Intelligent Transportation Systems and Emissions: Predictive Modeling Approach Using Artificial Neural Networks
Publication: Journal of Infrastructure Systems
Volume 18, Issue 2
Abstract
Environmental or air-quality effects of Intelligent Transportation Systems (ITS) are very difficult to measure. Some researchers have attempted to quantify the effects of individual ITS application on emissions; yet, the effects of ITS as a whole on ambient air-quality have not been investigated. This paper shows how to model the relationship between ITS and ambient airquality. The multiple artificial neural networks (ANN) training with the data yielded a model for predicting the concentrations. In addition, the ANN made the measurement of the effect of ITS on concentrations in ambient air possible. Data pertaining to 59 U.S. cities (urbanized area) were used for this work. Input variables used were related to transportation, local characteristics, and ITS applications. Output variable was the annual average concentration of in ambient air. The -fold cross-validation technique was used to train the ANN. There was an unusual finding: in contrast to the common assumptions, concentration increased with ITS intensity and that may be suggestive of causing conformity problems and may jeopardize the ITS project and the transportation program.
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Information & Authors
Information
Published In
Journal of Infrastructure Systems
Volume 18 • Issue 2 • June 2012
Pages: 113 - 118
Copyright
© 2012. American Society of Civil Engineers.
History
Received: Oct 18, 2009
Accepted: Jul 22, 2011
Published online: Aug 11, 2011
Published in print: Jun 1, 2012
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