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.

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.

scholarly journals Predicting Yield Losses in Rice Mixed-Weed Species Infestations in California

Weed Science ◽  
2016 ◽  
Vol 65 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Whitney B. Brim-DeForest ◽  
Kassim Al-Khatib ◽  
Albert J Fischer
Keyword(s):  
Yield Loss ◽  
Information Criterion ◽  
Average Error ◽  
Canopy Closure ◽  
Weed Species ◽  
Major Barrier ◽  
Combined Models ◽  
Relative Cover ◽  
Predicted Values ◽  
Using Data

Although many pests constrain rice production, weeds are considered to be the major barrier to achieving optimal yields. A predictive model based on naturally occurring mixed-species infestations in the field would enable growers to target the specific weed group that is the greatest contributor to yield loss, but as of now no such models are available. In 2013 and 2014, two empirical hyperbolic models were tested using the relative cover at canopy closure of groups of weed species as independent variables: grasses, sedges, broadleaves, grasses and sedges combined, grasses and broadleaves combined, and all weed species combined. Models were calibrated using data from experiments conducted at the California Rice Experiment Station, in Biggs, CA, and validated across four sites over 2 years, for a total of 7 site-year combinations. Of the three major weed groups, grasses, sedges, and broadleaves, the only groups positively related to yield loss in the multispecies infestation were grasses. At the model calibration site, grasses and sedges combined best predicted yield loss (corrected Akaike information criterion [AICc]=−21.5) in 2013, and grasses alone best predicted yield loss (AICc=−19.0) in 2014. Across the validation sites, the model using grasses and sedges combined was the best predictor in 5 out of 7 site-years. Accuracy of the predicted values at the model validation sites ranged from 6% mean average error to 17% mean average error. No single model and set of parameters accurately predicted losses across all years and locations, but relative cover of grasses and sedges combined at canopy closure was the best estimate over the most sites and years.

Simulation Approach to Traffic Noise Modeling: American Automobile Manufacturers Association Community Noise Model Version 4.0

1997 ◽  
Vol 1601 (1) ◽  
pp. 64-70 ◽  
Author(s):  
Roger L. Wayson ◽  
John M. MacDonald ◽  
Ronald Eaglin ◽  
Barbara Wendling
Keyword(s):  
Simulation Model ◽  
Traffic Noise ◽  
The United States ◽  
Point Sources ◽  
Noise Model ◽  
Community Noise ◽  
Model Free ◽  
Sound Levels ◽  
Acceleration And Deceleration ◽  
Interrupted Flow

Several models are available for predicting traffic noise levels. The FHWA-promulgated model, STAMINA 2.0, is the most widely used noise model in the United States and is used to model free-flow vehicular traffic. STAMINA 2.0 cannot directly model interrupted-flow traffic. Sound levels from interrupted-flow traffic can be approximated with STAMINA 2.0 using the method presented in NCHRP Report 311. This method is time-consuming and difficult to use. These limitations demonstrate the need for a traffic noise model that can model the acceleration and deceleration behavior of interrupted-flow traffic. The University of Central Florida has developed the American Automobile Manufacturers Association Community Noise Model (CNM). The CNM is a traffic simulation model that determines sound levels at receivers by modeling vehicles as discrete moving point sources. The vehicle energy is determined from acceleration, deceleration, idle, and cruise reference energy mean emission level curves. Sound energy attenuation is calculated from distance, ground absorption, and user input barriers. The model sums the energy at receivers from all vehicles and then calculates the Leq noise level at the receivers. It is demonstrated that the CNM predicts receiver Leq levels that are very close to STAMINA 2.0 results for constant-speed traffic. The CNM can also accurately predict sound levels at receivers located before and after intersections. In addition to the advantages of a real simulation model, the CNM is user friendly, allowing the user to place lanes and receivers using the mouse.

Modeling and Characterization of a Photovoltaic Array Based on Actual Performance Using Cascade-Forward Back Propagation Artificial Neural Network

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Ammar Mohammed Ameen ◽  
Jagadeesh Pasupuleti ◽  
Tamer Khatib ◽  
Wilfried Elmenreich ◽  
Hussein A. Kazem
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Measured Data ◽  
Mean Bias Error ◽  
Output Current ◽  
Pv System ◽  
Proposed Model ◽  
Artificial Neural ◽  
Predicted Values

This paper proposes a novel prediction model for photovoltaic (PV) system output current. The proposed model is based on cascade-forward back propagation artificial neural network (CFNN) with two inputs and one output. The inputs are solar radiation and ambient temperature, while the output is output current. Two years of experimental data for a 1.4 kWp PV system are utilized in this research. The monitored performance is recorded every 2 s in order to consider the uncertainty of the system’s output current. A comparison between the proposed model and other empirical and statistical models is done in this paper as well. Moreover, the ability of the proposed model to predict performance with high uncertainty rate is validated. Three statistical values are used to evaluate the accuracy of the proposed model, namely, mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). These values are used to measure the deviation between the actual and the predicted data in order to judge the accuracy of the proposed model. A simple estimation of the deviation between the measured value and the predicted value with respect to the measured value is first given by MAPE. After that, the average deviation of the predicted values from measured data is estimated by MBE in order to indicate the amount of the overestimation/underestimation in the predicted values. Third, the ability of predicting future records is validated by RMSE, which represents the variation of the predicted data around the measured data. Eventually, the percentage of MBE and RMSE is calculated with respect to the average value of the output current so as to present better understating of model’s accuracy. The results show that the MAPE, MBE, and RMSE of the proposed model are 7.08%, −0.21 A (−4.98%), and 0.315 A (7.5%), respectively. In addition to that, the proposed model exceeds the other models in terms of prediction accuracy.

scholarly journals Exploiting ground effects for surface transport noise abatement

Noise Mapping ◽  
2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Keith Attenborough ◽  
Imran Bashir ◽  
Shahram Taherzadeh
Keyword(s):  
Noise Reduction ◽  
Traffic Noise ◽  
Square Lattice ◽  
Artificial Roughness ◽  
Noise Mitigation ◽  
Noise Abatement ◽  
Surface Transport ◽  
Lattice Design ◽  
Parallel Wall ◽  
Ground Effects

AbstractGrowing demand on transportation, road and railway networks has increased the risk of annoyance from these sources and the need to optimise noise mitigation. The potential traffic noise reduction arising from use of acoustically-soft surfaces and artificial roughness (0.3 m high or less) is explored through laboratory experiments, outdoor measurements at short and medium ranges and predictions. Although the applicability of ground treatments depends on the space usable for the noise abatement and the receiver position, replacing acousticallyhard ground by acoustically-soft ground without or with crops and introducing artificial roughness configurations could achieve noise reduction along surface transport corridors without breaking line of sight between source and receiver, thereby proving useful alternatives to noise barriers. A particularly successful roughness design has the form of a square lattice which is found to offer a similar insertion loss to regularly-spaced parallel wall arrays of the same height but twice the width. The lattice design has less dependence on azimuthal source-receiver angle than parallel wall configurations.

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