Estimating young novice drivers' compliance with graduated driver licensing restrictions: A novel approach

Objectives: Current methods of estimating compliance with graduated driver licensing (GDL) restrictions among young drivers with intermediate driver's licenses—which include surveys, direct observations, and naturalistic studies—cannot sufficiently answer many critical foundational questions: What is the extent of noncompliance among the population of young intermediate drivers? How does compliance change over the course of licensure? How does compliance differ by driver subgroup and in certain driving environments? This article proposes an alternative and complementary approach to estimating population-level compliance with GDL nighttime and passenger restrictions via application of the quasi-induced exposure (QIE) method.

Methods: The article summarizes the main limitations of previous methods employed to estimate compliance. It then introduces the proposed method of borrowing the fundamental assumption of the QIE method—that young intermediate drivers who are nonresponsible in clean (i.e., one and only one responsible driver) multivehicle crashes are reasonably representative of young intermediate drivers on the road—to estimate population-based compliance. I describe formative work that has been done to ensure this method can be validly applied among young intermediate drivers and provide a practical application of this method: an estimate of compliance with New Jersey's passenger restrictions among 8,006 nonresponsible 17- to 20-year-old intermediate drivers involved in clean 2-vehicle crashes from July 2010 through June 2012.

Results: Over the study period, an estimated 8.4% (95% confidence interval, 7.8%, 9.0%) of intermediate drivers' trips were not in compliance with New Jersey's GDL passenger restriction. These findings were remarkably similar to previous estimates from more resource-intensive naturalistic studies (Goodwin et al. 2006 Goodwin AH, Wells JK, Foss RD, Williams AF. Encouraging compliance with graduated driver licensing restrictions. J Safety Res. 2006;37(4):343–351.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]; Klauer et al. 2011 Klauer SG, Simons-Morton B, Lee SE, Ouimet MC, Howard EH, Dingus TA. Novice drivers' exposure to known risk factors during the first 18 months of licensure: The effect of vehicle ownership. Traffic Inj Prev. 2011;12(2):159–168.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]).

Conclusion: Studies can practically apply proposed methods to estimate population-level compliance with GDL passenger and night restrictions; examine how compliance varies by relevant driver, vehicle, and environmental factors; and evaluate the implementation of a GDL provision or other intervention aimed at increasing compliance with these restrictions. Important considerations and potential limitations and challenges are discussed.     

Are there higher pedestrian fatalities in larger cities?: A scaling analysis of 115 to 161 largest cities in the United States

Objective: In 2012, 4,743 pedestrians were killed in the United States, representing 14% of total traffic fatalities. The number of pedestrians injured was higher at 76,000. Therefore, 36 out of 52 of the largest cities in the United States have adopted a citywide target of reducing pedestrian fatalities. The number of cities adopting the reduction goal during 2011 and 2012 increased rapidly with 8 more cities. We examined the scaling relationship of pedestrian fatality counts as a function of the population size of 115 to 161 large U.S. cities during the period of 1994 to 2011. We also examined the scaling relationship of nonpedestrian and total traffic fatality counts as a function of the population size.

Methods: For the data source of fatality measures we used Traffic Safety Facts Fatality Analysis Reporting System/General Estimates System annual reports published each year from 1994 to 2011 by the NHTSA. Using the data source we conducted both annual cross-sectional and panel data bivariate and multivariate regression models. In the construction of the estimated functional relationship between traffic fatality measures and various factors, we used the simple power function for urban scaling used by Bettencourt et al. (2007 Bettencourt LMA, Lobo J, Helbing D, Kühnert C, West GB. Growth, innovation, scaling and the pace of life in cities. Proc Natl Acad Sci USA. 2007;104:7301–7306.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar], 2010 Bettencourt LMA, Lobo J, Strumsky D, West GB. Urban scaling and its deviations: revealing the structure of wealth, innovation and crime across cities. PLoS ONE. 2010;5:e13541.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) and the refined STIRPAT (stochastic impacts by regression on population, affluence, and technology) model used in Dietz and Rosa (1994 Dietz T, Rosa EA. Rethinking the environmental impacts of population, affluence and technology. Human Ecology Review. 1994;1:277–300. [Google Scholar], 1997 Dietz T, Rosa EA. Effects of population and affluence on CO₂ emissions. Proc Natl Acad Sci USA. 1997;94:175–179.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) and York et al. (2003 York R, Rosa EA, Dietz T. STIRPAT, IPAT and IMPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecol Econ. 2003;46:351–365.[Crossref], [Web of Science ®] , [Google Scholar]).

