| ARTICLE |
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| Year : 2014 | Volume
: 16
| Issue : 68 | Page : 63--67 |
Development of a traffic noise prediction model for an urban environment
Asheesh Sharma1, GL Bodhe2, G Schimak3
1 Environmental Systems Design Modeling Division, CSIR-National Environmental Engineering Research Institute, Nagpur, Maharashtra, India
2 Analytical Instrumentation Division, CSIR-National Environmental Engineering Research Institute, Nagpur, Maharashtra, India
3 Department Information Management, Austrian Institute of Technology, GmbH, Donau-City-Strasse 1, A1220 Vienna, Austria
Correspondence Address:
Asheesh Sharma
Environmental Systems Design Modeling Division, CSIR-National Environmental Engineering Research Institute, Nehru Marg, Nagpur - 440 020, Maharashtra
India
 Source of Support: Department of Science and Technology, New Delhi, India., Conflict of Interest: None  | Check |
DOI: 10.4103/1463-1741.127858
The objective of this study is to develop a traffic noise model under diverse traffic conditions in metropolitan cities. The model has been developed to calculate equivalent traffic noise based on four input variables i.e. equivalent traffic flow (Q e ), equivalent vehicle speed (S e ) and distance (d) and honking (h). The traffic data is collected and statistically analyzed in three different cases for 15-min during morning and evening rush hours. Case I represents congested traffic where equivalent vehicle speed is <30 km/h while case II represents free-flowing traffic where equivalent vehicle speed is >30 km/h and case III represents calm traffic where no honking is recorded. The noise model showed better results than earlier developed noise model for Indian traffic conditions. A comparative assessment between present and earlier developed noise model has also been presented in the study. The model is validated with measured noise levels and the correlation coefficients between measured and predicted noise levels were found to be 0.75, 0.83 and 0.86 for case I, II and III respectively. The noise model performs reasonably well under different traffic conditions and could be implemented for traffic noise prediction at other region as well.
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