Traffic noise measurement and prediction in Hong Kong

Pao Yue-kong Library Electronic Theses Database

Traffic noise measurement and prediction in Hong Kong


Author: Leung, Kai-hong
Title: Traffic noise measurement and prediction in Hong Kong
Degree: M.Eng.
Year: 2007
Subject: Hong Kong Polytechnic University -- Dissertations.
Traffic noise -- China -- Hong Kong -- Measurement.
Traffic noise -- China -- Hong Kong -- Forecasting.
Department: Dept. of Building Services Engineering
Pages: vi, 92, 7, 69 leaves : col. ill., col. maps ; 30 cm.
Language: English
InnoPac Record:
Abstract: Although having been challenged about its accuracy, the Calculation of Road Traffic Noise (CRTN) prediction method which was originated in the United Kingdom remains its position as the sole instrument for traffic noise estimation in Hong Kong. This dissertation aims to examine the accuracy and suitability of the CRTN model for roads in Hong Kong by comparing the difference between the predicted and the actual traffic noise level LA 10 by using the model and conducting direct measurements on roads simultaneously. Twenty-nine measurements and predictions were conducted in fifteen locations in Hong Kong. The results show that the CRTN could predict the traffic noise level fairly accurately with a mean percentage error of only 1.8% and a mean difference between the predicted and measured LA10 of+0.4 dB(A). A large coefficient of determination (R2) also indicates that the predicted noise levels could correlate closely with the measured levels. Comparisons between the CRTN model with other regression models proposed by local researchers were also conducted. The results further affirm that the CRTN model would be reliable for predictions in Hong Kong. These positive results being contrary to that obtained in previous studies are believed to be due to the difference in site selection. To prevent discrepancy in measured noise level that may affect the accuracy of the CRTN predictions, it is therefore recommended that measurements in complex or congested roads and during rainy and windy days be avoided in future investigations of the accuracy of the CRTN prediction model.

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