Study on the Annoyance Response in the Area Exposed by Road Traffic Noise and Railway Noise

Ko, J.H.; Chang, Suk Yoon; Son, Jin-Hee; K, Liu

doi:10.5050/ksnve.2010.20.2.172

Cited by 2 publications

(2 citation statements)

References 8 publications

(1 reference statement)

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“…(8,9) . 일본의 경우 철도소음에 대한 반응 은 도로 소음에 대한 반응보다 약 10 dB가 더 높았 으며 (8) 국내 철도소음에 대한 반응 곡선도 일본의 경 우와 같이 도로 소음에 대한 반응보다 높았다 (9,10) . 의 결과이다.…”

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Railway Noise Exposure-response Model based on Predicted Noise Level and Survey Results

Son¹,

K²,

Chang³

2011

Transactions of the Korean Society for Noise and Vibration Engi

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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

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Railway Noise Exposure-response Model based on Predicted Noise Level and Survey Results

Son¹,

K²,

Chang³

2011

Transactions of the Korean Society for Noise and Vibration Engi

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“…It is concluded that the method of dichotomizing data and logistic regression was suitable to handle the predicted noise level and survey data. 나라도 예외가 아니다 (5,6) . .…”

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Application Method of Logistic Regression Analysis for Annoyance Prediction Model Based on Predicted Noise Level

Son¹,

K²,

Choung³

et al. 2010

Transactions of the Korean Society for Noise and Vibration Engi

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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.

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Study on the Annoyance Response in the Area Exposed by Road Traffic Noise and Railway Noise

Cited by 2 publications

References 8 publications

Railway Noise Exposure-response Model based on Predicted Noise Level and Survey Results

Railway Noise Exposure-response Model based on Predicted Noise Level and Survey Results

Application Method of Logistic Regression Analysis for Annoyance Prediction Model Based on Predicted Noise Level

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