Impact of noise on school bus drivers’ visual reaction time: An experimental approach with road safety implications Noise and Reaction Time

Abstract

Background

Fast reaction time is essential for safe and effective driving.

Objective

The objective of this research was to ascertain the visual reaction time of school bus drivers and the effect of additional noise on this time.

Methods

28 volunteer professional school bus drivers were included in the study. Each participant was in both the experimental and control groups. Vehicle simulation was used to determine the reaction time of the participants. The participants’ throttle response time was measured in the presence and absence of an 85 dBA (A-weighted decibels) supplemental noise. The Wilcoxon rank-order sign test and linear mixed-effects regression models were used for the statistical analysis.

Results

The reaction time of the participants without additional noise was calculated to be 0.10 ± 0.02 (0.06–0.15 s). It was found that the noise statistically increased the reaction time of the participants.

Conclusions

The additional noise in the vehicle has been shown to have a detrimental effect on the reaction time of school bus drivers, thereby impacting their cognitive functions. The findings emphasise the practical significance of noise control strategies in both occupational and transportation contexts, with the objective of enhancing safety and cognitive efficiency.

Keywords

accidents cognitive workload environmental factors occupational health safety and health transportation

Introduction

Road traffic accidents, which occur annually across the globe, are recognised as the primary source of human casualties and the catalyst for economic and societal impairment.¹ Despite the decreasing frequency of school bus accidents, single-vehicle accidents involving school buses pose a heightened risk when compared to other vehicle types.² This underscores the importance of conducting research in this area. A comprehensive body of research on school bus safety has been instrumental in ensuring the safety of school bus passengers.³ Accidents are categorised into three distinct categories: vehicle-related factors, human factors such as a lack of safety awareness among drivers and passengers, and environmental factors including the condition of the roads and traffic density.⁴ The time and accuracy of decision-making in emergency situations, along with the speed at which actions are implemented, often determine whether a situation escalates into a traffic accident. The ability to react swiftly and perform multiple tasks simultaneously are critical components of driving performance. Consequently, drivers’ reaction time is a significant predictor of traffic accident involvement.⁵ While reaction time alone is not necessarily a cause or prevention of accidents, it is a significant indicator. Driver-related factors are not independent of external factors.

One of the external factors that drivers are exposed to is noise. A common workplace and environmental hazard is noise. Exposure to noise has auditory and non-auditory effects on humans. A review of the non-auditory effects of noise reported that more research is needed to examine these effects.⁶ Drivers are exposed to both external traffic noise and internal vehicle noise. This noise level has been reported to be between 65.9–79 decibels (dB) in city bus divers in Iran.⁷ In another study conducted in Iran, the noise level that city bus drivers were exposed to was reported as 79.50 dBA (A-weighted decibels).⁸ In a study conducted to examine the effects of noise, it was determined that drivers’ short-term memory and attention were affected by noise.⁹ The relevant research is on non-professional drivers. Research has identified a correlation between reaction time and various factors, including driver distractions, contrast sensitivity and age.^10–12 A study conducted to examine physiological responses to different noise parameters and mental workload levels concluded that subjects had to exert more effort to cope with the harmful effects of adverse noise exposure.¹³ Noise is a distracting and annoying factor.¹⁴ A few previous studies have found that reaction times in miners, construction workers, healthcare workers, and metal industry workers and students are increased by noise.^15–19 The situation may be different for professional drivers. A single study on young drivers found that the sound of music increased reaction times.²⁰ School buses are a form of public transportation with an average of 19 students. Unlike other types of public transportation, they are made up of children between the ages of 5–15. In-vehicle noise may be higher. In a school in Turkey where acoustic improvements were made, the noise level was reported as 66.38 dBA during class hours and 78.78 dBA during breaks.²¹ As indicated by the available data, school bus drivers are exposed to considerable noise levels in the course of their professional duties. However, they may have adapted to this situation.

The objective of this research was to ascertain the visual reaction time of school bus drivers and the effect of 85 decibels of additional noise on this time.

