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

Abstract

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.

An Unhelmeted Motorcyclist Not Holding Both Handlebars - A Photographic Idea for Education Presented as an Initial Scoping Study

“…Merali and colleague's street imagery study using google maps has been previously referred to in the introduction and further analysis of the data was done according to motorcyclist category and whether the streets were residential or not (Merali et al, 2020).…”

“…This could include improved education or awareness as a result of ideas such as that which is presented in this manuscript, or improving law enforcement, increasing fines for non-compliance or making helmets more affordable and less cost to purchase. Merali and colleagues mention that street imagery in the future maybe automated via machine learning (Merali et al, 2020). That hence may facilitate the ease of capturing and/ or re-capturing data, analysis and re-analysis to monitor helmet usage rates in the future while also assessing and monitoring change.…”

“…Merali and colleagues report that the majority of Thailand's road traffic deaths occur on motorised two-wheeled or three-wheeled vehicles (Merali et al, 2020). The authors developed a novel method of estimating motorcycle helmet wearing utilising Google Maps in Bangkok.…”

Artificial intelligence and the future of global health

“…In one study, researchers used street imagery available online and machine learning to estimate helmet use prevalence. 63 In another study, a large government dataset of road injuries and data mining techniques were used to predict road injury severity. 64…”

GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography

“…In addition to phone and photo data, GSV images are another data source that are even more consistent, cost-effective, and scalable. Recent studies [320,[328][329][330] that have employed GSV images have shown the data's great potential for large-scale comparative analysis. For example, Goel et al [328] collected 2000 GSV images from 34 cities to predict travel patterns at the city level.…”