NDLI: Machine-learning methodology for energy efficient routing

Content Provider	IET Digital Library
Author	Masikos, Michail Demestichas, Konstantinos Adamopoulou, Evgenia Theologou, Michael
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.
Starting Page	255
Ending Page	265
Page Count	11
ISSN	1751956X
Volume Number	8
e-ISSN	17519578
Issue Number	Issue 3, May (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/8/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2013.0006
Journal	IET Intelligent Transport Systems
Publisher Date	2013-08-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Ecodriving Assistance System Economical Driving Behaviour Electric Vehicle Electrical Engineering Computing Emission Reductions Energy Conservation Energy Consumption Predictions Energy Efficient Routing Fuel Economy Knowledge Engineering Technique Learning in AI Machine Learning Functionality Machine Learning Methodology Road Segments Road Traffic Road Vehicle Traffic Engineering Computing Transportation Vehicle Route
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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