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

Sara Naderpour, Seyed Taghi Heydari, Kamran Bagheri Lankarani, Seyed Abbas Motevalian
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Abstract

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 preventing 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 frequency 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 safety violation (-0.01).

Conclusions: This study's main finding is that age and riding frequency are the main variables indirectly affecting crash involvement. Therefore, periodic training courses for younger riders is essential in order to decreasing crash involvements.

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社会人口特征、骑车行为和摩托车碰撞事故:结构方程模型法。
背景:涉及摩托车驾驶员的交通事故发生率不断上升,已成为公共卫生和道路安全方面的一个令人担忧的问题。此外,骑行行为及其先决因素已被确定为评估、干预和预防机动车驾驶员交通伤害的潜在决定因素。本研究旨在通过骑行行为要素,确定一组人口统计学和摩托车相关变量对碰撞的潜在预测影响:研究样本是通过时间地点抽样法从伊朗三个城市选出的 1,611 名摩托车手。他们回答了摩托车驾驶员行为问卷(MRBQ)和包括社会人口学和骑行相关项目在内的一般问卷。选择的数据分析方法是结构方程模型(SEM),通过 4.1.0 版 R 软件的 0.6-8 版 Lavaan 软件包进行分析:所有参与者均为男性(100%),平均年龄为 28.1(SD=8.5)岁。约 24.4% 的骑行者在过去一年中至少经历过一次车祸,大多数骑行者没有摩托车驾照(80.1%)。SEM 模型显示,驾驶执照(0.06)和骑行频率(0.09)对车祸发生率有直接影响。一些潜变量,包括超速违规(0.13)、特技(0.11)和交通违规(0.07)对撞车史有正向影响,而安全违规(-0.07)对撞车史有负向影响。年龄与撞车史之间存在间接效应,其中介效应为超速(-0.04)、特技(-0.04)、交通违规(-0.02)和安全违规(0.01)。此外,骑行频率对撞车事故的间接影响由超速(0.01)、交通违规(0.006)和安全违规(-0.01)中介:本研究的主要发现是,年龄和骑行频率是间接影响碰撞事故的主要变量。因此,为了减少碰撞事故的发生,对年轻骑手进行定期培训是非常必要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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