Spatial heterogeneous impact of built environment on household auto ownership levels: evidence from analysis at traffic analysis zone scales

Ding, Chuan; Wang, Yupeng; Yang, Jun; Liu, Chao; Lin, Yaoyu

doi:10.1179/1942787515y.0000000004

Cited by 41 publications

(17 citation statements)

References 31 publications

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“…Therefore, car ownership and use control policy needs to be developed based on local conditions. However, there are some similar results with prior studies conducted in western countries [16,[22][23][24]40]. It was found that residential density, land use mixture, bus stop density, and intersection density had significant effects on household car ownership.…”

Section: Discussionsupporting

confidence: 78%

“…People living in a neighborhood with high residential density are more likely get to work by other modes of travel besides driving. Land use mixture was found to be significantly associated with using cars to commute to work at the 95% level, which is consistent with studies conducted by Ding et al [23]. This might be explained by the neighborhood land use mixture generating more non-motorized travel for non-commuting purposes because the compact land use provides a higher probability for shorter travel distance to shops and hospitals, but not significantly for commuting purposes [67].…”

Section: Car Use Modelsupporting

confidence: 81%

“…Furthermore, trip-related information is another factor that was found to have a significant influence on car use. For example, Ding et al [23] confirmed that travel cost and time had a significant influence on mode choice after controlling for the built environment and socio-demographic characteristics.…”

Section: Other Factors Influencing Car Ownership and Usementioning

confidence: 83%

“…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%

“…The impacts of the built environment on car ownership and use have gained more and more attention due to its contribution to traffic congestion and environmental issues in developed countries [22][23][24]. However, there are several major gaps that need to be filled.…”

Section: Introductionmentioning

confidence: 99%

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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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