Mobile phone use during driving: Effects on speed and effectiveness of driver compensatory behaviour

Gap acceptance behaviour and crash risks of mobile phone distracted young drivers at roundabouts: A random parameters survival model

Navigating through complex road geometries, such as roundabouts, poses significant challenges and safety risks for drivers. These challenges may be exacerbated when drivers are distracted by mobile phone conversations. The interplay of road geometry, driving state, and driver characteristics in creating compound risks remains an underexplored area in existing literature. Proper understanding of such compound crash risk is not only crucial to improve road geometric design but also to educate young drivers, who are particularly risk-takers and to devise strict penalties for mobile phone usage whilst driving. To fill this gap, this study examines crash risks associated with gap acceptance manoeuvres at roundabouts in the simulated environment of the CARRS-Q driving simulators, where 32 licenced young drivers were exposed to a gap acceptance scenario in three phone conditions: baseline (no phone conversation), handheld, and hands-free. A parametric random parameters survival modelling approach is adopted to understand safety margins-characterised by gap times-during gap acceptance scenarios at roundabouts, concurrently uncover driver-level heterogeneity with mobile phone distraction and capture repeated measures of experiment design. The model specification includes the handheld phone condition as a random parameter and hands-free phone condition, acceleration noise, gap size, crash history, and gender as non-random parameters. Results suggest that the majority of handheld distracted drivers have smaller safety margins, reflecting the negative consequences of engaging in handheld phone conversations. Interestingly, a group of drivers in the same handheld phone condition have been found to exhibit cautious/safer behaviour, as evidenced by longer gap times, reflecting their risk compensation behaviour. Female distracted drivers are also found to exhibit safer gap acceptance behaviour compared to distracted male drivers. The findings of this study shed light on the compound risk of mobile phone distraction and gap acceptance at roundabouts, requiring policymakers and authorities to devise strict penalties and laws for distracted driving.

Speeding behavior and associated factors in Buenos Aires, Argentina

OBJECTIVE

Excessive speed is a major risk factor for serious injuries and death. However, speeding remains a pervasive problem around the world. This study aimed to investigate the factors associated with speeding behavior in the city of Buenos Aires, Argentina.

METHODS

A sample of vehicles (n = 34,967) from ten locations in the city was observed in two waves during 2021. Measurements were made at different times and days of the week. Observation sites were free of intersections, traffic lights, speed bumps and cameras, allowing drivers to speed freely. Data on speed, drivers and vehicle types were collected. Factors associated with speeding were identified through logistic regression analyses.

RESULTS

15.3% of vehicles were observed to be speeding. Roads with posted speed limits of 40 km/h showed higher speeding compared to 60 km/h roads. 77% of vehicles traveled above 30 km/h on local roads, and 30% above 50 km/h on avenues. Motorcycles, both commercial and private, showed a higher percentage of speeding compared to all other vehicles. Speeding was lower among women, among adults over 60 years of age, and among those using cell phones.

CONCLUSION

It is crucial to strengthen strategies for increased compliance with speed limits. Actions targeting motorcyclists must be a priority.

Self-Regulation of Driving Behavior Under the Influence of Cannabis: The Role of Driving Complexity and Driver Vision

OBJECTIVE

This study analyzed the self-regulation behaviors of drivers under the influence of cannabis and its relationship with road complexity and some driver traits, including visual deterioration.

BACKGROUND

Cannabis is the illicit drug most often detected in drivers; its use results in significant negative effects in terms of visual function. Self-regulation behaviors involve the mechanisms used by drivers to maintain or reduce the risk resulting from different circumstances or the driving environment.

METHODS

Thirty-one young, occasional cannabis users were assessed both in a baseline session and after smoking cannabis. We evaluated the visual function (visual acuity and contrast sensitivity) and driver self-regulation variables of both longitudinal and lateral control as the speed adaptation and standard deviation of lateral position (SDLP).

RESULTS

Visual function was significantly impaired after cannabis use. Recreational cannabis use did not result in self-regulation, although some road features such as curved roads did determine self-regulation. Male participants adopted mean faster driving speeds with respect to the speed limit. Driver age also determined better lateral control with lower SDLPs. In addition, visual impairment resulting from cannabis use (contrast sensitivity) was linked with self-regulation by changes in longitudinal and lateral control.

CONCLUSION

Contrast sensitivity could be a good indicator of individual visual status to help determine how drivers self-regulate their driving both in normal conditions and while under the influence of cannabis.

APPLICATION

The findings provide new insights about driver self-regulation under cannabis effects and are useful for policy making and awareness campaigns.

Evaluation of the visual-manual resources required to perform calling and navigation tasks in conventional mode with a portable phone and in full- touch mode with an embedded system

This study investigates the differences in a driver's visual-manual behaviour when performing secondary tasks while driving under the full-touch mode (FTM) and the conventional mode (CM). To this end, 30 participants were recruited to perform secondary tasks while driving two vehicles equipped with different HMI system interaction modes. The results show that compared to the CM, in the FTM, fewer visual-manual resources are required to perform the calling task, but for the navigation task, this requirement is higher. Additionally, in both modes, the driver exhibited self-regulation visual-manual behaviour when performing secondary tasks as the driving speed increased. However, the effect of the driving speed on visual-manual behaviour was greater in the FTM than in the CM. The main limitation of this study is that the effect of the difference between the two experimental vehicles on the findings was not considered, however, this does not affect the generalisation of the findings. Practitioner summary: Potential applications of this study include improving drivers' knowledge about the effect of performing secondary tasks in different modes on driving safety, and this study also provides useful insights human-machine co-driving systems to develop user-friendly control strategies and for automotive companies to improve the full-touch interactive mode for automotive companies.

Reconstruction of a Car-Running Pedestrian Accident Based on a Humanoid Robot Method

Due to the characteristics of multibody (MB) and finite element (FE) digital human body models (HBMs), the reconstruction of running pedestrians (RPs) remains a major challenge in traffic accidents (TAs) and new innovative methods are needed. This study presents a novel approach for reconstructing moving pedestrian TAs based on a humanoid robot method to improve the accuracy of analyzing dynamic vehicle-pedestrian collision accidents. Firstly, we applied the theory of humanoid robots to the corresponding joints and centroids of the TNO HBM and implemented the pedestrian running process. Secondly, we used rigid-flexible coupling HBMs to build pedestrians, which can not only simulate running but also analyze human injuries. Then, we validated the feasibility of the RP reconstruction method by comparing the simulated dynamics with the pedestrian in the accident. Next, we extracted the velocity and posture of the pedestrian at the moment of collision and further validated the modeling method through a comparison of human injuries and forensic autopsy results. Finally, by comparing two other cases, we can conclude that there are relative errors in both the pedestrian injury results and the rest position. This comparative analysis is helpful for understanding the differences in injury characteristics between the running pedestrian and the other two cases in TAs.

Analysis of speed reductions and crash risk of aggressive drivers during emergent pre-crash scenarios at unsignalized intersections

Aggressive driver behavior (ADB) is often linked with road crashes, especially during crash imminent situations. Previous studies demonstrated that ADB was positively correlated with collision risk; however, this relationship has not quantified evidently. This study aimed to analyze drivers' collision risk and speed reduction behavior during an emergent pre-crash scenario (such as a conflict encroaching into an unsignalized intersection at different critical time gaps) using a driving simulator. The effect of ADB on crash risk is investigated using the time to collision (TTC). Further, drivers' collision evasive behavior is analyzed using speed reduction time (SRT) survival probabilities. Fifty-eight Indian drivers are identified as aggressive, moderately aggressive, and, non-aggressive based on aggressive indicators such as vehicle kinematics (percentage of the time spent in speeding and rapid accelerations, maximum brake pressure, etc.). Two separate models are built to analyze ADB effects on TTC and SRT using a Generalized Linear Mixed Model (GLMM) and a Weibull Accelerated Failure Time (AFT) model, respectively. From the results, it can be observed that aggressive drivers' TTC and SRT are reduced by 82% and 38%, respectively. Compared to a 7 sec conflict approaching time gap, TTC is reduced by 18%, 39%, 51%, and 58% for 6 sec, 5 sec, 4 sec, and 3 sec conflict approaching time gaps, respectively. The estimated SRT survival probabilities for aggressive, moderately aggressive and non-aggressive drivers are 0%, 3% and 68% at 3 sec of conflict approaching time gap, respectively. SRT survival probability increased by 25% for matured drivers and decreased by 48% for drivers who tend to engage in frequent speeding. Important implications of the study findings are discussed.

