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Comparison of Artificial Neural Networks and Support Vector Machines for Weigh-In-Motion Based Truck Type Classification

Authors:
Yueren Wang
Ian Flood
Raja R A Issa

Keywords: artificial neural network; empirical modeling; support vector machine; truck weigh-in-motion

Abstract:
The paper develops and compares a comprehensive range of configurations of artificial neural networks and support vector machines for solving the truck classification by weigh-in-motion problem. A local scatter point smoothing schema is also demonstrated as a means of selecting an optimal set of design parameters for each model type. Three main model formats are considered: (i) a monolithic structure with a one versus all strategy for selecting truck type; (ii) an array of sub-models each dedicated to one truck type with a one versus all truck type selection strategy; and (iii) an array of sub-models each dedicated to selecting between pairs of trucks. Overall, the SVM approach was found to outperform the ANN based models. The paper concludes with some suggestions for extending the work to a broader scope of problems.

Pages: 140 to 145

Copyright: Copyright (c) IARIA, 2015

Publication date: June 21, 2015

Published in: conference

ISSN: 2308-3484

ISBN: 978-1-61208-416-9

Location: Brussels, Belgium

Dates: from June 21, 2015 to June 26, 2015