Abstract Road traffic noise is a significant environmental risk factor to the human health, resulting in the necessity for accurate noise prediction models with less uncertainty. Several standards and methods are available in commercial noise modeling software to predict noise levels from road traffic noise, such as ISO 9613-2, CNOSSOS-EU, RLS 19, NMPB2008, RMG2012 and Nord2000. However, many of these standards were developed for specific national conditions and thus have limitations in wider applications. For instance, the RLS 19 standard is most suitable for the German road network, the NMPB2008 standard is tailored to French conditions, the RMG2012 is Dutch and the Nord2000 standard is predominantly applied in Northern Europe. Similarly, the use of CNOSSOS-EU is limited to European Union member states that have transposed the method into national law, as recommended by the Environmental Noise Directive (END), due to varying road surface characteristics across countries. In contrast, the ISO 9613-2 standard, entitled “Acoustics – Attenuation of sound during propagation outdoors – Part 2: Engineering method for the prediction of sound pressure levels outdoors” provides a general engineering framework for estimating noise levels from several sources, such as road traffic noise. This paper presents the results of a thorough investigation in which predicted noise levels from ISO 9613-2 and CNOSSOS-EU models were compared with measured data for a road section with relatively low traffic volume. The purpose of this study is to identify the modeling approach that delivers the most realistic predictions, offering guidance on selecting appropriate methods and techniques for similar traffic conditions. The findings contribute to a better understanding of the applicability of the evaluated methods for noise modeling of roads with low traffic volumes, highlighting their respective strengths and limitations. This comparison can support environmental authorities and acoustic consultants in selecting the most suitable prediction method for local and national assessments, and it provides a basis for improving model calibration and adaptation in future studies.
Odeh et al. (Thu,) studied this question.