Results: We found that the scaling relationship display diseconomies of scale or sublinear for pedestrian fatalities. However, the relationship displays a superlinear relationship in case of nonpedestrian fatalities. The scaling relationship for total traffic fatality counts display a nearly linear pattern. When the relationship was examined by the 4 subgroups of cities with different population sizes, the most pronounced sublinear scaling relationships for all 3 types of fatality counts was discovered for the subgroup of megacities with a population of more than 1 million.

Conclusions: The scaling patterns of traffic fatalities of subgroups of cities depend on population sizes of the cities in subgroups. In particular, 9 megacities with populations of more than 1 million are significantly different from the remaining cities and should be viewed as a totally separate group. Thus, analysis of the patterns of traffic fatalities needs to be conducted within the group of megacities separately from the other cities with smaller population sizes for devising prevention policies to reduce traffic fatalities in both megacities and smaller cities.     

Literature review on risky driving videos on YouTube: Unknown effects and areas for concern?

Objective: Entry of terms reflective of extreme risky driving behaviors into the YouTube website yields millions of videos. The majority of the top 20 highly subscribed automotive YouTube websites are focused on high-performance vehicles, high speed, and often risky driving. Moreover, young men are the heaviest users of online video sharing sites, overall streaming more videos, and watching them longer than any other group. The purpose of this article is to review the literature on YouTube videos and risky driving.

Methods: A systematic search was performed using the following specialized database sources—Scopus, PubMed, Web of Science, ERIC, and Google Scholar—for the years 2005–2015 for articles in the English language. Search words included “YouTube AND driving,” “YouTube AND speeding,” “YouTube AND racing.”

Results: No published research was found on the content of risky driving videos or on the effects of these videos on viewers. This literature review presents the current state of our published knowledge on the topic, which includes a review of the effects of mass media on risky driving cognitions; attitudes and behavior; similarities and differences between mass and social media; information on the YouTube platform; psychological theories that could support YouTube's potential effects on driving behavior; and 2 examples of risky driving behaviors (“sidewalk skiing” and “ghost riding the whip”) suggestive of varying levels of modeling behavior in subsequent YouTube videos.

Conclusions: Every month about 1 billion individuals are reported to view YouTube videos (ebizMBA Guide 2015 ebizMBA Guide. Top 15 most popular websites. 2015. Available at: http://www.ebizmba.com/articles/most-popular-websites [Google Scholar]) and young men are the heaviest users, overall streaming more YouTube videos and watching them longer than women and other age groups (Nielsen 2011 Nielsen. State of the media: the social media report. Q3. 2011. Available at: http://www.nielsen.com/us/en/insights/reports/2011/social-media-report-q3.html [Google Scholar]). This group is also the most dangerous group in traffic, engaging in more per capita violations and experiencing more per capita injuries and fatalities (e.g., Parker et al. 1995 Parker D, Reason J, Manstead ASR, Stradling SG. Driving errors, driving violations and accident involvement. Ergonomics. 1995;38:1036–1048.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]; Reason et al. 1990 Reason J, Manstead A, Stradling S, Baxter J, Campbell K. Errors and violations on the roads: a real distinction? Ergonomics. 1990;33:1315–1332.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]; Transport Canada 2015 Vingilis E, Yilderim-Yenier Z, Fischer P, et al. Self-concept as a risky driver: Mediating the relationship between racing video games and on-road driving violations in a community-based sample. Transp Res Part F Traffic Psychol Behav. 2016;43:15–23. [Google Scholar]; World Health Organization 2015 World Health Organization. Road traffic injuries. Fact sheet no. 358. 2015. Available at: http://www.who.int/mediacentre/factsheets/fs358/en/# Accessed March 14, 2016. [Google Scholar]). YouTube also contains many channels depicting risky driving videos. The time has come for the traffic safety community to begin exploring these relationships.     

