Analysis of the distraction impact on driving performance across driving styles: A driving simulator study in various speed conditions

Mobina Faqani; Habibollah Nassiri; Mahdi Rezaei; Mohsen Ramezani

doi:10.1371/journal.pone.0336480

. 2025 Dec 4;20(12):e0336480. doi: 10.1371/journal.pone.0336480

Analysis of the distraction impact on driving performance across driving styles: A driving simulator study in various speed conditions

Mobina Faqani ^1,^2,^¤, Habibollah Nassiri ^2,^*, Mahdi Rezaei ³, Mohsen Ramezani ¹

Editor: Quan Yuan⁴

PMCID: PMC12677451 PMID: 41343537

Abstract

Distracted driving is a mounting global issue, prompting numerous naturalistic and simulator-based investigations. This study investigates the impact of hands-free (HF) conversation and texting distractions on driving performance during car-following experiments. Three experiments were designed: a baseline (control) condition, HF conversation, and text messaging. Driving data were collected from 40 participants of driving simulator experiments, conducted under six different speed conditions: (i) free-flow, (ii) coherent moving flow, (iii) synchronized flow, (iv) jam density, (v) recovery from jam density, and (vi) collision avoidance. To analyze driving performance across various mobile phone distracted driving (MPDD) experiments, participants are partitioned into three distinct groups: aggressive, moderate, and conservative, based on their driving styles using k-means clustering. Statistical analyses, including t-tests, Friedman Test, and Wilcoxon Signed-Rank Test, were conducted to evaluate driving performance metrics such as Standard Deviation of Lateral Position (SDLP) across conditions (i)-(iv), Acceleration Reaction Time (ART) in condition (v), and Time to Initial Braking Location (TIBL) in condition (vi).

The findings indicated that HF conversation had no effect on SDLP in the free-flow condition. However, it led to a reduction in SDLP for the conservative group in the coherent moving flow condition, for both moderate and conservative groups in the synchronized flow condition, and for the moderate group in the jam density condition. Additionally, HF conversation was associated with a decrease in ART among conservative participants, while it significantly increased TIBL for both moderate and conservative groups. Conversely, texting led to an increase in SDLP for moderate and conservative participants in the free-flow condition and for the moderate group in the coherent moving flow condition. However, it resulted in a reduction in SDLP for the conservative group in the coherent moving flow condition. Texting had no significant effect on SDLP in the jam density condition or on ART. However, it significantly increased TIBL among moderate and conservative participants. These findings can inform legislation, policy development, countermeasures, and future research.

Introduction

Mobile phone distracted driving (MPDD) has emerged as a critical area of concern in road safety, with substantial evidence pointing to its role in increasing the risk of traffic incidents [1–5]. In 2016, distraction contributed to 9% of all fatal crashes, underscoring its detrimental impact on safety [6]. Drivers who read or compose text messages exhibit slower stimulus-response times, lower speeds, poorer lane tracking, shorter headways, and fewer forward glances—factors that together heighten the likelihood of safety-critical events compared with undistracted driving [7–9]. One study reported that phone use lengthened reaction time by roughly 30% and markedly impaired the driver’s ability to keep a safe gap to the lead vehicle [7]. Crash-risk estimates range from two- to nine-fold higher for drivers engaged in mobile-phone tasks than for those who are not distracted [10,11]. Data from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2) further show that hand-based phone activities raise crash odds ratios substantially; texting while driving, for instance, increases risk by a factor of about 6.1 [12,13]. The study conducted indicated that there is a relationship between the use of cellular telephones while driving and a subsequent crash with fatalities. The researchers found out that the use of phone for texts and Internet to be positively associated with road crash fatalities with incremental risks of 8.4%, and 54.6%, respectively [14]. Similarly, Perez et al. (2024) concluded that distractions from smartphone apps and social media—combined with reduced situational awareness and risk perception—may significantly elevate crash risk [15].

A sizeable literature also documents how widespread this behaviour is across regions and demographic groups. For instance, research conducted in China found that 84.1% of surveyed drivers (from a sample size of 414) reported engaging in phone conversations at least weekly while driving [16]. In the United States, a comprehensive roadside observation study revealed that 48% of distracted drivers, out of a sample of 3,265, were seen using mobile phones, and 16.6% were observed texting or dialing [17]. In New Zealand, over half of drivers (sample size of 1,057) admitted to sending or reading between 1 to 5 text messages weekly while driving [18]. Among Australian drivers, 61% of a sample of 484 reported engaging in high-risk mobile phone activities, such as texting or browsing, while driving [19]. In the United Kingdom, 29% of a sample of drivers (n = 314) reported making phone calls daily while driving, with 30% reading text messages and 22% sending texts on a daily basis [20]. Similarly, a survey conducted in Tehran, Iran, found that an alarming 88% of respondents engaged in mobile phone use while driving (sample size of 824) [21]. Also, a survey conducted in Mashhad, Iran, revealed that approximately 93% of drivers use their cell phones while driving at least once per week, with 32.5% reporting consistent cell phone use while driving (n = 255) [22].

Addressing the impact of mobile phone distractions on driving safety requires a nuanced examination of driver performance and behavior under various conditions. Extensive reviews in recent years have highlighted the broad scope of research on mobile phone distractions, focusing on how these distractions impact driving performance, with specific effects on behaviors like speed selection, lane deviations, steering control [23], and even gaze behavior [24,25]. Prior research indicates that MPDD can profoundly influence driving dynamics, though the specific effects vary widely across contexts. For instance, while some studies suggest that cognitive distractions increase variability in lateral control, as evidenced by the standard deviation of the lateral position (SDLP) [26–28], others have reported a reduction [29] or negligible impact on SDLP [30,31].

Research on the influence of MPDD under varying driving conditions also shows mixed results; for instance, young drivers have shown significant lane deviation during nighttime cell phone use [32]. Dialing and conversing on mobile phones, whether hands-free (HF) or handheld, have both been associated with elevated mental workload, impaired peripheral detection, and a notable increase in lateral deviation during dialing [33]. The presence of MPDD has also been demonstrated to alter driving patterns across age groups and distraction scenarios. Studies conducted in simulators with drivers of different ages reveal that mobile phone usage during unexpected driving situations increases lateral variability and diverges from typical driving behavior seen in non-distracted conditions [34].

