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Development of prediction models of transportation noise for roundabouts and signalized intersections.
- Source :
-
Transportation Research Part D: Transport & Environment . Feb2022, Vol. 103, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- • Various traffic noise prediction models for roundabouts and signalized intersections were developed. • The models were based on the calculation of road traffic noise (CORTN) method. • The general and type-specific noise models for intersections of different sizes were compared. • The intersection type-specific models fit the measured data better than the general models. Accurate transportation noise prediction models are needed by governments and researchers to predict traffic noise levels originating from roadways. This study aims to develop traffic noise prediction models appropriate for roundabouts and signalized intersections of different sizes. Noise measurements , traffic volumes, and different site characteristics were collected at eight two-lane and three-lane roundabouts and signalized intersections. A set of customized general and type-specific noise prediction models were developed and compared. The models were based on the calculation of road traffic noise (CORTN) method. The results indicated that intersection type-specific models fit the measured data for the different intersection types better than general models for the studied conditions. Using these models is expected to be advantageous in terms of predicting traffic noise at roundabouts and signalized intersections. The proposed models could help governments, policymakers, traffic engineers, and urban planners assess and implement necessary noise management and mitigation strategies. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13619209
- Volume :
- 103
- Database :
- Academic Search Index
- Journal :
- Transportation Research Part D: Transport & Environment
- Publication Type :
- Academic Journal
- Accession number :
- 155103347
- Full Text :
- https://doi.org/10.1016/j.trd.2022.103174