학술논문
도로교통소음예측식 RLS-90 및 RLS-19 비교 연구 / A Comparative Study on Road Traffic Noise Prediction Formulas RLS-90 and RLS-19
Document Type
Dissertation/ Thesis
Author
Source
Subject
Language
Korean
Abstract
본 연구에서는 1990년에 독일에서 발표한 도로교통소음 예측식인 RLS-90과 2019년에 독일에서 발표한 교체 규정서인 RLS-19를 실측데이터와 각각의 예측식에 따른 예측값을 비교·분석하여 두 식의 차이점을 도출하고 소음지도를 통한 소음노출면적을 산정하여 비교하고자 한다. 예측식 RLS-90과 2019년에 발표한 예측식 RLS-19의 큰 차이점인 차량 분류 방식을 중심으로 비교하였고, 소음측정을 위해 대상지점을 선정하였다. 실측값과의 비교를 위해 예측식에 따른 차량 구분을 소음예측프로그램에 기입하여 계산한 각각의 예측값과 차이를 비교·분석하였으며, 주간 소음도를 기준으로 한 소음지도를 작성하였다. 예측식 비교를 위한 소음예측프로그램으로는 SoundPlan(Ver. 9.0)을 이용하였다. 예측식 RLS-90과 RLS-19를 이용하여 실측 소음도와 비교하였을 때 예측식 RLS-19의 야간 시간대의 예측값과 실측값의 오차 평균은 예측식 RLS-90의 예측값 보다 실측값과 정확한 값을 보였으며, 두 예측식들 사이의 주간 시간대의 예측값과 실측값의 오차 평균은 미미한 차이를 보였다. 예측식 RLS-19를 이용하여 산정한 소음노출면적의 경우 주간 소음 환경기준을 초과한 면적은 예측식 RLS-90를 이용하여 산정한 소음노출면적보다 더 넓게 분석되었다. 현재 우리나라가 사용하고 있는 차량 구분을 소형, 대형 두 가지 종류로 구분하는 RLS-90 예측식의 예측값 보다 대형 차량을 세부적으로 분류한 RLS-19 예측식의 예측값이 실측값과 더 근사하다고 분석되었다. RLS-19로 도로소음예측 시 기존에 사용하던 RLS-90의 예측보다 더 강도 높은 소음대책을 마련할 필요가 있다고 판단되며, 대형차의 통행량이 더욱 많은 도로 및 오토바이의 통행량이 높은 도로 등 다양한 도로 상황에 따른 연구가 필요할 것으로 사료된다.
In this study, the difference between the two equations is derived by comparing and analyzing the measured data and the predicted values according to each predicted equation, and the noise exposure area through the noise map is calculated and compared between the road traffic noise prediction formula RLS-90 published in Germany in 1990 and the replacement regulation RLS-19 published in Germany in 2019. The vehicle classification method, which is a big difference between the predicted equation RLS-90 and the predicted equation RLS-19 published in 2019, was compared and the target point was selected for noise measurement. For comparison with the measured values, the difference was compared and analyzed with each predicted value calculated by entering the vehicle classification according to the predicted equation into the noise prediction program, and a noise map was prepared based on the daytime noise level. SoundPlan (Ver. 9.0) was used as a noise prediction program for comparison of the predicted equation. Compared to the measured noise using predictive RLS-90 and RLS-19, the error average of the predicted value and the measured value in the night time zone of predictive RLS-19 was more accurate than the predicted value of predictive RLS-90, and the error average of the predicted value and the measured value in the daytime between the two predictive equations showed a slight difference. In the case of the noise exposure area calculated using the predicted equation RLS-19, the area exceeding the daytime noise environment standard was analyzed wider than the noise exposure area calculated using the predicted equation RLS-90. It was analyzed that the predicted value of the RLS-19 prediction equation, which classifies large vehicles in detail, is closer to the measured value than the predicted value of the RLS-90 prediction equation, which divides the vehicle classification currently used in Korea into two types: small and large. When predicting road noise with RLS-19, it is judged that it is necessary to prepare more intensive noise measures than the previously used RLS-90 prediction, and research on various road conditions such as roads with more traffic for large cars and roads with higher traffic for motorcycles is needed.
In this study, the difference between the two equations is derived by comparing and analyzing the measured data and the predicted values according to each predicted equation, and the noise exposure area through the noise map is calculated and compared between the road traffic noise prediction formula RLS-90 published in Germany in 1990 and the replacement regulation RLS-19 published in Germany in 2019. The vehicle classification method, which is a big difference between the predicted equation RLS-90 and the predicted equation RLS-19 published in 2019, was compared and the target point was selected for noise measurement. For comparison with the measured values, the difference was compared and analyzed with each predicted value calculated by entering the vehicle classification according to the predicted equation into the noise prediction program, and a noise map was prepared based on the daytime noise level. SoundPlan (Ver. 9.0) was used as a noise prediction program for comparison of the predicted equation. Compared to the measured noise using predictive RLS-90 and RLS-19, the error average of the predicted value and the measured value in the night time zone of predictive RLS-19 was more accurate than the predicted value of predictive RLS-90, and the error average of the predicted value and the measured value in the daytime between the two predictive equations showed a slight difference. In the case of the noise exposure area calculated using the predicted equation RLS-19, the area exceeding the daytime noise environment standard was analyzed wider than the noise exposure area calculated using the predicted equation RLS-90. It was analyzed that the predicted value of the RLS-19 prediction equation, which classifies large vehicles in detail, is closer to the measured value than the predicted value of the RLS-90 prediction equation, which divides the vehicle classification currently used in Korea into two types: small and large. When predicting road noise with RLS-19, it is judged that it is necessary to prepare more intensive noise measures than the previously used RLS-90 prediction, and research on various road conditions such as roads with more traffic for large cars and roads with higher traffic for motorcycles is needed.