• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1140-1143
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• 2007

The purpose of this study is the improvement of the prediction model of highway noise. It includes the measurement and analysis of predicted noise levels by various programs in types of road and environments. The results of the measurement are compared with the noise levels predicted by improved highway noise prediction model and domestic prediction models, (Improved highway noise prediction model was considered ASJ-2003, ISO-9613 part2 and noise power of road surface types at Korean highway road.)

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.856-857
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• 2014

국내에서 이용되는 예측모델은 국립환경과학원식, 도로공사의 HW-NOISE, KHTN, 소음지도에 이용되는 외국의 RLS90, NMPB 등이 있다. 이러한 예측모델은 예측 방법 및 표현에 따라 예측식 2D(국립환경과학원식, HW-NOISE)와 3D로 예측(KHTN, RLS90, NMPB 등)으로 구분할 수 있다. 본 연구는 도로 주변 공동주택에서의 소음실측 및 예측식별 예측값을 통하여 예측식의 오차 및 오차의 원인을 분석하고 예측식의 적용방법에 대하여 고찰하였다.

• Journal of Environmental Policy
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• v.11 no.3
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• pp.49-65
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• 2012

Road traffic noise is closely related to road traffic environment, including traffic volumes by type and travel speed. In this study, we stated some issues and analyzed the relationships between road traffic noise and traffic flow characteristics. First, in attempt to find the answer to the question "When does the loudest traffic noise occur?" we reviewed the issue in the terms of traffic flow. As a result of analyzing level of service through Korea Highway Traffic Noise model, the actual maximum noise occurred in level of service D rather than level of service E, on the capacity state. It shows that maximum noise would be most likely to occur right before and after the peak hours. Second, this paper was looking for the method of a more easy and accurate traffic speed estimation to predict traffic noise. This paper proposed sketch planning techniques of speed-volume curve by level of service on Korea Highway Capacity Manual. As a result of trend line modeling, it was judged that quadratic form is a suitable function, and coefficient of determination ($R^2$) was higher than 0.96, respectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.1143-1151
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• 2013

Reasonable methods are needed to predict the noise level of new/existing roads and to select means of noise reduction. In this study, the noise reduction effects of both soundproof walls and quiet pavements were predicted. The noise reduction effects of quiet pavements were predicted by measurement data obtained using the CPX method in test pavements. The noise reduction effect was predicted by KHTN program when applied to soundproof walls and quiet pavement. As a result, the predicted noise level was similar to the measured one. The design method was suggested by an economic analysis using noise benefit of predicted noise reduction. The research suggests that the optimum alternative has to be determined using noise prediction method and life-cycle cost analysis.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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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.

• International Journal of Highway Engineering
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• v.17 no.6
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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.