Multivariate adaptive regression splines models for vehicular emission prediction

Oduro, SD; Metia, S; Duc, H; Hong, G; Ha, QP

Multivariate adaptive regression splines models for vehicular emission prediction

Oduro, SD Metia, S

Duc, H Hong, G

Ha, QP

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Publication Type:: Journal Article
Citation:: Visualization in Engineering, 2015, 3 (1)
Issue Date:: 2015-12-01

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Field	Value	Language
dc.contributor.author	Oduro, SD	en_US
dc.contributor.author	Metia, S https://orcid.org/0000-0002-5676-1146	en_US
dc.contributor.author	Duc, H	en_US
dc.contributor.author	Hong, G https://orcid.org/0000-0002-1905-1161	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.date.issued	2015-12-01	en_US
dc.identifier.citation	Visualization in Engineering, 2015, 3 (1)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/36305
dc.description.abstract	© 2015, Oduro et al. Background: Rate models for predicting vehicular emissions of nitrogen oxides (NO X) are insensitive to the vehicle modes of operation, such as cruise, acceleration, deceleration and idle, because these models are usually based on the average trip speed. This study demonstrates the feasibility of using other variables such as vehicle speed, acceleration, load, power and ambient temperature to predict (NO X) emissions to ensure that the emission inventory is accurate and hence the air quality modelling and management plans are designed and implemented appropriately. Methods: We propose to use the non-parametric Boosting-Multivariate Adaptive Regression Splines (B-MARS) algorithm to improve the accuracy of the Multivariate Adaptive Regression Splines (MARS) modelling to effectively predict NO X emissions of vehicles in accordance with on-board measurements and the chassis dynamometer testing. The B-MARS methodology is then applied to the NO X emission estimation. Results: The model approach provides more reliable results of the estimation and offers better predictions of NO X emissions. Conclusion: The results therefore suggest that the B-MARS methodology is a useful and fairly accurate tool for predicting NO X emissions and it may be adopted by regulatory agencies.	en_US
dc.relation.ispartof	Visualization in Engineering	en_US
dc.relation.isbasedon	10.1186/s40327-015-0024-4	en_US
dc.title	Multivariate adaptive regression splines models for vehicular emission prediction	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	3	en_US
utslib.for	0902 Automotive Engineering	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0906 Electrical and Electronic 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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic 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.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	3	en_US

Abstract:

© 2015, Oduro et al. Background: Rate models for predicting vehicular emissions of nitrogen oxides (NO X) are insensitive to the vehicle modes of operation, such as cruise, acceleration, deceleration and idle, because these models are usually based on the average trip speed. This study demonstrates the feasibility of using other variables such as vehicle speed, acceleration, load, power and ambient temperature to predict (NO X) emissions to ensure that the emission inventory is accurate and hence the air quality modelling and management plans are designed and implemented appropriately. Methods: We propose to use the non-parametric Boosting-Multivariate Adaptive Regression Splines (B-MARS) algorithm to improve the accuracy of the Multivariate Adaptive Regression Splines (MARS) modelling to effectively predict NO X emissions of vehicles in accordance with on-board measurements and the chassis dynamometer testing. The B-MARS methodology is then applied to the NO X emission estimation. Results: The model approach provides more reliable results of the estimation and offers better predictions of NO X emissions. Conclusion: The results therefore suggest that the B-MARS methodology is a useful and fairly accurate tool for predicting NO X emissions and it may be adopted by regulatory agencies.

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http://hdl.handle.net/10453/36305