Validation of quasi-induced exposure representativeness assumption among young drivers

Objective: Young driver studies have applied quasi-induced exposure (QIE) methods to assess relationships between demographic and behavioral factors and at-fault crash involvement, but QIE's primary assumption of representativeness has not yet been validated among young drivers. Determining whether nonresponsible young drivers in clean (i.e., only one driver is responsible) 2-vehicle crashes are reasonably representative of the general young driving population is an important step toward ensuring valid QIE use in young driver studies. We applied previously established validation methods to conduct the first study, to our knowledge, focused on validating the QIE representativeness assumption in a young driver population.

Methods: We utilized New Jersey's state crash and licensing databases (2008–2012) to examine the representativeness assumption among 17- to 20-year-old nonresponsible drivers involved in clean multivehicle crashes. It has been hypothesized that if not-at-fault drivers in clean 2-vehicle crashes are a true representation of the driving population, it would be expected that nonresponsible drivers in clean 3-or-more-vehicle crashes also represent this same driving population (Jiang and Lyles 2010 Jiang XG, Lyles RW. A review of the validity of the underlying assumptions of quasi-induced exposure. Accid Anal Prev. 2010;42:1352–1358.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). Thus, we compared distributions of age, gender, and vehicle type among (1) nonresponsible young drivers in clean 2-vehicle crashes and (2) the first nonresponsible young driver in clean crashes involving 3 or more vehicles to (3) all other nonresponsible young drivers in clean crashes involving 3 or more vehicles. Distributions were compared using chi-square tests and conditional logistic regression; analyses were conducted for all young drivers and stratified by license status (intermediate vs. fully licensed drivers), crash location, and time of day of the crash.

Results: There were 41,323 nonresponsible drivers in clean 2-vehicle crashes and 6,464 nonresponsible drivers in clean 3-or-more-vehicle crashes. Overall, we found that the distributions of age, gender, and vehicle type were not statistically significantly different between the 3 groups; in each group, approximately one fourth of drivers were represented in each age from age 17 through 20, half were males, and approximately 80% were driving a car/station wagon/minivan. In general, conclusions held when we evaluated the assumption within intermediate and fully licensed young drivers separately and by crash location and time.

Conclusions: It appears that the representativeness assumption holds among the population of young NJ drivers. We encourage young driver studies utilizing QIE methods to conduct internal validation studies to ensure appropriate application of these methods and we propose utilization of QIE methods to address broader foundational and applied questions in young driver safety.     

Biofidelity assessment of the 6-year-old ATDs in lateral impact

Objectives: The objective of this study was to assess and compare the current lateral impact biofidelity of the shoulder, thorax, abdomen, and pelvis of the Q6, Q6s, and Hybrid III (HIII) 6-year-old anthropomorphic test devices (ATDs) through lateral impact testing.

