A new probability statistical model for traffic noise prediction on free flow roads and control flow roads

Feng Li; Shaoyi Stephen Liao; Ming Cai

doi:10.1016/j.trd.2016.10.019

Author(s)

Feng Li
Shaoyi Stephen Liao
Ming Cai

Related Research Unit(s)

Department of Information Systems

Detail(s)

Original language	English
Pages (from-to)	313-322
Journal / Publication	Transportation Research Part D: Transport and Environment
Volume	49
Online published	10 Nov 2016
Publication status	Published - Dec 2016

Link(s)

DOI	DOI https://doi.org/10.1016/j.trd.2016.10.019 Final Published version
	Check@CityULib
Link to Scopus	https://www.scopus.com/record/display.uri?eid=2-s2.0-85010216148&origin=recordpage
Permanent Link	https://scholars.cityu.edu.hk/en/publications/publication(b74bb5d3-6577-4f07-8391-990270797792).html

Abstract

A new traffic noise prediction approach based on a probability distribution model of vehicle noise emissions and achieved by Monte Carlo simulation is proposed in this paper. The probability distributions of the noise emissions of three types of vehicles are obtained using an experimental method. On this basis, a new probability statistical model for traffic noise prediction on free flow roads and control flow roads is established. The accuracy of the probability statistical model is verified by means of a comparison with the measured data, which has shown that the calculated results of L_eq, L₁₀, L₅₀, L₉₀, and the probability distribution of noise level occurrence agree well with the measurements. The results demonstrate that the new method can avoid the complicated process of traffic flow simulation but still maintain high accuracy for the traffic noise prediction.