Applying mathematical modeling to predict road traffic noise in Phuket Province, Thailand

© Int. J. of GEOMATE. Road traffic is the most significant source of noise in an urban city and is considered not only an environmental nuisance but also a threat to public health. Therefore, this study aimed to determine road traffic noise levels in Phuket Province, including Muang Phuket, Thalang,...

Full description

Saved in:
Bibliographic Details
Main Authors: Withida Patthanaissaranukool, Kulnapa Bunnakrid, Tanasri Sihabut
Other Authors: King Chulalongkorn Memorial Hospital, Faculty of Medicine Chulalongkorn University
Format: Article
Published: 2020
Subjects:
Online Access:https://repository.li.mahidol.ac.th/handle/123456789/49896
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Mahidol University
Description
Summary:© Int. J. of GEOMATE. Road traffic is the most significant source of noise in an urban city and is considered not only an environmental nuisance but also a threat to public health. Therefore, this study aimed to determine road traffic noise levels in Phuket Province, including Muang Phuket, Thalang, and Kathu District; and to compare them with predicted noise levels using NMTHAI 1.2. Traffic noise level, traffic volume and speed of vehicles were measured on main roads including Yaowarat, Ratsada, Montri, Patipat, Ban Muangmai, Ban Kain, Ban Lipon, Baramee and Vichitsongkram Road. The results showed that traffic noise in Muang Phuket, Thalang and Kathu Districts were 70.0-70.9, 72.9-74.7 and 74.6-74.8 dBA, respectively. The result revealed that traffic noise levels obtained from the model were higher than measured noise at an average of 4.8±2.3 dBA. A high correlation was observed between predicted and measured traffic noise levels (R2 = 0.655, P < 0.01). Speed of vehicles and traffic volume were key variables affecting traffic noise level with a correlation coefficient of 0.752 and 0.702 at 99% confidence level, respectively. The model performed reasonably well under different traffic noise conditions and could predict traffic noise of other cities in Thailand.