An engineering method to determine the attenuation due to ground effects in traffic noise prediction for long straight road based on the ISO method

2012 ◽  
Vol 60 (4) ◽  
pp. 374-378 ◽  
Author(s):  
Jian-Qiang Zhao ◽  
Ying Chen ◽  
Bo Hu
Keyword(s):  
Traffic Noise ◽  
Engineering Method ◽  
Noise Prediction ◽  
Ground Effects

Road Traffic Noise Prediction Model

2012 ◽  
Vol 3 (4) ◽  
pp. 110-112
Author(s):  
Rahul Singh ◽  
◽  
Parveen Bawa ◽  
Ranjan Kumar Thakur
Keyword(s):  
Prediction Model ◽  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Noise Prediction

Application of the Boundary Element Method and Modal Analysis to Tire Acoustics Problems

1993 ◽  
Vol 21 (2) ◽  
pp. 66-90 ◽  
Author(s):  
Y. Nakajima ◽  
Y. Inoue ◽  
H. Ogawa
Keyword(s):  
Finite Element ◽  
Boundary Element Method ◽  
Boundary Element ◽  
Acoustic Radiation ◽  
Traffic Noise ◽  
Noise Prediction ◽  
Prediction System ◽  
Tire Noise ◽  
Acoustic Contribution ◽  
Element Method

Abstract Road traffic noise needs to be reduced, because traffic volume is increasing every year. The noise generated from a tire is becoming one of the dominant sources in the total traffic noise because the engine noise is constantly being reduced by the vehicle manufacturers. Although the acoustic intensity measurement technology has been enhanced by the recent developments in digital measurement techniques, repetitive measurements are necessary to find effective ways for noise control. Hence, a simulation method to predict generated noise is required to replace the time-consuming experiments. The boundary element method (BEM) is applied to predict the acoustic radiation caused by the vibration of a tire sidewall and a tire noise prediction system is developed. The BEM requires the geometry and the modal characteristics of a tire which are provided by an experiment or the finite element method (FEM). Since the finite element procedure is applied to the prediction of modal characteristics in a tire noise prediction system, the acoustic pressure can be predicted without any measurements. Furthermore, the acoustic contribution analysis obtained from the post-processing of the predicted results is very helpful to know where and how the design change affects the acoustic radiation. The predictability of this system is verified by measurements and the acoustic contribution analysis is applied to tire noise control.

SPEED DISTRIBUTION INFLUENCE IN ROAD TRAFFIC NOISE PREDICTION

2013 ◽  
Vol 12 (3) ◽  
pp. 493-501 ◽  
Author(s):  
Gerardo Iannone ◽  
Claudio Guarnaccia ◽  
Joseph Quartieri
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Speed Distribution

scholarly journals Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction

2021 ◽  
Vol 11 (13) ◽  
pp. 6030
Author(s):  
Daljeet Singh ◽  
Antonella B. Francavilla ◽  
Simona Mancini ◽  
Claudio Guarnaccia
Keyword(s):  
Machine Learning ◽  
Linear Models ◽  
Prediction Models ◽  
Road Traffic ◽  
Traffic Noise ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Volume Percentage

A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. Leq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.

Reliability of Dynamap traffic noise prediction

2019 ◽  
Vol 156 ◽  
pp. 142-150 ◽  
Author(s):  
Roberto Benocci ◽  
Alessandro Molteni ◽  
Marco Cambiaghi ◽  
Fabio Angelini ◽  
H. Eduardo Roman ◽  
...  
Keyword(s):  
Traffic Noise ◽  
Noise Prediction

Traffic noise prediction model of an Indian road: an increased scenario of vehicles and honking

2020 ◽  
Vol 27 (30) ◽  
pp. 38311-38320
Author(s):  
Chaitanya Thakre ◽  
Vijaya Laxmi ◽  
Ritesh Vijay ◽  
Deepak J. Killedar ◽  
Rakesh Kumar
Keyword(s):  
Prediction Model ◽  
Traffic Noise ◽  
Noise Prediction

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.

Road traffic noise prediction model in Thailand

1999 ◽  
Vol 58 (2) ◽  
pp. 123-130 ◽  
Author(s):  
Thanaphan Suksaard ◽  
Phaka Sukasem ◽  
S.Monthip Tabucanon ◽  
Ichiro Aoi ◽  
Kiyotsugu Shirai ◽  
...  
Keyword(s):  
Prediction Model ◽  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Noise Prediction
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