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

Author

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  • Xiaoquan Wang

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Chunfu Shao

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China)

  • Chaoying Yin

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Chengxiang Zhuge

    (Department of Geography, University of Cambridge, Downing Place, Cambridge CB2 3EN, UK)

  • Wenjun Li

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

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.

Suggested Citation

  • Xiaoquan Wang & Chunfu Shao & Chaoying Yin & Chengxiang Zhuge & Wenjun Li, 2018. "Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun," Sustainability, MDPI, vol. 10(2), pages 1-15, February.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:2:p:484-:d:131443
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    References listed on IDEAS

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    Cited by:

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    2. Xiaoquan Wang & Chunfu Shao & Chaoying Yin & Chengxiang Zhuge, 2018. "Exploring the Influence of Built Environment on Car Ownership and Use with a Spatial Multilevel Model: A Case Study of Changchun, China," IJERPH, MDPI, vol. 15(9), pages 1-14, August.
    3. Che Wan Jasimah Wan Mohamed Radzi & Hashem Salarzadeh Jenatabadi & Ayed R. A. Alanzi & Mohd Istajib Mokhtar & Mohd Zufri Mamat & Nor Aishah Abdullah, 2019. "Analysis of Obesity among Malaysian University Students: A Combination Study with the Application of Bayesian Structural Equation Modelling and Pearson Correlation," IJERPH, MDPI, vol. 16(3), pages 1-17, February.
    4. Wang, Xiaoquan & Yin, Chaoying & Zhang, Junyi & Shao, Chunfu & Wang, Shengyou, 2021. "Nonlinear effects of residential and workplace built environment on car dependence," Journal of Transport Geography, Elsevier, vol. 96(C).
    5. Gang Cheng & Shuzhi Zhao & Di Huang, 2018. "Understanding the Effects of Improving Transportation on Pilgrim Travel Behavior: Evidence from the Lhasa, Tibet, China," Sustainability, MDPI, vol. 10(10), pages 1-13, October.
    6. José Gerardo Carrillo-González & Guillermo López-Maldonado & Juan Lopez-Sauceda & Francisco Perez-Martinez, 2023. "Method for Selecting the Vehicles That Can Enter a Street Network to Maintain the Speed on Links above a Speed Threshold," Sustainability, MDPI, vol. 15(13), pages 1-29, June.
    7. Zheng, Yan & Wang, Shengyou & Dong, Chunjiao & Li, Wenquan & Zheng, Wen & Yu, Jingcai, 2022. "Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).

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