Comparison of concurrent cognitive load measures during n-back tasks.

The cognitive load experienced by humans is an important factor affecting their performance. Cognitive overload or underload may result in suboptimal human performance and may compromise safety in emerging human-in-the-loop systems. In driving, cognitive overload, due to various secondary tasks, such as texting, results in driver distraction. On the other hand, cognitive underload may result in fatigue. In automated manufacturing systems, a distracted operator may be prone to muscle injuries. Similar outcomes are possible in many other fields of human performance such as aviation, healthcare, and learning environments. The challenge with such human-centred applications is that the cognitive load is not directly measurable. Only the change in cognitive load is measured indirectly through various physiological, behavioural, performance-based and subjective means. A method to objectively assess the performance of such diverse measures of cognitive load is lacking in the literature. In this paper, a performance metric for the comparison of different measures to determine the cognitive workload is proposed in terms of the signal-to-noise ratio. Using this performance metric, several measures of cognitive load, that fall under the four broad groups were compared on the same scale for their ability to measure changes in cognitive load. Using the proposed metrics, the cognitive load measures were compared based on data collected from 28 participants while they underwent n-back tasks of varying difficulty. The results show that the proposed performance evaluation method can be useful to individually assess different measures of cognitive load.

Integrating visual large language model and reasoning chain for driver behavior analysis and risk assessment.

Driver behavior is a critical factor in driving safety, making the development of sophisticated distraction classification methods essential. Our study presents a Distracted Driving Classification (DDC) approach utilizing a visual Large Language Model (LLM), named the Distracted Driving Language Model (DDLM). The DDLM introduces whole-body human pose estimation to isolate and analyze key postural features-head, right hand, and left hand-for precise behavior classification and better interpretability. Recognizing the inherent limitations of LLMs, particularly their lack of logical reasoning abilities, we have integrated a reasoning chain framework within the DDLM, allowing it to generate clear, reasoned explanations for its assessments. Tailored specifically with relevant data, the DDLM demonstrates enhanced performance, providing detailed, context-aware evaluations of driver behaviors and corresponding risk levels. Notably outperforming standard models in both zero-shot and few-shot learning scenarios, as evidenced by tests on the 100-Driver dataset, the DDLM stands out as an advanced tool that promises significant contributions to driving safety by accurately detecting and analyzing driving distractions.

Unraveling the differences in distracted driving injury severities in passenger car, sport utility vehicle, pickup truck, and minivan crashes.

Distracted driving poses a significant risk on the roadway users, with the level of distraction and crash outcomes varying depending on the type of vehicle. Drivers of passenger cars, sport utility vehicles (SUVs), pickup trucks, minivans experience distinct levels of distraction, leading to potential crashes. This study investigates into the severity of driver injuries resulting from distracted driving in these vehicle categories, shedding light on the variations in single-vehicle crashes. Focusing on single-vehicle crashes in Florida during 2019, involving passenger cars, SUVs, pickup trucks, and minivans caused by distracted driving, the study examines various distractions such as, electronic communication devices (cell phones), electronic devices (navigation systems, music players), internal and external disturbances, texting, and inattentive driving. To analyze the severity of injuries resulting from distracted driving in passenger cars, SUVs, pickup trucks, and minivans, the study employs random parameter multinomial logit models with heterogeneity in means and variances. The model estimates highlight thirty-five significant factors influencing the severity of driver injuries resulting from distracted driving. Notably, the impact of these factors varies significantly depending on the vehicle type (i.e., passenger cars, SUVs, pickup trucks, and minivans). While many explanatory variables are specific to each vehicle type, only one factor (restraint belt usage) is common across all vehicle types, with varying magnitudes in injury outcomes. The likelihood ratio tests indicate that injury severity must be analyzed and modeled separately for passenger cars, SUVs, pickup trucks, and minivans. Vehicle characteristics play a crucial role in driver distraction and crash outcomes. Analyzing a year of crash data, categorized by four vehicle types, has provided valuable insights into distracted driving patterns in passenger cars, SUVs, pickup trucks, and minivans, influencing potential prevention strategies. To combat against distracted driving effectively, priority should be given to driver education and training, roadway design, vehicle technology, enforcement, and automobile insurance. The automobile industry, especially for passenger cars, SUVs, pickup trucks, and minivans, should consider implementing advanced in-vehicle technologies tailored to the specific characteristics of each vehicle type (e.g., advanced driver assistance systems (ADAS)) to proactively prevent driver distraction. These proactive measures will contribute significantly to enhancing road safety and reducing the risks associated with distracted driving.

