• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1273-1279
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• 2001

Precise highway traffic noise simulation and reduction require the accurate data for sound power levels emitted by vehicles, varied to road surface, traffic speed, vehicle types and makers, different from countries to countries. In this study, we have elaboratively measured domestic 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 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 shows little difference within 1 dB.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.12
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• pp.1191-1198
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• 2012

Highway traffic noise is the one of the most frequent pollutant source of environmental claims in Korea for the last 10 years. For abating the noise from the highways, characteristics of highway noise source should be investigated and identified before performing the abatements. Highway noises are affected by traffic volume, vehicle types and speed, tyre and pavement types. In this study, highway noises which measured from different pavements have been analyzed and compared. Especially, the noise from the asphalt concrete pavement, cement concrete pavement and low-noise pavement have been measured simultaneously at the same traffic condition and compared each other. Hopefully, the data of the study may be used for abating highway noise and further studies.

• Journal of Environmental Science International
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• v.16 no.12
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• pp.1393-1399
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• 2007

This research helps you understand the road traffic noise levels by using a noise map. We have observed the change of the road traffic noise levels around $07:00{\sim}08:30\;and\;22:00{\sim}23:00$ using the noise map in the city. The road traffic noise level is very high both at noon and at night around a beltway and an interchange that is linked with a highway. It seems that the main route of so many vehicles, which are at neighboring cities such as N city and D and H districts and which avoid traffic jams in the city, is the beltway and interchange. The road traffic noise level of a nearby express bus terminal, railroad station, and airport is more than 75 dB at noon and 65 dB at night. The road traffic noise level of G city at night is observed to be more than 55 dB. The noise levels of a residence area and a university are higher than a road with high noise levels when the commuters drive to work. The end of the day exceeds 11 o'clock because of a culture level of development that arouses spare time, eating out, adults' drinking culture, nightlife of the youth, etc. Therefore, the road traffic noise level is high during late night hours, and it exceeds regulatory guidelines(55 dB(A)). It also damages the residence area that is located near the road.

• Journal of the Korean association of regional geographers
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• v.19 no.3
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• pp.541-550
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• 2013

Noise is defined to be unwanted (or undesirable) sounds, and it can be subdivided into occupational and environmental noises. The occupational noise is largely emitted from workplaces, but the environmental noise is produced from various sources such as transportation, construction, and even neighborhood. Transportation, particularly road vehicles, has been recognized to play a major role in emitting environmental noise and having adverse effects on urban communities. Considering this issue, this research investigated how severe the impacts of road traffic noise on a variety of land uses in a highly urbanized area, Fulton County, Georgia, United States. Daytime and nighttime road traffic noise maps were derived with Traffic Noise Model that was developed by Federal Highway Administration at United States Department of Transportation. Individual noise maps were overlaid on three-tiered land use type maps in order to evaluate the magnitude of noise impacts on the study area, as a noise exposure assessment. This study found that many land use parcels and buildings were exposed to noise levels exceeding the guideline values of the World Health Organization and the Atlanta Code of Ordinance. Therefore, it would be required to take an action that can protect many urban residents from ill effects of road traffic noise.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2003.11a
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• pp.89-93
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• 2003

The ambient noise in modem society is the factor to give people serious influence. Noise barriers installation is mostly used in the apartment estate to protect countermeasure about the road traffic ambient noise. The purpose of this study is to suggest reference data fur design of traffic noise reduction in apartment estate. It finds out how the traffic noise is measured about a noise barriers and physical characteristics. The 57dB was measured from the noise barriers in a 2m point. An ambient noise's of the part to belong noise attenuation is big at the sound arresting area of the noise barriers as the distance far.

• Journal of Environmental Health Sciences
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• v.49 no.6
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• pp.353-361
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• 2023

Background: Noise has been linked to an increased risk of various health issues, including stress, anxiety, insomnia, and cardiovascular disorders. However, research on the relationship between residential traffic noise and mental health in South Korea is limited. Objectives: This study aims to examine the correlation between regional traffic noise levels and mental health using an ecological study design. Methods: In this ecological study, all data were collected from the Korean Statistical Information Service. Traffic noise data in residential areas and the prevalence of mental health indices in 44 cities with noise monitoring stations were obtained. Pearson correlation coefficients were calculated to assess the relationship between regional traffic noise levels and mental health indices. Results: Daytime traffic noise levels were found to have a positive correlation with stress perception (r=0.428, p=0.004) and showed a borderline positive correlation with depressive symptom prevalence (r=0.273, p=0.073). In cities with airports, significant correlations was observed between 24-hour average noise levels and depressive symptoms (r=0.604, p=0.010), whereas this relationship was not observed in cities without airports (r=0.048, p=0.813). Multiple regression analysis, which adjusted for factors such as age, gender ratio, smoking, obesity, unemployment, urban size, and the presence of an airport, confirmed the relationship between 24-hour average noise levels and stress perception (beta=0.287, p=0.024). Conclusions: This study identified correlations between regional traffic noise levels and depressive symptoms or stress perception in adults, with a more pronounced effect in areas with airports. These findings suggest a potential impact of residential traffic noise on mental health, highlighting the importance of preventive measures like noise management in urban planning to promote mental well-being.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.10
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• pp.1075-1082
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• 2009

Using GIS data of C-si as basic data when making noise map of road traffic, we estimated exactly the noise excess areas and consequently suggested the population and the area exposed to road traffic noise accurately. We made 3D noise map to assess regional distribution of noise quantitatively. The noise map consists of noise prediction model based on data base such as traffic volume and speed changes for estimating quantitatively the noise and 3D urban space model which includes locations of noise sources, 3D buildings, topography and roads. We made noise standard map according to land use conditions and compared this map to road traffic noise map, and consequently made excess noise map. Using excess noise map, we assessed areas which exceed environmental noise level standards and noise guidelines quantitatively and effectively through GIS spatial analysis, and consequently more accurate noise exposed area and noise exposed population could be estimated. To show buildings' outer walls noise exposure, we analyzed 3D urban noise distributions using 3D-analysis of GIS.

• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.277-282
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• 2006

Road traffic noise is not produced by any one factor rather occurs as a composition of various factors. Its occurrence is made by running engine noise, tire frictional, and exhaust noise etc. The quality of the noise depends on the size of the vehicles, rotation and engine speed, vehicle load, package state of the road and incline etc. The occurrence of noise level of heavy trucks appears louder than smaller vehicles and the noise levels produced differs according to speed and load etc between similar size vehicles. Other factors such as traffic density, average speed, mixing rate of heavy vehicles, and the distance between vehicles also generate road traffic noise. In this paper we examine 2, 4, and 6-lane roads in Jeonju. Consequently, this study examined the means used to measure road traffic noise. It was found that when there is a large traffic density and the average velocity is below 70 km/hr, the noise level could receive a relative proper value by the current measuring means. But in the case of night-time, it was found that the current measuring method is inapposite.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.475-478
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• 2007

In this study, we estimated the effect of the traffic noise in urban area using the LiDAR data. The propagation of noise has a strong relationship between distance and shape of surface. Therefore, it is necessary to consider the distribution of buildings for estimating noise assessment in urban area because noise propagations will be affected by buildings. For this, we were developed DEM and DBM using the LiDAR data in order to analyze the propagation of traffic noise precisely in urban area. The level of traffic noise were calculated by investigating the real volume of traffic in study area. The SoundPLAN S/W and RLS90 algorithm was used for traffic noise assessment.