Comparative analysis of mental workload and performance between young and elderly drivers: Implications for road safety and age-related driving challenges

1. Introduction

The issue of traffic accidents and their consequential damages poses significant challenges to societies, affecting both human well-being and imposing substantial economic burdens on communities. Statistics reveal that more than half a million lives are lost annually and approximately 50 million individuals worldwide suffering from disabilities because of driving accidents [1]. Disturbingly, traffic accidents stand as the leading cause of death among young people and rank as the third most prominent cause of mortality across all age groups. This situation not only takes away lives prematurely but also causes a lot of financial burden for countries [2]. On a global scale, the statistics are concerning, as road accidents account for an estimated 1.2 million fatalities and 50 million injuries annually [3]. Shockingly, road accidents held the eighth position in terms of years of life lost due to untimely death or disability on a global scale in 2010. Most of these accidents disproportionately affect low- and middle-income countries, accounting for around 90% of the fatalities and injuries. This holds particularly true in the context of Iran, where road traffic accidents stand as a significant cause of mortality, injury, and property damage. Furthermore, these accidents are estimated to consume 6.46% of the nation’s gross national income. Unfortunately, the incidence of accidents, influenced by multiple factors, has been consistently increasing. [4]. Considering the distressing figures pertaining to fatalities and injuries stemming from accidents, it is imperative to spotlight the contributory factors in a bid to mitigate these occurrences. Road traffic accidents are the outcome of a complex interplay involving human, vehicle, and environmental/road factors. Notably, research underscores the leading role of the human element in these incidents [5]. Capitalizing on the advancements in the automotive sector, recent studies have underscored the significance of human factors in shaping road accidents. Across various nations, comprehensive studies have thoroughly examined the three key elements vehicle-related, road/environmental, and human factors. Evidently, human errors account for a substantial 66.8% of the causative factors, followed by vehicle-related issues at 25.6%, while environmental factors contribute to 7.6% [6].

Thus, driving can be viewed as a combination of three key elements: the human factor (driver), the vehicle, and the environment [7]. Human factors are a critical component in road safety, as emphasized in various studies [8]. A significant portion of driving accidents attributed to human factors arise when drivers are unable to meet the required mental and physical demands. In such scenarios, the risk of human error is notably high [7]. Maintaining an appropriate level of workload is essential; it not only minimizes the likelihood of errors but also bolsters driver performance. When faced with high complexity, drivers encounter intense mental demands, often surpassing their cognitive capacity. This leads to cognitive overload, a state that can precipitate errors [9]. Additionally, if the mental workload is less than the driver’s capacity, it can result in boredom and fatigue, increasing the likelihood of mistakes [7]. Further research underscores that the complexities inherent in certain driving conditions, particularly on intricate roadways, are linked to longer reaction times and a heightened propensity for errors. These elements collectively exacerbate the cognitive load on drivers, a significant factor in road safety [10].

Researchers suggest that one of the key determinants of road accidents lies in the workload inherent to driving. This workload is molded by a convergence of various of factors, encompassing prolonged durations behind the wheel, varying traffic conditions, and external physical factors such as high temperatures during summer and extreme cold during winter and the interaction of the driver with the passengers as well as with the outside environment [11]. However, the engagement in additional activities during driving, such as conversations, music listening, news hearing, or mobile phone usage, introduces a layer of complexity, ultimately heightening workload and subsequently driver distraction. This, in turn, detracts from the attention allocated to the primary task of driving. Researchers suggest that the frequency of driving accidents is closely connected to the mental burden experienced by the driver. It’s widely noted in research that a considerable portion of driving accidents arise due to distractions and lapses in concentration. [10].

Given that driving is an inherently dynamic task involving a continuous stream of information assimilation and decision-making, ensuring safety, and averting driving accidents necessitates a thorough grasp and accurate interpretation of the surrounding environment and traffic scenarios. In urban settings like Tehran, where dense traffic prevails, particularly in central locales like Azadi Street, the ability of drivers to promptly adapt to shifting environmental circumstances becomes paramount. This adaptability is crucial to curbing the likelihood of errors and accidents. The concept of workload, quantifying the information processing demanded per unit of time to achieve satisfactory performance, comes into sharp focus in such dynamic settings. In scenarios like navigating heavy traffic, drivers’ workload and performance are subject to fluctuations, contingent on a range of variables. The factors influencing this intricate interplay encompass an array of environmental attributes, including traffic density, the conduct of fellow drivers, the presence of passengers and pedestrians, prevailing weather conditions, air quality, vehicle specifications, speed, road conditions, and individual driver characteristics such as age, gender, and experience.

