By: Ramos-Montañez, Fiori F., Valle-Bayona, José J., Instituto de Seguridad Minera – ISEM. 

Abstract

Purpose: Evaluate the impact of training mining drivers using machinery simulators, considering variables such as perception, attention, efficiency, and safety. The influence of covariates such as age, occupational group, company, duration of training, and work experience was explored.

Materials and Methods: A heavy machinery simulator was used to train and evaluate a sample of 117 mining drivers in various driving skills. Data on perception, attention, efficiency, and safety were collected before and after training, as well as information on covariates such as age, occupational group, company, duration of training, and work experience. An analysis was performed using Wilcoxon's signed-rank test for repeated measures, before and after training.

Results: A significant improvement was observed in the perception, attention, efficiency, and safety skills of mining drivers after training with the simulator (p<0.001). Age, occupational group, company, duration of training, and work experience also influenced the results.

Conclusions: Training using machinery simulators has a positive impact on the driving skills of mining drivers. Age, occupational group, company, duration of training, and work experience are important factors to consider in the effectiveness of training.

Recommendations: It is suggested that machinery simulators continue to be used for training mining drivers, adapting training programs to the specific characteristics of drivers and working conditions in the mining industry. In addition, it is recommended that longitudinal studies be conducted to evaluate the long-term impact of training on road safety and efficiency in mining.

Keywords: Driving Simulator, Road Safety, Driving Efficiency, Driving Safety.

Introduction

Background

Driving safety and efficiency have become significantly important issues worldwide, given the constant increase in the number of vehicles and the growing complexity and extent of road networks. Although the mortality rate related to traffic accidents has stabilized and even decreased considering the expansion of motorized vehicles in recent years (WHO, 2018), numbers remain alarmingly high, resulting in human and economic costs that are unacceptable in all countries, regardless of their level of development.

Particularly alarming is the fact that injuries caused by road accidents are the second leading cause of death among young adults aged 12 to 26 (Hernández, 2024), with new or inexperienced drivers constituting a particularly significant segment. At the same time, rising pollution levels, especially in urban areas, and the continuing depletion of fossil fuels underscore the urgency of reducing their consumption (Rentschler, 2023).

The implementation of efficient driving techniques and the gradual transition from internal combustion engine (ICE) vehicles to electric vehicles are emerging as imperative measures to address these issues (Alanazi, 2023).

In this regard, achieving a safe and sustainable transport system requires drivers with safe and efficient driving skills, which is particularly important in the current context. In line with continuous technological advances, simulation environments have expanded their realism and learning potential, providing an accelerated path for skill acquisition in various fields.

Driving simulators, in this sense, enable the acquisition, development, and evaluation of driving skills without exposing participants to the risks inherent in real driving under various conditions and events in which safety and efficiency parameters can be measured. These simulators have been widely used in training programs in many countries, demonstrating their versatility and effectiveness in driver instruction (Faus, 2023).

Research in driving simulation has several advantages over research based on real driving, one of the most notable being the ability to create virtual environments with fully controllable parameters, which would be costly and challenging in real driving (Alonso, 2023).

In the educational field, simulators are valuable tools for concept formation and knowledge building, allowing their application in contexts that are inaccessible or difficult to recreate for participants. This methodology encourages user participation, promoting meaningful learning based on their own experience (Feldstein, 2020).

Although there are driving simulators designed to train new drivers, most of these systems tend to focus on the recreational aspect, neglecting the development of fundamental skills for responsible driving (Barbaroux, 2022). It is therefore very important to explore and promote simulators that focus on comprehensive skills training that contributes to safe and efficient driving in the interests of safer and more sustainable transport.

Problem to be Addressed

Safety in passenger and cargo transportation within the mining industry in Peru remains a constant concern, given the high rates of traffic accidents reported in the country (Buitron, 2023). Drivers of vehicles responsible for transporting people and goods in this sector are exposed to a series of risk factors that impair their driving ability, thereby increasing the likelihood of accidents.

One of the main factors affecting driver safety in the mining industry is the altitude at which mining activities take place. These operations are carried out in high-mountain areas, where reduced oxygen levels can impair drivers’ physical and mental performance, thereby increasing the risk of traffic accidents (Matamala, 2021). In addition, working at high altitudes can cause fatigue and stress, impairing drivers’ reaction capacity when facing unexpected situations on the road (Olarte, 2022).

