IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-16-8016-8_2.html
   My bibliography  Save this book chapter

A Spatio-temporal Distribution Model for Determining Origin–Destination Demand from Multisource Data

In: Logic-Driven Traffic Big Data Analytics

Author

Listed:
  • Shaopeng Zhong

    (Dalian University of Technology
    Southwest Jiaotong University)

  • Daniel (Jian) Sun

    (Chang’an University
    Shanghai Jiao Tong University)

Abstract

A scientific understanding of the spatio-temporal distribution of road travel demand is a prerequisite for formulating effective countermeasures to traffic congestion. Accordingly, this chapter analyzes the relationship between urban built environment attributes and origin–destination (OD) demand in the specific spatial structure of a city, thereby guiding decision-makers on how to solve traffic congestion problems. Multisource data and a Dirichlet multinomial regression model are used to reveal the functional zones and spatial structure of a city. A spatial autoregressive model is then applied to reveal the relationship between urban built environment attributes and the spatio-temporal distribution of OD demand. Finally, data from the downtown area of Chengdu (China) are used to validate the model and method and analyze their performance.

Suggested Citation

  • Shaopeng Zhong & Daniel (Jian) Sun, 2022. "A Spatio-temporal Distribution Model for Determining Origin–Destination Demand from Multisource Data," Springer Books, in: Logic-Driven Traffic Big Data Analytics, chapter 0, pages 33-52, Springer.
    • Handle: RePEc:spr:sprchp:978-981-16-8016-8_2
    DOI: 10.1007/978-981-16-8016-8_2
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:sprchp:978-981-16-8016-8_2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.