Moreover, findings indicate that engaging in phone conversations while driving significantly elevates cognitive load, which can lead to reduced speed, extended following distances, and increased variability in lane positioning [35]. Similarly, a study leveraging virtual reality (VR) simulations to assess the impact of cellphone positioning on driving performance noted that drivers with cell phones positioned in their lap exhibited more cautious behavior by checking speed and lane position more frequently, with reduced lateral deviation, reduced time spent looking at the cellphone, and increased time in the correct lane [36]. Additional research indicates that drivers using hands-free devices have better vehicle control than those using handheld phones, as measured by steering patterns and lateral positioning [37]. Meanwhile, texting on messaging platforms like WhatsApp has been shown to impair driving performance, particularly among older drivers, with significant increases in SDLP and collision likelihood [38].

There is ongoing debate regarding reaction times when drivers engage in mobile phone conversations. Reaction time represents how quickly a driver can respond to control the vehicle in a particular situation. Certain authors contend that reaction times decrease when drivers engage in mobile phone conversations [26]. Additionally, other studies in this area suggest that under cognitive distraction, reaction times are prolonged compared to situations without distraction [9,27,39–43]. A study investigated braking responses among young drivers while conversing on hands-free and handheld cell phones compared to a baseline condition, revealing significantly slower reaction times, movement times, and total response times in both cell phone conditions as opposed to the baseline, indicating that conversing on cell phones, whether hands-free or handheld, leads to a reduction in the speed of information processing [44]. A study aimed to assess the reaction times of college-age drivers to peripheral traffic events while engaged in mobile phone conversations. Using a driving simulator, it found that distracted driving due to phone conversations led to reaction times over 40% longer compared to un-distracted conditions. Additionally, this impairment was nearly double for provisional license holders [41]. In a series of driving simulator experiments exploring various distractions like mobile phone use, text reading, and watching a DVD while driving, mobile phone conversations significantly increased mental workload and impacted driving behavior, showing reduced speed, increased headway, and lateral position variability [35].

While previous studies have examined either driving style classification or the effects of mobile phone distraction on driving behavior independently, the combined influence of mobile phone distraction on drivers with varying driving styles remains largely underexplored. This study aims to fill this gap by addressing the following research question: How does mobile phone distraction—specifically hands-free (HF) conversations, which primarily involve cognitive distraction, and texting, which primarily involves manual distraction—affect driving performance, including lateral maneuvering across different traffic conditions, Acceleration Reaction Time (ART), and Time to Initial Braking Location (TIBL), both with and without accounting for distinct driving styles (aggressive, moderate, and conservative)?

Aggressive driving typically involves higher speeds, sudden accelerations, abrupt steering, and sharp maneuvers, while conservative driving is characterized by smoother maneuvers and more gradual acceleration and deceleration. A moderate driving style, as the term suggests, sits between these two extremes [45]. Previous studies have categorized drivers based on behavioral observations and driving metrics, such as speed variance, acceleration distribution, and lane deviation [46–48], or through surveys that measure self-reported driving behaviors [49–51]. For this study, driving style classification considers speed, acceleration, and distance to the lead vehicle. Given our research objectives—to examine the effects of mobile phone distraction, to categorize drivers into distinct driving styles, and to compare performance outcomes with and without considering driving style—the study is designed around a car-following task. Participants follow a lead vehicle through six different traffic conditions: free-flow, coherent-moving flow, synchronized flow, traffic jam, recovery from traffic jam, and collision avoidance. We hypothesize that traffic congestion and the relative position of the lead vehicle [52–55] significantly affect driving aggression, making this scenario suitable for exploring the interaction between driving style and distraction type.

The structure of this paper is as follows: Sect Materials and methods presents the experimental setup, data collection, and methods used to analyze MPDD impacts on driving performance, followed by Sect Results, which details the findings and their implications under varying traffic conditions. The final section, Sect Discussion, concludes with key insights, emphasizing the relevance of these findings to road safety and potential future research directions, including enhancements in experimental design and exploration of additional variables influencing driver behavior under distraction.

Materials and methods

Participants characteristics

A total of 40 healthy participants neurologically healthy participants with normal vision and hearing abilities, all licensed drivers, were recruited for this study, comprising 7 females and 33 males. The participants’ ages ranged from 18 to 42 years (M = 28.68, SD = 6.9), with driving experience ranging from 1 to 22 years (M = 9.1, SD = 6.44). Of the sample, 55% reported using a mobile phone while driving more than three times per week, indicating a relatively high prevalence of mobile phone use in their driving routines. Additionally, 10% of participants reported having been involved in a road accident while using a mobile phone. In a pre-survey, participants rated the potential for mobile phone use to cause distraction and accidents at an average of 3.18 out of 4, indicating a strong awareness of the associated risks.

To minimize potential learning effects among participants, each individual completed three experimental conditions—baseline, hands-free (HF) conversation, and texting—in a randomized order. This randomization ensured that no specific sequence influenced participant performance, maintaining the integrity and validity of the experimental results.

Apparatus and stimuli

The experiments were conducted using a medium-fidelity driving simulator at the Road Traffic Injury Research Center in Tabriz, Iran. The simulator was equipped with all essential vehicle operation components, including a steering wheel, accelerator, and brake pedals, providing a realistic driving environment for participants (see Fig 1). The simulator features a cabin modeled after the Peugeot 405 with an autogear, providing participants with an immersive and realistic driving experience. To enhance realism, the simulator is equipped with an environmental noise system that replicates authentic road sounds and operational noise.

The driving scenario was projected onto three 43-inch screens strategically placed around the cabin, providing a wide 270 ^° horizontal field of view and a vertical field of view 38 ^° to 40 ^° for the driver. Each screen featured a $1920 \times 1080$ pixel resolution, full high-definition capabilities, and high brightness for enhanced clarity. This immersive setup allowed participants to experience a near-surround panoramic vision, closely replicating real-world driving conditions and ensuring a highly realistic simulation environment.

Experimental procedure

Participants were eligible for the study if they held a valid driver’s license and had at least one year of driving experience. Individuals with a history of neurological disorders, uncorrected vision impairments, or conditions affecting cognitive or motor functions were excluded. Recruitment was conducted through university announcements and social media advertisements.