As age advances, there exists a vulnerability to cognitive decline, potentially compromising an individual’s capability to navigate the roads safely. This, in turn, poses risks not only to the driver but also to passengers and other road users [12]. Research underscores a distinct pattern in road accident prevalence, with two demographic groups particularly susceptible: young individuals under the age of 25 and older individuals above 60 years old [13]. Compelling data on accident occurrences underscores those older drivers, specifically those aged above 75, face the highest probability of being entangled in road accidents, and notably, older individuals are often found at fault in these incidents [14]. Regrettably, the implications of such accidents for the elderly extend beyond mere involvement, as this group exhibits a heightened likelihood of incurring injuries, severe harm, and even fatalities during these events [12].

As we examine the evolving demographic landscape marked by aging populations, a critical concern emerges. The projected surge in the number of elderly individuals underscores the anticipated escalation of vehicle accidents within this demographic. Against this backdrop, the imperative to identify and mitigate risk factors influencing the occurrence of road accidents gains paramount significance. Such an endeavor is not merely aimed at minimizing accidents but also at curbing the direct and indirect costs associated with these incidents [15]. Moreover, the role of inattention has been unequivocally identified as a significant catalyst in a considerable portion of accidents. This facet notably sets apart experienced drivers from novices, thereby accentuating its importance [15]. Respected authorities in the domains of human factors and transportation safety converge on the notion that the intimate connection between the driver and their attention exerts a profound influence on driving performance. In this context, the concept of selective attention assumes pivotal significance. The contemporary landscape is marked by the rapid proliferation of in-vehicle technologies, which introduces a plethora of secondary activities that coax drivers into situations demanding dual or multitasking capabilities, thus elevating the complexity of driving. Devices like audio-visual systems, smartphones, and navigation tools exemplify this trend, holding the potential to divert the driver’s attention and concentration [16].

Research consistently illuminates that driver distractions occupy a prominent position among the prime catalysts of road accidents. Beyond visual distractions, the spectrum extends to encompass non-visual and cognitive distractions. Activities such as attending to voice messages or participating in phone conversations exert discernible influences on a driver’s ability to manage speed, control acceleration, and navigate lateral deviations, as substantiated by empirical findings [17].

Most of the research evaluating drivers’ workload and mental performance has been carried out in simulated settings. However, driving in real-life conditions involves factors such as street traffic, environmental elements like rain or snow, and human interactions including engagement with other drivers, pedestrians, and numerous distractions like using mobile phones for calls, texting, or social media, adjusting radio settings, changing music, consuming alcohol or drugs, eating, drinking, talking to passengers, admiring scenic views, responding to requests or paying attention to surrounding events, using maps, navigators, and GPS devices, and ultimately dealing with driver fatigue or drowsiness. All these factors could significantly impact a driver’s cognitive performance. For instance, heavy traffic can increase the volume of information and inputs, making it harder for the brain to process this information. This can negatively affect the driver’s cognitive performance, leading to longer response times or more errors in driving. Interaction with other drivers and pedestrians can also increase cognitive demands and diminish concentration. Therefore, driving in more natural and realistic conditions can affect a driver’s cognition. Consequently, more research is needed in real-life environments to obtain results that closely mirror reality. This research might provide deeper insights into cognition and the impact of age on driving performance. Hence, this study aims to investigate the impact of mental workload on the performance of young and older drivers in a dynamic driving scenario to compare cognitive performance, workload perception, and driving outcomes between the two age groups.

2. Methods

2.2. Research tools

2.2.1. Cognitive performance measurements

In this study, three tools including Stroop Software, Continuous Performance Test (CPT), and Focused Attention Test (FAT) were used to measure cognitive performance.