Use of smartphone apps while driving: Variations on driving performances and perceived risks.

Distracted driving increases the crash frequencies on the road and subsequently leads to fatalities involved with crashes. As technology has evolved, drivers are continuously exposed to newer technology in their vehicles and applications in their phones, which has led to technology representing one of the main secondary tasks that distract drivers on the road. The impact of technology-involved distraction appears to be different by the type of distraction since a secondary task that can be exceedingly distracting to the driver causes more reckless and risky driving. Moreover, the impact of distracted driving may differ by roadway geometries since distracted drivers' performance may vary depending on how actively they interact with other vehicles or surrounding environments. This study aims to understand the impacts of smartphone application distractions, in particular social media activities (e.g., video, feed, message), on different road geometries using a mixed-method analysis consisting of a survey, a driving simulator experiment, and individual interview. Results from the interview and simulation experiments show that most social media activities cause unsafe lane changes regardless of road geometry. Among various social-media activities, watching reels (videos) represent an unintentional but deeper level of engagement that consequently causes a driver to deviate in their lane, make unintentional lane changes, suddenly change their speed and acceleration, and headway. The interview also revealed varying levels of risk perception about distracted driving, in particular the lower level of risk perception in using GPS and music applications. This study concludes that the distractions caused by smartphone applications and social media activities combined with lower awareness and risk perception could significantly elevate the crash risks.

Exploring the Behaviour Change Wheel and the Theoretical Domains Framework in interventions for mobile phone driver distraction: A scoping review.

Mobile phone use while driving remains a significant traffic safety concern. Although numerous interventions have been developed to address it, there is a gap in the synthesis of relevant information through a comprehensive behaviour change lens. This scoping review uses the Behaviour Change Wheel (BCW) and the Theoretical Domains Framework (TDF) to examine the literature to (a) identify behavioural constructs targeted in interventions for mobile phone use while driving, (b) determine if the intervention success varied by sociodemographic group (e.g., age, gender, driving experience), and (c) map interventions to TDF domains to highlight areas for future research. Following the PRISMA extension for scoping reviews, we searched seven databases and identified 5,202 articles. After screening, 50 articles detailing 56 studies met the following inclusion criteria: (a) intervention studies, (b) providing details on methods and results, (c) written in English, and (d) targeting any driver behaviour related to mobile phone use while driving with a bottom-up approach, using not regulation or law enforcement, but individuals' psychological processes, such as cognitive, behavioural, and emotional. Findings show that most interventions targeted young drivers and were typically effective. Except for a few studies, the effectiveness of interventions targeting different sociodemographic groups either remained untested or revealed nonsignificant differences. This finding points to a gap in the literature, indicating a need for further investigation into the efficacy of interventions for different groups, and for tailoring and testing them accordingly. The interventions also often targeted multiple TDF domains, complicating the interpretation of the relative efficacy of specific domains. Most frequently targeted domains included beliefs and consequences, emotions, knowledge, social influence, social/professional role and identity, and behavioural regulation. Physical skills and optimism domains were not targeted in any intervention. Further, almost all interventions addressed deliberate engagement in mobile phone distractions, while the automatic and fast processes involved in such behaviours were often overlooked. Mobile phone distractions are in part habitual behaviours, yet the existing mitigation efforts mostly assumed intentional engagement. More focus on the habitual nature of mobile phone distractions is needed.

Driving errors as a function of listening to music and FM radio: A simulator study.