Another critical factor for driver safety is the prevalence of sleep disorders. Drivers who undertake long and frequent journeys may experience sleep disorders such as insomnia or apnea, which compromise their attention and concentration behind the wheel (Yuda, 2020).

Likewise, road conditions and the presence of geographical hazards represent significant risk factors that affect driver safety in mining operations. In many mining areas, access roads are precarious and poorly maintained, hindering traffic flow and increasing the likelihood of accidents. Furthermore, in high-mountain regions, adverse weather conditions such as snowfall, rain, blizzards, and dense fog may occur, further complicating driving (Zhang, 2020).

Night driving also represents a significant risk factor that can affect driver safety. In the mining sector, it is common for vehicles to travel at night in order to avoid traffic and ensure timely arrival at work sites. However, this type of night driving can diminish drivers’ perception and reaction capacity, particularly in high-mountain areas or under adverse weather conditions (Luzzi, 2020).

In summary, all these factors have a significant impact on the safety and efficiency of drivers operating vehicles for passenger and cargo transportation in the mining industry. To mitigate the risks associated with driving in this context, it is essential to implement preventive measures that include driver training, road infrastructure improvements, the adoption of vehicle safety technologies, and strict compliance with driving safety protocols. In this regard, driver assessment through simulators emerges as a valuable tool for identifying areas of improvement and designing targeted training programs, with the aim of reducing the inherent risks of driving in the mining industry in Peru.

Objectives

General Objective

To evaluate the impact of training for mining drivers through machinery simulators, considering variables such as perception, attention, efficiency, and safety. The influence of covariates such as age, occupational group, company, duration of training, and work experience is explored.

Specific Objectives

a) To analyze the effect of simulator-based training on the perception and attention of drivers across different age groups, assessing significant changes in their performance before and after training.

b) To determine the influence of simulator-based training on driving efficiency and safety among drivers with varying levels of experience and operating different types of vehicles, through a comparison of results obtained before and after training.

c) To evaluate the effectiveness of different variables, such as course duration and type of company, on the final outcome of simulator-based training, identifying which factors significantly influence the improvement of drivers’ skills and the reduction of risks associated with driving.

Scope

The proposed scope of the study focuses on evaluating the impact of training for mining drivers through machinery simulators, considering variables such as perception, attention, efficiency, and safety. The study also seeks to explore the influence of covariates such as age, occupational group, company, training duration, and work experience on these aspects.

What the study does not include, nor can it guarantee, is the direct implementation of changes in training policies or in the labor practices of mining companies. Nor can it guarantee a specific reduction in traffic accidents or an absolute improvement in the efficiency and safety of mining drivers, since this would depend on various external and contextual factors beyond the scope of the study.

Theoretical Framework

Driving simulators represent a fundamental tool in the training and evaluation of drivers in Peru’s mining industry (Laquiticona, 2022). These devices accurately replicate the behavior of a driving system under diverse conditions, providing an immersive experience that allows drivers to be trained and evaluated safely and efficiently.

There are two main types of simulators used in this industry: automobile simulators and heavy machinery simulators. The former, equipped with hardware that replicates the interior of a real vehicle, allow users to immerse themselves in various scenarios and weather conditions, interacting with other agents during the simulation. On the other hand, heavy machinery simulators train operators in the handling of equipment such as cranes, backhoes, and dump trucks, providing a realistic experience that familiarizes users with the controls and functions of each type of machinery (Santillán, 2022).

Technological advancements have enabled the development of increasingly sophisticated simulators, capable of delivering a comprehensive and realistic driving experience. Since their beginnings in the 1960s, simulators have evolved significantly, progressing from mechanical devices to high-tech computer-based systems. Today, the most advanced simulators incorporate high-quality 3D graphics and physical motion reproduction, providing a highly realistic driving experience (Criscione, 2018).

Driver evaluation through simulators is a crucial tool for measuring both driver efficiency and safety. Designing an effective assessment requires defining specific objectives and creating driving scenarios that allow for the evaluation of the targeted skills (Garcia, 2001). It is important to take into account factors such as altitude, climate, and geography, as well as the specific characteristics of the vehicle used in mining activities (Mayora, 2008).

During the evaluation, different aspects of driver efficiency and safety can be measured, such as speed, distance traveled, reaction time, and the ability to handle risk situations (Mora, 2004). In addition, the driver’s behavior during the test should be evaluated, including their willingness to comply with safety regulations and their ability to make sound decisions in critical situations (Valero, 2008).