2.2.1.1 Stroop Software

The Stroop Color-Word Test (SCWT) stands as a neuropsychological assessment designed to measure selective attention and the capacity for cognitive interference control. It entails the presentation of a stimulus featuring a specific color name printed in ink of a different color. The task assigned to the participant involves naming the ink color while disregarding the written word. The Stroop effect manifests when the processing of one aspect of the stimulus (e.g., color) disrupts the simultaneous processing of another aspect (e.g., the word). This test is a fundamental tool harnessed by psychologists and researchers to probe cognitive proficiencies like information processing, swiftness, and selective attention, all of which demand a certain level of cognitive control. Selective attention, a fundamental cognitive ability, enables individuals to respond to distinct environmental stimuli while effectively filtering out irrelevant ones. The phenomenon of the Stroop effect encapsulates the delayed reaction time observed when the color and the word of a stimulus are incongruent. For instance, Figure 1 visually illustrates Stroop test scenarios featuring congruent (left side) and incongruent (right side) combinations of colors and spoken word sounds. In cases where the colors of the words align with the spoken word sounds, accurate responses ensue, whereas disparities in these aspects yield incorrect responses. The semantic significance of the word induces cognitive conflict, substantiating the Stroop effect’s efficacy as a tool for assessing cognitive performance.

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Figure 1

Visual examples of stroop test trials.

In this test, there are 48 congruent color-word pairs (where the color of the word matches its meaning, e.g., the word “red” in red color), and 48 incongruent color-word pairs (where the color of the word does not match its meaning, e.g., the word “blue” in red color). The incongruent stimuli are presented with a stimulus onset asynchrony of 800 milliseconds and a stimulus duration of 2000 milliseconds. The task for the participants is to select the correct color only. Individuals who experience anxiety due to lack of sufficient concentration may not perform successfully in the administration of the Stroop test.

2.2.1.2 Continuous performance test (CPT)

The Continuous Performance Test is employed to assess sustained attention across different age groups. This test serves as a suitable instrument for measuring vigilance, attention maintenance, and depth of processing. The primary aim of the Continuous Performance Test is to measure sustained attention and vigilance. It is widely used in cognitive assessment, especially in cases involving attention deficits and is particularly valuable in evaluating executive functions associated with attention impairment. The main objectives are to measure sustained attention and the secondary objective is to measure inhibitory control or response inhibition. The Persian version of this test, conducted via software, consists of 150 Persian characters as stimuli. Among these, 30 items (20%) serve as target stimuli. The inter-stimulus interval is one second. The duration of the test administration, including a training phase designed to enhance the participants’ understanding before the main phase, is 200 seconds. In this procedure, participants are required to attend to a relatively simple set of stimuli for a certain period, and upon the appearance of a target stimulus, they are expected to respond by pressing a key. For this study, the number 4 was chosen as the target stimulus among the digits 1 to 9. The test assesses both “target errors” and “response errors.” Target omission errors occur when participants fail to respond to a target stimulus, indicating difficulties in stimulus detection. Response errors occur when participants respond to non-target stimuli, indicating weaknesses in response inhibition. Omission errors can be interpreted as inattentiveness to stimuli, and response errors reflect difficulties in impulsivity. In this study, performance scores were calculated by dividing the number of correct responses by the mean reaction time, and both omission and commission errors were considered. Additionally, the average reaction time was calculated. The test’s reliability was assessed through a pilot study using the test-retest method, yielding reliability of 0.87, 0.76, and 0.81 for omission errors, commission errors, and reaction time, respectively.

The Continuous Performance Test is a tool used to measure sustained attention in different age groups. It functions as a fitting apparatus to assess vigilance, the ability to sustain attention, and the depth of information processing. This test’s primary objective lies in quantifying sustained attention and vigilance, rendering it a prevalent choice in cognitive evaluations, particularly in cases where attention deficits are in question. It holds promise in appraising executive functions entailing attention-related impairments. The core goals encompass the measurement of sustained attention, while a secondary objective focuses on assessing inhibitory control or response inhibition.

Employing software, the Persian version of this test comprises a stimulus set featuring 150 Persian characters. Among these, 30 stimuli (equivalent to 20%) are designated as target stimuli. Intervals between successive stimuli stand at one second. The overall duration of the test, inclusive of a preparatory training phase aimed at enhancing participants’ comprehension before the core assessment, spans 200 seconds. During this evaluation, participants are tasked with attentively monitoring a relatively straightforward sequence of stimuli over the specified time frame. The pivotal action involves responding to a target stimulus by pressing a designated key. In this study’s context, the digit 4 was chosen as the target stimulus from the range of digits 1 to 9. The assessment categorizes performance into “target errors” and “response errors.” Instances of target omission errors manifest when participants neglect to react to a target stimulus, indicative of challenges in detecting stimuli. Conversely, response errors emerge when participants respond to stimuli that are not designated as targets, underscoring issues with response inhibition. Omission errors allude to lapses in attentiveness to stimuli, while response errors underscore difficulties with impulsivity.

