Impact of distracted driving on safety and traffic flow

How does distraction affect cyclists' severe crashes? A hybrid CatBoost-SHAP and random parameters binary logit approach

Cyclists are among the most vulnerable road users, increasingly subject to various sources of distraction, including the use of mobile phones and engagement in other tasks while navigating urban environments. Understanding and mitigating the impact of these distractions on cyclist safety is crucial. Despite the importance of this issue, the effect of distraction on injury severity in cycling crashes has not been extensively studied. This research analyzes four years of U.S. crash data (2019-2022) from the Crash Report Sampling System (CRSS) database, employing a hybrid framework that integrates CatBoost-based SHAP algorithm and the random parameters binary logit model with heterogeneity in means and variances (RPBL-HMV). The proposed approach confirms the significant role of cyclist distraction in crash injury severity. Subsequently, the analysis identifies several factors influencing the likelihood of severe injuries in distracted cyclist crashes. Crashes involving the front of motor vehicles, occurring in rural areas, on two-way roads, at higher speed limits, and during weekends were associated with a higher probability of severe injuries. Conversely, crashes at T-intersections, involving the side or rear of motor vehicles, where cyclists wore helmets, or during rush hour were linked to a reduced likelihood of severe injuries. Notably, interaction effects reveal nuanced patterns. For instance, while crossing roadway actions and rush hour periods individually decrease the likelihood of severe crashes, their combination increases the probability of such outcomes. The findings suggest targeted safety measures and policy interventions aimed at enhancing cyclist safety and promoting safer cycling environments by mitigating distraction-related risks.

Principal Components Analysis of Driving Simulator Variables in Novice Drivers

Objective

Although driving simulators are powerful tools capable of measuring a wide-ranging set of tactical and operational level driving behaviors, comparing these behaviors across studies is problematic because there is no core set of driving variables to report when assessing driving behavior in simulated driving scenarios. To facilitate comparisons across studies, researchers need consistency in how driving simulator variables combine to assess driving behavior. With inter-study consistency, driving simulator research could support stronger conclusions about safe driving behaviors and more reliably identify future driver training goals. The purpose of the current study was to derive empirically and theoretically meaningful composite scores from driving behaviors of young people in a driving simulator, utilizing driving data from across a variety of driving environments and from within the individual driving environments.

Method

One hundred ninety adolescent participants aged 16 years or 18 years at enrollment provided demographic data and drove in a high-fidelity driving simulator. The simulated scenario included 4 distinct environments: Urban, Freeway, Residential, and a Car Following Task (CFT). A Principal Components Analysis (PCA) was conducted on the variable output from the driving simulator to select optimal factor solutions and loadings both across the multi-environmental drive and within the four individual driving environments.

Results

The PCA suggested two components from the multi-environmental simulated drive: vehicle control and speed. The individual driving environments also indicated two components: vehicle control and tactical judgment.

Conclusion

These findings are among the first steps for identifying composite driving simulator variables to quantify theoretical conceptualizations of driving behavior. Currently, driving behavior and performance measured by driving simulators lack "gold standards" via driving scores or benchmarks. The composites derived in this analysis may be studied for further use where driving behavior standards are increasingly sought by clinicians and practitioners for a variety of populations, as well as by parents concerned about the readiness of their novice driving teen.

Examining the impact of interventions in reducing self-reported engagement in distracted driving: A systematic review

Despite the implementation of legal countermeasures, distracted driving remains a prevalent concern for road safety. This systematic review (following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines) summarised the literature on the impact of interventions targeting attitudes/intentions towards, and self-reported engagement in, distracted driving. Studies were eligible for this review if they examined self-reported behaviour/attitudes/intentions pertaining to distracted driving at baseline and post-intervention. Databases searched included PubMed, ProQuest, Scopus, and TRID. The review identified 19 articles/interventions, which were categorised into three intervention types. First, all program-based interventions (n = 6) reduced engagement in distracted driving. However, there were notable limitations to these studies, including a lack of control groups and difficulties implementing this intervention in a real-world setting. Second, active interventions (n = 9) were commonly utilised, yet a number of studies did not find any improvements in outcomes. Finally, four studies used a message-based intervention, with three studies reporting reduced intention and/or engagement in distracted driving. There is opportunity for message-based interventions to be communicated effortlessly online and target high-risk driving populations. However, further research is necessary to address limitations highlighted in the review, including follow-up testing and control groups. Implications are discussed with particular emphasis on areas where further research is needed.

Modeling distracted driving behavior considering cognitive processes

The modeling of distracted driving behavior has been studied for many years, however, there remain many distraction phenomena that can not be fully modeled. This study proposes a new method that establishes the model using the queuing network model human processor (QN-MHP) framework. Unlike previous models that only consider distracted-driving-related human factors from a mathematical perspective, the proposed method reflects the information processing in the human brain, and simulates the distracted driver's cognitive processes based on a model structure supported by physiological and cognitive research evidence. Firstly, a cumulative activation effect model for external stimuli is adopted to mimic the phenomenon that a driver responds only to stimuli above a certain threshold. Then, dual-task queuing and switching mechanisms are modeled to reflect the cognitive resource allocation under distraction. Finally, the driver's action is modeled by the Intelligent Driver Model (IDM). The model is developed for visual distraction auditory distraction separately. 773 distracted car-following events from the Shanghai Naturalistic Driving Study data were used to calibrate and verify the model. Results show that the model parameters are more uniform and reasonable. Meanwhile, the model accuracy has improved by 57% and 66% compared to the two baseline models respectively. Moreover, the model demonstrates its ability to generate critical pre-crash scenarios and estimate the crash rate of distracted driving. The proposed model is expected to contribute to safety research regarding new vehicle technologies and traffic safety analysis.

Demographic characteristics and trends of cell phone use while driving citations in selected states in the United States, 2010-2020

OBJECTIVE

Distracted driving is a leading cause of motor vehicle crashes, and cell phone use is a major source of in-vehicle distraction. Many states in the United States have enacted cell phone use laws to regulate drivers' cell phone use behavior to enhance traffic safety. Numerous studies have examined the effects of such laws on drivers' cell phone use behavior based on self-reported and roadside observational data. However, little was known about who actually violated the laws at the enforcement level. This study sought to uncover the demographic characteristics of drivers cited for cell phone use while driving and whether these characteristics changed over time since the enactment of cell phone laws.

METHODS

We acquired useable traffic citation data for 7 states in the United States from 2010 to 2020 and performed descriptive and regression analyses.

