Environmental impact studies are strongly related to road traffic noise, especially in urban areas. A long term exposure to road traffic noise, in fact, can lead to relevant effects, both auditory (e.g. sleep disturbance, hearing loss, etc.) or not auditory (e.g. stress, anxiety, cardiovascular problems, etc.). A proper modelling of noise production and propagation is a challenging issue, especially in areas where the complexity of sources, receivers and other objects makes difficult to use standard predictive formulas, such as the usual Traffic Noise predictive Models (TNMs). The collection of experimental data is always advisable, in order to control the predictive tools and eventually tune their parameters. In this paper, the author presents a set of advanced tools for noise modelling, particularly aimed at the prediction of non-conventional situations, such as road intersections, traffic jams, extreme traffic flow, etc., where the standard TNMs usually fail. The main idea is to implement a dynamical approach in the traffic noise prediction, i.e. to include the dependence of noise emission by kinematical parameters, such as speed, position and eventually acceleration. This can be achieved by means of different approaches, some of them resumed in the paper, for instance cellular automata, traffic theory (Fundamental Diagram), source power dependence from the speed, etc.. The implementation of these models in easy to use tools represents the new horizon in traffic noise prediction.
Advanced Tools for Traffic Noise Modelling and Prediction
GUARNACCIA, CLAUDIO
2013-01-01
Abstract
Environmental impact studies are strongly related to road traffic noise, especially in urban areas. A long term exposure to road traffic noise, in fact, can lead to relevant effects, both auditory (e.g. sleep disturbance, hearing loss, etc.) or not auditory (e.g. stress, anxiety, cardiovascular problems, etc.). A proper modelling of noise production and propagation is a challenging issue, especially in areas where the complexity of sources, receivers and other objects makes difficult to use standard predictive formulas, such as the usual Traffic Noise predictive Models (TNMs). The collection of experimental data is always advisable, in order to control the predictive tools and eventually tune their parameters. In this paper, the author presents a set of advanced tools for noise modelling, particularly aimed at the prediction of non-conventional situations, such as road intersections, traffic jams, extreme traffic flow, etc., where the standard TNMs usually fail. The main idea is to implement a dynamical approach in the traffic noise prediction, i.e. to include the dependence of noise emission by kinematical parameters, such as speed, position and eventually acceleration. This can be achieved by means of different approaches, some of them resumed in the paper, for instance cellular automata, traffic theory (Fundamental Diagram), source power dependence from the speed, etc.. The implementation of these models in easy to use tools represents the new horizon in traffic noise prediction.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