During the experimental preparation phase, participants were provided with an iPhone 8 and given time to familiarize themselves with the device for texting purposes. They also completed a demographic questionnaire, collecting information such as age, gender, driving experience, and education level. To ensure they were comfortable with the procedure, participants first received verbal instructions and then practiced using the driving simulator for five minutes. This practice session allowed them to become accustomed to the simulator by steering and operating the accelerator and brake pedals. Notably, none of the participants reported experiencing simulator sickness during this phase.

During the experimental trials, participants were instructed to drive according to their natural driving habits, while consistently following the lead vehicle and strictly avoiding any overtaking or lane changes under any circumstances. To minimize external influences, participants were required to refrain from consuming alcohol or caffeinated beverages for 24 hours before the experiment and to ensure adequate rest the night before.

This study was conducted in compliance with the ethical guidelines approved by the Research Ethics Committee of Iran University of Medical Sciences and adhered to the principles outlined in the Declaration of Iran. Written informed consent was obtained from all participants before their involvement in the study. The experimental sessions were conducted between 9:00 a.m. and 12:00 p.m, from 22 February 2023 to 6 May 2023. Participation was voluntary and generally uncompensated; however, participants recruited during their working hours were compensated based on their regular job wages as a token of appreciation.

Scenario design

The car-following experiment employed a two-factor mixed design: three types of distraction (baseline, hands-free conversation, and texting) and six traffic conditions (free-flow, coherent moving flow, synchronized flow, jam density, recovery from jam density, and collision avoidance). The total driving duration of 574.931 seconds was segmented into six distinct traffic conditions: 19.45 to 64.5 seconds for the first condition, 67.288 to 231.175 seconds for the second, 236.731 to 414.5 seconds for the third, 417.285 to 473.961 seconds for the fourth, 496.733 to 516.19 seconds for the fifth, and 566.171 to 574.931 seconds for the sixth. The speed profile of the lead vehicle under different traffic conditions is illustrated in Fig 2.

The driving scenario was conducted on a 5.1-km two-way straight rural road, featuring three lanes in each direction. Each lane measured 3.75 meters in width, with a 1-meter shoulder on either side. The speed limit was set at 80 km/h, and the weather remained clear and sunny throughout the experiment. To create a controlled yet realistic environment, the road was free of parked cars and pedestrians, with simulated random traffic in the opposite direction and vehicles occasionally passed from the adjacent lanes.

Cognitive task

The mobile phone tasks were intentionally designed to sufficiently distract drivers and increase their cognitive workload. Two task levels were implemented: hands-free conversation and texting.

In the hands-free conversation task, participants listened to complete arithmetic questions and were required to provide appropriate verbal responses. The arithmetic questions involved calculating the sum of two-digit numbers presented in a random difficulty order. For instance, the sum 67+89 = 156 is considered more challenging, while 10+20 = 30 is regarded as a simpler calculation.

In the texting task, participants received the same arithmetic questions and were required to both compute and text their answers. Similar to the conversation task, the arithmetic problems involved summing two-digit numbers with varying difficulty levels.

Both the hands-free calling and texting tasks were initiated precisely at the beginning of the respective experimental conditions and continued until the end of the scenario, ensuring consistent exposure to cognitive load throughout the task duration.

Driving data acquisition

Eight key driving data were collected from the simulator at a sampling rate of 50 Hz (see Table 1). These variables capture Speed-related metrics (e.g., speed, Relative speed, Longitudinal acceleration, deceleration), Lane-keeping behavior (e.g., lane deviation, standard deviation of lane position, time spent out of lane), Steering control (e.g., steering angle, steering reversal rate, smoothness of steering), Braking patterns (e.g., brake reaction time, braking force, braking duration), and Headway and following behavior (e.g., time headway, time-to-collision, car-following distance).

Table 1. Driving variables recorded from the simulator.

Variable	Unit
Speed	m/s
Relative speed	m/s
Space headway	m
Longitudinal acceleration	m/s²
Degree of acceleration pedal	^°
Degree of brake pedal	^°
Steering wheel angle	^°
Lane deviation	m

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Methodology

Building on previous research, factors such as congestion [52–54] and the (relative) position of the lead vehicle [55] have been shown to significantly influence driving aggression levels. Therefore, we designed a car-following experiment under six different traffic conditions. Since a aggressive driving style is generally characterized by higher speeds, sudden acceleration and deceleration, abrupt steering angle changes, and harsh lateral and longitudinal maneuvers, whereas a calm driving style is associated with relatively lower speeds, gradual acceleration and deceleration, smooth maneuvers, and mild steering adjustments. A moderate or normal driving style falls between these two extremes [45].

To quantify driving style, we extracted key driving features, including the average, maximum, minimum, 25th percentile, 75th percentile, standard deviation, and variance of eight critical variables (Table 1). For computational efficiency, Min-Max Normalization (Feature Scaling) was applied, transforming the data to a [0,1] range.

Our assumption was that participants exhibited one of two distinct driving styles—aggressive or non-aggressive—under conditions (i) to (iv) in the baseline experiment. The assumption of two clusters was theoretically motivated by the hypothesis that drivers tend to fall into one of two broad behavioral profiles—typically referred to as “conservative” and “aggressive” styles. This choice was further supported by internal validation metrics, including the elbow method, silhouette analysis, and the gap statistic, all of which indicated that k = 2 was an appropriate and data-driven choice. Conditions (i)–(iv) were used to distinguish aggressive from non-aggressive drivers because they represent steady-state car-following in free-flow or mildly congested traffic, where headway choice, acceleration, braking and steering are largely voluntary. In contrast, conditions (v) and (vi)—stop-and-go queues and lead-vehicle cut-ins—provoke highly reactive responses that obscure underlying style differences; clustering metrics such as Max-S, Max-ABS-LA and Min-SH did not form distinct groups in these conditions. We therefore excluded conditions (v) and (vi) from style classification and reserved them as performance testbeds for assessing the impact of MPDD, avoiding circular reasoning.