RESULTS

Male drivers were cited more for cell phone use while driving. Handheld and texting bans were associated with a greater proportion of cited drivers aged 40 and above, compared to texting-only bans. Trends in the citations issued based on drivers' age group following the enactment of different cell phone laws were also uncovered. The proportion of citations issued to drivers aged 60 and above increased over time but the temporal trend remained insignificant when population effect was considered.

CONCLUSIONS

This study examined the demographic characteristics of drivers cited for cell phone use while driving in selected states with texting-only bans or handheld and texting bans. The results reveal policy-based differences in trends in the proportion of citations issued to drivers in different age groups.

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.

Influence of distracted driving of online car-hailing drivers on overall driving performance

The distraction affects driving performance and induces serious safety issues. To better understand distracted driving, this study examines the influence of distracted driving on overall driving performance. This paper analyzes the distraction behavior (mobile phone use, entertainment activities, and passenger interference) under three driving tasks. The statistical results show that viewing or sending messages is common during driving. Smoking, phone calls, and talking to passengers are evident in cruising, ride request and drop-off, respectively. Then, overall driving performance is proposed based on velocity, longitudinal acceleration (longacc) and yaw_rate. It is divided into three categories, high, medium, and low, by k-means algorithms. The average speed increases from low to high performance; however, the longacc and yaw_rate decrease. Finally, the influence of distracted driving on overall driving performance is analyzed using C4.5 algorithm. The result shows that when time is peak, the probability of high performance (HP) is higher than off-peak. The possibility of HP increases with the increase of duration; the number of, talking to passengers, listening to music or radio, eating; the duration of, viewing or sending messages, phone calls; but reduces with the increase of the number of phone calls. These findings provide theoretical support for driving performance evaluation.

Optimization method to reduce the risky driving behaviors of ride-hailing drivers

INTRODUCTION

To promote the safety level of ride-hailing services, this study develops the Targeted and Differentiated Optimization Method of Risky Driving Behavior Education and Training (TDOM-RDBET) founded on driver type classification of high-risk drivers.

METHOD

Based on value and goal orientations, 689 drivers were classified into four driver types and were assigned to three groups, including an experimental group, a blank control group, and a general control group. This research preliminarily analyzes the effectiveness of the TDOM-RDBET to reduce mobile phone use while driving by assessing the main effects of the group and test session on the risk value ranking of mobile phone use while driving (AR), the frequency per 100 km of mobile phone use while driving (AF), and the frequency per 100 km of risky driving behaviors (AFR), as well as the interactive effects of the two factors on AR, AF, and AFR, based on a two-way analysis of variance (two-way ANOVA).

RESULTS

The results demonstrate an overall significant reduction in AR (F = 8.653, p = 0.003), AF (F = 11.027, p = 0.001), and AFR (F = 8.072, p = 0.005) for the experimental group after training. Moreover, significant interactive effects of the driver group × test session on AR (F = 7.481, p = 0.001) and AF (F = 15.217, p < 0.001) were found. AR was significantly lower for the experimental group than for the blank control group (p < 0.05) in the post-training condition. Moreover, AF was also significantly lower for the experimental group than for the blank control group (p < 0.05) and general control group (p < 0.05) in the post-training condition.

PRACTICAL APPLICATIONS

On the whole, it was preliminarily verified that the TDOM-RDBET is more effective than the general training method at modifying the risky driving behavior.

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.

How do the type and duration of distraction affect speed selection and crash risk? An evaluation using naturalistic driving data

Distracted driving is among the leading causes of roadway crashes worldwide. However, due to limitations of police-reported crash data, it is often challenging to understand the nature and magnitude of this problem. Distraction has also been shown to affect driver speed selection, which is important as both mean speed and speed variance have substantive impacts on crash risk. This study utilizes naturalistic driving data to investigate the relationship between the engagement in various secondary (non-driving) tasks and driver speed selection under different driving contexts. Separate analyses were conducted for low-speed and high-speed driving environments. Two-way random effects linear regression models were estimated for both speed regimes, while controlling for driver, roadway, and traffic characteristics. The differences were assessed based upon ten types of secondary tasks. In general, engagement in all tasks was found to decrease speeds in high-speed environments while the effects were mixed in low-speed settings. The changes in speeds were much pronounced for secondary tasks that include a combination of visual, manual, and cognitive distractions, such as cell phone use. Among all secondary tasks, an average episode of a driver talking on a handheld cellphone was associated with a 6-mph speed reduction in high-speed environments, but a 3.5-mph increase in low-speed settings. In addition to examining impacts on speed selection, the risk of involvement in crash and near-crash events was also evaluated in consideration of the type and duration of distraction. Interestingly, distractions tended to show similar relationships, in both direction and magnitude, with the risk of involvement in both crash and near-crash events. From a policy standpoint, this study provides further motivation for legislation and other programs aimed at curbing distracted driving.

Identifying distracted-driving events from on-road observations using a moving vehicle: A case study in New Jersey

Distracted driving is a major traffic safety concern in the USA. To observe and detect distracted-driving events, various methods (e.g., surveys, videos, and simulations) involving the collection of cross-sectional data from individual subjects have been used in the transportation field. In this study, we employed an unconventional approach of on-road observations using a moving vehicle to collect data on distracted-driving events for multiple subjects in New Jersey. A data-collection crew member continuously navigated selected corridors to record driver-distraction events. A GPS (Global Positioning System) tracker was used to timestamp and record the location of each incident. Two non-parametric tests (Mann-Whitney U test and Kruskal-Wallis test) were performed to identify the significance of the variations in distracted-driving behaviors due to changes in temporal variables (e.g., day of the week, season), the type of roadway, and the geometric properties of the roadway. The results indicated that cellphone use was the leading type of distraction. Additionally, "handheld phone use (phone to ear)," "fidgeting/grooming," "drinking/eating/smoking," and "talking to passengers" events were significantly affected by the time of day and the geometric properties of the roadway. The results of this study are expected to assist state and local agencies in promoting awareness of distracted driving with the aim of reducing the frequency and severity of distracted driving-related crashes.

A review of research on driving distraction based on bibliometrics and co-occurrence: Focus on driving distraction recognition methods

INTRODUCTION

The existing selection of driving distraction recognition methods is based on a specific research perspective and does not provide comprehensive information on the entire field of view.

