PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Spatial Analysis of the Impact of Urban Forms to Road-traffic Noisein a Highly Populated City
Taeho PARK1; Minho KIM2; Phillip KIM3; Seo Il CHANG4
1
2

University of Seoul, Republic of Korea

Sangmyung University, Republic of Korea
3

University of Seoul, Republic of Korea

4

University of Seoul, Republic of Korea

ABSTRACTRoad traffic noise is widely distributed in the cities and is more closely related to the urban resident people
than other noise sources. To understand the relationship between road-traffic noise level and urban formindicators such as ground space index, floor space index, road density, road length, traffic volume and so on,
spatial analysis was performed on highly populated city in South Korea. A system of 125m X 125m grid cells
is placed on the city. And the urban forms and noise levels were calculated in each grid. Noise levels werecalculated as energetical averages on each grid based on the noise map. Finally, it was simulated for changes
in road-traffic noise levels due to urban forms change situations such as land development, road construction,and land use change. This study can be a policy tool to consider residents' noise exposure or area exceeding
noise standard in the design process such as the redevelopment of highly populated urban city or the policy
decision making process for urban planning.
Keywords: Road-traffic noise, Urban form, Noise mapping, Ordinary least square,

1. INTRODUCTION
Urban noise is due to human activity. Human activities generate noise by driving cars, running
factories, using trains. In the case of highly populated cities there are relatively more human activities.
Especially, road-traffic noise is closely related to urban residential population compared to othernoise.
Much research has been done to understand the noise of the city accurately. The equation forpredicting noise is summarized. Since then, the computation power has been able to perform complex
calculations, and this equation has been applied to the entire city to produce a noise map that predicts
noise. However, for more complicated cities, there are more and more times to build and compute 3Dmodels for producing noise maps. Therefore, in this study, we devised a method to predict moreconcise and quick results by using city components such as floor space index, population density,
road length, road area, road density, and traffic volume.
In this study, the relationship between urban form and road-traffic noise level was derived from thehighly populated city of Korea (Gwangju metropolitan city) and the OLS(Ordinary Least square)model was created. The results were compared with the noise map results.

2. LITERATURE REVIEWIn a recent study, Salomons et al.(1) analyzed the changes in noise level due to urban form andurban density in Amsterdam and Rotterdam. In this study, As the GSI, FSI and population density
1

dreamer2442@uos.ac.kr

mhkim@smu.ac.kr
rlavlfflq154@uos.ac.kr
4schang@uos.ac.kr
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increase the noise level decreases and as the road density and traffic density increase, the noise level
increases. Silva et al.(2) analyzed the effect of the urban model on noise propagation. Analyzing 10
types of urban and 3 traffic scenarios. This study shows that noise levels increase as urban structures
become denser and open. Also, it is shown that the noise level is decreased when the urban form iscomplicated. Aguilera et al. (3) analyzed the relationship between urban form and road traffic noisein three European cities (Basel, Girona and Grenoble) through the Land Use Regression(LUR) model.
The model using only GIS(GIS-only model) showed coefficient of determination for 0.66 ~ 0.87.
The model with actual collection data of urban form(Best model) had coefficient of determination
for 0.7~0.89In this study, the OLS model, which is the most basic form of multiple regression analysis, isused to show the correlation between noise and Urban form. The OLS model is the simplest andcomputation time is short, but it can be difficult to estimate complex relationships.
For more accurate prediction, the influence range of the noise source(road) was expanded
considering the characteristic of the noise source. This is because noise affects not only theneighborhood but also the wider range. In a complex city, the factors related to the building that
insulated the noise are sufficient to analyze only the target area, but in the case of the noise source, itis necessary to expand the scope to the surrounding area.

3. Materials and Methods
3.1 Materials
The Gwangju metropolitan city, located in the south-west part of the Republic of Korea, isselected for this study. As of 2016, the total population of Gwangju metropolitan city was 1.517
million, the total length of roads was 27,022 km and the total city area for this study was 501.2 km 2 .
The data for this study were obtained from the National Geographic Information Institute, theGwangju Metropolitan Police Agency, the Gwangju Metropolitan City Office, and the Korean
National Statistical Office. Table 1 shows the types and parameters of the data used.
Table 1 – Summary of input data for this research
TypeParameter

Topography

Terrain elevation

RoadNetwork
VolumeVehicle

Speed
Type

Building
Population

Footprint

ProviderNational Geographic
Information InstituteNational Geographic
Information InstituteGwangju Metropolitan Police
AgencyGwangju Metropolitan CityOffice

Population for

Korean National Statistical‘-dong’ district

OfficeProduction year2106
2016

2016

2016
2016

3.2 Methods
Noise maps were produced for this study. Noise maps were produced with 3-Dimension using thedata presented in Section 3.1. As a result of the noise map, a façade noise map was derived. The results
were classified into 125m × 125m grid and the energetical average noise level in each grid was selected
as the representative noise level of each grid. Urban form was also classified by the same grid.

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Figure 1 shows the roads, buildings and sphere boundaries of Gwangju Metropolitan City. The redbox is a detailed representation of some areas. The red box is an enlarged view of some areas, and thesolid black line represents the 125m × 125m grid used in this study. Also, considering the propagation
characteristics of the roadside, a buffer area is set at the center of the grid. The road length index, roadarea index, and traffic speed of each buffer area are additionally used. The size of the buffer area is set
to 500m and 1,000m when the noise level is 1.5dB (A) and 1dB (A), respectively, when the receiver islocated 100m interval vertically on the road. Figure 2 shows the difference in noise level between thereceiver and the next receiver in 100m interval.

