Objective: A lower helmet-wearing rate and overspeeding in Pakistan are critical risk behaviors of motorcyclists, causing severe injuries. To explore the differences in the determinants affecting the injury severities among helmeted and non-helmeted motorcyclists in motorcycle crashes caused by overspeeding behavior, single-vehicle motorcycle crash data in Rawalpindi city for 2017-2019 is collected. Considering three possible crash injury severity outcomes of motorcyclists: fatal injury, severe injury and minor injury, the rider, roadway, environmental, and temporal characteristics are estimated.
Methods: To provide a mathematically simpler framework, the current study introduces parsimonious pooled random parameters logit models. Then, the standard pooled random parameters logit models without considering temporal effects are also simulated for comparison. By comparing the goodness of fit measure and estimation results, the parsimonious pooled random parameters logit model is suitable for capturing the temporal instability. Then, the non-transferability among helmeted and non-helmeted overspeeding motorcycle crashes is illustrated by likelihood ratio tests and out-of-sample prediction, and two types of models provide robust results. The marginal effects are also calculated.
Results: Several variables, such as age, cloudy and weekday indicators illustrate temporal instability. Moreover, several variables are observed to only show significance in non-helmeted models, showing non-transferability across helmeted and non-helmeted models.
Conclusions: More educational campaigns, regulation and enforcement, and management countermeasures should be organized for non-helmeted motorcyclists and overspeeding behavior. Such findings also provide research reference for the risk-compensating behavior and self-selected group issues under overspeeding riding considering the usage of helmets.
Keywords: Temporal stability; injury severity; motorcycle crashes; out-of-sample prediction; overspeeding riding behavior; parsimonious pooled random parameters models.