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Assessment of Traffic Noise and Development of Noise Models for National Highway-06, Passing through Chhattisgarh, India


  • Department of Civil Engineering, Chhattisgarh Swami Vivekanand Technical University, North park Avenue, Sector-8, Bhilai, Chhattisgarh – 491107, India
  • Government Engineering College, Koni, Bilaspur – 495009, India
  • Department of Civil Engineering, Bhilai Institute of Technology, Bhilai House, Durg,Chhattisgarh – 491001, India


Background/Objectives: Traffic noise pollution is a major concern of the societies residing near the busy National highways. The recognition of this noise as the main source of environmental pollution has compelled the construction engineers & traffic planners to bring forth the models that enable us to predict noise from basic variables. These noise prediction models are very much needed as helping tools in the design of roads& traffic and in assessing frequent changes in traffic noise scenario. In India, vehicular traffic is increasing tremendously, which has given birth to overcrowded roads and simultaneously to noise pollution. Therefore, it is expected to develop noise prediction models suitable for national highways pertaining to Indian context Methods: This paper is the zest of a research conducted on National highway-06 with the sole purpose of setting up traffic noise models based on existing traffic conditions. Traffic noise levels have been measured by noise meter SL4023 SD by adopting standard procedure. Findings: In the process of observation & measurement of noise data, noise levels were found alarmingly& consistently high. After the analysis coefficient of correlation (R2) for the developed model for Supela (Location 1) and Power house (Location 2) are found 0.78 and0.70 respectively, which is lying in acceptable range. Applications: It can be concluded that traffic noise prediction models so developed is theoretically acceptable and conveniently applicable for real traffic conditions of National highway-06passing through Chhattisgarh, India.


Model, Noise, Traffic Noise Prediction.

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