Motorcycle rider error and engagement in distracting activities: A study using the Attention-Related Driving Errors Scale (ARDES-M)

Assessing the interdependence of rider fault-status and injury severity in motorcycle rear-end crashes: insights from bivariate probit and XGBoost-SHAP models

This study examines the interdependent relationship between fault status and injury severity in motorcycle rear-end crashes in Thailand using data from 1,549 crashes (2011-2015) integrated from the Department of Highway's Accident Information Management System and Traffic Information Movement System. This article employs a bivariate probit model alongside various boosting techniques for simultaneous estimation of injury severity and at-fault status. Among the tested models (AdaBoost, CatBoost and LightGBM), both the bivariate probit and XGBoost-Endogenous models demonstrate superior performance in accuracy and F1-score. The bivariate probit model reveals that injury severity is significantly influenced by rider characteristics (age, gender), road features, and traffic conditions. Riders under 55 years old, female riders and those on roads with depressed medians or higher traffic volume show lower injury severity risk. Conversely, drunk riding, nighttime crashes on unlit roads, and higher truck traffic percentages increase severe injury likelihood. The XGBoost model corroborates these findings, identifying traffic volume, truck percentage and nighttime conditions on unlit roads as the most crucial predictors of injury severity. Regarding fault status, younger riders and those using safety equipment show a higher probability of being at-fault. This novel analytical approach provides valuable insights for motorcycle safety policy development and future research directions.

Development of an AI-Integrated Smart Helmet for Motorcycle Accident Prevention: A Feasibility Study

Introduction

The purpose of this research was to develop a smart helmet, including emphasizing the AI integration and the device's role in enhancing road safety with a mechanism that stimulates the driver to recognize which vehicle is approaching and the speed levels of the vehicle while it is moving, and to assess the satisfaction and feasibility of drivers while using the smart helmet.

Methods

This study included 139 participants who were general people in Thailand. The research model consists of four research and development steps: research, design and development, implementation, and evaluation. The questionnaires included general information, satisfaction, and feasibility of using a smart helmet.

Results

The study showed that males had a greater number of participants (63.31%), aged between 21 and 40 years (64.03%), higher education (73.38%), and most of the participants were university students (90.64%). Overall, satisfaction with using smart helmets was high (mean = 4.20, SD = 0.83), and the overall possibility of using smart helmets was very high (mean = 4.33, SD = 0.75).

Conclusion

The participants' reflections indicated that smart helmets can be a possibility for further development and are highly feasible practical application devices. Moreover, the smart helmet is beneficial to riders in terms of warning functions to prevent and monitor accidents. Nurses and health care providers may use these results to develop programs or devices that can encourage people to be aware of harm on the road while riding motorcycles.

Unveiling the road to safety: Understanding the factors influencing motorcycle accidents among riders in rural Chiang Mai, Thailand

Background

Motorcycle accidents pose a significant threat to traffic safety in Thailand, particularly in rural areas where the severity of these accidents often results in prolonged medical treatment and a reduction in the quality of life of the affected individual.

Objectives

To investigate the prevalence and the factors associated with motorcycle accidents among motorcycle riders in rural areas in Chiang Mai, Thailand.

Method

A cross-sectional study was conducted from December 2022 to March 2023 via an anonymous survey in Chiang Mai, Thailand. A total of 308 participants engaged with the survey. The data about background information, motorcycle details, personal protective equipment, risky behaviors, attitude toward riding, and history of motorcycle accidents in the prior six months were collected and analyzed by binary logistic regression.

Results

Of 308 participants, the mean age was 56 years old (SD = 14.2), females were 56.8 % (N = 175), 51 % had co-morbidity, and 40.6 % were active alcohol drinkers. The prevalence of individuals who experienced a motorcycle accident within the previous six months was 57.1 %. Notably, the most unsafe riding behavior was not wearing a helmet while riding, which had a prevalence of more than 80 % in both the accident and non-accident groups. The study found significant associated factors for motorcycle accidents in rural communities, including the history of alcohol consumption (aOR 1.71, 95 % CI: 1.05,2.79), changing lanes without using turn signals (aOR 1.93, 95 % CI: 1.07,3.48) and those who strongly disagree with the notion that listening to music while riding is dangerous (aOR 2.80, 95 % CI: 1.06, 7.43).

Conclusion

Over half of motorcycle riders have been in accidents. These findings emphasize the need to enforce drunk-driving and traffic laws. Comprehensive motorcycle rider education and safety training are needed to encourage responsible riding.

Measurement invariance of the driving inattention scale (ARDES) across 7 countries

The Attention-Related Driving Errors Scale (ARDES) is a self-report measure of individual differences in driving inattention. ARDES was originally developed in Spanish (Argentina), and later adapted to other countries and languages. Evidence supporting the reliability and validity of ARDES scores has been obtained in various different countries. However, no study has been conducted to specifically examine the measurement invariance of ARDES measures across countries, thus limiting their comparability. Can different language versions of ARDES provide comparable measures across countries with different traffic regulations and cultural norms? To what extent might cultural differences prevent researchers from making valid inferences based on ARDES measures? Using Alignment Analysis, the present study assessed the approximate invariance of ARDES measures in seven countries: Argentina (n = 603), Australia (n = 378), Brazil (n = 220), China (n = 308). Spain (n = 310), UK (n = 298), and USA (n = 278). The three-factor structure of ARDES scores (differentiating driving errors occurring at Navigation, Manoeuvring and Control levels) was used as the target theoretical model. A fixed alignment analysis was conducted to examine approximate measurement invariance. 12.3 % of the intercepts and 0.8 % of the item-factor loadings were identified as non-invariant, averaging 8.6 % of non-invariance. Despite substantial differences among the countries, sample recruitment or representativeness, study results support resorting to ARDES measures to make comparisons across the country samples. Thus, the range of cultures, laws and collision risk across these 7 countries provides a demanding assessment for a cultural-free inattention while-driving. The alignment analysis results suggest that ARDES measures reach near equivalence among the countries in the study. We hope this study will serve as a basis for future cross-cultural research on driving inattention using ARDES.