Temporal Instability of Factors Affecting Injury Severity in Helmet-Wearing and Non-Helmet-Wearing Motorcycle Crashes: A Random Parameter Approach with Heterogeneity in Means and Variances

Abstract

Not wearing a helmet, not properly strapping the helmet on, or wearing a substandard helmet increases the risk of fatalities and injuries in motorcycle crashes. This research examines the differences in motorcycle crash injury severity considering crashes involving the compliance with and defiance of helmet use by motorcycle riders and highlights the temporal variation in their impact. Three-year (2017–2019) motorcycle crash data were collected from RESCUE 1122, a provincial emergency response service for Rawalpindi, Pakistan. The available crash data include crash-specific information, vehicle, driver, spatial and temporal characteristics, roadway features, and traffic volume, which influence the motorcyclist’s injury severity. A random parameters logit model with heterogeneity in means and variances was evaluated to predict critical contributory factors in helmet-wearing and non-helmet-wearing motorcyclist crashes. Model estimates suggest significant variations in the impact of explanatory variables on motorcyclists’ injury severity in the case of compliance with and defiance of helmet use. For helmet-wearing motorcyclists, key factors significantly associated with increasingly severe injury and fatal injuries include young riders (below 20 years of age), female pillion riders, collisions with another motorcycle, large trucks, passenger car, drivers aged 50 years and above, and drivers being distracted while driving. In contrast, for non-helmet-wearing motorcyclists, the significant factors responsible for severe injuries and fatalities were distracted driving, the collision of two motorcycles, crashes at U-turns, weekday crashes, and drivers above 50 years of age. The impact of parameters that predict motorcyclist injury severity was found to vary dramatically over time, exhibiting statistically significant temporal instability. The results of this study can serve as potential motorcycle safety guidelines for all relevant stakeholders to improve the state of motorcycle safety in the country.

Exploring University Campus Community’s Perceptions and Barriers toward Biking: A Case Study of the University of Tennessee Knoxville

“…Non-bicyclists tend to avoid biking due to perceived dangers or lack of safe route options. High-speed traffic and a high proportion of vehicular traffic raise cyclists' safety concerns [10] [34] [35] [36] [37]. Another study explored individual preferences for cycling environments and found the willingness of people to travel up to 20 minutes extra for a better facility [38].…”

Analysis of pedestrian accident severity by considering temporal instability and heterogeneity

“…Hosseini et al [ 13 ] investigated data from bicycle collisions in Los Angeles, California, from 2012 to 2017 and found that the effects of certain factors, such as cyclists over the age of 55 years, cyclist negligence, and rear-end collisions, differed between years; the reason for these changes was unclear. Ijaz et al [ 14 ]used motorcycle collision data for 2017 to 2019 from Rawalpindi, Pakistan, to examine the temporal instability of factors affecting the severity of helmeted and unhelmeted motorcycle collision injuries. They found that the likelihood of fatal injuries in unhelmeted collisions might be lower during nonpeak hours than during peak hours, and helmeted collisions are more likely to result in fatal and minor injuries on workdays than on weekends.…”

Joint Analysis of Crash Frequency by Severity Based on a Random Parameters Approach

“…The aforementioned random parameters approach (the basic random parameters model) assumes that the distributional mean for the normal distribution of a random parameter is the same across observations and that all of the random parameters are independent, which is not true in many cases due to the complex interactions of both observed and unobserved variables. A more flexible way to improve the basic random parameters model is by allowing the mean to vary across observations (i.e., assuming heterogeneous means of random parameters) [25][26][27][28] and the correlation of random parameters (i.e., introducing covariance of random parameters) [29][30][31]. Recent studies have proven that the goodness-of-fit, prediction accuracy, and explanatory analysis can be significantly enhanced by using the improved random parameters approach [25][26][27][28][29][30][31].…”

“…A more flexible way to improve the basic random parameters model is by allowing the mean to vary across observations (i.e., assuming heterogeneous means of random parameters) [25][26][27][28] and the correlation of random parameters (i.e., introducing covariance of random parameters) [29][30][31]. Recent studies have proven that the goodness-of-fit, prediction accuracy, and explanatory analysis can be significantly enhanced by using the improved random parameters approach [25][26][27][28][29][30][31]. Although the abovementioned random parameters approach has been increasingly adopted for either crash-frequency or injury-severity analysis to account for unobserved heterogamy, it has rarely been used for the joint analysis of crash frequency and injury severity (i.e., jointly modeling for crash frequency by severity level).…”