Vehicular emissions prediction with CART-BMARS hybrid models

Oduro, SD; Ha, QP; Duc, H

Vehicular emissions prediction with CART-BMARS hybrid models

Oduro, SD Ha, QP

Duc, H

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Publication Type:: Journal Article
Citation:: Transportation Research Part D: Transport and Environment, 2016, 49 pp. 188 - 202
Issue Date:: 2016-12-01

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Field	Value	Language
dc.contributor.author	Oduro, SD	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.author	Duc, H	en_US
dc.date.available	2020-05-25T19:14:24Z
dc.date.issued	2016-12-01	en_US
dc.identifier.citation	Transportation Research Part D: Transport and Environment, 2016, 49 pp. 188 - 202	en_US
dc.identifier.issn	1361-9209	en_US
dc.identifier.uri	http://hdl.handle.net/10453/54544
dc.description.abstract	© 2016 Vehicular emission models play a key role in the development of reliable air quality modeling systems. To minimize uncertainties associated with these models, it is essential to match the high-resolution requirements of emission models with up-to-date information. However, these models are usually based on average trip speed, not on environmental parameters like ambient temperature, and vehicle's motion characteristics, such as speed, acceleration, load and power. This contributes to the degradation of its predictive performance. In this paper, we propose to use the non-parametric Classification and Regression Trees (CART), the Boosting Multivariate Adaptive Regression Splines (BMARS) algorithm and a combination of them in hybrid models to improve the accuracy of vehicular emission prediction using on-board measurements and the chassis dynamometer testing. The experimental comparison between the proposed CART-BMARS hybrid model with the BMARS and artificial neural networks (ANNs) algorithms demonstrates its effectiveness and efficiency in estimating vehicular emissions.	en_US
dc.relation.ispartof	Transportation Research Part D: Transport and Environment	en_US
dc.relation.isbasedon	10.1016/j.trd.2016.09.012	en_US
dc.subject.classification	Logistics & Transportation	en_US
dc.title	Vehicular emissions prediction with CART-BMARS hybrid models	en_US
dc.type	Journal Article
utslib.citation.volume	49	en_US
utslib.for	090201 Automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels)	en_US
utslib.for	0502 Environmental Science and Management	en_US
utslib.for	1205 Urban and Regional Planning	en_US
utslib.for	1507 Transportation and Freight Services	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	49	en_US

Abstract:

© 2016 Vehicular emission models play a key role in the development of reliable air quality modeling systems. To minimize uncertainties associated with these models, it is essential to match the high-resolution requirements of emission models with up-to-date information. However, these models are usually based on average trip speed, not on environmental parameters like ambient temperature, and vehicle's motion characteristics, such as speed, acceleration, load and power. This contributes to the degradation of its predictive performance. In this paper, we propose to use the non-parametric Classification and Regression Trees (CART), the Boosting Multivariate Adaptive Regression Splines (BMARS) algorithm and a combination of them in hybrid models to improve the accuracy of vehicular emission prediction using on-board measurements and the chassis dynamometer testing. The experimental comparison between the proposed CART-BMARS hybrid model with the BMARS and artificial neural networks (ANNs) algorithms demonstrates its effectiveness and efficiency in estimating vehicular emissions.

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http://hdl.handle.net/10453/54544