Probabilistic model for urban traffic noise analyses using real sound signals

Abstract Vehicular traffic is pointed out as a major source of urban noise pollution today. In this paper, we evaluated the precision of a new probabilistic model for urban traffic noise analyses. The proposed model adopts real sound signals and the Monte Carlo method in simulations. Probability distributions of traffic variables were obtained in-situ on two urban roads. The acoustic signals and corresponding energies of single pass-by of vehicles were obtained usingsound signal recordings on test tracks under free-field condition. The model simulates vehicular traffic noise on urban roads in free or in trafficlight controlled flow and considers the influence of bus stops.The proposed model calculates different acoustic descriptors, such as Statistical sound levels (LA10 and LA90), Equivalent continuous sound level (LAeq), Trafficnoise index (TNI) and Noise pollution level (LNP). Furthermore, it allowsthe listening of simulated noise. The experimental results indicate that theproposed model is reliable and accurate for vehicular traffic noise prediction.