OBJECTIVES: Driving is a dynamic activity that takes place in a constantly changing environment, carrying safety implications not only for the driver but also for other road users. Despite the potentially life-threatening consequences of incorrect driving behavior, drivers often engage in activities unrelated to driving. This study aims to investigate the frequency and types of errors committed by drivers when they are distracted compared to when they are not distracted. METHODS: A total of 64 young male participants volunteered for the study, completing four driving trials in a driving simulator. The trials consisted of different distraction conditions: listening to researcher-selected music, driver-selected music, FM radio conversation, and driving without any auditory distractions. The simulated driving scenario resembled a semi-urban environment, with a track length of 12 km. RESULTS: The findings of the study indicate that drivers are more prone to making errors when engaged in FM radio conversations compared to listening to music. Additionally, errors related to speeding were found to be more prevalent across all experimental conditions. CONCLUSIONS: These results emphasize the significance of reducing distractions while driving to improve road safety. The findings add to our understanding of the particular distractions that carry higher risks and underscore the necessity for focused interventions to reduce driver errors, especially related to FM radio conversations. Future research can delve into additional factors that contribute to driving errors and develop effective strategies to promote safer driving practices.

Driver behavior while using Level 2 vehicle automation: a hybrid naturalistic study.

Vehicle automation is becoming more prevalent. Understanding how drivers use this technology and its safety implications is crucial. In a 6-8 week naturalistic study, we leveraged a hybrid naturalistic driving research design to evaluate driver behavior with Level 2 vehicle automation, incorporating unique naturalistic and experimental control conditions. Our investigation covered four main areas: automation usage, system warnings, driving demand, and driver arousal, as well as secondary task engagement. While on the interstate, drivers were advised to engage Level 2 automation whenever they deemed it safe, and they complied by using it over 70% of the time. Interestingly, the frequency of system warnings increased with prolonged use, suggesting an evolving relationship between drivers and the automation features. Our data also revealed that drivers were discerning in their use of automation, opting for manual control under high driving demand conditions. Contrary to common safety concerns, our data indicated no significant rise in driver fatigue or fidgeting when using automation, compared to a control condition. Additionally, observed patterns of engagement in secondary tasks like radio listening and text messaging challenge existing assumptions about automation leading to dangerous driver distraction. Overall, our findings provide new insights into the conditions under which drivers opt to use automation and reveal a nuanced behavioral profile that emerges when automation is in use.

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review.

Driver behaviour monitoring is a broad area of research, with a variety of methods and approaches. Distraction from the use of electronic devices, such as smartphones for texting or talking on the phone, is one of the leading causes of vehicle accidents. With the increasing number of sensors available in vehicles, there is an abundance of data available to monitor driver behaviour, but it has only been available to vehicle manufacturers and, to a limited extent, through proprietary solutions. Recently, research and practice have shifted the paradigm to the use of smartphones for driver monitoring and have fuelled efforts to support driving safety. This systematic review paper extends a preliminary, previously carried out author-centric literature review on smartphone-based driver monitoring approaches using snowballing search methods to illustrate the opportunities in using smartphones for driver distraction detection. Specifically, the paper reviews smartphone-based approaches to distracted driving behaviour detection, the smartphone sensors and detection methods applied, and the results obtained.

Prevalence of distracted driving by driver characteristics in the United States.

INTRODUCTION: Distracted driving is a long-standing traffic safety concern, though common secondary tasks continually evolve. The goal of this study was to measure the prevalence of self-reported distracted driving behaviors, including activities made possible in recent years by smartphones. METHODS: We conducted a nationwide survey of 2,013 U.S. licensed drivers (ages 16 +). We created four aggregate distraction categories from 18 individual secondary tasks to estimate the proportion of drivers study-wide and by demographic characteristics belonging to each category, defined as those who regularly did (during most or all drives in the previous 30 days) one or more secondary task within each category. Logistic regression estimated the adjusted odds of drivers belonging to each aggregate distraction category by demographic characteristics. RESULTS: Sixty-five percent of drivers reported doing at least one of the 18 secondary tasks regularly, and half did at least one device-based task regularly in the past 30 days. Non-device task prevalence trended downward with age, while device-based task prevalence was consistent among younger drivers before declining beginning with age 35. Males (OR, 1.53; 95% CI, 1.16, 2.02), parents of children ages 18 and younger (OR, 1.47; 95% CI, 1.10, 1.96), and participants who drive in the gig economy (OR, 3.85; 95% CI, 2.73, 5.43) had higher adjusted odds of engaging in "modern" device-based distractions enabled by smartphones (e.g., making video calls, watching videos, using social media) than other drivers. Many drivers are using hands-free capabilities when available for tasks, but for some tasks more than others. CONCLUSIONS: Regular distracted driving is widespread with most behavior concentrated among drivers younger than age 50, though no age group or other demographic studied abstains. PRACTICAL APPLICATIONS: Stakeholders can use these findings to develop countermeasures for distracted driving by targeting specific secondary tasks and the demographics most likely to report regularly doing them.