Persistent effects of mobile phone conversation while driving after disconnect: Physiological evidence and driving performance

Cognitive workload has been known as a key factor in traffic accidents, which can be highly increased by talking on the phone while driving. A wide range of studies around the world investigated the effects of mobile phone conversations on driving performance and traffic accidents. But less noticed is the durability of cognitive effects of mobile phone conversations. This study aimed to determine the effects of different types of mobile phone conversations on physiological response and driving performance during and after the conversation. Heart rate, heart rate variability (physiological response), Standard deviation of lane position (SDLP), and the relative distance between two cars (driving performance) of 34 samples (male and female) in the driving simulator were recorded. In this study, three types of conversations (neutral, cognitive, and arousal) were used. Neutral conversation did not pursue specific purpose questions. Cognitive conversations were simple mathematical problem-solving questions and arousal conversations aimed at arousing participant emotions. Each conversation was used as a secondary task in a condition. The study had three conditions; in each condition the participant drove for 15 min. Each condition consisted of 5 min of driving (Background), 5 min of driving and conversation (dual tasks) and 5 min of driving after conversation to trace the effects of the conversation. Vehicle speed was 110 km/h in each of the three conditions using car-following scenario. The results showed that neutral conversations had no significant effects on physiological response. Though, arousal conversations had significant effects on physiological responsiveness and driving performance during conversations, where it was even more significant after disconnection. Therefore, the content of the conversation determines the amount of cognitive load imposed on the driver. Considering the persistence of cognitive effects caused by conversation, the risk of traffic accidents is still high even after disconnection.

Determinants of self-efficacy of driving behavior among young adults in the UAE: Impact of gender, culture, and varying environmental conditions in a simulated environment

Research on traffic accidents have acknowledged that human error is the leading cause of road accidents around the world. In the UAE, those aged between 18 and 30 years are involved in the most accidents. As a result, this study examines the perception, attitude and driving behavior of young adults in the UAE. Virtual Reality (VR) was used to examine driving behavior because it offers alternatives to assess driving behavior with a high degree of immersive experience in a safe and replicable environment. Participants drove through a virtual environment that resembled the urban environment of Abu Dhabi in the UAE, which included six traffic events. A sample of 12 females and 27 males also completed a pre and post-simulation questionnaire to report and evaluate their personal driving experience in Abu Dhabi. The volunteer group represented young drivers with limited driving experience and diverse cultural backgrounds. Results indicated that male drivers were less adhering to safe driving behavior compared to females. Even though both males and females exceeded the designated speed limit, males traveled longer distances over the limit. Additionally, it was found that young drivers tend to overestimate their skills with factors like gender, cultural background, and driving experience being key contributors. The results indicate that traffic authorities should take into consideration different approaches in the formulation of policies related to young drivers with periodic reassessment of skills and training to enhance the safety of driving in the UAE and the region.

Analysis of mobile phone use engagement during naturalistic driving through explainable imbalanced machine learning

While driver distraction remains an issue in modernized societies, technological advancements in data collection, storage and analysis provide the means for deeper insights of this complex phenomenon. In this research, factors influencing when driver distraction through mobile phone use occurs during naturalistic driving are investigated. Naturalistic data from a 6-stage, 230-driver experiment are exploited, in which drivers installed a non-intrusive driving recording application in their devices and conducted their trips normally across a 21-month timespan, coupled with corresponding questionnaire data. The various experiment stages involved providing progressively more behavioral feedback to drivers while continuing to record them. Subsequently, supervised Machine Learning XGBoost algorithms were employed to model the contributions of naturalistic driving and questionnaire features to the decision to engage mobile phone use. Mobile phone use percentages were heavily skewed towards zero, therefore imbalanced ML with a minority-oversampling approach in a binary format was employed. To increase the explainability offered by the algorithm, SHAP values were calculated for the informative features. Results indicate that the decision of drivers to use a mobile while driving is governed by a number of complex, non-linear relationships. Total trip distance is the most significant predictor variable by a wide margin, with mean SHAP values of 0.79 towards affecting the model decisions for the probability of mobile phone use of each driver. However, other variables influence the final predictions as well, such as the number of tickets in the last three years (m.SHAP = 0.30), declared mobile phone use (m.SHAP = 0.26), the amount and variety of provided feedback (m.SHAP = 0.17) (i.e. experiment phase number) and family member numbers (m.SHAP = 0.09) decrease the probability of using a mobile phone while driving. Conversely, increases in driver experience (m.SHAP = 0.22), driver age (m.SHAP = 0.11), engine capacity (m.SHAP = 0.11) and total kilometers driven annually (m.SHAP = 0.08) increase the probability of using a mobile phone in naturalistic driving conditions. SHAP dependency plots reveal non-linear effects present in almost all variables. Fuel consumption had a particularly strong non-linear effect, as higher values of this variable lead to both higher and lower probability of drivers using a mobile phone, deviating from the safer average. Legislation, campaigns and enforcement measures can be restructured to take advantage of gains margins in terms of understanding and predicting driver distraction behavior, as explored in the present study.

Texting While Driving: A Literature Review on Driving Simulator Studies

Road safety is increasingly threatened by distracted driving. Studies have shown that there is a significantly increased risk for a driver of being involved in a car crash due to visual distractions (not watching the road), manual distractions (hands are off the wheel for other non-driving activities), and cognitive and acoustic distractions (the driver is not focused on the driving task). Driving simulators (DSs) are powerful tools for identifying drivers' responses to different distracting factors in a safe manner. This paper aims to systematically review simulator-based studies to investigate what types of distractions are introduced when using the phone for texting while driving (TWD), what hardware and measures are used to analyze distraction, and what the impact of using mobile devices to read and write messages while driving is on driving performance. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 7151 studies were identified in the database search, of which 67 were included in the review, and they were analyzed in order to respond to four research questions. The main findings revealed that TWD distraction has negative effects on driving performance, affecting drivers' divided attention and concentration, which can lead to potentially life-threatening traffic events. We also provide several recommendations for driving simulators that can ensure high reliability and validity for experiments. This review can serve as a basis for regulators and interested parties to propose restrictions related to using mobile phones in a vehicle and improve road safety.

Drivers' self-regulatory behaviors in active and responsive scenarios

OBJECTIVE

Drivers usually appear to self-regulate their driving behaviors in situations considered to be challenging, such as mobile phone-distracted driving. It is important to clarify how drivers self-regulate their actual behaviors. In addition, few studies investigated driver distraction in active and responsive scenarios. Therefore, the present study aimed to gain a better understanding of drivers' actual self-regulation of driving behaviors and phone use behaviors while mobile phone-distracted driving in active and responsive scenarios. The contribution of compensatory beliefs to self-regulation was also explored.

METHODS

This study was conducted using a 2 (mobile phone use behaviors: phone calling vs. WeChat messaging) × 2 (scenarios: active vs. responsive) within-group design. A total of 34 participants completed a driving simulation experiment. The dependent variables of drivers' driving behaviors, phone use behaviors, and physiological data were collected. Participants' compensatory belief was also measured.

RESULTS

The results showed that the speed reduction in the stages with WeChat messaging was significantly greater than that in the stages with phone calls, and the speed reduction in the responsive scenario was significantly greater than that in the active scenario. Participants would adopt relatively equal phone-use-related self-regulatory behaviors in active and responsive scenarios. Participants with higher compensatory beliefs had relatively greater speed reduction in most scenarios, but fewer phone-use-related self-regulatory behaviors. In addition, the respiratory rate could contribute to evaluating the changes in drivers' physiological status during phone calling-distracted driving.

CONCLUSIONS

Participants would self-regulate driving behaviors and phone use behaviors according to different distracted driving tasks and scenarios. The driving-related self-regulation in WeChat messaging scenarios and responsive scenarios was greater. There was a trend in the effect of compensatory beliefs on actual self-regulatory behaviors, which needs to be further verified in the future. This study contributes to the verification of the different actual driving-related and phone-use-related self-regulatory behavior of drivers in active and responsive mobile phone distracted driving scenarios.

