Citation

Wang C, Ijaz M, Chen F, Zhang Y, Cheng J, Zahid M. Anal. Meth. Accid. Res. 2022; 36: e100249.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.amar.2022.100249

PMID

unavailable

Abstract

With rapid growth in motorcycle use and relatively low helmet-wearing usage rates, injuries and fatalities resulting from motorcycle crashes in Pakistan are a critical concern. To investigate possible temporal instability and differences in the factors that determine resulting injury severities between male and female non-helmet wearing motorcyclists, this study estimated male and female injury severity models using a random parameter logit approach with heterogeneity in means and variances. Motorcycle crash data between 2017 and 2019 from Rawalpindi, Pakistan, were used for the model estimation. With four possible crash injury severity outcomes (injury, minor injury, severe injury, and fatal injury), a wide variety of explanatory variables were considered, including the characteristics of riders, vehicles, roadways, environments, crashes, and temporal considerations. Temporal shifts in the effects of explanatory variables were confirmed using a series of likelihood ratio tests. While the effects of several explanatory variables are relatively temporally stable, those of most variables vary considerably across the years. In addition, out-of-sample simulations underscore the temporal shifts from year to year and the differences between male and female motorcyclist-injury severity. The findings suggest that factors such as effective enforcement countermeasures and relevant educational campaigns can be implemented to reduce injury severity. The statistically significant differences between male and female non-helmeted injury severity models underscore the importance of policies that separately target male and female motorcycle rider safety.

Language: en

Keywords

Injury severity; Non-helmet wearing motorcyclist crash; Out-of-sample prediction; Random parameters logit model; Temporal stability