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Investigating the Effect of Drivers’ Training Courses on Commercial Drivers’ Success Rate for Qualification


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International Journal of Management Science and Business Administration

Volume 9, Issue 4, May 2023, Pages 35-41

Investigating the Effect of Drivers’ Training Courses on Commercial Drivers’ Success Rate for Qualification

DOI: 10.18775/ijmsba.1849-5664-5419.2014.94.1003  
URL: https://doi.org/10.18775/ijmsba.1849-5664-5419.2014.94.1003 

1 Abbas Mahmoudabadi, 2 Fatemeh Pourhossein Ghazimahalleh

1 Department of Industrial Engineering, MehrAstan University, Guilan, Iran
2 Graduated Student, Department of Information and Commerce Technology, Guilan, Iran

Abstract: In training and examining professional drivers who serve as commercial drivers for trucks and coaches, studying the effects of training courses on their success rate for professional qualifications is a crucial concern for transport authorities in developing training programs. To this purpose, the statistical methods of Kolmogorov-Smirnov and paired two-sample mean analysis have been utilized to investigate the statistical similarity of success rates for two groups of drivers who receive permission by taking training courses and those who receive it without taking training courses. Data for commercial drivers across twenty-one provinces of the West-Asian country of Iran has been collected for a year and categorized into two groups and twenty-one observations. The results revealed that their distribution functions and the mean success rates are not different for the two groups of drivers. Since the results of success rates are the same, 1) training courses do not have enough efficiency to affect success rates, or 2) exams could not adequately evaluate the skill and knowledge of drivers. Therefore, transport authorities are recommended to redesign training courses and exams for drivers interested in serving as commercial drivers.   

Keywords: Commercial Drivers, Training Succes Rate, Drivers’ Training, Professional Qualification, Statistical Similarity

1. Introduction

Compliance with laws is one of the most prominent manifestations of civic rights in the social sphere nowadays (Nwagwu et al., 2020). While this commitment is discussed in various fields, some of which are neglected in transportation, leading to traffic violations (Ako, 2019). One of the major contributors to traffic issues and accidents across all communities is driving offenses believed to be preventable if drivers are put under comprehensive and efficient training courses (Gichaga, 2017). The prevalence of such offenses differs based on the cultural, social, economic, and geographic characteristics of each society (Kelacha, 2021), and the various environmental, regulatory, and enforcement factors contribute to driving offenses and human factors are also considered to be the leading cause (Lee and Al-Mansour, 2020). According to studies conducted in developed countries, the identification of human factors and the use of education to address and improve driving behaviors have been found to play a significant role in preventing and managing traffic accidents (Nancy, 2021). So, the Global Plan for the Decade of Action for Road Safety emphasizes the driver as a crucial element in its approach to reducing driving-related accidents and injuries worldwide (Timmermans et al., 2020). Education is additionally acknowledged as a sustainable competitive advantage for enhancing the awareness and skills of the workforce in modern times (Farooq and Juhasz, 2020), so in terms of road safety, providing driver education is regarded as the topmost priority for investment in human capital (Timmermans et al., 2019). One of the critical concerns of transportation authorities dealing with designing training programs for commercial drivers is to study their inclination toward training courses. This is because the effectiveness of training programs depends on the individual’s willingness to engage in the activities and follow the guidelines provided (Nwakaire and Kobani, 2022). Therefore, managing the training process and developing driver education programs require careful consideration of individuals’ motivations and drivers’ desires to participate. By evaluating educational programs, decision-makers are empowered to devise tailored educational systems for each region, enhancing the knowledge and skills of the human resources involved in traffic and transportation. While maintaining productivity, an approach can be adapted to create desirable behavioral changes for both individual and societal needs, facilitating the achievement of organizational goals. On the other hand, it is essential to design training courses according to the real needs of participants to ensure their usefulness and that they lead to behavioral changes during and after these courses (Mahmoudabadi and Moghadam, 2020). In countries such as Iran, where commercial drivers do not legally take training courses prior to receiving qualifications, it is crucial to take care of developing drivers’ training courses, so the main aim of the present research work is to ensure that taking training courses have the significant effect on enhancing drivers’ skills and their professional knowledge.