Predicting collision cases at unsignalized intersections using EEG metrics and driving simulator platform

Unsignalized intersection collision has been one of the most dangerous accidents in the world. How to identify road hazards and predict the potential intersection collision ahead are challenging problems in traffic safety. This paper studies the feasibility of EEG metrics to forecast road hazards and presents an improved neural network model to predict intersection collision based on EEG metrics and driving behavior. It is demonstrated that EEG metrics show significant differences between collision and non-collision cases. It indicates that EEG metrics can serve as effective indicators to predict the collision probability. The drivers with higher relative power in fast frequency band (alpha and beta), lower relative power in slow frequency band (delta and theta) are more likely to have conflicts. The prediction using three machine learning models (Multi-layer perceptron (MLP), Logistic regression (LR) and Random forest (RF)) based on three input datasets (only EEG metrics, only driving behavior and combined EEG metrics with driving behavior) are compared. The results show that for single time point prediction, MLP model has the highest accuracy among three machine learning models. The model solely based on EEG metrics datasets has higher accuracy than driving behavior as well as combined datasets. However, for multi-time point prediction, the accuracy of MLP is only 73.9%, worse than LR and RF. We improved the MLP model by adding attention mechanism layer and using random forest model to select important features. As a consequence, the accuracy is greatly improved and reaches 88%. This study demonstrates the importance and feasibility of EEG signals to identify unsafe drivers ahead. The improved neural network model can be helpful to reduce intersection accidents and improve traffic safety.

Distractions by work-related activities: The impact of ride-hailing app and radio system on male taxi drivers

Use of ride-hailing mobile apps has surged and reshaped the taxi industry. These apps allow real-time taxi-customer matching of taxi dispatch system. However, there are also increasing concerns for driver distractions as a result of these ride-hailing systems. This study aims to investigate the effects of distractions by different ride-hailing systems on the driving performance of taxi drivers using the driving simulator experiment. In this investigation, fifty-one male taxi drivers were recruited. During the experiment, the road environment (urban street versus motorway), driving task (free-flow driving versus car-following), and distraction type (no distraction, auditory distraction by radio system, and visual-manual distraction by mobile app) were varied. Repeated measures ANOVA and random parameter generalized linear models were adopted to evaluate the distracted driving performance accounting for correlations among different observations of a same driver. Results indicate that distraction by mobile app impairs driving performance to a larger extent than traditional radio systems, in terms of the lateral control in the free-flow motorway condition and the speed control in the free-flow urban condition. In addition, for car-following task on urban street, compensatory behaviour (speed reduction) is more prevalent when distracted by mobile app while driving, compared to that of radio system. Additionally, no significant difference in subjective workload between distractions by mobile app and radio system were found. Several driver characteristics such as experience, driving records, and perception variables also influence driving performances. The findings are expected to facilitate the development of safer ride-hailing systems, as well as driver training and road safety policy.

Talking on the Phone While Driving: A Literature Review on Driving Simulator Studies

Distracted driving is a growing concern around the world and has been the focus of many naturalistic and simulator-based studies. Driving simulators provide excellent practical and theoretical help in studying the driving process, and considerable efforts have been made to prove their validity. This research aimed to review relevant simulator-based studies focused on investigating the effects of the talking-on-the-phone-while-driving distraction on drivers' behavior. This work is a scoping review which followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. The search was performed on five databases, covering twenty years of research results. It was focused on finding answers to three research questions that could offer an overview of the main sources of distraction, the research infrastructure, and the measures that were used to analyze and predict the effects of distractions. A number of 4332 studies were identified in the database search, from which 83 were included in the review. The main findings revealed that TPWD distraction negatively affects driving performance, exposing drivers to dangerous traffic situations. Moreover, there is a general understanding that the driver's cognitive, manual, visual, and auditory resources are all involved, to a certain degree, when executing a secondary task while driving.

The use of risk homeostasis theory to reduce smartphone use during low-speed driving

Driving while distracted by smartphones is an unsafe behavior and constitutes a serious worldwide road safety issue. In line with the risk homeostasis theory, during high-speed driving, drivers perceive smartphone usage as an unwarranted risk and in most cases refrain from doing so. During low-speed driving, however, drivers often use their smartphones, as they do not perceive this as inherently unsafe, even though it is. The goal of this study was to examine an intervention, based on the risk homeostasis theory, aimed at decreasing the use of smartphones while driving at low speeds. Thirty-seven young drivers participated in the research group that aimed to alter drivers' risk perceptions, decision making, and behavior. The study also included a control group of 33 young drivers. All of the participants' smartphone usage was monitored using a dedicated application that measured both the number of times drivers touched their smartphone screens while driving and the driving speed each time the screen was touched. The results indicate that drivers in the research group decreased their smartphone usage while driving, unlike the control group drivers who did not alter their behavior. In conclusion, a risk homeostasis-based intervention can decrease dangerous and unsafe driving behavior, even when such behavior is not perceived as significantly dangerous. Furthermore, additional types of risky and unsafe driving behaviors may be decreased using this type of intervention.

Effects of Gesture-Based Interaction on Driving Behavior: A Driving Simulator Study Using the Projection-Based Vehicle-in-the-Loop

OBJECTIVE

We observe the driving performance effects of gesture-based interaction (GBI) versus touch-based interaction (TBI) for in-vehicle information systems (IVISs).

BACKGROUND

As a contributing factor to a number of traffic accidents, driver distraction is a significant problem for traffic safety. More specifically, visual distraction has a strong negative impact on driving performance and risk perception. Thus, the implementation of new interaction systems that use midair gestures to encourage glance-free interactions could reduce visual distraction among drivers.

METHODS

In this experiment, participants drove a projection-based Vehicle-in-the-Loop. The projection-based technology combines a visual simulation with kinesthetic, vestibular, and auditory feedback from a car on a test track. While driving, participants used GBI or TBI to perform IVIS tasks. To investigate driving behavior related to critical driving situations and car-following maneuvers, vehicle data based upon longitudinal and lateral driving were collected.

RESULTS

Participants reacted faster to critical driving situations when using GBI compared to TBI. For drivers using TBI, steering performance decreased and time headway to a preceding vehicle was higher.

CONCLUSION

Gestures provide a safe alternative to in-vehicle interactions. Moreover, GBI has fewer effects on driver distraction than TBI.

APPLICATION

Potential applications of this research include all in-vehicle interaction systems used by drivers.

Analysis of Driver’s Socioeconomic Characteristics Relating to Speeding Behavior and Crash Involvement: A Case Study in Lahore

Speeding is one of the risky behaviors which results in accident involvement causing fatalities and severe injuries. This paper aimed to identify the significant socio-economic characteristics of drivers concerning their speeding behavior and crash involvement. A questionnaire was designed consisting of driver’s demographic features, involvement in an accident, penalty on speed violations, and statements on speeding behavior in terms of exceeding the speed limits by 10 km/h or more on roads with different speed limits of 60, 80, 100, and 120 km/h per standard operating speeds on various road types in Pakistan. This survey was conducted in Lahore city and a total of 551 usable samples were obtained. A latent variable of drivers’ speeding behavior was introduced; factor loadings were estimated, and an observed variable of drivers’ crash experience was defined as the drivers’ crash involvement. Ordered regression analysis using the probit function was conducted on speeding behavior and crash involvement. The ordinal analysis revealed that the drivers’ age, gender, marital status, employment, vehicle engine size, type of vehicle they drive, and driving frequency per day are good predictors of speeding behavior. Similarly, male drivers’ age, vehicle engine size, and type of vehicle they drive were significant predictors of their likelihood to be involved in an accident. The young, single, and male drivers and drivers of cars with an engine capacity above 1.5 L were more likely to speed and be involved in crashes. These findings provide a clear understanding of a specific group of drivers who have a higher probability of speeding and crash involvement. There is a need to focus on specific demographic factors in the formulation of traffic safety policies and managing speedy drivers’ behaviors.

Profiling drivers to assess safe and eco-driving behavior - A systematic review of naturalistic driving studies

Road accidents and vehicular emissions are two significant issues related to road transportation, affecting both human life and the environment. Prior research suggests that driver behavior is a crucial factor in the majority of road crashes and is a significant factor influencing fuel consumption and vehicle emission. Significant improvement in driving behavior can be achieved by providing feedback to drivers about their driving behavior. An increasing interest among researchers to identify risky and non-economical driving maneuvers has led to the development of driver behavior profiling, i.e., rating/categorizing drivers into different categories based on how they drive. To get an insight into different parameters and methodology adopted by researchers for categorizing drivers into different categories, this paper presents a systematic review of studies on driver behavior profiling. In the present paper, PRISMA approach was adopted to shortlist the most relevant studies for systematic review out of 1231 initial studies, which were extracted using the relevant keywords. The findings from our study suggest that the selection of parameters for profiling the driver will depend on the application of the profiling scheme, type of device used for extracting data, and importance of parameter in rating criteria. Further, the findings suggest that significant improvement in driving behavior can be achieved by providing feedback to the drivers about their driving behavior and by implementing usage-based insurance schemes. It is also suggested that future studies shall focus on using smartphone devices for the collection of driver data as smartphones are nowadays easily accessible to everyone.