METHOD

We conducted a systematic review of previous studies, aiming to come up with appropriate research methods to identify the driver's distraction state. First, this article selects four sets of search keywords related to driving distraction discrimination from five databases (Web of Science, ScienceDirect, Springer Link, IEEE, and TRID) and identifies 1,620 peer-reviewed documents from 2000 to 2020; these 1,620 documents underwent bibliographic analysis and co-occurrence network analysis. The co-occurrence coupling relationship is analyzed from the aspects of time, country, publication, author and keywords. Second, 37 papers published were screened, and the driving distraction recognition methods proposed by these 37 papers were summarized and analyzed.

RESULTS

The results show that this field has been prevalent since 2013; countries such as the United States, Britain, Germany, Australia, China, and Canada are in the forefront of research in this field, and the cooperation between related countries is relatively close. The cooperation between authors is characterized by aggregation, and the mobile phone as the main keyword is almost connected to other keyword nodes; the recognition model of deep learning algorithm based on video surveillance data sources has become the mainstream hot spot distraction recognition method. The recognition model of machine learning algorithm based on vehicle dynamics data, driver physiology, and eye movement data sources has specific advantages and disadvantages.

PRACTICAL APPLICATIONS

The results can help people to understand the current situation of driving distraction comprehensively and systematically, provide better theoretical support for researchers to choose the subsequent driving distraction recognition model, and provide research direction for driving distraction recognition in the future.

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.

Analysis of distracted driving crashes in New Jersey using mixed logit model

INTRODUCTION

Distracted driving is a concern for traffic safety in the 21st century, and can be held responsible for the increasing propensity and severity of traffic crashes. With the advent of mobile technologies, distractions involving the use of cellphones while driving have emerged, and young drivers in particular are getting more and more engaged in these distractions. Texting or receiving phone calls while driving are offenses in most states, and they are punished with fiscal penalties. Awareness campaigns have also been arranged over recent decades across the United States in order to minimize crashes due to distracted driving. The severity of such crashes depends on driver behavior, which can also be affected by various factors like the geometric design of the roadway, lighting and environmental conditions, and temporal variables.

METHOD

In this study, we analyzed data on five years (2015-2019) of crashes involving cellphone use in New Jersey using a mixed logit model. As estimated model parameters can vary randomly across roadway segments in this approach, this allowed us to account for unobserved heterogeneities relating to roadway characteristics, environmental factors, and driver behavior. A pseudo-elasticity analysis was further employed to observe the sensitivity of the significant explanatory variables to crash severity.

RESULTS

We found that higher speed limits and a larger total number of vehicles involved both increased crash severity, while higher annual average daily traffic (AADT) levels and the presence of an urban road setting reduced it.

PRACTICAL APPLICATIONS

These findings will help decision-makers to comprehend what the significant contributing factors associated with crash injury severity due to distracted driving are, and how to implement necessary interventions to reduce this severity.

Risk-taking behaviors of e-scooter users: A survey in Paris

Risk-taking behavior is often held responsible for increased crash involvement. We designed and undertook a face-to-face road survey (N = 459) in order to explore incident involvement history, driving attitudes and perceived risk among e-scooter users is Paris, France. Three risk factors were specifically explored: (i) riding after having consumed alcohol, (ii) riding after having consumed drugs, and (iii) using the smartphone while riding. The relationship between these factors and user attributes (such as age and gender) and travel behavior (such as frequency of e-scooter usage and trip duration) was examined using logit and mixed logit specifications and a structural equation model. Empirical evidence suggests that it is more likely for young and male riders to develop risky behaviors. Longer trip durations seem to be associated with risk-taking behaviors.

Dynamics of Covid-19 mortality and social determinants of health: a spatiotemporal analysis of exceedance probabilities

PURPOSE

To determine the association of social factors with Covid-19 mortality and identify high-risk clusters.

METHODS

Data on Covid-19 deaths across 3,108 contiguous U.S. counties from the Johns Hopkins University and social determinants of health (SDoH) data from the County Health Ranking and the Bureau of Labor Statistics were fitted to Bayesian semi-parametric spatiotemporal Negative Binomial models, and 95% credible intervals (CrI) of incidence rate ratios (IRR) were used to assess the associations. Exceedance probabilities were used for detecting clusters.

RESULTS

As of October 31, 2020, the median mortality rate was 40.05 per 100, 000. The monthly urban mortality rates increased with unemployment (IRRadjusted:1.41, 95% CrI: 1.24, 1.60), percent Black population (IRRadjusted:1.05, 95% CrI: 1.04, 1.07), and residential segregation (IRRadjusted:1.03, 95% CrI: 1.02, 1.04). The rural monthly mortality rates increased with percent female population (IRRadjusted: 1.17, 95% CrI: 1.11, 1.24) and percent Black population (IRRadjusted:1.07 95% CrI:1.06, 1.08). Higher college education rates were associated with decreased mortality rates in rural and urban counties. The dynamics of exceedance probabilities detected the shifts of high-risk clusters from the Northeast to Southern and Midwestern counties.

CONCLUSIONS

Spatiotemporal analyses enabled the inclusion of unobserved latent risk factors and aid in scientifically grounded decision-making at a granular level.

The mere presence of a mobile phone: Does it influence driving performance?

The ubiquity of mobile phones has led to a rapid increase in its presence and use in vehicles, especially among young adults (up to 25 years), who are generally the least experienced group of drivers. The potential for phones to draw attention away from the main driving task has significant consequences for road safety. Previous studies have found that the mere presence of a mobile phone can be distracting by impairing attention in experimental non-driving contexts. However, the effect of phone presence, independent to usage, has not yet been examined in the context of driving. As such, the present study examined whether the mere presence of a mobile phone, its proximity to the driver, and power status (on/off) influenced the driving performance of young drivers. Additionally, this study assessed whether the effects of phone presence and proximity were moderated by an individual's level of dependence on, or emotional attachment to, their phone. A sample of 127 undergraduate psychology students (M = 19.76, SD = 1.63) were randomly assigned to one of four conditions: (1) phone absent (control), (2) phone on, in holder, (3) phone off, in holder, and (4) phone on, in pocket. They all completed the same simulated drive, and were measured for degree of phone dependence and phone emotional attachment. Overall, drivers in all the phone present conditions made significantly more driving errors (speeding and collision) compared to those in the phone absent (control) condition, irrespective of proximity to the phone and whether it was on or off. Phone dependence, but not phone emotional attachment, moderated the effect of phone presence on speeding behaviour. These findings suggest that the mere presence of a phone is distracting for drivers, especially so for those who are highly dependent on their phone, which may place them at a greater risk of a distraction induced crash.

