Statistical Road-Traffic Noise Mapping Based on Elementary Urban Forms in Two Cities of South Korea

Phillip Kim, Hunjae Ryu, Jong-June Jeon and Seo Il Chang
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Phillip Kim: Department of Energy and Environmental System Engineering, University of Seoul, Seoul 02504, Korea
Hunjae Ryu: Korean Educational Environments Protection Agency, Chungcheongbuk-do 28166, Korea
Jong-June Jeon: Department of Statistics & Graduate School, Department of Urban Big Data Convergence, University of Seoul, Seoul 02504, Korea
Seo Il Chang: School of Environmental Engineering & Graduate School, Department of Urban Big Data Convergence, University of Seoul, Seoul 02504, Korea

Sustainability, 2021, vol. 13, issue 4, 1-17

Abstract: Statistical models that can generate a road-traffic noise map for a city or area where only elementary urban design factors are determined, and where no concrete urban morphology, including buildings and roads, is given, can provide basic but essential information for developing a quiet and sustainable city. Long-term cost-effective measures for a quiet urban area can be considered at early city planning stages by using the statistical road-traffic noise map. An artificial neural network (ANN) and an ordinary least squares (OLS) model were developed by utilizing data on urban form indicators, based on a 3D urban model and road-traffic noise levels from a normal noise map of city A (Gwangju). The developed ANN and OLS models were applied to city B (Cheongju), and the resultant statistical noise map of city B was compared to an existing normal road-traffic noise map of city B. The urban form indicators that showed multi-collinearity were excluded by the OLS model, and among the remaining urban forms, road-related urban form indicators such as traffic volume and road area density were found to be important variables to predict the road-traffic noise level and to design a quiet city. Comparisons of the statistical ANN and OLS noise maps with the normal noise map showed that the OLS model tends to under-estimate road-traffic noise levels, and the ANN model tends to over-estimate them.

Keywords: road-traffic noise; urban forms; artificial neural network; statistical noise map (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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