Real-time monitoring of driver distraction: State-of-the-art and future insights.

Driver distraction and inattention have been found to be major contributors to a large number of serious road crashes. It is evident that distraction reduces to a great extent driver perception levels as well as their decision making capability and the ability of drivers to control the vehicle. An effective way to mitigate the effects of distraction on crash probability, would be through monitoring the mental state of drivers or their driving behaviour and alerting them when they are in a distracted state. Towards that end, in recent years, several inexpensive and effective detection systems have been developed in order to cope with driver inattention. This study endeavours to critically review and assess the state-of-the-art systems and platforms measuring driver distraction or inattention. A thorough literature review was carried out in order to compare and contrast technologies that can be used to detect, monitor or measure driver's distraction or inattention. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The results indicated that in most of the identified studies, driver distraction was measured with respect to its impact to driver behaviour. Real-time eye tracking systems, cardiac sensors on steering wheels, smartphone applications and cameras were found to be the most frequent devices to monitor and detect driver distraction. On the other hand, less frequent and effective approaches included electrodes, hand magnetic rings and glasses.

The effect of mobile phone ringtone on visual recognition during driving: Evidence from laboratory and real-scene eye movement experiments.

OBJECTIVE: To determine the effect of mobile phone ringtones on visual recognition during driving, laboratory and real-scene eye movement experiments were conducted with simulated and real driving tasks, respectively. Competition for visual attention during driving increases with the integration of sounds, which is related to driving safety. METHOD: We manipulated the physical (long exposure duration vs. short exposure duration) and psychological (self-related vs. non-self-related) properties of mobile phone ringtones presented to drivers. Estimates were based on linear mixed models (LMMs) and generalized linear mixed models (GLMMs). RESULTS: Self-related ringtones had a greater influence on driving attention than non-self-related ones, and the interaction between exposure duration and self-relatedness was significant. Furthermore, the impact of the mobile phone ringtone occurred in real time after the ringtone stopped. CONCLUSION: These results highlight the importance of considering the impact of ringtones on driving performance and demonstrate that ringtone properties (exposure duration and self-relatedness) can affect cognitive processes.

Evaluation of intersection crashes using naturalistic driving data through the lens of future I-ADAS.

OBJECTIVE: Intersection advanced driver assistance systems (I-ADAS) with the capability to detect possible collisions and perform evasive braking have the potential to reduce the number of intersection crashes. However, these systems will encounter many challenges caused by the complexity of real-world driving conditions. The purpose of this study is to use real-world naturalistic driving data to conduct an initial exploration of the potential challenges for future I-ADAS in straight crossing path (SCP), left turn across path/lateral direction (LTAP/LD), and left turn across path/opposite direction (LTAP/OD) crash configurations. METHODS: Intersection crashes were selected from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study. The SHRP 2 dataset includes front-facing, driver-facing, rear-facing, and a hands/feet-facing video and vehicle speed, steering, accelerator, and brake time-series data. This data was reviewed to understand driver sightline obstructions, driver distractions, and initiation of driver responses. The estimated time to collision (TTC) from the precipitating event, defined as when either vehicle entered the intersection without the right-of-way, was computed based on the distance to the impact point divided by the current velocity of the subject vehicle. RESULTS: The median impact speed was 18.0 km/h for SCP and LTAP/LD crashes and 16.1 km/h for LTAP/OD crashes. The median TTC from the precipitating event was 1.35 s for SCP and LTAP/LD crashes and 1.44 s for LTAP/OD crashes. For SCP crashes, the three main sightline obstruction scenarios were slower vehicles traveling in the same direction waiting to turn right, vehicles in the closer crossing lane, and a parked truck. For LTAP/OD crashes, the sightline obstruction was often oncoming vehicles in a closer lane blocking the view of another vehicle. CONCLUSION: Sightline obstructions could present a challenge for future I-ADAS to activate in SCP, LTAP/LD, and LTAP/OD crashes. This study utilized naturalistic driving data to complete a comprehensive analysis of intersection crashes, including driver distractions, evasive maneuvers, and sightline obstructions that can assist in the development of I-ADAS. This analysis is not possible with police-reported crash data only, which does not contain necessary details on the driver and surrounding environment.