“Just One Short Voice Message”—Comparing the Effects of Text- vs. Voice-Based Answering to Text Messages via Smartphone on Young Drivers’ Driving Performances

Despite the well-known distracting effects, many drivers still engage in phone use, especially texting and especially among young drivers, with new emerging messaging modes. The present study aims to examine the effects of different answering modes on driving performance. Twenty-four students (12 females), aged between 19 and 25 years (M = 20.83, SD = 1.53), volunteered for the study. They accomplished the Lane Change Task (LCT) with baseline and dual-task runs in a driving simulator. In dual-task runs, participants answered text messages on a smartphone by voice or text message with varying task complexity. Driving performance was measured by lane deviation (LCT) and subjective measures (NASA-TLX). Across all trials, driving performance deteriorated during dual-task runs compared with the baseline runs, and subjective demand increased. Analysis of dual-task runs showed a benefit for voice-based answering to received text messages that leveled off in the complex task. All in all, the benefits of using voice-based answering in comparison with text-based answering were found regarding driving performance and subjective measures. Nevertheless, this benefit was mostly lost in the complex task, and both the driving performance and the demand measured in the baseline conditions could not be reached.

Effects of Mobile Phone Use on Driving Performance: An Experimental Study of Workload and Traffic Violations

The use of communication technologies, e.g., mobile phones, has increased dramatically in recent years, and their use among drivers has become a great risk to traffic safety. The present study assessed the workload and road ordinary violations, utilizing driving data collected from 39 young participants who underwent a dual-task while driving a simulator, i.e., respond to a call, text on WhatsApp, and check Instagram. Findings confirmed that there are significant differences in the driving performance of young drivers in terms of vehicle control (i.e., lateral distance and hard shoulder line violations) between distracted and non-distracted drivers. Furthermore, the overall workload score of young drivers increases with the use of their mobile phones while driving. The obtained results contribute to a better understanding of the driving performance of distracted young drivers and thus they could be useful for further improvements to traffic safety strategies.

Sleep-deprived car-following: Indicators of rear-end crash potential

Safety assessment among sleep-deprived drivers is a challenging research area with only a few sleep-related studies investigating safety performance during car-following. Therefore, this study aimed to measure the effects of partial sleep deprivation on driver safety during car-following. Fifty healthy male drivers with no prior history of any sleep-related disorders, drove the driving simulator in three conditions of varying sleep duration: a baseline (no sleep deprivation), test session (TS1) after one night of PSD (sleep ≤4.5 h/night) and TS2 after two consecutive nights of PSD. The reduced sleep in PSD sessions was monitored using an Actiwatch. Karolinska Sleepiness Scale was used to indicate loss of alertness among drivers. Each drive included a car-following task to measure longitudinal safety indicators based on speed and headway management: normalized time exposed to critical gap (TECG'), safety critical time headway and speed variability with respect to leading vehicle's speed (SPV). Crash potential index (CPI) was also determined from deceleration rate of drivers during car-following and was found correlated with other indicators. Therefore, to determine the aggregate influence of PSD on safety during car-following, CPI was modelled in terms of TECG, SPV, THW and other covariates. All safety metrics were modelled using generalized mixed effects regression models. The results showed that compared to the baseline drive, critical time headway decreased by 0.65 and 1.08 times whereas speed variability increased by 1.34 and 1.28 times during the TS1 and TS2, respectively, both indicating higher crash risk. However, decrease in TECG' by 64 % and 56 % during TS1 and TS2, respectively indicate compensatory measures to avoid risks due to sleep loss. A fractional regression model of crash potential revealed that low time-headway and higher speed variability and high time exposed to critical gap (TECG') significantly contribute to higher CPI values indicating higher safety risk. Other covariates such as sleep duration, professional driving experience and history of traffic violations were also associated with safety indicators and CPI, however no significant effects of age were noticed in the study. The study findings present the safety indicators sensitive to rear-end crashes specifically under PSD conditions, which can be used in designing collisions avoidance systems and strategies to improve overall traffic safety.

Mobile phone use while driving: Development and validation of knowledge, attitude, and practice survey instruments

INTRODUCTION

Instruments that assess the knowledge, attitude, and practice (KAP) of mobile phone use serve as a primary assessment tool on which mobile phone distracted driving interventions can be designed. The objective of this study is to develop and validate KAP-modeled survey instruments that measure the knowledge of mobile phone hazards while driving (KMPHD), the attitude of drivers towards mobile phone use while driving (AMPUD), and the practice of mobile phone use while driving (PMPUD).

METHOD

This study was a cross-sectional analytical survey conducted in Ibadan, Nigeria. Three instruments were designed to measure KMPHD, AMPUD, and PMPUD. Content validity, item analysis, exploratory factor analysis were conducted, and items were excluded based on the collective results of the analysis. The domains of the constructs and the reliability of the instruments are reported. A confirmatory factor analysis was used to assess the regression weights of each item and the model fit.

RESULTS

From an original list of 13, 12, and 10 items in the KMPHD, AMPUD, and PMPUD instruments, a final list of 7, 5, and 7 items were generated in each survey instrument, respectively. Two domains of the knowledge of hazards and practice of mobile phone use were obtained, and attitude to phone use while driving was a single domain. The reliabilities (Cronbach alpha) of the KMPHD (0.881), AMPUD (0.954), and PMPUD (0.920) were sufficiently high. Also, all items in the three instruments had moderate-to-high regression coefficients, and the model fits of the instruments were good.

CONCLUSIONS

This study provides KAP-modeled survey instruments that can be used to assess a population-based knowledge, attitude, and practice of mobile phone use while driving. Practical Applications: This survey instrument can be used in assessing baseline knowledge, attitude, and practice of phone use while driving and determine the focus and effectiveness of mobile phone-induced distracted driving interventions.

Applications of brain imaging methods in driving behaviour research

Applications of neuroimaging methods have substantially contributed to the scientific understanding of human factors during driving by providing a deeper insight into the neuro-cognitive aspects of driver brain. This has been achieved by conducting simulated (and occasionally, field) driving experiments while collecting driver brain signals of various types. Here, this sector of studies is comprehensively reviewed at both macro and micro scales. At the macro scale, bibliometric aspects of these studies are analysed. At the micro scale, different themes of neuroimaging driving behaviour research are identified and the findings within each theme are synthesised. The surveyed literature has reported on applications of four major brain imaging methods. These include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS) and Magnetoencephalography (MEG), with the first two being the most common methods in this domain. While collecting driver fMRI signal has been particularly instrumental in studying neural correlates of intoxicated driving (e.g. alcohol or cannabis) or distracted driving, the EEG method has been predominantly utilised in relation to the efforts aiming at development of automatic fatigue/drowsiness detection systems, a topic to which the literature on neuro-ergonomics of driving particularly has shown a spike of interest within the last few years. The survey also reveals that topics such as driver brain activity in semi-automated settings or neural activity of drivers with brain injuries or chronic neurological conditions have by contrast been investigated to a very limited extent. Potential topics in driving behaviour research are identified that could benefit from the adoption of neuroimaging methods in future studies. In terms of practicality, while fMRI and MEG experiments have proven rather invasive and technologically challenging for adoption in driving behaviour research, EEG and fNIRS applications have been more diverse. They have even been tested beyond simulated driving settings, in field driving experiments. Advantages and limitations of each of these four neuroimaging methods in the context of driving behaviour experiments are outlined in the paper.