Material and method

Study design

The research is of an experimental type. Participants’ general information, reaction times and reaction times in a noisy environment were measured. The reaction time in a noisy environment constituted the experimental group data, and the reaction time in an environment without additional noise constituted the control group data.

Study population and sample

The universe of the study is drivers working in a school bus company in Turkiye (N = 40). For 80% power and assuming a medium effect size (Cohen's d = 0.5), a sample size of approximately 26 was calculated to be sufficient. Inclusion criteria are being a bus driver, not having a sleep problem the day before, and being a volunteer. Exclusion criteria are having any neurological and orthopedic deficits involving the lower extremities, having any joint limitations in the lower extremities, a history of previous eye disorders, not stabilizing comorbidity. 5 participants were appointed as reserves and the research began with 31 participants. 3 participants were excluded from the study due to inclusion and exclusion criteria. The sample included 28 participants. 56 reaction time data were analyzed, 28 experimental and 28 control.

Data collection

The data were collected in between January 30 and February 20, 2025. On the first day of the study, the participants were informed about the nature of the research. participants’ birth years, gender, height, and body weight were questioned in order to determine their ages. Their body mass index (BMI) was subsequently calculated. The participants’ education status, work experience, existing health problems, and assistive device use data were collected face-to-face using a prepared form. On the other days of the study, the visual reaction time of each participant was measured in both the experimental and control conditions. Half of the participants underwent the experimental condition first, while the other half started with the control condition. This counterbalancing procedure was implemented to minimize potential order effects. The visual reaction time was measured with a driving simulator, which is a safe, repeatable, and precise measurement method specific to the participants’ profession.²² The driving simulator equipment is the same size as the real vehicle equipment. The cabin is equipped with an adjustable seat, seat belt, handbrake, steering wheel, signal lever, wiper lever, ignition key, and analogous equipment. It also contains all indicators found in real vehicles, including signal indicators, flashers, seat belt warning lamp, oil lamp, and others. Furthermore, it is furnished with a gear lever, brake, clutch, gas pedal, rearview mirror, and additional mirrors. It has an advanced sound simulation and gives engine sound and similar warning sounds according to the engine speed when the engine is started and while driving. There are indicators and a city road simulation on a monitor in front of the driver (Figure 1). All simulations were conducted in a controlled laboratory setting during periods of minimal background activity to reduce environmental distractions. Moreover, the simulator settings—such as lighting, screen brightness, interface layout, and input devices—were kept constant across all trials to ensure uniformity. Participants were also given standardized instructions and were tested individually to avoid interpersonal interference. These measures helped reduce variability caused by external factors, thereby increasing the internal validity of the results. The drive was completed in approximately 25 min for each reaction time measurement. Prior to the initiation of the measurement process, participants were provided with an opportunity to undergo a trial drive. Participants were directed to maintain a constant velocity of 70 kilometers per hour. Braking tasks (a pedestrian crossing the street) were performed at irregular locations along the driving route. No accidents occurred during the test. In the control condition, participants drove at a constant speed and responded as described above in the braking task. In the experimental condition, the same participants performed the braking task while being exposed to 85 dBA white noise while driving. Specifically, broadband white noise (approximately 20 Hz–20,000 Hz) was delivered into the experimental environment via a pre-recorded audio file played on a laptop computer. The sound was set at an intensity level of 85 dB(A) and played continuously throughout the task using the laptop's built-in speakers. Each measurement was performed 3 times at the same time of day for each participant and the average Gas-off reaction time (RT) was recorded. The environment and braking Gas-off reaction time were recorded with the computer program Tabim Driving School Automation SQL 2.84.3.27. Prior to the commencement of the study, the simulator was meticulously calibrated to ensure the uniform measurement of visual stimulus timing and brake response.

Figure 1.

Driving simulator.

