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

State-of-the-Art Prediction and Control of Road Traffic Noise in France

1998 ◽  
Vol 1626 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Michel C. Bérengier ◽  
Fabienne Anfosso-Lédée
Keyword(s):  
Sound Propagation ◽  
Road Traffic ◽  
Numerical Models ◽  
Traffic Noise ◽  
Low Noise ◽  
Road Traffic Noise ◽  
Road Noise ◽  
The Road ◽  
Road Pavement ◽  
Prediction And Control

Because traffic noise is considered by the French population as the primary environmental nuisance, prediction of road traffic noise and development of efficient noise control techniques is very important. The first step is to analyze the source, the main part of which is due to the contact between tires and the road pavement. Many efforts have been devoted to the assessment of a reliable measurement method, and a classification of road pavements in relation to noise has been established for some years. To abate road traffic noise, special attention has been paid to low-noise pavements. Thus, the modeling of the absorption properties of porous asphalts has been particularly studied in the past 10 years. The second step is to understand the physics of sound propagation outdoors, especially the meteorological effects on the propagation of road traffic noise. Both theoretical and experimental approaches have been undertaken. Finally, the effect of road noise barriers of any shape on the propagation of road noise and their interaction with porous road surfaces have been investigated by using numerical models.

scholarly journals Road Traffic Noise Prediction with Neural Networks - A Review

2011 ◽  
Vol 2 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Kranti KUMAR ◽  
Manoranjan PARIDA ◽  
Vinod Kumar KATIYAR
Keyword(s):  
Neural Networks ◽  
Noise Exposure ◽  
Prediction Models ◽  
Road Traffic ◽  
Traffic Noise ◽  
Network Models ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Neural Network Models ◽  
The Road

This paper aims to summarize the findings of research concerning the application of neural networks in traffic noise prediction. Noise is an environmental agent, regarded as a stressful stimulus. Noise exposure causes changes at different levels in living beings, such as the cardiovascular, endocrine and nervous system. Study of traffic noise prediction models began in 1950s to predict a single vehicle sound pressure level at the road side. After that, several traffic noise prediction models such as FHWA, CORTN, STOP and GO, MITHRA, ASJ etc. were developed depending upon various parameters and conditions. Complexity of error identification by means of classical approaches has led to researchers and designers to explore the possibility of neural solution to the problem of traffic noise prediction. Present study is focused on review of various neural network models developed for road traffic noise prediction.

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

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.

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.

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