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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.

References
  1. Banerjee, D. 2012: "Research on road traffic noise and human health in India: Review of literature from 1991 to current", Noise and Health, Vol 14, Issue 58, pp. 113-118.
  2. Motor transport statistics of Maharashtra 2010 – 2011. Available online on: www. mahatranscom. in/pdf/STATISTICAL. BOOK. 10-11. pdf
  3. Reddy, R. B. , Ramachandaiah, A. 1995: "Traffice noise in some typical urban lanes", Journal of Acoustic Society of India, Vol 23, Issue 4, pp. 53-57.
  4. Mishra, R. K. , Parida, M. , Rangnekar S. 2010: "Evaluation and analysis of traffic noise along bus rapid transit system corridor", International Journal of Environmental Science and Technology, Vol 7, Issue 4, pp. 737-750.
  5. Sharma, A, Bodhe G. L, Schimak G. 2014: "Development of a traffic noise prediction model for an urban environment". Noise Health, Vol 16, Issue 68, pp. 63-67.
  6. Shukla, K. , Jain, S. S. , Parida, M. , Srivastava, J. B. 2009: "Performance of FHWA model for predicting traffic noise: A case study of Metropolitan city, Lucknow (India)", Transport, Vol 24, Issue 3, pp. 234-240.
  7. Steele, C. 2012: "A critical review of some traffic noise prediction models", Applied Acoustics, Vol 62, Issue 3, pp. 271–287.
  8. Francis, Giovanni, L. 2012: "Environmental modeling for traffic noise in urban area", American Journal of Environmental Science, Vol 8, Issue 4, pp. 345-351.
  9. Lecture 6, Objective. 1: "To learn about the standard UK method for the calculation of road traffic noise", Environmental Noise Control. Page 1/12. University of Bradford. Available online at: www. staff. brad. ac. uk/kvhorosh/CV6505M/Lecture_06. PDF.
  10. Tyagi, V. , Kalyanaraman, S. , Krishnapuram, R. 2012: "Vehicular traffic density state estimation based on cumulative road acoustic", IEEE Transactions on Intelligent Transportation Systems, Vol 13, Issue 3, pp. 1156–1166.
  11. Wani, K. A. , Jaiswal, Y. K. 2005: "Assessment of Noise Pollution in Gwalior, M. P, India", Advance in Bioresearch, Vol 1, Issue 1, pp. 54-60.
  12. Bin, J. , Rui, J. , Qing-Song, W. , Mao-bin, H. 2005: "Honk effect in the two lane cellular automaton model for traffic flow", Physica A, Vol 348, pp. 544-552.
  13. Nanda, S. K. , Tripathy, D. P. , Patra, S. K. 2008: "Fuzzy inference system-based noise prediction models for opencast mines", International Journal of Mining Reclamation and Environment, Vol 23, Issue 4, pp. 242-260.
  14. Kalaiselvi, R. , Ramachandraiah, A. 2010: "Environmental noise mapping study for heterogeneous traffic conditions", Proceedings of 20th International Congress on Acoustic, ICA, Sydney, Australia, pp. 23-27.
  15. Nayef, M. A. , Rukaibi A. F. 2012: "Neural Network Models for Traffic Noise Quality Prediction: a comparative study", Journal of Civil Environment Engineering, Vol 2, Issue 1. Available online at: http://dx. doi. org/10. 4172/2165-784X. 1000106
  16. Arora, J. K. , Mosahari, P. V. 2012: "Artificial Neural Network Modeling of Traffic Noise in Agra-Firozabad Highway", International Journal of Computer Applications, Vol 56, Issue 2, pp. 0975–8887.
  17. Genaro, N. , Torija, A. , Ridao, A. R. , Requena, I. , Ruiz, D. P. , Zamorano, M. 2010: "A neural network based model for urban noise prediction", Journal of Acoustic Society America, Vol 128, Issue 4, pp. 1738-1746.
  18. Provisional Population Totals, Census of India 2011: Available online at: http://www. censusindia. gov. in/2011-provresults/paper2/data_files/India2/Table_2_PR_Cities_1Lakh_and_Above. pdf
  19. SoundPro, 2011: Sound Level Meter, 3M make, St. Paul, MN 55144-1000, USA.
  20. Rajakumara, H. N. , Gowda, R. M. M. 2009: "Road Traffic Noise Prediction Model under Interrupted Traffic Flow Condition", Environ Modeling and Assessment, Vol 14, pp. 251–257.
  21. Jang, S. 1993: "Adaptive network-based Fuzzy Inference System". IEEE Journal, Vol 23, Issue 3, pp. 665-685.
  22. Takagi, T. , Sugeno, M. 1985: "Fuzzy identification of system and its application to modeling and control", IEEE. Trans. Syst. Man. Cyber, Vol 15, pp. 116-132.
  23. Kukolji. 2002: "Design of adoptive T-S-K fuzzy models", Appl. Soft Compt, Vol 2, pp. 89-103.
Index Terms

Computer Science
Information Sciences

Keywords

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

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