The need for road traffic noise monitoring is growing in urban areas due to the growth of vehicles number and to the consequent increase of risk for human health. Noise measurements cannot be performed everywhere, or even in a large number of sites, because of high costs and time consumption. For this reasons, Road Traffic Noise predictive Models (RTNMs) can be implemented to estimate the noise levels at any distance, knowing certain parameters needed as input of the RTNM. In this paper, the main statistical RTNMs are presented, together with the implementation of two innovative and advanced models: the EU suggested model (CNOSSOS-EU) and a research model presented by Quartieri et al. (2010). These models will be compared with noise measurements performed in different sites and with different traffic conditions, in order to avoid bias from geometry or other features of the area under study. The main conclusion is that the application of innovative models and the inclusion of dynamical information about traffic flow, will lead to better results with respect to statistical models.

Statistical and semi-dynamical road traffic noise models comparison with field measurements

Guarnaccia, Claudio
;
Quartieri, Joseph
2018

Abstract

The need for road traffic noise monitoring is growing in urban areas due to the growth of vehicles number and to the consequent increase of risk for human health. Noise measurements cannot be performed everywhere, or even in a large number of sites, because of high costs and time consumption. For this reasons, Road Traffic Noise predictive Models (RTNMs) can be implemented to estimate the noise levels at any distance, knowing certain parameters needed as input of the RTNM. In this paper, the main statistical RTNMs are presented, together with the implementation of two innovative and advanced models: the EU suggested model (CNOSSOS-EU) and a research model presented by Quartieri et al. (2010). These models will be compared with noise measurements performed in different sites and with different traffic conditions, in order to avoid bias from geometry or other features of the area under study. The main conclusion is that the application of innovative models and the inclusion of dynamical information about traffic flow, will lead to better results with respect to statistical models.
2018
978-073541698-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4717925
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