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ORIGINAL ARTICLE
Year : 2022 | Volume
: 26
| Issue : 4 | Page : 266-272
Driving fine and its relationship with dangerous driving behaviour among heavy vehicle Drivers
Masoud Motalebi Kashani1, Hossein Akbari2, Hamidreza Saberi1, Reihaneh Ghorbanipour3, Fahimeh Karamali4
1 Social Determinant of Health Research Center, Kashan, Iran
2 Trauma Research Center, Kashan, Iran
3 Department of Occupational Health, School of Health, Tehran University of Medical Sciences, Tehran, Iran
4 Department of Health, Safety and Environmental Management, School of Health, Kashan University of Medical Sciences, Kashan, Iran
Correspondence Address:
Fahimeh Karamali
Kashan University of Medical Sciences, Kashan
Iran
Source of Support: None, Conflict of Interest: None
CheckDOI: 10.4103/ijoem.ijoem_45_22
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.
Keywords: Aberrant driving behaviors, professional drivers, structural equation modeling, traffic fines
Around 700000 professional drivers working on heavy vehicles are estimated to work in the road transportation system across Iran. Heavy vehicles usually include tanker, trailer, lorry, and buses, involved in carrying products and also passengers.[1] The related studies showed that drivers are mostly exposed to mixed workload indexes, like night shifts, irregular schedules, and extended periods of work, and, the potential danger of traffic accidents, as well as uncomfortable environmental factors, such as constant concentration of pollutants, heat stress, noise.[2],[3] Furthermore some studies which have focused on professional drivers revealed a high prevalence of minor psychiatric disorders leading to road traffic accidents and injury.[4],[5]
FMCSA (Federal Motor Carrier Safety Administration) in 2014, reported 4,161 deaths and 132,000 injuries in remarkable truck and bus accidents in the United States. Europe also recorded 1357 deaths from bus and Freight Forwarding accidents in 2013. Major truck accidents leading to deaths cost an average of $ 3.6 million per accident. The cost of injuries resulting from these accidents is about $ 200,000 for each person.[6] Almost 1.35 million people die each year as a RTAs (result of road traffic accidents) and 93% of road traffic fatalities happen in low- and middle-income countries. Iran has shown one of the highest mortality rates from RTAs among middle-income countries.[7] The health and safety of drivers as well as the large community involved in this type of transportation system depend on the potential health risk associated with these drivers. To minimize injury and improve road safety, factors related to risky driving in this population should also be identified and prevented.
The related literature revealed the role of risky behaviors in road traffic accidents and injuries. Studies in China,[8] India[9] and Egypt[10] reported frequent risky behaviors among drivers of heavy vehicles.[11],[12],[13],[14] indicated that dangerous driving behaviors play a significant role in traffic fines. Studies also have shown traffic fines due to dangerous driving behaviors maybe are correlated with fatigue due to exceeded working hours, using drugs, and alcohol drinking.[15],[16] It is required to understand driving behaviors to figure out the effect of driving behaviors on fines traffic. The way individuals choose to drive is defined as driving behaviors analyzed over several years.[17] However, risky driving consists of many behaviors, which increased the risk of injury and accident.[18] In this study, the DBQ methodology has also been used, which has two factors: violations and errors. Over the past 20 years, the DBQ or its modified version has been used by about 200 studies.[19] A study has shown traffic law violations are correlated with fatigue due to excessive working hours, using drugs, and alcohol drinking.[20] Other Studies obtained Similar results in this field.[21],[22] A study done in China showed that drivers of heavy motor vehicles with less experience are more involved in road traffic accidents.[23] The mentioned cause–effect relationship has been also reported by other authors.[21],[23],[24],[25],[26] Also the results of.[27] showed that there was an inverse relationship between education level and dangerous driving behaviors. increasing education level descended the risk of dangerous driving.