To quantify performance, this study computed scores by dividing the number of accurate responses by the mean reaction time. Both omission and commission errors were factored into this assessment. Furthermore, the average reaction time was derived as part of the evaluation process. Ascertaining the test’s reliability necessitated a pilot study utilizing the test-retest method. The ensuing reliability coefficients stood at 0.87, 0.76, and 0.81 for omission errors, commission errors, and reaction time, respectively.

2.2.1.3 Focused attention test (FAT)

The Focused Attention Test serves as an instrumental tool for measuring selective attention capabilities and the adeptness of visual and auditory attention systems across diverse age demographics. Its applicability extends notably to the evaluation of individuals engaged in industrial, military, and driving.

This assessment unfolds in two distinct stages. Commencing with the Focused Attention Test, the screen exhibits a pair of alphabet letters, such as “M” and “S”. Once these letters are displayed, the test initiation is signaled, prompting participants to identify and select the presented letters. Crucially, any other letters shown should not elicit a response. The temporal gap between the presentation of two stimuli is precisely half a second, with this interval being modifiable based on requirements. Additionally, the composition of the displayed letters can be adjusted to suit specific testing scenarios.

Distributed Attention Test (DAT): Subsequently, the Distributed Attention Test takes center stage. This phase entails the exhibition of the previously mentioned letters positioned on opposing sides of the screen. Participants are tasked with responding solely to the right-side stimulus using their right hand and exclusively to the left-side stimulus using their left hand. The preservation of consistent response patterns is imperative, with any deviation considered an error. Each stimulus garners a presentation time of 200 milliseconds, while a one-second interval separates consecutive stimuli.

It is noteworthy that all three tests, including the Focused Attention Test, were conceptualized, and crafted by the Institute of Cognitive Behavioral Sciences Research “Sina.” The significance of these assessments is found in their ability to analyze the complexities of attention distribution and the precision of responses. This detailed examination provides valuable insights into cognitive performance, which are applicable and beneficial across a range of professional fields.

2.2.2. Workload assessment

Workload pertains to the level of attentional resources required to meet both objective and subjective criteria, influenced by factors such as task demands, external support, and experience. Balancing workload is critical, as both insufficient and excessive demands can strain an individual’s adaptive capacities, leading to compromised performance when surpassing capacity limits in either direction. Current viewpoints suggest that load capacity displays a degree of flexibility, but it tends to increase when multiple tasks compete for the same mental resources [18].

2.2.2.1 DALI questionnaire

The DALI questionnaire, a version of the NASA-TLX, is a crucial tool for measuring the mental workload involved in driving tasks. The questionnaire follows a similar method, breaking down workload into six main areas: Attention (Q: how much attentional demand is present- to think about, to decide, to choose, to think for), visual (Q: how much visual demand is present), auditory (Q: how much auditory demand is present), temporal (Q: how much temporal demand is present), interference (Q: how much disturbance occurs during your driving activity simultaneously with any other supplementary tasks such as phoning, using systems, radio or...) and situational stress (Q: how great is your level of stress/constraints while driving- fatigue, insecure feelings, irritation, discouragement, etc). The assessment process for DALI entails the presentation of a question for each of these dimensions. This method assesses workload during the driving task and contains six factors: Participants are tasked with rating their personal perceptions about each aspect on a scale from 0 (low) to 5 (high) based on their own driving experiences. This method measures the different types of pressure they feel while driving. The DALI questionnaire gives a detailed view of how workload changes during driving by looking at these different areas. The validity and reliability of the Persian version of this questionnaire were obtained in the study by Zakarian and his colleagues [19].

