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Title: Study of Vehicular Traffic Noise and Its Prediction
Authors: Kumar, Narotam
Supervisor: Kumar, Paras
Keywords: Mathematical model;T-Test;Traffic Noise
Issue Date: 16-Sep-2009
Abstract: The major contribution of the traffic noise, towards overall noise pollution scenario, is a well known established fact. Traffic noise from highways creates problems for surrounding areas, especially when there are high traffic volumes and high speeds. Vehicular traffic noise problem as contributed by various kinds of vehicles like heavy, medium trucks/buses, automobiles and two wheelers. Many western countries have developed different prediction models based on L10, Leq and other characteristics. In India, the transportation sector is growing rapidly and number of vehicles on Indian roads is increasing at very fast rate. This has lead to overcrowded roads and pollution. So, a need is being felt to develop a noise prediction model suitable for Indian conditions. The present work discusses the fundamentals of acoustics and analysis of vehicular traffic noise. A mathematical model is developed in Patiala city (Punjab) for a site at sirhind road. A large number of sets of data were recorded for 15 minutes duration at different dates/timings in a random/staggered manner in order to account for statistical temporal variations in traffic flow characteristics. The noise measurement parameters to be recorded were Leq, L10, Lmax and Lmin. Sound level meter (CESVA SC 310) was used for these measurements. In this mathematical model which is used for predicting L10 or Leq level included the following parameters. 1. Total vehicle volume/hr 2. Percentage of heavy vehicles 3. Average vehicle speed The Noise levels Leq and L10 used in regression analysis for prediction. It was concluded that value of R2 ranges from 0.1 to 0.3. The paired t-test was also carried out successfully for goodness-of-fitness. This kind of present work on noise has first time carried out in Mechanical Engineering Department, Thapar University, Patiala.
Appears in Collections:Masters Theses@MED

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