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

Abstract

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.

Built Environment and Parking Availability: Impacts on Car Ownership and Use

“…It is found that household size has no significant influence on commuting mode choice. What's more, commuting distance is positively associated with the probability of car commuting, which is consistent with the previous study [23].…”

“…Built environment consists of some measurements, including diversity, density, distance to transit, and destination accessibility, which have been summarized as "D variables". Land use mixture is a key component representing diversity, which has received much attention [23][24][25]. For instance, Acker and Witlox [25] treated car ownership as a mediating variable between built environment and car use and confirmed that land use mixture had a significant influence on car use at the significance level of 99% (Coeff.…”

“…Household size and structure are both factors that could influence car ownership and use. Different household structures can generate different car travel demand, such as education and healthcare purposes [23].…”

Understanding the Effects of Improving Transportation on Pilgrim Travel Behavior: Evidence from the Lhasa, Tibet, China

“…Older adults with lower incomes, those who drive frequently, and those who live closer to downtown are more likely to have benefited from Oakville's free bus program [32]. Better transit services and street connectivity can reduce car dependency more effectively in mid-sized and small cities [33]. A preliminary exploratory analysis sheds light on the differences in activity participation, trip chaining, time-of-day preferences for trip departure, and transportation mode selection by non-workers in Bangalore city [34].…”

“…In particular, we expected that a group of pilgrims over the age of 48 may have more time for pilgrimage, and may, therefore, show different mobility patterns when compared to people under 48 years old. Most previous studies in this field have included overall income as a variable explaining mode choice instead of personal disposable income, which has higher accuracy [33]. To this end, the multinomial logit model developed in this study was complemented with a multilevel model extension.…”