Different Types Of Distraction Research Articles

Mobile phone conversation during nighttime driving: Effects on driving behavior

Objectives This study aims to investigate the impact of mobile phone use (specifically, conversation), considering various use modes, on driving behavior at night. Mobile phone use is a source of driver distraction and has been associated with increased accident risk. Driving at night also entails higher accident risk and severity compared to daytime driving. Several studies have investigated the impact of mobile phone use on driving behavior; however, only a few have explored the differences between the different use modes. Most present studies involved daytime driving, although mobile phone use at night is equally if not more prevalent. Method A driving simulator experiment was designed in which 55 participants drove under nighttime simulator conditions, in different road environments (urban and rural) and under different types of distraction: no distraction, handheld, wired earphone, and speaker mode. The drives were performed during late afternoon and evening hours to resemble nighttime conditions both in the simulator and in the actual environment. Participants also completed a questionnaire for collection of different types of data. Results Results highlight the effect of mobile phone use on driving behavior, through specific indicators. Mobile phone use resulted in reduced 85th percentile driving speed and 85th percentile acceleration and increased reaction time and lateral deviation. However, safer stopping distance was observed in rural roads. Parameters relative to mobile phone use familiarity and exposure were found to mitigate mobile phone use effects. Conclusions Mobile phones affect driving behavior at night in a similar manner to that noted in several different studies considering daytime driving. The hands-free regulation should be revisited, because driver distraction also occurred under this particular use mode. Further research is required considering mobile phone use familiarity and exposure and effects of mobile phone use, because the latter is reduced with an increase in the former. Stopping distance, an understudied but more immediate surrogate measure of road safety, was increased with mobile phone use, mainly as a result of the risk compensation behavior that drivers adopt, indicating that more research is required in this field.

Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network.

In this study, we propose an electrophysiological analysis-based brain network method for the augmented recognition of different types of distractions during driving. Driver distractions, such as cognitive processing and visual disruptions during driving, lead to distinct alterations in the electroencephalogram (EEG) signals and the extracted brain networks. We designed and conducted a simulated experiment comprising 4 distracted driving subtasks. Three connectivity indices, including both linear and nonlinear synchronization measures, were chosen to construct the brain network. By computing connectivity strengths and topological features, we explored the potential relationship between brain network configurations and states of driver distraction. Statistical analysis of network features indicates substantial differences between normal and distracted states, suggesting a reconfiguration of the brain network under distracted conditions. Different brain network features and their combinations are fed into varied machine learning classifiers to recognize the distracted driving states. The results indicate that XGBoost demonstrates superior adaptability, outperforming other classifiers across all selected network features. For individual networks, features constructed using synchronization likelihood (SL) achieved the highest accuracy in distinguishing between cognitive and visual distraction. The optimal feature set from 3 network combinations achieves an accuracy of 95.1% for binary classification and 88.3% for ternary classification of normal, cognitively distracted, and visually distracted driving states. The proposed method could accomplish the augmented recognition of distracted driving states and may serve as a valuable tool for further optimizing driver assistance systems with distraction control strategies, as well as a reference for future research on the brain-computer interface in autonomous driving.

How Games Help Us Research Age-Related Memory Changes

Working memory is the ability to hold information in our minds for a short time, often while we work on it. We need it for schoolwork, solving problems, and many aspects of life. Some people can hold more information in mind than others, and as we get older the amount we can hold tends to decline. If we understand what limits working memory and how working memory changes with age, we can figure out ways to improve it. A person’s ability to focus on relevant information and ignore distraction seems to be important. Our research group looked at how well people can ignore different types of distraction, which might limit their working memory. To sample a large number of people with a range of abilities, we used a game played by 29,631 people. The results gave us clues about how our ability to ignore distraction affects our working memory, and we identified a specific type of distraction that seems to particularly affect working memory in older adults.