Accordingly, K-means clustering was employed separately for each of these conditions to classify participants into aggressive or non-aggressive driving styles. To ensure methodological robustness, we tested alternative clustering techniques such as hierarchical clustering (with and without PCA) and DBSCAN. However, these methods either resulted in imbalanced groupings, lower silhouette scores, or poor separability. In contrast, K-means with k = 2 consistently produced more stable and interpretable clusters. We explored different combinations of driving features across conditions (i) to (iv) to maximize the similarity in driving style classification. Additionally, we assessed cluster quality, stability, and interpretability, with a particular focus on the significance of K-means cluster centers.

Subsequently, we introduced a new participant-specific metric, the degree of conservativeness (Eq 1), defined as:

D C = 1 - \frac{1}{4} \sum_{i = 1}^{4} D S_{i}

(1)

DC = Degree of Conservativeness
DS_i = Driving Style score in condition i

Based on this metric, participants were categorized as follows: 0 (entirely aggressive), 1 (entirely conservative), and 0.25, 0.5, or 0.75 (moderate drivers). Finally, we evaluated the impact of MPDD on driving performance, specifically analyzing Standard Deviation of Lateral Position (SDLP), Acceleration Reaction Time (ART), and Time to initial Braking Location (TIBL) using the paired t-test and the Wilcoxon Signed-Rank Test (Fig 3).

Clustering algorithm and statistical overview.

K-means Clustering: K-means clustering is an unsupervised machine learning algorithm used to partition a dataset into k clusters based on feature similarity. The algorithm aims to minimize intra-cluster variance by iteratively assigning data points to the nearest cluster centroid and updating the centroids until convergence.

Paired t-test: The paired t-test was used to compare driving performance metrics before and after MPDD, assuming the data follows a normal distribution. This test evaluates whether the mean difference between paired observations is statistically significant.

Null hypothesis (H₀): There is no significant difference in the mean values of the driving performance metric between the two conditions (e.g., control vs. distracted).
Alternative hypothesis (H₁): There is a significant difference in the mean values of the driving performance metric between the two conditions.

Wilcoxon Signed-Rank Test: Given that normality cannot always be assumed, we also conducted the Wilcoxon Signed-Rank Test as a non-parametric alternative to the paired t-test. It evaluates whether the median difference between paired observations is significantly different from zero.

Null hypothesis (H₀): There is no significant difference between the paired conditions; the median of the differences is zero.
Alternative hypothesis (H₁): There is a significant difference between the paired conditions; the median of the differences is not zero.

Friedman test: The Friedman test is a non-parametric statistical test used to detect differences in treatments across multiple related groups. It is an alternative to repeated-measures ANOVA when the assumption of normality is violated.

Null hypothesis (H₀): There is no significant difference between the conditions (the median ranks are the same)).
Alternative hypothesis (H₁): At least one condition differs significantly from the others.

If the p-value $≺$ a (e.g., 0.05), the null hypothesis is rejected, indicating a significant difference between conditions. If significant, post-hoc pairwise comparisons (e.g., Wilcoxon signed-rank test) are performed to determine which conditions differ.

Results

Driving style classification

To classify participants into aggressive and non-aggressive driving groups, we employed the k-means clustering method with k = 2. This classification focused on driving behavior during four traffic conditions (i)-(iv); free-flow, coherent moving flow, synchronized flow, and jam density; observed in the baseline experiment. Key features analyzed in this clustering process included maximum speed (Max-S), maximum absolute value of longitudinal acceleration (Max-ABS-LA), and minimum space-headway (Min-SH), as shown in Fig 4. To ensure consistency in feature dimensions, all variables were normalized prior to clustering.

The results revealed that 15 participants (37.5%) consistently exhibited the same driving style across all four conditions. Meanwhile, 16 participants (40%) maintained the same driving style in three out of the four conditions, and 9 participants (22.5%) displayed an equal mix of both aggressive and non-aggressive driving styles across the four conditions.

Based on our hypothesis that driving styles vary under different traffic conditions, we further evaluated participants’ driving styles by averaging their behavior across the four conditions. This analysis indicated that 10% of participants consistently displayed an aggressive driving style, 27.5% exhibited a fully conservative style, and 62.5% demonstrated a moderate driving style. The distribution of driving styles is presented in Fig 5.

Impact of MPDD on lateral vehicle control

The standard deviation of lateral position (SDLP) is a widely used parameter for assessing the lateral control of vehicles, as highlighted in previous studies [56–59]. To evaluate SDLP in the Mobile Phone Distraction Driving (MPDD) experiments relative to the baseline, we first assessed the normality of the data. If the data followed a normal distribution, a t-test was performed; otherwise, the Wilcoxon Signed-Rank Test was applied. The Friedman test was used to assess whether SDLP significantly differed between the two driving experiments (control and MPDD) within each driving style. When a significant effect was found, post-hoc pairwise comparisons were conducted using the Wilcoxon Signed-Rank Test to determine in which traffic condition (i–iv) the differences occurred. All statistical analyses were performed with a significance level of p < 0.05.

Engaging in hands-free (HF) conversation while driving significantly reduced SDLP in coherent, synchronized, and jam-density conditions, suggesting a stabilizing effect on lateral control under these traffic scenarios. Conversely, texting while driving resulted in a significant increase in SDLP during free-flow and coherent moving flow conditions, indicating a marked impact on lateral control (see Fig 6). The results of the Wilcoxon signed-Rank and the t-test are reported in Tables 2, 3, and 4.

Fig 6 — Horizontal lines indicate statistical comparisons between baseline and MPDD, with significance levels: "NS" (not significant) or "*" (significant at 0.05).

Table 2. Wilcoxon Signed-Rank Test results for the effect of HF conversation on SDLP across different traffic conditions. Significant differences are indicated by p < 0.05.

Measure	Condition	Control (Median, [IQR])	HF conversation (Median, [IQR])	W	z	p-value
SDLP	Free-flow	(0.30, [0.19])	(0.27, [0.18])	344	−0.887	0.38264
	Coherent moving flow	(0.31, [0.14])	(0.22, [0.10])	225	−2.487	0.01202^*
	jam density	(0.14, [0.22])	(0.08, [0.10])	171	−3.212	0.00094^*

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Table 3. T-test results for the effect of HF conversation on SDLP across different traffic conditions. Significant differences are indicated by p < 0.05.