Assessment of the Influence of Technology-Based Distracted Driving on Drivers' Infractions and Their Subsequent Impact on Traffic Accidents Severity

Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions; (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions; (iii) distracted motorcycles and squad riders are found more likely to speed; (iv) the probability of committing infractions by distracted drivers increases on streets and highways; and, finally, (v) drivers’ infractions lead to serious injuries.

Which accuracy levels of positioning technologies do drivers really need in connected vehicle settings for safety?

Based on Global Navigation Satellite Systems (GNSS) and Internet of Vehicles (IoV), collision warning systems can provide drivers specific warning information such as the position of hazardous vehicles. However, positioning technologies vary in accuracy. Highly accurate positioning technologies are theoretically favorable for driving safety but are relatively difficult to use widely due to the high cost. While many researchers compared accuracies of different positioning technologies or explored methods to improve accuracy, few directly investigated the effect of different positioning accuracies for hazardous vehicles on driving safety. The present study conducted a laboratory experiment to explore the effect of four positioning accuracies of hazardous vehicles (i.e., highest: 1.5 m, high: 5 m, medium: 8.5 m, and low: 20 m, each value represented the radius of a circle and the estimated positions of hazardous vehicle were within this circle) and different traffic densities (high: one car per 150 m and low: one car per 1500 m) on driver behaviors. Thirty participants were distributed into high and low traffic density groups. Each participant received auditory warning information, which provided positions of hazardous vehicles relative to the ego vehicle concerning four accuracy levels. Driving safety (i.e., number of collisions and min TTC), driving performance (i.e., brake onset reaction time, steer onset reaction time, and mean deceleration), and subjective workload for warnings with different positioning accuracies of hazardous vehicles were recorded and analyzed. Results suggested the following: 1) Under low traffic density, four positioning accuracies showed no significant differences on driving safety and performance as well as subjective workload. 2) Under high traffic density, the medium positioning accuracy (8.5 m) exerted no significant differences on driving safety and performance compared with the highest positioning accuracy (1.5 m), which had the fastest brake onset reaction time, the least number of collisions, and the largest mean deceleration. Moreover, when traffic density was high, warnings with the highest (1.5 m), high (5 m), and medium (8.5 m) positioning accuracies generated a significantly lower workload than the warning with low (20 m) positioning accuracy. In conclusion, low positioning accuracy (20 m), which was a general accuracy of A-GNSS (no added cost) or GNSS (SPP) (relatively low cost) in dense urban area, was feasible for providing warnings under low traffic density. Medium positioning accuracy (8.5 m), which was the accuracy of A-GNSS (no added cost) in open-sky area, was acceptable for supporting warning systems under high traffic density. These findings have implications for promoting the application of positioning technologies to realize warnings in the near future.

The effects of sleep deprivation and text messaging on pedestrian safety in university students

STUDY OBJECTIVES

This study assesses the impact of sleep deprivation and text messaging on pedestrian injury risk.

METHODS

A total of 36 university students engaged in a virtual reality pedestrian environment in two conditions: sleep deprived (no sleep previous night) and normal sleep (normal sleep routine). Sleep was assessed using actigraphy and pedestrian behavior via four outcomes: time to initiate crossing, time before contact with oncoming vehicle, hits/close calls, and looks left/right. During each condition, participants made half the crossings while text messaging. Participants also completed the Useful Field of View test, the Psychomotor Vigilance Test, and Conners' Continuous Performance Test in both conditions.

RESULTS

While sleep deprived, students crossed significantly closer to oncoming vehicles compared with after normal sleep. While text messaging, crossed closer to vehicles and took longer to initiate crossings. Safety risks were amplified through combined sleep deprivation plus text messaging, leading to more virtual hits and close calls and shorter time before vehicle contact while crossing. Sleep-deprived students demonstrated impairments in functioning on cognitive tests.

CONCLUSIONS

University students' pedestrian behavior was generally riskier, and their cognitive functioning was impaired, when sleep deprived compared with after normal sleep. This effect was exacerbated when distracted by text messaging.

The Impact of driver distraction and secondary tasks with and without other co-occurring driving behaviors on the level of road traffic crashes

Driving safety is typically affected by concurrent non-driving tasks. These activities might negatively impact the trips' outcome and cause near-crash or crash incidents and accidents. The crashes impose a tremendous social and economic cost to society and might affect the involving individuals' quality of life. As it stands, road injuries are ranked among top-ten leading causes of death by the World Health Organization. Distracted driving is defined as an attention diversion of the driver toward a competing activity. It was shown in numerous studies that distracted driving increase the probability of near-crash or crash events. By leveraging the statistical power of the large SHRP2 naturalistic data, we are able to quantify the preponderance of specific distractions during daily trips and confirm the causality factor of an ubiquitous non-driving task in the crash event. We show that, except for phone usage which happens more frequently in near-crash and crash categories than in baseline trips, both distracted driving and secondary tasks occur almost uniformly in different types of trips. In this study, we investigate the impact of the co-occurrence of distracted driving with other driving behaviors and secondary tasks. It is found that the co-occurrence of distracted driving with other driving behaviors or secondary tasks increase the chance of near-crash and crash events. This study's findings can inform the design and development of more precise and reliable driving assistance and warning systems.

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.

Reaction time and brake pedal force after total knee replacement: timeframe for return to car driving

PURPOSE

This prospective cohort study aimed to examine objective and subjective parameters in patients who underwent total knee replacement (TKR) to assess from when on driving a car can be deemed safe again.

METHODS

Thirty patients (16 women, 14 men, age 66 ± 11 years) who received TKR of the right knee and 45 healthy controls (26 women, 19 men, age 32 ± 9 years) were asked to perform an emergency braking manoeuvre using a driving simulator. Brake pedal force (BPF), neuronal reaction time (NRT), brake reaction time (BRT), and subjective parameters (pain, subjective driving ability) were measured preoperatively as well as 5 days, 3-4, and 6 weeks after TKR.

RESULTS

Preoperative NRT was 506 ± 162 ms, BRT 985 ± 356 ms, and BPF 614 ± 292 N. NRT increased to 561 ± 218 ms, BRT to 1091 ± 404 ms and BPF decreased to 411 ± 191 N 5 days after TKR. Three weeks after surgery, NRT was 581 ± 164 ms and BRT 1013 ± 260 ms, while BPF increased to 555 ± 200 N. Only BPF showed significant differences (p < 0.01). In week 6, all parameters were restored to baseline levels; patients showed significant pain decrease and evaluated their driving ability as "good" again.

