Intelligent Performance-enhanced Green Vehicle Consumption Prediction

Road transportation energy conservation is of major importance not only for environmental, but also for economic reasons. This paper discusses the topic of vehicle energy consumption prediction through advanced, intelligence-based methods. The concept is to exploit historic data collected by each vehicle travelling on the road network in order to gain knowledge about the travelling characteristics of each piece of road and then utilizing it to predict consumption of future journeys. The work presented attempts a leap forward from proof-of-concept implementations already assessed. In particular, it focuses on enhancing the performance of the prediction process, in terms of both execution time and resources needed, mainly through the introduction of machine learning engine clustering. To this end, a detailed performance evaluation of the proposed method and an analysis of the resulting benefits are presented.

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  • Supplemental Notes:
    • Abstracts used with permission of ERTICO – ITS Europe.
  • Corporate Authors:

    ERTICO - ITS Europe

    Avenue Louise 326, Blue Tower, 2nd Floor
    Brussels,   Belgium  B-1050
  • Authors:
    • Adamopoulou, Evgenia
    • Demestichas, Konstantinos
    • Asthenopoulos, Vasilis
    • Kosmides, Pavlos
    • Gorini, Marco
  • Conference:
  • Publication Date: 2015


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 12p
  • Monograph Title: 22nd ITS World Congress. Proceedings

Subject/Index Terms

Filing Info

  • Accession Number: 01603208
  • Record Type: Publication
  • Report/Paper Numbers: ITS-1784
  • Files: TRIS
  • Created Date: Jun 17 2016 12:49PM