A deep understanding of variables that may involve violating traffic laws can be crucial in explaining the high road traffic accident rate. The aims of the study were to explore the factors associated with unsafe behaviors and getting traffic fines among a sample of Iranian heavy-vehicle professional drivers.
HypothesesFocusing on driving behaviors, demographic characteristics have been showing more influence on driving behaviors.[28],[29]
H1: demographic and driving characteristics are associated with driving behaviors.
There are some studies that have shown demographic characteristics (age, marital, education, etc.) as one of the main predictors of higher levels of traffic fines.[24],[30]
H2: demographic and driving characteristics are associated with traffic fines.
The following hypothesis statements are based on the scientific findings that driving behaviors has a greater impact on traffic fines.[11],[31],[32],[33]
H3: Each driving behaviours category have a significant impact on increasing traffic fines.
Subjects and MethodsStudy design
The present cross-sectional study was conducted in Iran, from February 2019 to September 2020.
Study population
The sample size was determined based on observed variables.[34],[35] It shows that the size of the sample should be at least 10–15 times the number of observed variables. In this study, the sample size was 10 times the number of 32 observed variables, that is the data from 320 drivers were surveyed. The method of data collection was simple convenient random sampling. Out of the 320 professional drivers, 17 did not return the measures or had more than 20% missing data. From 303 professional drivers of heavy vehicles who were referred to the Occupational Medicine Clinic for periodic work-related health examinations, Data was collected and used in the analyses.
Study tools
The main instrument of the study was a questionnaire consisting of 32 items to measure the observed variables of this study. The questionnaire was adopted from previous studies in this area of research Which consists of 2 sections, including DBQ and demographic information.
Independent variables: Demographic information
Several questions about drivers' driving and demographic characteristics were provided in the first section of the questionnaire. This section included drivers' marital status, age, educational status, medical backgrounds, and diseases, such as hypertension, pulmonary and cardiovascular diseases, diabetes, vision weakness, other diseases, alcohol consumption, and smoking. The information regarding each participant's driving characteristics was also obtained, including the type of vehicle, hours of occupational driving in a day, work shift, days of occupational driving in a week, speed (km/h), and years of occupational driving experience.
Mediator variables: DBQ measurement
The Iranian 15-item modified DBQ based on the original DBQ developed by[23] was used to evaluate risky driving behaviors. The validity and reliability of the questionnaire were approved in a previous study by.[36] The Alpha coefficient and Spearman's correlation coefficient in test-retest were calculated at 0.83 and 0.72 respectively. The participants had to report how often they committed each act (15 acts) while driving in the past year. Five items were designed for risky violation, and four items for slip, and lapse. Highway Violations contained three items, Mistake contained three items. a five-point Likert scale was used to measure the items. Higher scores indicated more Aberrant driving behaviors.
Dependent variables
Traffic fine was assessed through self-report. The subjects had to report the number of all their fines over the past 3 years.
Data analysis
To investigate the participants' characteristics, descriptive statistics were applied, and to determine the inter-correlations among the variables, bivariate Pearson correlation analysis was run. The analyses were by IBM SPSS Statistics 22 (IBM Corporation, Armonk, NY, USA) To develop an acceptable measurement model describing the link between latent constructs and measurement variables, CFA (confirmatory factor analysis) is recommended performed. Hence, the way by which measurement variables accurately reflect their latent variables.
ResultsDescriptive analysis
[Table 1] reveals the participants' features. The sample consisted of Passenger vehicles driver (20.1%) and Commercial and goods vehicles driver (79.9%) with an average age of 43.15 years (SD = 10.29). About 94% of the drivers were married. The average driving experience of the participants was 11.26 years. The average daily driving rate for professional drivers was 11.73 hours in a day and 5.15 day of driving in a week. In total, their average speed was 85.94 km/h. Most participants revealed that they do not use drugs (97%), alcohol (93.4%), and cigarettes (63.4%).