Measure	Condition	Control (Mean, SD)	HF conversation (Mean, SD)	t(df)	p-value	Effect Size (d)
SDLP	Synchronized flow	(0.29, 0.12)	(0.21, 0.09)	5.50	0.00000^*	0.870

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Table 4. Wilcoxon Signed-Rank Test results for the effect of texting on SDLP across different traffic conditions. Significant differences are indicated by p < 0.05.

Measure	Condition	Control (Median, [IQR])	Texting (Median, [IQR])	W	z	p-value
SDLP	Free-flow	(0.30, [0.19])	(0.40, [0.31])	121	−3.885	0.00004^*
	Coherent moving flow	(0.31, [0.14])	(0.31, [0.14])	231	−2.406	0.01527^*
	Synchronized flow	(0.27, [0.14])	(0.24, [0.15])	407	−0.04	0.97349
	Jam density	(0.14, [0.22])	(0.13, [0.14])	350	−0.806	0.42797

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Further analysis was performed to evaluate the impact of MPDD on SDLP across different driving styles. The results of the Friedman test are presented in Table 5. Given the presence of a significant effect, post-hoc pairwise comparisons using the Wilcoxon Signed-Rank Test were conducted to identify which specific traffic conditions exhibited significant differences within each driving style. This analysis revealed that engaging in HF conversation significantly reduced SDLP among participants with a moderate driving style under synchronized and jam-density conditions. Similarly, participants with a conservative driving style exhibited a significant decrease in SDLP during coherent moving flow, synchronized flow, and jam-density conditions when engaging in HF conversation. In contrast, texting while driving led to a significant increase in SDLP for participants with a moderate driving style in free-flow and coherent moving flow conditions. Among conservative drivers, texting while driving resulted in increased SDLP during free-flow conditions but decreased SDLP during synchronized flow (see Fig 7). The results of the Wilcoxon signed-Rank is reported in Table 6.

Table 5. The Friedman Test results for HF conversation and texting across driving styles.

	Driving style	$χ^{2}$ (df)	p-value	W
HF conversation	Aggressive	18.75(7)	0.00901^*	0.67
	Moderate	59.08(7)	0.00000^*	0.338
	Conservative	29.848(7)	0.0001^*	0.388
Texting	Aggressive	17.991(7)	0.01201^*	0.643
	Moderate	78.453(7)	0.00000^*	0.448
	Conservative	28.879(7)	0.00015^*	0.375

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Table 6. Wilcoxon Signed-Rank Test results for HF conversation and texting across driving styles and traffic conditions.

	Driving style	Condition	W	z	p-value
HF conversation	Aggressive	Free-flow	0.00	1.53	0.125
		Coherent moving flow	3.00	0.49	0.625
		Synchronized flow	0.00	1.53	0.125
		Jam density	5	0.00	1.000
	Moderate	Free-flow	113.00	1.31	0.19081
		Coherent moving flow	119.00	1.15	0.2521
		Synchronized flow	56.00	2.96	0.00309^*
		Jam density	62.00	2.77	0.00558^*
	Conservative	Free-flow	26.00	0.558	0.57715
		Coherent moving flow	4.00	2.71	0.00684^*
		Synchronized flow	2.00	2.98	0.00293^*
		Jam density	10.00	2.03	0.04199^*
Texting	Aggressive	Free-flow	0.00	1.53	0.125
		Coherent moving flow	3.00	0.49	0.625
		Synchronized flow	0.00	1.53	0.125
		Jam density	0.00	1.53	0.125
	Moderate	Free-flow	63.00	2.74	0.00613^*
		Coherent moving flow	73.00	2.44	0.01472^*
		Synchronized flow	132.00	0.80	0.42614
		Jam density	121.00	1.10	0.27518
	Conservative	Free-flow	5.00	2.58	0.00977^*
		Coherent moving flow	28.00	0.39	0.7002
		Synchronized flow	6.00	2.47	0.01367^*
		Jam density	23.00	0.82	0.41309

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The Mean SDLP under different cognitive workloads based on driving styles is presented in Fig 8. These results emphasize the significant impact of mobile phone distractions on lateral vehicle control across different traffic conditions and driving styles, with texting posing a particularly pronounced risk to SDLP.

Impact of MPDD on reaction time

In car-following scenarios, driver reaction time can be characterized by the time taken to respond during acceleration and deceleration maneuvers [60]. In this study, we focus on two specific reaction time metrics. In condition (v), representing recovery from jam density, the primary metric of interest is Acceleration Reaction Time (ART). ART is defined as the duration between a driver’s observation of the leading vehicle initiating acceleration and the driver’s subsequent activation of the gas pedal to begin accelerating. Condition (vi), which focuses on collision avoidance, is assessed based on Time to Initial Braking Location (TIBL). TIBL is the time it takes for the driver’s vehicle to reach the location where the leading vehicle begins braking.

To examine ART and TIBL in the MPDD experiments relative to the baseline, we first assessed the normality of these variables. we first assessed the normality of the data. If the data followed a normal distribution, a t-test was performed; otherwise, the Wilcoxon Signed-Rank Test was applied. The findings indicated that engaging in hands-free (HF) conversation while driving significantly reduced ART, whereas texting did not produce a significant effect. AdditionallyF, MPDD had a significant impact on increasing TIBL, with texting exerting a greater influence. (see Fig 9). The results of the Wilcoxon signed-Rank are reported in Tables 7 and 8.

Table 7. Wilcoxon Signed-Rank Test results for the effect of HF conversation on ART and TIBL (*p < 0.05).

Measure	Control (Median, [IQR])	HF Conversation (Median, [IQR])	W	z	p-value
ART	(2.12, [0.83])	(1.67, [0.88])	211	−2.68	0.00666^*
TIBL	(2.16, [1.97])	(3.25, [3.08])	123	−3.15	0.00117^*

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Table 8. Wilcoxon Signed-Rank Test results for the effect of texting on ART and TIBLeak (*p < 0.05).

