Does the Built Environment Make a Difference? An Investigation of Household Vehicle Use in Zhongshan Metropolitan Area, China

Abstract

Abstract:To address worsening urban traffic and environmental issues, planners and policy makers in China have begun to recognize the importance of shaping vehicle use through the built environment. However, very few studies can be found that examine the relationship between the built environment and vehicle use in the Chinese context. With data collected in Zhongshan Metropolitan Area, this study examined how two built environment representations-simple measures and neighborhood types-were related to household car trips and motorcycle trips in China. The results of the negative binomial regression models showed that the household socio-demographic measures displayed significant association, and the built environment representations enhanced the explanatory powers. All else being equal, households in Zhongshan would generate less car and motorcycle trips if located in neighborhoods with denser land use development, better transit service and less connective street networks. In order to shape vehicle use, the findings provided informative insights for planners and policy makers to form a relatively high density of land use developments, slow down the construction of street networks, provide more jobs adjacent to residential areas and facilitate easy access to public transportation services.

Who Will Use Pre-Trip Traveler Information and How Will They Respond? Insights from Zhongshan Metropolitan Area, China

“…The level of urbanization, income per capita, private car ownership per household and motorcycle ownership per household in Zhongshan (up to 2012) were 1.7-, 1.8-, 1.8-, and 2.8-times China's average, respectively [24,25]. As of the time of research data collection in 2009, the modal split of car and motorcycle in Zhongshan were 9.2% and 43.4%, respectively [26,27], covering more than half of daily trips. In China's three largest coastal urban agglomerations, the Yangtze River Delta urban agglomerations, the Pearl River Delta urban agglomerations and the Jing-Jin-Ji urban agglomerations, there are nearly 20 medium-sized cities with similar levels of economic development, urbanization and motorization as Zhongshan.…”

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

“…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.…”

“…In recent years, the number of cars has increased rapidly due to rapid urbanization, rising incomes, and demand for motorized travel, especially in developing countries [1,2]. Car ownership per 1000 people increased to 77 in 2013, which is almost 100 times what it was in 1990 in mainland China [3].…”

Measuring the Spatial Dimension of Automobile Ownership and Its Associations with Household Characteristics and Land Use Patterns: A Case Study in Three Counties, South Florida (USA)

“…Based on the literature and data availability, the model calibrated three categories of factors, household characteristics, built environment, and life style, simultaneously taking into account the interactive effects between households and built environment [34,35] (Table 1). It has been justified that residential locations are associated with auto ownership levels [35,53], and the preferences of households' residential sites are argued to be partly reflected by an index describing the degree of land use mix [44]. Accordingly, this paper employed such an index as a potential explanatory variable.…”