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Adoptive Neuro-Fuzzy Inference System for Traffic Noise Prediction

by Asheesh Sharma, Ritesh Vijay, G. L. Bodhe, L. G. Malik
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 13
Year of Publication: 2014
Authors: Asheesh Sharma, Ritesh Vijay, G. L. Bodhe, L. G. Malik
10.5120/17243-7579

Asheesh Sharma, Ritesh Vijay, G. L. Bodhe, L. G. Malik . Adoptive Neuro-Fuzzy Inference System for Traffic Noise Prediction. International Journal of Computer Applications. 98, 13 ( July 2014), 14-19. DOI=10.5120/17243-7579

@article{ 10.5120/17243-7579,
author = { Asheesh Sharma, Ritesh Vijay, G. L. Bodhe, L. G. Malik },
title = { Adoptive Neuro-Fuzzy Inference System for Traffic Noise Prediction },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 13 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 14-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume98/number13/17243-7579/ },
doi = { 10.5120/17243-7579 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:26:06.959560+05:30
%A Asheesh Sharma
%A Ritesh Vijay
%A G. L. Bodhe
%A L. G. Malik
%T Adoptive Neuro-Fuzzy Inference System for Traffic Noise Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 13
%P 14-19
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An adaptive neuro-fuzzy inference system (ANFIS) is implemented to evaluate traffic noise under heterogeneous traffic conditions of Nagpur city, India. The major factors which affect the traffic noise are traffic flow, vehicle speed and honking. These factors are considered as input parameters to ANFIS model for traffic noise estimation. The proposed ANFIS model has implemented for traffic noise estimation at eight locations. The results have been compared and analyzed with observed noise levels and the coefficient of co-relation between observed and predicted noise level was found to be in range of 0. 70 to 0. 95. The model performance has also been compared with Federal Highway Administration (FHWA), Calculation of road traffic noise (CRTN) and regression noise models and it is observed that the model performs better than conventional statistical noise model. The proposed noise model is completely generalized and problem independent so it can be easily modified to prediction traffic noise under various traffic criteria and serve as first hand tool for traffic noise assessment.

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Index Terms

Computer Science
Information Sciences

Keywords

Heterogeneous traffic adaptive neural network based fuzzy inference system noise prediction honking.

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