Noise is one of the most prevalent sources of environmental pollution, and vehicular traffic noise is considered one of the most invasive type of pollution and often the most intrusive of all. The trend of noise pollution modelling varies from the smart result of classic regressive models to the performance of many assessment models based on mathematical expression, genetic algorithms and neural networks. In this study, multilayer feed forward back propagation neural network has been developed to predict vehicular traffic noise in urban area. The proposed ANN model has been used to predict the equivalent continuous sound level (Led) in dB (A). The model input parameters are the characteristics of the vehicular traffic flows (total vehicle, percentage of heavy vehicles and average vehicle speed) and the typology of the roads (width of the roadway). The predicted Led from neural network approach and the regression analysis have also compared with the filed measurement. The results show how the neural network approach provides better performance than the classical solution based on statistical analysis.

Artificial Neural Network for Traffic Noise Modelling

Leonardi
;
2015

Abstract

Noise is one of the most prevalent sources of environmental pollution, and vehicular traffic noise is considered one of the most invasive type of pollution and often the most intrusive of all. The trend of noise pollution modelling varies from the smart result of classic regressive models to the performance of many assessment models based on mathematical expression, genetic algorithms and neural networks. In this study, multilayer feed forward back propagation neural network has been developed to predict vehicular traffic noise in urban area. The proposed ANN model has been used to predict the equivalent continuous sound level (Led) in dB (A). The model input parameters are the characteristics of the vehicular traffic flows (total vehicle, percentage of heavy vehicles and average vehicle speed) and the typology of the roads (width of the roadway). The predicted Led from neural network approach and the regression analysis have also compared with the filed measurement. The results show how the neural network approach provides better performance than the classical solution based on statistical analysis.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12318/5907
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