Citation

Kashani MM, Akbari H, Saberi H, Ghorbanipour R, Karamali F. Indian J. Occup. Environ. Med. 2022; 26(4): 266-272.

Copyright

(Copyright © 2022, Indian Association of Occupational Health, Publisher Medknow Publications)

DOI

10.4103/ijoem.ijoem_45_22

PMID

37033749

PMCID

PMC10077724

Abstract

CONTEXT: 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. AIMS: Explore the factors associated with unsafe behaviors and getting traffic fines among a sample of Iranian heavy-vehicle professional drivers. SETTINGS AND DESIGN: The present cross-sectional study was conducted in Iran, from February 2019 to September 2020.

METHODS AND MATERIAL: 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. STATISTICAL ANALYSIS USED: This article used structural equation modeling for Statistical analysis.

RESULTS: 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.

CONCLUSIONS: 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.

Language: en

Keywords

structural equation modeling; professional drivers; Aberrant driving behaviors; traffic fines