An emotional artificial neural network for prediction of vehicular traffic noise

Abstract

Road traffic is a leading source of environmental noise pollution in large cities, which greatly affects the health and well-being of people. A reliable method for the prediction of road traffic noise is required for monitoring and assessment of traffic noise exposure. This study presents the first application of the Emotional Artificial Neural Network (EANN), as a new generation of neural network method for modeling the road traffic noise in Nicosia, North Cyprus. The efficiency of the EANN model was validated in comparison with the classical feed-forward neural network (FFNN) using two different scenarios with different input combinations. In the first scenario, vehicular classification (the number of cars, medium vehicles, heavy vehicles) and average speed were considered as the models' inputs. In the second scenario, the total traffic and percentage of heavy vehicles were used instead of the classification where the input parameters were total traffic volume, average speed and percentage of heavy vehicles. Application of the EANN model in the prediction of road traffic noise could improve the efficiency of the FFNN, MLR and empirical models at the verification stage up to 14%, 35% and 37%, respectively. Classifying the traffic volume into sub-classes (in scenario 1) before feeding them into the models improved the performance of the EANN and FFNN models at the verification stage by 8% and 12%, respectively. Sensitivity analysis of the input parameters indicated that total traffic volume is the most relevant factor influencing road traffic noise in the study area followed by the number of cars, medium vehicles, heavy vehicles, average speed and percentage of heavy vehicles, respectively.