1.1. Distribution Similarity and Concept

Checking the similarity of distribution functions is one of the statistical methods to examine the relevancy between two variables. In this case, two data sets are commonly compared based on the similarity of their distribution functions through statistical tests (Pastore and Calcagnì, 2019). There are many measures to check the similarity of two distribution functions (Lee, 1999), but they depend on the method used. The Kolmogorov-Smirnov test, also known as the KS test and primarily utilized in nonparametric hypothesis testing (Sahinturk and Özcan, 2017), is one of the statistical tests that compare the behavior of two related samples. It is a goodness of fit and nonparametric test of the equality of continuous or discrete one-dimensional probability distributions to compare the statistical probabilities of two samples (Vrbik, 2018), so it is conventionally utilized to compare an accurate distribution sample with a reference probability distribution (Arnold and Emerson, 2011). When comparing two samples, each record or sample in one population is compared individually to the same observation in another. The overall concept behind the KS test is to investigate the maximum distance between the cumulative distribution functions of two samples, which represents the unlikeness of two distribution shapes (Lopes et al., 2007). Therefore, it is utilized to assess the similarity between the expected and the experimental or observational distribution functions for checking the fitness of the experimental data to the expected distribution function (Simard and L’Ecuyer, 2011). Although this ability commonly supports data analyzers for testing normality, where the existing normality is necessary to perform analyzing procedures (Drezner et al., 2010), it can also be utilized in other distribution functions and existing similarities for two data sets (Mahmoudabadi and Abdous, 2020). It can likely be developed that the difference between two distribution functions is formulated to check if they are the same.

1.2. Research Statement

Following those mentioned above, the study has been conducted to investigate the similarities and dissimilarities between the distribution functions of drivers who receive commercial driving permissions (qualifications) in two groups, including those taking examination and training courses and examinations without training courses. The results would support transport authorities in investigating the effects of training courses and exams on drivers’ skills and making decisions on training commercial drivers based on their willingness to take training courses. In other words, in a country where drivers can optionally select the way of receiving permission with or without examination and taking training courses, the study investigates whether the driving training programs would be successful.

2. Literature Review

Due to the importance of training programs for promoting road safety in traffic and enhancing transport productivity, many studies have been conducted in this area. In a study conducted by Nwadinigwe et al. (2018), the effectiveness of road safety education was evaluated on the knowledge and behavior of commercial drivers. The results of the study suggested that the educational program significantly impacted the behavior of commercial drivers. Furthermore, the study also highlighted the joint influence of factors such as the educational background of commercial drivers, the duration of their road safety learning, their behavior and knowledge, and eventually, their perception of the road safety education program on road safety (Nwadinigwe et al., 2018). The efficacy of training courses on transportation drivers was also investigated by Koushki et al. (2022). According to their research findings, attending these courses improves driver skills, increases learning levels, and reduces accidents (Koushki et al., 2020). In Mexico, a study was conducted by Bergoffen et al. (2022) to examine driver training for commercial vehicle operators. Their research showed that individuals who receive technical knowledge and skills training should undergo periodic validation and monitoring. Their study also revealed the necessity for a standardized national curriculum for each LFC class in the country, with certification granted to LFC drivers upon meeting national standards (Bergoffen et al., 2022).