Data analysis

The statistical analysis of the data was conducted using IBM Statistics 21st version. Categorical data were expressed as numerical values and percentages, while numerical data were reported as mean and standard deviation. The Shapiro-Wilk normality test was applied to assess whether the reaction time data showed a normal distribution. The test results (p = 0.013) revealed that the data were not normally distributed. The Wilcoxon signed ranks test was employed to assess the statistical significance of the observed difference between groups with and without additional noise given that reaction time is not normally distributed. In this study, because each participant was exposed to both noise and no-noise conditions, inter-measurement dependence was present. Therefore, a linear mixed model (LMM) was preferred, which can account for within-individual variation and evaluate fixed (noise, age, experience) and random effects (individual) within the same model. In this model, the dependent variable was reaction time, and the independent variable was noise condition (present/absent). The model incorporated age and experience as confounding factors, given their potential impact on reaction time. Initially, BMI and education variables were incorporated into the model; however, subsequent analyses of model fit and statistical significance revealed that these variables contributed no additional value to the model and were thus removed. Individuals were defined as random effects. The assumption of normality of residuals was tested using both visual inspection and statistical tests. The normal Q–Q plot showed no major deviations from normality, and the Shapiro–Wilk test was non-significant (W = 0.964, p = 0.090), indicating that the residuals were approximately normally distributed. The significance of the fixed effects in the model was assessed using the F test, and partial eta squared (ηp²) was calculated for effect sizes. The level of significance was set at p < 0.05.

Results

The study included 28 male professional service drivers aged 27–64. Descriptive data of the participants are given in Table 1. All participants started work at 07:30 in the morning, 16 of them worked until 15:00 and the others finished later. Weekly working hours varied between 9–15 h. Experience varied between 4–20 years. 10 (35.7%) of the participants wore glasses. They did not use any assistive devices other than glasses. Sleep duration the day before was 6.46 ± 0.99 h. In this study, a linear mixed model analysis was employed to examine the effects of noise presence, age, and experience on reaction time (RT). The adequacy of the linear mixed model was evaluated using information criteria. The model yielded a Restricted Log Likelihood of −240.38, an Akaike Information Criterion of −236.38, and a Bayesian Information Criterion of −232.52. These values indicate a satisfactory model fit with adequate parsimony. The analysis results indicate that all fixed effects contributed significantly to the model. The presence of noise significantly increased reaction time (F(1, 46.62) = 25.60, p < 0.001, ηp² = 0.354). The age variable also had a significant effect on reaction time (F(1, 45.84) = 17.55, p < 0 .001, ηp² = 0.277); as age increased, reaction time lengthened. However, reaction time decreased markedly with increasing experience (F(1, 45.84) = 11.58, p = 0.001, ηp² = 0.202). The findings indicate that the presence of noise and individual demographic characteristics (age and experience) significantly and strongly influence reaction time. Full results are presented in Table 2. A post-hoc power analysis was conducted based on the observed partial eta squared (ηp²) values using the formula:

f = \sqrt (η^{2} / (1 - η^{2}))

Table 1.

Participant characteristics.

Variables	Mean	SD
Age (years)	46.71	9.29
Height (m)	1.73	0.09
Weight (kg)	79.25	8.11
BMI (kg/m²)	26.54	3.75
Experience (years)	10.14	4.70
Weekly working hours	12.04	2.52
		n(%)
Education Status	Elementary School	3 (10.7)
	Middle School	11 (39.3)
	High School	14 (50.0)

Table 2.

Linear mixed model outcome with the reaction time of braking as dependent variable.

Predictor	Estimate	SE	CI is the 95% confidence interval		t	P	ηp²
Intercept	0.072	0.014	0.044	0.100	5.100	<0.001
Noise	−0.026	0.005	−0.036	−0.016	−5.060	<0.001	0.354
Age	0.002	0.000	0.001	0.002	4.189	<0.001	0.277
Experience	−0.003	0.001	−0.004	0.001	−3.403	0.001	0.202

Estimate is the standardized beta value, ηp² is the efect size The reference category for noise condition was ‘noise present (1)’. The regression coefficient for ‘noise absent (0)’ reflects the change in reaction time compared to the noise condition.

