Exploring car ownership and car use in neighborhoods near metro stations in Beijing: Does the neighborhood built environment matter?

Li, Shengxiao; Zhao, Pengjun

doi:10.1016/j.trd.2017.07.016

Cited by 100 publications

(77 citation statements)

References 58 publications

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“…In China, the housing source is a factor influencing travel behavior. Residents living in Danwei housing are found to significantly generate shorter commuting distance either by public transport or car than those with other housing sources [32].…”

Section: Determinants Of Car Ownership and Use In Chinamentioning

confidence: 99%

“…Built environment characteristics are summarized as the "D variables" including density, design, diversity, destination accessibility and distance to transit [29][30][31][32][33]. A few studies have investigated the influence of the built environment on car dependency as problems related to driving gained more attention [6].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The authors compared the commuting travel by car in 2005 and 2009 during which, the metro underwent explosive construction. Li et al [32] employed a regression model to explore the influence of the built environment in neighborhoods near metro stations on car ownership and use, in which distance to the metro station was found to be a significant factor.…”

Section: The Impacts Of Built Environment On Car Ownership and Usementioning

confidence: 99%

“…For example, many people living in suburban areas have to work downtown where most employment opportunities concentrate, but the public transit service is lagging [32]. Many studies claim the residents living in the suburbs generate more travel time by car [64].…”

Section: Determinants Of Car Ownership and Use In Chinamentioning

confidence: 99%

“…Table 1 summarizes the existing studies. It shows that descriptive analysis and statistical models are most commonly used in the existing studies such as the negative binomial regression [1,32], the structural equation model [22,37,50,53,63,65], multilevel ordered probit model [23], the ordered logit model [36], the multinomial logit model [38], the ordinary least squares regression [38], and the logistic regression model [59,66]. The models can identify the influencing factors and measure the power of these factors simultaneously, which can help to understand the role played by built environment in influencing car ownership and use.…”

Section: Models For Built Environment and Car Dependencymentioning

confidence: 99%

See 4 more Smart Citations

Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun

et al. 2018

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Concerns about transportation energy consumption and emissions force urban planners and policy makers to pay more attention to the effects of car ownership and use on the environment in China. However, few studies have investigated the relationship between the built environment and car ownership and use in China, especially in mid-sized and small cities. This study uses Changchun, China as a case study and examines the potential impacts of the built environment and socio-demographics on car ownership and use for commuting simultaneously using Bayesian multilevel binary logistic models. Furthermore, the spatial autocorrelation of car ownership and use is recognized across traffic analysis zones (TAZs), which are specifically represented by the conditional autoregressive (CAR) model. The estimated results indicate that socio-demographic characteristics have significant effects on car ownership and use. Moreover, the built environment measured at the TAZ level still shows a significant association with other factors controlled. Specifically, it suggests that denser residential density, compact land use, better transit services and street connectivity can reduce car dependency more effectively. This study provides new insights into how the built environment influences the car ownership and use, which can be useful for urban planners and policy makers to develop strategies for reducing car dependency.

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Section: Determinants Of Car Ownership and Use In Chinamentioning

confidence: 99%