Measure	Control (Median, [IQR])	Texting (Median, [IQR])	W	z	p-value
ART	(2.12, [0.83])	(2.26, [1.64])	299	−1.49	0.139
TIBL	(2.16, [1.97])	(3.57, [3.32])	97	−3.57	0.00018^*

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Further analysis assessed the impact of MPDD on reaction times by driving style, revealing significant differences in ART and TIBL for specific driver groups (see Fig 10). HF conversation notably reduced ART among conservative participants, while MPDD significantly increased TIBL in both moderate and conservative participants. The results of the Wilcoxon signed-Rank Test are reported in Table 9.

Table 9. Wilcoxon Signed-Rank Test results for HF conversation and texting.

		Driving style	W	z	p-value
ART	HF conversation	Aggressive	1.00	−1.46	0.250
		Moderate	101.00	−1.66	0.1014
		Conservative	11.00	−1.96	0.05371*
	Texting	Aggressive	0.00	−1.83	0.125
		Moderate	137.00	−0.69	0.5077
		Conservative	32.00	−0.09	0.96582
TIBL	HF conversation	Aggressive	2.00	−1.10	0.375
		Moderate	60.00	−2.16	0.03012*
		Conservative	6.00	−1.96	0.05469*
	Texting	Aggressive	2.00	−1.10	0.375
		Moderate	50.00	−2.49	0.01147*
		Conservative	2.00	−2.43	0.01172*

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The Mean ART and TIBL according to driving styles under MPDD conditions are illustrated in Fig 11. In summary, while HF conversation reduces ART, particularly among conservative participants, MPDD significantly increases TIBL, especially in moderate and conservative participants. These findings highlight how texting may affect driver response behaviors differently from HF conversation, particularly in car-following scenarios.

Discussion

This paper evaluates the impact of MPDD on drivers’ performance, including SDLP, ART, and TIBL. Driving styles were extracted through data normalization, feature selection, clustering, and the definition of degree of conservativeness feature. Additionally, the effect of MPDD on drivers’ performance in relation to their driving styles, was assessed. Driving data were collected from 40 participants in driving simulator experiments, encompassing three experimental conditions: a baseline (control) condition, hands-free conversation, and texting. These car-following experiments were conducted under six distinct speed conditions: (i) free-flow, (ii) coherent moving flow, (iii) synchronized flow, (iv) jam density, (v) recovery from jam density, and (vi) collision avoidance. For the evaluation, statistical analyses, including t-tests, the Wilcoxon Signed-Rank Test, and the Friedman test, were performed.

Our findings demonstrate clear, condition- and style-specific effects of MPDD on driving performance. Tables 10 and 11 summarize the influence of distraction types (hands-free vs. texting) on SDLP, ART, and TIBL across aggressive, moderate, and conservative drivers. .

Table 10. Summary of the effect of MPDD on SDLP across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (significant decrease)).

		HF conversation			Texting
		Aggressive	Moderate	Conservative	Aggressive	Moderate	Conservative
SDLP	Free Flow	NS			+
	Free Flow	NS	NS	NS	NS	+	+
	Coherent moving flow	–			+
	Coherent moving flow	NS	NS	–	NS	+	NS
	Synchronized flow	–			NS
	Synchronized flow	NS	–	–	NS	NS	–
	Jam density	–			NS
	Jam density	NS	–	–	NS	NS	NS

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Table 11. Summary of the effect of MPDD on ART and TIBL across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (significant decrease).

		HF conversation			Texting
		Aggressive	Moderate	Conservative	Aggressive	Moderate	Conservative
ART	Recovery from jam density	–			+
ART	Recovery from jam density	NS	NS	–	NS	+	+
TIBL	Collision avoidance	NS			+
TIBL	Collision avoidance	NS	NS	NS	NS	+	+

Open in a new tab

Texting generally led to significant increases in SDLP across several traffic regimes, especially for moderate and conservative drivers under free-flow and coherent-moving flow conditions. Conversely, hands-free conversations had mixed effects: under jam density and synchronized flow, we observed a reduction in SDLP for conservative drivers. These results align with those of Giot et al. (2022) and Rumschlag et al. (2015), who reported that texting degrades lane-keeping more consistently than verbal distractions [56,57].

Interestingly, in specific low-speed, high-density situations, hands-free conversation appeared to stabilize lane control, possibly by increasing cognitive engagement without overloading visual-manual resources. This pattern supports the nuanced findings of Verster and Hartman (2015) and Caird et al. (2018), who suggested that hands-free interactions can sometimes enhance lane centering when drivers self-regulate speed and attention [1,58].

A particularly notable result emerged during the recovery from jam density phase. Here, texting significantly lengthened ART for moderate and conservative drivers, indicating impaired readiness to accelerate following congestion. However, hands-free conversation showed either negligible or decreased ART in this phase for some driver types, especially conservative drivers. This suggests a possible vigilance-enhancing effect of verbal interaction during low-demand traffic conditions.

These outcomes partially diverge from the aggregate findings of Caird et al. (2018), whose meta-analysis showed consistent reaction time impairments for all phone use types [1]. Our traffic-state-specific approach may account for this discrepancy by isolating traffic regimes where task demands interact differently with cognitive distraction.

TIBL during the collision avoidance phase was significantly impacted by texting across most driver categories, particularly moderate and conservative participants. While hands-free conversation had little effect on TIBL, the significant increases seen during texting align with prior studies showing degraded braking and hazard anticipation during visual-manual phone use [61–63].

Our findings corroborate previous studies that identified texting as a major threat to lane-keeping and reaction time performance [32,64]. For instance, Yan et al. (2022) found a pronounced SDLP increase on straight segments during texting. Similarly, Chouhan et al. (2025) quantified texting as raising crash probability by over 27% due to reaction-time impairments. Moreover, studies like Ortega et al. (2021) and Andrikopoulou et al. (2025) demonstrated that mobile phone use worsens lateral deviation and reduces driver control, particularly among younger drivers or during multitasking events [63,65].

Driving style emerged as a critical moderating factor. moderate drivers exhibited the highest texting-related increases in SDLP and ART, likely due to inherently higher speed variability and less cautious behavior. On the other hand, conservative drivers showed more pronounced sensitivity to hands-free conversations, particularly in lane-keeping heightened self-regulation under dual-task demands.

This style-specific pattern is consistent with Zhang et al.(2024), who found that conservative drivers displayed better takeover control in autonomous vehicles under distraction, while aggressive drivers had longer reaction times and lower braking intensity in urgent situations [66,67].