For the noise predictor (ηp² = 0.354), this corresponded to a large effect size (f ≈ 0.74), and the achieved power exceeded 0.99, indicating high statistical confidence in the observed effect. Similarly, age and driving experience also showed large effect sizes and high power values (f = 0.62 and f = 0.50, respectively). Following the implementation of a control variable to adjust for the effects of age and experience, the estimated marginal mean reaction time was determined to be 0.095 ± 0.003 s in the condition that was devoid of supplementary noise, and 0.121 ± 0.004 s when the presence of additional noise was observed. The 95% confidence intervals for these means were 0.089–0.101 s and 0.112–0.130 s, respectively. The findings of this study suggest that the presence of additional noise has a substantial impact on reaction time, even when controlling for potential confounding variables (see Table 3).

Table 3.

Estimated marginal means of reaction time according to noise condition (controlling for age and experience).

Noise Condition	Mean Reaction Time (s)	Std. Error	95% Confidence Interval
No Noise (0)	0.095	0.003	0.089–0.101
Noise Present (1)	0.121	0.004	0.112–0.130

Covariates appearing in the model were evaluated at the following values: Age = 46.71 years, Experience = 10.14 years.

Discussion

This study was conducted to determine the reaction time of school bus drivers and the effect of additional noise on this time. The gas-off reaction time of the drivers was calculated as 0.10 ± 0.02 s. The present study demonstrates that occupational noise exposure significantly impairs reaction time in professional service drivers, even after adjusting for age and driving experience.

The reaction time of the school bus drivers was calculated as 0.10 ± 0.02 s. This time is quite short and we thought it was normal for a professional driver. The mean brake reaction time for drivers to a pedestrian hazard was reported as one second and 1.1 s by a vehicle simulation device.^23,24 Brake reaction time includes the sum of perception time and motor time (transfer foot from the gas pedal to the brake pedal). We measured the gas off reaction time, so the results of the two studies seem to be different. In one particular study, researchers methodically determined and compared the reaction time of drivers utilizing three distinct measurement methods: mouse/keyboard capture recorder, driving simulator, and the Kielce Track. The study's findings indicated that the lowest recorded reaction time was between 0.02 and 0.33 s when using the mouse/keyboard capture recorder, followed by the driving simulator with a range of 0.03 to 1.2 s. The highest recorded reaction time was observed to be between 0.6 and 1.5 s in a test conducted with the Kielce Track on a driving track.²⁵ In studies measuring the reaction time to a visual stimulus during actual driving, reaction times were reported to be between 0.45 and 0.680 s on average.^26,27 A plethora of studies have reported conflicting results with regard to reaction times. This can be attributed to the fact that the group measured, the method of measurement, and the thing measured differ from each other.

In this study, it was determined that the visual reaction time of the drivers increased when there was additional noise in the vehicle. Gas-off reaction time refers to a simple reaction time. However, simple reaction time is also of two types: reflex time and conditioned reflex time. When a driver encounters an obstacle while driving, as in this study, taking action to stop is an example of conditioned reflex time. Learning processes are effective in this reflex. However, the action occurs as a result of the combination of cognitive skills such as visual perception, attention, motor planning and organization, which the cortex is involved in. Noise is an additional mental workload, and it is expected that it has increased the reaction time by affecting cognitive processes.²⁸ In support of this, the factors that will distract the drivers’ attention are talking to the passengers, controlling the behavior of the child/animals being carried, using a mobile phone, operating the devices in the vehicle (air conditioning, radio, navigation), stress, alcohol, weakness or unhealthiness, eating or drinking while driving, the driver daydreaming, looking for something in the vehicle; and stress resulting from inexperience in driving have been reported.²⁷ It is known that visual/auditory attention is significantly reduced by noise.²⁹ Driver distraction results in loss of control over the perception of the road scene. It has been determined that the complex reaction time of a driver talking on a mobile phone increases by 18.1% compared to a driver not talking on the phone while driving conventionally.¹⁰ Although conducted on older drivers. Stajan and colleagues found that additional tasks (such as entering a three-digit number on a numeric keypad, arguing verbally for or against a topic of general interest, remembering prices posted at gas stations along the road) increased reaction time.³⁰ A study conducted to examine physiological responses to different noise parameters and mental workload levels concluded that subjects had to exert more effort to cope with the harmful effects of adverse noise exposure.¹³ A few previous studies have found that reaction times in miners, construction workers, healthcare workers, and metal industry workers and students are increased by noise.^15–19 A single study on young drivers found that the sound of music increased reaction times.²⁰ Our research findings are consistent with those of previous studies. It is known that drivers’ reaction times are affected by age and experience.^12,31 This research found that the increase in reaction time resulting from noise exposure was independent of age and experience. Acceptable noise limits in public transport vehicles typically range between 70 and 80 dBA.³² Both road safety and the occupational health of school bus drivers should be considered when implementing noise reduction measures. A review of this topic recommended taking an integrated approach, combining multiple abatement strategies to address noise from various sources in the workplace.³³ A combination of engineering, administrative and behavioural interventions is more effective at reducing noise levels and health risks than an isolated approach. Future studies could also examine the most effective strategies for limiting the negative impact of noise on reaction time.

