Modeling traffic noise in a mountainous city using artificial neural networks and gradient correction |
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Affiliation: | 1. School of Civil Engineering, Chongqing Jiaotong University, 66 Xuefu Rd., Nan’an Dist., Chongqing 400074, PR China;2. School of Transportation Engineering, Chongqing Jiaotong University, 66 Xuefu Rd., Nan’an Dist., Chongqing 400074, PR China;3. Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, 851 Neyland Drive, Knoxville, TN 37996, USA |
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Abstract: | Different regions have established traffic noise prediction models to adapt to their particular environmental characteristics. This paper aimed to develop a traffic noise prediction model for mountainous cities. In China, the traffic noise prediction model HJ 2.4-2009, which itself is based on the sound pressure level corrected for roadway gradients (RGs), has been receiving widespread acceptance. On the basis of the model in HJ 2.4-2009, the RG correction coefficient was proposed to modify the original model and a per-vehicle noise prediction model was built using a multilayer feedforward artificial neural network (ANN) model. The data collected from a municipal road of a hilly city, Chongqing, was used to train and validate the ANN model. The predictor variables comprised the per-vehicle noise value, vehicle type, vehicle velocity, and roadway gradient. The results showed that the modified HJ 2.4-2009 model incorporating the gradient correction coefficient achieved a significantly higher R2 for mountainous cities than the original model. Besides, the ANN-based noise prediction model achieved considerable accuracy improvement over the empirical predictive equations. |
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Keywords: | Traffic noise Gradient correction Per-vehicle noise Multilayer feedforward artificial neural network Mountainous city |
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