Methods: A series of lateral impact pendulum tests, vertical drop tests, and Wayne State University (WSU) sled tests was performed, based on the procedures detailed in ISO/TR 9790 (1999) and scaling to the 6-year-old using Irwin et al. (2002 Irwin AL, Mertz HJ, Elhagediab AM, Moss S. Guidelines for assessing the biofidelity of side impact dummies of various sizes and ages. Stapp Car Crash J. 2002;46:297–319.[PubMed] , [Google Scholar]). The HIII used in this study was tested with the Ford-designed abdomen described in Rouhana (2006 Rouhana SW. Abdominal impact injury research—a review. J Biomech. 2006;39(Suppl 1):S157–S158. [Google Scholar]) and Elhagediab et al. (2006 Elhagediab AM, Hardy WN, Rouhana SW. Advancements in the rate-sensitive abdomen for the Hybrid III family of dummies. J Biomech. 2006;39(Suppl 1):S158.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). The data collected from the 3 different ATDs were filtered using SAE J211 (SAE International 2003 SAE International. Surface Vehicle Recommended Practice. Instrumentation for Impact Test—Part 1—Electronic Instrumentation. Warrendale, PA: SAE International; 2003. SAE Standard J211-1. [Google Scholar]), aligned using the methodology described by Donnelly and Moorhouse (2012 Donnelly BR, Moorhouse K. Optimized phasing of PMHS response curves for biofidelity targets. Paper presented at: IRCOBI Conference; 2012. [Google Scholar]), and compared for each body region tested (shoulder, thorax, abdomen, and pelvis). The biofidelity performance in lateral impact for the 3 ATDs was assessed against the scaled biofidelity targets published in Irwin et al. (2002 Irwin AL, Mertz HJ, Elhagediab AM, Moss S. Guidelines for assessing the biofidelity of side impact dummies of various sizes and ages. Stapp Car Crash J. 2002;46:297–319.[PubMed] , [Google Scholar]), the abdominal biofidelity target suggested in van Ratingen et al. (1997 van Ratingen M, Twisk D, Schrooten M, Beusenberg M. Biomechanically based design and performance targets for a 3-year-old child crash dummy for frontal and side impact. Paper presented at: 41st Stapp Car Crash Conference; 1997. [Google Scholar]), and the biofidelity targets published in Rhule et al. (2013 Rhule H, Donnelly B, Moorhouse K, Kang YS. A methodology for generating objective targets for quantitatively assessing the biofidelity of crash test dummies. Paper presented at: 23rd Enhanced Safety of Vehicles Conference; 2013. [Google Scholar]). Regional and overall biofidelity rankings for each of the 3 ATDs were performed using both the ISO 9790 biofidelity rating system (ISO/TR 9790 1999) and the NHTSA's external biofidelity ranking system (BRS; Rhule et al. 2013 Rhule H, Donnelly B, Moorhouse K, Kang YS. A methodology for generating objective targets for quantitatively assessing the biofidelity of crash test dummies. Paper presented at: 23rd Enhanced Safety of Vehicles Conference; 2013. [Google Scholar]).

Results: All 3 6-year-old ATD's pelvises were rated as least biofidelic of the 4 body regions tested, based on both the ISO and BRS biofidelity rating systems, followed by the shoulder and abdomen, respectively. The thorax of all 3 ATDs was rated as the most biofidelic body region using the aforementioned biofidelity rating systems. The HIII 6-year-old ATD was rated last in overall biofidelity of the 3 tested ATDs, based on both rating systems. The Q6s ATD was rated as having the best overall biofidelity using both rating systems.

Conclusions: All 3 ATDs are more biofidelic in the thorax and abdomen than the shoulder and pelvis, with the pelvis being the least biofidelic of all 4 tested body regions. None of the 3 tested 6-year-old ATDs had an overall ranking of 2.0 or less, based on the BRS ranking. Therefore, it is expected that none of the 3 ATDs would mechanically respond like a postmortem human subject (PMHS) in a lateral impact crash test based on this ranking system. With respect to the ISO biofidelity rating, the HIII dummy would be considered unsuitable and the Q-series dummies would be considered marginal for assessing side impact occupant protection.     

German taxi drivers' experiences and expressions of driving anger: Are the driving anger scale and the driving anger expression inventory valid measures?

Objective: The objective of this article was 2-fold: firstly, we wanted to examine whether the original Driving Anger Scale (DAS) and the original Driving Anger Expression Inventory (DAX) apply to German professional taxi drivers because these scales have previously been given to professional and particularly to nonprofessional drivers in different countries. Secondly, we wanted to examine possible differences in driving anger experience and expression between professional German taxi drivers and nonprofessional German drivers.

Methods: We applied German versions of the DAS, the DAX, and the State–Trait Anger Expression Inventory (STAXI) to a sample of 138 professional German taxi drivers. We then compared their ratings to the ratings of a sample of 1,136 nonprofessional German drivers (Oehl and Brandenburg n.d. Oehl M, Brandenburg S. Driving anger in Germany: validation of the Driving Anger Scale (DAS). Saf Sci. n.d. submitted.  [Google Scholar]).

Results: Regarding our first objective, confirmatory factor analysis shows that the model fit of the DAS is better for nonprofessional drivers than for professional drivers. The DAX applies neither to professional nor to nonprofessional German drivers properly. Consequently, we suggest modified shorter versions of both scales for professional drivers. The STAXI applies to both professional and nonprofessional drivers. With respect to our second objective, we show that professional drivers experience significantly less driving anger than nonprofessional drivers, but they express more driving anger.