Table 1: Descriptive statistics of the different variables for different groups of professional driversCorrelation among latent variable
The relationship among risky driving behaviors variables of the study was examined by running the Pearson correlation coefficient. There was a significant and positive correlation between risky driving behaviors variables (i.e. slip, highway violations, mistake, and risky violations) (P < 0.01) [See [Table 2] for more details].
Table 2: Descriptive statistics, Cronbach's alpha, and correlations of self-reported dangerous driving behaviorsValidity and reliability of the questionnaires
Cronbach's alpha coefficient was used to perform reliability analysis of the revised scale.[37] In this study, the combined reliability of each factor was 0.6-0.677 and Cronbach's alpha coefficients of the revised DBQ scales were 0.772. It indicates that the questionnaire was very reliable and can be utilized for the structural equation modeling [[Table 2] illustrates the details of the analysis].
Measurement model
In order to test the model goodness of fit, CFA with a maximum likelihood for the DBQ was used, since there might be a kind of misfit within the model, 'MIs (modification indices)' was used to re-specify the model. Fit indices after retesting increased from the first model: relative Chi-square (x2/df) of 2.145, NFI = 0.82 and CFI = 0.89 indicated not being too far from the suggested value of >0.90 for a good fit. Likewise, TLI = 0.86 which is not far from the suggested value of 0.90, its RMSEA value was lower than 0.08. [These results are shown in [Table 3] and [Figure 2]].
Figure 2: Measurement model, confirmatory factor analysis for driving behavior questionnaire[Figure 2] reveals the CFA results. There were significant (P <0.001) factor loadings of each latent variable (above 0.3), which is the minimum value recommended to accept for a factor loading.[37]
Structural model
To begin with, an SEM analysis was performed to examine the model represented in [Figure 1]. After analysis of several SEM models, [Figure 3] reveals the ideal SEM model results. The prediction of a dependent variable from an independent variable was shown by the arrows in the figures. All the following models are final, with a significance level of ≤ 0.05.
Controlling the modification indices suggested that there were strong correlations. The mentioned correlations were added to the model and tested again.
The fit indices revealed that the model fit the data in a good way (X2 = 362.214, X2/df = 2.195, P < 0.01, RMSEA = 0.063, CFI = 0.865, NFI = 0.783, TLI = 0.828) [Table 3].
Figure 3 shows the results of the estimated model and demonstrates the standardized path coefficients of the significant structural relationships (P < 0.05) among the tested variables.
Driver's age, hours of driving per day, smoking, and driving behaviors (slip) indicated an explanation as 08% of the total variance in traffic fines. In this model, risky driving behaviors (slip β = 0.14) in a significant way predicted traffic fines. In addition, driver's age (β= -0.15), smoking (β = -0.136), and hours of driving (β = -0.118) had a significant influence on traffic fines. Smoking (β = 0.12) and educational level (β= -0.56) had a significant influence on risky violation behavior. Educational level (β= -0.53) and Years of Experience (β = -0.16) affected slip behaviors negatively. Education had a negative effect on highway behaviors (β = −0.21). Hours of driving (β = -0.14) and educational level (β = -0.28) had a negative effect on mistake behaviors.
It was revealed that the direct effects of the seven demographic and driving characteristics on traffic fines were not significant; whereas Driver's educational level (indirect effect = -0.075), Years of experience (indirect effect = -0.023) exerted a negative and indirect influence on traffic fines via driving behaviors.
