Analysis of Vehicle Emissions and Prediction of Gross Emitter using Remote Sensing Data

Interest has focused on the analysis of vehicle emission based on the remote sensing data during the last two decades. This paper proposes an artificial neural network model for predicting taxi gross emitters using remote sensing data. Firstly, it introduces the field test in Guangzhou, and then analyzes the various factors from the emission data. Secondly, after doing principal components analysis and selecting algorithm and architecture, the back-propagation neural network model with 8-17-1 architecture was established as the optimal approach. It gives a percentage of hits of 93%. Finally, comparison among our former research results and aggression analysis results were presented. The results show the potentiality and validity of the proposed method in the prediction of taxi gross emitters