Spatial statistical modeling of road traffic noise for supporting strategic regional planning

Road traffic, which is an essential element of urban form indicators, exposes people to noise and air pollution throughout the surroundings of modern people. Therefore, in order to attain the urban health equity, a strategic city planning that considers environmental risk stressors such as noise and air quality is required. In this study, the noise prediction model that can be used for the preliminary urban environmental planning was statistically estimated and the estimated model was simulated to the other cities at the regional scale. Using the façade noise map and the urban form indicators such as population density, building-related, traffic-related and land-use-related data in the Gwangju metropolitan city, Korea, the spatial autoregressive (SAR) model, which explains 91% of the road traffic noise, was estimated. Especially, the noise barrier variables were added to improve the applicability in the strategic regional planning, which were not included in the previous studies. This estimated model was simulated to Cheongju and Suwon cities in Korea. Although the errors were caused by the difference in the urban densities between the estimated city and the simulated ones, the results showed that the less density difference between them is, the more accurate simulated noise level is.