DiscussionThe present study investigated risk factors and relationships in driving unsafe behaviors and getting traffic fines among a sample of Iranian heavy-vehicle professional drivers. The findings of the study revealed that drivers with higher educational backgrounds are involved in lower unsafe behavior which is in line with[27],[33],[37],[38],[39]. Therefore, providing education and training programs concerning safety behavior can improve drivers' attitudes and work performance. focused on traffic regulations that drivers with higher educational levels want to have.[40]
The results of the study showed that the smoking habit is the main factor for risky behaviors among heavy vehicle drivers. Using cigarette, alcohol, and stimulant substances is common among professional drivers to reduce the symptoms of fatigue.[41],[42],[43] Previous studies have shown that addiction to drugs is related to an increased risk of drivers' misbehaviors.[15],[16] Like several studies evaluating the increased risk of misbehaviors and/or accidents.[42],[44] Long-term smoking hurts the regions of the brain influencing executive functioning, including inhibitory control and self-monitoring, and may possibly consequence in increased risk-taking.[45]
Our findings showed that more hours of driving had a positive relationship with traffic fines among professional drivers. In order to transport more passengers and carry more loads in one day, heavy vehicle drivers commit more break traffic laws, such as speeding, overtaking, not keeping the necessary distance, and crossing red lights. These findings are consistent with the studies of.[21],[22] No rules have been in Iran for heavy vehicles limiting the number of continuous driving hours or the total driving hours per day. Some studies revealed a significant correlation between hours of driving and mistake.[20] In a previous study longer hours of driving per week have been related to increased fatigue, decreased attention, and judgment of driver while driving and thus resulted in dangerous driving behaviors. However, the present study represented contradictory findings. It may be due to different vehicles, different countries, and the influence of other factors, including age, driving experience, environmental factors, and study designs, such as using different instruments for measuring the variables.
In the current study, the years of driving experience were considered related to dangerous driving behaviors. There is also some evidence in the literature to support the study results.[21],[23],[24],[26] However,[25] concluded that lack of driving experience is related to increased traffic fines. Additionally, our results showed an inverse association between age with violation of traffic rules.[43],[46],[47],[48],[49] concluded the same. It seems older drivers exhibit a lower probability of committing traffic violations and getting fines. With increasing age and driving experience, drivers may involve in less risky attitudes and behavior and therefore lower risk of being involved in traffic crashes and injuries.[43] Since older drivers like to be more cautious and considered the cost and benefits of the violation, fear of being fined can prevent them from committing traffic fines.
Considering the dangerous driving behaviors scale in the present analysis, it was revealed that only slip predicted dangerous driving behaviors. Some the empirical studies have shown that more traffic fines when drivers experience considerable rates of dangerous driving.[11],[13],[14] Slip center on the unwitting deviation of action, intention, and memory failure. Inattention and confusion among heavy vehicle drivers can happen as the result of work pressure, economic problems, low rest between traversed roads, negligible facilities, etc.
Limitation
The present research had some obvious limitations. First, the study analysis was limited to a few demographics factors of professional drivers. Further, it will be helpful to undertake a study that evaluates other factors that can influence risky driving behaviors. Second, the DBQ data and number of fines self-reported may potentially have errors due to faulty memory.
ConclusionsThe study was an attempt to examine the relationships of effective factors in driving unsafe behaviors and getting traffic fines among a sample of Iranian heavy-vehicle professional drivers. The main factors of the driver to get involved in traffic violation and dangerous driving behavior, are as follows: (a) driver × s education level; (b); driver's age and experience (c) hours of driving (d) driver × s smoking habit (f) slip. Albeit the etiology for these high-risk driving behaviors was not possible to be examined by this study, such a realization is significant for impressive prevention attempts. It was concluded that the variable affecting a traffic dangerous driving behavior can be the driver's education level. Training courses in the field of increasing attention and precision in observations and judgments of drivers is useful to reduce traffic fines. By attempting to change attitudes, beliefs and social norms are possible to decrease the violations. So, it is useful to understand the ways to alter the driver's attitudes.
Acknowledgement
Authors would like to appreciate Vice Chancellor of Research & Technology kashan University of Medical Sciences for providing financial support to conduct this work (Approval code: 97139).
Ethical approval
This study was approved by Kashan University of Medical Sciences (Grant number = 97139) and approved by the Ethics Committee (IR.KAUMS.NUHEPM.REC.1397.047).
Financial support and sponsorship
Source(s) of support: Kashan University of Medical Sciences, Kashan.
Conflicts of interest
There are no conflicts of interest.
References
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