IDEAS home Printed from https://ideas.repec.org/a/ids/eujine/v16y2022i6p651-678.html
   My bibliography  Save this article

The charging infrastructure design problem with electric taxi demand prediction using convolutional LSTM

Author

Listed:
  • Seong Wook Hwang
  • Sunghoon Lim

Abstract

The authors present a charging infrastructure design problem with electric taxi demand prediction. Due to environmental concerns, electric vehicle adoption has significantly increased in the transportation sector. However, the use of electric vehicles is not highly commercialised in the taxi industry, because the immature charging network and frequent charging decrease taxi revenue efficiency. Therefore, charging infrastructure needs to be built in urban areas in consideration of operational requirements of the taxi industry. The authors first design a convolutional long short-term memory model that predicts taxi demand, along with hotspots. Then, based on the predicted taxi demand in hotspots, a mixed integer linear programming model is proposed to optimise the location of recharging stations to minimise the cost of locating stations and charging service. Also, we propose a heuristic algorithm to solve realistic and practical problems. Lastly, a case study is presented to validate the proposed research. [Submitted: 28 April 2021; Accepted: 5 September 2021]

Suggested Citation

  • Seong Wook Hwang & Sunghoon Lim, 2022. "The charging infrastructure design problem with electric taxi demand prediction using convolutional LSTM," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 16(6), pages 651-678.
  • Handle: RePEc:ids:eujine:v:16:y:2022:i:6:p:651-678
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=126633
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:ids:eujine:v:16:y:2022:i:6:p:651-678. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=210 .

    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.