Machine Learning Regressors Calibrated on Computed Data for Road Traffic Noise Prediction Article Swipe
Domenico Rossi
,
Aurora Mascolo
,
Daljeet Singh
,
Cláudio Guarnaccia
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/make7040133
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/make7040133
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.
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- https://doi.org/10.3390/make7040133
- https://www.mdpi.com/2504-4990/7/4/133/pdf?version=1761987315
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- 46
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All OpenAlex metadata
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https://doi.org/10.3390/make7040133Digital Object Identifier
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Machine Learning Regressors Calibrated on Computed Data for Road Traffic Noise PredictionWork title
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enPrimary language
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2025Year of publication
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2025-11-01Full publication date if available
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Domenico Rossi, Aurora Mascolo, Daljeet Singh, Cláudio GuarnacciaList of authors in order
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https://doi.org/10.3390/make7040133Publisher landing page
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https://www.mdpi.com/2504-4990/7/4/133/pdf?version=1761987315Direct link to full text PDF
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goldOpen access status per OpenAlex
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0Total citation count in OpenAlex
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46Number of works referenced by this work
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