The Impact of Urban Spatial Structure on Travel Demand in the United States

Bento, Antonio M.; Cropper, Maureen; Mobarak, Ahmed Mushfiq; Vinha, Katja

doi:10.1596/1813-9450-3007

Cited by 76 publications

(78 citation statements)

References 2 publications

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“…The effects of this program is very similar to the results of Bento et al (2005), where the authors implemented a similar simulation that moves a household from a city which has the same characteristics as Georgia to a city which has the same characteristics as Boston. In Bento et al,…”

Section: Simulation Using the Urban/rural Dimensionsupporting

confidence: 57%

“…Bento et al (2005) specify disaggregate models of commute mode choice, automobile ownership and annual vehicle miles traveled (VMT). They construct diversified measures of urban form and transit supply: measures of city shape, density of the road network, spatial distribution of population, jobs-housing balance, and bus route and rail miles supplied.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although disaggregate studies (Bento et al, 2005) that include a rich set of socioeconomic control variables are less subject to residential self-selection bias, it is still possible that residents in high density areas differ in some unobservable characteristics that influence their travel behavior. The only way to deal with this possibility is to construct a joint model of residential density and travel behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

The impact of residential density on vehicle usage and energy consumption

Kim

Brownstone

2009

Journal of Urban Economics

401

205

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This paper investigates the impact of residential density on vehicle usage and fuel consumption.The empirical model accounts for both residential self-selection effects and non-random missing data problems. While most previous studies focus on a specific region, this paper analyzes national level data from the 2001 National Household Travel Survey. Comparing two households that are equal in all respects except residential density, the household residing in an area that is 1000 housing units per square mile denser (roughly 50% of the sample average) will drive 1341 (6.9%) less miles per year and will consume 65 (7.0%) fewer gallons of fuel than the household in the less dense area. The joint effect of the contextual density measure (density in the context of its surrounding area) and residential density is quantitatively larger than the sole effect of residential density. A simulation moving a household from suburban to urban area reduces household annual mileage by 18%.

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Section: Simulation Using the Urban/rural Dimensionsupporting

confidence: 57%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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The impact of residential density on vehicle usage and energy consumption

Kim

Brownstone

2009

Journal of Urban Economics

401

205

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“…Regarding the effect of density, the intra-city research is somewhat mixed, with some studies finding minimal (or no) effects of density after controlling for demographic effects (Crane and Crepeau, 1998), while others find significant effects (Geurs et al, 2006). Results from inter-city comparisons are also mixed, although the larger studies (such as Kenworthy and Laube (1999); Badoe and Miller (2000); Ewing et al (2001); Bento et al (2003); Baum-Snow and Kahn (2005)) generally support the notion that some measure of population distribution is important, although even here there are exceptions, such as the review by Boarnet and Crane (2001). It is generally understood that many other factors besides macro-scale density have an effect on travel behaviour in cities.…”

Section: Reviewmentioning

confidence: 99%

Urban Structure and Commuting in Australian Cities

Rickwood

Glazebrook

2009

Urban Policy and Research

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Studies looking at the relationship between urban form and travel behaviour have generally considered spatial information on coarse metropolitan or local government area scales. We analyse ABS Census data at the Collection District level for the metropolitan areas of the mainland Australian state capital cities, and at various spatial scales for an in-depth analysis of commuting in Sydney. The analyses suggest that the relationship between travel behaviour and urban form is complex, and that simple analyses of density alone are likely to overstate the effect of both metropolitan and neighbourhoodscale population density on mode choice, but that these variables serve as useful proxies for more complex measures of urban structure. IntroductionThere has been much interest by urban researchers in a variety of disciplines on the relationship between urban form and travel behaviour. The quantity of research in this area is large enough to support large bodies of work investigating (and often advocating) plans and policies to influence urban travel behaviour. For example, research indicating that higher population density, mixed land use, and transit provision result in significant shifts in travel behaviour has been the ammunition for those arguing for urban planning policies that favour higher-density development around public transport nodes (Cervero and Kockelman, 1997;Newman and Kenworthy, 1999). The motivation for such urban compaction is often environmental, although social, health, economic and aesthetic concerns are also cited (Newman and Kenworthy, 1989;Frumkin et al., 2004). Such policies, however, have not had unequivocal support, either in the research community (Troy, 1996;Levinson and Kumar, 1997;Holloway and Bunker, 2005), or amongst the general urban citizenry.The principal aim of this article is not to advocate for or against particular planning policies, as there is an extensive literature on this already 1 . Instead, we will give a brief overview pointing to some of the major relevant publications on both sides, and then proceed with our more descriptive task -conducting an analysis of how spatial structure affects journey-to-work travel behaviour in contemporary Australian cities. We take a spatially disaggregated approach, but without adopting anything as sophisticated as the discrete choice model backed random utility framework typical in modern transport microsimulation, as such an approach would require extensive data collection and model calibration that would preclude us covering all mainland state capitals. Despite the simpler nature of our approach, we believe the breadth and spatial resolution of our coverage provides useful insights into how inter 1 The nature of the data we analyse, however, makes some general policy related remarks inevitable.

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“…Nevertheless, while several studies from North America point to a mitigating influence of urban design on car ownership and use (e.g. Bento et al 2005;Potoglou and Kanaroglou 2008;Van Acker and Witlox 2010), there have been relatively fewer studies that have investigated this linkage in the European context (some exceptions include Hedel 2008 andBuehler 2011). Given that choices about land development tend to have long-lasting impacts that span over decades, quantification of the influence of landscape pattern on car use is highly significant to the formulation of contemporary planning strategies.…”

Section: Introductionmentioning

confidence: 99%

Landscape pattern and car use: Linking household data with satellite imagery

Keller

Vance

2013

Journal of Transport Geography

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Abstract:Landscape pattern has long been hypothesized to influence automobile dependency.Because choices about land development tend to have long-lasting impacts that span over decades, understanding the magnitude of this influence is critical to the design of policies to reduce emissions and other negative externalities associated with car use. Combining household survey data from Germany with satellite imagery and other geo-referenced data sources, we undertake an econometric analysis of the relation between landscape pattern and automobile dependency. Specifically, we employ a two-part model to investigate two dimensions of car use, the discrete decision to own a car and, conditional upon ownership, the continuous decision of how far to drive. Results indicate that landscape pattern, as captured by measures of both land cover (e.g. the extent of open space and landscape diversity) and land use (e.g. the density of regional businesses) are important predictors of car ownership and use. Other policy-relevant variables, such as fuel prices and public transit infrastructure, are also identified as correlates. Based on the magnitude of our estimates, we conclude that carefully considered land development and zoning measures -ones that encourage dense development, diverse land cover and mixed land use -can have beneficial impacts in reducing car dependency that extend far into the future.

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The Impact of Urban Spatial Structure on Travel Demand in the United States

Cited by 76 publications

References 2 publications

The impact of residential density on vehicle usage and energy consumption

The impact of residential density on vehicle usage and energy consumption

Urban Structure and Commuting in Australian Cities

Landscape pattern and car use: Linking household data with satellite imagery

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