Studied frequently, training with a progressive approach is another contributing factor. A study examined the effectiveness of a traffic safety education program for the Oman Traffic Safety Administration through program design and implementation. The study’s findings demonstrated that implementing multiple educational systems for residents was highly desirable, but the program also required continuous planning, monitoring, and improvement (Hamdania et al., 2019). The results of another study conducted by researchers (Yahaya and Abubakar, 2022) demonstrate a significant difference in risky behavior in road accidents between commercial vehicle drivers who have participated in training programs and those who have not. The study’s executive summary suggests that the government and other relevant organizations, such as the National Road Safety Corps and vehicle inspection officers, collaborate to organize a program that educates commercial vehicle drivers about behaviors that make them more prone to road accidents. Eventually, qualified drivers should be hired from among those who have received training (Yahaya and Abubakar, 2022). Nwadinigwe et al. evaluated commercial drivers’ understanding of traffic safety training. The results of their study indicate that the road traffic safety training program significantly impacts drivers’ compliance with road traffic laws and regulations.

Additionally, it was observed that drivers’ experience would affect their adherence to road traffic laws and regulations (Nwadinigwe et al., 2019). In their review study, Garcia and colleagues examined the effectiveness of training techniques for vehicle operators. In this review, some of the topics of the theoretical lessons and others convenient were taught to the operators. In the end, they were asked to participate in a test to evaluate the impact of the techniques learned in the classes on the performance of individuals. The test results showed that the selected plan was very successful as a desired short-term effect and required the continuation of the course in the long run (Garcia et al., 2019). In terms of evaluating the efficacy of training courses on improving drivers’ skills, Mahmoudabadi and Moghadam (2020) had a field study by performing the Kirk-Patrick model in which truck drivers, consignees, customers, and experts who work in Road Maintenance and Transport Organization (RMTO) have been interviewed through filling out a standard questionnaire modified to transport courses. They showed that the written examination-based training courses do not significantly affect drivers’ reactions, learning, behaviors, and performance in freight transportation.

To summarize, what can be realized from the above studies is that taking and examining, and training drivers’ courses significantly influence road safety and transportation. However, the question here is why they do not significantly affect drivers’ behavior in Iran. In addition, developing appropriate training courses is crucial to managing drivers who take examination and training courses, mainly commercial truck drivers whose exposure to traffic is higher than other road users.

3. Research Methodology

As stated in the previous section, comparing acceptance rates between two groups of drivers who received permission following training courses and without training courses is now being investigated in twenty-one selected provinces in the West-Asian country of Iran. The selection was based on data available in both groups. In the present research work, the well-known statistical goodness of the appropriate method of the Kolmogorov-Smirnov test is utilized, followed by checking the results by performing a simple test of two samples’ mean difference test. Each sample has twenty-one observations attributed to each selected province. The main stages of this research work include descriptions of how the data was collected and the hypotheses defined, followed by utilizing the Kolmogorov-Smirnov and verified by the paired sample mean test. Figure 1 depicts an overall image of what is followed as the research methodology in the present study.

Step 1: Dividing qualified drivers into two groups of Trained and Untrained
Step 2: Collecting data for each group of drivers in terms of Accept or Reject
Step 3: Obtaining Success Rates and their Proportions for all observations
Step 4: Calculating Cumulative proportions for observations
Step 5: Calculating the Absolute differences for observations and find the maximum as KS stat
Step 6: Verifying the results by performing another test

Figure 1: The overall steps of the research methodology

4. Data Collection and Analysis

The first step in implementing the research study is to collect appropriate data. Data from two groups of drivers, composed of those who receive commercial driving permission with training courses and without training, has been collected in each selected province in the country. Each selected province is a so-called observation where data has been received from the “Research, Training, and Innovation Center,” the responsible branch directed by the Road Maintenance and Transportation Organization (www.rmto.ir). It composes all drivers’ certificates issued in the 1401 Sonar calendar (March 21, 2022, to March 20, 2023). Table 1 tabulates the number of two groups of drivers mentioned above ordered alphabetically by the names of provinces. The number of trained and untrained drivers are tabulated, including accepted, rejected, and the success rate. As shown in Table 1, the success rate differs from province to province and drivers from group to group. In addition, the overall success rate for trained drivers is more significant than those examined as untrained drivers. However, it should be statistically checked to ensure that there is a significant effect on examination results.

