Driver Skill Research Articles

Investigation on Factors Affecting Cognitive Skills in Detection of Driving Fatigue

Road safety is a worldwide concern where a high number of fatalities and injuries are caused by traffic accidents. One major issue contributing to these incidents is driving while fatigued, which has been recognized as a contributing factor to accidents in many countries. Being tired while driving is a state that encompasses both physical and mental exhaustion and can greatly impair a person's ability to drive safely. Fatigue negatively affects a driver's cognitive abilities, resulting in decreased performance behind the wheel. The aim of this study is to analyse the effect of driving duration, body mass index (BMI), types of roads and gender factor in causing fatigue while driving. Cognitive skills of drivers while driving was measured through the electroencephalogram (EEG) signal gathered from Emotiv EPOC device to measure brain activity. Relationship between cognitive skills and fatigue signalling is then analysed using regression analysis in Design-Expert software. Results indicated that as mental fatigue increases, there is a noticeable decrease in the relative power of beta rhythm, supporting previous research findings. Results on correlation show that the longer the drivers drove, the wearier they become. Obese people experience more fatigue when driving than those with a normal or overweight BMI. Driving on straight roads resulted in greater fatigue compared to driving on winding ones, with female participants indicating higher levels of tiredness than male participants. These insights are crucial for road safety authorities to tailor their supervision methods based on individual characteristics and to design effective driver fatigue warnings.

Somatic Factors Predict On-Road Driving Skills in Older Drivers and Drivers with Mild Cognitive Impairment.

On-road driving skills can be impaired in older drivers and drivers with mild cognitive impairment (MCI) due to different driving-relevant deficits. Among these deficits, somatic factors have received little attention so far. In a prospective observational on-road driving study, we examined whether somatic factors can predict on-road driving skills in a mixed sample of healthy older drivers and drivers with MCI (n = 99) and whether the inclusion of age explains additional variance. Somatic factors included the number of prescribed drugs, visual acuity, peripheral visual field integrity, mobility of the cervical spine, and hearing impairment. A hierarchical regression analysis was used to predict on-road driving skills by adding the somatic factors in the first step and age in the second step. Results revealed that the combination of somatic factors significantly predicted on-road driving skills (R2adjusted = 0.439). The inclusion of age led to a significant increase of explained variance (R2adjusted = 0.466). Our results suggest that somatic factors can accurately predict on-road driving skills in healthy older drivers and drivers with MCI. In addition, our results suggest that there is a significant but rather small effect of age beyond somatic changes.

Identifying interactions among factors related to death occurred at the scene of traffic accidents: Application of “logic regression” method

AimTraffic accidents are caused by several interacting risk factors. This study aimed to investigate the interactions among risk factors associated with death at the accident scene (DATAS) as an indicator of the crash severity, for pedestrians, passengers, and drivers by adopting “Logic Regression” as a novel approach in the traffic field. MethodA case-control study was designed based on the police data from the Road Traffic Injury Registry in northwest of Iran during 2014–2016. For each of the pedestrians, passengers, and drivers’ datasets, logic regression with “logit” link function was fitted and interactions were identified using Annealing algorithm. Model selection was performed using the cross-validation and the null model randomization procedure. Resultsregarding pedestrians, “The occurrence of the accident outside a city in a situation where there was insufficient light” (OR = 6.87, P-value<0.001) and “the age over 65 years” (OR = 2.97, P-value<0.001) increased the chance of DATAS. “Accidents happening in residential inner-city areas with a light vehicle, and presence of the pedestrians in the safe zone or on the non-separate two-way road” combination lowered the chance of DATAS (OR = 0.14, P-value<0.001). For passengers, “Accidents happening in outside the city or overturn of the vehicle” combination (OR = 8.55, P-value<0.001), and “accidents happening on defective roads” (OR = 2.18, P-value<0.001) increased the odds of DATAS; When “driver was not injured or the vehicle was two-wheeled”, chance of DATAS decreased for passengers (OR = 0.25, p-value<0.001). The odds of DATAS were higher for “drivers who had a head-on accident, or drove a two-wheeler vehicle, or overturned the vehicle” (OR = 4.03, P-value<0.001). “Accident on the roads other than runway or the absence of a multi-car accident or an accident in a non-residential area” (OR = 6.04, P-value<0.001), as well “the accident which occurred outside the city or on defective roads, and the drivers were male” had a higher risk of DATAS for drivers (OR = 5.40, P-value<0.001). ConclusionBy focusing on identifying interaction effects among risk factors associated with DATAS through logic regression, this study contributes to the understanding of the complex nature of traffic accidents and the potential for reducing their occurrence rate or severity. According to the results, the simultaneous presence of some risk factors such as the quality of roads, skill of drivers, physical ability of pedestrians, and compliance with traffic rules play an important role in the severity of the accident. The revealed interactions have practical significance and can play a significant role in the problem-solving process and facilitate breaking the chain of combinations among the risk factors. Therefore, practical suggestions of this study are to control at least one of the risk factors present in each of the identified combinations in order to break the combination to reduce the severity of accidents. This may have, in turn, help the policy-makers, road users, and healthcare professionals to promote road safety through prioritizing interventions focusing on effect size of simultaneous coexistence of crash severity determinants and not just the main effects of single risk factors or their simple two-way interactions.