For this reason, driving simulators represent an invaluable tool for enhancing safety and efficiency in Peru’s mining industry. Their ability to train and assess drivers in a safe and controlled virtual environment contributes significantly to reducing accident risks and improving the quality of passenger and cargo transportation in this industry, which is crucial for the country.

Method of Solution

A random sample of truck drivers of buses and semi-trailer trucks carrying concentrate (10-20 MT) attending a training course using a Simumak Simestruck Gold simulator was included, comprising 117 workers, between October 2023 and February 2024.

The training course with the simulator used the Simescar 4WD 2.0 software and was adjusted in duration according to driver profiles. The content and program of the training sessions were essentially the same across the groups of workers, who came from eight different companies.

Psychological tests measuring perception, attention, and concentration were administered to all course participants under standardized conditions by a psychology professional experienced in test application, processing, and analysis, obtaining individualized scores.

For data analysis, univariate descriptive statistics were obtained. For quantitative variables, averages and standard deviations or medians were calculated depending on the assumption of normality of the variables evaluated using the Shapiro-Wilk test.

In the first sample, the Wilcoxon signed-rank test for repeated measures was applied to assess differences between scores before and after the training sessions.

Covariates such as age ranges, occupational group, company, training duration, and work experience were identified.

Hybrid graphs in the form of raincloud plots were generated to visualize the observed differences.

Statistical analyses were carried out using JASP version 0.17.1, an open-source statistical software^t, as well as SPSS v.25 (Statistical Package for the Social Sciences).

Results

The Shapiro-Wilk test showed that the scores obtained did not follow a normal distribution (p < 0.05). Consequently, non-parametric tests were used to analyze the paired data sample (m₁ = 117).

The Wilcoxon test was applied to the scores measured before and after training, yielding p <0.001 and showing a statistically significant difference between the scores for perception, attention, and concentration.

The measurements of efficiency and safety taken before and after the training also showed a statistically significant difference (p <0.001).

See Figures 1 to 5.

The influence of age, company of origin, driver’s experience, position, and course duration was analyzed, resulting in 30 reference tables (Annex No. 1).

Once normality and homoscedasticity of the measurements were obtained, the non-parametric Wilcoxon test was applied to evaluate the assessment scores before and after the training. The covariates were analyzed, and the values are shown in Table 3.

Training has significant effects on perception, attention and concentration, efficiency, and safety for drivers in the 30-39 and 50-59 age groups. However, for the 40-49 age group, only perception and attention show significant improvements, while efficiency and safety in this group do not show significant gains.

The effects of the training vary depending on the company. Some companies show significant improvements in all measured areas, while others show significant improvements only in certain areas or no significant improvement at all.

Drivers with different years of experience also show varying responses to the training. Those with less experience (under 10 years) and those with 10-14 years of experience show significant improvements in all measured areas. However, for those with 15-19 years of experience, improvements are only significant in perception, attention, and efficiency, but not in safety.

Drivers of different types of vehicles and the duration of the course also show differences in training effectiveness. Bus and semi-trailer drivers show significant improvements in all measured areas. Additionally, course duration appears to have an impact, as longer courses (6 hours) show significant improvements in all areas, whereas shorter courses (5 hours) show significant improvements in only some areas.

Drivers who are considered competent show significant improvements in all measured areas, whereas those still in training only show significant improvements in some areas.

Discussion

The results indicate that the covariates did not significantly affect training performance, and that improvement was associated with a driver profile of age 30-39, less than 10 years of experience, completing longer courses, and being classified as “competent.”

It is important to mention that previous research has evaluated the impact of training on heavy machinery operators in other industrial contexts or specifically within the mining industry (Ryan, 2007; Gürer, 2021; Zujovic, 2021). Our study follows others in the mining sector involving bus drivers or concentrate transport drivers, but conducted from a preventive approach to the risk of fatigue and drowsiness in drivers (Gomero, 2017).

Our study complements the need to establish a baseline profile and intermediate progress profiles in the development of strategies to enhance the skills and competencies of mining drivers. In this regard, a significant group of formal-sector mining companies is already interested in incorporating the use of simulators into their preventive strategies (Santillán, 2022).

It should be noted that, according to several studies, training mining vehicle drivers using simulators improves efficiency, reduces errors, and increases safety and productivity by replicating real working conditions in a virtual environment. It also reduces financial and time costs for training a driver while enhancing the safety and productivity of their work (Eiter, 2020; Artem, 2021).