Distracted driving recognition based on functional connectivity analysis between physiological signals and perinasal perspiration index

Automatic detection of distracted driving is essential to ensure safety of drivers. In this paper, a novel set of features were extracted from thermal and physiological signals in order to detect and recognize distraction of drivers. Thermal video data which measured the temperature of different areas of the face, heart rate, breathing rate and behavioural signals were used while various types of distractions including cognitive, emotional and sensory-motor were applied to the subjects. The proposed discriminator features were extracted by different functional connectivity methods between the perinasal perspiration extracted from thermal images of the face and physiological variables of heart rate and breathing rate. After feature extraction, binary classification methods were applied to detect the distractions. The results showed that using functional connectivity features significantly increased the accuracy of distraction detection system (up to 99.16% with a highly acceptable F1 score of 0.99). Hence, the proposed model significantly improved (p-value<0.001) the detection of driver’s distraction compared to previous studies. Functional connectivity between perinasal perspiration and breathing signals was the most informative feature for distraction detection purpose. This feature set explained almost all modulations in physiological changes by distractions. For the first time, we designed a system capable of recognizing types of distractions including sensory-motor, cognitive, emotional distractions and also normal state. We used the same feature set to recognize different types of distractions by using three-class and four-class classifiers. The suggested methods distinguished three types of distractions with the best accuracy of 81.94% related to cognitive, sensory-motor distraction and no-distraction states. We also tried to discriminate two types of cognitive distractions, analytic and mathematical distractions. The recognition system classified two types of cognitive distractions with an accuracy of 91.78%. The results suggest that there is important and complementary information in the connectivity between facial temperature signals and physiological variables for distraction detection and recognition.

SAFECAR: A Brain–Computer Interface and intelligent framework to detect drivers’ distractions

As recently reported by the World Health Organization (WHO), the high use of intelligent devices such as smartphones, multimedia systems, or billboards causes an increase in distraction and, consequently, fatal accidents while driving. The use of EEG-based Brain–Computer Interfaces (BCIs) has been proposed as a promising way to detect distractions. However, existing solutions are not well suited for driving scenarios. They do not consider complementary data sources, such as contextual data, nor guarantee realistic scenarios with real-time communications between components. This work proposes an automatic framework for detecting distractions using BCIs and a realistic driving simulator. The framework employs different supervised Machine Learning (ML)-based models on classifying the different types of distractions using Electroencephalography (EEG) and contextual driving data collected by car sensors, such as line crossings or objects detection. This framework has been evaluated using a driving scenario without distractions and a similar one where visual and cognitive distractions are generated for ten subjects. The proposed framework achieved 83.9% F1-score with a binary model and 73% with a multiclass model using EEG, improving 7% in binary classification and 8% in multi-class classification by incorporating contextual driving into the training dataset. Finally, the results were confirmed by a neurophysiological study, which revealed significantly higher voltage in selective attention and multitasking.

Towards Imbalanced Multiclass Driver Distraction Identification

Driver distraction is one of the leading causes of driving-related accidents worldwide. The ability to detect driver distraction preemptively is crucial to reducing the number of such accidents. This paper utilizes a novel multimodal dataset of thermal, visual, near-infrared, and physiological signals recorded from 45 subjects in order to identify distraction. We explore imbalanced distraction identification as a four-class problem across different types of distractions in order to resemble real-life scenarios, where the occurrence of different distractors varies. Our study analyzes the effectiveness of using early fusion across different modalities, a variety of window sizes, and data balancing schemas using synthetic instances. Moreover, we explore the effects of introducing subject-specific knowledge when training such identification models.

Hazard-based duration approach for understanding pedestrian crossing risk exposure at signalised intersection crosswalks – A case study of Kolkata, India

Pedestrian signal violation is a significant concern among policymakers and traffic engineers as this leads to a pedestrian-vehicle crash. The waiting time for a pedestrian at intersections is crucial in street-crossing decision-making. Once pedestrians terminate their waiting behaviour during the red-light period, they would cross in the red light and put themselves in danger. A total of 2089 red-light arriving pedestrian observations were made at eight intersection crosswalks across Kolkata city (India). With the help of hazard-based duration models, the waiting duration till signal violation has been analysed. Kaplan–Meier curve has been plotted to understand the survival probabilities. A semi-parametric Cox Proportional Hazard model was used to understand the different factors influencing signal violation behaviour. However, the proportional hazard assumption was not satisfied. Therefore, a parametric Accelerated Failure Time (AFT) model was used to understand the various covariates that affect the waiting duration. The results highlighted that 49.5% of pedestrians crossed immediately after arriving at the intersection in the red-light phase. A pedestrian’s probability of crossing the road when it is unsafe, i.e., motor vehicles still have green or yellow, varies with the waiting time. As waiting time increases, pedestrians get impatient and violate the traffic signal. This violation places them at an increased risk of being struck by a motor vehicle. The covariate analysis using the AFT model showed that pedestrian glance/looking behaviour, different types of distractions, signal cycle length, carrying luggage, and traffic plying on the road impacted signal violation behaviour. Signal modifications by reducing red-light phase length for pedestrians might be the most efficient means of reducing the likelihood of signal violation and being hit by a motor vehicle.

