scholarly journals Factors affecting motorcycle helmet use in the population of Greater Athens, Greece

1999 ◽  
Vol 5 (4) ◽  
pp. 264-267 ◽  
Author(s):  
A. Skalkidou ◽  
E. Petridou ◽  
F. C Papadopoulos ◽  
N. Dessypris ◽  
D. Trichopoulos
2014 ◽  
Vol 16 (3) ◽  
pp. 276-282 ◽  
Author(s):  
Kim T. Thai ◽  
Andrew S. McIntosh ◽  
Toh Yen Pang

2015 ◽  
Vol 85 ◽  
pp. 102-110 ◽  
Author(s):  
Mahdi Quchaniyan Haqverdi ◽  
Seyedehsan Seyedabrishami ◽  
John A. Groeger

2002 ◽  
Vol 92 (8) ◽  
pp. 1352-1355 ◽  
Author(s):  
Kimberly M. Auman ◽  
Joseph A. Kufera ◽  
Michael F. Ballesteros ◽  
John E. Smialek ◽  
Patricia C. Dischinger

2010 ◽  
Author(s):  
Megan L. Ranney ◽  
Michael J. Mello ◽  
Janette B. Baird ◽  
Peter R. Chai ◽  
Melissa A. Clark
Keyword(s):  

2019 ◽  
Vol 26 (2) ◽  
pp. 103-108
Author(s):  
Hasan S. Merali ◽  
Li-Yi Lin ◽  
Qingfeng Li ◽  
Kavi Bhalla

IntroductionThe majority of Thailand’s road traffic deaths occur on motorised two-wheeled or three-wheeled vehicles. Accurately measuring helmet use is important for the evaluation of new legislation and enforcement. Current methods for estimating helmet use involve roadside observation or surveillance of police and hospital records, both of which are time-consuming and costly. Our objective was to develop a novel method of estimating motorcycle helmet use.MethodsUsing Google Maps, 3000 intersections in Bangkok were selected at random. At each intersection, hyperlinks of four images 90° apart were extracted. These 12 000 images were processed in Amazon Mechanical Turk using crowdsourcing to identify images containing motorcycles. The remaining images were sorted manually to determine helmet use.ResultsAfter processing, 462 unique motorcycle drivers were analysed. The overall helmet wearing rate was 66.7 % (95% CI 62.6 % to 71.0 %). Taxi drivers had higher helmet use, 88.4% (95% CI 78.4% to 94.9%), compared with non-taxi drivers, 62.8% (95% CI 57.9% to 67.6%). Helmet use on non-residential roads, 85.2% (95% CI 78.1 % to 90.7%), was higher compared with residential roads, 58.5% (95% CI 52.8% to 64.1%). Using logistic regression, the odds of a taxi driver wearing a helmet compared with a non-taxi driver was significantly increased 1.490 (p<0.01). The odds of helmet use on non-residential roads as compared with residential roads was also increased at 1.389 (p<0.01).ConclusionThis novel method of estimating helmet use has produced results similar to traditional methods. Applying this technology can reduce time and monetary costs and could be used anywhere street imagery is used. Future directions include automating this process through machine learning.


2015 ◽  
Vol 16 (8) ◽  
pp. 809-817 ◽  
Author(s):  
Millicent Awialie Akaateba ◽  
Ibrahim Yakubu ◽  
Bernard Afiik Akanpabadai Akanbang
Keyword(s):  

2008 ◽  
Vol 40 (4) ◽  
pp. 1627-1633 ◽  
Author(s):  
Dang Viet Hung ◽  
Mark R. Stevenson ◽  
Rebecca Q. Ivers

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