Open AccessMachine‐learning methodology for energy efficient routing
Author(s) -
Masikos Michail,
Demestichas Konstantinos,
Adamopoulou Evgenia,
Theologou Michael
Publication year - 2014
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2013.0006
Subject(s) - computer science , routing (electronic design automation) , energy (signal processing) , artificial intelligence , machine learning , computer network , mathematics , statistics
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.