Abstract
A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. Leq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.
Highlights
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The results obtained by using the four machine learning models are compared on the basis of r, R2, mean square error (MSE) and accuracy
A traffic noise prediction approach using machine learning methods has been presented, which considers the parameter of honking noise
Summary
An inevitable outcome of this continuous production and buying of these vehicles, especially the private ones, has been an increase in the noise and air pollution levels due to the hard traffic congestion, especially in critical areas of the cities [1,2,3]. The effects of air pollution are well known to the general public. To address the problem of environmental pollution, many predictive models and experimental studies have been developed for the traffic management as efficient solution to reduce vehicle journey times and, environmental impact. In [4], carbon dioxide (CO2 ) emissions and unnecessary fuel consumption are addressed in the framework of route management for autonomous vehicles in urban areas. There is a relatively lesser awareness of the harmful effects of traffic noise on the human population. Some of the adverse effects [5,6,7,8]
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