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The association between transit access and auto ownership: evidence from Guangzhou, China

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  • Xiaoyan Huang
  • Xiaoshu Cao
  • Jason Cao

Abstract

In many developing countries, massive investment in transit infrastructure is concurrent with the proliferation of automobiles. Planners expect that investment can slow the growth of auto ownership. However, few studies have examined the relationships between transit access and auto ownership in developing countries, whereas research in developed countries offers mixed findings and the outcomes may not be applicable to developing countries. This study employs a random effect ordered probit model on data collected from Guangzhou residents in 2011--2012. We find that transit access is negatively associated with auto ownership, after controlling for demographics and other built environment variables. This result suggests that, although income is the dominant driver for auto ownership in growing developing countries, transit investment is a promising strategy to slow the growth of auto ownership. This study also highlights the importance of addressing spatial dependency in clustered data.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:transp:v:39:y:2016:i:3:p:269-283
    DOI: 10.1080/03081060.2016.1142223
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    Cited by:

    1. Mitra, Suman K. & Saphores, Jean-Daniel M., 2017. "Carless in California: Green choice or misery?," Journal of Transport Geography, Elsevier, vol. 65(C), pages 1-12.
    2. 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.
    3. Jie Ma & Xin Ye & Cheng Shi, 2018. "Development of Multivariate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in Xiaoshan District of Hangzhou, China," Sustainability, MDPI, vol. 10(10), pages 1-17, October.
    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. Zhong, Shaopeng & Bushell, Max, 2017. "Impact of the built environment on the vehicle emission effects of road pricing policies: A simulation case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 235-249.
    6. Arefeh Nasri & Carlos Carrion & Lei Zhang & Babak Baghaei, 2020. "Using propensity score matching technique to address self-selection in transit-oriented development (TOD) areas," Transportation, Springer, vol. 47(1), pages 359-371, February.
    7. Liu, Changqing & Li, Lei, 2020. "How do subways affect urban passenger transport modes?—Evidence from China," Economics of Transportation, Elsevier, vol. 23(C).
    8. Suchi Kapoor Malhotra & Howard White & Nina Ashley O. Dela Cruz & Ashrita Saran & John Eyers & Denny John & Ella Beveridge & Nina Blöndal, 2021. "Studies of the effectiveness of transport sector interventions in low‐ and middle‐income countries: An evidence and gap map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(4), December.
    9. 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.

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