in which travel behavior is modeled as a function of the built environment and socioeconomic characteristics.
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However, a light rail and pricing scenario48 showed similar modal shares but much larger reductions in VMT, primarily from a reduction in the length of trips. Model results indicated that land use policies and transit investments could reduce VMT by 5 to 7 percent over a 20-year time horizon compared with the status quo scenario.
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, because the former are based on applications of aggregate models that differ substantively from the disaggregate models on which the elasticity estimates are based. For example, simulated system-level changes such as "adding 75 miles of new light rail investment" are not generally translated into "percentage changes in density" (which would need to be averaged across the region, somehow)
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case studies Many of the studies reviewed in the previous sections suggest that reducing VMT in any significant way through changes in the built environment would require a broad range of measures, from increasing density, to substantial investment in transit, to pricing policies that better reflect the externalities of automobile travel. The committee identified two locations that have had considerable success in implementing such policies -- Portland, Oregon, and Arlington County, Virginia.
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The final element was the creation of Metro, an elected regional governance body, which not only operated as the area's MPO but also held the power of the purse, with broad taxing authority and responsibility for implementing the area's ambitious development plans. The evidence indicates that Portland's policies to steer growth into more compact, mixed-use development have paid off, not only in revitalizing the downtown and many of its neighborhoods but also in changing travel behavior, the primary concern of this study.
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Over the same period, according to Metro's Data Resource Center, population density levels increased by 18 percent, from 3,136 to 3,721 persons per square mile, holding constant the urban growth area boundary.52 A large fraction of the increase came from constructing single-family housing on small lots.53 The relatively small size of the Portland urban area, due to the urban growth boundary, has also resulted in shorter average trip lengths. Portland demonstrates that the built environment can be changed in ways that encourage more compact development and less automobile dependence, but its experience may be difficult to replicate widely.
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census, 39 percent of those living in the Metrorail corridors use transit to get to work, and another 10 percent walk or bicycle; only 40 percent commute alone. In comparison, outside the Metrorail corridors, about 17 percent commute by transit, about 54 Arlington County itself has a population density of about 8,062 per square mile, one of the highest densities in the country (Arlington County Planning Department 2008)
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Like Portland, Arlington County demonstrates what can be done through a combination of land use plans and transit investment to promote development and at the same time reduce automobile travel. The county's success can be attributed to leadership and early recognition of development potential; good planning and design, including rezoning of land adjacent to Metrorail stations to allow high-density development; a healthy economic base; and above all, the foresight to take advantage of massive investment in a new regional transit system to channel development.
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Using the example of Boston, one of the densest metropolitan areas, and Atlanta, one of the most sprawling, the researchers simulate the effect of moving sample households from a city with the urban form and transit supply characteristics of Atlanta to a city with the characteristics of Boston, with the effect that VMT could be lowered by as much as 25 percent, an estimate
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references Abbreviation TRB Transportation Research Board Arlington County Planning Department.
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2008. Examining the Impacts of Residential Self Selection on Travel Behavior: Methodologies and Empirical Findings.
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In Transportation Research Record: Journal of the Transportation Research Board, No. 1780, Transportation Research Board, National Research Council, Washington, D.C., pp.
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2008. Examining the Impacts of Residential Self-Selection on Travel Behavior: A Focus on Methodologies.
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The goal is that by 2040, two-thirds of jobs and 40 percent of households will be located in and around centers and corridors served by light rail transit (LRT)
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The Pearl District arose from a decision to use construction of the Portland streetcar line as a means to leverage large-scale redevelopment of a functionally obsolete warehouse and industrial zone in downtown Portland. The city entered into an innovative agreement with developers, requiring them to meet ambitious housing density levels
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Orenco Station was designated one of a number of "town centers" along the Westside LRT line in the 2040 regional plan and is generally viewed as the most ambitious and successful such community to date. It contains 1,800 homes, mixed with office and retail spaces, in the town of Hillsboro, situated close to a large employment center in the metropolitan area's high-tech corridor.
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More than half of all the central city development within the past decade has been within one block of the streetcar line. A wide array of studies has demonstrated the effect of these land use and transportation developments on travel behavior.
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the increase came from the construction of single-family housing on small lots.58 The relatively small size of the Portland urban area, due to the UGB, has also resulted in shorter average trip lengths. Several studies have examined the travel behavior of Portland residents before and after moving to housing located adjacent to an 58 According to the American Housing Survey, nearly three-fourths of the new lots constructed in the Portland metropolitan area between 1998 and 2002 were built on lots smaller than ¼ acre, and 65 percent of these were single-family dwellings.
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Results of a travel behavior survey of more than 7,500 households in four counties (Clackmas, Multnomah, and Washington Counties in Oregon and Clark County in Washington) clearly indicate that good transit service and mixed-use neighborhoods have had a significant influence on reducing automobile use and ownership (see Annex 3-1 Table 1)
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The Portland metropolitan area's success is due to a host of political, regulatory, and economic factors, some of which are unique to the region but all of which may still offer useful lessons for other parts of the country: • Early leadership from a visionary governor and a supportive state legislature willing to pass strong state planning laws, including urban growth boundaries; • Strong public support for LRT investments and advocacy from citizens groups (in particular, the 1000 Friends of Oregon) capable of litigating when relevant authorities were not following planning requirements; • Unique powers of Metro to influence planning and investments for regional transportation and land use;
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arlington county, virginia, tod corridors The Washington, D.C., area's 103-mile, 86-station Metrorail system is arguably the nation's best example of a modern rapid transit system built specifically to incorporate a goal of shaping regional growth. The system, which opened in 1976, is overseen by the Washington Metropolitan Area Transit Authority (WMATA)
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. Since 1980, county office space has nearly doubled to about 44 million square feet, with almost 80 percent located within the two Metrorail corridors (Arlington County Planning Department 2008)
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Some of Arlington County's success may be attributable to unique local factors such as strong, stable support among the county board, manager, and other key local officials; a large base of locally rooted jobs in federal government agencies and related contracting organizations; and a manageable physical size (approximately 26 square miles) that made it possible for planners and officials to have a good grasp of the territory and communicate effectively with the community.
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Related keys to success have included the following: • A variety of strategies to attract private investments around stations, such as targeted infrastructure improvements and incentive-based, permissive zoning measures; • Rezoning of land adjacent to stations to high density while maintaining relatively low density and protecting greenspace in surrounding neighborhoods; • Dedication to continually pressing for top-quality design for housing and office developments, with a strong focus on creating attractive, walkable spaces; and • Proactive public outreach and community involvement, with business alliances, neighborhood groups, and individual residents frequently being invited to express their opinions on the design and scale of new developments through neighborhood meetings, workshops, and interactive websites. references Abbreviations FHWA Federal Highway Administration NRDC National Resources Defense Council Arlington County Planning Department.
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Transportation Research Board of the National Academies, Washington, D.C.
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