Table 1: The number of commercial drivers accepted (Acc.) and rejected (Rej.) in the process of qualification

Row Province Trained Drivers   Untrained Drivers  
Acc. Rej. Total Rate Acc. Rej. Total Rate
1 Ardabil 342 268 610 56.07% 538 266 804 66.92%
2 Bushehr 26 70 96 27.08% 227 246 473 47.99%
3 Chehar-Mahal-o-B 923 576 1499 61.57% 36 53 89 40.45%
4 East-Azerbaijan 144 64 208 69.23% 3480 3249 6729 51.72%
5 Fars 1595 736 2331 68.43% 1784 1155 2939 60.70%
6 Golestan 101 155 256 39.45% 560 291 851 65.80%
7 Guilan 446 200 646 69.04% 299 171 470 63.62%
8 Hamedan 530 291 821 64.56% 1186 420 1606 73.85%
9 Isfahan 56 23 79 70.89% 1924 769 2693 71.44%
10 Kerman 312 99 411 75.91% 1203 589 1792 67.13%
11 Kermanshah 1724 608 2332 73.93% 5 6 11 45.45%
12 Kohgiluye 156 62 218 71.56% 152 17 169 89.94%
13 Kordestan 1224 597 1821 67.22% 169 296 465 36.34%
14 Markazi 120 43 163 73.62% 333 264 597 55.78%
15 Mazandaran 1076 301 1377 78.14% 69 38 107 64.49%
16 North-Khorasan 305 195 500 61.00% 364 263 627 58.05%
17 Qazvin 0 31 31 0.00% 938 370 1308 71.71%
18 Razavi-Khorasan 775 448 1223 63.37% 3 6 9 33.33%
19 Sistan-O-Baluchestan 884 690 1574 56.16% 4 126 130 3.08%
20 South-Khorasan 754 228 982 76.78% 259 112 371 69.81%
21 West-Azerbaijan 1073 537 1610 66.65% 1699 990 2689 63.18%
Total 12566 6222 18788 66.88% 15232 9697 24929 61.10%


4.1. Defining the Hypotheses

The first step of performing a statistical test is to define its hypothesis. Since the Kolmogorov-Smirnov and the Paired Two Sample for Means tests here are utilized to check the similarity between drivers who have taken the training courses or not, the null and competitive hypotheses are defined as follows:

For the Kolmogorov-Smirnov test:

H0: Trained and untrained drivers have the same success rates and come from the same distribution functions.

H1: Trained and untrained drivers have different success rates and come from different distribution functions.

For Paired Two-Sample for Means test:

H0: Trained and untrained drivers have the same means of acceptance rates.

H1: Trained and untrained drivers have different means of acceptance rates.

4.2. Mathematical Analysis and Discussion

The first step for utilizing the Kolmogorov-Smirnov test is calculating the proportion for each observation here, known as a province. The acceptance rate for the first observation of trained drivers in the province of Ardabil is calculated as , followed by the same role for untrained drivers, resulting in %66.9. The above role has been performed, and the results are tabulated in the third and the sixth columns of Table 2.

The next step is to calculate the proportion of the rate based on the observations. Since the sum of all success rates for the trained group equals 12.907, the proportion for the first observation (province) is calculated as . The others have been obtained by following the same role and tabulated in columns four and seven in Table 2.

The third step is to calculate the cumulative proportion for all observations. For each observation (province), the cumulative proportion of success rates of both trained and untrained groups are calculated and represented in the fifth and eighth columns of Table 2. As shown, both end at 1 for both trained and untrained groups.

The fourth step is to calculate the absolute difference between the cumulative proportions. The first observation of the province of Ardabil is calculated as , and the rest are calculated and tabulated as the same the above way and tabulated in the last column of Table 2.