Assessment of the Decadal Prediction Skill of Sahel Rainfall in CMIP5 and CMIP6

Abstract Accurately predicting the decadal variations in Sahel rainfall has important implications for the lives and economy in the Sahel. Previous studies found that the decadal variations in sea surface temperature (SST) in the Atlantic, Mediterranean Sea, Indian Ocean, and Pacific contribute to those in Sahel rainfall. This study evaluates the decadal prediction skills of Sahel rainfall from all the available hindcasts contributing to phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6), in comparison with the related uninitialized simulations. A majority of the prediction systems show high skill with regard to Sahel rainfall. The high skill may be partly attributed to external forcings, which are reflected in good performance of the respective uninitialized simulations. The decadal prediction skills of the key SST drivers and their relationships with the Sahel rainfall are also assessed. Both the hindcasts and the uninitialized simulations generally present high skill for the Atlantic multidecadal variability (AMV) and Mediterranean Sea SST indices and low skill for the Indian Ocean basin mode (IOBM) and interdecadal Pacific variability (IPV) indices. The relationship between the Sahel rainfall and the AMV or Mediterranean Sea SST index is reasonably captured by most prediction systems and their uninitialized simulations, while that between the Sahel rainfall and the IOBM or IPV index is captured by only a few systems and their uninitialized simulations. The high skill of the AMV and Mediterranean Sea SST indices as well as the reasonable representations of their relationships with the Sahel rainfall by both the hindcasts and uninitialized simulations probably plays an important role in predicting the Sahel rainfall successfully. Significance Statement Predicting Sahel rainfall on the decadal time scale is of great importance. This study provides a thorough evaluation of the decadal prediction skills of Sahel rainfall in the current decadal prediction systems participating in phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). A majority of the systems achieve high prediction skill of Sahel rainfall, which probably results from the high prediction skill of some key sea surface temperature (SST) drivers, especially in the Atlantic and Mediterranean Sea SST, and their relationships with Sahel rainfall. This study provides a reference for better understanding the predictability of Sahel rainfall.

Driving techniques for drivers with mobility disabilities.

“Driving technique is the way of using the road consciously chosen by the driver. The key to appropriate driving technique is the driver's knowledge, skills and characteristics.” [1] The above-mentioned skills refer only to the manner of using the vehicle by an able-bodied driver (driver candidate and full-fledged driver). The techniques used by the vehicle driver affect the possibility of making mistakes while driving and the time needed to perform manoeuvres. Generally accepted driving standards and principles do not cover how to drive a vehicle adapted to the needs of a disabled driver. These skills are therefore not developed during the course preparing candidates with disabilities to become drivers, or at a later stage after obtaining driving licenses. The aim of the article is to present ways of driving a vehicle using the most common adaptive devices used by people with mobility disabilities. The research used the case study method, i.e. a case study involving the analysis and discussion of real situations [2]. The lack of information relating to driving techniques adapted to the needs of drivers with disabilities is a gap that should be filled. The study describes the processes relating to driving techniques of a vehicle with the most commonly used adaptation sets.

