Kashani Masoud Motalebi, Akbari Hossein, Saberi Hamidreza, Ghorbanipour Reihaneh, Karamali Fahimeh
Social Determinant of Health Research Center, Kashan, Iran.
Trauma Research Center, Kashan, Iran.
Indian J Occup Environ Med. 2022 Oct-Dec;26(4):266-272. doi: 10.4103/ijoem.ijoem_45_22. Epub 2022 Dec 24.
There is a significant difference between actual and existing statistics of traffic fines; since some invisible fines and most of the visible traffic violations cannot be recorded by traffic officers. Therefore, dealing with driving fines and road fatalities is considered an important issue in social and public management worldwide.
Explore the factors associated with unsafe behaviors and getting traffic fines among a sample of Iranian heavy-vehicle professional drivers.
The present cross-sectional study was conducted in Iran, from February 2019 to September 2020.
This study used the driver behavior questionnaire (DBQ), demographic and driving characteristics, the number of fines, and structural equation modeling. Also, in this study 320 professional drivers participated.
This article used structural equation modeling for Statistical analysis.
The results of structural equation modeling analysis indicated that the data fit well with the theoretical model proposed in this study. The number of fines was directly predicted by both demographic and driving characteristics and risky driving behaviors. A significant relationship was observed between, driving hours, driving experience, and smoking, respectively, with a mistake, slip, and risky violation. There was a negative correlation between education and all four sub-scales of risky driving behaviors.
In order to reduce traffic fines, training courses on increasing attention and precision in drivers' observations and judgments are useful. The courses can decrease traffic violations by trying to change beliefs, attitudes, and social norms. It is therefore helpful to understand the ways to change the drivers' attitudes.
交通罚款的实际统计数据与现有统计数据存在显著差异；因为一些隐性罚款以及大部分显性交通违规行为交通警察无法记录。因此，处理驾驶罚款和道路死亡事故被视为全球社会和公共管理中的一个重要问题。
在伊朗重型车辆职业司机样本中探究与不安全行为及交通罚款相关的因素。
本横断面研究于2019年2月至2020年9月在伊朗进行。
本研究使用了驾驶员行为问卷（DBQ）、人口统计学和驾驶特征、罚款数量以及结构方程模型。此外，本研究有320名职业司机参与。
本文使用结构方程模型进行统计分析。
结构方程模型分析结果表明，数据与本研究提出的理论模型拟合良好。罚款数量由人口统计学和驾驶特征以及危险驾驶行为直接预测。分别观察到驾驶时长、驾驶经验和吸烟与失误、疏忽和危险违规行为之间存在显著关系。教育与危险驾驶行为的所有四个子量表之间存在负相关。
为了减少交通罚款，开展关于提高驾驶员观察和判断注意力及准确性的培训课程是有用的。这些课程可以通过试图改变信念、态度和社会规范来减少交通违规行为。因此，了解改变驾驶员态度的方法是有帮助的。
