Analysis of risky driving behaviors among bus drivers in China: The role of enterprise management, external environment and attitudes towards traffic safety

结构方程建模 背景(考古学) 运输工程 毒物控制 人为因素与人体工程学 心理干预 工程类 伤害预防 职业安全与健康 计算机安全 应用心理学 心理学 环境卫生 计算机科学 医学 生物 精神科 机器学习 病理 古生物学
作者
Lianzhen Wang,Yuping Wang,Lingyun Shi,Huizhi Xu
出处
期刊:Accident Analysis & Prevention [Elsevier BV]
卷期号:168: 106589-106589 被引量:36
标识
DOI:10.1016/j.aap.2022.106589
摘要

Several studies have shown that enterprise management (e.g. culture, salary) and external environment (e.g. traffic congestion) predict risky driving behaviors and accident involvement. However, this process has not been systematically investigated in bus drivers. The present study uses structural equation model to assess whether enterprise management and external environment are associated with risky self-reported driving behaviors both directly and indirectly, through the effects of attitudes towards traffic safety in a large sample of bus drivers. Three hundred and thirty-one bus drivers (mean age = 39.5, SD = 5.6 years) completed a structured and anonymous questionnaire measuring enterprise management, external environment, attitudes toward traffic safety, and self-reported risky driving behaviors (i.e., speeding, fatigue driving, running the light) in the last 6 months. Structural equation modeling analysis revealed that enterprise management, and external environment were associated with risky driving behaviors both directly and indirectly. In particular both of them were directly correlated with bus drivers' attitudes toward traffic safety which, in turn, were related to the five types of self-reported risky driving behaviors. The present findings suggest that measures related to the impact factors could be carried out to reduce the probabilities of the risky driving behaviors among bus drivers, such as improving the salary level of bus drivers, setting up bus lanes and priority signals to alleviate road congestion, optimizing shift schedules, implementing effective safety education, etc. These findings can provide the empirical basis for evidence-based road safety interventions in the context of public transport.
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