IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-642-32054-5_77.html
   My bibliography  Save this book chapter

A Comparative Study on Predict Effects of Railway Passenger Travel Choice Based on Two Soft Computing Methods

In: Liss 2012

Author

Listed:
  • Yan Xi

    (Beijing Jiaotong University)

  • Li Zhu-Yi

    (University of Illinois, Urbana Champaign)

  • Long Cheng-Xu

    (Beijing Jiaotong University)

  • Kang Shu

    (Beijing Jiaotong University)

  • Gao Yue

    (Beijing Jiaotong University)

  • Li Jing

    (Beijing Jiaotong University)

Abstract

The travelling factors acting on the railway passengers changes greatly with the passengers’ choice. With the help of the modern information computing technology, the factors were integrated to realize quantitative analyze according to the travel purpose and travel cost. The detailed comparative study was implemented with the two soft computing method: genetic algorithm, BP neural network. The two methods with different idea, applicable range applicable and the key parameters set were also studied in this model. The analyzed methods were also proved effective and applied for predicting the railway passengers travel choice through the empirical study with soft-computing supporting.

Suggested Citation

  • Yan Xi & Li Zhu-Yi & Long Cheng-Xu & Kang Shu & Gao Yue & Li Jing, 2013. "A Comparative Study on Predict Effects of Railway Passenger Travel Choice Based on Two Soft Computing Methods," Springer Books, in: Zhenji Zhang & Runtong Zhang & Juliang Zhang (ed.), Liss 2012, edition 127, pages 543-552, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-32054-5_77
    DOI: 10.1007/978-3-642-32054-5_77
    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:978-3-642-32054-5_77. 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.