Skip to main content

News about Scitation

In December 2016 Scitation will launch with a new design, enhanced navigation and a much improved user experience.

To ensure a smooth transition, from today, we are temporarily stopping new account registration and single article purchases. If you already have an account you can continue to use the site as normal.

For help or more information please visit our FAQs.

banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
/content/asa/journal/jasa/140/4/10.1121/1.4964786
1.
Burgess, M. A. (1977). “ Noise prediction for urban traffic conditions—Related to measurement in Sydney metropolitan area,” Appl. Acoust. 10, 17.
http://dx.doi.org/10.1016/0003-682X(77)90002-0
2.
Canelli, G. B. , Gluck, K. , and Santoboni, S. (1983). “ A mathematical model for evaluation and prediction of the mean energy level of traffic noise in Italian towns,” Acustica 53, 3136.
3.
Civicioglu, P. (2012). “ Transforming geocentric Cartesian coordinates to geodetic coordinates by using differential search algorithm,” Comput. Geosci. 46, 229247.
http://dx.doi.org/10.1016/j.cageo.2011.12.011
4.
Fagotti, C. , and Poggi, A. (1995). “ Traffic noise abatement strategies. The analysis of real case not really effective,” in Proceedings of the 18th International Congress for Noise Abatement, Bologna, Italy, pp. 223233.
5.
Genaro, N. , Torija, A. , Ramos-Ridao, A. , Requena, I. , Ruiz, D. P. , and Zamorano, M. (2010). “ A neural network based model for urban noise prediction,” J. Acoust. Soc. Am. 128, 17381746.
http://dx.doi.org/10.1121/1.3473692
6.
Givargis, S. , and Karimi, H. (2010). “ A basic neural traffic noise prediction model for Tehran's roads,” J. Environ. Manage. 91, 25292534.
http://dx.doi.org/10.1016/j.jenvman.2010.07.011
7.
Griffiths, I. D. , and Langdon, F. J. (1968). “ Subjective response to road traffic noise,” J. Sound Vib. 8, 1632.
http://dx.doi.org/10.1016/0022-460X(68)90191-0
8.
Gundogdu, O. , Gokdag, M. , and Yuksel, F. (2005). “ A traffic noise prediction method based on vehicle composition using genetic algorithms,” Appl. Acoust. 66(7), 799809.
http://dx.doi.org/10.1016/j.apacoust.2004.11.003
9.
ISO. (1996). ISO 9613-2:1996{E}, Acoustics—Attenuation of Sound During Propagation Outdoors, Part 2: General Method of Calculation (International Standards Organization).
10.
Mihajlov, D. , Prascevic, M. , and Cvetkovic, D. (2012). “ An analysis of the environmental noise levels on the territory of the city of Nis,” in Proceedings of the 23rd National and 4th International Conference “Noise and Vibration,” Niš, Serbia, pp. 4958.
11.
Nielsen, H. L. (1996). “ Road traffic noise: Nordic prediction method,” Nordic Council of Ministers.
12.
Prascevic, M. R. , Mihajlov, D. I. , and Cvetkovic, D. S. (2014). “ Measurement and evaluation of the environmental noise levels in the urban areas of the city of Nis,” Environ. Monitor. Assess. 186(2), 11571165.
http://dx.doi.org/10.1007/s10661-013-3446-2
13.
Rahmani, S. , Mousavi, S. M. , and Kamali, M. J. (2011). “ Modeling of road-traffic noise with the use of genetic algorithm,” Appl. Soft Comput. 11(1), 10081013.
http://dx.doi.org/10.1016/j.asoc.2010.01.022
14.
Richtlinien für den Lärmschutz an Straßen (RLS), Germany, 1990.
http://aip.metastore.ingenta.com/content/asa/journal/jasa/140/4/10.1121/1.4964786
Loading
/content/asa/journal/jasa/140/4/10.1121/1.4964786
Loading

Data & Media loading...

Loading

Article metrics loading...

/content/asa/journal/jasa/140/4/10.1121/1.4964786
2016-10-14
2016-12-04

Abstract

Available traffic noise prediction models are usually based on regression analysis of experimental data, and this paper presents the application of soft computing techniques in traffic noise prediction. Two mathematical models are proposed and their predictions are compared to data collected by traffic noise monitoring in urban areas, as well as to predictions of commonly used traffic noise models. The results show that application of evolutionary algorithms and neural networks may improve process of development, as well as accuracy of traffic noise prediction.

Loading

Full text loading...

/deliver/fulltext/asa/journal/jasa/140/4/1.4964786.html;jsessionid=H1QdE3ta7YAjRrcDK3cHl7iZ.x-aip-live-03?itemId=/content/asa/journal/jasa/140/4/10.1121/1.4964786&mimeType=html&fmt=ahah&containerItemId=content/asa/journal/jasa
true
true

Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
/content/realmedia?fmt=ahah&adPositionList=
&advertTargetUrl=//oascentral.aip.org/RealMedia/ads/&sitePageValue=asadl.org/jasa/140/4/10.1121/1.4964786&pageURL=http://scitation.aip.org/content/asa/journal/jasa/140/4/10.1121/1.4964786'
Right1,Right2,Right3,