CONCLUSION

BPF was the only parameter displaying a significant postoperative decrease. However, preoperative patients' baseline levels and subjective confidence in driving ability were only reached 6 weeks after the operation. These results indicate that a minimum waiting period of 6 weeks should be considered before patients can safely participate in road traffic at their individual preoperative safety level again.

LEVEL OF EVIDENCE

II.

Drivers' Braking Behavior Affected by Cognitive Distractions: An Experimental Investigation with a Virtual Car Simulator

In this study, a cohort of 78 university students performed a driving experience in a virtual urban scenario, by means of a car driving simulator, to examine effects of a planned hands-free mobile phone conversation on young drivers’ braking behaviors. To this aim, a control group was left free to drive without any imposed cognitive task. An experimental group faced the same scenario while engaged in a phone call. The conversation via earphones was arranged to diminish the amount of cognitive resources allocated to the driving task. For both groups, the analyses focused on the moment at which a child entered a pedestrian crossing from a sidewalk. The results of a mixed two-way ANOVA showed the presence of a significant difference for distracted and non-distracted drivers with the absence of gender-related differences across the two groups. Distracted participants assumed lower initial speeds, took the first action to stop at shorter distances from the zebra crossing, and had more difficulty in keeping speed variations under control. These findings suggest that the distraction induced by the use of earphones may induce risk compensation behaviors and delay pedestrian perception. Moreover, the effects on the participants’ braking behavior suggest that the procedure adopted to increase cognitive load, based on a story retelling, is an effective method to analyze the impact of hands-free cellphone use on driving skills in a car simulation experiment.

Demographic, driving experience, and psychosocial predictors of adolescent distracted driving beliefs

PURPOSE

The current study had three aims: 1) describe distracted driving beliefs among adolescents by various distraction types (i.e., talking on a hands-free/hands-held cell phone, texting or emailing, taking "selfies," and updating/checking social media); 2) examine the factor structure of distracted driving beliefs; and 3) test whether individual difference factors, shown in prior work to be related to distracted driving behavior, significantly predicted factors of distracted driving beliefs.

METHODS

Three hundred seventy-nine high school students enrolled in non-mandatory Driver's Education courses completed surveys of distracted driving beliefs, sensation seeking, and demographics.

RESULTS

A factor analysis revealed four factors of distracted driving beliefs: 1) self-acceptance of interacting with a cell phone while driving; 2) perceived peer acceptance of interacting with a cell phone while driving; 3) perceived threat of distracted driving to personal safety; and 4) self- and peer- acceptance of talking on a cell phone while driving. Adolescents perceived a greater threat to safety and less self- and peer-acceptance of interacting with cell phones while driving (i.e., texting/emailing, updating/posting to social media, taking selfies) than talking on a cell phone while driving. In general, men, those with more driving experience, higher in sensation seeking, and those placing more importance on checking notifications on a phone had riskier beliefs about distracted driving.

CONCLUSION

Findings suggest adolescent distracted driving beliefs are influenced by individual difference factors, providing some knowledge about the motivations for distracted driving. Future work should consider novel strategies for intervening to reduce this common yet extremely dangerous behavior among adolescents.

Predictors of accidents in people with mild cognitive impairment, mild dementia due to Alzheimer's disease and healthy controls in simulated driving

OBJECTIVES

To examine the driving variables that predict accident probability in mild dementia due to Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy older control drivers in simulated driving. To compare the three groups in mean performance and in frequency of scores exceeding 1.5 SD from the mean.

METHODS/DESIGN

Participants were 37 drivers with MCI, 16 drivers with AD, and 21 control drivers over the age of 52. Driving measures were derived from four rural driving conditions: moderate traffic without and with distraction and high traffic without and with distraction. The measures were z-transformed based on the performance of 90 control drivers of different ages. Two unexpected incidents occurred per condition, requiring the sudden breaking to avoid an accident.

RESULTS

Drivers with AD showed significantly lower average speed, speed variability, greater headway distance, headway variability and average reaction time (RT) than control drivers. Drivers with MCI showed significantly lower average speed, greater headway distance and average RT than control drivers in the two conditions of distraction. No differences were found in accident probability. Drivers with AD had more deviant scores than both control drivers and drivers with MCI in most comparisons. Predictors of accident probability were average RT, speed variability and lateral position variability but MCI and AD status were not significant predictors in any of the regression models.

CONCLUSIONS

Despite significant differences in performance, drivers with MCI and AD did not differ in accident probability from control drivers. An individualized approach of examining individual driving performance is recommended.

Investigating in-vehicle distracting activities and crash risks for young drivers using structural equation modeling

Distracted driving has been considered one of the main reasons for traffic crashes in recent times, especially among young drivers. The objectives of this study were to identify the distracting activities in which young drivers engage, assess the most distracting ones based on their experiences, and investigate the factors that might increase crash risk. The data were collected through a self-report questionnaire. Most participants reported frequent cell phone use while driving. Other reported activities include adjusting audio devices, chatting with passengers, smoking, eating, and drinking. A structural equation model was constructed to identify the latent variables that have a significant influence on crash risk. The analysis showed that in-vehicle distractions had a high effect on the crash likelihood. The results also indicated that dangerous driving behavior had a direct effect on the crash risk probability, as well as on the rash driving latent variables. The results provide insight into distracted driving behavior among young drivers and can be useful in developing enforcement and educational strategies to reduce this type of behavior.

Influence of traffic congestion on driver behavior in post-congestion driving

Traffic congestion is more likely to lead to aggressive driving behavior that is associated with increased crash risks. Previous studies mainly focus on driving behavior during congestion when studying congestion effects. However, the negative effects of congestion on driving behavior may also affect drivers' post-congestion driving. To fill this research gap, this study examined the influence of traffic congestion on driver behavior on the post-congestion roads (i.e., the roads travelled right after congestion). Twenty-five subjects participated in a driving simulation study. They were asked to complete two trials corresponding to post-congestion and non-congestion conditions, respectively. Driver behavior quantified by driving performance measures, eye movement measures, and electroencephalogram (EEG) measures was compared between the two conditions. Ten features were selected from the measures with statistical significance. The selected features were integrated to characterize drivers' response patterns using a hierarchical clustering method. The results showed that driver behavior in post-congestion situations became more aggressive, more focused in the forward area but less focused in the dashboard area, and was associated with lower power of the β-band in the temporal brain region. The clustering results showed more aggressive and lack-of-aware response patterns while driving in post-congestion situations. This study revealed that traffic congestion negatively affected driver behavior on the post-congestion roads. Practical implications for driving safety education was discussed based on the findings from the present study.

