Modelling traffic noise in a wide gradient interval using artificial neural networks

As classical traffic noise prediction models lack a deeper consideration of the impact of the gradient, the characteristics of longitudinal gradients from multiple roads were collected as data in the mountain city of Chongqing county, which was chosen as the entry point, to study the noise characteristics for a wide range of road gradients and to build a traffic noise prediction model based on artificial neural networks (ANNs). The field data consisted of traffic volumes, heavy-vehicle ratios, average vehicle speeds, road gradients, and corresponding equivalent sound pressure levels. An optimal ANN model was determined and compared with two classical models. The results demonstrated that a one-hidden-layer ANN model was suitable for traffic noise prediction in mountain cities and presented better predictive performance than the conventional models. The best-performing ANN model yielded a determination coefficient of 0.9447 and a mean-squared error of 0.2708 dBA. Moreover, this study confirmed that road gradients were significant for constructing traffic noise prediction models.