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A basic neural traffic noise prediction model for Tehran's roads.

Sh Givargis1, H Karimi

  • 1HoushAfzar Research Institute, No. 56, Iranshahr St. Tehran 15816-15434, Iran. nirari6757@gmail.com

Journal of Environmental Management
|August 4, 2010
PubMed
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An artificial neural network model effectively predicts traffic noise levels (L(Aeq,1h)) near Tehran roads. This AI approach matches the accuracy of the established UK Calculation of Road Traffic Noise (CORTN) model.

Area of Science:

  • Environmental acoustics
  • Artificial intelligence in environmental science

Background:

  • Traffic noise pollution is a significant environmental concern in urban areas like Tehran.
  • Accurate prediction of sound pressure levels is crucial for urban planning and public health.

Purpose of the Study:

  • To develop and validate an artificial neural network (ANN) model for predicting hourly A-weighted equivalent sound pressure levels (L(Aeq,1h)).
  • To assess the performance of the ANN model against the UK Calculation of Road Traffic Noise (CORTN) approach for road traffic noise in Tehran.

Main Methods:

  • Utilized data from 50 sampling locations within 4m of carriageway edges on five Tehran roads.
  • Employed a data-driven approach, splitting data into training (60%), testing (20%), and holdout (20%) subsets for model development and validation.

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  • Applied non-parametric statistical tests, including Wilcoxon matched-pairs signed-rank and Kolmogorov-Smirnov tests, to evaluate model efficiency.

    • Main Results:

    • The developed ANN model demonstrated statistically sound performance for traffic noise prediction in Tehran.
    • No significant difference was found between the ANN model's prediction errors and those of a calibrated CORTN model.

    Conclusions:

    • Artificial neural networks offer a viable and statistically robust method for predicting road traffic noise.
    • The ANN model provides a competitive alternative to traditional methods like CORTN for noise assessment in urban environments.