Targeting young driver emotions can reduce their cellphone distractions.

OBJECTIVE: This study sought to evaluate the relationship between young drivers' intention to engage in cellphone distractions while driving and their emotions towards the associated risks. First, we assessed whether the emotions of guilt, shame, and fear are associated with young drivers' intention to engage in cellphone distractions, through an extended Theory of Planned Behavior (TPB) model. Second, we evaluated whether road signs that may evoke these negative emotions reduce cellphone use intentions among young drivers. METHODS: An online survey was conducted with young drivers (18 to 25 years old) from Ontario, Canada. 403 responses were collected, of which, 99 responses were used to evaluate the first objective and all 403 responses were used to evaluate the second objective. RESULTS: Anticipating feelings of guilt, shame, and fear negatively predicted the intention to engage in cellphone distractions, above and beyond the standard TPB constructs (i.e., attitudes, subjective norms, and perceived behavioral control). When prompted with potentially emotion-evoking road signs (children crossing, my mom/dad works here), an increase in anticipated feelings of these emotions corresponded with lower intention to engage in cellphone distractions. CONCLUSION: Countermeasures that target young driver emotions toward distracted driving risks may be effective in reducing their distraction engagement. Future studies in more controlled environments can investigate causal relationships between emotions and distracted driving among young drivers.

Cell phone-related driver distraction: Habits predict behavior over and above the theory of planned behavior variables.

INTRODUCTION: Habits have often been overlooked in studies investigating cell phone-related driver distractions. This paper examines the association between habits and cell phone-related driver distractions within a mediation model based on the Theory of Planned Behavior (TPB). Additionally, it explores potential differences in behaviors across urban and rural driving environments and between males and females. METHOD: We conducted an online survey in China with 1,016 respondents, measuring attitudes, subjective norms, perceived behavioral control, self-reported behavior, and habits associated with cell phone use while driving. RESULTS: Data was analyzed using a two-stage structural equation modeling approach. Results indicate that the measurement model provided a good fit to the data and was invariant across urban and rural driving environments, as well as across genders. The latent path model investigating mediation also demonstrated a good fit and revealed that TPB variables (attitudes, subjective norms, and perceived behavioral control) partially mediated the relationship between cell phone-related habits and cell phone use while driving. The structural model was invariant across driving environments but not across genders, for which the extent of the differences were limited. Moreover, habits were strongly associated with subjective norms and perceived behavioral control, emerging as the strongest predictor of cell phone-related distractions. CONCLUSIONS: The findings suggest that habits should be considered in research on phone-related distracted driving behaviors and in the development of intervention designs.

Evaluating the Impact of a Multifaceted Distracted Driving Prevention Program.