What is the difference between perceived and actual risk of distracted driving? A field study on a real highway

Distracted driving is a leading cause of traffic accidents. It is influenced by driver attitude toward secondary tasks; however, field-based studies on the effects of low-perceived-risk tasks on lateral driving have rarely been reported. A total of 17 experienced non-professional drivers were recruited to participate in two secondary tasks: a cognitive experiment (conversation) and a visual distraction experiment (observation of following vehicles), each representing low-perceived-risk secondary tasks. One-way analysis of variance (ANOVA) was conducted to evaluate the effects of low-perceived-risk tasks on lateral driving performance. ANOVA results indicated that compared with baseline (no task) lateral performance, lane-keeping ability was enhanced during cognitive distractions. In the visual distraction experiment, more than 50% of the distractions required 1–2 s. Lane deviation and its growth rate increased with the duration of distraction. Compared with cognitive distraction, lane deviation increased significantly with visual distraction, and lane-keeping performance was seriously impaired. For low-perceived-risk tasks, visual distractions impaired driving safety more seriously, compared with cognitive distractions, suggesting that drivers misjudge the risks associated with visual tasks. These results can contribute to the design of advanced driving-assistance systems and improve professional driver programs, potentially reducing the frequency of traffic accidents caused by distracted driving.

Risk Assessment of Commercial dangerous -goods truck drivers using geo-location data: A case study in China

The primary objective of this study is to understand the relationship between driving risk of commercial dangerous-goods truck (CDT) and exposure factors and find a way to evaluate the risk of specific transportation environment, such as specific transportation route. Due to increasing transportation demand and potential threat to public, commercial dangerous goods transportation (CDGT) has drawn attention from decision makers and researchers within governmental and non-governmental safety organization. However, there are few studies focusing on driving risk assessment of commercial dangerous-goods truck by environmental factors. In this paper we employ survival analysis methods to analyze the impact of risk exposure factors on non-accident mileage of commercial dangerous-good truck and assess risk level of specific driving environment. Using raw location data from six transportation companies in China, we derive a set of 17 risk exposure factors that we use for model parameters estimation. The survival model and hazard model were estimated using the Weibull distribution as the baseline distribution. The results show that four factors - weather, traffic flow, travel time and average velocity have a significant impact on the non-accident mileage of driver in this company, and the assessment results of survival function and hazard function are robust to the different levels of testing data. The employment time has some effect on the results but does not result in a significant difference in most cases, and the task stability has little impact on the results. The findings of this study should be useful for decision makers and transportation companies to better risk assessment of CDT.

Virtual Reality for Pediatric Traumatic Brain Injury Rehabilitation: A Systematic Review

Objective. Pediatric traumatic brain injury (TBI) is associated with physical and psychobehavioral impairment in children. Effective rehabilitation programs postinjury are critical for children with TBI. Virtual reality (VR) has been increasingly adopted for brain injury rehabilitation. However, scientific synthesis is lacking in evaluating its effectiveness in pediatric TBI rehabilitation. This article aimed to conduct a systematic review on the effectiveness of VR-based pediatric TBI rehabilitation. Methods. A systematic literature search was conducted in PubMed, PsycInfo, SCOPUS, CENTRAL, BioMed Central, CiNAHL, and Web of Science through November 2015. Personal libraries and relevant references supplemented the search. Two authors independently reviewed the abstracts and/or full text of 5824 articles. Data extraction and qualitative synthesis was conducted along with quantitative assessment of research quality by 2 authors. Results. A positive impact was found for VR-based interventions on children's physical rehabilitation post-TBI. The quality of research evidence was moderate, which largely suffered from small samples, lack of immersive VR experience, and lack of focus on socioemotional outcomes post-TBI. Conclusions. The present review identified positive effects of VR interventions for pediatric TBI rehabilitation especially in physical outcomes. Future research should include larger samples and broader post-TBI outcomes in children using VR-based interventions.

The role of pre-crash driving instability in contributing to crash intensity using naturalistic driving data

While the cost of crashes exceeds $1 Trillion a year in the U.S. alone, the availability of high-resolution naturalistic driving data provides an opportunity for researchers to conduct an in-depth analysis of crash contributing factors, and design appropriate interventions. Although police-reported crash data provides information on crashes, this study takes advantage of the SHRP2 Naturalistic Driving Study (NDS) which is a unique dataset that allows new insights due to detailed information on driver behavior in normal, pre-crash, and near-crash situations, in addition to trip and vehicle performance characteristics. This paper investigates the role of pre-crash driving instability, or driving volatility, in crash intensity (measured on a 4-point scale from a tire-strike to an injury crash) by analyzing microscopic vehicle kinematic data. NDS data are used to investigate not only the vehicle movements in space but also the instability of vehicles prior to the crash and their contribution to crash intensity using path analysis. A subset of the data containing 617 crash events with around 0.18 million temporal trajectories are analyzed. To quantify driving instability, microscopic variations or volatility in vehicular movements before a crash are analyzed. Specifically, nine measures of pre-crash driving volatility are calculated and used to explain crash intensity. While most of the measures are significantly correlated with crash intensity, substantial positive correlations are observed for two measures representing speed and deceleration volatilities. Modeling results of the fixed and random parameter probit models revealed that volatility is one of the leading factors increasing the probability of a severe crash. Additionally, the speed prior to a crash is highly correlated with intensity outcomes, as expected. Interestingly, distracted and aggressive driving are highly correlated with driving volatility and have substantial indirect effects on crash intensity. With volatile driving serving as a leading indicator of crash intensity, given the crashes analyzed in this study, early warnings and alerts for the subject vehicle driver and proximate vehicles can be helpful when volatile behavior is observed.

Effect of Using Mobile Phones on Driver's Control Behavior Based on Naturalistic Driving Data

Distracted driving behaviors are closely related to crash risk, with the use of mobile phones during driving being one of the leading causes of accidents. This paper attempts to investigate the impact of cell phone use while driving on drivers' control behaviors. Given the limitation of driving simulators in an unnatural setting, a sample of 134 cases related to cell phone use during driving were extracted from Shanghai naturalistic driving study data, which provided massive unobtrusive data to observe actual driving process. The process of using mobile phones was categorized into five operations, including dialing, answering, talking and listening, hanging up, and viewing information. Based on the concept of moving time window, the variation of the intensity of control activity, the sensitivity of control operation, and the stability of control state in each operation were analyzed. The empirical results show strong correlation between distracted operations and driving control behavior. The findings contribute to a better understanding of drivers' natural behavior changes with using mobiles, and can provide useful information for transport safety management.