Simulators provide a realistic working environment, allowing participants to quickly develop their skills for atypical and emergency situations (Orr, 2009). Furthermore, within mining operations, simulators can also improve decision-making processes by enabling prospective experiments at a lower cost, which accelerates the iterative process and reduces uncertainty in decision-making (Tang, 2019).

Chenxi (2022), in a study, confirms the effectiveness of simulator and virtual reality training for driving school students, showing positive results in subjective response, knowledge mastery, driving behavior, and exam pass rates. In their study, a hierarchical evaluation method was adopted to assess the effect of driving simulator training plus virtual reality on subjective reaction levels, knowledge mastery, driving behavior, and exam pass rates.

According to Pradhan (2023), driver training with simulators enhances the understanding and use of advanced driver assistance systems, thereby improving real-time knowledge and awareness of system states. When evaluating the effects of different training methods on drivers’ use and comprehension of vehicle automation, the results indicated that training is associated with greater knowledge of these systems.

In our study, the effects of covariates were explored. In this regard, previous research has analyzed the influence of variables such as age, work experience, and training duration, among others, on the effectiveness of driver training or on driving performance.

Covariates are quantitative variables related to the dependent variable and that may predict the outcome under study (Coeli, 2021). Thus, when the design includes any covariate variable, it is referred to as a covariate design. In our study, the identification of these covariates dependent on participant characteristics is based on other attributes of the environment (driving system ecology) that act as counterparts in the development of driver skills and competencies (Caffo, 2020).

It is very common in studies on driver safety that environmental attributes, and less frequently those of work interfaces, are identified in patterns that condition the emergence of risk factors.

Some of these factors, individually or in isolation, and which contribute in terms of risk load, may outline a set of risk factors or their association (Choudhary, 2022).

In mining, some of these factors have been identified in previous studies. Siyurin (2022) found that risk factors for mining dump truck drivers include length of service, work severity, whole-body vibrations, exposure to noise, and musculoskeletal and hearing disorders, all of which affect occupational health risks. Thus, the occupational health risk of dump truck drivers is determined by length of service, work severity, the impact of whole-body vibrations and noise, as well as concomitant musculoskeletal and hearing disorders, all of which must be taken into account when preventing occupational diseases.

Aliabadi (2022) proposed in a study that risk factors for mine drivers include awkward body posture, exposure to vibrations, and age. It was found that awkward body posture has the greatest impact on musculoskeletal discomfort among mine truck drivers. The author used Random Forest algorithms to investigate musculoskeletal discomfort among mine truck drivers, taking into account human vibrations and awkward body postures, and the results showed that the adverse effects of these factors on driver discomfort exceeded the exposure limit.

The results obtained are not yet comparable with effectiveness studies, which correspond to ex-post measurements conducted under company-controlled conditions. In this regard, our findings contribute to the dimension of efficiency and efficacy measurements (Abich, 2021).

However, our study is comparable with the results obtained in studies that evaluated the effectiveness of heavy machinery simulators in driver training, particularly in similar mining or industrial environments (Caignet, 2007).

Alonso (op. cit.), in analyzing the original literature on driving simulators as a tool for driver training/instruction, had already indicated that the use of driving simulators is promising in terms of training effectiveness, but with limitations such as small sample sizes and lack of follow-up, as well as the need for further research to improve the training programs that employ them.

Our study also highlights the need to establish adaptation patterns to the participant’s profile, which was implemented through the trainers working with groups of employees by company.

The participant profile with the least benefit, as mentioned, achieved lower specific expected results, as also reported by some studies in relation to age. Thus, Cuenen (2019), in a study, reported that simulator-based training improves the driving performance of older drivers, enhancing measures such as lateral control and right-of-way behavior, and that specific feedback is crucial for certain skills. In their article, the authors investigated whether simulator-based driving training can improve specific measures of driving ability in older drivers and found that driving multiple times in a simulator improves performance in certain driving skills, such as lateral control.

In our study, two variables obtained from the measurements performed by the simulator software were efficiency and safety. When reviewing the literature on factors that influence driver safety and efficiency in the mining industry, there are many factors such as fatigue, stress, weather conditions, among others, and how training can mitigate these effects.

However, the comparative statistical tests of the scores before and after training indicated that there were statistically significant differences in all groups.