Driver Distraction Recognition Using Wearable IMU Sensor Data

Distracted driving has become a major cause of road traffic accidents. There are generally four different types of distractions: manual, visual, auditory, and cognitive. Manual distractions are the most common. Previous studies have used physiological indicators, vehicle behavior parameters, or machine-visual features to support research. However, these technologies are not suitable for an in-vehicle environment. To address this need, this study examined a non-intrusive method for detecting in-transit manual distractions. Wrist kinematics data from 20 drivers were collected using wearable inertial measurement units (IMU) to detect four common gestures made while driving: dialing a hand-held cellular phone, adjusting the audio or climate controls, reaching for an object in the back seat, and maneuvering the steering wheel to stay in the lane. The study proposed a progressive classification model for gesture recognition, including two major time-based sequencing components and a Hidden Markov Model (HMM). Results show that the accuracy for detecting disturbances was 95.52%. The accuracy associated with recognizing manual distractions reached 96.63%, using the proposed model. The overall model has the advantages of being sensitive to perceptions of motion, effectively solving the problem of a fall-off in recognition performance due to excessive disturbances in motion samples.

How drivers adapt their behaviour to changes in task complexity: The role of secondary task demands and road environment factors

The present study investigates the impact of different sources of task complexity such as driving demands and secondary task demands on driver behaviour. Although much research has been dedicated to understanding the impact of secondary task demands or specific road traffic environments on driving performance, there is little information on how drivers adapt their behaviour to their combined presence. This paper aims to describe driver behaviour while negotiating different sources of task complexity, including mobile phone use while driving (i.e., calling and texting) and different road environments (i.e., straight segments, curves, hills, tunnels, and curves on hills). A driving simulator experiment was conducted to explore the effects of different road scenarios and different types of distraction while driving. The collected data was used to estimate driving behaviour through a Generalized Linear Mixed Model (GLMM) with repeated measures. The analysis was divided into two phases. Phase one aimed to evaluate driver performance under the presence and absence of pedestrians and oncoming traffic, different lanes width and different types of distraction. The second phase analysed driver behaviour when driving through different road geometries and lane widths and under different types of distraction. The results of the experiment indicated that drivers are likely to overcorrect position in the vehicle lane in the presence of pedestrians and oncoming traffic. The effect of road geometry on driver behaviour was found to be greater than the effect of mobile phone distraction. Curved roads and hills were found to influence preferred speeds and lateral position the most. The results of this investigation also show that drivers under visual-manual distraction had a higher standard deviation of speed and lateral position compared to the cognitive distraction and the non-distraction condition.

“Texting &amp; Driving” Detection Using Deep Convolutional Neural Networks

The effects of distracted driving are one of the main causes of deaths and injuries on U.S. roads. According to the National Highway Traffic Safety Administration (NHTSA), among the different types of distractions, the use of cellphones is highly related to car accidents, commonly known as “texting and driving”, with around 481,000 drivers distracted by their cellphones while driving, about 3450 people killed and 391,000 injured in car accidents involving distracted drivers in 2016 alone. Therefore, in this research, a novel methodology to detect distracted drivers using their cellphone is proposed. For this, a ceiling mounted wide angle camera coupled to a deep learning–convolutional neural network (CNN) are implemented to detect such distracted drivers. The CNN is constructed by the Inception V3 deep neural network, being trained to detect “texting and driving” subjects. The final CNN was trained and validated on a dataset of 85,401 images, achieving an area under the curve (AUC) of 0.891 in the training set, an AUC of 0.86 on a blind test and a sensitivity value of 0.97 on the blind test. In this research, for the first time, a CNN is used to detect the problem of texting and driving, achieving a significant performance. The proposed methodology can be incorporated into a smart infotainment car, thus helping raise drivers’ awareness of their driving habits and associated risks, thus helping to reduce careless driving and promoting safe driving practices to reduce the accident rate.