Examining driver distraction in the context of driving speed: An observational study using disruptive technology and naturalistic data

Considering the number of people who have been involved in crashes associated with driver distractions, it is important to understand the characteristics of distracted driving on public roadways. While experiments have indicated that driver distractions are associated with slower driving speeds, the methodologies tend to have limited external validity. Observational studies are often conducted under limited circumstances - be it time or location. Therefore, in order to better understand the nature of driver distractions, the authors investigated the relationships between driving speed, posted speed limits, and phone handling frequency through naturalistic driving data obtained (via disruptive technology) from 8,240 mobile application users on state-maintained highways throughout Texas. As a measure of manual distractions, a phone handling rate (PHR; times/hours driven) was calculated based on phone rotations. Within-subject comparisons were drawn for driving speed and posted speed limits under normal driving conditions and distracted conditions. The analysis revealed a strong negative correlation between PHR and driving speed (r_s = -0.87). Paired t-tests revealed significantly lower driving speeds (p = 0.000 < 0.01, d = -0.48, η = 0.69) and posted speed limits (p = 0.000 < 0.01, d = -0.20, η = 0.42) during phone handling events when compared to driving without phone handling. On average, users drove 3.26 mph slower in distracted conditions than in undistracted conditions. Driving speed had a larger effect size than posted speed limits. The findings were in line with existing theories and experiments as well as other observational studies conducted at fixed locations. Although this research did not reveal causal relations, it is noteworthy that speed reduction with manual distractions was observed under real road conditions. Spatial analyses are recommended to conduct in order to paint a more thorough picture of speed reduction, its relationship to space, and crash risks related to distracted driving.

Factors affecting behavior of mobile phone use while driving and effect of mobile phone use on driving performance

This study analyzed factors affecting behavior of mobile phone use while driving and its effects on driving performance, in terms of speed, lateral position, steer deviation, steer speed, following distance, perception-reaction time, and occurrence of a near miss situation. To investigate the factors affecting behavior of mobile phone use while driving, 1106 respondents from four different regions in Thailand participated in the questionnaire survey study. Theory of Planned Behavior (TPB) was used to explain these factors including two additional extended factors which are risk perception and law enforcement knowledge. The outcome of this part shows that attitude, norm, and law enforcement knowledge significantly affect the intention and behavior of the drivers. Even though approximately 90 percent of drivers realized that using a mobile phone while driving was dangerous and against the law, they have reported that they still use mobile phone while driving. To determine the effect of mobile phone use on driving performance, a 2-lane, straight rural highway, with a leading vehicle and an unexpected "STOP" sign, were simulated in order to examine the driving performance of drivers "without a phone", "talking on a phone call", and "texting a message" conditions. The results found that using mobile phone while driving can reduce speed and following distance, but increase lateral deviation, steer deviation, steer speed, perception-reaction time, and number of near misses leading to higher risks for road crashes.

Characterizing phone usage while driving: Safety impact from road and operational perspectives using factor analysis

Phone use while driving (PUWD) is one of the most crucial factors of distraction related traffic crashes. This study utilized an unsupervised learning method, known as factor analysis, on a unique distracted driving dataset to understand PUWD behavior from the roadway geometry and operational perspectives. The results indicate that the presence of a shoulder, median, and access control on the relatively higher functional class roadways could encourage more PUWD events. The roadways with relatively lower speed limits could have high PUWD event occurrences if the variation in operating speed is high. The results also confirm the correlations between the frequency of PUWD events and the frequency of distracted crashes. This relationship is strong on urban roadways. For rural roadways, this correlation is only strong on the roadways with a large amount of PUWD events. The findings could help transportation agencies to identify suitable countermeasures in reducing distraction related crashes. Moreover, this study provides researchers a new perspective to study PUWD behavior rather than only focus on drivers' personalities.

Analyzing driver behavior under naturalistic driving conditions: A review

For a decade, researchers working in the area of road safety have started exploring the use of driving behavior data for a better understanding of the causes related to road accidents. A review of the literature reveals the excellent potential of naturalistic driving studies carried out by collecting vehicle performance data and driver behavior data during normal, impaired, and safety-critical situations. An in-depth understanding of driver behavior helps analyze and implement pre-crash safety measures - the development of enforcement policies, infrastructure design, and intelligent vehicle safety systems. The present paper attempts to review the naturalistic driving studies that have been undertaken so far. The paper begins with an overview of different methods for collecting unobtrusive driver behavior data during their day to day trip, followed by a discussion of various factors affecting driving behavior and their influence on vehicle performance parameters. The paper also discusses the strategies mentioned in the literature for improving driving behavior using naturalistic driving studies to enhance road safety. Some of the major findings of this review suggest that i) driver behavior is a major cause in the majority of the road accidents ii) drivers generally reduce their speed and increases headway as a compensatory measure to reduce the workload imposed during distracting activity and adverse weather conditions iii) mobile phone has emerged as a potential device for collecting naturalistic driving data and, iv) improvement in driving behavior can be achieved by providing feedback to the drivers about their driving behavior. This can be done by implementing usage-based insurance schemes such as pay as you drive (PAYD), pay how you drive (PHYD), and manage how you drive (MHYD). While a considerable amount of research has been done to analyze driving behavior under naturalistic conditions, some areas which are yet to be explored are highlighted in the present paper.

Detection and response to critical lead vehicle deceleration events with peripheral vision: Glance response times are independent of visual eccentricity

Studies show high correlations between drivers' off-road glance duration or pattern and the frequency of crashes. Understanding drivers' use of peripheral vision to detect and react to threats is essential to modelling driver behavior and, eventually, preventing crashes caused by visual distraction. A between-group experiment with 83 participants was conducted in a high-fidelity driving simulator. Each driver in the experiment was exposed to an unexpected, critical, lead vehicle deceleration, when performing a self-paced, visual-manual, tracking task at different horizontal visual eccentricity angles (12°, 40° and 60°). The effect of visual eccentricity on threat detection, glance and brake response times was analyzed. Contrary to expectations, the driver glance response time was found to be independent of the eccentricity angle of the secondary task. However, the brake response time increased with increasing task eccentricity, when measured from the driver's gaze redirection to the forward roadway. High secondary task eccentricity was also associated with a low threat detection rate and drivers were predisposed to perform frequent on-road check glances while executing the task. These observations indicate that drivers use peripheral vision to collect evidence for braking during off-road glances. The insights will be used in extensions of existing driver models for virtual testing of critical longitudinal situations, to improve the representativeness of the simulation results.

Characteristics of driver cell phone use and their influence on driving performance: A naturalistic driving study

Cell phone use while driving is becoming a key problem in traffic safety as it causes visual-manual distraction and has been linked to increases in crash rates. The use of hand-held phones has been banned in several countries, yet research comparing the safety of hands-free phone use with hand-held has produced inconsistent results. Analysis of specific phone use characteristics could help move this traffic safety problem toward a solution, but few studies have considered the influence on driving performance of specific sets of phone use characteristics in combination with other factors such as driving context and driver demographic characteristics. The main objective of this paper is therefore to identify and analyze these factors to determine their effects on driving performance indicators such as speed changes. To this end, 1244 phone events were collected from 52 drivers from the Shanghai Naturalistic Driving Study (SH-NDS), the first naturalistic driving data in China. Because subtasks within a phone event may cause different visual-manual distractions, a hierarchical coding structure for phone events was built. A total of 5662 eyes-off-road (EOR) cases and 4237 subtasks were extracted. The results showed that on average, the participating drivers used the phone for 6.08 % of their driving time; for 17 % of phone use time, drivers used both hands to manipulate the phone; and their average EOR time was 3.16 s, which is equivalent to driving blindly for 22.82 m at an average speed of 7.22 m/s, or 26 km/hr. The effect of phone use on driving performance, including speed, headway, and lane offset, was analyzed with ANOVA. Results showed that standard deviations (SD) of all three parameters were significantly lower during phone periods than during baseline periods. The speed SD during phone use was 0.95 lower than baseline, the headway SD was 2.48 lower, and the absolute lane offset SD was 685.72 lower than baseline. These lower SDs indicate that drivers operated their vehicles with less fluctuation during phone use. While we were unable to find similar differences in mean speed and mean headway, mean lane offset was also significantly lower with phone use than without. A decision tree was developed to identify the factors influencing driver speed change. Results showed that drivers increased, decreased, or maintained speed depending on the type and duration of phone tasks, the duration of the trip, and the type of roadway. Greater understanding of the specific aspects of phone use and their influences on driver distraction and performance will permit the development of more effective countermeasures, including legislation, enforcement, blocking technologies, social norms education, and sending Do Not Disturb messages to callers, all of which will be required to mitigate continued deaths and injuries from phone use while driving.

