Development of a highway geometry based noise abatement model

Exposure to noise generated from traffic has led to significant annoyance and sleep deprivation in people. Continuous exposure to traffic noise can lead to mental instability and reduce the learning rate of children. Many cases of cardiovascular diseases, sleep disturbance, and cognitive impairment have been reported as a result of traffic noise. With increasing population of cars, health problems resulting from traffic noise will continue to increase and it is of utmost priority to abate this noise. Noise abatement is one of the most effective strategies in reducing noise level for communities close to roadways, although it is also one of the major cost drivers in highway projects to reduce noise. Extensive noise modeling is required to determine the feasibility of abatement choices; however, traffic noise models usually do not interact with highway geometries in selecting optimized noise abatement options. The Traffic Noise Model (TNM) developed by Federal Highway Administration (FHWA) was used to compute highway traffic noise and evaluate the effect of noise abatements. A parametric study was conducted on a total of 23,093 scenarios generated to examine ten major variables influencing roadway noise level. A noise prediction model was developed using multiple regression analysis incorporating parameters such as roadway geometry (horizontal offsets and elevation differences), barrier height, receiver height, traffic volume, roadway section, road-surface material, barrier types, speed and roadway section. This model can be used to examine possible noise mitigation strategies to make the best decision at design stage of a roadway.

• Record URL:
  https://doi.org/10.1016/j.trip.2021.100382
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2590198221000890
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/25901982
• Supplemental Notes:
  • © 2021 Mehdi Shokouhian et al. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Shokouhian, Mehdi
  • 0000-0001-9823-0661
  • Owolabi, Oludare
  • Efe, Steve
  • Ikiriko, Sotonye
  • 0000-0001-9675-6600
  • Tsegaye, Tiyobistiya
• Publication Date: 2021-6

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Photos; References; Tables;
• Pagination: 100382
• Serial:
  • Transportation Research Interdisciplinary Perspectives
  • Volume: 10
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2590-1982
  • Serial URL: https://www.journals.elsevier.com/transportation-research-interdisciplinary-perspectives
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Feasibility analysis; Geometric design; Highway design; Mathematical models; Multiple regression analysis; Noise control; Parametric analysis; Traffic noise
• Identifier Terms: U.S. Federal Highway Administration
• Subject Areas: Design; Environment; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01778024
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 28 2021 1:41PM