The recently released FHWA traffic noise model (TNM) and prediction program provides many improvements over the existing STAMINA program. However, the quick adoption of TNM could be hindered by its more detailed input, including the site terrain, and its relatively long run times. This paper examines updating the vehicle noise coefficients in STAMINA so that its use might be extended while the traffic noise community is being trained on TNM and while TNM’s weaknesses are dealt with. STAMINA has a well-documented deficiency of overpredicting traffic noise levels by about 3 dBA. This deficiency might be overcome by updating the vehicle noise emission coefficients. A recent source of traffic noise data is the 1995 reference energy mean emission levels (REMEL) study, which provides vehicle noise data for TNM. This paper presents the results of fitting the REMEL vehicle noise data to the STAMINA vehicle noise equation with a logarithmic regression. These updated noise coefficients are then tested with four example problems to evaluate the effectiveness of updating the STAMINA vehicle noise levels alone. The predicted noise levels from the updated STAMINA as well as the original 1975 noise levels are compared to the FHWA-TNM 1.0 predictions. The results show that the updated STAMINA reduces the overprediction relative to TNM by about 1.5 dBA. Updating only the vehicle noise coefficients, however, has little effect on the prediction of insertion loss because the STAMINA diffraction model was unchanged.
Fitting the FHWA Traffic Noise Model Noise Emissions for Use in STAMINA 2.0
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 1670 , 1 ; 59-68
1999-01-01
Article (Journal)
Electronic Resource
English
Fitting the FHWA Traffic Noise Model Noise Emissions for Use in STAMINA 2.0
British Library Conference Proceedings | 1999
|Part 2 -- Noise - Fitting the FHWA Traffic Noise Model Noise Emissions for Use in STAMINA 2.0
Online Contents | 1999
|FHWA highway traffic noise prediction model
SLUB | 1978
|