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Commuting Mode Choice in a High-Density City: Do Land-Use Density and Diversity Matter in Hong Kong?

Author

Listed:
  • Yi Lu

    (Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong, China
    City University of Hong Kong Shenzhen Research Institute, Shenzhen 518057, China)

  • Guibo Sun

    (Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong, China)

  • Chinmoy Sarkar

    (Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong, China)

  • Zhonghua Gou

    (School of Engineering and Built Environment, Griffith University, Gold Coast, QLD 4215, Australia)

  • Yang Xiao

    (Department of Urban Planning, Tongji University, Shanghai 200092, China)

Abstract

Hong Kong is a densely populated and transit-oriented Chinese city, which provides an ideal urban environment with which to study the various successful facets of land use policy as a model for potential replication to curb increasing car use in other Chinese cities. We examine the commuting mode choice of 203,900 households living in 4768 street blocks in Hong Kong from 2011 census. A street block is the smallest planning unit, made up of one or more housing estates with a homogenous built environment and socioeconomic status. The built environment is measured using the five Ds framework, an international dimensioning framework for classifying and measuring attributes of the built environment for physical activity and travel behaviors. Generalized, multi-level mixed models were applied to detect the associations between travel choice and built environment characteristics, while adjusting for socioeconomic status. Design and destination accessibility had greater effects on the choices to walk and take public transport than on the choice to drive. Density and diversity had only marginal effects on mode choice. Unexpectedly, distance to the urban center had the opposite effect on automobile use to that found in Western studies. Hong Kong residents living close to the urban center were more likely to drive for commuting trips. The contrasting findings between our study and Western studies suggest that the associations between a high-density built environment and travel choice vary with urban context.

Suggested Citation

  • Yi Lu & Guibo Sun & Chinmoy Sarkar & Zhonghua Gou & Yang Xiao, 2018. "Commuting Mode Choice in a High-Density City: Do Land-Use Density and Diversity Matter in Hong Kong?," IJERPH, MDPI, vol. 15(5), pages 1-13, May.
  • Handle: RePEc:gam:jijerp:v:15:y:2018:i:5:p:920-:d:144704
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    References listed on IDEAS

    as
    1. Reid Ewing & Robert Cervero, 2010. "Travel and the Built Environment," Journal of the American Planning Association, Taylor & Francis Journals, vol. 76(3), pages 265-294.
    2. Christopher F Baum, 2008. "Stata tip 63: Modeling proportions," Stata Journal, StataCorp LP, vol. 8(2), pages 299-303, June.
    3. Lawrence Frank & Mark Bradley & Sarah Kavage & James Chapman & T. Lawton, 2008. "Urban form, travel time, and cost relationships with tour complexity and mode choice," Transportation, Springer, vol. 35(1), pages 37-54, January.
    4. Ng, Shu Wen & Norton, Edward C. & Popkin, Barry M., 2009. "Why have physical activity levels declined among Chinese adults? Findings from the 1991-2006 China health and nutrition surveys," Social Science & Medicine, Elsevier, vol. 68(7), pages 1305-1314, April.
    5. Yi Zhang & Yuan Li & Qixing Liu & Chaoyang Li, 2014. "The Built Environment and Walking Activity of the Elderly: An Empirical Analysis in the Zhongshan Metropolitan Area, China," Sustainability, MDPI, vol. 6(2), pages 1-17, February.
    6. Cervero, Robert, 1996. "Mixed land-uses and commuting: Evidence from the American Housing Survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(5), pages 361-377, September.
    7. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
    8. Schwanen, Tim & Mokhtarian, Patricia L., 2005. "What Affects Commute Mode Choice: Neighborhood Physical Structure or Preferences Toward Neighborhoods?," University of California Transportation Center, Working Papers qt4nq9r1c9, University of California Transportation Center.
    9. Yi Lu & Zhonghua Gou & Yang Xiao & Chinmoy Sarkar & John Zacharias, 2018. "Do Transit-Oriented Developments (TODs) and Established Urban Neighborhoods Have Similar Walking Levels in Hong Kong?," IJERPH, MDPI, vol. 15(3), pages 1-14, March.
    10. Khattak, Asad J. & Rodriguez, Daniel, 2005. "Travel behavior in neo-traditional neighborhood developments: A case study in USA," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(6), pages 481-500, July.
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