scholarly journals RESEARCH ON THE EFFECTS OF PAVEMENT SURFACE CONDITION ON ROAD TRAFFIC NOISE

2012 ◽  
Vol 1 (8) ◽  
Author(s):  
Mirza Pozder
Keyword(s):  
Prediction Models ◽  
Road Traffic ◽  
Surface Condition ◽  
Traffic Noise ◽  
Noise Pollution ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Noise Emission ◽  
Pavement Surface ◽  
Pollution Problem

The noise is meant by all unwanted sounds. As the years were passing by the noise has become more and more intense. European Union adopted Directive 2002/49/EC recognizing noise pollution problem. During the processes of planning and designing, and after the construction of new roads, it is of major importance to determine the level of traffic noise which is going to occur or which has already occurred. For that purposes mathematical models for prediction of noise emission and dispersion have been used. The aim of this paper is to represent the results of the research on the effects of pavement surface condition on road traffic noise. Research results were used to develop noise prediction models.

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 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.

Application of BP Neural Network in Traffic Noise Prediction

2012 ◽  
Vol 518-523 ◽  
pp. 3838-3842
Author(s):  
Gang Liu ◽  
Li Hua Lin ◽  
Rui Tian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Urban Areas ◽  
Road Traffic ◽  
Traffic Noise ◽  
Noise Pollution ◽  
Measured Data ◽  
Sound Level ◽  
Road Traffic Noise ◽  
Noise Prediction

A model of BP neural network for road traffic noise prediction was established by using some road traffic parameters in this paper. Then use this model to predict equivalent continual sound level of road traffic noise, After an overview about the neural approach, the learning; and production phase results are shown and contrasted with the measured data. They point out how good is the approach proposed to model noise pollution in urban areas.

scholarly journals Comparison of Road Traffic Noise prediction models: CNOSSOS-EU, Nord2000 and TRANEX

2021 ◽  
Vol 270 ◽  
pp. 116240
Author(s):  
Jibran Khan ◽  
Matthias Ketzel ◽  
Steen Solvang Jensen ◽  
John Gulliver ◽  
Erik Thysell ◽  
...  
Keyword(s):  
Prediction Models ◽  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Noise Prediction

scholarly journals Road traffic noise in areas surrounding intersections

2014 ◽  
Vol 13 (1) ◽  
pp. 137-147 ◽  
Author(s):  
Marek Motylewicz ◽  
Władysław Gardziejczyk
Keyword(s):  
Noise Level ◽  
Prediction Models ◽  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Organization And Management ◽  
Traffic Composition ◽  
Intersection Geometry ◽  
The Impact

Over the period of the last few years special attention has been paid to the issue of protecting built-up areas against excessive traffic noise. Prediction and assessment of the level of noise in areas surrounding intersections was of primary interest to the researchers. Studies in this field have been carried out in numerous research centers in Poland and around the world. Existing noise prediction models describe the noise level in areas close to intersections with different degree of accuracy, depending on the number and type of factors included in the model. The paper presents the results of studies on the equivalent noise level in the surroundings of various types of intersections. It shows the impact of the following factors on traffic noise: geometry, traffic organization, traffic composition and distribution as well as the distance from the intersection. It was stated that apart from the basic traffic parameters (intensity and composition) and the characteristics of entries, some factors, including intersection geometry, organization and management of traffic and the traffic conditions should be analysed in greater detail.

scholarly journals Parameter Estimation for Traffic Noise Models Using a Harmony Search Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Deok-Soon An ◽  
Young-Chan Suh ◽  
Sungho Mun ◽  
Byung-Sik Ohm
Keyword(s):  
Parameter Estimation ◽  
Prediction Models ◽  
Road Traffic ◽  
Traffic Noise ◽  
Harmony Search ◽  
Measurement Data ◽  
Road Surface ◽  
Road Traffic Noise ◽  
Sound Power ◽  
Noise Prediction

A technique has been developed for predicting road traffic noise for environmental assessment, taking into account traffic volume as well as road surface conditions. The ASJ model (ASJ Prediction Model for Road Traffic Noise, 1999), which is based on the sound power level of the noise emitted by the interaction between the road surface and tires, employs regression models for two road surface types: dense-graded asphalt (DGA) and permeable asphalt (PA). However, these models are not applicable to other types of road surfaces. Accordingly, this paper introduces a parameter estimation procedure for ASJ-based noise prediction models, utilizing a harmony search (HS) algorithm. Traffic noise measurement data for four different vehicle types were used in the algorithm to determine the regression parameters for several road surface types. The parameters of the traffic noise prediction models were evaluated using another measurement set, and good agreement was observed between the predicted and measured sound power levels.

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