A new version of the Behaviour of Young Novice Drivers Scale (BYNDS). Insights from a randomised sample of 700 German young novice drivers

In Germany, every year 66,000 road crashes lead to death or injury of young novice drivers. This makes them twice as likely to be involved in, or cause, vehicle crashes compared to their older and more experienced counterparts. This study aims to address this societal issue by developing a better understanding of the German young driver problem. For this purpose, we created an updated, 55-item strong version of the Behaviour of Young Novice Drivers Scale (BYNDS), originally developed by Scott-Parker et al. in 2010. To make the new version of the BYNDS understandable for German young novice drivers, this research used a new method of translation in combination with extensive pre-testing. As a result, we identified possible threats for response errors such as retrospective formulated questions or double negations. Due the adjustment of the possible sources of error the presented version of the BYNDS is semantically and conceptually different from the original. However, due to the application of the updated version of the BYNDS in a robust sample of 700 participants, this paper presents the first reliable and validated tool to measure novices risky driving behaviour in Germany. Moreover, it offers an updated and extended version of the BYNDS that allows practitioners but also researchers to broaden their understanding of young driver risk.

A Comparative Study of Accident Risk Related to Speech-Based and Handheld Texting during a Sudden Braking Event in Urban Road Environments

The use of mobile phones while driving is a very common phenomenon that has become one of the main causes of traffic accidents. Many studies on the effects of mobile phone use on accident risk have focused on conversation and texting; however, few studies have directly compared the impacts of speech-based texting and handheld texting on accident risk, especially during sudden braking events. This study aims to statistically model and quantify the effects of potential factors on accident risk associated with a sudden braking event in terms of the driving behavior characteristics of young drivers, the behavior of the lead vehicle (LV), and mobile phone distraction tasks (i.e., both speech-based and handheld texting). For this purpose, a total of fifty-five licensed young drivers completed a driving simulator experiment in a Chinese urban road environment under five driving conditions: baseline (no phone use), simple speech-based texting, complex speech-based texting, simple handheld texting, and complex handheld texting. Generalized linear mixed models were developed for the brake reaction time and rear-end accident probability during the sudden braking events. The results showed that handheld texting tasks led to a delayed response to the sudden braking events as compared to the baseline. However, speech-based texting tasks did not slow down the response. Moreover, drivers responded faster when the initial time headway was shorter, when the initial speed was higher, or when the LV deceleration rate was greater. The rear-end accident probability respectively increased by 2.41 and 2.77 times in the presence of simple and complex handheld texting while driving. Surprisingly, the effects of speech-based texting tasks were not significant, but the accident risk increased if drivers drove the vehicle with a shorter initial time headway or a higher LV deceleration rate. In summary, these findings suggest that the effects of mobile phone distraction tasks, driving behavior characteristics, and the behavior of the LV should be taken into consideration when developing algorithms for forward collision warning systems.

Factors determining speed management during distracted driving (WhatsApp messaging)

The objective of this work was to investigate self-regulation behaviours, particularly speed management, under distracted conditions due to WhatsApp use. We also studied the influence of different environments and driver characteristics, introducing visual status as one of them. Seventy-five drivers were evaluated in a simulator study involving two test sessions under baseline and texting conditions. A cluster analysis was used to identify two groups with different visual capacity .Lastly, possible predictors of speed management were studied developing a generalised linear mixed model. Our results show that drivers reduced their speeds in the presence of more demanding driving conditions; while replying to a WhatsApp message, on curved road segments and when parked cars are present. Driving speed also correlated with driver characteristics such as age or dual task experience and human factors such as self-perceived risk. Finally, although there were significant differences in visual capacity between the two groups identified, the model did not identify visual capacity membership as a significant predictor of speed management. This study could provide a better understanding of the mechanisms drivers use when WhatsApp messaging and which environments and driver conditions influence how speed is managed.

Effects of Partial Sleep Deprivation on Braking Response of Drivers in Hazard Scenarios

This study aimed at modeling the Response Time (RT) and Total Braking Time (TBT) of drivers under Partial Sleep Deprivation (PSD). Fifty male participants drove the driving simulator in three experimental conditions: two test sessions and a baseline. The two test sessions were conducted after one and two nights of PSD (sleep = 4.25 ± 0.5 h), respectively. Sleep reduction was recorded using a wrist-worn Actiwatch. The baseline session was conducted after full rest (7-8 h sleep/day for a week). The order of test sessions and baseline was randomized. Each test included two hazard events: 1) pedestrians crossing a road and 2) parked vehicles merging into a roadway. Karolinska Sleepiness Scale (KSS) and Sleepiness Symptoms Questionnaire (SSQ) ratings were also recorded during each drive. Four separate models using parametric accelerated failure time (AFT) with Weibull distribution were developed for RT and TBT in the two events. The models were chosen with clustered heterogeneity to account for intra-group heterogeneity due to repeated measures across tests. In the case of pedestrians crossing event, RT increased by 10% in the first test session and no significant effect observed on RT in the second test session. The overall TBT reduced by 25% and 28% during the first and second PSD sessions, respectively. In the case of vehicle merging event, both response time and total braking time delayed by 44% and 17% respectively after PSD. Other factors such as age, experience, work-rest hours, KSS and SSQ rating, often exercising, approaching speed and braking force were also found significant in the analysis. The parametric AFT approach adopted in this study showed the change in 'response time' and 'total braking time' concerning the type of hazard scenario and partial sleep-deprivation.

Using European naturalistic driving data to assess secondary task engagement when stopped at a red light

PROBLEM

Some evidence exists that drivers choose to engage in secondary tasks when the driving demand is low (e.g., when the car is stopped). While such a behavior might generally be considered as rather safe, it could be argued that the associated diversion of attention away from the road still leads to a reduction of situational awareness, which might increase collision risk once the car regains motion. This is especially relevant for texting, which is associated with considerable eyes-off-the-road-time. Nonetheless, it seems that previous research has barely addressed the actual engagement in secondary tasks while waiting at a red light (as compared to just addressing the tasks' mere prevalence).

OBJECTIVE

The present study investigated secondary task engagement while stopped at a red light using European naturalistic driving data collected through the UDRIVE project. Attention was given to the whole engagement process, including simple prevalence and the tasks' relation (in terms of start/end) to the red light period. Moreover, given that texting is one of the most problematic forms of distraction, it was characterized in more detail regarding glance behavior.

METHOD

Videos of 804 red light episodes from 159 drivers were annotated. Glance behavior was also coded for a sub-set of 75 texting events and their matched baselines. Results, conclusions and practical applications: Drivers engaged in at least one secondary task across almost half of the annotated red light episodes. Drivers who texted while stopped spent most of the time looking at their cell phone. Consequently, drivers might not have been prepared for potentially unexpected events once the light turned green. Further, drivers concluded texting a considerable number of times well after the red light period, which has potential implications for traffic safety.

Alcohol-impaired driving in rural and urban road environments: Effect on speeding behaviour and crash probabilities

Speeding behaviour is known to influence crash risk among alcohol-impaired drivers, but this relationship is scarcely explored. The present study investigated the effects of different Blood Alcohol Concentrations (BAC) levels on driving performance with respect to mean speed of drivers and their ability to avoid crashes during sudden events while driving. Eighty-two drivers participated in the simulation driving experiment at four BAC levels (0%, 0.03 %, 0.05 % and 0.08 % BAC) in rural and urban driving scenarios. Two sudden events (pedestrian crossing and road crossing by parked vehicles (a car and a truck) in the perpendicular direction of traffic) were designed to evaluate the crash probabilities in both the driving scenarios. Generalized linear mixed models were developed to analyse the effects of BAC levels and driver attributes (e.g., age, gender) on mean speeds and crash probabilities. Results for mean speed showed that, compared to sober state, drivers drove 3.5 kmph, 5.76 kmph and 8.78 kmph faster at 0.03 %, 0.05 % and 0.08 % BAC respectively in the rural environment and this increment was 3.6 kmph, 3.69 kmph and 4.13 kmph in the urban environment. The model results for crash probabilities revealed that 0.03 %, 0.05 % and 0.08 % BAC levels increased the crash probabilities by 1.9 times, 2 times and 3 times in case of the rural environment and 2 times, 2.3 times and 3.5 times respectively in the urban driving environment.

