Traffic noise measurement, mapping, and modeling using soft computing techniques for mid-sized smart Indian city

Abstract

The present study presents an investigation into the utilization of artificial neural networks (ANN) and multiple linear regression model (MLR) for the prediction of traffic noise levels in various locations of Dhanbad city at varying intervals. Traffic noise indices measurements were carried out using sound pressure level meter (SLM). The prediction of equivalent A-weighted sound level (LAeq) and the sound level exceeding 10 percent of the time (L10) is carried out using various influencing factors such as number of cars, number of 2 wheelers, number of 3 wheelers, number of heavy vehicle, number of medium commercial vehicles, and traffic speed. The findings demonstrate the ANN model's proficiency in comparison of MLR model for providing precise prediction accuracy of traffic noise indices with a R2 of 0.94 for LAeq and 0.91 for L10. Furthermore, the frequency spectrum analysis reveals that high peaks were observed at lower frequencies ranging from 31.5 Hz to 50 Hz, middle frequencies from 500 to 800 Hz and higher frequencies from 3.5 kHz to 5 kHz. The noise maps at varying intervals revealed that most of the locations are having higher noise levels due to increase in vehicular movement. The study emphasizes on the potential significance of the proposed neural network-based prediction model in collaboration with noise mapping as a vital tool for the anticipation of traffic noise levels and the formulation of noise mitigation strategies in context of the mid-sized smart city like Dhanbad, India.