Investigation of influential factors of tire/pavement noise: A multilevel Bayesian analysis of full-scale track testing data

Abstract

Tire/pavement noise is a primary source of traffic noise that continuously interferes with human life. In this study, we employed a Bayesian multilevel model to reveal the causal relationship between the resulting noise and the considered variables and quantify their uncertainties. The experimental design comprised four primary factors: pavement type, tire type, driving speed, and trailer weight. We also included the impacts of pavement surface characteristics and environmental factors. The results show the proposed Bayesian model is more predictive than its fixed effects counterpart. Among the considered factors, the influence of speed dominates all other factors on the noise levels. The tire/pavement noise increases with macrotexture depth while decreases with the porosity of the surface material. The porous asphalt pavement attenuates noise more significantly at a higher speed. The noise level increases statistically when the tire load increases, while it decreases as the air temperature increases.