• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.101-104
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• 2007

All of noise prediction model have it's own features in the case of modeling conditions, so it is very important to know the features of each model case by case for a proper modeling, especially using at the Environmental Impact Assessment. For prediction of highway traffic noise and abating the noise by barriers, two kinds of prediction model, HW-NOISE, KHTN(Korea Highway Traffic Noise) has been mainly used in Korea. In this study, the features of these models were described at the same conditions. The properties of sound power from a road, diffraction characteristics from a barrier, sound pressure level decaying in each model were investigated. Using the results, it will be anticipated that the proper using of prediction models in the works of highway noise abating.

• 한국소음진동공학회논문집
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• 제12권8호
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• pp.581-588
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• 2002

Precise highway traffic noise simulation and reduction require the accurate data for sound power levels omitted by vehicles, varied to road surface, traffic speed, vehicle types and makers, different from countries to countries. In this study, we have elaboratively measured Korea highway traffic noise and parameters affecting noise levels at the nearside carriageway edge. From numerical simulation using the measured results for highway traffic noise, we propose not only two correction factors to enhance the accuracy of Korea highway traffic sound power estimation using ASJ Model-1998 but also its typical power spectrum according to road surface type. The measured and predicted highway traffic noise levels using the proposed sound power show little difference within 1 dB.

• 한국환경복원기술학회지
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• 제3권3호
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• pp.37-44
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• 2000

The objective of this study is on suggesting the simulation model of 10 selected plants for the traffic noise barrier through field experiment by computer. The field experiment was carried out at the traffic noise barrier of Honam highway. The results are as follows. 1. It was identified that Paederia scandens, Celastrus Orbiculatus, Lonicera japonica, Wisteria floribunda, Parthenocissus tricuspidata and Parthenocissus quinquefolia grows well vertically and takes 3 years in covering completely. 2. It was identified that Trachelospermum asiaticum and Hedera rhombea, evergreen Climber(vine), grows slowly in comparative with other deciduous Climbers(vine), but give drivers good landscapes in winter. So those have considerable value in the south region of Taejeon. 3. It was identified that Wisteria jl.oribunda, Lonicera japonica, Paederia scandens, Clematis mandschurica and Campsis grandijlora showed good view in flowering period. 4. It was identified that auxiliary materials for inducing growth were needed in other plants except Parthenocissus tricuspidata, Parthenocissus quinquefolia. 5. It was identified that subsequent research about the auxiliary materials for inducing growth and adequate planting distance of each plants is needed for actual application.

• 환경정책연구
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• 제11권3호
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• pp.49-65
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• 2012

도로교통소음은 자동차의 이동 즉 교통량과 통행속도등 도로교통환경과 밀접하게 관련되는바 본 논문에서는 교통류특성과 도로교통소음과의 관계를 분석하고자 몇 가지 문제를 제기하였다 첫째는 "최대 소음이 언제 발생하는가?"라는 문제로 교통량이 많은 첨두시간대에 과연 최대소음이 발생할 것인지를 교통류측면에서 검토하고자 하였다. KHTN 모델을 통해서 LOS를 분석한 결과 실제 최대 소음은 용량상태인 LOSE가 아닌 LOSD에서 발생하는 것으로 나타났다. 이는 실제 첨두시간대에는 교통량은 많지만 통행속도가 낮기 때문에 오히려 첨두시간을 전후하여 최대소음이 발생할 가능성이 높다는 것을 보여준다. 둘째는 교통소음예측에 이용되는 차종별 통행속도 추정에 관한 것으로, 보다 쉬우면서도 정확성을 지닌 통행속도추정기법을 찾고자 하였다. 이를 위해 스케치 플래닝 기법으로 도로용량편람의 '고속도로 기본구간의 속도 교통량 곡선과 서비스수준' 그래프를 이용하는 방법을 제시하였다. 각 설계속도별 추세선 모형식 산정결과 최적의 함수유형은 2차 다항식이며 각각 $R^2$가 0.96이상의 값을 가지고 있어 적합한 모형으로 판단된다.

• 한국도로학회논문집
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• 제13권3호
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• pp.111-119
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• 2011

본 연구에서는 실시간으로 개별 차량에 대한 소음 예측 시뮬레이션을 통해 매 순간 소음지도와 Lmax, Lmin 등을 얻을 수 있는 소음 예측 모델을 제시하였다. GIS 지형처리 기법을 이용해 공간 모델 처리 기법을 바탕으로 실시간 교통소음예측 시스템 모델을 제안하고, 객체지향기법을 이용해 개발하였다. 실시간 소음 시뮬레이션 모델을 이용하여 교통 흐름의 변화에 따라 소음레벨, 소음지도 변화, Lmin, Lmax값을 한눈에 파악하거나 비교할 수 있다. 현장에서 수행한 소음측정치와 예측치를 비교한 결과, 대부분 거리에서 Leq는 2~3db, Lmax는 3~4db 이내의 차이를 나타내어 소음예측의 신뢰성이 양호함을 확인할 수 있었다. 개발된 시스템을 이용해 민감도 분석을 수행한 결과, 대형차 비율, 차량 속도, 방음벽 높이에 따라 소음레벨의 차이를 보였고, 특히 방음벽 높이는 Leq나 Lmin보다 Lmax에 큰 영향을 미치는 것을 알 수 있었다.