Drivers' gap acceptance behaviours at intersections: A driving simulator study to understand the impact of mobile phone visual-manual interactions

Mobile phone use is often considered to be the main source of distraction on the road. Gap acceptance at intersections is a frequent and complex driving task that requires high visual attention from drivers. This study aims to investigate the effect of mobile phone use on the gap acceptance manoeuvre at intersections. Different mobile phone use positions, intersection type, gap size and driver characteristics were considered in the study. A total of 41 licenced drivers drove in an advanced driving simulator in three phone use conditions: baseline (no phone use), using the phone under the steering wheel (covert) and using the phone above the steering wheel (overt). Drivers drove the simulator three times and experienced two intersection types (straight-forward vs. left-turn) and two gap sizes (4 s vs. 7 s) during each drive. A parametric accelerated failure time (AFT) duration model was developed to evaluate the intersection crossing completion time of drivers. The results showed no significant difference of gap acceptance behaviours between the two phone use positions. The distraction task did not affect drivers' gap acceptance decision, but it increased the crossing completion time by over 10 % compared to baseline. Besides, drivers behaved conservatively at intersections while using a mobile phone, such as adopting a larger deceleration, waiting a longer time, and mainting a larger distance to the front vehicle, etc. However, these compensational behaviours were not helpful in improving the intersection traffic situation regarding both safety and efficiency. Intersection type and gap size were both significant factors of gap acceptance decision and crossing completion time. Additionally, younger drivers were more likely to accept a gap than older drivers, and female drivers spent longer time to cross the intersection than males.

Modelling braking behaviour and accident probability of drivers under increasing time pressure conditions

Drivers apply brakes to reduce the speed of a vehicle based on the perceived risk while approaching a certain event. Inadequate or excessive braking can lead to serious consequences. The current study analyses the braking behaviour and accident probability of the drivers under increasing time pressure conditions. Two perilous events (pedestrian crossing and obstacle overtaking) were designed to examine Brake Pedal Force (BPF) and Brake-To-Maximum Brake (BTMB) transition time on a driving simulator. Eighty-five Indian licensed drivers drove the simulator in three different time pressure conditions: No Time Pressure (NTP) (baseline), Low Time Pressure (LTP), and High Time Pressure (HTP). Random parameters Tobit model was used for analysing BPF and duration analysis approach was considered for BTMB analysis. Further, generalized linear mixed model with logit link function was used to study the effect of BPF and BTMB on accident probability of the drivers. The model results showed that gender, driving profession, approach speed, age, driving history, and driving condition significantly affected braking behaviour of the drivers. It was observed that in pedestrian crossing event, LTP and HTP driving conditions resulted in 42.31 % and 87.28 % increase in BPF and 13 % and 23 % reduction in BTMB respectively with respect to NTP driving condition and the corresponding changes were slightly lower in case of obstacle overtaking event. The accident probability model showed that female drivers needed 119.70 % and 186.08 % more BPF and 37.55 % and 58.51 % less BTMB in LTP and HTP driving conditions respectively to have equivalent risk levels as observed for male drivers. Further, non-professional drivers had to increase their BPF by 166.83 % in LTP and 219.93 % in HTP to offset their increased accident risk as compared to professional drivers under time pressure conditions.

Mobile Phone Use in a Car-Following Situation: Impact on Time Headway and Effectiveness of Driver's Rear-End Risk Compensation Behavior via a Driving Simulator Study

Mobile phone use while driving has become one of the leading causes of traffic accidents and poses a significant threat to public health. This study investigated the impact of speech-based texting and handheld texting (two difficulty levels in each task) on car-following performance in terms of time headway and collision avoidance capability; and further examined the relationship between time headway increase strategy and the corresponding accident frequency. Fifty-three participants completed the car-following experiment in a driving simulator. A Generalized Estimating Equation method was applied to develop the linear regression model for time headway and the binary logistic regression model for accident probability. The results of the model for time headway indicated that drivers adopted compensation behavior to offset the increased workload by increasing their time headway by 0.41 and 0.59 s while conducting speech-based texting and handheld texting, respectively. The model results for the rear-end accident probability showed that the accident probability increased by 2.34 and 3.56 times, respectively, during the use of speech-based texting and handheld texting tasks. Additionally, the greater the deceleration of the lead vehicle, the higher the probability of a rear-end accident. Further, the relationship between time headway increase patterns and the corresponding accident frequencies showed that all drivers’ compensation behaviors were different, and only a few drivers increased their time headway by 60% or more, which could completely offset the increased accident risk associated with mobile phone distraction. The findings provide a theoretical reference for the formulation of traffic regulations related to mobile phone use, driver safety education programs, and road safety public awareness campaigns. Moreover, the developed accident risk models may contribute to the development of a driving safety warning system.

Tracing the physiological response and behavioral performance of drivers at different levels of mental workload using driving simulators

INTRODUCTION

The use of mobile phones while driving is known to be a distraction factor and a cause of accidents. The way in which different kinds of conversations affect the behavioral performance of the driver as well as the persistence of the effects are not yet fully understood.

METHOD

In this study, in addition to comparing brain function and behavioral function in dual task conditions in three conversations types, the persistent effects of these types of conversations have also been traced.

RESULTS

The results show that the content of the mobile phone conversation while driving is the cause of the persistent changes in behavioral and brain functions. Increased time headway and lane departure was observed during and up to 5 min after the emotional conversation was finished. EEG bands also varied in different types of conversations. Cognitive conversations caused an increase in the activity of the alpha and beta bands while emotional conversations enhanced the rate of gamma and beta bands. A meaningful correlation was found between changes in the theta and alpha bands and changes in behavioral performance both during the dual task condition and after the conversation was finished, was also observed.

CONCLUSIONS

The content of the conversation is one of the most important factors that increase the risk of road accidents. This can also deteriorate the behavioral performance of the driver and can have persistent effects on behavioral performance and the brain. Practical applications: The findings of this study provide a basis to measure and tracing drivers' cognitive distractions induced by different levels of mental workload through physiological and behavioral performances.

Laboratory analysis of driving behavior and self-perceived physiological impairment at 0.03%, 0.05% and 0.08% blood alcohol concentrations

BACKGROUND

Alcohol-impaired driving is a critical factor leading to traffic crashes worldwide. Since decades, researchers from the developed nations have been investigating the effects of alcohol intoxication on their drivers. However, alcohol effects on the drivers of developing counties remain understudied. The extent of alcohol impairment may vary among the drivers of developed and developing nations due to the differences in drivers' physiology, driving conditions, drinking habits, policy enforcement and driving attitude.

METHODS

This study examined the effects of different Blood Alcohol Concentration (BAC) levels (0%, 0.03%, 0.05%, and 0.08%) on 75 licensed Indian drivers (56 males and 19 females) with the help of a fixed-base driving simulator. Subjects completed driving simulations for all the BAC levels in a driving environment representing heterogeneous traffic conditions. Performance measures included longitudinal and lateral driving control indicators (obtained from simulator) along with the self-reported physiological changes in the drivers (obtained from questionnaire) with varying BAC levels.

RESULTS

Mean speed was the only performance measure significantly affected at 0.03% BAC. At 0.05% BAC, mean speed and mean steering wheel angle were the two significantly impaired measures. At 0.08% BAC, all the driving performance measures showed significant impairment except steering wheel angle variability. Physiological characteristics of drivers deteriorated with rising BAC levels.

CONCLUSIONS

Alcohol significantly impaired the physiology of Indian drivers resulting in impairment in their driving performance. Mean speed was the only performance measure significantly affected by all the BAC levels, making it a suitable parameter to detect the alcohol-impaired state of the drivers.

A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving

Driver distraction is one of the major causes of traffic accidents. In recent years, given the advance in connectivity and social networks, the use of smartphones while driving has become more frequent and a serious problem for safety. Texting, calling, and reading while driving are types of distractions caused by the use of smartphones. In this paper, we propose a non-intrusive technique that uses only data from smartphone sensors and machine learning to automatically distinguish between drivers and passengers while reading a message in a vehicle. We model and evaluate seven cutting-edge machine-learning techniques in different scenarios. The Convolutional Neural Network and Gradient Boosting were the models with the best results in our experiments. Results show accuracy, precision, recall, F1-score, and kappa metrics superior to 0.95.

