Traffic Noise Mapping in Urban 3D Area by Using GIS and CORTN Model

Urban communities have been developing and they are being industrialized. These developments have some benefits for these communities; however, they have created some significant problems. One of these problems in this area is traffic and road congestion and following that, noise pollution on urban areas. These days, noise pollution is one the considerable problems that the residents of crowded places in urban communities are dealing with. Noise pollution not only decreases the quality of urban life, but also its mental and physical side effects are lasting for years. Therefore, considering this issue, attentions must be paid to this topic and conducting research is necessary. The results of these researches can contribute to offering solutions and analyses for planning for controlling the noise pollution on urban communities. In this study we aim to present a 3D model of an urban area in which the amount of noise pollution caused by traffic on the surfaces of buildings is specified in the form of color maps with noise scales. For this, at first, a comprehensive analysis of the proposed models in the area of predicting noise was done. The models were CORTN،FHWA،CNR،RLS90،SCM1. Each of These models has calculating relations and characteristics unique to them. After analyzing these models from calculation and structural aspects, we chose the CORTN model for the predicting the noise. This model was chosen as the final model in predicting the noise due to its simplicity and calculability of all of its parameters and also its ability for calculating the noise scales in different heights. Then, with creating an arranged network of the areas in the 3D example in the level of region and doing three stages of refining on them, the amount of noise in all areas was calculated. These three steps of refinery were done for decreasing the extra and unnecessary points in a way that the remaining areas have logical long and wide distance from each other and be consistent of the outer surfaces of the buildings. Because for predicting the noise we just need those areas that are on the outer surfaces of the buildings and we do not need to predict the noise in other areas. With doing these three stages of refinery, the number of sample areas was decreased from 896000 to 11456. This reduction in the number of areas did not influence the final accuracy. However, because of the significant reduction, it increased the speed of calculation and the saving in time. It is worthy to note that all calculations related to predicting noise were conducted by through formulas in Microsoft Excel. Finally, after the calculating and predicting the noise in all sample areas, we use Kirging interpolation model to create interpolation maps of areas in the ArcGIS software and these maps were merged with the surfaces of the buildings by Sketch UP software. Then, the created model was moved to ArcGIS and finally, the built model was presented in it. The results of this study show the distribution of noise pollution on different surfaces. Knowing this awareness can help us in better planning for choosing the kind of usage of different floors in a building. Because we cannot decrease the number of cars in the urban communities these days and we are dealing with increasing number of cars, therefore, we must conduct such researches in order to deal with this kind of pollution an use them in designing and constructing the buildings.