Statistical noise level distribution modelling for road traffic noise predictions

Pao Yue-kong Library Electronic Theses Database

Statistical noise level distribution modelling for road traffic noise predictions

 

Author: Ng, Chun-hung
Title: Statistical noise level distribution modelling for road traffic noise predictions
Degree: Ph.D.
Year: 2008
Subject: Hong Kong Polytechnic University -- Dissertations.
Traffic noise -- Measurement -- Statistical methods.
Department: Dept. of Building Services Engineering
Pages: xxxiv, 210 p. : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2276238
URI: http://theses.lib.polyu.edu.hk/handle/200/3789
Abstract: The main objective of this study is to seek for a reliable prediction scheme for the traffic noise levels distribution where the outcome will be contributable to the development of an environmental noise theory and a reliable and informative prediction scheme for the aural environment affected by traffic noise. The study began with an intensive noise survey carried out in the HKSAR to identify the existing noise problem outside the building facade of the residential premises. Further analysis has been made on the effectiveness of the short time span noise measurement, which is the current local practice, in assessing traffic noise in the living environment in accordance with the local noise control ordinance. Statistical structures of the environmental noise found in the HKSAR have been examined. The addition of two noise records with similar and different statistical structures has also be reviewed and studied respectively. Deficiency of the algorithm in common practice is also quantified in this study. A new traffic noise propagation noise model has been developed for the prediction of road traffic noise level distributions. It is a source-path-receiver model with moving noise sources which are governed by the known vehicle type and speed distributions. The simulation outcomes show that the predicted noise level distribution provides a good approximation when the dominant noise source is road traffic. As a result, the model can be applied to the prediction of noise level distribution in both the planning and design stage of a new road and the road traffic noise analysis of an existing road with minimal resources on noise surveys.

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