The differentiated effects of MPDD on lateral and longitudinal control based on driving style highlight the potential for personalized safety interventions. Advanced driver-monitoring systems (DMS) could improve risk prediction by incorporating behavioral style metrics (e.g., headway, speed variability) alongside distraction detection.

Limitation and future work

The current research has certain limitations that suggest potential directions for future studies. The study includes a total of 40 participants, which, based on previous research, is a reasonable sample size [1]. However, only four participants exhibited aggressive driving behavior, which may be insufficient for robust statistical analysis. As a result, detecting significant effects within this subgroup was challenging. Furthermore, the experiment is limited to a specific scenario, focusing solely on the impact of mobile phone use and a cognitive task on driving performance. Future research should consider a larger and more diverse sample, incorporating additional behavioral patterns and broader driving scenarios to enhance the generalizability of the findings. Task order was counterbalanced through random assignment to mitigate learning effects; however, future studies should employ a Latin-square experimental design, which offers a more rigorous and standardized means of controlling sequence effects. While this study utilized a medium-fidelity driving simulator, the use of high-fidelity simulators in future work may enhance validity. Although driving style in the present study was identified using performance-based metrics such as SDLP, ART, and TIBL, we acknowledge that gaze behaviors also represent an important dimension of driving style. Integrating an eye-tracking system into future experiments will enable a more detailed analysis of drivers’ visual scanning patterns, including gaze shifts, blink rates, and fixation durations, which are critical for assessing distraction and driving safety. Moreover, future studies could integrate gaze-based measures alongside performance indicators to provide a more comprehensive characterization of driving style [68]. Examining the relationship between these visual behaviors and driving performance under various mobile phone usage conditions will provide deeper insights into the mechanisms of driver distraction, ultimately contributing to the development of more effective safety interventions.

Acknowledgment

We gratefully acknowledge the support of the Sharif University of Technology, the Traffic Injury Research Center, and the University of Sydney in making this research possible.

Data Availability

The data underlying our study are publicly available in the following GitHub repository: https://github.com/Faqani1993/driving-distraction-performance-styles-data.

Funding Statement

The author(s) received no specific funding for this work.

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PONE-D-25-19007Analysis of the distraction impact on driving performance across driving styles: A driving simulator study in various speed conditionsPLOS ONE

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Academic Editor

PLOS ONE

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Reviewer #1: Yes

Reviewer #2: Partly

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #2: Yes

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Reviewer #1: This paper focuses on a interesting and meaningful topic, where the intersection between driving style and driving performance under MPDD conditions were investigated. However, there are several issues to be clarified.

1. Page 4, Lines 96-100. It would be better to describe more information of participants, such as the accident experience, vision and hearing conditions.

2. Page 4, Line 101. The authors adopted randomized order to minimize potential learning effects. Although the randomized order is valid to some extent, the use of a Latin-square experimental design is more standardized and effective.

3. Page 4, Line 106. The driving simulator shown in Fig 1 does not appear to have motion simulation capabilities and is therefore not a high-fidelity driving simulator. In the field of driving behavior analysis, driving simulators that only have visual and auditory simulations are usually referred to as medium-fidelity driving simulators.

4. Page 5, Line 132-133. The instructions for the participants need to be further clarified. For example, the participants were required to follow the lead vehicle and were not allowed to overtake the vehicle or change lanes under any circumstances.

5. Page 6, Table 1. The initial case form of the variables in the table is not standardized.

6. Page 7, Lines 187-193. Why the authors only adopt conditions (i) to (iv) to recognize participants driving style? There were six condition in total.

Reviewer #2: Although the topic of this manuscript holds certain practical significance and the authors have made efforts in reviewing the relevant literature, there remain notable shortcomings. Specifically, the research objectives and innovative contributions are not clearly articulated; the experimental design suffers from potential confounding factors and lacks sufficient detail; and the definition and interpretation of key variables are not rigorous.

1. In the Introduction, the authors state that mobile phone-related distracted driving (MPDD) has become a key concern in road traffic safety. However, the cited references [6]–[12] mainly document the co-occurrence of distracted driving and MPDD across different countries, without providing concrete evidence on how MPDD specifically increases crash risk. It is recommended that more targeted literature be included to clarify the specific mechanisms through which MPDD impacts driving safety.

2. Despite conducting a relatively comprehensive review of existing studies, the Introduction fails to extract the core research question and clearly define the study's objectives. This lack of focus obscures the novelty and significance of the research. The authors are advised to better articulate the research motivation and explicitly state the scientific problem this study aims to address, along with its potential contributions.

3. The experiment involves six types of traffic conditions, yet each condition lasts only for a short period, and the vehicle’s state changes continuously. This setup may introduce interference effects that compromise the drivers’ ability to perform the tasks as intended, thereby undermining the reliability of the collected data. The experimental design should be refined, or the strategies used to mitigate such interference should be clarified. Additionally, Figure 2 lacks necessary details; it is recommended to include the time–speed profiles at key event points to improve clarity.

4. The current description of the mobile phone task is insufficient to evaluate whether it effectively induces cognitive distraction. Detailed information should be provided on the experimental procedures, such as how distraction tasks are triggered, the content and duration of phone calls, the type and demands of text messaging tasks, etc. Moreover, the method for quantifying cognitive load should be specified, and evidence should be presented to confirm that the distraction task significantly increases cognitive demand on the driver.

5. The study classifies participants into two driving style groups. However, the criteria for this classification are unclear, and the rationale for choosing the K-means clustering method has not been justified. The authors should explain why K-means is suitable for this context and clarify its methodological advantages in this study.

6. The Discussion section is overly brief and mainly repeats the results without engaging in comparative analysis with existing literature. It is recommended to expand this section by contrasting the study’s findings with prior research and analyzing possible reasons for any discrepancies, in order to highlight the scientific value and implications of the work.

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PLoS One. 2025 Dec 4;20(12):e0336480. doi: 10.1371/journal.pone.0336480.r002

Author response to Decision Letter 1

6 Aug 2025

Dear Editorial Office,

We have thoroughly addressed all reviewer comments in the uploaded rebuttal letter and responded to the editor's remarks within the cover letter.

