Update of the Montevideo Traffic Noise Prediction Model

Urban areas continue to grow steadily, leading to increases in vehicular traffic and urban noise levels, which may affect public health and quality of life. In this context, it is essential to develop tools capable of estimating and analyzing traffic-related noise. This work presents an update of the urban noise prediction model developed for Montevideo in 2000, incorporating new data obtained from the acoustic map of Montevideo 2024–2025 and recalibrating its parameters to current traffic conditions. To this end, variables such as traffic flow and composition, vehicle speed, and the presence of anomalous acoustic events (e.g., horns, braking, or modified exhaust systems) were analyzed. A statistical analysis of the recorded sound levels was also carried out to identify anomalous acoustic events and evaluate their influence on noise levels. Based on this analysis, correction factors related to these events and to traffic speed were incorporated into the model. The results show that the updated model improves predictive performance compared with the original formulation, reducing the percentage of errors outside the ±3 dBA range. Overall, the model constitutes a useful tool for estimating urban noise in Montevideo during daytime hours for traffic densities between 200 and 3000 veh/h, although challenges remain due to the high variability of the acoustic environment.

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Publication Details

Journal
Urban Science
Published
2026-09-15
DOI
https://doi.org/10.3390/urbansci10090528
Primary Topic
Noise Effects and Management
Type
article
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Update of the Montevideo Traffic Noise Prediction Model

Alice Elizabeth González, Pablo Gianoli-Kovar, Valentina La Manna
Urban Science
Noise Effects and Management
article

Update of the Montevideo Traffic Noise Prediction Model

Alice Elizabeth González, Pablo Gianoli-Kovar, Valentina La Manna
article en

Abstract

Urban areas continue to grow steadily, leading to increases in vehicular traffic and urban noise levels, which may affect public health and quality of life. In this context, it is essential to develop tools capable of estimating and analyzing traffic-related noise. This work presents an update of the urban noise prediction model developed for Montevideo in 2000, incorporating new data obtained from the acoustic map of Montevideo 2024–2025 and recalibrating its parameters to current traffic conditions. To this end, variables such as traffic flow and composition, vehicle speed, and the presence of anomalous acoustic events (e.g., horns, braking, or modified exhaust systems) were analyzed. A statistical analysis of the recorded sound levels was also carried out to identify anomalous acoustic events and evaluate their influence on noise levels. Based on this analysis, correction factors related to these events and to traffic speed were incorporated into the model. The results show that the updated model improves predictive performance compared with the original formulation, reducing the percentage of errors outside the ±3 dBA range. Overall, the model constitutes a useful tool for estimating urban noise in Montevideo during daytime hours for traffic densities between 200 and 3000 veh/h, although challenges remain due to the high variability of the acoustic environment.

Urban ScienceVol. 10(9)
Universidad de la República de Uruguay (UY)
Sustainable cities and communities
Openalex Percentile: Top 7%
Noise Effects and Management
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Update of the Montevideo Traffic Noise Prediction Model — Alice Elizabeth González, Pablo Gianoli-Kovar, et al. · Urban Science (2026) | TGRS Research Map | TGRS