Noise control in bus rapid transit systems in Istanbul
2015
0 views
0 downloads
Advisor: Yrd. Doç. Dr. Mustafa Sinan Yardım
Abstract (EN)
Nowadays, the negative effects of high noise levels on human health and psychology are well known. "Metrobus systems" are urban rapid transit systems which are used extensively in the daytime hours. Determination of noise exposure levels at the metrobus stations are getting more important to detect precautions for the protection of human health. In this study, the noise levels were determined at the selected stations. Data were collected at 12 different stations and peak hours, which morning 07:00-09:00, evening 17:00-19:00 and noon 12:00-14:00, from 21 July 2015 to 27 July 2015. The results show that dominant noise levels were between 80 dBA and 96 dBA. This noise levels may cause to psychological and physiological effects on passenger and staff who work at the station, even it may cause hearing problems and damage to depending on the duration time. Vehicle volume, average vehicle speed and % heavy vehicles rate data was obtained from the I.B.B. Traffic Control Centre. Relationship between the data and noise levels were examined. Sound level is increasing with the increasing volume and speed of vehicles. Heavy vehicles rate with no correlation between the sound level.
Author
Berivan Akgün
Institution
How to Cite
Berivan Akgün (Master Thesis). Noise control in bus rapid transit systems in Istanbul, 2015, Yıldız Technical University.
Keywords
EN
License
Tüm Hakları Saklıdır
This work is shared under the specified license terms.
More theses from Yıldız Technical University
- An investigation on the relationship between problem solving and critical thinking skill, and academic achievement of vocational and technical high school students(2017)
- Examining ?Historical housing structures" within the confines of protecting ecological balance(2012)
- Approximate solutions of integral equations(2012)
- The annotative dictionary of Kutadgu Bilig in terms of vocabulary(2013)
- Stepper motor speed control with labVIEW(2014)
- Determining supply chain risk factors in food industry(2014)