A probability distribution prediction method for expressway traffic noise. (February 2022)

• Record Type: Journal Article
• Authors: Li, Feng; Xue, Wangxing; Rong, Ying; Du, Canyi; Tang, Jilong; Zhao, Youting
• Is Part Of: Transportation research. Volume 103(2022)
• Publication Date: 2022-02
• Subjects: Traffic noise -- Expressway -- Probability distribution model -- Monte Carlo simulation -- Gaussian mixture model; Transportation -- Research -- Periodicals; Transportation -- Environmental aspects -- Periodicals; 354.76
• DOI: 10.1016/j.trd.2022.103175
• Languages: English
• ISSNs: 1361-9209

Abstract

Abstract: The prediction and evaluation of expressway traffic noise is essential work of environmental management. A Monte Carlo simulation method for instantaneous sound levels is introduced according to the characteristics of free-flow traffic on expressways. The sound power levels of light and heavy vehicles are measured by field experiments, and a probability model of vehicle noise emission is established. Noise under different traffic volumes and distances is simulated by the Monte Carlo method. A probability distribution model of traffic noise with traffic volume and distance as parameters is given. A comparison of the calculated results and measured data shows that the proposed model can effectively predict the probability distribution of traffic noise and can be used to predict common traffic noise indicators. The prediction errors are less than 3 dB(A) for L eq, L 10, L 50, and L 90 ; less than 1.3 dB(A) for σ ; and less than 13 dB(A) for TNI .