This effect is also evident in the results of other studies, such as that of Larue (2018), which points out that driving simulators improve driver education by engaging novice drivers in procedural and higher-level skills. Novice drivers perceive simulators as effective tools that align with their expectations for driver training. The study concludes that young people are likely to engage in technologically enhanced driver education using driving simulators and found that, despite focusing heavily on procedural skills, novice drivers believed that simulators could also be used to obtain higher-level skills. Moreover, they considered them consistent with their expectations of road safety education, suggesting that novice drivers would be willing to further develop this countermeasure through simulators.

With regard to older drivers, Urlings (2019) found that both driving simulator-based training and computer-based training improved knowledge of traffic signs, general driving skills, and specific aspects of driving in at-risk older drivers. In the study, training features that had been shown to be effective in previous research on older driver training were incorporated into both a computer-based intervention and a driving simulator program, in order to examine the effect of both training formats on overall driving ability and on specific aspects of driving in older drivers at risk of reduced driving capacity.

Our study focused on courses lasting less than one week, which raises questions about the sustainability of behavioral changes and the potential curve of extinction or replacement of these behaviors.

This study did not address the importance of continuous training and the reskilling for mining drivers, highlighting the need for data to support the requirement of ongoing updates, feedback, and improvement either outside or after the course.

In this regard, Lavalliere (2017) pointed out in a study that simulator training with video-based feedback improved the driving skills of older drivers, demonstrating an effective transfer to road performance, unlike classroom programs without specific feedback. The results suggest that simulator training was effectively transferred to road performance and suggest that driving programs should include active practice sessions with specific feedback on driving.

Conclusions

1. Training using simulators significantly improves drivers' perception, attention, and concentration. The results show significant improvements in these areas, suggesting that training helps drivers to be more alert and aware of their surroundings while driving.

2. Road safety benefits from simulator training, with a significant improvement in road safety, as measured by tests conducted in the simulator, indicating that trained drivers are better able to handle risky situations more effectively, thereby reducing the likelihood of accidents.

3. The effectiveness of training varies according to the driver’s age and experience. Younger drivers and those with less experience tend to show greater improvements compared to older and more experienced drivers. This suggests that training may be particularly beneficial for less experienced drivers who are still developing their driving skills.

4. The duration of the course influences training outcomes. Longer courses yield better results compared to shorter ones. This indicates that a greater investment of time in training can lead to more significant improvements in drivers’ skills.

5. Training is effective for drivers of different types of vehicles. Both bus drivers and semi-trailer drivers show significant improvements across all measured areas. This indicates that simulator-based training can be successfully adapted to different types of vehicles and driving environments.

Recommendations

1. Implement training programs that include specific exercises aimed at improving drivers’ perception, attention, and concentration. This may involve mindfulness practices, attention-focused exercises, and simulations of complex traffic situations to enhance responsiveness and concentration during driving.

2. Continue and expand simulator-based training programs focused on improving road safety. These programs should include defensive driving practices, identification and response to hazardous situations, and awareness of factors contributing to traffic accidents.

3. Customize training programs to address the specific needs of each driver group, taking into account age and experience level. For example, younger and less experienced drivers may require a stronger focus on developing basic driving skills, while older and more experienced drivers may benefit from refresher and reskilling programs that target challenges associated with their driving experience.

4. Prioritize the allocation of resources to longer training courses, as these have proven to be more effective in improving drivers’ skills. This may involve extending the duration of existing courses or developing new training programs that address key areas of improvement in greater depth.

5. Expand and diversify simulator-based training programs to cover a wider range of vehicle types and driving environments. This will ensure that all drivers, regardless of the type of vehicle they operate, have access to effective training that enhances their driving skills and road safety.

Acknowledgments

To Engineer Giakarlo Carhuamaca, Services Coordinator of the Operations Department at the Mining Safety Institute (ISEM, Instituto de Seguridad Minera), for his invaluable support in facilitating access to the information required for the completion of this study. His contributions were essential to the development of our research.

To Mr. José Sondor, trusted administrative assistant, for his exceptional work in establishing and maintaining the data management sequence, which was crucial for the accurate analysis and interpretation of the results obtained.

We also sincerely thank Psychologist Irina Vílchez for her outstanding guidance and dedication during the fieldwork related to the collection of data from the psychological tests. Her expertise and commitment were fundamental in ensuring the quality and reliability of the information gathered.

We are deeply grateful for the support and collaboration provided by these professionals, whose contributions have significantly enriched this work.

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