The Kolmogorov-Smirnov stat is the maximum difference between the cumulative proportions of two groups of trained and untrained drivers for the seventeenth observation (Qazvin), equal to 0.06266. The obtained value should be compared to the critical values of the KS test. The test here is applicable in 21 observations, so the critical value of KS(0.95%, 21)=0.287 is greater than the obtained value of 0.06266. It shows that the success rates for two groups of trained and untrained drivers come from the same distribution functions. Training courses do not significantly affect success rates, so whether the courses have not been designed well or if exams should be performed effectively should be investigated. The results may be better depicted by a graph like Figure 2, composed of two lines. The dashed line represents the cumulative proportions for trained drivers’ acceptance rate, and the solid line represents the above measure for untrained drivers. The most significant difference between the two lines is detected in the seventeenth observation, known as the KS stat. The results are the same as what was concluded in a previous study (Mahmoudabadi and Moghadam, 2020), where a field study evaluated the effects of training courses on drivers’ skills and the other of intercity transport.

Table 2: Success rates, proportions, cumulative proportions, and differences for two groups of drivers

Trained Untrained  
Row Province Success Rate Proportion Cumulative Proportion Success Rate Proportion Cumulative Proportion Difference
1 Ardabil 0.561 0.0434 0.0434 0.669 0.0557 0.0557 0.01229
2 Bushehr 0.271 0.0210 0.0644 0.480 0.0400 0.0957 0.03127
3 Chehar-Mahal 0.616 0.0477 0.1121 0.404 0.0337 0.1294 0.01725
4 East-Azerbaijan 0.692 0.0536 0.1658 0.517 0.0431 0.1724 0.00667
5 Fars 0.684 0.0530 0.2188 0.607 0.0506 0.2230 0.00421
6 Golestan 0.395 0.0306 0.2494 0.658 0.0548 0.2778 0.02844
7 Guilan 0.690 0.0535 0.3028 0.636 0.0530 0.3308 0.02793
8 Hamedan 0.646 0.0500 0.3529 0.738 0.0615 0.3923 0.03941
9 Isfahan 0.709 0.0549 0.4078 0.714 0.0595 0.4518 0.04398
10 Kerman 0.759 0.0588 0.4666 0.671 0.0559 0.5077 0.04107
11 Kermanshah 0.739 0.0573 0.5239 0.455 0.0379 0.5455 0.02165
12 Kohgiluye 0.716 0.0554 0.5793 0.899 0.0749 0.6204 0.04110
13 Kordestan 0.672 0.0521 0.6314 0.363 0.0303 0.6507 0.01929
14 Markazi 0.736 0.0570 0.6885 0.558 0.0465 0.6972 0.00870
15 Mazandaran 0.781 0.0605 0.7490 0.645 0.0537 0.7509 0.00186
16 North-Khorasan 0.610 0.0473 0.7963 0.581 0.0483 0.7992 0.00294
17 Qazvin 0.000 0.0000 0.7963 0.717 0.0597 0.8589 0.06266
18 Razavi-Khorasan 0.634 0.0491 0.8454 0.333 0.0278 0.8867 0.04133
19 Sistan-O-Baluch. 0.562 0.0435 0.8889 0.031 0.0026 0.8892 0.00037
20 South-Khorasan 0.768 0.0595 0.9484 0.698 0.0581 0.9474 0.00098
21 West-Azerbaijan 0.666 0.0516 1.0000 0.632 0.0526 1.0000 0.00000
Total 12.907 12.008 Max 0.06266


Figure 2: The difference between cumulative proportions of Success Rates for Trained and Untrained drivers

Another statistical test can also be utilized to check the similarity between the success rates for two groups of drivers over the selected provinces, the so-called Paired Two Sample for Means, which investigates whether the two samples are the same or different. In most cases, the paired differences are compared to zero, and the t-test is utilized to make the final decision. Table 3 tabulates the test results where two samples are the proportion of success rates of trained and untrained drivers in provinces. As shown, the t-stat, obtained by the calculation process, is near zero, and the P-value for the two-tail test is equal to 0.459. The two-tail test is utilized because both sides’ differences should be checked. It means that the state is not in the binding domain, so the null hypothesis is accepted, and that means that the two means of success rates over the observations are the same. Since they were defined as the proportion of the success rates of trained and untrained drivers, transport authorities are recommended to redesign training courses and exams to qualify drivers for serving as commercial drivers.