Behind the wheel: Probing into personality, skills, and driving behavior’s role in bus rapid transit crashes

Objective Personality traits and driving skills are significantly associated with driving behaviors and crashes. In the case of professional bus drivers, the relationships amongst these variables have not been sufficiently examined in terms of road crashes. Therefore, this study seeks to examine the relationship between personality traits, driving skills, driving behaviors, and crash involvement among Bus Rapid Transit (BRT) drivers. Methods The study employed a comprehensive data collection strategy involving self-reported questionnaires, including the driver behavior questionnaire, driver skill inventory, and Big Five inventory, alongside Global Positioning System (GPS)-extracted speeding data from a sample of 166 drivers. To explore the relationship between variables, the study utilized the Partial Least Squares Structural Equation Model (PLS-SEM) as the analytical method. Result The findings reveal that self-reported violations and actual speeding performed by drivers were positively associated with crash involvement, whereas positive driving behavior negatively influences violation, errors, speeding and crash involvement. The study also found that the safety skills were negatively associated with violations, errors, and speeding, while higher perceptual-motor skills were associated with higher instances of speeding violations, resulting to a higher possibility of getting involved in a crash. Finally, the study reveals that certain personality traits (extraversion and neuroticism) were positively associated with violations, errors, and speeding, leading to a higher risk of getting involved in crashes, whereas certain personality traits (conscientiousness and agreeableness) were associated with safe driving. Conclusion The study findings offer valuable insights into the predictors of crashes among professional BRT drivers, which can be used to enhance driving practices, ensuring the safety of the public. Moreover, these findings provide transportation agencies with better management and decision-making capabilities to implement effective interventions to improve road safety.

Peran Kepuasan Pelanggan Dalam Memediasi Pengaruh Kualitas Layanan Dan Citra Merek Terhadap Loyalitas Pelanggan: Studi Pada Pelanggan GO-JEK Di Denpasar

Online transportation is one form of the rapid development of technology. One company that has become a pioneer in providing online-based transportation services is PT. Indonesian Go-Jek. PT. Go-Jek Indonesia was pioneered by Nadiem Makarim in 2010 which at that time only served motorcycle taxi orders via telephone, but since January 2015, Go-Jek has launched a mobile application that can be used as a medium for ordering Go-Jek transportation online. Go-Jek itself is an intermediary that connects motorcycle taxi drivers with customers, where customers can monitor the position of motorcycle taxi drivers that have been ordered. This study aims to determine the role of customer satisfaction in mediating the effect of service quality and brand image on customer loyalty. The sample in this study amounted to 120. The sampling technique used was purposive sampling technique, namely the technique of determining the sample with certain criteria. This study used data collection techniques using the questionnaire method using the Google form. The data analysis technique in this study is the SEM analysis technique using the SmartPLS software analysis tool. The results of this study indicate that service quality has a positive and significant effect on customer satisfaction and loyalty. Brand image has no positive and significant effect on customer satisfaction and loyalty. Customer satisfaction has a positive and significant effect on customer loyalty. Customer satisfaction partially mediates service quality on customer loyalty. Customer satisfaction does not mediate brand image on customer loyalty. Go-Jek management is expected to continue to improve the quality of their services, especially in improving the discipline and skills of drivers, so that the company is able to provide maximum service.

