Exploring the Influence of Built Environment on Car Ownership and Use with a Spatial Multilevel Model: A Case Study of Changchun, China.

Xiaoquan Wang,Chunfu Shao,Chaoying Yin,Chengxiang Zhuge

doi:10.3390/ijerph15091868

Abstract

Although the impacts of built environment on car ownership and use have been extensively studied, limited evidence has been offered for the role of spatial effects in influencing the interaction between built environment and travel behavior. Ignoring the spatial effects may lead to misunderstanding the role of the built environment and providing inconsistent transportation policies. In response to this, we try to employ a two-step modeling approach to investigate the impacts of built environment on car ownership and use by combining multilevel Bayesian model and conditional autocorrelation (CAR) model to control for spatial autocorrelation. In the two-step model, the predicting car ownership status in the first-step model is used as a mediating variable in the second-step car use model. Taking Changchun as a case study, this paper identifies the presence of spatial effects in influencing the effects of built environment on car ownership and use. Meanwhile, the direct and cascading effects of built environment on car ownership and use are revealed. The results show that the spatial autocorrelation exists in influencing the interaction between built environment and car dependency. The results suggest that it is necessary for urban planners to pay attention to the spatial effects and make targeted policy according to local land use characteristics.

Highlights

Car dependency is one of the most influential contributing factors to air pollution, traffic congestion, and energy consumption [1]
This study aims to examine the direct and cascading effects of the built environment on car ownership and use based on a two-step model, in which conditional autocorrelation (CAR) model is combined to the Bayesian multilevel model to address the spatial effects
A two-step model can provide an insight into the link between the built environment and travel behavior by revealing the direct and cascading effects of built environment on car ownership and use

Summary

Introduction

Car dependency is one of the most influential contributing factors to air pollution, traffic congestion, and energy consumption [1]. It is widely believed that car use can increase the risk of health problem due to more sedentary behavior than other travel modes [2]. A growing body of literature has focused on the link between built environment and travel behavior in order to reduce car dependency through promoting sustainable urban planning strategies in developed countries [3,4,5]. It is viewed as a long-term effective solution to the negative effect of car dependency on the environment to promote high-density and compact urban development strategies due to the likelihood to engage in active travel. It is a good opportunity for policy

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