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Development Of A Statistical Model For Predicting A Traffic Noise (case Study: Irbid-jordan)

Submitted2021-05-27
Last Update2021-09-30
TitleDevelopment Of A Statistical Model For Predicting A Traffic Noise (case Study: Irbid-jordan)
Author(s)Author #1
Name: Taisir Khedaywi
Org: Jordan University of Science and Technology
Country: Jordan
Email: Khedaywi@just.edu.jo

Author #2
Name: Nabil AlKofahi
Org: Jadara University
Country: Jordan
Email: Alkofahi@jadara.edu.jo

Author #3
Name: Lila Alomarri
Org: Jordan university of science and technology
Country: Jordan
Email: lailaalomari@yahoo.com

Other Author(s)
Contact AuthorAuthor #2
Alt Email: nabil_alkofahi@hotmail.com
Telephone: 00962795052757
KeywordsTraffic noise, Traffic noise model, Predicted noise, CRTN
AbstractTraffic noise has been recognized as environmental pollution in many cities. This study aims to develop a statistical model that can estimate road traffic noise as a function of traffic volume (passenger cars, medium vehicles, heavy vehicles), speed, number of lanes, pedestrian volume, parking type (on-street and off-street), grade of the road segment and temperature to study the impact of these variables on the noise level. Thirty sites were selected in urban areas for arterial and ring roads in Irbid city. These sites were categorized as: traffic signal intersections, roundabouts and sections. The model shows that heavy vehicle volume is the main influencing factor that affects the noise level. Also, increasing the number of lanes by one lane in any approach will increase the noise level by 0.50 dB (decibel). Holding other factors constant, the model shows that the increase of (1 km/hr) in speed will increase traffic noise by 0.15 dB. Results show that the present and predicted noise level in the future (2030) is high and exceeds the maximum allowable limit (60 dB) for all sites. The developed model compared with the British method [Calculation of Road Traffic Noise (CRTN)] was found to be compatible.
Paperview paper 5999.pdf (455KB)

http://www.just.edu.jo