Traffic Noise Prediction for Delhi-NCR using Multiple RegressionModelling Approach


  • Suman Mann Department of Civil Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana, India 131039
  • Gyanendra Singh Department of Civil Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana, India 131039



Traffic Noise Prediction Model, Noise Pollution, Significant Variables, Multiple Regression Modelling


Traffic noise prediction models are crucial for designing highways to implement preventive measures against traffic noise pollution by analyzing future trends. This study aims to identify the traffic, road geometrical, and environmental parameters that escalate traffic noise pollution, enabling rectification of influencing factors and enhancement of strategies to reduce this pollution. A traffic noise prediction model was developed for the highways of Delhi-NCR using the Multiple Regression approach, incorporating various traffic, geometric, and environmental parameters. Statistical analysis was conducted, and the model was formulated based on data collected from 31 sampling stations on two major Delhi highways. Significant variables identified include the number of lanes, average building height, international roughness index, temperature, wind speed, and humidity. The model’s validity is affirmed by a coefficient of determination R2 = 0.75, indicating a good fit.


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How to Cite

S. Mann and G. Singh, “Traffic Noise Prediction for Delhi-NCR using Multiple RegressionModelling Approach”, J. Comput. Mech. Manag, vol. 2, no. 6, pp. 1–8, Dec. 2023.



Original Articles


Received 2023-11-14
Accepted 2023-12-04
Published 2023-12-31