How do built-environment factors affect travel behavior? A spatial analysis at different geographic scales

Hong, Jinhyun; Shen, Qing; Zhang, Lei

doi:10.1007/s11116-013-9462-9

Cited by 187 publications

(111 citation statements)

References 46 publications

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“…By applying a cross-sectional design, most of the existing studies offer a snap shot of travel behavior and environmental outcomes in different places at one point in time and while these studies reveal the correlation between urban form and its impacts, they do not prove causality (Handy 2005). In the case of sprawl and transportation, researchers have argued about the possibility of a 'selfselection' bias which is particularly ignored in a cross-sectional framework (Brownstone and Golob 2009;Hong et al 2013). The idea is that individuals who would prefer to walk or take transit than drive choose to live in more compact neighborhoods that are conducive to walking and taking transit (Handy 2005).…”

Section: Gaps In Existing Urban Form Studiesmentioning

confidence: 99%

“…Handy and Clifton (2001) found that residents with a higher frequency of walking to the store (compared to other neighborhoods) did in fact choose their neighborhood because they like to walk to the store. On the other hand, the travel impacts of neighborhood characteristics are found to be significant in many studies even after controlling for the influence of residential self-selection (Cao et al 2009;Hong et al 2013). After reviewing possible methodologies that can be adopted to relieve the selfselection impact, Mokhtarian and Cao (2008) developed six categories: direct questioning, statistical control, instrumental variable models, sample selection models, other joint models (joint discrete choice models and structural equations models), and longitudinal designs.…”

Section: Gaps In Existing Urban Form Studiesmentioning

confidence: 99%

“…With rapid urbanization throughout the world, these concerns resonate in the planning processes of a growing number of cities that are developing strategies for sustainable urban development (Runhaar et al 2009;Yigitcanlar and Teriman 2014). By shaping the spatial settings for human activities through tools like zoning, land use planning is considered a fundamental-and long-term approach for mitigating the environmental impact of transportation (Hong et al 2013). Promoting high density, compact, and mixed-use urban development is widely viewed as an effective planning strategy for increasing the efficiency of public transportation, decreasing dependence on automobiles, and subsequently reducing vehicle miles traveled (VMT) and emissions.…”

Section: Introductionmentioning

confidence: 99%

“…Zolnik 2011;Hong et al 2013;Bereitschaft and Debbage 2013). We explored these longitudinal impacts and considered sprawl as different patterns of density, land use mix, urban centrality, and street pattern (Ewing et al 2002).…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the dynamic impacts of urban form on transportation and environmental outcomes in US cities

Kashem

Irawan

Wilson

2014

Int. J. Environ. Sci. Technol.

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Previous urban sprawl studies have typically taken a cross-sectional approach without examining how sprawling urban areas are performing over time. Longitudinal studies of individual or household travel behavior and built-environment preference have made some progress in this direction, but very few studies have explored the longitudinal interaction of urban form with transportation and environmental outcomes. This study begins to fill this gap by evaluating the transportation and environmental impact dynamics of several key dimensions of urban sprawl: density, land use mix, centrality, and street connectivity. The central hypothesis is that while the built environment is durable and development patterns change slowly, the impacts of urban sprawl are dynamic rather than static and are accelerated in more sprawling cities relative to less sprawling cities. To test this hypothesis, a panel dataset of 7 years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) was developed for 60 Metropolitan Statistical Areas of the US and a hybrid modeling framework that incorporates fixed and random effects is applied to evaluate different transportation and environmental outcomes over time. We found that the influence of urban centrality or the strength of urban subcenters on the travel pattern of commuters and transit users is dynamic. This suggests that people living in cities with stronger centers drive less and use public transportation more over time. For environmental outcomes, we found that cities with higher density have experienced a significant decrease in ambient ozone and PM 2.5 concentrations after controlling city-specific variables.

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Section: Gaps In Existing Urban Form Studiesmentioning

confidence: 99%

Section: Gaps In Existing Urban Form Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the dynamic impacts of urban form on transportation and environmental outcomes in US cities

Kashem

Irawan

Wilson

2014

Int. J. Environ. Sci. Technol.

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“…The Bayesian multilevel model can be considered as an extension of regression models that produce different coefficients by subject groups (Hong et al 2011;Shen et al 2011). Subjects in the same level/group are likely to be similar to each other in terms of their observable characteristics.…”

Section: Multilevel Bayesian Regression Model Specificationmentioning

confidence: 99%

How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities

Zhang¹,

Hong²,

Nasri³

et al. 2012

JTLU

Self Cite

148

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Mixed findings have been reported in previous research regarding the impact of built environment on travel behavior-i.e., statistically and practically significant effects found in a number of empirical studies and insignificant correlations shown in many other studies. It is not clear why the estimated impact is stronger or weaker in certain urban areas and how effective a proposed land use change/policy will be in changing certain travel behavior. This knowledge gap has made it difficult for decision makers to evaluate land use plans and policies according to their impact on vehicle miles traveled (VMT), and consequently, their impact on congestion mitigation, energy conservation, and pollution and greenhouse gas emission reduction.This research has several objectives: (1) re-examine the effects of built-environment factors on travel behavior, in particular, VMT in five US metropolitan areas grouped into four case study areas; (2) develop consistent models in all case study areas with the same model specification and datasets to enable direct comparisons; (3) identify factors such as existing land use characteristics and land use policy decision-making processes that may explain the different impacts of built environment on VMT in different urban areas; and (4) provide a prototype tool for government agencies and decision makers to estimate the impact of proposed land use changes on VMT.The four case study areas include Seattle, WA; Richmond-Petersburg and Norfolk-Virginia Beach, VA; Baltimore, MD; and Washington, DC. Our empirical analysis employs Bayesian multilevel modeling method with various person-level socioeconomic and demographic variables, and five built-environment factors including residential density, employment density, entropy (measuring level of mixed-use development), average block size (measuring transit/walking friendliness), and distance to city center (measuring decentralization and level of infill development).Our findings show that promoting compact, mixed-use, small-block, and infill developments can be effective in reducing VMT per person in all four case study areas. However, the effectiveness of land use plans and policies encouraging these types of land development is different both across case study areas and within the same case study area. We have identified several factors that potentially influence the connection between built environment shifts and VMT changes including urban area size, existing built environment characteristics, transit service coverage and quality, and land use decision-making processes.

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The Relationship Between Urban Environment and Travel Behavior

Antipova

2018

Urban Environment, Travel Behavior, Health, and Resident Satisfaction

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How do built-environment factors affect travel behavior? A spatial analysis at different geographic scales

Cited by 187 publications

References 46 publications

Evaluating the dynamic impacts of urban form on transportation and environmental outcomes in US cities

Evaluating the dynamic impacts of urban form on transportation and environmental outcomes in US cities

How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities

The Relationship Between Urban Environment and Travel Behavior

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