Development of hybrid algorithms for vehicular emissions modelling and prediction
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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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