Table 3: Paired Two Sample for Means for proportions acceptance rates of trained and untrained drivers

Statistical Measure  Trained Untrained
Mean 0.614595371 0.571807619
Variance 0.034782195 0.034005579
Observations 21 21
Pearson Correlation 0.034393887
Hypothesized Mean Difference 0
Degree of Freedom 40
t Stat 0.747607223
P(T<=t) one-tail 0.229534933
t Critical one-tail 1.683851014
P(T<=t) two-tail 0.459069866
t Critical two-tail 2.02107537


5. Conclusion

Since in managing training courses for commercial drivers, the efficiency of training courses is essential; it is necessary to investigate the similarity of success rates of drivers, those who have taken training courses, and others without training courses. The overall concept behind the research work is to check the similarity between the drivers who received commercial certificates after taking training courses and without training courses. Data for two groups of drivers, one examined following taking training courses and the other examined without taking training courses, have been collected in each province. The well-known goodness of fit test of Kolmogorov-Smirnov has been utilized to check if the distribution functions for both success rates are the same or different. The results revealed that their distribution functions for success rates of trained and untrained drivers are the same, approved by performing the simple statistical test of Paired Two Sample Means. It means that the training courses have little effect on improving drivers’ professional skills, so the transport authorities should redesign training courses and exams in the process of drivers’ qualifications.

Further research in this field should focus on specific measures, such as the quality of training courses for commercial drivers. Transport authorities are also recommended to check the difference between the training materials applied in other countries, such as Europe, where they follow a harmonized procedure for drivers’ training programs to adapt training courses with them.


