Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

Xu, Z; Wei, T; Easa, S; Zhao, X; Qu, X

Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

Xu, Z Wei, T Easa, S Zhao, X Qu, X

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Publication Type:: Journal Article
Citation:: Computer-Aided Civil and Infrastructure Engineering, 2018, 33 (3), pp. 209 - 219
Issue Date:: 2018-03-01

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Field	Value	Language
dc.contributor.author	Xu, Z	en_US
dc.contributor.author	Wei, T	en_US
dc.contributor.author	Easa, S	en_US
dc.contributor.author	Zhao, X	en_US
dc.contributor.author	Qu, X https://orcid.org/0000-0003-0973-3756	en_US
dc.date.issued	2018-03-01	en_US
dc.identifier.citation	Computer-Aided Civil and Infrastructure Engineering, 2018, 33 (3), pp. 209 - 219	en_US
dc.identifier.issn	1093-9687	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132226
dc.description.abstract	© 2018 Computer-Aided Civil and Infrastructure Engineering In this research, by taking advantage of dynamic fuel consumption–speed data from Internet of Vehicles, we develop two novel computational approaches to more accurately estimate truck fuel consumption. The first approach is on the basis of a novel index, named energy consumption index, which is to explicitly reflect the dynamic relationship between truck fuel consumption and truck drivers’ driving behaviors obtained from Internet of Vehicles. The second approach is based on a Generalized Regression Neural Network model to implicitly establish the same relationship. We further compare the two proposed models with three well-recognized existing models: vehicle specific power (VSP) model, Virginia Tech microscopic (VT-Micro) model, and Comprehensive Modal Emission Model (CMEM). According to our validations at both microscopic and macroscopic levels, the two proposed models have stronger performed in predicting fuel consumption in new routes. The models can be used to design more energy-efficient driving behaviors in the soon-to-come era of connected and automated vehicles.	en_US
dc.relation.ispartof	Computer-Aided Civil and Infrastructure Engineering	en_US
dc.relation.isbasedon	10.1111/mice.12344	en_US
dc.subject.classification	Civil Engineering	en_US
dc.title	Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	33	en_US
utslib.for	0905 Civil Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	33	en_US

Abstract:

© 2018 Computer-Aided Civil and Infrastructure Engineering In this research, by taking advantage of dynamic fuel consumption–speed data from Internet of Vehicles, we develop two novel computational approaches to more accurately estimate truck fuel consumption. The first approach is on the basis of a novel index, named energy consumption index, which is to explicitly reflect the dynamic relationship between truck fuel consumption and truck drivers’ driving behaviors obtained from Internet of Vehicles. The second approach is based on a Generalized Regression Neural Network model to implicitly establish the same relationship. We further compare the two proposed models with three well-recognized existing models: vehicle specific power (VSP) model, Virginia Tech microscopic (VT-Micro) model, and Comprehensive Modal Emission Model (CMEM). According to our validations at both microscopic and macroscopic levels, the two proposed models have stronger performed in predicting fuel consumption in new routes. The models can be used to design more energy-efficient driving behaviors in the soon-to-come era of connected and automated vehicles.

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http://hdl.handle.net/10453/132226