Predictive Modeling of Road Traffic Noise in Chennai Using Machine Learning Techniques

Noise pollution is a significant environmental concern, particularly in urban areas, where it adversely impacts public health and overall quality of life. Chennai, a bustling metropolitan city in India, faces elevated levels of road traffic noise due to its dense population, high vehicular density, and unique urban landscape. Addressing this challenge, this study develops a robust predictive model for traffic noise using advanced regression techniques, including polynomial regression, linear interpolation, and Gaussian Process Regression (GPR). The model leverages noise level data collected at 5-meter intervals, incorporating environmental factors such as barriers, wind, and humidity. The data were preprocessed, and relevant features were selected to ensure accurate noise level predictions at finer 1-meter intervals. Validation using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score confirms the model's reliability. The results demonstrate the potential of the proposed approach to assist urban planners and policymakers in formulating effective noise mitigation strategies for Chennai's unique urban environment.