2005The Journal of the Acoustical Society of AmericaRequires access

Development of method for assessing traffic noise in certain typical conditions

Weixiong Wu

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Abstract

Assessing traffic noise is difficult in certain typical conditions in New York City due to changed street geometries, challenges of collection of non-traffic noise components, and levels of existing noise affected by heavy traffic at adjacent streets, among other variables. In general, a proportional model, i.e., a logarithmic equation to compute total passenger car equivalents (PCEs), is employed to assess traffic noise impacts based upon the noise methodology and the noise criteria under the City Environmental Quality Review (CEQR) guidelines. However, in some typical conditions, such as significant changes in roadway or street geometry, roadways that currently carry no or very low traffic volumes, and existing noise levels that are the result of multiple sources, the FHWA Traffic Noise Model (TNM) can be used to better compute project-generated traffic components. This paper presents a development of noise analysis method dealing with these conditions. Once a proportional model identifies any potential noise impacts for screening purposes, TNM computations can be conducted for more thorough and detailed noise analyses. The results demonstrate that while a proportional model provides a practical and convenient noise analysis for most situations, TNM can provide more accurate noise assessments for the conditions listed above.

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Assessing traffic noise is difficult in certain typical conditions in New York City due to changed street geometries, challenges of collection of non-traffic noise components, and levels of existing noise affected by heavy traffic at adjacent streets, among other variables. In general, a proportional model, i.e., a logarithmic equation to compute total passenger car equivalents (PCEs), is employed to assess traffic noise impacts based upon the noise methodology and the noise criteria under the City Environmental Quality Review (CEQR) guidelines. However, in some typical conditions, such as significant changes in roadway or street geometry, roadways that currently carry no or very low traffic volumes, and existing noise levels that are the result of multiple sources, the FHWA Traffic Noise Model (TNM) can be used to better compute project-generated traffic components. This paper presents a development of noise analysis method dealing with these conditions. Once a proportional model identifies any potential noise impacts for screening purposes, TNM computations can be conducted for more thorough and detailed noise analyses. The results demonstrate that while a proportional model provides a practical and convenient noise analysis for most situations, TNM can provide more accurate noise assessments for the conditions listed above.

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Available abstract

Assessing traffic noise is difficult in certain typical conditions in New York City due to changed street geometries, challenges of collection of non-traffic noise components, and levels of existing noise affected by heavy traffic at adjacent streets, among other variables. In general, a proportional model, i.e., a logarithmic equation to compute total passenger car equivalents (PCEs), is employed to assess traffic noise impacts based upon the noise methodology and the noise criteria under the City Environmental Quality Review (CEQR) guidelines. However, in some typical conditions, such as significant changes in roadway or street geometry, roadways that currently carry no or very low traffic volumes, and existing noise levels that are the result of multiple sources, the FHWA Traffic Noise Model (TNM) can be used to better compute project-generated traffic components. This paper presents a development of noise analysis method dealing with these conditions. Once a proportional model identifies any potential noise impacts for screening purposes, TNM computations can be conducted for more thorough and detailed noise analyses. The results demonstrate that while a proportional model provides a practical and convenient noise analysis for most situations, TNM can provide more accurate noise assessments for the conditions listed above.

Key concepts: Noise (video), Traffic noise, Computer science, Logarithm, Roadway noise, Computation, Noise barrier, Noise pollution

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