IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i5p920-d144704.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/5/920/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/15/5/920/
    Download Restriction: no
    ---><---

    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. 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.
    3. 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.
    4. Christopher F Baum, 2008. "Stata tip 63: Modeling proportions," Stata Journal, StataCorp LP, vol. 8(2), pages 299-303, June.
    5. 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.
    6. 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.
    7. 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.
    8. 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..
    9. 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.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tao Zhang & Yibo Yan & Qi Chen & Ze Liu, 2022. "Evaluation Method of Composite Development Bus Terminal Using Multi-Source Data Processing," Land, MDPI, vol. 11(10), pages 1-14, October.
    2. Yanyan Gu & Yandong Wang, 2022. "Using weighted multilayer networks to uncover scaling of public transport system," Environment and Planning B, , vol. 49(6), pages 1631-1645, July.
    3. 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.
    4. Chen Cao & Feng Zhen & Xianjin Huang, 2022. "How Does Perceived Neighborhood Environment Affect Commuting Mode Choice and Commuting CO 2 Emissions? An Empirical Study of Nanjing, China," IJERPH, MDPI, vol. 19(13), pages 1-17, June.
    5. Le Zhang & Xiaoxiao Xu & Yanlong Guo, 2022. "Comprehensive Evaluation of the Implementation Effect of Commercial Street Quality Improvement Based on AHP-Entropy Weight Method—Taking Hefei Shuanggang Old Street as an Example," Land, MDPI, vol. 11(11), pages 1-19, November.
    6. Lingzhu Zhang & Yu Ye & Wenxin Zeng & Alain Chiaradia, 2019. "A Systematic Measurement of Street Quality through Multi-Sourced Urban Data: A Human-Oriented Analysis," IJERPH, MDPI, vol. 16(10), pages 1-24, May.
    7. Yi Lu & Long Chen & Yiyang Yang & Zhonghua Gou, 2018. "The Association of Built Environment and Physical Activity in Older Adults: Using a Citywide Public Housing Scheme to Reduce Residential Self-Selection Bias," IJERPH, MDPI, vol. 15(9), pages 1-13, September.
    8. Ming Chen & Ting Wang & Zongshi Liu & Ye Li & Meiting Tu, 2024. "Nonlinear and Threshold Effects of the Built Environment on Dockless Bike-Sharing," Sustainability, MDPI, vol. 16(17), pages 1-18, September.
    9. Le Zhang & Xueyan Li & Yanlong Guo, 2024. "Research on the Influencing Factors of Spatial Vitality of Night Parks Based on AHP–Entropy Weights," Sustainability, MDPI, vol. 16(12), pages 1-20, June.
    10. Qi Chen & Yibo Yan & Xu Zhang & Jian Chen, 2022. "A Study on the Impact of Built Environment Elements on Satisfaction with Residency Whilst Considering Spatial Heterogeneity," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
    11. Jixiang Liu & Longzhu Xiao, 2024. "Socioeconomic differences in effect size: predicting commuting mode choice of migrants and locals using a light gradient boosting approach," Transportation, Springer, vol. 51(1), pages 1-24, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Li, Jingjing & Kim, Changjoo & Sang, Sunhee, 2018. "Exploring impacts of land use characteristics in residential neighborhood and activity space on non-work travel behaviors," Journal of Transport Geography, Elsevier, vol. 70(C), pages 141-147.
    3. Steven R Gehrke & Kelly J Clifton, 2019. "An activity-related land use mix construct and its connection to pedestrian travel," Environment and Planning B, , vol. 46(1), pages 9-26, January.
    4. Cao, Xinyu, 2006. "The Causal Relationship between the Built Environment and Personal Travel Choice: Evidence from Northern California," University of California Transportation Center, Working Papers qt07q5p340, University of California Transportation Center.
    5. Lara Engelfriet & Eric Koomen, 2018. "The impact of urban form on commuting in large Chinese cities," Transportation, Springer, vol. 45(5), pages 1269-1295, September.
    6. Thomas Klinger & Martin Lanzendorf, 2016. "Moving between mobility cultures: what affects the travel behavior of new residents?," Transportation, Springer, vol. 43(2), pages 243-271, March.
    7. Millward, Hugh & Spinney, Jamie & Scott, Darren, 2013. "Active-transport walking behavior: destinations, durations, distances," Journal of Transport Geography, Elsevier, vol. 28(C), pages 101-110.
    8. Umer Mansoor & Mohammad Tamim Kashifi & Fazal Rehman Safi & Syed Masiur Rahman, 2022. "A review of factors and benefits of non-motorized transport: a way forward for developing countries," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 1560-1582, February.
    9. Janke, Julia, 2021. "Re-visiting residential self-selection and dissonance: Does intra-household decision-making change the results?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 379-401.
    10. 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.
    11. Ali Ardeshiri & Akshay Vij, 2019. "A lifestyle-based model of household neighbourhood location and individual travel mode choice behaviours," Papers 1902.01986, arXiv.org, revised Nov 2019.
    12. Cao, XinYu, 2007. "The Causal Relationship between the Built Environment and Personal Travel Choice: Evidence from Northern California," Institute of Transportation Studies, Working Paper Series qt1n90z8h8, Institute of Transportation Studies, UC Davis.
    13. Khan, Mobashwir & M. Kockelman, Kara & Xiong, Xiaoxia, 2014. "Models for anticipating non-motorized travel choices, and the role of the built environment," Transport Policy, Elsevier, vol. 35(C), pages 117-126.
    14. Altieri, Marcelo & Silva, Cecília & Terabe, Shintaro, 2020. "Give public transit a chance: A comparative analysis of competitive travel time in public transit modal share," Journal of Transport Geography, Elsevier, vol. 87(C).
    15. Cao, Xinyu (Jason) & Mokhtarian, Patricia L. & Handy, Susan L., 2009. "The relationship between the built environment and nonwork travel: A case study of Northern California," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(5), pages 548-559, June.
    16. Sehatzadeh, Bahareh & Noland, Robert B. & Weiner, Marc D., 2011. "Walking frequency, cars, dogs, and the built environment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(8), pages 741-754, October.
    17. Kamruzzaman, Md. & Baker, Douglas & Washington, Simon & Turrell, Gavin, 2013. "Residential dissonance and mode choice," Journal of Transport Geography, Elsevier, vol. 33(C), pages 12-28.
    18. Van Acker, Veronique & Ho, Loan & Stevens, Larissa & Mulley, Corinne, 2020. "Quantifying the effects of childhood and previous residential experiences on the use of public transport," Journal of Transport Geography, Elsevier, vol. 86(C).
    19. Ding, Yu & Lu, Huapu, 2016. "Activity participation as a mediating variable to analyze the effect of land use on travel behavior: A structural equation modeling approach," Journal of Transport Geography, Elsevier, vol. 52(C), pages 23-28.
    20. Toşa, Cristian & Sato, Hitomi & Morikawa, Takayuki & Miwa, Tomio, 2018. "Commuting behavior in emerging urban areas: Findings of a revealed-preferences and stated-intentions survey in Cluj-Napoca, Romania," Journal of Transport Geography, Elsevier, vol. 68(C), pages 78-93.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:15:y:2018:i:5:p:920-:d:144704. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.