Road Rage and Anger-Provoking Situations in Sulaimani City- Iraq

Driver behavior factors, including road rage factor, are often responsible for increasing traffic accidents that result in deaths, physical injuries, psychological problems, and financial losses. In order to improve traffic safety problems in our country, road rage issues related to anger-provoking situations were explored. To collect data, two questionnaire survey forms related to road rage level, traffic characteristics, and demographics of the drivers were prepared. The road rage questionnaire form included 12 questions related to popular anger-provoking situations. Six Likert scales were scored starting from 0% to 100%. Out of 600 spread forms, 463 valid forms were accepted to be analyzed. Different participants having different gender, age, education level, driving experience, vehicle type, daily travel time, vehicles’ price, traffic accidents, and the road rage history were contributed. A statistical software program (SPSS 26) was utilized to analyze the data. It was obtained that there are significant correlations among road rage-provoking anger questions and the total overall rate of the road rage. On the other hand, the results showed that male drivers are angrier than female drivers during driving. The older the drivers, the less overall road rage values were obtained. The overall road rage value decreased as the education level of the drivers raised to higher level. Driver skills had significant effects on the results of the overall road rage value of the drivers in which the drivers having 4 to 6 years of driving license were angrier than the other drivers, while drivers having driving license more than 6 years had the less feeling of anger during driving. Regarding types of vehicles, the bus, taxi, and then truck drivers had more overall road rage values than PC and SUV drivers. As the drivers traveled more distances, the obtained results of the overall road rage values became greater. The drivers who have high-price vehicles were angrier than those have low-price vehicles. The Drivers’ accident history did not significantly relate to overall road rage values. In addition, the drivers who did not have any road rage history in their real life had the smallest overall road rage values; while those drivers faced physical fighting and engaged police stations due to road rage problems had the greatest overall road rage value.

Risky driving behavior among individuals with Autism, ADHD, and typically developing persons

IntroductionMany individuals with Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) often experience difficulty with driving, including difficulty with obtaining a driver’s license as well as driving safely and efficiently. Such difficulties negatively impact their ability to function independently and participate in daily activities that require driving. ASD and ADHD commonly occur co-morbidly and share many overlapping clinical features. Few studies have directly compared the nature of difficulties in driving safety outcomes between ASD and ADHD. The overarching goal of the current study was to characterize and compare self-reported driving behavior among young autistic drivers, ADHD drivers, and typically developing (TD) drivers. MethodFifty-four participants (14 ASD, 20 ADHD, 20 TD); ages 16–30) completed the Autism Spectrum Quotient and ADHD Adult Rating scale as a method of screening of symptoms. All three groups then completed the Driving Behavior Questionnaire (DBQ), which measured self-reported driving violations, driving errors, and overall risky driving behavior. The three groups of ASD, ADHD, and TD individuals were then compared regarding symptomology and driving behavior differences. ResultsOne-way ANOVAs indicated group differences in DBQ total scores and DBQ errors. Drivers with ADHD reported significantly greater overall risky driving behaviors and driving errors compared to ASD and TD drivers. There were no significant differences between ASD and TD drivers in reported risky driving behaviors and errors. Linear regressions indicated that among all drivers, self-reported ADHD symptoms were significantly associated with higher levels of self-reported overall risky driving and driving errors, regardless of diagnostic group. DiscussionRisky driving and driving errors may be more closely related to symptoms that are characteristic of ADHD. This has implications for individuals with ADHD and autistic individuals who often show or report higher rates of ADHD symptoms. Future studies should compare driving skills of ASD and ADHD drivers using objective measures of driving performance, such as driving simulators or on-road tests.

Deep learning assisted three triboelectric driving operation sensors for driver training and behavior monitoring

Improving the driving skills of drivers, particularly during the training stage, is crucial in reducing the likelihood of road traffic accidents. In this work, a driver training assistance system (DTAS) is developed for driver training and behavior monitoring. The DTAS integrates three triboelectric driving operation sensors, including gear shift sensor, steering angle sensor, and pedal sensors. Through the ingenious structural design of contact-separation and freestanding-triboelectric-layer mode, these triboelectric sensors have the characteristics of simple structure, easy manufacture and installation, and self-powered, which avoids the complex wiring problem in the limited space of the vehicle. The basic electrical performance test of triboelectric sensors and driving simulation experiment show that the developed DTAS can monitor the driver behavior and provide the feedback on each driver’s operation process in real-time. Combined with deep learning (DL) technology, the DTAS can identify whether the driving operation of drivers in specific training scenarios is correct or not, with an accuracy rate of 97.5%. This work is aimed at assisting the novice or learner drivers in driving training, which helps to improve their driving skills and form good driving habits. The proposed scheme can provide new ideas for the innovative exploration of driving training modes without coaching.

Effect of driving training on car crashes and driving skills in older people: A systematic review and meta-analysis.

