Recent advancements in the challenges and strategies of globally used traffic noise prediction models

Environ Sci Pollut Res Int. 2022 Jul;29(32):48168-48184. doi: 10.1007/s11356-022-20693-1. Epub 2022 May 18.

Abstract

It is the need of an era to develop efficient traffic noise prediction models with optimum accuracy. In this context, the present work tries to comprehend the performance-related potential parameters based on earlier published articles worldwide that are responsible for deviation in noise values for different traffic noise prediction models and find out critical gaps. This study reviewed the process involved in source modeling and sound propagation algorithms, applicability, limitations, and recent modification in 9 principal traffic noise prediction models adapted by different countries all around the globe. The result of this review shows that many researchers had carried out comparative analysis among various traffic noise prediction models, but no emphasis was made on the recent modifications, limitations associated with those models, and strategies involved without ignoring the propagation and attenuation mechanism in the developing phase of these models. The findings of this study revealed that the major challenge for any traffic noise prediction model to be efficient enough is the inclusion of all the factors responsible for the generation and deviation of traffic noise before reaching the receiver. These responsible factors include a factor for source emission, sound propagation and attenuation, road characteristics, and other miscellaneous factors such as absorption characteristics of building facades, honking, and dynamic behavior of traffic. This study adds to the broader domain of research and will be used as reference material for future traffic noise modeling strategies.

Keywords: CRTN; FHWA; Leq; MITHRA; Principal traffic noise models; RLS90; Sound propagation.

Publication types

  • Review

MeSH terms

  • Environmental Monitoring
  • Forecasting
  • Models, Theoretical
  • Noise, Transportation*