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 using sound signal recordings on test tracks under free-field condition. The model simulates vehicular traffic noise on urban roads in free or in traffic light controlled flow and considers the influence of bus stops. The proposed model calculates different acoustic descriptors, such as Statistical sound levels (L_A10 and L_A90), Equivalent continuous sound level (L_Aeq), Traffic noise index (TNI) and Noise pollution level (L_NP). Furthermore, it allows the listening of simulated noise. The experimental results indicate that the proposed model is reliable and accurate for vehicular traffic noise prediction.