PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Artificial neural network analysis of the relationship between roadtraffic noise and air pollutants and urban form indicators
Phillip KIM 1; Hunjae RYU 2; Jong June JEON3; Seo IL CHANG 4; Nokil PARK 5
1

Department of Energy and Environmental System Engineering, University of Seoul, Korea
2

Korea Educational Environments Protection Agency, Korea
3

4

Department of Statistics, University of Seoul, Korea

School of Environmental Engineering, University of Seoul, Korea
5

Atlanta Regional Commission, USA

ABSTRACTRoad-traffic noise and air pollutants have adverse effect to urban dweller’s health and life quality. For
management of the noise and air pollutants, noise and air pollution maps can be used to provide quantitative
information of noise and air pollution exposure levels. In this study, the more efficient method of noise andair pollution mapping was developed statistically. Noise and air pollutants’ exposure change by small-scaled
alteration of urban planning can be predicted by the method. The relationship between road-traffic noise level
and air pollutants and urban forms for roads, buildings and land-use was analyzed by artificial neural network
analysis. The selected representative urban form indicators are road-related(traffic volume, speed), buildingrelated(floor space index, ground space index), and land-use-related indicators. The artificial neural networkmodel was optimized by adjusting the number of hidden nodes and layers. The 2/3 of data sets extracted froma region was used for the model development to select the model with the least prediction error. The selectedmodel was applied to the remaining 1/3 of data sets for verification. The result from the artificial neuralnetwork model were compared with that from engineering model
Keywords: Artificial neural network, Road-traffic noise, Urban form indicators

1. INTRODUCTION
Noise and air pollutants that could adversely affect the life and health of urban habitants share roadtraffic as a common source. Roads are social infrastructures that is essential for the maintenance anddevelopment of society. Therefore, it is practically impossible to reduce noise and air pollutants due
to reduction or elimination of roads. A noise map is created using the road-traffic noise prediction
model which takes into account the noise level emitted from the road and the processes in which thenoise is absorbed, reflected and diffracted in the propagation path. And air pollutants map is createdusing emission level of air pollutants and dispersion model. The management of road-traffic noise andair pollutants is based on using of noise map and air pollutants map.
But creating noise map and air pollutants map requires verification through in-situ measurement
and using considerable computation resource in the calculation process. And it needs a lot of cost,time and manpower in the creation process. So, it is necessary to research how to estimate road-traffic
noise level and air pollutants concentration in urban area with minimum cost, time and manpower. In
1rlavlfflq154@uos.ac.kr
pgryuno1@schoolkeepa.or.kr
3
jj.jeon@uos.ac.kr
4schang@uos.ac.kr
5npark@atlantaregional.org
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this study, the basic research was carried out to estimate the road-traffic noise level and air pollutants
concentration based on statistical method. Among various statistical methods, the artificial neuralnetwork method was used.
Kumar, et al.(2015) fitted an artificial neural network model based on measured road-traffic noiseand road information(traffic volume, heavy vehicle ratio and traffic speed). Predicted road-traffic
noise level of artificial neural network model is similar to measured road-traffic noise level than thatof linear regression model.(1) Garg et al.(2015) predicted road-traffic noise level based on traffic
volume by car types(car, two-wheelers, medium commercial vehicles, three-wheelers bus and trucks)
and traffic speed by car types(light vehicle, heavy vehicle) with artificial neural network model. Leq
and L10 were used as unit. The artificial neural network model showed higher explanatory power thanmultiple linear regression model.(2) In the study of comparing predicted road-traffic noise levels
based on conventional road-traffic noise prediction(UK, Northern Europe, Italy and France) andartificial neural network model, the results of artificial neural network model were most similar tomeasured noise level.(3) In the above research cases, the artificial neural network models were createdbased on measured noise level and road information(Traffic volume, speed and etc.) at a certain point.
As a result, predicted road-traffic noise levels by ANN model were similar to the measured roadtraffic noise levels compared than the conventional road-traffic noise prediction and linear regression.
However, it is difficult to predict the representative road-traffic noise level in urban area, because themodel reflects geographical and topographical characteristics of the certain point. Ryu, et al.(2014)
predicted road-traffic noise of urban area using statistical method. A system of grid cells was placedon the study area, and the representative values of road-traffic noise level and urban form indicators
were calculated in each grid cell. And the relationship between road-traffic noise and urban form
indicators was analyzed using spatial statistical analysis.(4) In the case of predicting air pollutants
spatial distribution, artificial neural network model was created based on road information(Traffic
volume, Heavy vehicle ratio) and meteorological information(temperature, wind speed, humidity andsun radiation). And the model predicted concentration of PM2.5 at various height of highway side.(5)In this study, the relationships between urban form indicators and road-traffic nose, air pollutants
were analyzed with artificial neural network model. And the critical urban form indicators that affectto road-traffic noise and air pollutant’s occurrence were analyzed.

