A Comparison between Traffic Noise Experimental Data and Predictive Models Results

 Abstract—Traffic Noise predictive Models (TNMs) are often used in order to predict and/or monitor road traffic noise impact on environment. Usually a statistical approach is followed in the most used model building and compiling. A large set of experimental data is collected on one or more sites under investigation and on these data a best fit is performed with a functional relation. A logarithmic function is generally postulated and the fit parameters are evaluated and used to predict noise level for any traffic flow. This procedure produce of course a kind of "site bias" in the prediction, since the parameters are evaluated on data collected in that specific area and conditions. In this paper, after having summarized some of the most known models, a comparison between simulated and experimental data is performed, in order to highlight the behaviour of models in two different sites. The strong dependence on the site, and consequently on the vehicle flow volume and typology, will be sketched, together with the influence of climatic parameters.