Effective analysis of noise levels due to vehicular traffic in urban area using deep learning with OALO model

Abstract

Nowadays, there has been an exponential grow in the number of vehicles moving on the roads, causing an unavoidable intensification of levels in the traffic noise. There is no second opinion on the fact the ever-zooming noise levels have adversely affected the health and welfare of a substantial section of society, especially those who are residing in the immediate vicinity of highways and urban roads. In this regard, a novel method intended for the improvement of the vehicular traffic noise prediction techniques namely the Deep Neural Network (DNN) is introduced. For optimizing the weight of DNN structure, we designed a meta-heuristic approach termed as the Oppositional based Antlion Optimization (OALO). Using the data of observed noise levels, traffic volume and average speed of vehicles, the noise parameters such as Equivalent continuous (A-weighted) sound level Leq and Percentile exceeded sound level, L10 are predicted. The predicted noise levels are compared with experimental and other existing prediction models. It is observed that the proposed DNN-OALO approach attains high accuracy and also accomplished a positive correlation between actual and predicted noise levels.