Intelligent Transportation Systems and NO_2 Emissions: Predictive Modeling Approach Using Artificial Neural Networks

摘要

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 NO_2 concentrations. In addition, the ANN made the measurement of the effect of ITS on NO_2 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 NO_2 in ambient air. The K-fold cross-validation technique was used to train the ANN. There was an unusual finding: in contrast to the common assumptions, NO_2 concentration increased with ITS intensity and that may be suggestive of causing conformity problems and may jeopardize the ITS project and the transportation program.