3. Results

3.1. Participant demographics

The study’s cohort comprised ten drivers classified under the “young” age group (below 35 years), characterized by an average age of 30 ± 3.5 years and an average professional tenure of 8 ± 2 years. In parallel, an additional ten individuals belonged to the “older” age bracket (above 55 years), with an average age of 61 ± 5.3 years and a substantial average work experience of 30 ± 4 years. Regarding the temporal context of the predefined round-trip route, driving durations ranged from a minimum of 30 minutes to a maximum of 45 minutes. Table 1, based on the independent t-test, displays the values of attention effort, visual demand, auditory demand, stress, temporal demand, and interference, along with their significance levels in two age groups: young and elderly. According to this table, the effort of attention, visual demand, auditory demand, and interference showed significant differences between the young and elderly groups (p-value < 0.05). Additionally, the components of stress and temporal demand did not exhibit significant differences between young and elderly drivers (p-value > 0.05). It is important to note that the normality of the variables under study in this research, including workload and cognitive performance tests, was assessed using the Kolmogorov-Smirnov test, and the normality of the data was confirmed (p-value > 0.05).

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Table 1.

Comparative Analysis of Workload Components in Young and Older Drivers: Independent t-test Results.

Age	Workload	p-value
Young driver Older driver	Effort of Attention	0.012
Young driver Older driver	Visual Demand	0.001
Young driver Older driver	Auditory Demand	0.001
Young driver Older driver	Stress	0.328
Young driver Older driver	Temporal Demand	0.67
Young driver Older driver	Interference	0.035

Due to the broad spectrum of each workload subscale, ranging from zero to one hundred, and for a better understanding of relationships, in this section, we investigated workload subscales in two conditions: low and high. Table 2 examines the cognitive performance of drivers in each workload subscale and compares it before and after driving (Effort of Attention, Visual Demand, Auditory Demand, Stress, Temporal Demand, and Interference). According to this table, which displays p-values, individuals who reported high and low workload in the Effort of Attention subscale showed a significant difference in Stroop task performance before and after driving (p-value < 0.05). In other words, driving has influenced the Stroop task performance of these individuals. Additionally, in individuals with high Effort of Attention, the FAT test showed a significant difference before and after driving. Visual, auditory, Temporal Demand, and Interference in individuals with high Effort of Attention also had a significant change before and after driving (p-value < 0.05).

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Table 2.

Impact of workload levels on cognitive performance: A comparative analysis before and after driving.

	Effort of Attention		Visual Demand		Auditory Demand		Stress		Temporal Demand		Interference
Variable	low	high	low	high	low	high	low	high	low	high	low	high
SCWT	0.04	0.019	0.066	0.012	0.33	0.014	0.27	0.14	0.39	0.014	0.21	0.019
CPT	0.8	0.92	0.015	0.024	0.53	0.011	0.81	0.58	0.35	0.03	0.59	0.032
FAT	0.175	0.032	0.512	0.018	0.56	0.02	0.17	0.25	0.22	0.032	0.15	0.014

Figures 3 and 4 illustrate the Stroop task performance (average time and average errors) in two groups of young and elderly individuals before and after driving. According to the obtained results, Stroop task performance (average time) showed a significant difference in both young and elderly groups before and after driving (p-value <  0.05). Stroop task performance based on the average errors also decreased after driving in both groups, but this reduction was not statistically significant.

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Figure 2.

The driving route of drivers between Enghelab Square and Azadi Square.

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Figure 3.

Stroop performance (Average time) before and after driving.

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Figure 4.

Stroop performance (Average error) before and after driving.

The primary objective of this study was to examine and draw comparisons between the mental workload and performance levels of both young and older drivers. To achieve this, a range of cognitive tests such as the Stroop test, Continuous Performance test, and Focused Attention test were administered, alongside the utilization of the DALI questionnaire for workload assessment. These evaluations were carried out in conjunction with a dynamic and realistic driving scenario to simulate real-world conditions.

The study was conducted on one of Tehran’s busiest roads, known for its heavy traffic throughout most of the day. Findings from this research revealed significant differences in the components of attention, visual and auditory demand, and interference between the two age groups –younger and older drivers. Notably, these elements were more pronounced in older drivers compared to their younger counterparts (p < 0.05). The study also indicated that visual and auditory demand contributed most to the mental workload experienced by drivers. Given that driving predominantly involves visual tasks, this observation aligns logically with expectations. Driving experience for older drivers appears to be characterized by greater complexity and less adaptability concerning cognitive requisites. A body of prior studies in this domain has consistently underscored age-related declines in both cognitive and psychomotor proficiencies pertinent to driving [20]. Remarkably, the present study aligns with these observations and demonstrates that visual demand tends to be lower in older individuals compared to their younger counterparts. This finding aligns with parallel research indicating that age-related constriction of the visual field may hinder environmental perception, thereby impacting driving proficiency. [21]. Consequently, there is an elevated risk of accidents and related injuries among the elderly. Research has consistently shown that the incidence of accidents is higher in older individuals, with a notably increased likelihood among drivers aged 75 and above. This heightened risk in the elderly population may stem from the amplified workload they experience while driving. [22]. The visual and auditory capacities of older drivers may be strained by the substantial influx of information from both the external environment and within the vehicle, particularly in congested driving conditions.[23] An escalation in the dispersion of visual and auditory demands contributes to heightened workload, stress, and fatigue, setting the stage for potentially hazardous behaviors. [24].

