Date on Master's Thesis/Doctoral Dissertation

12-2008

Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department

Civil and Environmental Engineering

Committee Chair

Cohn, Louis F. (Louis Franklin), 1948-

Author's Keywords

Air quality; Intelligent transportation system; Artificial neural networks

Subject

Intelligent transportation systems; Air quality; Air--Pollution

Abstract

Environmental or air quality impacts 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. The objective of this research was to model the relationship between ITS and ambient air quality. The multiple Artificial Neural Networks (ANN) training with the data yielded a model for predicting the air quality. In addition, the ANN made the measurement of the effect of ITS on air quality possible. Data pertaining to sixty US cities (urbanized area) were used for this research. Input variables used were related to transportation and local characteristics, and ITS applications. Output variables were the annual average concentrations of CO, Ozone, and N02 in ambient air. The K-fold cross validation technique was used to train the ANN. The results of ANN model were compared with that of a Multiple Regression (MR) model showing the supremacy of ANN over MR. The ANN model results show that the Mean Absolute Errors (MAEs) in prediction vary from 5 to 20 %. This variance is justified since the factors related with industries, which contribute significantly to air pollution, have not been taken into consideration in this study. There were some unusual findings: in contrast to the common assumptions, N02 concentration increases with ITS intensity, and Ground Level Ozone concentration, in ambient air, seemed to be more transportation-dependent as compared with that of CO and N02• A recommendation for further research on this topic is to include more input variables, especially those which are relatcd with industries, to improve the accuracy of prediction. Scientific experimentations have also been recommended to corroborate the unusual findings.

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