Optimization of Dataset Generation for a Multilinear Regressive Road Traffic Noise Model

Abstract

According to the European Environmental Agency, road traffic noise is one of the worst and most prevalent kinds of environmental pollutants, which causes health problems to a constantly increasing number of people in urban areas throughout Europe. It has been proved that prolonged exposure to sound levels exceeding 55 dBA is harmful and causes severe problems like sleep disturbances, tiredness, lack of concentration, high blood pressure and, in the worst case, sudden death. A precise and constant evaluation of sound level in inhabited areas is therefore desired (and in some cases compelled by laws), but collection of actual noise data is not easy and sometimes not possible. For this reason, Road Traffic Noise (RTN) models are very handy: one can (more or less precisely) estimate the noise emitted in a certain area having certain road traffic characteristics. The application of RTN models, anyway, also has problems. First of all, an RTN model has to be built and calibrated by using real collected noise data. Moreover, when trying to apply an RTN model on road traffic situations that are far away from the site of collection, the models generally fail. To overcome such problems, in this contribution, a road traffic dataset has been computed by randomly generating values of traffic variables like the number of vehicles per unit of time, their velocities, and their distance from the receiver. Then, by applying a multiregressive function on the dataset, the obtained coefficients have been used to calibrate and validate the presented model. The three steps (generation of the dataset, calibration of the model, and validation on a real dataset) are detailly investigated.