Traffic noise can be classified among the worst factors in terms of damage to people's health and well-being. The trend of noise pollution modeling variable from the smart result of classic regressive models in the performance of many assessment models based on mathematical expressions, genetic algorithms and neural networks (of GRNN type, General Regression Neural Network). A methodological approach for the quantitative analysis of traffic noise in urban settings is proposed in the study. We present an analysis of the acoustic data measured in the city of Villa S. Giovanni (Italy), simultaneous measurement is of noise levels and vehicle flow and composition were done. Different prediction models are compared and a classification for the best assessment tool in the analysis of the equivalent level of noise Leq is given. The results show how the neural network approach provides better performance than the classical solution based on statistical analyses. The GRNN network is best suited to the simulation of the phenomenon seems and for the application in more complex areas, with greater variability in the traffic patterns, such as the case considered.

Environmental modeling for traffic noise in urban area

LEONARDI, Giovanni
;
2012

Abstract

Traffic noise can be classified among the worst factors in terms of damage to people's health and well-being. The trend of noise pollution modeling variable from the smart result of classic regressive models in the performance of many assessment models based on mathematical expressions, genetic algorithms and neural networks (of GRNN type, General Regression Neural Network). A methodological approach for the quantitative analysis of traffic noise in urban settings is proposed in the study. We present an analysis of the acoustic data measured in the city of Villa S. Giovanni (Italy), simultaneous measurement is of noise levels and vehicle flow and composition were done. Different prediction models are compared and a classification for the best assessment tool in the analysis of the equivalent level of noise Leq is given. The results show how the neural network approach provides better performance than the classical solution based on statistical analyses. The GRNN network is best suited to the simulation of the phenomenon seems and for the application in more complex areas, with greater variability in the traffic patterns, such as the case considered.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12318/6585
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