A pilot investigation on the effects of combination transcranial direct current stimulation and speed of processing cognitive remediation therapy on simulated driving behavior in older adults with HIV

Cognitive impairments seen in people living with HIV (PLWH) are associated with difficulties in everyday functioning, specifically driving. This study utilized speed of processing cognitive remediation therapy (SOP-CRT) with transcranial direct current stimulation (tDCS) to gauge the feasibility and impact on simulated driving. Thirty PLWH (M _age = 54.53, SD = 3.33) were randomly assigned to either: sham tDCS SOP-CRT or active tDCS SOP-CRT. Seven indicators of simulated driving performance and safety were obtained. Repeated measures ANOVAs controlling for driver's license status (valid and current license or expired/no license) revealed a large training effect on average driving speed. Participants who received active tDCS SOP-CRT showed a slower average driving speed (p = 0.020, d = 0.972) than those who received sham tDCS SOP-CRT. Non-significant small-to-medium effects were seen for driving violations, collisions, variability in lane positioning, and lane deviations. Combination tDCS SOP-CRT was found to increase indices of cautionary simulated driving behavior. Findings reveal a potential avenue of intervention and rehabilitation for improving driving safety among vulnerable at-risk populations, such as those aging with chronic disease.

Distraction of cyclists: how does it influence their risky behaviors and traffic crashes?

Background

Undisputedly, traffic crashes constitute a public health concern whose impact and importance have been increasing during the past few decades. Specifically, road safety data have systematically shown how cyclists are highly vulnerable to suffering traffic crashes and severe injuries derived from them. Furthermore, although the empirical evidence is still very limited in this regard, in addition to other human factors involved in cycling crashes, distractions while cycling appear to be a major contributor to the road risk of cyclists.

Objectives

The main objectives of this study were, first, to explore the prevalence and trends of cycling distractions within an international sample of bike users, and second, to determine the influence of such distractions on road crashes suffered by cyclists, simultaneously considering the explanatory role of risky behaviors (errors and traffic violations) as potentially mediating variables between cycling distractions and traffic crashes.

Methods

For this cross-sectional study, we analyzed the data obtained from 1,064 cyclists—61.2% male and 38.8% female—from 20 different countries, who answered an on-line questionnaire on cycling-related features, habits, behaviors and accidents.

Results

The prevalence of different cycling distractions oscillated between 34.7% and 83.6%. The most common distractions were those related to the behavior of other users, physical elements of the road, weather conditions and phone calls. Age trends and differences were also found, thus establishing a positive association between age and distractibility during cycling. Furthermore, the effect of distractions on traffic crashes of cyclists was significant when tested together with age, risk perception and risky behaviors on the road.

Conclusion

The results of this study support the hypotheses that distractions have a major prevalence among bike users, and that they play a significant role in the prediction of the traffic crash rates of cyclists, through the mediation of risky behaviors.

Visualization and analysis of mapping knowledge domain of road safety studies

Mapping knowledge domain (MKD) is an important application of visualization technology in Bibliometrics, which has been extensively applied in psychology, medicine, and information science. In this paper we conduct a systematic analysis of the development trend on road safety studies based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) articles published between 2000 and 2018 using the MKD software tools VOSviewer and Sci2 Tool. Based on our analysis, we first present the annual numbers of articles, origin countries, main research organizations and groups as well as the source journals on road safety studies. We then report the collaborations among the main research organizations and groups using co-authorship analysis. Furthermore, we adopt the document co-citation analysis, keywords co-occurrence analysis, and burst detection analysis to visually explore the knowledge bases, topic distribution, research fronts and research trends on road safety studies. The proposed approach based on the visualized analysis of MKD can be used to establish a reference information and research basis for the application and development of methods in the domain of road safety studies. In particular, our results show that the knowledge bases (classical documents) of road safety studies in the last two decades have focused on five major areas of "Crash Frequency Data Analysis", "Driver Behavior Questionnaire", "Safety in Numbers for Walkers and Bicyclists", "Road Traffic Injury and Prevention", and "Driving Speed and Road Crashes". Among the research topics, the five dominant clusters are "Causation and Injury Severity Analysis of Road Accidents", "Epidemiologic Study and Prevention of Road Traffic Injury", "Intelligent Transportation System and Active Safety", "Young drivers' driving behavior and psychology", and "Older drivers' psychological and physiological characteristics". Finally, the burst keywords in research trends include Cycling, Intelligent Transportation Systems, and Distraction.

The effects of age on crash risk associated with driver distraction

Background

Driver distraction is a major contributing factor to crashes, which are the leading cause of death for the US population under 35 years of age. The prevalence of secondary-task engagement and its impacts on distraction and crashes may vary substantially by driver age.

Methods

Driving performance and behaviour data were collected continuously using multiple cameras and sensors in situ for 3542 participant drivers recruited for up to 3 years for the Second Strategic Highway Research Program Naturalistic Driving Study. Secondary-task engagement at the onset of crashes and during normal driving segments was identified from videos. A case-cohort approach was used to estimate the crash odds ratios associated with, and the prevalence of, secondary tasks for four age groups: 16-20, 21-29, 30-64 and 65-98 years of age. Only severe crashes (property damage and higher severity) were included in the analysis.

Results

Secondary-task-induced distraction posed a consistently higher threat for drivers younger than 30 and above 65 when compared with middle-aged drivers, although senior drivers engaged in secondary tasks much less frequently than their younger counterparts. Secondary tasks with high visual-manual demand (e.g. visual-manual tasks performed on cell phones) affected drivers of all ages. Certain secondary tasks, such as operation of in-vehicle devices and talking/singing, increased the risk for only certain age groups.

Conclusions

Teenaged, young adult drivers and senior drivers are more adversely impacted by secondary-task engagement than middle-aged drivers. Visual-manual distractions impact drivers of all ages, whereas cognitive distraction may have a larger impact on young drivers.