The present study is subject to a number of methodological limitations that have a detrimental effect on its external validity. Firstly, although driving simulators offer controlled environments for experimental consistency, they do not fully replicate the complexity of real-world driving. Participants were made aware that they were participating in a simulation and were likely to be more alert and task-focused than drivers in naturalistic settings. It is evident that real-world factors such as fatigue, prolonged attention demands, multitasking, and unpredictable distractions were not captured in this setup. This limitation results in a restriction of the generalisability of the reaction time findings. Moreover, while the study measured the effects of continuous noise, real-life noise exposure – such as that experienced by school bus drivers – is dynamic and often unpredictable. The simulation environment was found to have a lack of dedicated soundproofing, which may have resulted in uncontrolled auditory influences. The combination of these factors has the effect of reducing the ecological validity of the findings. It is therefore recommended that future studies consider the implementation of naturalistic driving conditions or field experiments with a view to enhancing generalizability. A further limitation of this study is the absence of participant blinding. It is evident that the presence or absence of background noise was discernible to the participants, thereby ensuring their full awareness of the condition they were experiencing. This awareness may have influenced their performance due to expectation or performance bias, potentially affecting the measured reaction times. Whilst it is challenging to blind participants in experiments involving auditory noise, future studies may wish to consider the use of more subtle or masked conditions in order to minimise such biases.

Conclusion

This study contributes to filling the knowledge gap by determining that noise increases the visual reaction times of school bus drivers. First, the findings have implications for occupational health and safety, as increased reaction times under noise exposure may indicate reduced alertness or delayed responsiveness in school bus drivers. Although this study did not directly assess fatigue or auditory effects, the observed delay in visual reactions suggests that persistent exposure to noise could indirectly compromise drivers’ performance and safety. Implementing preventive measures such as monitoring cabin noise levels and improving acoustic conditions in vehicles may therefore contribute to safer working environments. Second, the results highlight the impact of noise on driving safety, as delayed reaction times can elevate the risk of accidents, particularly in environments that require rapid responses to unpredictable traffic situations. Third, the study underscores the necessity of enhancing awareness of noise pollution as a critical occupational hazard within the transportation sector. Policymakers should consider incorporating noise monitoring and reduction standards into occupational health regulations and driver training programs. Future research should focus on the effectiveness of specific noise mitigation interventions and their long-term impact on driver performance, safety outcomes, and overall well-being.

Footnotes

Acknowledgements

The authors would like to thank all participants for their valuable contribution to this study.

Ethical approval

For the ethical compliance of the research, permission was obtained from the Çankırı Karatekin University Health Sciences Ethics Committee dated 25.01.2025 and numbered 60c1181ac3944bf9.

Informed consent

Informed consent was obtained from all participants.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The APC fee for this article was covered by Necmettin Erbakan University.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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