2. DATA ANALYSIS AND DEFINITION
2.1 Gridding study areas
Gwangju metropolitan city in Republic of Korea and Fulton county in Atlanta, USA were selected
as study areas. Gwangju was chosen to analyze the relationship between road-traffic noise and urban
form indicators, and Fulton was chosen to analyze the relationship between air pollutants and urban
form indicators. Gwangju had total area of 501km2 and population of 1.46 million as 2017 when thenoise map was created. And Fulton had total area of 1380km2 and population of 1 million as 2015
when the air pollutants map created. A system of 200m×200m grid cells is placed on the study areas.
The number of valid grid cell of Gwangju and Fulton is 5,933 and 26,717, respectively. Figure 1 showsthe example of Gwangju city’s grid cell system application.

Figure 1 – Grid cell system application to Gwangju(c) Google Maps / Digital Globe / SK telecom

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2.2 Estimation of representative value of road-traffic noise level and air pollutants
The representative value of road-traffic noise level of a grid cell was calculated based on existing
façade noise map. The noise map of Gwangju was created by commercial noise mapping software,SoundPLAN 7.4.(6) The power averaged façade noise level of each grid cell was calculated anddefined as representative road-traffic noise level of a grid cell. And the representative value of air
pollutants of grid cell was calculated base on existing air pollutants map. The air pollutants map was
created by two software. The emission level was calculated by MOVES(Motor Vehicle Emission
Simulator) developed by US EPA(Environmental Protection Agency).(7) And the R-line 1.2 whichcalculate air pollutants dispersion in atmosphere was used to calculate representation value of air
pollutants concentration.(8) Carbon monoxide(CO) and PM2.5 concentrations were used as
representative value of air pollutants. Figure 2 shows the noise map of Gwangju. And figure 3 shows
the air pollutants map of Fulton.

Figure 2 – Road-traffic noise map of Gwangju

Figure 3 – Air pollutants map of Fulton6737

2.3 Estimation of representative value of urban form indicators
The 3D urban models of each city used to extract representative value of urban form indicators.
The models were created using ArcMap 10.3.(9). The urban form indicators fall into four categories,
population, building road and land-use-related.(10) Population density(Pd) was chosen as populationrelated urban form indicator. The data of Gwangju includes the population data, but the data of Fulton
did not include population data. So, in this study, the population-related urban form indicator was
excepted. Similarly as in our previous study, Ground Space Index(GSI) and Floor Space Index(FSI)
were chosen as building-related urban form indicators.(4, 10) Traffic volume(Q), heavy-vehicle
ratio(PH), traffic speed(V) and road area density(Ra) were chosen as road-related urban form
indicators.(4, 10) Land-use-related urban form indicators are consist of residential(LR),commercial(LC), industrial(LI) and green(LG) area ratios.(4, 10) The calculation formula anddefinition of urban form indicators were shown in Table 1.

Type

Buildingrelated

Roadrelated

Land-userelated

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Table 1 – Formula and definition of urban form indicators
Variable
Calculation formula
Definition of variables in formula
Ground space index(GSI, m2 /m2 )‫= ܫܵܩ‬σೖ ீೖ஺‫ܩ‬௞ : building coverage of building k‫ ܣ‬: area of each grid cell(40,000m2 )Floor space index(FSI, m2 /m2 )‫= ܫܵܨ‬σ௞ ‫ܩ‬௞ ‫ܨ‬௞‫ܣ‬‫ܩ‬௞ : building coverage of building k‫ܨ‬௞ : number of floors of building k‫ ܣ‬: area of each grid cell(40,000m2 )Traffic volume(Q, vehicles/h)ܳ=σ௜ ܳ௜ ܴ௜ܴ௜ܳ௜ : traffic volume of road i
ܴ௜ : length of road i

Heavy vehicle ratio(PH, %)ܲ‫= ܪ‬σ௜ ܲ‫ܪ‬௜ ܴ௜ܴ௜ܲ‫ܪ‬௜ : heavy vehicle ratio of road i
ܴ௜ : length of road i

Traffic speed(V, km/h)ܸ=σ௜ ܸ௜ ܴ௜ܴ௜ܸ௜ : traffic speed of road i
ܴ௜ : length of road i

Road area ratio(Ra, m2 /m2 )ܴܽ =σ௜ ܴ௜ ‫ݓ‬௜‫ܣ‬ܴ௜ : length of road i‫ݓ‬௜ : width of road i‫ ܣ‬: area of each grid cell(40,000m2 )Residential area
ratio
(LR)

‫= ܴܮ‬‫ܮ‬ோ௔‫ܣ‬‫ܮ‬ோ௔ : area of residential area‫ ܣ‬: area of each grid cell(40,000m2 )Commercial area
ratio
(LC)

