Using street imagery and crowdsourcing internet marketplaces to measure motorcycle helmet use in Bangkok, Thailand

Measuring motorcycle helmet use is critical for countries to target enforcement and measure the impact of new laws for road safety for motorcyclists. Current methods to measure helmet use are time-consuming and costly and involve roadside observation and review of hospital records. This novel method, using street imagery and crowdsourcing internet marketplaces, has the ability to revolutionize how this life-saving intervention is measured by dramatically reducing time and cost.

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Introduction: The 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.

Methods: Using 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.

Results: After 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). Conclusion: This 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.

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Year: 2019
Level of government: National/Federal government


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