An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront. (January 2022) - Baccoli, Roberto; Sollai, Federico; Medda, Andrea; Piccolo, Antonio; Fadda, Paolo
Abstract: In this research study an adaptive recurrent artificial nonlinear neural network identification model has been developed and experimentally tested for dynamically predicting the traffic noise level L e q, 1 ′ with a time refinement of 1 min. The model has been successfully applied in three selected positions, representative of the waterfront in a Mediterranean port city. Several maritime cities are exposed to a wide range of road traffic fluctuations that negatively impact liveability in the area concerned. Large volumes of road traffic periodically access the port, dynamically affecting the acoustic scenario in neighbouring areas, especially in seaside towns during the tourist season. A signalized intersection, a roundabout, and a wide entrance to a vehicular underpass have been analyzed in the course of two characteristic periods, during which traffic ranged widely from normal to peak yearly intensity. Detailed traffic data for 15 road lanes and noise sequence regressors have been considered as input data sources. This exploratory investigation reveals a good predictive performance of the model developed, the prediction error of L e q, 1 ′ falling prevalently within the range ±0.5 dB. The experimental profile of L e q, 1 ′ is well reflected by the simulated sequence, and the auto and cross correlation functions confirm how well the identified neural model is able to explain the functional dependence underlying the experimental observations. Highlights: An adaptative ANN model for short-term prediction of traffic noise is developed. A NARX architecture simulates the data generation mechanism of traffic noise event. A waterfront context with marked seasonal traffic variations in port activities is considered. The model successfully predicts L e q, 1 ′ ( τ ) during off- and peak season traffic volume. Simulations with a resolution of 1 min and prediction error < ±0.5 dB are performed.