Patterns of motorcycle helmet use - A naturalistic observation study in Myanmar

Gender disparities in observed motorcycle helmet use in Madagascar: female motorcyclists behave safer but have lower overall protection

BACKGROUND

Although motorcycle helmets can save lives in case of a crash, no helmet use data are available for many countries. When data is available, it is often only analysed as a global average, preventing targeted road safety education and legislative action. In this study, we conducted a detailed analysis of motorcycle helmet use in the capital of Madagascar.

METHODS

Using a cross-sectional observational field survey framework, we observed 17 230 individual motorcycles. We systematically recorded motorcycle riders' helmet use, position on the motorcycle, rider numbers and gender.

RESULTS

We found a general helmet use of 76.1%. Observed drivers had a significantly higher helmet use (84.6%) than passengers (47.7%), and subsequently helmet use per motorcycle decreased significantly when the number of riders per motorcycle increased. Female drivers had significantly higher helmet use than male drivers, and female passengers had significantly higher helmet use than male passengers. That is, on the same position of the motorcycle, female riders behaved safer than male riders. However, since female riders were more often passengers than drivers, their average helmet use was lower than that of male riders overall. Contrary to findings from other countries, motorcycle helmet use did not differ significantly throughout the day but was relatively constant.

CONCLUSION

Our results show the potential for injury and fatality prevention in Madagascar through increased passenger helmet use. This increase would also proportionally benefit female riders more than male riders. Findings regarding road safety legislation's applied impact, education, enforcement and future research needs are discussed.

10 city analysis of child passenger helmet use

INTRODUCTION

Road traffic injuries are the leading cause of death in children over age five. Most of these deaths occur in low- and middle-income countries. Vulnerable road users, such as motorcyclists and their passengers, are at higher risk. Helmets have significantly decreased morbidity and mortality for motorcyclists; however, they are often unused. The second phase of the Bloomberg Philanthropies Initiative for Global Road Safety was launched in 2015 to improve road safety in 10 cities. This study focuses on child passenger helmet use data from that study to understand the prevalence of helmet use and factors that are associated with helmet use.

METHODS

The 10 cities selected were Accra, Addis Ababa, Bandung, Bangkok, Bogota, Fortaleza, Ho Chi Minh City, Mumbai, Sao Paulo, and Shanghai. Eight rounds of roadside observational data were collected from February 2015 to April 2019. Observers noted correct child motorcycle passenger helmet use and other site observations including weather patterns, traffic volume, and road surface conditions. A multivariable Poisson regression model was used to examine correct helmet use trends over time. A multivariable logistic regression model was fitted for correct child passenger helmet use in all cities controlling for weather, observation time, number of passengers, and driver's correct helmet use.

RESULTS

This dataset contained 99,846 motorcycle child passenger observations across the 10 cities. The highest prevalence of correct child passenger helmet use was in Sao Paulo at 97.33%. Six cities had under 25% correct helmet use for child passengers. Examining helmet use over time, only five cities had a significant increase, four cities had no change, and Ho Chi Minh City demonstrated a decrease. In the multivariable regression model, child passengers had higher odds of wearing helmets in adverse weather conditions, early mornings, if the driver wore a helmet, and if there were fewer passengers.

CONCLUSIONS

The prevalence of correct child passenger helmet utilization shows large variation globally and is concerningly low overall. Enhanced enforcement in combination with media campaigns may have contributed to increasing helmet use prevalence over time. Further research is needed to understand reasons for low child passenger helmet use in most cities.

Helmet wearing behavior where people often ride motorcycle in Ethiopia: A cross-sectional study

Background

Road traffic accidents are a major global concern that affects all people regardless of their age, sex, wealth, and ethnicity. Injuries and deaths due to motorcycles are increasing, especially in developing countries. Wearing helmet is effective in reducing deaths and injuries caused by motorcycle accidents.

Objectives

To assess the magnitude of helmet wearing behavior and its determinants among motorcycle riders in Sawula and Bulky towns, Gofa zone, Southern Ethiopia.

Methods

A community-based cross-sectional study was conducted from April, 15 to May 25, 2020, among 422 motorcycle drivers in Sawula and Bulky towns, where people often drive motorcycles. A stratified sampling technique was used to recruit sampled drivers in a face-to-face interview. Data were entered into EPI-data version 3.1 software and exported to SPSS version 23 software to manage analysis. Descriptive analyses such as frequency, percentage, mean and standard deviation were performed as necessary. Logistic regression models were fitted to identify the predictors of helmet wearing behavior. Adjusted odds ratios (AOR) with 95% confidence interval (CI) were used to determine the magnitude and strength of the association.

Results

A total of 403 motorcycle drivers participated in the study which gave a 95.5% response rate. Among 403 motorcycle riders, only 12.4% (95% CI, 9.2 to 15.6%) wore helmets while driving motorcycles. Having license [AOR 3.51(95% C.I 1.56–7.89)], driving distance >10Km [AOR 2.53(95% C.I 1.08–5.91)], History of exposure to accident [AOR 2.71(95% C.I 1.32–5.55)], driving experience of ≥10 years [AOR 2.98 (95% C.I 1.25–7.09)] and high perceived susceptibility to accident [AOR 3.10(95% C.I 1.29–7.46)] had statistically significant association with helmet wearing compared to their counterparts.

Conclusions

This study found that helmet-wearing behavior was very low. Having a license, driving distance, exposure to accidents, driving experience, and accident risk perception were determinants of helmet wearing behavior. These determinants imply the need for interventions that focus on behavioral change communications such as awareness creation campaigns and mandatory helmet wearing laws.

Detecting motorcycle helmet use with deep learning

The continuous motorization of traffic has led to a sustained increase in the global number of road related fatalities and injuries. To counter this, governments are focusing on enforcing safe and law-abiding behavior in traffic. However, especially in developing countries where the motorcycle is the main form of transportation, there is a lack of comprehensive data on the safety-critical behavioral metric of motorcycle helmet use. This lack of data prohibits targeted enforcement and education campaigns which are crucial for injury prevention. Hence, we have developed an algorithm for the automated registration of motorcycle helmet usage from video data, using a deep learning approach. Based on 91,000 annotated frames of video data, collected at multiple observation sites in 7 cities across the country of Myanmar, we trained our algorithm to detect active motorcycles, the number and position of riders on the motorcycle, as well as their helmet use. An analysis of the algorithm's accuracy on an annotated test data set, and a comparison to available human-registered helmet use data reveals a high accuracy of our approach. Our algorithm registers motorcycle helmet use rates with an accuracy of -4.4% and +2.1% in comparison to a human observer, with minimal training for individual observation sites. Without observation site specific training, the accuracy of helmet use detection decreases slightly, depending on a number of factors. Our approach can be implemented in existing roadside traffic surveillance infrastructure and can facilitate targeted data-driven injury prevention campaigns with real-time speed. Implications of the proposed method, as well as measures that can further improve detection accuracy are discussed.