Discussion of “Improving Traffic-Noise Model Insertion Loss Accuracy Based on Diffraction and Reflection Theories” by Ning Shu, Louis F. Cohn, Roswell A. Harris, and Teak K. Kim

2008 ◽  
Vol 134 (9) ◽  
pp. 391-392
Author(s):  
Aaron L. Hastings ◽  
Judith L. Rochat
Keyword(s):  
Insertion Loss ◽  
Traffic Noise ◽  
Noise Model

Florida Noise Barrier Evaluation and Computer Model Validation

2003 ◽  
Vol 1859 (1) ◽  
pp. 72-78 ◽  
Author(s):  
Roger Wayson ◽  
John MacDonald ◽  
Ahmed EI-Assar ◽  
Win Lindeman ◽  
Mariano Berrios
Keyword(s):  
Insertion Loss ◽  
Traffic Noise ◽  
Sound Level ◽  
Noise Model ◽  
Barrier Method ◽  
Noise Barriers ◽  
Zone Length ◽  
Sound Levels ◽  
Noise Barrier ◽  
Source Level

The results of a project that investigated the effectiveness of in situ noise barriers in Florida are presented. The prediction accuracy of the FHWA Traffic Noise Model (TNM) is compared with STAMINA 2.0 and 2.1 (Florida-specific). A total of 20 barrier sites were visited during a 3-year period that resulted in 844 discrete 20-min equivalent sound level (Leq) measurements behind the barriers. Barrier insertion loss was determined using the ANSI indirect barrier method. A methodology was developed to estimate shadow zone length created behind highway noise barriers. All of the barriers tested were effective (>5 dB:LAeq insertion loss at distances equivalent to the first row of homes, where LAeq is the A-weighted Leq) except one site because of marginal additional shielding from a berm–barrier combination. Only three sites had an insertion loss of less than 5 dB at distances representative of the second row of homes. Overall, measurements indicate that the barriers provide substantial sound level reduction for residents along the highway. TNM was the best prediction model when considering all test sites; however, the STAMINA models were more accurate at predicting source level. TNM predictions using the Average pavement input overpredicted the reference sound levels measured at these sites. TNM predictions using the OGAC (open-graded asphalt concrete) input were improved (under 2 dB:LAeq of error) over those using the Average pavement type input. This result is expected because Florida uses an open-graded asphalt friction mix.

Improving Traffic-Noise Model Insertion Loss Accuracy Based on Diffraction and Reflection Theories

2007 ◽  
Vol 133 (5) ◽  
pp. 281-287 ◽  
Author(s):  
Ning Shu ◽  
Louis F. Cohn ◽  
Roswell A. Harris ◽  
Teak K. Kim
Keyword(s):  
Insertion Loss ◽  
Traffic Noise ◽  
Noise Model

Acoustic Modeling of Meteorological Effects on Roadway Noise

2021 ◽  
pp. 036119812199458
Author(s):  
Roger L. Wayson ◽  
Kenneth Kaliski
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Model Development ◽  
The United States ◽  
Noise Model ◽  
Road Traffic Noise ◽  
Atmospheric Conditions ◽  
Noise Propagation ◽  
Transportation Research ◽  
Meteorological Effects

Modeling road traffic noise levels without including the effects of meteorology may lead to substantial errors. In the United States, the required model is the Traffic Noise Model which does not include meteorology effects caused by refraction. In response, the Transportation Research Board sponsored NCHRP 25-52, Meteorological Effects on Roadway Noise, to collect highway noise data under different meteorological conditions, document the meteorological effects on roadway noise propagation under different atmospheric conditions, develop best practices, and provide guidance on how to: (a) quantify meteorological effects on roadway noise propagation; and (b) explain those effects to the public. The completed project at 16 barrier and no-barrier measurement positions adjacent to Interstate 17 (I-17) in Phoenix, Arizona provided the database which has enabled substantial developments in modeling. This report provides more recent information on the model development that can be directly applied by the noise analyst to include meteorological effects from simple look-up tables to more precise use of statistical equations.

Traffic Noise Model vs. Extreme Topography

2003 ◽  
Vol 1859 (1) ◽  
pp. 65-71
Author(s):  
Michael A. Staiano
Keyword(s):  
Traffic Noise ◽  
Severe Case ◽  
Sound Attenuation ◽  
Noise Model ◽  
Interstate Highway ◽  
Site Model ◽  
Sound Levels

Traffic noise exposures were measured at various locations adjacent to an Interstate highway and compared with sound levels predicted by the FHWA Traffic Noise Model (TNM). The prediction procedure underestimated the measured sound attenuation by 6 to 12 A-weighted decibels. Various TNM site model configurations were evaluated in an effort to improve agreement between measurements and predictions. For the site tested—a severe case with relatively distant receptors and extreme topography—variations in ground impedance (including a median ground zone) had little benefit or were counterproductive, while adding topographic detail via terrain lines helped somewhat. The best agreement resulted from the incorporation of a tree zone for the wooded site. However, this benefit is thought to be chance, because the site was not only relatively lightly wooded but also thinly foliaged at the time of the on-site measurements.

Comparison of Traffic Noise Model 2.5 with 2.1 and Measured Data

2005 ◽  
Vol 1941 (1) ◽  
pp. 149-154
Author(s):  
A. A. El-Aassar ◽  
R. L. Wayson ◽  
J. M. MacDonald
Keyword(s):  
Traffic Noise ◽  
Measured Data ◽  
Noise Model ◽  
Average Error ◽  
Matched Pairs ◽  
Federally Funded ◽  
Reference Measurement ◽  
Previous Version ◽  
Ground Effects ◽  
Predicted Values

Traffic Noise Model Version 2.5 (TNM 2.5) will soon be the official traffic noise model required by the FHWA for federally funded projects. TNM was updated from Version 2.1 to 2.5 to address two major issues: the overprediction found in the previous version of TNM and an anomaly related to diffraction points. This research focused on comparing the TNM 2.5 predicted results with TNM 2.1 predicted values and with measured data from 18 barrier locations in Florida. Matched pairs of predicted and measured differences between the data for TNM 2.5 and TNM 2.1 were evaluated and a direct comparison of the two models was made. This research demonstrated that the predicted results from TNM 2.5 had an average error for all 18 barrier locations of less than 1 dB. However, when each of the sites is evaluated individually, TNM 2.5 has a tendency to underpredict slightly at many of the evaluated barrier locations. Finally, TNM 2.5-predicted results tend to be about 3 dB(A) on average less than TNM 2.1 at a defined reference measurement position, which is relatively unaffected by ground effects or diffraction, and about 1 dB less at microphone positions behind evaluated barriers when compared with TNM 2.1.

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