Recognizing Distractions for Assistive Driving by Tracking Body Parts

Busy life as well as the prevalence of infotainment is increasingly making people more occupied even during tasks that require serious attention. One such task is driving and at the same time getting involved in activities that may distract drivers cognitively from watching the road and cause fatal accidents. This paper presents a method that is capable of monitoring different types of distractions, such as talking and texting on cell phone, casual eating, and operating cabin equipment while driving, so that a driver can be assisted to remain cautious on the road. The proposed method automatically detects and tracks fiducial body parts of a driver from video captured by a camera mounted on the front windshield inside a vehicle. Relative distances between the tracking trajectories are used as features that represent actions of the driver. Then, the well-known kernel support vector machine is applied for recognizing a particular distraction from the features extracted from body parts. The proposed feature is also compared with previously employed features for tracking-based human action recognition schemes to substantiate its better result in terms of mean accuracy and robustness for distraction recognition. The effectiveness of the proposed method of distraction recognition is also analyzed with respect to tracking errors.

Prevalence of nonuse of helmet and seat belt and distracted driving on national highway in Pakistan

Background: Pakistan is a developing country with high number of road traffic injuries. High magnitude of road traffic injuries highlights deficiencies in road traffic laws which are not at par with current safety requirements resultantly fail to address important risk factors such as distracted driving, non-use of seat belt and helmet. Purpose: - To assess effect of deficient traffic laws related of distracted driving, seat belt and helmet usage in term of estimate prevalence of different types of distractions and use of helmet by motorcycle riders and pillion riders and seat belts by the drivers and the passengers on National Highway in Pakistan that can lead to road crashes and increasing the injuries. Methods: Roadside survey was carried on N-5 National Highway to estimate number of distracted drivers, use of helmet by riders and pillion riders and use of seat belt by drivers and passengers. Results: Three types of distraction were observed i.e. Mobile Phone distraction (24%); Equipment related distraction (1%) and eating related distraction (1%) amounting to 29% of drivers distracted ;car drivers (29%), other vehicle drivers (6%), motorcyclists (3%) and trailer drivers (3%). Likewise; 64% drivers were found without seat belt, LTV drivers (85%), car drivers (74%) and truck drivers (69%). Similarly; 97% passengers were found without seat belt, LTV passengers (100%), truck passenger (99%), cars passenger (97%), Oil tanker and trailer passenger (90%). Helmet violation was high with 87.4% motorcycle riders and 100% pillion riders were found without helmet. Conclusion: Lack of enforcement due to deficient traffic laws result in RTIs in Pakistan. There is need to improve legislation on different forms of distractions, use of seat belt among passengers and helmet use by pillion riders; so that enforcement efforts can be made to address risk factors effectively to meet Decade of Action 2011-2020 targets.

Which factors lead to driving errors? A structural equation model analysis through a driving simulator experiment

As driving error is a main contributory factor of road accidents, its causes and consequences are of great interest in the road safety decision making process. This paper investigates several factors (including driver distraction, driver characteristics and road environment) that affect overall driving error behaviour and estimates a new unobserved variable which underlines driving errors. This estimation is performed with data obtained from a driving simulation experiment in which 95 participants covering all ages were asked to drive under different types of distraction (no distraction, conversation with passenger, cell phone use) in rural and urban road environment, as well as in both low and high traffic conditions. Driving error was then modeled as a latent variable based on several individual driving simulator parameters. Subsequently, the impact of several risk factors such as distraction, driver characteristics as well as road environment on driving error were estimated directly. The results of this complex model reveal that the impact of driver characteristics and area type are the only statistically significant factors affecting the probability of driving errors. Interestingly, neither conversing with a passenger nor talking on the cell phone have a statistically significant impact on driving error behaviour which highlights the importance of the present analysis and more specifically the development of a measure that represents overall driving error behaviour instead of individual driving errors variables.