OBJECTIVE: The aim of this study is to evaluate undergraduate college students' attitude changes toward distracted driving after participating in a multifaceted distracted driving prevention program. METHODS: This study used a quasi-experimental, pre- post-test design. Participants were undergraduate college students who were aged 18 or older and had a valid driver's license. The Questionnaire Assessing Distracted Driving was used to measure participants' attitudes and behaviors. All participants completed the entire Questionnaire Assessing Distracted Driving survey and then participated in the distracted driving prevention program that consisted of a 10-minute narrated recorded PowerPoint lecture followed by a distracted driving simulation. Descriptive statistics were calculated to describe the study sample. The Questionnaire Assessing Distracted Driving data were analyzed to ascertain any statistically significant changes in responses from pre- to postintervention. RESULTS: From pre- to post-test, there were statistically significant increases in the number of participants who reported they would tell friends to stop texting and driving if they were a passenger, refrain from texting while driving, and wait until reaching home before retrieving their cell phones from the floor of the vehicle. Participants perceived a greater threat from drivers talking on phones or texting/emailing from pre- to post-test. Moreover, attitudes toward talking on a handheld device, talking on a hands-free phone, and texting/emailing became more negative from pre- to post-test. CONCLUSION: The intervention helped promote negative attitudes toward distracted driving in a sample of college students immediately after participating in a distracted driving prevention program.

Driver distraction and in-vehicle interventions: A driving simulator study on visual attention and driving performance.

Driving simulator studies are popular means to investigate driving behaviour in a controlled environment and test safety-critical events that would otherwise not be possible in real-world driving conditions. While several factors affect driving performance, driving distraction has been emphasised as a safety-critical issue across the globe. In this context, this study explores the impact of distraction imposed by mobile phone usage, i.e., writing and reading text messages, on driver behaviour. As part of the greater i-DREAMS project, this study uses a car driving simulator experimental design in Germany to investigate driver behaviour under various conditions: (I) monitoring scenario representing normal driving conditions, (II) intervention scenario in which drivers receive fixed timing in-vehicle intervention in case of unsafe driving manoeuvres, and (III) distraction scenario in which drivers receive in-vehicle interventions based on task completion capability, where mobile phone distraction is imposed. Besides, eye-tracking glasses are used to further explore drivers' attention allocation and eye movement behaviour. This research focuses on driver response to risky traffic events (i.e., potential pedestrian collisions, and tailgating) and the impact of distraction on driving performance, by analysing a set of eye movement and driving performance measures of 58 participants. The results reveal a significant change in drivers' gaze patterns during the distraction drives with significantly higher gaze points towards the i-DREAMS intervention display (the utilised advanced driver assistance systems in this study). The overall statistical analysis of driving performance measures suggests nearly similar impacts on driver behaviour during distraction drives; a higher deviation of lateral positioning was noted irrespective of the event risk levels and lower longitudinal acceleration rates were observed for pedestrian collisions and non-critical events during distracted driving.

Injury severity of single-vehicle large-truck crashes: accounting for heterogeneity.

This research examines the injury severity of single-vehicle large-truck crashes in Florida while exploring the role of heterogeneity. A random parameter ordered logit (RPOL) model was applied to 27,505 single-vehicle large-truck crashes from 2007 to 2016 in Florida, and the contributing factors were identified. Random parameters and interaction effects were introduced to the model to determine the heterogeneity and its potential sources. The results suggested that driving speed of 76-120 mph and defective tires were the most influential factors in crash injury severity, increasing the probability of severe crashes. Regarding truckers' attributes, asleep or fatigued conditions and driving under the influence were correlated with a higher possibility of severe crashes. Interestingly, the results showed that truckers from outside the state of Florida were less likely to cause severe single-vehicle large-truck crashes compared to their Floridian counterparts. Y-intersections were also found as a high-risk location for single-vehicle large-truck crashes, leading to more severe outcomes. Regarding heterogeneity, the results indicated that the impacts of driving speed (26-50 mph) and light condition (dark - not lighted) significantly varied among the observations, and these variations could be attributed to driver action, vision obstruction, driver distraction, roadway type and roadway alignment.

An XGBoost approach to detect driver visual distraction based on vehicle dynamics.

