Traffic Noise: 1/f Characteristics

Traffic Noise: 1/f Characteristics

K. B. Patange (Deogiri College, India), A. R. Khan (Maulana Azad College, India), S. H. Behere (Dr. Babasaheb Ambedkar Marathwada University, India) and Y. H. Shaikh (Shivaji Arts, Commerce and Science College, India)
DOI: 10.4018/978-1-4666-3890-7.ch006
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In this paper, the study of traffic noise is presented from the point of view of 1/f noise. Samples of Traffic Noise are collected from selected locations from busy roads of Aurangabad city in Maharashtra state (India) and data is analyzed. It is observed that in many cases the traffic noise possesses pink noise (1/f noise) prevailing over appreciable range of frequency. The log log plot of noise power versus frequency results in a straight line with a slope approximately equal to unity confirming the presence of pink noise. After certain frequency, the noise power no longer behaves like pink noise (1/f noise) and becomes more or less constant with random fluctuations. Plots of noise power versus frequency on log log basis for different locations studied are presented and the inferences are discussed.
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1. Introduction

Traffic noise is considered as one of the important sources of noise pollution that adversely affects human health in residential urban areas (Onuu, 2000; Martin, 2002; Gambart, Myncke, & Cops, 1976). Low frequency noise is common as background noise in urban environments arising due to many artificial sources like road vehicles, aeroplanes, industrial machinery, artillery and mining explosions and air movement machinery. This includes wind turbines, compressors, and indoor ventilation and air conditioning units etc. (Tempest, 1985; Leventhall, 1988). Low-frequency noise or flicker noise has been found in many systems (Li, 2009). Intense low frequency noise may produce clear symptoms like respiratory impairment and aural pain (Von Gierke & Nixon, 1976). The 978-1-4666-3890-7.ch006.m01behavior generally persists over low frequencies (Sinha, 1996). The power spectra of large variety of complex systems exhibit 1/ f behavior at low frequencies. It is widely accepted that 1/ f noise and self-similarity are characteristic signatures of complexity (Gilden, Thornton, & Mallon, 1995; Wong, 2003). Self-similarity, scale invariance and fractal nature are found to be characteristics of many natural phenomena (Shaikh, Khan, Pathan, Patil, & Behere, 2009; Shaikh, Khan, Iqbal, Behere, & Bagare, 2008). 1/f noise refers to the phenomenon of the spectral density, 978-1-4666-3890-7.ch006.m02of a stochastic process (Ward & Greenwood, 2007) having the form978-1-4666-3890-7.ch006.m03 = constant978-1-4666-3890-7.ch006.m04Where978-1-4666-3890-7.ch006.m05is frequency, on an interval bounded away from both zero and infinity. Spectral density (power distribution in the frequency spectrum) is such a property, which can be used to distinguish different types of noise (Wikipedia, n. d.). This classification by spectral density is given “color” terminology. The spectral density of white noise is flat (α = 0), while pink noise has α = 1, and brown noise has α = 2. During last 80 years since the first observation by Johnson (1925), long-memory processes with long-term correlations and 978-1-4666-3890-7.ch006.m06 (with 978-1-4666-3890-7.ch006.m07) behavior of power spectra at low frequencies have been observed in physics, technology, biology, astrophysics, geophysics, economics, psychology, language and even music (Wong, 2003; Press, 1978; Hooge, Kleinpenning, & Vandamme, 1981; Dutta & Horn, 1981; Kogan, 1985; Weissman, 1988; West & Shlesinger, 1990; Van Vliet, 1991; Zhigalskii, 1997; Milotti, 2002) and in traffic flow too (Yale University, n. d.).

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