Comparison of classical statistical methods and artificial neural network in traffic noise prediction
- Faculty of Philology and Arts, University of Kragujevac, Jovana Cvijića bb, 34000 Kragujevac (Serbia)
- Faculty of Economics, University of Kragujevac, Djure Pucara Starog 3, 34000 Kragujevac (Serbia)
- Faculty of Economics, University of Niš, Trg kralja Aleksandra Ujedinitelja, 18000 Niš (Serbia)
- Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac (Serbia)
- College of Applied Mechanical Engineering, Trstenik (Serbia)
Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. The output variable of the network is the equivalent noise level in the given time period L{sub eq}. Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model.
- OSTI ID:
- 22447491
- Journal Information:
- Environmental Impact Assessment Review, Vol. 49; Other Information: Copyright (c) 2014 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); ISSN 0195-9255
- Country of Publication:
- United States
- Language:
- English
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