Development of hybrid algorithms for vehicular emissions modelling and prediction

Oduro, Seth Daniel

Development of hybrid algorithms for vehicular emissions modelling and prediction

Oduro, Seth Daniel

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Publication Type:: Thesis
Issue Date:: 2016

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Field	Value	Language
dc.contributor.author	Oduro, Seth Daniel
dc.date.accessioned	2016-11-17T00:54:04Z
dc.date.available	2016-11-17T00:54:04Z
dc.date.issued	2016
dc.identifier.uri	http://hdl.handle.net/10453/62169
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_AU
dc.description.abstract	The overwhelming accumulation of traffic volumes and relentless changes in travel-related characteristics significantly increase vehicular emissions, and hence, seriously affect urban air quality. It is difficult, however, to accurately estimate vehicular emissions in traffic intersections, junctions, and at signalized roadways because rate models for predicting vehicular emissions are insensitive to the vehicle modes of operations, such as cruising, idling, acceleration and deceleration. The reason is that these models are usually based on the average trip speed, not vehicle dynamics. These contribute to the increased complexity of such a model and degradation of its predictive performance. This thesis advocates the feasibility of using variables such as vehicle speed, acceleration, load, power and ambient temperature to predict transport emissions to ensure that emission inventories are accurate for the sake of air quality modelling and management planning. A variety of algorithms has been developed, based on Multivariate Adaptive Regression Splines (MARS), Boosting Multivariate Adaptive Regression Splines (BMARS), Artificial Neural Networks (ANNs), as well as the non-parametric Classification and Regression Trees (CART) and a combination of them in hybrid models to improve the accuracy of the emission prediction using vehicles’ on-board measurements and chassis dynamometer testing. Several performance indices are used to evaluate: accuracy, flexibility and computational efficiency. The obtained results suggest that the CART-BMARS hybrid methodology appears to be a useful and fairly accurate tool for predicting microscale vehicle emissions and may be adopted by regulatory agencies. The significance of this thesis is in providing of feasible and effective solutions for the implementation of vehicular emissions models to address the problem of air quality modelling and control in metropoles and mega-cities.	en_AU
dc.format	Thesis (PhD)
dc.language.iso	en_AU	en_AU
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/62169/2/02whole.pdf
dc.rights	au.edu.uts.lib/ppc
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Vehicular emissions.	en
dc.subject	Urban air quality.	en
dc.subject	Rate models for predicting vehicular emissions.	en
dc.subject	Multivariate Adaptive Regression Splines (MARS).	en
dc.subject	Boosting Multivariate Adaptive Regression Splines (BMARS).	en
dc.subject	Artificial Neural Networks (ANNs).	en
dc.subject	Non-parametric Classification and Regression Trees (CART).	en
dc.subject	CART-BMARS hybrid methodology.	en
dc.title	Development of hybrid algorithms for vehicular emissions modelling and prediction	en_AU
dc.type	Thesis	en_AU
utslib.copyright.status	open_access

Abstract:

The overwhelming accumulation of traffic volumes and relentless changes in travel-related characteristics significantly increase vehicular emissions, and hence, seriously affect urban air quality. It is difficult, however, to accurately estimate vehicular emissions in traffic intersections, junctions, and at signalized roadways because rate models for predicting vehicular emissions are insensitive to the vehicle modes of operations, such as cruising, idling, acceleration and deceleration. The reason is that these models are usually based on the average trip speed, not vehicle dynamics. These contribute to the increased complexity of such a model and degradation of its predictive performance. This thesis advocates the feasibility of using variables such as vehicle speed, acceleration, load, power and ambient temperature to predict transport emissions to ensure that emission inventories are accurate for the sake of air quality modelling and management planning. A variety of algorithms has been developed, based on Multivariate Adaptive Regression Splines (MARS), Boosting Multivariate Adaptive Regression Splines (BMARS), Artificial Neural Networks (ANNs), as well as the non-parametric Classification and Regression Trees (CART) and a combination of them in hybrid models to improve the accuracy of the emission prediction using vehicles’ on-board measurements and chassis dynamometer testing. Several performance indices are used to evaluate: accuracy, flexibility and computational efficiency. The obtained results suggest that the CART-BMARS hybrid methodology appears to be a useful and fairly accurate tool for predicting microscale vehicle emissions and may be adopted by regulatory agencies. The significance of this thesis is in providing of feasible and effective solutions for the implementation of vehicular emissions models to address the problem of air quality modelling and control in metropoles and mega-cities.

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http://hdl.handle.net/10453/62169