‫= ܥܮ‬‫ܮ‬஼௔‫ܣ‬‫ܮ‬஼௔ : area of commercial area‫ ܣ‬: area of each grid cell(40,000m2 )Industrial area ratio(LI)‫= ܫܮ‬‫ܮ‬ூ௔‫ܣ‬‫ܮ‬ூ௔ : area of industrial area‫ ܣ‬: area of each grid cell(40,000m2 )Green area
ratio(LG)‫= ܩܮ‬‫ீܮ‬௔‫ܣ‬‫ீܮ‬௔ : area of green area‫ ܣ‬: area of each grid cell(40,000m2 )3. ARTIFICIAL NEURAL NETWORK ANALYSIS
3.1 Model development
Artificial neural network method was used to analyze the relationship between urban form
indicators and road-traffic noise, air pollutants. The whole dataset of Gwangju which is consist ofurban form indicators and road-traffic noise level, were divided two, training dataset and test dataset.
The artificial neural network model for analyzing relationship between urban form indicators androad-traffic noise were fitted by training dataset. And explanatory power of the model was verified bytest dataset. The number of training and test dataset is 3,955 and 1,978. In the process of fitting
artificial neural network model, training dataset was divided into training and verification dataset.
Difference between road-traffic noise level predicted by artificial neural network model and that ofnoise map was calculated by mean squared prediction error(MSE). Similarly, to analyze therelationship between urban form indicators and air pollutants, the whole dataset of Fulton county was
divided into training and test dataset. The number of training and test dataset is 17,811 and 8,906.
Error analysis was also performed as in the case of noise.
Artificial neural network model is based on mathematical calculations finding the point where theerror between existing data and predicted data is minimized. Because it is through complicatedcalculation process, statistical analysis software R, which could be performed automatically, wasused.(11) R has various package to create artificial neural network model. The nnet package which
could create artificial neural network model including single hidden layer was used.(12)The optimized artificial neural network model could be fitting by change of various parameters. In
this study, the number of hidden nodes, decay parameter which prevent over-fitting to test dataset
were changed. Maxit parameter which specifies the maximum number of repetitions in fitting process
was fixed to 200, and the other parameters were applied as default. The number of hidden nodes waschanged 1 to 50, decay parameter was changed e -2 to e 7 and 0.
In case of road-traffic noise, the artificial neural network model was optimized when the numberof hidden nodes was 48 and decay parameter was e -2 . And in case of CO, the number of hidden nodes
was 38, decay parameter was e -2 , and in case of PM2.5 the number of hidden nodes was 13, decay
parameter was 0. The example of finding minimum MSE were shown in Figure 4.

4. Result
4.1 Model application to test dataset
The three artificial neural network models(road-traffic noise, CO, PM2.5) were applied to test
dataset to analyze explanatory power. The correlation coefficient of road-traffic noise level based onnoise map and that of artificial neural network model was 0.74. In case of CO, the correlation
coefficient was 0.74 and in case of PM2.5 correlation coefficient was 0.75. The detailed correlation
coefficient and coefficient of determination were shown in Table 2.

Figure 4 - Mean squared prediction error of road-traffic noise by changing number of hidden nodes anddecay parameter

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Table 2 –Correlation coefficient and coefficient of determination
Correlation

R-squared

Adjustedcoefficient(r)(R2)

R-squared(R2adj)

Noise level

0.74

0.54

0.54

CO concentration

0.74

0.54

0.54

PM2.5 concentration

0.75

0.56

0.56

Figure 5 shows the comparison between predicted road-traffic noise level based on noise map androad-traffic noise level based on artificial neural network model.

Figure 5 – Comparison between noise levels from a noise map and ANN model
Figure 6 and 7 show the comparison between predicted CO and PM2.5 concentration based onnoise map and CO and PM2.5 concentration based on artificial neural network model.

Figure 6 - Comparison between CO concentrations from a noise map and ANN model

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Figure 7 - Comparison between PM2.5 concentrations from a noise map and ANN model

4.2 Relative importance of urban form indicators
The Garson algorithm was used to analyze the relative importance of urban form indicators on thegeneration of road-traffic noise and air pollutants. The NeuralNettools package which includes Garson
analysis was used.(13) In case of road-traffic noise, relative importance of urban form indicators wasin order of FSI, GSI and LI. In case of CO, relative importance of urban form indicators was inorder of FSI, V and LR. And in case of CO, relative importance of urban form indicators was in orderof LC, Ra and FSI. Figure 8 shows the relative importance of urban form indicators when predictroad-traffic noise level and concentration of CO and PM2.5 based on artificial neural network model.

Figure 8 – Relative importance of urban form indicators in road-traffic noise, CO and PM2.5 prediction

5. SUMMARY
The artificial neural network model was fitted to analyze relationship between urban form
indicators and road-traffic noise, air pollutants. Artificial neural network model which is prediction
road-traffic noise level’s explanatory power was 0.54. In case of CO concentration explanatory power
was 0.54 and in case of PM2.5 concentration explanatory power was 0.56. Road-traffic noise level,
CO and PM2.5 concentrations which were predicted by artificial neural network model shows linear
relationship with predicted result of nose and air pollutants map. As a result of analyzing relative
importance of urban form indicators on the generation of road-traffic noise and air pollutants, theimportance of urban form indicators was different by pollutant’s type. The most significant urban
form indicator was FSI

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ACKNOWLEDGEMENTS
This research was supported by Basic Science Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03035253)

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