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Car Ownership Behavior Model Considering Nonlinear Impacts of Multi-Scale Built Environment Characteristics

Author

Listed:
  • Lan Wu

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Xiaorui Yuan

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Chaoyin Yin

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Ming Yang

    (Nanjing Institute of City and Transport Planning Co., Ltd., Nanjing 210008, China)

  • Hongjian Ouyang

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

Abstract

To explore the nonlinear influence of a multi-scale built environment on residents’ car ownership behavior, combined with the data set of residents’ individual information and travel-related data from the China Labor Force Dynamic Survey report, eight variables are selected to describe the built environment from multiple scales. The gradient-boosting iterative decision tree model including individual family attributes and neighborhood-scale and city-scale built-environment attributes is constructed. The results show that the individual family attributes have the greatest cumulative impact on car ownership behavior (46.3%). The built environment based on neighborhood scale and city scale also has a significant impact on residents’ car ownership behavior, these being 33.94% and 19.76%, respectively. The distance to the city center at the neighborhood scale is positive correlated with car ownership. The number of buses per 10,000 people and road area per capita in the city scale are also positive correlated with car ownership. Therefore, in order to slow down the increase in car ownership, the built environment can be optimized and adjusted at neighborhood scale and city scale.

Suggested Citation

  • Lan Wu & Xiaorui Yuan & Chaoyin Yin & Ming Yang & Hongjian Ouyang, 2023. "Car Ownership Behavior Model Considering Nonlinear Impacts of Multi-Scale Built Environment Characteristics," Sustainability, MDPI, vol. 15(12), pages 1-14, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9658-:d:1172588
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    References listed on IDEAS

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    1. Guan, Xiaodong & Wang, Donggen, 2019. "Influences of the built environment on travel: A household-based perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 710-724.
    2. Ao, Yibin & Yang, Dujuan & Chen, Chuan & Wang, Yan, 2019. "Exploring the effects of the rural built environment on household car ownership after controlling for preference and attitude: Evidence from Sichuan, China," Journal of Transport Geography, Elsevier, vol. 74(C), pages 24-36.
    3. Ding, Chuan & Cao, Xinyu, 2019. "How does the built environment at residential and work locations affect car ownership? An application of cross-classified multilevel model," Journal of Transport Geography, Elsevier, vol. 75(C), pages 37-45.
    4. Ding, Chuan & Wang, Donggen & Liu, Chao & Zhang, Yi & Yang, Jiawen, 2017. "Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 65-80.
    5. Shen, Qing & Chen, Peng & Pan, Haixiao, 2016. "Factors affecting car ownership and mode choice in rail transit-supported suburbs of a large Chinese city," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 31-44.
    6. 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).
    7. Chaoying Yin & Xiaoquan Wang & Chunfu Shao & Jianxiao Ma, 2022. "Exploring the Relationship between Built Environment and Commuting Mode Choice: Longitudinal Evidence from China," IJERPH, MDPI, vol. 19(21), pages 1-15, October.
    8. Ding, Chuan & Cao, Xinyu & Yu, Bin & Ju, Yang, 2021. "Non-linear associations between zonal built environment attributes and transit commuting mode choice accounting for spatial heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 22-35.
    9. Su, Shiliang & Zhao, Chong & Zhou, Hao & Li, Bozhao & Kang, Mengjun, 2022. "Unraveling the relative contribution of TOD structural factors to metro ridership: A novel localized modeling approach with implications on spatial planning," Journal of Transport Geography, Elsevier, vol. 100(C).
    10. Peng Zang & Hualong Qiu & Fei Xian & Linchuan Yang & Yanan Qiu & Hongxu Guo, 2022. "Nonlinear Effects of the Built Environment on Light Physical Activity among Older Adults: The Case of Lanzhou, China," IJERPH, MDPI, vol. 19(14), pages 1-15, July.
    11. Du, Qiang & Zhou, Yuqing & Huang, Youdan & Wang, Yalei & Bai, Libiao, 2022. "Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership," Journal of Transport Geography, Elsevier, vol. 102(C).
    12. Chetan Doddamani & M. Manoj, 2023. "Analysis of the influences of built environment measures on household car and motorcycle ownership decisions in Hubli-Dharwad cities," Transportation, Springer, vol. 50(1), pages 205-243, February.
    13. Zhang, Junyi & Yu, Biying & Chikaraishi, Makoto, 2014. "Interdependences between household residential and car ownership behavior: a life history analysis," Journal of Transport Geography, Elsevier, vol. 34(C), pages 165-174.
    14. Ben Clark & Kiron Chatterjee & Steve Melia, 2016. "Changes in level of household car ownership: the role of life events and spatial context," Transportation, Springer, vol. 43(4), pages 565-599, July.
    15. Xiaoyan Huang & Xiaoshu Cao & Jason Cao, 2016. "The association between transit access and auto ownership: evidence from Guangzhou, China," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(3), pages 269-283, April.
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