Traffic noise prediction using machine learning and monte carlo data augmentation: A case study on the Patiala city in India

Abstract

Traffic noise pollution is a serious problem in the modern urban areas especially to design new architecture of smart cities, highways, hospitals, schools for an efficient and healthy environment. To analyse this aspect, we have proposed a machine learning based prediction of sound pressure level on an original dataset collected in Patiala city in India. Vehicular traffic and sound pressure level data was collected on different sites in the city. A total of 502 data samples on the identified sites were obtained for the study. Further this data is augmented using Monte Carlo simulation to 10 times of its initial size and the Artificial Neural Networks (ANN) have been trained and compared with other Machine learning methods for the vehicular traffic noise prediction. The input parameters in the model are traffic volume Q, percentage of heavy vehicles P and the average speed of vehicles V and the output parameter is the equivalent continuous sound pressure level, Leq dB(A). The experimental results show ANN which is trained on the augmented data using Monte Carlo simulations outperforms other advanced methods making it an effective measure for vehicular traffic noise prediction to develop a healthy environment which is free of noise pollution.