Cargando…

Sociodemographic characteristics, riding behavior and motorcycle crash involvement: a structural equation modeling approach

BACKGROUND: The increasing rate of traffic crashes involving motorcyclists have turned into a public health and road safety concern. Furthermore, riding behaviors and their precedent factors have been identified as potential determinants for assessing, intervening, and prevent-ing traffic injuries o...

Descripción completa

Detalles Bibliográficos
Autores principales: Naderpour, Sara, Heydari, Seyed Taghi, Bagheri Lankarani, Kamran, Motevalian, Seyed Abbas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Kermanshah University of Medical Sciences 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369328/
https://www.ncbi.nlm.nih.gov/pubmed/36588299
http://dx.doi.org/10.5249/jivr.v15i1.1784
Descripción
Sumario:BACKGROUND: The increasing rate of traffic crashes involving motorcyclists have turned into a public health and road safety concern. Furthermore, riding behaviors and their precedent factors have been identified as potential determinants for assessing, intervening, and prevent-ing traffic injuries of motorists. This study aimed to identify the effects of a set of demographic and motorcycle-related variables as potential predictors on collision through riding behavior components. METHODS: The study sample was 1,611 motorcyclists who were selected through time-location sampling method from three cities in Iran. They responded a Motorcycle Rider Behavior Questionnaire (MRBQ) and a general questionnaire including sociodemographic and riding-related items. The chosen method to analyze the data was Structural Equation Modeling (SEM) through Lavaan package version 0.6-8 of R software version 4.1.0. RESULTS: All participants were male (100%) with a mean age of 28.1(SD=8.5) years. About 24.4% of riders experienced at least one crash during the last year and the majority of riders did not hold a motorcycle license (80.1%). The SEM model showed that riding license (0.06) and fre-quency of riding (0.09) had a direct effect on crash involvement. Some latent variables including speed violation (0.13), stunts (0.11) and traffic violation (0.07) had positive effects and safety violation (-0.07) had a negative effect on crash history. There were indirect effects between age and history of crash mediated by speed violation (-0.04), stunts (-0.04), traffic violation (-0.02) and safety violation (0.01). Also, the indirect effects of riding frequency on crash involvement were mediated by speed violation (0.01), traffic violation (0.006) and safe-ty violation (-0.01). CONCLUSIONS: This study’s main finding is that age and riding frequency are the main variables indirectly af-fecting crash involvement. Therefore, periodic training courses for younger riders is essential in order to decreasing crash involvements.