Evaluation and Modelling of Traffic Noise on the Asian Highway in Golestan National Park, Iran

Abstract

Abstract. The increasing number of vehicles on Iran's highways and major roads has led to an increase in noise levels. As a result, traffic is now considered a main source of noise pollution. This paper reports on the modelling of traffic noise levels in Golestan National Park, Golestan using vehicle data and other environmental features. For the evaluation of noise and the recording of independent environmental variables, Sampling stations were selected using a systematic-random method at 76 points at various distances and between 0-250 meters from the road. At each sampling point, traffic flow (number and speed of vehicles, number of horn beeps) was measured for 15 minutes from 8 am to 8 pm. Simultaneously other environmental variables were assessed, including the geometry of the road surface and location conditions .The best multivariable regression based on the correlation coefficient (R) and the coefficient of determination (R2) was achieved. The R-square (73%) and the adjusted R-square (68%) of the regression equation were 73% and 68% respectively. The results of modelling show that the most important variables affecting noise pollution are distance from the road, roughness coefficient, speed of medium-weight vehicles, relative humidity, and height and number of light vehicles. There is a negative correlation with distance from the road and noise pollution.The accuracy of the model was found to be about ±5 dB. Therefore, the model is suggested for the prediction of traffic noise on the Asian Highway in Golestan National Park.

Application of M5P Model Tree and Artificial Neural Networks for Traffic Noise Prediction on Highways of India

“…Thus, M5P model tree in figure 4 shows that traffic volume, road roughness, average building height, percentage of 4Wheelers, speed variance and no of lanes are most significant variables. These results are similar to the findings reported in literature [45][46][47]. 14 linear models LM1-LM14 are given in figure 5 and each model equation can be used for different set of conditions like LM1 can be used for prediction when traffic volume is less than 534.5 and IRI is less than 7.125m/Km and average building height is less than 5.25m [48].…”

Traffic Noise Prediction for Delhi-NCR using Multiple RegressionModelling Approach

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