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Related Experiment Video

Updated: Jun 24, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

Urban daytime traffic noise prediction models.

Elaine Carvalho da Paz¹, Paulo Henrique Trombetta Zannin

¹Laboratory of Environmental and Industrial Acoustics and Acoustic Comfort - LAAICA, UFPR - Federal University of Paraná, Jardim das Américas, 55-81531-990, Curitiba, Parana, Brazil. epaz@bol.com.br

Environmental Monitoring and Assessment

|April 9, 2009

Summary

New mathematical models accurately predict urban highway noise levels. These models utilize linearity and class intervals for improved assessment of traffic noise, offering a valuable tool for urban planning and environmental studies.

Related Experiment Videos

Last Updated: Jun 24, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

Area of Science:

Environmental Science
Acoustics
Urban Planning

Background:

Urban highways generate significant acoustic environments.
Accurate assessment of traffic noise is crucial for urban planning and public health.

Purpose of the Study:

To develop and validate mathematical models for predicting key sound levels from urban highways.
To assess the accuracy and characteristics of the new models compared to existing literature.

Main Methods:

In situ measurements of the acoustic environment.
Development of mathematical models for sound levels (L (eq), L (10), L (50), L (90)).
Statistical validation of generated models using traffic variables and sound level correlations.

Main Results:

Four groups of statistically validated mathematical models for daytime sound levels were generated.
The new models demonstrate accuracy comparable to existing traffic noise prediction models.
Key differentiating characteristics include model linearity and the application of class intervals.

Conclusions:

The developed mathematical models provide an accurate and effective method for assessing and predicting daytime traffic noise.
The linearity and class interval application offer distinct advantages over current models in the literature.
These findings support improved urban noise management strategies.