• 한국도로학회논문집
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• 제17권6호
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• pp.11-18
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• 2015

PURPOSES : The purpose of this thesis is to evaluate the effectiveness of an active noise cancellation (ANC) system in reducing the traffic noise level against frequencies from the predictive model developed by previous research. The predictive model is based on ISO 9613-2 standards using the Noble close proximity (NCPX) method and the pass-by method. This means that the use of these standards is a powerful tool for analyzing the traffic noise level because of the strengths of these methods. Traffic noise analysis was performed based on digital signal processing (DSP) for detecting traffic noise with the pass-by method at the test site. METHODS : There are several analysis methods, which are generally divided into three different types, available to evaluate traffic noise predictive models. The first method uses the classification standard of 12 vehicle types. The second method is based on a standard of four vehicle types. The third method is founded on 5 types of vehicles, which are different from the types used by the second method. This means that the second method not only consolidates 12 vehicle types into only four types, but also that the results of the noise analysis of the total traffic volume are reflected in a comparison analysis of the three types of methods. The constant percent bandwidth (CPB) analysis was used to identify the properties of different frequencies in the frequency analysis. A-weighting was applied to the DSP and to the transformation process from analog to digital signal. The root mean squared error (RMSE) was applied to compare and evaluate the predictive model results of the three analysis methods. RESULTS : The result derived from the third method, based on the classification standard of 5 vehicle types, shows the smallest values of RMSE and max and min error. However, it does not have the reduction properties of a predictive model. To evaluate the predictive model of an ANC system, a reduction analysis of the total sound pressure level (TSPL), dB(A), was conducted. As a result, the analysis based on the third method has the smallest value of RMSE and max error. The effect of traffic noise reduction was the greatest value of the types of analysis in this research. CONCLUSIONS : From the results of the error analysis, the application method for categorizing vehicle types related to the 12-vehicle classification based on previous research is appropriate to the ANC system. However, the performance of a predictive model on an ANC system is up to a value of traffic noise reduction. By the same token, the most appropriate method that influences the maximum reduction effect is found in the third method of traffic analysis. This method has a value of traffic noise reduction of 31.28 dB(A). In conclusion, research for detecting the friction noise between a tire and the road surface for the 12 vehicle types needs to be conducted to authentically demonstrate an ANC system in the Republic of Korea.

• 대한환경공학회지
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• 제28권11호
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• pp.1198-1206
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• 2006

본 연구는 국내 도시개발 및 택지개발에서 많이 사용되고 있는 국립환경과학원식('99)에 대한 평가 및 검증을 수행하였다. 국립환경과학원식(NIER('99))은 두 변수 사이의 일차적인 관계가 얼마나 강한 정도를 제시하기 위해 결정계수($R^2$)와 표본 Pearson 상관계수(r)를 실측치와 예측치를 토대로 층별로 평가한 결과 1층 92.4%(r=0.96), 3층 38.7%(r=0.66), 5층 42$(r=0.65), 7층 7.5%(r=0.27), 10층 28.4%(r=0.53), 13층 35.6%(r=0.60), 15층 52.7%(r=0.73) 등의 결과를 보였다 선형 회귀를 통해 반응 변수(Y)와 예측 변수(X) 사이의 선형 관계를 조사하여 모형화하고 검증하기 위한 결과 1층을 제외한 모든 층에서 종속변수를 설명할 수 있는 기여율이 60% 이하로 회귀모형의 설명력이 상당히 떨어지는 것이 1.5 m 이상 높이에서 예측식 수립이 필요할 것으로 판단된다. 또한 등분산성을 토대로 잔차(residual) 대 적합지(fitted value)를 선택하여 예측식을 검증한 결과 1층의 경우 이상적 분포로 적합치에서 잔차들이 -5와 5 사이에 분포되어 있지만 1층을 제외한 나머지 층에 대해서는 이분산 혹은 비선형 분포로 잔차들이 -5에서 5사이에 분포되고 있는 것을 확인 할 수 있었다.

• 한국도로학회논문집
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• 제19권4호
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• pp.1-7
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• 2017

PURPOSES : This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors. METHODS: Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling. RESULTS : The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables. CONCLUSIONS : Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.