THESIS
2001
Abstract
A regression model for predicting L
10 noise levels at roadsides due to urban road traffic in Hong Kong was developed. It was found that urban traffic noise depends on the logarithmic functions of the hourly traffic flow (logQ), the number of heavy vehicles (logHV) and the average speed of vehicles (logV). Analysis of variance (ANOVA) and Tukey's studentized method confirmed that the hourly traffic flow and the number of heavy vehicles are the most significant factors in urban traffic noise. The regression model was analyzed using scattering diagrams and compared with the widely used CRTN model. Results show that the regression model would give an accurate prediction in an urban area with tall buildings and a high proportion of heavy vehicles (weight exceeding 1,525 kg)....[
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A regression model for predicting L
10 noise levels at roadsides due to urban road traffic in Hong Kong was developed. It was found that urban traffic noise depends on the logarithmic functions of the hourly traffic flow (logQ), the number of heavy vehicles (logHV) and the average speed of vehicles (logV). Analysis of variance (ANOVA) and Tukey's studentized method confirmed that the hourly traffic flow and the number of heavy vehicles are the most significant factors in urban traffic noise. The regression model was analyzed using scattering diagrams and compared with the widely used CRTN model. Results show that the regression model would give an accurate prediction in an urban area with tall buildings and a high proportion of heavy vehicles (weight exceeding 1,525 kg).
The statistical frequency distribution of urban traffic noise was obtained based on the noise levels measured in the winter months of 2000. It was found that urban traffic noise is characterized by chi-squared distributions with a degree of freedom of 6 for interrupted traffic flow and with a different dof of 12 for free traffic flow. Two noise sources with the same or different chi-squared distributions can be combined using the method of probability combination. The combined statistical noise parameters L
10, L
50 and L
90 can be calculated from the new distribution.
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