• DocumentCode
    12246
  • Title

    Machine-learning methodology for energy efficient routing

  • Author

    Masikos, Michail ; Demestichas, Konstantinos ; Adamopoulou, Evgenia ; Theologou, Michael

  • Author_Institution
    Inst. of Commun. & Comput. Syst., Athens, Greece
  • Volume
    8
  • Issue
    3
  • fYear
    2014
  • fDate
    May-14
  • Firstpage
    255
  • Lastpage
    265
  • Abstract
    Eco-driving assistance systems encourage economical driving behaviour and support the driver in optimising his/her driving style to achieve fuel economy and consequently, emission reductions. Energy efficiency is also one of the most pertinent issues related to the autonomy of fully electric vehicles. This study introduces a novel methodology for energy efficient routing, based on the realisation of dependable energy consumption predictions for the various road segments constituting an actual or potential vehicle route, performed mainly by means of machine-learning functionality. This proposed innovative methodology, the functional architecture implementing it, as well as demonstrative experimental results are presented in this study.
  • Keywords
    electric vehicles; electrical engineering computing; energy conservation; learning (artificial intelligence); road traffic; road vehicles; traffic engineering computing; ecodriving assistance systems; economical driving behaviour; electric vehicles; emission reductions; energy consumption predictions; energy efficient routing; fuel economy; machine learning functionality; machine learning methodology; road segments; vehicle route;
  • fLanguage
    English
  • Journal_Title
    Intelligent Transport Systems, IET
  • Publisher
    iet
  • ISSN
    1751-956X
  • Type

    jour

  • DOI
    10.1049/iet-its.2013.0006
  • Filename
    6818488