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Development of Multivariate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in Xiaoshan District of Hangzhou, China

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  • Jie Ma

    (Key Laboratory of Road and Traffic Engineering of Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Xin Ye

    (Key Laboratory of Road and Traffic Engineering of Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Cheng Shi

    (College of Architecture and Urban Planning, Tongji University, Shanghai 201804, China)

Abstract

With the rapid increase of motorization in China, transitions have taken place in regards to traditional private transportation modes. This paper aims to understand four types of vehicle ownership within a household, including automobile, motorcycle, electric bicycle and human-powered bicycle. This study presents a cross-sectional multivariate ordered probit model, with a composite marginal likelihood estimation approach that accommodates the effects of explanatory variables, and capturing the dependence among the propensity to household vehicle ownership. The sample data are obtained from the residents’ household travel survey of Xiaoshan District, Hangzhou, in 2015, which can analyze the significant effects of sociodemographic attributes and built environment attributes. Interestingly, the major findings suggest that: (1) The households with higher income tend to own more automobiles, yet the effect is not obvious with a small value of elasticity, which is similar to developed countries. (2) The household education level, which takes a positive effect on automobile ownership, is a more elastic factor than income. (3) The higher population density contributes to less ownership of automobiles and motorcycles, due to traffic congestions and parking challenges. (4) There is a large substitutive relation between automobile and electric bicycle/motorcycle, and the vehicle ownership of electric bicycle/motorcycle and bicycle are mutually promoted, while motorcycle and electric-bicycle are mutually substituted.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3660-:d:175324
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    References listed on IDEAS

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    1. Bhat, Chandra R. & Sen, Sudeshna, 2006. "Household vehicle type holdings and usage: an application of the multiple discrete-continuous extreme value (MDCEV) model," Transportation Research Part B: Methodological, Elsevier, vol. 40(1), pages 35-53, January.
    2. Toshiyuki Yamamoto, 2009. "Comparative analysis of household car, motorcycle and bicycle ownership between Osaka metropolitan area, Japan and Kuala Lumpur, Malaysia," Transportation, Springer, vol. 36(3), pages 351-366, May.
    3. Scott, Darren M. & Kanaroglou, Pavlos S., 2002. "An activity-episode generation model that captures interactions between household heads: development and empirical analysis," Transportation Research Part B: Methodological, Elsevier, vol. 36(10), pages 875-896, December.
    4. Bhat, Chandra R. & Guo, Jessica Y., 2007. "A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels," Transportation Research Part B: Methodological, Elsevier, vol. 41(5), pages 506-526, June.
    5. Liu, Yangwen & Tremblay, Jean-Michel & Cirillo, Cinzia, 2014. "An integrated model for discrete and continuous decisions with application to vehicle ownership, type and usage choices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 315-328.
    6. Fang, Hao Audrey, 2008. "A discrete-continuous model of households' vehicle choice and usage, with an application to the effects of residential density," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 736-758, November.
    7. Tian Wu & Hongmei Zhao & Xunmin Ou, 2014. "Vehicle Ownership Analysis Based on GDP per Capita in China: 1963–2050," Sustainability, MDPI, vol. 6(8), pages 1-23, August.
    8. Bhat, Chandra R. & Srinivasan, Sivaramakrishnan, 2005. "A multidimensional mixed ordered-response model for analyzing weekend activity participation," Transportation Research Part B: Methodological, Elsevier, vol. 39(3), pages 255-278, March.
    9. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    10. Zhang, Zhao & Jin, Wen & Jiang, Hai & Xie, Qianyan & Shen, Wei & Han, Weijian, 2017. "Modeling heterogeneous vehicle ownership in China: A case study based on the Chinese national survey," Transport Policy, Elsevier, vol. 54(C), pages 11-20.
    11. Abay, Kibrom A. & Paleti, Rajesh & Bhat, Chandra R., 2013. "The joint analysis of injury severity of drivers in two-vehicle crashes accommodating seat belt use endogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 74-89.
    12. Tang, Jinjun & Zhang, Shen & Chen, Xinqiang & Liu, Fang & Zou, Yajie, 2018. "Taxi trips distribution modeling based on Entropy-Maximizing theory: A case study in Harbin city—China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 430-443.
    13. Yibin Ao & Chuan Chen & Dujuan Yang & Yan Wang, 2018. "Relationship between Rural Built Environment and Household Vehicle Ownership: An Empirical Analysis in Rural Sichuan, China," Sustainability, MDPI, vol. 10(5), pages 1-18, May.
    14. Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November.
    15. Bhat, Chandra R. & Pulugurta, Vamsi, 1998. "A comparison of two alternative behavioral choice mechanisms for household auto ownership decisions," Transportation Research Part B: Methodological, Elsevier, vol. 32(1), pages 61-75, January.
    16. Ziwen Ling & Christopher R. Cherry & John H. MacArthur & Jonathan X. Weinert, 2017. "Differences of Cycling Experiences and Perceptions between E-Bike and Bicycle Users in the United States," Sustainability, MDPI, vol. 9(9), pages 1-18, September.
    17. Matas, Anna & Raymond, José-Luis & Roig, José-Luis, 2009. "Car ownership and access to jobs in Spain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(6), pages 607-617, July.
    18. Yi Zhang & Wei Wu & Yuan Li & Qixing Liu & Chaoyang Li, 2014. "Does the Built Environment Make a Difference? An Investigation of Household Vehicle Use in Zhongshan Metropolitan Area, China," Sustainability, MDPI, vol. 6(8), pages 1-21, August.
    19. Ferdous, Nazneen & Eluru, Naveen & Bhat, Chandra R. & Meloni, Italo, 2010. "A multivariate ordered-response model system for adults' weekday activity episode generation by activity purpose and social context," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 922-943, September.
    20. Wells, Peter & Lin, Xiao, 2015. "Spontaneous emergence versus technology management in sustainable mobility transitions: Electric bicycles in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 371-383.
    21. Chung, William & Zhou, Guanghui & Yeung, Iris M.H., 2013. "A study of energy efficiency of transport sector in China from 2003 to 2009," Applied Energy, Elsevier, vol. 112(C), pages 1066-1077.
    22. Jonathan Weinert & Chaktan Ma & Christopher Cherry, 2007. "The transition to electric bikes in China: history and key reasons for rapid growth," Transportation, Springer, vol. 34(3), pages 301-318, May.
    23. West, Sarah E., 2004. "Distributional effects of alternative vehicle pollution control policies," Journal of Public Economics, Elsevier, vol. 88(3-4), pages 735-757, March.
    24. Sabreena Anowar & Naveen Eluru & Luis F. Miranda-Moreno, 2014. "Alternative Modeling Approaches Used for Examining Automobile Ownership: A Comprehensive Review," Transport Reviews, Taylor & Francis Journals, vol. 34(4), pages 441-473, July.
    25. Xin Ye & Ke Wang & Yajie Zou & Dominique Lord, 2018. "A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-17, May.
    26. Huo, Hong & Wang, Michael, 2012. "Modeling future vehicle sales and stock in China," Energy Policy, Elsevier, vol. 43(C), pages 17-29.
    27. 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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