Machine Learning Regressors Calibrated on Computed Data for Road Traffic Noise Prediction

Noise is one of the main pollutants in urban contexts, even if it is not perceived as severe as other pollutants. Transportation, specifically road traffic, accounts for most of the urban environmental noise, and its monitoring is very important and sometimes compelled by law. To do this, two different approaches are possible: a direct measurement campaign or a simulation approach. The so-called Road Traffic Noise Models (RTNMs) are used for this second scope. In recent years, noise assessment has also been experimented with through Machine Learning (ML) techniques: ML is very interesting mainly because it is usable in unusual road traffic conditions, like in the presence of roundabouts and/or stops and traffic lights, or more generally when the free flow aspect is not verified, and the classic RTNMs fail. In this contribution, a large and comprehensive study on four different ML regressors is presented. After careful hyperparameter tuning, regressors have been calibrated by using two different approaches: a classic train/test split on real road traffic data, and by using a computed dataset. Results show a quantitative and qualitative description of the outputs of the ML regressors functioning, and how their calibration by using computed data instead of real data can give good output simulations.