Gap acceptance behavior at unsignalized intersections: Effects of using a phone and a music player while driving

Objectives: The present study is an attempt to analyze and compare the distraction effects caused by the use of a phone and a music player at unsignalized intersections. Method: Eighty-eight participants performed simulated driving experiments where they faced a sequence of gaps in the major road traffic at 2 unsignalized intersections. In this process, their driving behavior was evaluated in terms of gap acceptance probability, accepted lag, and maneuver completion time. These parameters were modeled with a generalized estimating equation (GEE) method by considering distraction, demographic factors, driving history, maneuver types, and driving attributes in the approach and completion zones as independent variables. Results: The results showed that gap acceptance probability decreased by 46% during the conversation task, whereas it increased by 66% during the music player task. Lower gap acceptance could be a compensatory behavior adopted by drivers during the conversation task, whereas no such measure was adapted during the music player task. The results indicate that a higher approach speed during the music player task might have led to increased gap acceptance. Further, though the effect of distraction on the accepted lag was not evident, the completion time was reduced during the conversation task. Conclusions: Overall, the results suggest that drivers are more likely to adopt a compensatory measure in complex driving situations only if they perceive a high risk. Hence, drivers are exposed to a greater risk while operating a music player, because this is not perceived as risky behavior.

"Mate! I'm running 10 min late": An investigation into the self-regulation of mobile phone tasks while driving

The adaptive behaviour of mobile phone distracted drivers has been a topic of much discussion in the recent literature, but the mechanisms of behavioural adaptation are still unclear. This study investigated the influence of driving demands, secondary task characteristics, and personal characteristics on behavioural adaptation of mobile phone distracted drivers. In particular, distracted drivers' self-regulation at strategic, tactical, and operational levels was investigated through a driving simulator experiment. In a high-fidelity driving simulator, participants driving through various driving conditions (e.g. interactions with pedestrian crossings, signalized intersections, merging ramps, roundabouts, etc.) needed to decide where and how to perform the following four mobile phone tasks: (a) ring a doctor and cancel an appointment, (b) text a friend and tell him/her that the participant will be arriving 10 min late, (c) share the doctor's phone number with a friend, and (d) take a 'selfie'. At a strategic level, the decision to pull over was modelled as a function of self-reported personal/attitudinal characteristics with a logistic regression model. Similarly, tactical self-regulation (decision to engage in a task while driving in a specific situation) and operational self-regulation (decision to temporarily stop the mobile phone task) were modelled as a function of driving demands and personal/attitudinal characteristics using a random-effects logistic regression model, which accounts for correlations resulting from multiple observations of a driver. Results suggest that tactical self-regulation is more common among distracted drivers followed by operational and strategic self-regulation. Personal beliefs regarding how safe it is to use the mobile phone for texting/browsing while driving were predictors of self-regulation for all levels. Drivers were observed to use the mobile phone more when the driving demands are low, e.g. while stopped at an intersection. This research suggests that distracted drivers engage in various levels of self-regulation, and future research could be focused on further theoretical refinement and development of technology-based interventions.

Effects of Visual and Acoustic Distraction on Driving Behavior and EEG in Young and Older Car Drivers: A Driving Simulation Study

Driving safety depends on the drivers' attentional focus on the driving task. Especially in complex situations, distraction due to secondary stimuli can impair driving performance. The inhibition of distractors or inadequate prepotent responses to irrelevant stimuli requires cognitive control, which is assumed to be reduced with increasing age. The present EEG study investigated the effects of secondary acoustic and visual stimuli on driving performance of younger and older car drivers in a driving simulator task. The participants had to respond to brake lights of a preceding car under different distraction conditions and with varying task difficulties. Overall, the anticipation of high demanding tasks affected braking response behavior in young and especially in older adults, who showed reduced cognitive control to task-relevant braking stimuli, as reflected by a smaller P3b. In a more easy (perception only) task, simultaneously presented acoustic stimuli accelerated braking response times (RTs) in young and older adults, which was associated with a pronounced P2. In contrast, secondary visual stimuli increased braking RTs in older adults, associated with a reduced P3b. In a more difficult (discrimination) task, braking response behavior was impaired by the presence of secondary acoustic and visual stimuli in young and older drivers. Braking RT increased (and the P3b decreased), especially when the responses to the secondary stimuli had to be suppressed. This negative effect was more pronounced with visual secondary stimuli, and especially so in the older group. In sum, the results suggest an impaired resistance to distractor interference and a reduced inhibition of prepotent responses in older drivers. This was most pronounced when the processing of task-relevant and irrelevant stimuli engage the same mental resources, for example, by sharing the same stimulus modality.

Effects of texting on accident risk during a sudden hazardous event: Analysis of predetection and postdetection phases

OBJECTIVE

The present study aims to quantify the effects of texting and driver behavior on the accident risk associated with a sudden event. Further, the study attempts to compare the effects of driving behavior of inexperienced young and professional drivers on risk during predetection and postdetection phases of the event.

METHODS

Forty-nine drivers from 2 categories-inexperienced young drivers and experienced professional drivers-took part in simulated experiments. The participants drove in a free-flow road environment under 3 driving conditions: no distraction (baseline) and writing short and long texts while driving. The participants were exposed to a sudden hazardous event during each drive. Accident probability during the sudden event was modeled with a generalized linear mixed model (with a logit link function).

RESULTS

As expected, both texting tasks increased accident risk, and the risk was much higher for inexperienced young drivers than for professional drivers. Time lapsed in reducing speed increased the odds for accident risk significantly. A comparative analysis of the driver categories showed that impairment in driving behavior due to the texting tasks was similar for both groups during the predetection phase. However, the risk associated with the texting tasks was higher for young drivers during the postdetection phase. A possible reason could be that young drivers had 65% and 75% higher approach speeds (than the professional drivers) during the short and long text tasks, respectively.

CONCLUSIONS

The results provide statistical evidence that increased speed is expressed as increased risk-taking behavior among young drivers, which subsequently is the main reason for their higher accident risk during texting tasks. Moreover, the results confirm that professional drivers are not able to mitigate the increased accident risk associated with texting tasks due to late detection of the event during the tasks.

Should I Text or Call Here? A Situation-Based Analysis of Drivers' Perceived Likelihood of Engaging in Mobile Phone Multitasking

This study investigated how situational characteristics typically encountered in the transport system influence drivers' perceived likelihood of engaging in mobile phone multitasking. The impacts of mobile phone tasks, perceived environmental complexity/risk, and drivers' individual differences were evaluated as relevant individual predictors within the behavioral adaptation framework. An innovative questionnaire, which includes randomized textual and visual scenarios, was administered to collect data from a sample of 447 drivers in South East Queensland-Australia (66% females; n = 296). The likelihood of engaging in a mobile phone task across various scenarios was modeled by a random parameters ordered probit model. Results indicated that drivers who are female, are frequent users of phones for texting/answering calls, have less favorable attitudes towards safety, and are highly disinhibited were more likely to report stronger intentions of engaging in mobile phone multitasking. However, more years with a valid driving license, self-efficacy toward self-regulation in demanding traffic conditions and police enforcement, texting tasks, and demanding traffic conditions were negatively related to self-reported likelihood of mobile phone multitasking. The unobserved heterogeneity warned of riskier groups among female drivers and participants who need a lot of convincing to believe that multitasking while driving is dangerous. This research concludes that behavioral adaptation theory is a robust framework explaining self-regulation of distracted drivers.

Driving behaviour while self-regulating mobile phone interactions: A human-machine system approach

Mobile phone distracted driving is a recurrent issue in road safety worldwide. Recent research on driving behaviour of distracted drivers suggests that in certain circumstances drivers seem to assume safer behaviours while using a mobile phone. Despite a high volume of research on this topic, self-regulation by mobile phone distracted drivers is not well understood as many driving simulator experiments are designed to impose an equal level of distraction to participants being tested for their driving performance. The aim of this research was to investigate the relationship between self-regulatory secondary task performance and driving. By a driving simulator experiment in which participants were allowed to perform their secondary tasks whenever they feel appropriate, the driving performance of 35 drivers aged 18-29 years was observed under three phone conditions including non-distraction (no phone use), hands-free interactions and visual-manual interactions in the CARRS-Q advanced driving simulator. Drivers' longitudinal and lateral vehicle control observed across various road traffic conditions were then modelled by Generalized Estimation Equations (GEE) with exchangeable correlation structure accounting for heterogeneity resulting from multiple observations from the same driver. Results show that the extent of engagement in the secondary task influence both longitudinal and lateral control of vehicles. Drivers who engaged in a large number of hands-free interactions are found to select lower driving speed. In contrast, longer visual-manual interactions are found to result in higher driving speed among drivers self-regulating their secondary task. Among the road traffic conditions, drivers distracted by their self-regulated secondary tasks are found to select lower speeds along the s-curve compared to straight and motorway segments. In summary, the applied human-machine system approach suggests that road traffic demands play a vital role in both secondary task management and driving performance.