Our findings indicated that among the various aspects of drivers’ workload, the levels of stress and temporal demand did not significantly differ between young and older drivers (p > 0.05). This could be attributed to the older drivers’ accumulated experience and their deeper familiarity with road conditions, potentially enabling them to cope with stress and temporal challenges more effectively. Since driving initially requires controlled, conscious effort, but gradually becomes more automatic with skill acquisition, experienced drivers may perceive less stress. Over time, as driving becomes more automatic for experienced drivers, it likely leads to them feeling less stressed while driving. Other results obtained in this study showed that the cognitive performance tests (Stroop, Continuous Performance Test, and Focused Attention Test) were significantly different in older people before and after driving (p-value < 0.05), but in young people before and After driving, the results of 3 cognitive performance tests showed no significant difference (p-value > 0.05). Research indicates that as age progresses, there is a notable decline in visual perception abilities, as well as in cognitive and psychomotor functions. [7]. As a result, in the elderly age group, compared to the young age group, there are more functional limitations, which includes impairment of physical health [25,26]. So aging drivers frequently manifest compromised abilities in tasks that necessitate the concurrent utilization of motor-cognitive skills and sensory capabilities, such as driving. As people get older, their driving skills tend to gradually get worse [14]. Exploring the underlying causes of accidents within this demographic reveal that the diminishing sensory and cognitive functions, coupled with age-related effects on the central nervous system (CNS), significantly contribute to the occurrence of such incidents [27]. Furthermore, the aging process brings about a deceleration in information transmission and a reduction in the efficiency of signaling within the nervous system, resulting in prolonged reaction times among older individuals. These alterations hold relevance in tasks that mandate rapid decision-making and responses, as is the case in driving scenarios. Consequently, the combination of various research outcomes strongly suggests that accidents involving elderly drivers are often rooted in the regression of cognitive functions (e.g., cognitive processing speed, sustained attention), psychomotor proficiencies (including manual dexterity), and perceptual capabilities.

The outcomes from the Continuous Performance Test and Focused Attention Test in older individuals revealed significant differences in these variables post-driving compared to pre-driving. These tests are employed to assess selective attention and measure the effectiveness of the attention system in handling visual and auditory information. Noteworthy is the revelation from research that well over 80 percent of information pertinent to driving is acquired through the visual modality [28]. The inevitable progression of aging is closely associated with changes in the visual system, which can lead to visual impairments and affect functions that rely on vision. [29]. This, in turn, has implications for driving competence. Paralleling this, the aging process also exerts deleterious effects on hearing capabilities, thereby potentially impacting an individual’s driving performance. The diminution of hearing acuity and the attenuation of auditory abilities can lead to the loss of auditory-received cues, which can disrupt the balance of driving performance [14].

The findings of this research revealed that there was a notable variance in reaction times during the Stroop test for both younger and older participants before and after driving, with this difference being statistically significant (p-value < 0.05). Furthermore, individuals who reported increased requirements for attention, visual and auditory processing, as well as those experiencing heightened stress and temporal demands, and encountering interference during driving, demonstrated significant disparities in their reaction times on the Stroop test across both pre- and post-driving conditions. These results indicate that driving, particularly under conditions of elevated workload across multiple dimensions, markedly diminishes selective attention due to the cognitive demands of the driving task (p-value < 0.05).