Kindly refer to the documents titled "rebuttal_letter" and "new_cover_letter" for our detailed, point-by-point responses.

Thank you for your time and consideration.

Sincerely,

Habibollah Nassiri

Associate Professor of Civil Engineering

Sharif University of Technology

nassiri@sharif.edu

Attachment

Submitted filename: rebuttal_letter.pdf

pone.0336480.s002.pdf^{(239.6KB, pdf)}

PLoS One. doi: 10.1371/journal.pone.0336480.r003

Decision Letter 1

Quan Yuan

14 Sep 2025

PONE-D-25-19007R1Analysis of the distraction impact on driving performance across driving styles: A driving simulator study in various speed conditionsPLOS ONE

Dear Dr. Nassiri,

Please address the reviewer's concern and revise the manuscript again.

Please submit your revised manuscript by Oct 29 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Quan Yuan, Ph.D.

Academic Editor

PLOS ONE

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If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Thank you for your rely and revisions. My previous concerns have been solved.

However, after reading the revised manuscript, I found that there is a new limiation. Although the authors adopted several metrics (e.g., SDLP, ART, and TIBL) to identify driving style, drivers' gaze behaviors are also important aspects of driving style. Thus, I recommend the authors to clarify that. You can refer to this article: Lane changing maneuver prediction by using driver’s spatio-temporal gaze attention inputs for naturalistic driving

Reviewer #2: (No Response)

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2025 Dec 4;20(12):e0336480. doi: 10.1371/journal.pone.0336480.r004

Author response to Decision Letter 2

25 Sep 2025

We would like to sincerely thank you for your time and thoughtful consideration of our manuscript. We have carefully addressed all of your comments and suggestions in the 'Response to Reviewers' file.

Attachment

Submitted filename: Response to Reviewers.pdf

pone.0336480.s003.pdf^{(125KB, pdf)}

PLoS One. doi: 10.1371/journal.pone.0336480.r005

Decision Letter 2

Quan Yuan

28 Oct 2025

Analysis of the distraction impact on driving performance across driving styles: A driving simulator study in various speed conditions

PONE-D-25-19007R2

Dear Dr. Nassiri,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Quan Yuan, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: Thank you for your rely and revisions. My previous concerns have been solved. The manuscript is suitable to publish now.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

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PLoS One. doi: 10.1371/journal.pone.0336480.r006

Acceptance letter

Quan Yuan

PONE-D-25-19007R2

PLOS ONE

Dear Dr. Nassiri,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Quan Yuan

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Attachment

Submitted filename: rebuttal_letter.pdf

pone.0336480.s002.pdf^{(239.6KB, pdf)}

Attachment

Submitted filename: Response to Reviewers.pdf

pone.0336480.s003.pdf^{(125KB, pdf)}

Data Availability Statement

The data underlying our study are publicly available in the following GitHub repository: https://github.com/Faqani1993/driving-distraction-performance-styles-data.

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PERMALINK

Analysis of the distraction impact on driving performance across driving styles: A driving simulator study in various speed conditions

Mobina Faqani

Habibollah Nassiri

Mahdi Rezaei

Mohsen Ramezani

Roles

Abstract

Introduction

Materials and methods

Participants characteristics

Apparatus and stimuli

Fig 1. The driving simulator at the Road Traffic Injury Research Center, Tabriz, Iran.

Experimental procedure

Scenario design

Fig 2. Lead vehicle speed profile across different traffic conditions: (i) free-flow; (ii) coherent moving flow; (iii) synchronized flow; (iv) jam density; (v) recovery from jam density; (vi) collision avoidance.

Cognitive task

Driving data acquisition

Table 1. Driving variables recorded from the simulator.

Methodology

Fig 3. The framework of the evaluation of the MPDD on driving performance based on driving style.

Clustering algorithm and statistical overview.

Results

Driving style classification

Fig 4. Results of k-means clustering in the baseline experiment (no distraction) across conditions (i)-(iv) (k = 2: aggressive and non-aggressive).

Fig 5. Distribution of participants’ conservativeness scores (0: aggressive; 0.25, 0.5, and 0.75: moderate; 1: conservative).

Impact of MPDD on lateral vehicle control

Fig 6. Box plots of SDLP across baseline, HF conversation, and texting for each traffic condition (i–iv).

Table 2. Wilcoxon Signed-Rank Test results for the effect of HF conversation on SDLP across different traffic conditions. Significant differences are indicated by p < 0.05.

Table 3. T-test results for the effect of HF conversation on SDLP across different traffic conditions. Significant differences are indicated by p < 0.05.

Table 4. Wilcoxon Signed-Rank Test results for the effect of texting on SDLP across different traffic conditions. Significant differences are indicated by p < 0.05.

Table 5. The Friedman Test results for HF conversation and texting across driving styles.

Fig 7. Box plots showing SDLP across experimental conditions, categorized by driving styles.

Table 6. Wilcoxon Signed-Rank Test results for HF conversation and texting across driving styles and traffic conditions.

Fig 8. Mean SDLP under MPDD, stratified by driving styles across traffic conditions (i–iv).

Impact of MPDD on reaction time

Fig 9. Box plots with individual data points visualizing the distribution of reaction times across MPDD experiments (NS means not significant at 0.05 and * means significant at 0.05).

Table 7. Wilcoxon Signed-Rank Test results for the effect of HF conversation on ART and TIBL (*p < 0.05).

Table 8. Wilcoxon Signed-Rank Test results for the effect of texting on ART and TIBLeak (*p < 0.05).

Fig 10. Box plots showing the ART and TIBLs across experiments, categorized by driving styles.

Table 9. Wilcoxon Signed-Rank Test results for HF conversation and texting.

Fig 11. Mean reaction time by driving style under MPDD.

Discussion

Table 10. Summary of the effect of MPDD on SDLP across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (significant decrease)).

Table 11. Summary of the effect of MPDD on ART and TIBL across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (significant decrease).

Limitation and future work

Acknowledgment

Data Availability

Funding Statement

References

Decision Letter 0

Quan Yuan

Roles

Author response to Decision Letter 1

Decision Letter 1

Quan Yuan

Roles

Author response to Decision Letter 2

Decision Letter 2

Quan Yuan

Roles

Acceptance letter

Quan Yuan

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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RESOURCES

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