The primary aim of this systematic review was to examine the efficacy of driving interventions with regard to a reduction in motor vehicle crashes and improvements in driving skills among older people. The secondary aim was to identify the optimal type (on-road or off-road) and dosage (period, sessions, and duration) of driving interventions for improving driving skills in older people. We searched MEDLINE, EMBASE, PsycINFO, and Scopus of Systematic Reviews for papers published from their inception to December 1, 2020, as well as the reference lists of the included papers. The selected studies were randomized controlled trials examining the effects of driving interventions among community-dwelling older drivers aged 65 years and over. A meta-analysis of two studies (n = 960) showed that driving interventions significantly reduced the number of motor vehicle crashes per person-years. Ten studies (n = 575) were included in the meta-analysis showing that the interventions significantly improved the driving skill scores. Driving skill scores significantly improvedafteron-road training, and in interventions of at least 3 h, 3 sessions, and 3 weeks. Driving interventions significantly improve driving skills and reduce motor vehicle crashes among older drivers aged 65 years and over. On-road training is more efficacious than off-road training and driving interventions of at least 3 h taking place in 3 sessions over a period of 3 weeksmay be required to improve driving skills in older drivers. Geriatr Gerontol Int 2023; 23: 771-778.

Providing a model of competences and skills of drivers in the internationalization of electronic businesses

Providing a model of competences and skills of drivers in the internationalization of electronic businesses

Deep Learning for Pothole Detection on Indonesian Roadways

Accidents are common on Indonesian roadways. Accidents are caused by vehicles, motorcycles, and public transportation. Road fatalities are caused by speeding, alcohol, distraction, fatigue, and poor road conditions. There are numerous car accidents on Indonesian roadways. 30% of Indonesian traffic incidents are explained by road infrastructure and environmental conditions, 61% by driver skill and personality, and 9% by vehicle variables such as vehicle standardization. Cars are damaged, immobilized, and crashed as a result of road conditions. Every hour, three people pass away in traffic in Indonesia, according to authorities. According to the BPS's 2021 Land Transportation Statistics report, 31.91 percent of Indonesia's roads were damaged, totaling 174,298 kilometers. Accidents among Indonesian motorists are becoming more common as roads deteriorate. Using a single camera, a deep learning algorithm can recognize and detect road degradation such as potholes and road cracks. Train and process the model using transfer learning and fine-tuning on the Nano YOLOv5 model architecture. After being validated in three major scenarios, the model performs well with the appropriate confidence level. The precision metric for the model is 0.8, while recall and mAP:0.5 are both 0.5.

Evaluating the impact of COVID-19 on traffic congestion and safety skills using structural equation modeling (SEM) and Auto-Regressive Integrated Moving Average (ARIMA)

The current work presented a comparative analysis of traffic demand and safety skills before and after control measures during the COVID-19 epidemic, acquired time-series change data curves, and constructed a prediction model after determining the trend of traffic demand over time. From a data analysis perspective, the paper draws some interesting conclusions about long span, coarse sampling studies. In terms of the study population, the paper did focus on the specificity of the global epidemic. Kuwait was selected as a case study. Traffic demand analysis was conducted using a Structural Equation Model (SEM), Auto-Regressive Integrated Moving Average (ARIMA), and safety skills questionnaire along with flow charts and demographic variables. These methods were utilized to study the impact of COVID-19 on traffic congestion and safety skills as well as to forecast the future traffic volumes. Results showed that traffic congestion had a significant reduction during COVID-19 as a result of the preventive safety measures taken to control the spread of the virus. Such reduced traffic volume was associated with a decrease in traffic violations and an increase in the safety skills and PM skills of drivers.

Bayesian analysis of Formula One race results: disentangling driver skill and constructor advantage.

Successful performance in Formula One is determined by combination of both the driver's skill and race-car constructor advantage. This makes key performance questions in the sport difficult to answer. For example, who is the best Formula One driver, which is the best constructor, and what is their relative contribution to success? In this paper, we answer these questions based on data from the hybrid era in Formula One (2014-2021 seasons). We present a novel Bayesian multilevel rank-ordered logit regression method to model individual race finishing positions. We show that our modelling approach describes our data well, which allows for precise inferences about driver skill and constructor advantage. We conclude that Hamilton and Verstappen are the best drivers in the hybrid era, the top-three teams (Mercedes, Ferrari, and Red Bull) clearly outperform other constructors, and approximately 88 % of the variance in race results is explained by the constructor. We argue that this modelling approach may prove useful for sports beyond Formula One, as it creates performance ratings for independent components contributing to success.

