IDEAS home Printed from https://ideas.repec.org/a/taf/rsrsxx/v7y2020i1p382-386.html
   My bibliography  Save this article

Estimating spatial patterns of commute mode preference in Beijing

Author

Listed:
  • Jiaoe Wang
  • Jie Huang
  • Fangye Du

Abstract

In the era of big data, multiple data sources have been employed in the study of land use and transportation for urban and regional planning purposes. This paper offers an example of how multiple data sources (e.g., mobile signalling data, taxi trips and transit trips from smartcard data) can be used to estimate the spatial pattern of commute mode preference in Beijing, China. The comparative analysis investigates the spatial pattern of commute mode preference by taxi at a fine resolution in Beijing. This work indicates how the preference for taxis can be seen in the north-east of the inner city, but not around employment centres. Equally, a complementary relationship is found between a preference for taxis and public transit that provides useful insights into modal choice at an intra-urban scale. These findings are useful in urban planning and transport management.

Suggested Citation

  • Jiaoe Wang & Jie Huang & Fangye Du, 2020. "Estimating spatial patterns of commute mode preference in Beijing," Regional Studies, Regional Science, Taylor & Francis Journals, vol. 7(1), pages 382-386, January.
    • Handle: RePEc:taf:rsrsxx:v:7:y:2020:i:1:p:382-386
    DOI: 10.1080/21681376.2020.1806104
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/21681376.2020.1806104
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/21681376.2020.1806104?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Yang Cao & Linxing Wang & Hao Wu & Shuqi Yan & Shuwen Shen, 2023. "Identification and Mechanism of Residents’ Regional Non-Commuting Flow Patterns Based on the Gradient Boosting Decision Tree Model: A Case Study of the Shanghai Metropolitan Area," Land, MDPI, vol. 12(9), pages 1-21, August.
    2. Zhitao Li & Xiaolu Wang & Fan Gao & Jinjun Tang & Hanmeng Xu, 2024. "Analysis of mobility patterns for urban taxi ridership: the role of the built environment," Transportation, Springer, vol. 51(4), pages 1409-1431, August.
    3. Jiaoe Wang & Yanan Li & Jingjuan Jiao & Haitao Jin & Fangye Du, 2023. "Bus ridership and its determinants in Beijing: A spatial econometric perspective," Transportation, Springer, vol. 50(2), pages 383-406, April.

    More about this item

    Statistics

    Access and download statistics

    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:taf:rsrsxx:v:7:y:2020:i:1:p:382-386. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/rsrs .

    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.