OBJECTIVES: Distracted driving such as reading phone messages during driving is risky, as it increases the probability of severe crashes. This study proposes an XGBoost model for visual distraction detection based on vehicle dynamics data from a driving simulation study. METHODS: A simulated driving experiment involving thirty-six drivers was launched. We obtained the vehicle dynamics parameters required for the model using the time window and fast Fourier transform methods, totaling 26 items. Meanwhile, the effects of varied time window sizes (1-7 s) and amount of input indications on model performance were studied. RESULTS: By conducting a comparative analysis, it has been determined that the ideal time window size is 5 s. Additionally, the optimal number of input indicators was found to be 23. The XGBoost model for distinguishing distractions achieved an accuracy rate of 85.68%, a precision rate of 85.83%, a recall rate of 83.85%, an F1 score of 84.82%, and an AUC value of 0.9319, which were higher than SVM and RF. The gain-based feature rank demonstrated that the standard deviation of vehicle sideslip rate and the mean amplitude of the 0-1 Hz spectrum component of the steering wheel angle were more crucial than other features. CONCLUSIONS: The research results indicate that the steering wheel angle and vehicle sideslip angle may be more conducive to identifying distractions. This XGBoost model could potentially be applied in advanced driving assistant systems (ADAS) to warn driver and reduce cellphone involved distracted driving.

Drive in the Moment: An evaluation of a web-based tool designed to reduce smartphone use among young drivers.

OBJECTIVE: Young drivers are overrepresented in road traffic crashes and fatalities. Distracted driving, including use of a smartphone while driving (SWD), is a major risk factor for crashes for this age group. We evaluated a web-based tool (Drive in the Moment or DITM) designed to reduce SWD among young drivers. METHODS: A pretest-posttest experimental design with a follow-up was used to assess the efficacy of the DITM intervention on SWD intentions and behaviors, and perceived risk (of having a crash and of being apprehended by the police) associated with SWD. One hundred and eighty young drivers (aged 17-25 years old) were randomly assigned to either the DITM intervention or a control group where participants completed an unrelated activity. Self-reported measures of SWD and perceptions of risk were obtained pre-intervention, immediately post-intervention and at a follow-up 25 days after the intervention. RESULTS: Participants who engaged with the DITM showed a significant reduction in the number of times they used their SWD at follow-up compared to their pre-intervention scores. Future intentions to SWD were also reduced from pre-intervention to post-intervention and follow-up. There was also an increase in the perceived risk of SWD following the intervention. CONCLUSIONS: Our evaluation of DITM suggests that the intervention had an impact on reducing SWD among young drivers. Further research is needed to establish which particular elements of the DITM are associated with reductions in SWD and whether similar findings would be identified in other age groups.

How is the duration of distraction related to safety-critical events? Harnessing naturalistic driving data to explore the role of driving instability.

INTRODUCTION: Due to a variety of secondary tasks performed by drivers, distracted driving has become a critical concern. At 50 mph, sending/reading a text for 5 seconds is equivalent to driving the length of a football field (360 ft) with eyes closed. A fundamental understanding of how distractions lead to crashes is needed to develop appropriate countermeasure strategies. A key question is whether distraction increases driving instability, which then further contributes to safety-critical events (SCEs). METHODS: By harnessing newly available microscopic driving data and using the safe systems approach, a subsample of naturalistic driving study data were analyzed, collected through the second strategic highway research program. Rigorous path analysis (including Tobit and Ordered Probit regressions) is used to jointly model the instability in driving (using coefficient of variation of speed) and event outcomes (including baseline, near-crash, and crash). The marginal effects from the two models are used to compute direct, indirect, and total effects of distraction duration on SCEs. RESULTS: Results indicate that a longer duration of distraction was positively but non-linearly associated with higher driving instability and higher chances of SCEs. Where, the chance of a crash and near-crash was higher by 34% and 40%, respectively, with a unit increase in driving instability. Based on the results, the chance of both SCEs significantly increases non-linearly with an increase in distraction duration beyond 3 seconds. For instance, the chance of a crash is 16% for a driver distracted for 3 seconds, which increases to 29% if a driver is distracted for 10 seconds. CONCLUSIONS AND PRACTICAL APPLICATIONS: Using path analysis, the total effects of distraction duration on SCEs are even higher when its indirect effects on SCEs through driving instability are considered. Potential practical implications including traditional countermeasures (changes in roadway environments) and vehicle technologies are discussed in the paper.