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Erratum: Combination of time series forecasting models with a microscopic and stochastic approach to predict road traffic noise [J. Acoust. Soc. Am. 159(3), 2754-2778 (2026)].

The Journal of the Acoustical Society of America·2026
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Combination of time series forecasting models with a microscopic and stochastic approach to predict road traffic noise.

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Improvement and Validation of a Smart Road Traffic Noise Model Based on Vehicles Tracking Using Image Recognition: EAgLE 3.0.

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A stochastic and microscopic model to predict road traffic noise by random generation of single vehicles' speeds.

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EAgLE: Equivalent Acoustic Level Estimator Proposal.

Claudio Guarnaccia1

  • 1Department of Civil Engineering, University of Salerno, I-84084 Fisciano, Italy.

Sensors (Basel, Switzerland)
|February 5, 2020
PubMed
Summary

A new method uses video analysis to estimate road traffic noise, overcoming limitations of traditional models. This approach, the Equivalent Acoustic Level Estimator (EAgLE), analyzes vehicle count, type, and speed from video feeds to predict noise levels accurately.

Keywords:
dynamic modelnoise controlroad traffic noise modelsensor concept

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Area of Science:

  • Environmental Science
  • Acoustics
  • Urban Planning

Background:

  • Road traffic noise is a significant environmental concern in smart cities, requiring accurate monitoring.
  • Traditional road traffic noise predictive models (RTNMs) often require extensive traffic data, limiting their large-scale application.
  • Existing monitoring methods may not be feasible for extensive measurement campaigns.

Purpose of the Study:

  • To introduce a novel, easily implementable methodology for estimating road traffic noise using video processing.
  • To develop the Equivalent Acoustic Level Estimator (EAgLE) for real-time noise assessment.
  • To enable large-scale noise monitoring using existing video infrastructure.

Main Methods:

  • Vehicle detection and classification (light vs. heavy) from video images.
  • Estimation of individual vehicle sound power levels and Sound Exposure Level (SEL) based on speed and type, using CNOSSOS-EU standards.
  • Calculation of the continuous equivalent sound level (Leq) by summing individual vehicle noise contributions.

Main Results:

  • The EAgLE technique demonstrated excellent agreement with measured Leq in preliminary tests on an Italian highway.
  • Performance comparison showed favorable results against other existing RTNMs.
  • The method effectively estimates noise levels using readily available video input parameters.

Conclusions:

  • The EAgLE methodology offers a practical and scalable solution for road traffic noise assessment.
  • The approach can leverage existing video surveillance networks for widespread noise monitoring in smart cities.
  • Further validation is recommended to confirm its potential for dedicated noise sensor development and smart infrastructure applications.