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
Link To Document :
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