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Related Experiment Video

Updated: Jun 16, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

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Published on: January 20, 2023

Efficient numerical modeling of traffic noise.

Erik M Salomons1, Han Zhou, Walter J A Lohman

  • 1TNO Built Environment and Geosciences, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands. erik.salomons@tno.nl

The Journal of the Acoustical Society of America
|February 9, 2010
PubMed
Summary
This summary is machine-generated.

An optimized numerical method accurately models traffic noise from line sources. This approach minimizes errors near sources, improving large-scale urban noise mapping efficiency.

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Area of Science:

  • Acoustics and Noise Pollution Control
  • Computational Physics
  • Environmental Engineering

Background:

  • Incoherent line sources, like road and rail traffic, are crucial for urban noise modeling.
  • Conventional numerical methods encounter significant errors at receiver positions near finite line sources.
  • These errors complicate the creation of accurate, large-scale urban noise maps.

Purpose of the Study:

  • To present an optimized numerical method for evaluating sound fields from incoherent line sources.
  • To address and resolve numerical errors in conventional methods, particularly near line sources.
  • To enhance the efficiency and accuracy of urban noise mapping.

Main Methods:

  • Distinguishing between point source and line source solutions for numerical integration.
  • Implementing proper segmentation of the line source (angular and linear).
  • Focusing on receiver positions close to the finite line source to mitigate errors.

Main Results:

  • The optimized method provides accurate sound field evaluations for incoherent line sources.
  • Numerical errors common in conventional methods are successfully avoided.
  • The technique is effective even at receiver positions near the line source.

Conclusions:

  • The presented optimized method offers a robust solution for modeling traffic noise.
  • It significantly improves the accuracy of noise mapping, especially in complex urban environments.
  • The method's efficiency in minimizing calculations makes it suitable for large-scale applications.