Difference with next point(dB(A))

Figure 1 – Square grid cells overlaid over the Gwangju metropolitan city.

4,543,532,52
1,510,500500

1000

1500

2000

Distance(m)Figure 2 – Noise difference with next point in 100m interval
The variables considered in this study are shown in Table 2. Ld was set as a dependent variable, andthe remaining variables were set as independent variables. The variables were excluded from the final
model when the variable was not significant or the variance inflation factor (VIF)> 7.5. In addition,
when the buffer area of 500 m and 1,000 m is set, a buffer area having a high coefficient ofdetermination of the model is selected even if the variables of both buffer area s are significant

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Table 2 – Explanation and unit for each variable of the initial statistical model
Variable

Explanation

Unit

Ld

Energetic average of noise levels during daytime

dB(A)

P

Population density

Person/m2

GSI*

Ground space index

m2/m2

FSI

Floor space index

m2/m2

Q

Traffic volume during daytime

Vehicle/hPH

Percentage of heavy vehicles during daytime%D*

Traffic density

Vehicle/kmRaRoad area index

m2/m2

Rl

Road length index

m/m

Ra,500*Road area in 500m buffer area

m2

Rl,500Road length in 500m buffer area

m2

S500

Traffic speed during daytime in 500m buffer area

km/h

Ra,1,000

Road area in 1,000m buffer aream2

Rl,1,000

Road length in 1,000m buffer aream2

S1,000Traffic speed during daytime in 1,000m buffer area

km/h

Note: * is the selected variables in the OLS model

4. Results
GSI, traffic density, and road area in 500m buffer area were found to be statistically significant.
In addition, the coefficients of determination in the 500m buffer area (R 2 = 0.473, adjusted R 2 =0.473) were higher than those of the 1,000m buffer area (R 2 = 0.458, adjusted R 2 = 0.457). The
created OLS model is shown in Table 3 below, and is summarized as Equation (1).
Table 3 – Results for ordinary least squares model
Variable

Estimated

t-ValueConstant

43.8114***349.557295

GSI*-3.392992***-6.708942

D*

0.550123***46.471753

Ra,500*0.000145***

71.453010

Note: *** indicates a statistically significant p-value(p < 0.01)

Ld  3.392992  GSI  0.550123  D  0.000145  R a,500(1)

Based on the estimated model, a noise map was created and compared with the existing noise map
results. The two results are shown in Figure 3 below. A scatter plot of the result is shown in Figure 4.
Comparing Figure 3- (A) and (B), the overall noise figure distribution is similar. In detail, theabsolute mean for the difference between the two noises is 5.88 and the skewness for the two noisedifferences (OLS model estimates - noisemap results) is 1.12. The noise map result is higher than theOLS model estimate. Noise map results are higher than OLS model estimates. The difference in6724

noise (OLS model estimation - Noise Diagnosis) among a total of 11,822 observations has a negativevalue of 6,349 and a positive value of 5,473. The coefficient of decision(R 2 ) for the two noises in
Figure 4 is 0.4735. The solid line in the figure is the case where the two noise levels are the same,and the dotted line is the trend line for the correlation of the two noise levels.

Figure 3 – Comparison of noisemap results(A) with OLS model estimates(B)Figure 4 – Scatter plot of the noise map results and the results of the OLS model estimates

5. CONCLUSIONSThis study deals with the method of estimating the noise level using the urban form as an alternative
method in a situation where the complexity of the city is increased and the noise map is difficult tomake due to the increase of the population. Numerous studies have been conducted on this method, andthere are many methods for improving the accuracy. In this study, we used OLS model, which is thesimplest model, and extended the influence range of road by setting buffer area at prediction point. Asa result, the coefficient of determination was 0.473. This decision coefficient was improved comparedwith 0.44 proposed by Ryu et al. (4). Also, as the results of Salomons et al.(1) and Ryu et al.(4)suggested, the tendency of the noise level to decrease with increasing GSI was confirmed.
The statistical noise estimation method using the Urban form can be a good way to find out thedistribution of urban noise easily and quickly. This methodology can be useful for decision -making
by enabling efficient information transmission without complicated simulations in preparation ofprojects such as planning or redevelopment of complex cities with many populations.

ACKNOWLEDGEMENTS

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This research was supported by Basic Science Research Program through the National Researc hFoundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01060891)

REFERENCES
1. Salomons E.M., Berghauser Pont M.. Urban traffic noise and the relation to urban density, form, andtraffic elasticity. Landscape and Urban Planning. 2012;108(1):2-16.
2. Silva L. T., Oliveira M., Silva J. F.. Urban form indicators as proxy on the noise exposure of buildings.
Applied acoustics. 2014;76:366-376.
3. Aguilera I, Foraster M, Basagaña X, Corradi E, Deltell A, Morelli X, Phuleria HC, Ragettli MS, Rivera
M, Thomasson A, Slama R, Künzli N.. Application of land use regression modelling to assess the spatial
distribution of road traffic noise in three European cities. Journal of Exposure Science & Environmental
Epidemiology. 2015;25(1): 97-105.
4. Ryu H., Pak I. K., Chun B. S. Chang S. I.. Spatial statistical analysis of the effects of urban form
indicators on road-traffic noise exposure of a city in South Korea. Applied acoustics. 2017;115:93-100.

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