Artificial neural network modeling for road traffic noise prediction

Several attempts have been made by the researchers to predict and model urban road traffic noise mathematically and statistically. There has been a lot of interest in the new techniques for analyzing data. Neural networks offer a new strategy with enormous potential for many tasks in the domain of geospatial planning. ANN technique for modeling provides smaller errors in comparison to other classical methods. Neural networks have been applied to many interesting problems in various areas including road traffic noise prediction. In the present study an attempt has been made to explore the application of neural networks to road traffic noise prediction in Lucknow city, capital of Uttar Pradesh, India. Traffic volume, speed and noise level data were collected at ten selected locations. For development of model, classified traffic volume (Car/Jeep/Van, Scooter/ Motorcycle, LCV/ Minibus, Bus, Truck and 3-Wheeler), traffic speed on both sides of the road were taken as input data. Output was estimated as L_eq. Performance of the model was tested by root mean square error (RMSE), mean absolute error (MAE) and coefficient of correlation (R). It was observed that there is no significant difference between observed and predicted noise levels in the present case, indicating the accuracy of model.