Which factors affect accident probability at unexpected incidents? A structural equation model approach

ABSTRACTConsidering that unexpected events are a major contributory factor of road accidents the main objective of this article is to investigate the effect of several parameters including overall driving performance, distraction sources, driver characteristics, as well as road and traffic environment on accident probability at unexpected incidents. For this purpose, a driving simulator experiment was carried out, in which 95 participants from all age groups were asked to drive under different types of distraction (no distraction, conversation with passenger, cell phone use) in different road and traffic conditions. Then, in the framework of the statistical analysis, driving performance is estimated as a new unobserved (latent) variable based on several individual driving simulator parameters while a structural equation model is developed investigating which factors lead to increased accident probability at unexpected incidents. Regarding driver distraction, results indicate that cell phone use has a negative effect on accident risk confirming the initial hypothesis that when talking on the cell phone drivers find it difficult to handle an unexpected incident and as a result are more likely to commit an accident. Overall, a risky driving profile is developed, completing the puzzle of the effect of driver distraction on driver behavior and road safety.

Structural equation model analysis for the evaluation of overall driving performance: A driving simulator study focusing on driver distraction

ABSTRACTObjective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures.Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures.Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance.Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.

QUIET PLEASE! Effect of distraction on simulated posterior segment surgical performance

PurposeTo determine the effect of distraction on posterior segment surgical performance using a virtual reality simulator in expert and novice ophthalmic surgeons.MethodsTwenty subjects were given 6 min to read an unpublished research paper and then were randomized into two groups. Group 1 subjects were allowed 3 min to complete a standardized vitreoretinal simulated task undistracted. Group 2 subjects were asked six questions on the research paper whilst completing the same task. Each subject then performed the alternate scenario. Finally, all participants were asked six questions on the research paper whilst not operating.ResultsThere was no evidence of a difference in the odometer values (p = 0.127), cognitive task score (p = 0.390) or overall surgical task scores (p = 0.113) between the two groups. The time taken by the distracted group was significantly greater (95% CI −26.03 to −1.67, t-test p = 0.028).ConclusionDistraction significantly increases the time taken to perform a simulated vitreoretinal surgical task for all grades of surgeon. More studies are required to understand the impact on different types of distraction on surgical performance.

Clinical and cognitive aspects of functional (psychogenic) tremor

Functional (psychogenic) tremor is the most common functional movement disorder. Characteristic clinical features, so called red flags, can help to make the clinical distinction of this type from other tremor disorders. The most common features include the variability of frequency and amplitude. Clinical examination should include different types of distraction including motor or cognitive tasks or testing the influence of suggestibility on tremor amplitude, frequency or direction. Patients often report sudden onset and remissions that may last for months or even years. In some cases, the tremor is only present in highly specific situations. Although functional tremor shares characteristics with voluntary actions, patients experience their abnormal movements as involuntary. Recent experimental approaches have revealed an impairment in sense of agency. The diagnosis can be supported by neurophysiological measurements including accelerometry, which achieved asensitivity of 89.5% and aspecificity of 95.9% in avalidated test battery, thus providing auseful additional diagnostic tool. Psychotherapeutic treatment is indicated in patients with and without evident psychological symptoms. Aspecific physiotherapeutic approach for functional tremor is re-trainment.

Distraction osteogenesis as a treatment of obstructive sleep apnea syndrome: A systematic review.

Background:To conduct a systematic review to answer the clinical question “What are the effectiveness of mandibular distraction osteogenesis (MDO) and its complications to treat patients with obstructive sleep apnea syndrome (OSAS)?”.Methods:A systematic search including a computer search with specific keywords, reference list search, and manual search were done. Relevant articles on MDO were assessed and selected in 3 rounds for final review based on 5 predefined inclusion criteria and followed by a round of critical appraisal. Different types of distraction and their treatment outcomes of OSAS were recorded with standardized form and analyzed.Results:Twelve articles were included in the final review. A total of 256 patients aged 7 days to 60 years were treated with either external or internal MDO, with a mean follow-up period of 6 to 37 months. The average distraction distance of 12 to 29 mm was achieved with various distraction protocols. The success rate for adult patients was 100%, and cure rates were ranged from 82% to 100%. The definition of success or cure for OSAS in children or infants was not defined. Therefore, there were no clearly reported success or cure rates for children/infants in the included studies. However, all studies reported that these patients showed significant improvement in OSAS, with many of them who avoided tracheostomy or had the tracheostomy decannulated. The complication rates were ranged from 0% to 21.4%, with most being from local wound infections or neurosensory disturbances.Conclusion:This systematic review showed that MDO was effective in resolving OSAS in adults with retrognathic mandible. MDO also showed promising results in infants or children with OSAS. From the results of this systematic review, we recommend to define the criteria of success or cure for OSAS surgery in children and infants. We also recommend setting up randomized controlled trials to compare MDO with traditional maxillomandibular advancement surgery for OSAS patients and to provide a better evidence on the success and complication rates of the techniques.

