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Predicting real-time roadside CO and NO2 concentrations using neural networks

Zito, P., Chen, H. and Bell, M.C. (2008) Predicting real-time roadside CO and NO2 concentrations using neural networks. IEEE Transactions on Intelligent Transportation Systems, 9 (3). pp. 514-522. ISSN 1524-9050

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Abstract

The main aim of this paper is to develop a model based on neural network (NN) theory to estimate real-time roadside CO and $hbox{NO}_{2}$ concentrations using traffic and meteorological condition data. The location of the study site is at a road intersection in Melton Mowbray, which is a town in Leicestershire, U.K. Several NNs, which can be classified into three types, namely, the multilayer perceptron, the radial basis function, and the modular network, were developed to model the nonlinear relationships that exist in the pollutant concentrations. Their performances are analyzed and compared. The transferability of the developed models is studied using data collected from a road intersection in another city. It was concluded that all NNs provide reliable estimates of pollutant concentrations using limited information and noisy data.

Item Type: Article
Copyright, Publisher and Additional Information: © Copyright 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds)
Depositing User: Sherpa Assistant
Date Deposited: 02 Oct 2008 13:37
Last Modified: 08 Feb 2013 17:07
Published Version: http://dx.doi.org/10.1109/TITS.2008.928259
Status: Published
Publisher: IEEE
Identification Number: 10.1109/TITS.2008.928259
URI: http://eprints.whiterose.ac.uk/id/eprint/4723

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