Evaluation Of The FHWA Traffic Noise Model

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Author/Creator ORCID

Date

2017

Department

Civil Engineering

Program

Doctor of Engineering

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This item is made available by Morgan State University for personal, educational, and research purposes in accordance with Title 17 of the U.S. Copyright Law. Other uses may require permission from the copyright owner.

Abstract

The goal of the present research is to gain clarity regarding the relationship between field measure traffic noise level and the Federal Highway Administration-Traffic Noise Model 2.5 (TNM) predicted noise level while utilizing the same data. This study advances the evaluation of the TNM prediction accuracy in previous research by designing a specific experiment rather than depending on survey of available data. A series of experiments were conducted to determine if any discrepancy (residual error) exists between sanitized noise level measured in the field and the noise level predicted by the TNM. Five-minutes traffic sound intervals were recorded by two sound meters located at 15 and 30 meter offset respectively from a roadway segment. One hundred and sixty-eight, five-minute data intervals were collected for this study. The intervals were segregated into four data sets: fully sanitized, environmentally sanitized, raw, and excluded. Certain criteria were used to define and identify environmentally sanitized and fully sanitized intervals of the raw data. Each data set was then utilized to create 300 randomly generated one-hour simulated sample subpopulation. A residual error was identified that improved the TNM far field prediction. The overall findings support the assertion made by the Federal Highway Administration's Office of Planning, Environment & Realty that the TNM 2.5 predicted noise levels correlate well with calibrated field-measured noise levels. However, a pattern to the TNM residual error within a very limited experiment was identified while using several sanitization schemes. Additional studies are required for this conclusion to be generalized for varying types of roads, traffic conditions, topography, and regions.