IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0174959.html
   My bibliography  Save this article

Driver behavior profiling: An investigation with different smartphone sensors and machine learning

Author

Listed:
  • Jair Ferreira Júnior
  • Eduardo Carvalho
  • Bruno V Ferreira
  • Cleidson de Souza
  • Yoshihiko Suhara
  • Alex Pentland
  • Gustavo Pessin

Abstract

Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving data and application of computer models to generate a classification that characterizes the driver aggressiveness profile. Different sensors and classification methods have been employed in this task, however, low-cost solutions and high performance are still research targets. This paper presents an investigation with different Android smartphone sensors, and classification algorithms in order to assess which sensor/method assembly enables classification with higher performance. The results show that specific combinations of sensors and intelligent methods allow classification performance improvement.

Suggested Citation

  • Jair Ferreira Júnior & Eduardo Carvalho & Bruno V Ferreira & Cleidson de Souza & Yoshihiko Suhara & Alex Pentland & Gustavo Pessin, 2017. "Driver behavior profiling: An investigation with different smartphone sensors and machine learning," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-16, April.
    • Handle: RePEc:plo:pone00:0174959
    DOI: 10.1371/journal.pone.0174959
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174959
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0174959&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0174959?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
    ---><---

    Citations

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


    Cited by:

    1. Christopher Blier-Wong & Hélène Cossette & Luc Lamontagne & Etienne Marceau, 2020. "Machine Learning in P&C Insurance: A Review for Pricing and Reserving," Risks, MDPI, vol. 9(1), pages 1-26, December.
    2. Xiamei Wen & Liping Fu & Ting Fu & Jessica Keung & Ming Zhong, 2021. "Driver Behavior Classification at Stop-Controlled Intersections Using Video-Based Trajectory Data," Sustainability, MDPI, vol. 13(3), pages 1-18, January.
    3. Maria Nadia Postorino & Giuseppe M. L. Sarnè, 2023. "Using Reputation Scores to Foster Car-Sharing Activities," Sustainability, MDPI, vol. 15(4), pages 1-24, February.

    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:plo:pone00:0174959. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.