The multiple regression analysis and path analysis in each dominant area of noise source are conducted to analyze the relationship between dependent variables like annoyance and independent ones such as noise and non-noise factors. The multiple regression analysis shows that impact of noise factors is the highest to annoyance in dominant areas of road traffic and railway noise.Meanwhile, impact of non-noise factors such as sensitivity and satisfaction of environment on annoyance is also high in these areas. The path analysis result for multivariate analysis between various independent and dependent variables is similar to that of the multiple regression analysis.
Predicted noise level has been used to assess the annoyance response since noise map was generalized and being the normal method to assess the environmental noise. Unfortunately using predicted noise level to derive the annoyance prediction curve caused some problems. The data have to be grouped manually to use the annoyance prediction curve. The aim of this paper is to propose the method to handle the predicted noise level and the survey data for annoyance prediction curve. This paper used the percentage of persons annoyed(%A) and the percentage of persons highly annoyed as the descriptor of noise annoyance in a population. The logistic regression method was used for deriving annoyance prediction curve. It is concluded that the method of dichotomizing data and logistic regression was suitable to handle the predicted noise level and survey data.
The suggested method of previous Son's study dichotomized subjective response data to modeling noise exposure-response. The method used maximum liklihood estimation instead of least square estimation and the noise exposure-response curve of the study was logistic regression analysis result.The method was originated to modeling community response rate such as %HA or %A. It can be useful when the subjective response was investigated based on predicted noise level. It is difficult to measure the single source emitting noise such as railway because various traffic noise sources combined in our life. The suggested method was adopted to model in this study and railway noise-exposure response curves were modeled because the noise level of this area was predicted data. The data of this study was used by previous Ko's paper but he dealt the area as combined noise area and divided the data by dominant noise source. But this study used all data of this area because the annoyance response to railway noise was higher than other noise according to the result of correlation analysis. The trend of the %HA and %A prediction model to train noise of this study is almost same as the model based on measured noise of previous Lim's study although the investigated areas and methods were different. . 이러한 소음에 단시간동안 노출될 경우 심각 한 질병을 유발하지는 않으나 방해와 불만을 초래 † 교신저자 ; 정회원, 서울시립대학교 환경공학부
To study annoyance response to combined transportation noise from road-traffic and railway, a social survey has been performed in a highly populated residential area in the city of Seoul, South Korea. To determine the noise exposure level in an interviewed household, a noise mapping software package was utilized and the noise exposure level from each single source was calculated separately and combined to get total exposure level. The prediction was partially validated by measurements. The households in the study area are classified into acoustically differentiated groups which are named as the road-traffic noise dominant, railway noise dominant, and combined noise dominant, respectively. %HA (percentage of highly annoyed people) curves to noise exposure level are presented for the acoustically classified three groups. Comparison of the %HA curves shows that more people are highly annoyed to railway noise than road-traffic noise, but the curve for combined noise is much closer to the %HA curve for road-traffic noise rather than the %HA curve for railway noise. Multiple-regression and path analysis of the survey data give the direct and indirect quantitative relationships between annoyance and noise and non-noise factors. Personal sensitivity to noise and satisfaction of living environment are primary contributing factors.
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