scholarly journals A practical prediction method using an elementary response wave form for the road traffic noise at a T-type intersection.

1988 ◽  
Vol 9 (4) ◽  
pp. 195-200 ◽  
Author(s):  
Mitsuo Ohta ◽  
Yasuo Mitani
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Prediction Method ◽  
Wave Form ◽  
Road Traffic Noise ◽  
The Road ◽  
Elementary Response

Study of the road traffic noise prediction method applicable to low-noise road surfaces

2010 ◽  
Vol 31 (1) ◽  
pp. 102-112 ◽  
Author(s):  
Keisuke Tsukui ◽  
Yasuo Oshino ◽  
Gijsjan van Blokland ◽  
Hideki Tachibana
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Prediction Method ◽  
Low Noise ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
The Road ◽  
Road Surfaces

scholarly journals Rapid Modeling Method for Road Traffic Noise Sources: A case study in Beijing

Noise Mapping ◽  
2015 ◽  
Vol 2 (1) ◽  
Author(s):  
L. Zhu ◽  
X. Li ◽  
C. Jiang ◽  
L. Liu ◽  
R. Wu ◽  
...  
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Modeling Method ◽  
Model Parameters ◽  
Road Traffic Noise ◽  
Emission Model ◽  
Noise Sources ◽  
The Road ◽  
The Mean ◽  
Rapid Modeling

AbstractBased on the local road traffic conditions in Beijing, China, this contribution proposes a rapid modeling method for road traffic noise sources. Since establishing the standardized experiment fields are expensive, real roads are used to determine the road traffic noise emission model in the method. Due to the similarity in the urban structures in China and Japan, this paper uses the ASJ- 2013 model as a template and replaces its model parameters with the ones output by an optimization program which minimizes the sum of absolute errors between the predicted and the measured LAeq. Real road experiments are conducted to verify the effectiveness and feasibility of the modeling method. The mean error of the model deduced by the method and the ASJ-2013 model is respectively 0.4 dB and 2.6 dB, and the mean absolute error of the two models is respectively 1.1 dB and 2.6 dB. The results of the real road experiments show that the road traffic noise sources deduced by the method are more accurate to conduct local noise prediction than those of other models.

Social survey on the road traffic noise effect on the quality of life in Moroccan cities

2008 ◽  
Vol 123 (5) ◽  
pp. 3811-3811
Author(s):  
Weam Kharbaoui ◽  
Mohammed Garoum ◽  
Abdelaziz Bahoussa ◽  
Mohammed Rhachi
Keyword(s):  
Quality Of Life ◽  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Social Survey ◽  
Noise Effect ◽  
The Road ◽  
On The Road

Acoustic Prediction Modeling and Sound Mapping of public transport users' exposure at a bus stop.

2021 ◽  
Vol 263 (2) ◽  
pp. 4058-4069
Author(s):  
Dayane Cristina Lima Estercio ◽  
Paulo Fernando Soares
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Sound Level ◽  
Road Traffic Noise ◽  
High Frequencies ◽  
Reference Models ◽  
The Road ◽  
Bus Stop ◽  
Specular Reflections ◽  
The City

The objective of this research is to develop a mathematical model to predict the road traffic noise level at the bus stop, to assess the level of noise that users of these urban facilities are exposed daily. To help assess the exposure and the environmental impact, sound mapping will be performed using the IMMI software. In the model, the calculation of direct paths and specular reflections and diffuse was adopted. The study was applied in three sections in the city of Maringá, Brazil. At each point, the user was simulated standing and sitting. The sound source was positioned on the axis of each strip, every five meters. In total, 5124 readings of source positions were evaluated in 84 measured points. For the validation, the Anderson-Kurze, Kang, Yang and Zhang, Bistafa and Naish model were applied, and then the t-Student test were applied. The results showed a correspondence between the developed model, the data of the measurements and the reference models in the range of 25 Hz to 10000 Hz, there was a greater variance between the models applied in the high frequencies. It is concluded that the model was able to estimate the sound level of the stretches evaluated.

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