IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-3-031-24907-5_26.html
   My bibliography  Save this book chapter

Energy-Efficient Driving Model by Clustering of GPS Information

In: Operations Research Proceedings 2022

Author

Listed:
  • Michael Breuß

    (BTU Cottbus-Senftenberg)

  • Ali Sharifi Boroujerdi

    (Volkswagen Infotainment GmbH)

  • Ashkan Mansouri Yarahmadi

    (BTU Cottbus-Senftenberg)

Abstract

In this paper we propose a novel approach to distinguish the style of drivers with respect to their energy efficiency. A unique property of the proposed method is that it relies exclusively on Global Positioning System (GPS) data. This setting is highly robust and available in practice as these GPS logs can easily be obtained. To rely on positional data alone means that all possible derived features from it will be highly correlated, so we have to consider a single feature. Here, we propose to explore the use of acceleration differences of a movement. Our strategy relies on agglomerative hierarchical clustering. The approach can be easily implemented to perform fast, even on huge amount of real-world data logs.

Suggested Citation

  • Michael Breuß & Ali Sharifi Boroujerdi & Ashkan Mansouri Yarahmadi, 2023. "Energy-Efficient Driving Model by Clustering of GPS Information," Lecture Notes in Operations Research, in: Oliver Grothe & Stefan Nickel & Steffen Rebennack & Oliver Stein (ed.), Operations Research Proceedings 2022, chapter 0, pages 213-219, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-24907-5_26
    DOI: 10.1007/978-3-031-24907-5_26
    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:lnopch:978-3-031-24907-5_26. 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.