MODELLING OF INTERRUPTED TRAFFIC FLOW NOISE

Abstract

In a rapidly urbanising country like India, the transportation sector is growing rapidly and the number of vehicles on Indian roads is increasing at a rate of more than 7% per annum. During the period of 50 years from 1951 to 2001, the number of vehicles has increased from 3 lakhs to 430 lakhs, i.e. an increase of almost 143 times. This has lead to overcrowded roads and noise pollution. The transportation sector is one of the major contributors to noise in urban area, accounting for as much as 55% of the total noise on the arterials. Interrupted traffic flow noise is associated with the typical congested conditions in the urban area and has different characteristics from the traffic noise generated by free flow traffic on rural highways. These differences arise from the various traffic flow characteristics such as heterogeneity of traffic, low speeds etc. This is in contrast to traffic on rural highways where the flow is generally continuous in nature. In this dissertation, three different types of models for noise prediction have been used, namely, FHWA Model, CORTN Model and Stop and Go Model. These models have been used to predict the noise level for urban area for interrupted traffic flow. The data collected at eight different sites of the National capital have been analysed and compared with the values predicted by each of three different models. After comparison of the result, it was observed that both FHWA and CORTN Models could be satisfactorily applied for Indian condition as they give acceptable result with a deviation of 7-10 dB (A) for FHWA and 5-8 dB (A) for CORTN Model. iii In addition, statistical analysis has been done on the data between measured and predicted values and a good agreement was obtained. From the results obtained from the statistical analysis, it can be observed that the three models, FHWA, CORTN and Stop and Go, give acceptable results at a confidence level of 95 %, with both observed t-values and F-values exceeding the corresponding critical values. The results obtained from the statistical analysis of the regression model developed using the data from the field studied conducted in Delhi shows an even better relationship between predicted and measured values at 95 % confidence level suggesting that the regression model can be safely used for prediction of Leg for urban conditions.