A Human-Machine Shared Control Framework Considering Time-Varying Driver Characteristics

The uncertainties of driver's behavior seriously affect road safety and bring significant challenges to the human-machine cooperative control. This paper proposes a human-machine shared control framework considering driver's time-varying characteristics to improve the co-driving cooperation performance. Firstly, the driving intention is introduced to describe the driver's involvement level through using Gauss-Bernoulli restricted Boltzmann machine method. And the index of driving ability is proposed to evaluate driver skills based on path-tracking errors. Then, a novel human-machine authority allocation strategy is designed by combining the two driving behavior characteristics and used to construct the driver-vehicle interaction system. Subsequently, a T-S fuzzy robust state-feedback shared control system is developed considering time-varying driver behaviors and vehicle states. Finally, the proposed shared steering system is validated by the driver-in-the-loop test bench. The results show that the proposed control method can reduce human-machine conflicts and has obvious superiority in improving performance of driving comfort, path tracking, and vehicle stability for the co-driving vehicles.

The short-term effectiveness of online group hazard perception training in experienced drivers

This study aimed to test short-term effectiveness of hazard perception training in a group of experienced drivers and to evaluate the role of traffic safety attitudes, risky driving intentions, and driving self-efficacy on training effectiveness. Seventy experienced drivers volunteered to participate in the study: 38 attended online hazard perception testing and training sessions and 32 participated only in hazard perception testing sessions. A newly developed online group intervention of two sessions (based on the framework of hazard avoidance and delivered by a driving instructor) for hazard perception training was delivered to the experimental group. The control group received no training. Lithuanian hazard prediction test (LHP12) was used to measure the level of hazard perception at pre-test and post-test assessments. Additionally, self-reported questionnaires on traffic safety attitudes, risky driving intentions, driving self-efficacy, and socio-demographics were used in pre-test assessment. Training helped to increase hazard perception skills of experienced drivers. Experienced drivers who participated in training scored higher in post-testing compared to those who received no training. Higher driving self-efficacy was related to greater improvement of hazard perception skills after training. Traffic safety attitudes and risky driving intentions were not related to the outcomes of hazard perception training. The newly developed online program for hazard perception skills training was found to be effective in experienced drivers. Future studies should be based on the analysis of hazard perception training effectiveness in a larger sample of experienced drivers, as well as on the investigation of long-term hazard perception training effects.

Study on the D evelopment and Verify o f the Korean- Pow ered Wheelchair Driving Skill Ability Assessment Tool(K-PWDSAAT)

Study on the D evelopment and Verify o f the Korean- Pow ered Wheelchair Driving Skill Ability Assessment Tool(K-PWDSAAT)

The Effectiveness of a Digital Game-Based Intervention on Hazard Perception and Visual Skills in Novice Drivers: A Single Blind, Randomized Controlled Trial

Novice drivers show poorer performance than experienced drivers in terms of visual skills and hazard perception. This study aimed at evaluating the effectiveness of a digital game-based intervention on hazard perception and visual skills in novice drivers. Forty-six novice drivers (6 men, 40 women) were randomized to the intervention group (n = 23; 20.79 ± 0.81 years) or control (n = 23; 20.65 ± 0.93 years) group. The intervention group received a game-based intervention in addition to a hazard perception training, whereas the control group received only the hazard perception training. Hazard perception and visual skills were assessed in both groups before and after the 14-day interventions. Between-group comparisons revealed significantly greater improvements in visual short time memory, visual closure, visual discrimination, figure-ground and total scores in the game-based group than in the control group (p < 0.05 for all). Our results showed that 14 days of game-based intervention enhanced hazard perception and visual skills in novice drivers. Using game-based interventions in driving rehabilitation is recommended to improve hazard perception and visual skills of novice drivers.

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

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.