  • Ako, D. (2019). Impact of road safety and accident prevention in Cameroon.Impact of Road Safety and Accidents Prevention in Cameroon (June 15, 2019). CrossRef
  • Arnold, T. B., & Emerson, J. W. (2011). Nonparametric goodness-of-fit tests for discrete null distributions. R Journal3(2). CrossRef
  • Bergoffen, G., Giacoman, R., Traslosheros, M., &Staplin, L. (2022).Case Study of Mexico’s Third-Party Entry-Level Driver Training for Commercial Vehicle Operators (No. FMCSA-RRR-15-018). United States. Department of Transportation. Federal Motor Carrier Safety Administration.
  • Drezner, Z., Turel, O., &Zerom, D. (2010). A modified kolmogorov-smirnov test for normality. Communications in Statistics: Simulation and Computation, 39(4), 693–704. doi:10.1080/03610911003615816.
  • Farooq, D., &Juhasz, J. (2020). Statistical Evaluation of Risky Driver Behavior Factors that Influence Road Safety based on Drivers Age and Driving Experience in Budapest and Islamabad. Transp. Eur, 1-18. CrossRef
  • Garcia-Osorio, F. J., Maldonado-Susano, A., & Dominguez-Vergara, N. (2019). Methods and results of training economically technical driving in Mexico. InEDULEARN19 Proceedings (pp. 7439-7448). IATED.
  • Gichaga, F. J. (2017). The impact of road improvements on road safety and related characteristics.IATSS Research40(2), 72-75.
  • Hamdania, H. A., Al Saadi, N., Al-Moqbali, E., Naidu, V. R., & Hasan, R. (2019). Design and Implementation of Educational Application for the Directorate of Traffic Safety, Oman.Journal of Student Research. CrossRef
  • Kelacha, A. M. (2021). Assessment of Traffic Safety Problems and Awareness of Road Users, the Case of Shashemene Town.International Journal of Transportation Engineering and Technology7(2), 33.
  • KoushkiJahromi, A., Ehsani Brown, A., &EhsaniBiroun, S. (2020). The evaluation of the training courses’ effectiveness on transportation drivers by Kirk Patrick’s Model.Journal of Transportation Research.
  • Lee, L. (1999). Measures of distributional similarity (pp. 25–32). doi:10.3115/1034678.1034693. CrossRef
  • Lee, S. M., & Al-Mansour, A. I. (2020). Development of a new traffic safety education material for future drivers in the Kingdom of Saudi Arabia.Journal of King Saud University-Engineering Sciences32(1), 19-26.
  • Lopes, R. H., Reid, I. D., & Hobson, P. R. (2007). The two-dimensional Kolmogorov-Smirnov test.
  • Mahmoudabadi, A., & Moghadam, P. (2020). Do the Written Examination-based Training Courses Affect Freight Drivers’ Skills? An Empirical Study in Iran based on the Kirk-Patrick Model, Proceedings of the International Conference on Industrial Engineering and Operations Management Dubai, UAE, March 10-12, 2020, 2965-2970.
  • Mahmoudabadi, A., &Abdous, H. (2020). Do the Coaches’ Crashes and Their Usage Exposure Come from the Same Distributions? Society & Sustainability, 2(3), 10–19. doi:10.38157/society_sustainability.v2i3.165. CrossRef
  • Nancy, T. (2021). Implementation of the safety riding program to reduce traffic accidents in Wamena City.Budapest International Research and Critics Institute-Journal (BIRCI-Journal)4(4), 8659–8665.
  • Nwadinigwe, I. P., Osarenren, N. A., &Otuagoma, F. A. (2018). Impact of road safety education on commercial drivers’ knowledge and behavior towards road traffic codes and safe driving in Delta State.International Journal of Educational Research5(1), 110-120.
  • Nwadinigwe, I. P., Osarenren, N. A., &Otuagoma, F. A. (2019). Assessment of commercial drivers’ perception of road safety education on compliance to road traffic rules and regulations in Delta State.International Journal of Educational Research6(1), 19-28.
  • Nwagwu, E. J., Udegbunam, K. C., &Uwaechia, O. G. (2020). Federal road safety corps and administration of traffic laws in South-east Nigeria: an appraisal.International Journal of injury control and safety promotion27(4), 510-519. CrossRef
  • Nwakaire, O. N., &Kobani, D. (2022). Creating learning opportunities for drivers for accident prevention on Nigerian roads.World Journal of Advanced Research and Reviews14(3), 653-658.
  • Pastore, M., &Calcagnì, A. (2019). Measuring distribution similarities between samples: A distribution-free overlapping index. Frontiers in Psychology, 10, 1089. doi:10.3389/fpsyg.2019.01089. CrossRef
  • Sahinturk, L., &Özcan, B. (2017). The Comparison of Hypothesis Tests Determining Normality and Similarity of Samples. Journal of Naval Science and Engineering, 13(2), 21–36.
  • Simard, R., &L’Ecuyer, P. (2011). Computing the two-sided Kolmogorov-Smirnov distribution. Journal of Statistical Software39, 1-18.
  • Timmermans, C. P., Alhajyaseen, W. K., Ross, V., & Nakamura, H. (2020). Introducing a multi-variate classification method: Risky driving acceptance among heterogeneous driver sub-cultures.Journal of safety research, 73, 81–91. CrossRef
  • Timmermans, C., Alhajyaseen, W., Reinolsmann, N., Nakamura, H., & Suzuki, K. (2019). The traffic safety culture of professional drivers in the State of Qatar. IATSS Res. CrossRef
  • Vrbik, J. (2018). Small-Sample Corrections to Kolmogorov–Smirnov Test Statistic. Pioneer Journal of Theoretical and Applied Statistics, 15(1–2), pp. 15–23.
  • Yahaya, M. Z., & Abubakar, S. (2022). Risk behavior on road traffic accident among commercial vehicle drivers in Jalingo Metropolitan, Taraba State, Nigeria.World Journal of advanced research and Reviews14(3), 241-247.

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