STUDY ON TRAFFIC NOISE MODEL AND DEVELOPMENT OF NOMOGRAPHS FOR TRAFFIC NOISE PREDICTION FOR INDIAN NATIONAL HIGHWAY

Abstract

The need foK controlling community noise is being felt with increasing intensity each passing day. Studies have shown that motor vehicles operating on highways constitute one of the major sources of noise, which have an impact on the community. This thesis presents the prediction model developed for Indian conditions for freely flowing traffic. The main aim has been to study the impact on community living near road side and to develop a methodology for prediction and, which could be made use of for further refinement of this model. In part 1, following the introduction to noise and its effects on humans, the review of the relevant literature available is presented in Chapter 2, which covers the whole spectrum of highway traffic noise — generation and control. This is followed by a brief description of some noise terminologies and various parameters affecting traffic noise in Chapters 1 and 3 respectively. In Part 2, Chapter 4, the scope and use of measurement and prediction methods are compared and the categories of noise prediction methods are outlined. Chapter 4, section 4.3 provides the overview of the strategy adopted for current model development. Chapter 5 covers the methodology adopted for making noise measurements and details its various aspects. The procedure adopted for the design of the data collection exercise is also given. Chapter 6 defines the different parameters incorporated in the prediction model and explains the prediction procedure in detail. In Part 3, Chapter 7, the prediction procedure is further explained by a calculation exercise making use of the data collected at one site on NH — 58. The results of the measurement and analysis are presented in Chapter 8. On the basis of the validation measurements at position Al on NH — 58, it is suggested that, the indiscriminate use of horn (especially pressure horn by the passenger buses) could also be analyzed for determining its effect on L eq and thereby its effect on the variation of prediction values.