Measuring working memory load effects on electrophysiological markers of attention orienting during a simulated drive

Intersection accidents result in a significant proportion of road fatalities, and attention allocation likely plays a role. Attention allocation may depend on (limited) working memory (WM) capacity. Driving is often combined with tasks increasing WM load, consequently impairing attention orienting. This study (n = 22) investigated WM load effects on event-related potentials (ERPs) related to attention orienting. A simulated driving environment allowed continuous lane-keeping measurement. Participants were asked to orient attention covertly towards the side indicated by an arrow, and to respond only to moving cars appearing on the attended side by pressing a button. WM load was manipulated using a concurrent memory task. ERPs showed typical attentional modulation (cue: contralateral negativity, LDAP; car: N1, P1, SN and P3) under low and high load conditions. With increased WM load, lane-keeping performance improved, while dual task performance degraded (memory task: increased error rate; orienting task: increased false alarms, smaller P3). Practitioner Summary: Intersection driver-support systems aim to improve traffic safety and flow. However, in-vehicle systems induce WM load, increasing the tendency to yield. Traffic flow reduces if drivers stop at inappropriate times, reducing the effectiveness of systems. Consequently, driver-support systems could include WM load measurement during driving in the development phase.

Does Talking on a Cell Phone, With a Passenger, or Dialing Affect Driving Performance? An Updated Systematic Review and Meta-Analysis of Experimental Studies

Objective An up-to-date meta-analysis of experimental research on talking and driving is needed to provide a comprehensive, empirical, and credible basis for policy, legislation, countermeasures, and future research. Background The effects of cell, mobile, and smart phone use on driving safety continues to be a contentious societal issue. Method All available studies that measured the effects of cell phone use on driving were identified through a variety of search methods and databases. A total of 93 studies containing 106 experiments met the inclusion criteria. Coded independent variables included conversation target (handheld, hands-free, and passenger), setting (laboratory, simulation, or on road), and conversation type (natural, cognitive task, and dialing). Coded dependent variables included reaction time, stimulus detection, lane positioning, speed, headway, eye movements, and collisions. Results The overall sample had 4,382 participants, with driver ages ranging from 14 to 84 years ( M = 25.5, SD = 5.2). Conversation on a handheld or hands-free phone resulted in performance costs when compared with baseline driving for reaction time, stimulus detection, and collisions. Passenger conversation had a similar pattern of effect sizes. Dialing while driving had large performance costs for many variables. Conclusion This meta-analysis found that cell phone and passenger conversation produced moderate performance costs. Drivers minimally compensated while conversing on a cell phone by increasing headway or reducing speed. A number of additional meta-analytic questions are discussed. Application The results can be used to guide legislation, policy, countermeasures, and future research.

Social and Non-social Hazard Response in Drivers with Autism Spectrum Disorder

Driving is a complex task that relies on manual, cognitive, visual and social skill. The social demands of driving may be challenging for individuals with Autism Spectrum Disorder (ASD) due to known social impairments. This study investigated how drivers with ASD respond to social (e.g., pedestrians) and non-social (e.g., vehicles) hazards in a driving simulator compared to typically developing drivers. Overall, participants responded faster to social hazards than non-social hazards. It was also found that drivers with typical development reacted faster to social hazards, while drivers with ASD showed no difference in reaction time to social versus non-social hazards. Future work should further investigate how social impairments in ASD may affect driving safety.

Detection of driver engagement in secondary tasks from observed naturalistic driving behavior

Distracted driving has long been acknowledged as one of the leading causes of death or injury in roadway crashes. The focus of past research has been mainly on the impact of different causes of distraction on driving behavior. However, only a few studies attempted to address how some driving behavior attributes could be linked to the cause of distraction. In essence, this study takes advantage of the rich SHRP 2 Naturalistic Driving Study (NDS) database to develop a model for detecting the likelihood of a driver's involvement in secondary tasks from distinctive attributes of driving behavior. Five performance attributes, namely speed, longitudinal acceleration, lateral acceleration, yaw rate, and throttle position were used to describe the driving behavior. A model was developed for each of three selected secondary tasks: calling, texting, and passenger interaction. The models were developed using a supervised feed-forward Artificial Neural Network (ANN) architecture to account for the effect of inherent nonlinearity in the relationships between driving behavior and secondary tasks. The results show that the developed ANN models were able to detect the drivers' involvement in calling, texting, and passenger interaction with an overall accuracy of 99.5%, 98.1%, and 99.8%, respectively. These results show that the selected driving performance attributes were effective in detecting the associated secondary tasks with driving behavior. The results are very promising and the developed models could potentially be applied in crash investigations to resolve legal disputes in traffic accidents.

Adolescent and adult drivers' mobile phone use while driving with different interlocutors

PURPOSE

We examined the frequency of adolescents' and their parents' mobile phone use while driving (MPUWD) in the context of their peer and parent-child interlocutors (i.e., communication partners), considering individual differences in perceived risk and symptoms of technology addiction.

METHODS

Ninety-four participants (47 parent-adolescent dyads) completed a survey battery measuring their symptoms of technology addiction, perceived risk of MPUWD, and MPUWD with family members and with their peers as assessed via the proportion of trips when drivers used a mobile phone to communicate.

RESULTS

For both adolescents and their parents across both types of interlocutors (parent-child, peer), stronger risk perceptions were associated with less MPUWD, and stronger symptoms of technology addiction were associated with more MPUWD. A three-way interaction among technology addiction, interlocutor (parent-child, peer), and driver (parent, adolescent) was observed. For adolescents, the association between technology addiction and MPUWD was significantly stronger for MPUWD with their peers than it was for their MPUWD with their parents; this association was not observed for parents. Parents engaged in MPUWD with their children as frequently as adolescents engaged in MPUWD with their peers.

CONCLUSIONS

Symptoms of technology addiction play a stronger role for adolescents' MPUWD with their peers than it does for adolescents' MPUWD with their parents. These and other driver-by-interlocutor interactions should be considered in future research on distracted driving and in prevention efforts.

Distracted Walking, Bicycling, and Driving: Systematic Review and Meta-Analysis of Mobile Technology and Youth Crash Risk

This article examined the impact of mobile technology on young pedestrians, bicyclists, and drivers. A systematic search yielded 41 articles meeting inclusion criteria: peer-reviewed, published before February 1, 2016, behavioral outcome related to pedestrian, bicycling, or driving in the presence of mobile technology use, youth sample. Eleven studies were meta-analyzed to evaluate increased risk for crash/near-crash while distracted. Risk of bias and quality of research were assessed. Across methodologies, developmental stages, and type of distracting task, mobile technology use impairs youth safety on the road. Quality of evidence was low (pedestrian) to moderate (driving). Findings are discussed from the perspective of cognitive and visual distractions. Policy and behavioral efforts should continue to reduce mobile technology use in transportation settings.