The diminution of driver performance, as evidenced in numerous studies, correlates with an escalation in workload. This increase in workload often results in a reallocation of focus to other tasks, subsequently leading to a decline in overall performance. Additionally, the augmentation of workload not only precipitates fatigue but also induces a reduction in concentration and an elevation in stress levels. These factors collectively exert a detrimental impact on cognitive performance, as demonstrated in various research findings [30]. In the past century, the complexity of driving conditions, including road environments and vehicular features, has elevated the mental workload associated with driving. This increase in cognitive demands becomes particularly challenging for older drivers, who may face greater difficulties navigating these complex conditions [24].

In the contemporary urban landscape, the act of driving has evolved into an intricate process, primarily owing to an array of factors that collectively shape the driving experience. The modern cityscape, characterized by bustling metropolises, an abundance of traffic signage, the integration of diverse and advanced in-vehicle technologies, augmented stress levels while driving, heightened fatigue, and occasional manifestations of emotional and risky behaviors during driving, all contribute to the intricate mix that influences shifts in workload and, consequently, driving performance [23]. In this context, the pervasive use of In-Vehicle Information Systems (IVIS), encompassing functionalities like radios, navigation systems, and audio setups, presents a paradoxical situation. While designed to enhance the driving experience, these systems can inadvertently lead to driver distraction, decreased precision, and an upswing in cognitive demands [31].

Another crucial determinant of workload is the intricate network of traffic and road signage. A case in point is the heavily congested route from Enghelab to Azadi Square in Tehran. This thoroughfare, filled with a high volume of traffic, serves as abundant elements that can collectively impact both mental workload and cognitive faculties of drivers. Factors such as noise pollution and air pollution arising from urban traffic, the dynamic and variable conditions resulting from heavy traffic, inadequacies in road infrastructure, non-standard road conditions, and a host of environmental variables encompassing traffic volume, the behaviors of fellow drivers, interactions with passengers and pedestrians, and atmospheric pollution, as well as vehicle-related attributes like speed and road conditions, and even individual driver characteristics including age, gender, and experience, all intertwine to influence facets of workload and ultimately driver performance. Existing studies in this domain consistently echo the findings of the present study. For instance, the shifting landscape of driving conditions, the evolution from physically oriented driving to a more cognitive dimension, and the transformation from interactions with other drivers on less congested routes to engagements within high-traffic and congested roadways collectively culminate in an increased vulnerability to human errors due to the mounting cognitive demands placed on drivers [23]. This underscores the intricate interplay between the ever-evolving urban driving landscape, cognitive demands, and the tendency for errors, highlighting the need for comprehensive strategies to address these multifaceted challenges. Initiatives ranging from improved road design, organized urban planning, innovative driver education programs, and the continued development of intelligent vehicle technologies could collectively contribute to safer and more efficient driving experiences in the increasingly complex urban environment.

5. Limitation

It should be noted that in this study, due to various limitations, physiological factors were not considered. Furthermore, to assess subjective workload, a self-reporting method (using questionnaires) was utilized. Therefore, it is recommended that future studies investigate the changes in physiological metrics (for example, eye movement, heart rate variability and brain signals) and their relationships with driving performance. Additionally, the use of other modern methods for assessing workload should be considered. Also, it is recommended that future studies also consider other intervening factors such as environmental factors.

Female drivers should also be considered in future studies. In this research, we focused exclusively on participants who are full-time drivers in Tehran and have extensive driving experience, which is why all the drivers in our study were male.

6. Conclusion

In conclusion, this study’s exploration of workload and driving performance among young and older drivers in the complex urban environment of Tehran provides valuable insights into the challenges faced by drivers of different ages. The research highlighted significant differences in attention, visual and auditory demand, and interference between younger and older drivers, with older drivers experiencing greater difficulty in these areas.

Also, the study found that while older drivers might have more experience, they also face greater challenges due to age-related sensory and cognitive declines. This is particularly evident in tasks requiring rapid decision-making and responses, which are essential in driving. The research also underscores the impact of the modern urban driving environment, characterized by heavy traffic, advanced in-vehicle technologies, and various external factors, on increasing workloads for all drivers.

The findings suggest that to enhance road safety, particularly for older drivers, there needs to be a holistic approach that considers not only the drivers’ cognitive and sensory abilities but also the complexity of the driving environment. This approach could include advancements in vehicle technology, better urban planning, and targeted driver training programs.

Overall, the study emphasizes the importance of continued research and innovation in understanding and addressing the multifaceted challenges of driving in modern urban environments, to ensure the safety and well-being of all drivers, regardless of age.