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Statistical model for traffic noise prediction in signalised roundabouts

Motylewicz M., Gardziejczyk W.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2020

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Vol. 68, nr 4

937--948

EN

The existing traffic noise prediction models in road intersections relate mainly to the typical solutions of intersection geometry and traffic organisation. There are no models for large and more complex intersections such as signalised roundabouts. This paper presents the results of studies on the development of a traffic noise prediction model for this type of intersection. The model was developed using a multiple regression method based on the results of field measurements of traffic parameters and noise levels in the vicinity of signalised roundabouts in Poland. The obtained model consists of two groups of variables affecting noise levels at the intersection. The first group determines in detail the inf luence of traffic and geometry of the closest entry. The second group shows the inf luence of more distant noise sources (traffic at the three remaining entries of the intersection) and the inf luence of the dimensions of the entire intersection. The developed model was verified through additional field measurements, as well as compared to the results of two methods of traffic noise prediction: the French ‘NMPB-Routes-2008’ and the German ‘RLS-90’. The obtained results confirmed a higher accuracy of calculations performed using the developed model in the range of: −1.2 dB ÷ +1.0 dB, while the ‘NMPB-Routes-2008’ and ‘RLS-90’ calculate precision were respectively: −2.8 dB ÷ +1.3 dB, and + 0.8 dB ÷ +5.2 dB. Therefore, the developed model allows for a more accurate prediction of noise levels in the vicinity of signalised roundabouts in a f lat terrain without buildings and noise barriers.

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Comparative Application of Radial Basis Function and Multilayer Perceptron Neural Networks to Predict Traffic Noise Pollution in Tehran Roads

Mansourkhaki A., Berangi M., Haghiri M.

Journal of Ecological Engineering

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2018

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Vol. 19, nr 1

113--121

EN

Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effects on humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to find new approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LAeq) by measuring the traffic volume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficient of determination (R2) and the Mean Squared Error (MSE). Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.

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Appraisal of Noise Level Dissemination Surrounding Mining and Industrial Areas of Keonjhar, Odisha: a Comprehensive Approach Using Noise Mapping

Lokhande S. K., Dhawale S. A., Pathak S. S., Gautam R., Jain M. C., Bodhe G. L.

Archives of Acoustics

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2017

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Vol. 42, No. 3

423--432

EN

Noise mapping is a well-established practice among the European nations, and it has been follow for almost two decades. Recently, as per guidelines of the Directorate General of Mines Safety (DGMS), India, noise mapping has been made mandatory in the mining expanses. This study is an effort to map the noise levels in nearby areas of mines in the northern Keonjhar district. The motive of this study is to quantify the existing A-weighted time-average sound level (LAeq, T) in the study area to probe its effects on the human dwellings and noise sensitive areas with the probability of future development of the mines, roads, and industrial and commercial zone. The LAeq, T was measured at 39 identified locations, including industrial, commercial, residential, and sensitive zones, 15 open cast mines, 3 major highways, and 3 haulage roads. With the utilisation of Predictor LimA Software and other GIS tools, the worked out data is mapped and noise contours are developed for the visualisation and identification of the extent and distribution of sound levels across the study area. This investigation discloses that the present noise level at 60% of the locations in silence and residential zone exposed to significantly high noise levels surpasses the prescribed limit of Central Pollution Control Board (CPCB), India. The observed day and night time LAeq, T level of both zones ranged between 43.2–62.2 dB(A) and 30.5–53.4 dB(A), respectively, whereas, the average Ldn values vary between 32.7 and 51.2 dB(A). The extensive mobility of heavy vehicles adjoining the sensitive areas and a nearby plethora of open cast mines is the leading cause of exceeded noise levels. The study divulges that the delicate establishments like schools and hospitals are susceptible to high noise levels throughout the day and night. A correlation between observed and software predicted values gives R2 of 0.605 for Ld, 0.217 for Ln, and 0.524 for Ldn. Finally, the mitigation measure is proposed and demonstrated using a contour map showing a significant reduction in the noise levels by 0–5.3 dB(A).