Investigating the Effect of Area Type and Traffic Conditions on Distracted Driving Performance

Although driver distraction can be considered as part of everyday driving it constitutes a basic contributory factor for increased risk for road accidents internationally. Within this content, cell-phone use and conversation with passenger are two critical in-vehicle distraction conditions with respect to driver behavior and safety. The objective of this research is the investigation of the effect of area and traffic conditions on driving performance of drivers while talking on the mobile phone or conversing with the passenger. For this purpose, a large driving simulator experiment is carried out, in which 95 drivers from all different age groups (young, middle aged and older) were asked to drive under different types of distraction (no distraction, conversation with passenger, cell phone use) in rural and urban road environment, in low and high traffic. In the next step, an appropriate modelling methodology has been developed, including first descriptive analysis in order to explore the large database. Then generalized linear models as well as generalized linear mixed models regarding average speed and reaction time were implemented in order to estimate the effect of the examined distraction sources as well as area and traffic characteristics on driving behaviour and road safety. Results indicate that female drivers, especially in rural areas, were found to have the worst reaction times, while being distracted (either conversing with a passenger or talking on the cell phone). This is probably explained by the fact that in urban area, the complex road environment alerts the drivers in order to self-regulate their driving to compensate for any decrease in attention to the driving task. Furthermore, regarding average speed, it is observed that in rural areas drivers reduce the speed while distracted either by talking on the mobile phone (older drivers) or by conversing with the passenger (young and middle aged drivers), especially at high traffic volume while in urban areas suggesting a driver's compensatory behaviour. The next steps of the present research could focus on the investigation of the impact of mobile phone use, not only when the drivers talk on mobile phone using a hand-held device, but also when they use a hands-free device, a Bluetooth, or when they type messages.

P10 How cell phone use affects reaction time of older drivers

BackgroundAlthough older drivers are involved in a few accidents in terms of absolute numbers, they represent one of the highest risk categories for accidents involving fatalities and serious injuries per number of drivers and per distance travelled. Furthermore, cell phone use is estimated to be an important cause of vehicle accidents. Even if cell phone use can be considered as part of everyday the increased use of cell phones from drivers of all age groups while driving makes the investigation of their influence on driving behaviour and on road safety very essential. MethodsDriving simulators have become a widely used tool for examining the impact of driver distraction and more specifically cell phone use, as examining distraction causes and impacts in a controlled environment helps provide insights into situations that are difficult to measure such as the reaction time of drivers.The objective of this research is the analysis of the reaction time of older drivers while talking on the cell phone. For this purpose, a large driving simulator experiment is carried out, in which 90 drivers from three different age groups (young, middle aged and older) were asked to drive under different types of distraction (no distraction, conversation with passenger, cell phone use) in rural and urban road environment, in low and high traffic. In addition, two unexpected events are set in each driving scenario, where the reaction time of each driver is recorded.To achieve the research objectives, a descriptive analysis took place through box plots, followed by a generalized linear mixed model, which was developed in order to estimate the effect of cell phone use in the reaction time at unexpected incidents. ResultsResults indicate that on the cell phone, while driving, leads to increased reaction time for all drivers and especially regarding female drivers, especially in rural areas. Furthermore, regarding age groups, older drivers talking on the cell phone achieved almost double reaction time than being undistracted, while urban area as well as high traffic increases the reaction time of older drivers significantly. ConclusionsResearch on older drivers is essential for policy makers, professionals involved in testing and rehabilitating older drivers, and those involved in designing and evaluating new driving environments and in-vehicle technologies. Results clearly suggest that mobile phone use while driving has a potential negative impact on road safety and leads to increased accident risk especially on older drivers.