Conclusions: We conclude that the STAXI can be applied to professional German taxi drivers. In contrast, for the DAS and the DAX we found particular shorter versions for professional taxi drivers. Especially for the DAX, most statements were too strong for German drivers to agree to. They do not show behaviors related to driving anger expression as they are described in the DAX. These problems with the original American DAX items are in line with several other studies in different countries. Future investigations should examine whether (professional) drivers from further countries express their anger as proposed by the DAX. In addition, professional drivers experience less driving anger (DAS) and less general trait anger (STAXI) than nonprofessional drivers, but they report more driving anger expression (DAX) and more current general state anger (STAXI). Subsequent studies should therefore focus on different types of anger within the group of professional drivers.     

Creating Pedestrian Crash Scenarios in a Driving Simulator Environment

Objective: In 2012 in the United States, pedestrian injuries accounted for 3.3% of all traffic injuries but, disproportionately, pedestrian fatalities accounted for roughly 14% of traffic-related deaths (NHTSA 2014 NHTSA. Traffic Safety Facts 2012 Pedestrians. Washington, DC: Author; 2014. DOT HS 811 888. [Google Scholar]). In many other countries, pedestrians make up more than 50% of those injured and killed in crashes. This research project examined driver response to crash-imminent situations involving pedestrians in a high-fidelity, full-motion driving simulator. This article presents a scenario development method and discusses experimental design and control issues in conducting pedestrian crash research in a simulation environment. Driving simulators offer a safe environment in which to test driver response and offer the advantage of having virtual pedestrian models that move realistically, unlike test track studies, which by nature must use pedestrian dummies on some moving track.

Methods: An analysis of pedestrian crash trajectories, speeds, roadside features, and pedestrian behavior was used to create 18 unique crash scenarios representative of the most frequent and most costly crash types. For the study reported here, we only considered scenarios where the car is traveling straight because these represent the majority of fatalities. We manipulated driver expectation of a pedestrian both by presenting intersection and mid-block crossing as well as by using features in the scene to direct the driver's visual attention toward or away from the crossing pedestrian. Three visual environments for the scenarios were used to provide a variety of roadside environments and speed: a 20–30 mph residential area, a 55 mph rural undivided highway, and a 40 mph urban area.

Results: Many variables of crash situations were considered in selecting and developing the scenarios, including vehicle and pedestrian movements; roadway and roadside features; environmental conditions; and characteristics of the pedestrian, driver, and vehicle. The driving simulator scenarios were subjected to iterative testing to adjust time to arrival triggers for the pedestrian actions. This article discusses the rationale behind creating the simulator scenarios and some of the procedural considerations for conducting this type of research.

Conclusions: Crash analyses can be used to construct test scenarios for driver behavior evaluations using driving simulators. By considering trajectories, roadway, and environmental conditions of real-world crashes, representative virtual scenarios can serve as safe test beds for advanced driver assistance systems. The results of such research can be used to inform pedestrian crash avoidance/mitigation systems by identifying driver error, driver response time, and driver response choice (i.e., steering vs. braking).     

Driving performance comparing older versus younger drivers

Objectives: A cross-sectional study was conducted at the Touro University California campus to compare differences in reaction times and driving performance of younger adult drivers (18–40 years) and older adult drivers (60 years and older). Each test group consisted of 38 participants.

Methods: A Simple Visual Reaction Test (SVRT) tool was used to measure reaction times. The STISIM Drive M100 driving simulator was used to assess driving parameters. Driving performance parameters included mean lane position, standard deviation of mean lane position measured, mean speed, standard deviation of mean speed, car-following delay, car-following modulus, car-following coherence, off-road accidents, collisions, pedestrians hit, and traffic light tickets.

Results: Compared to younger participants, older drivers experienced significantly slower reaction times (510.0 ± 208.8 vs. 372.4 ± 96.1 ms, P =.0004), had more collisions (0.18 ± 0.39 vs. none, P =.0044), drove slower (44.6 ± 6.6 vs. 54.9 ± 11.7 mph, P <.0001), deviated less in speed (12.6 ± 4.3 vs. 16.8 ± 6.3, P =.0011), and were less able to maintain a constant distance behind a pace car (0.42 ± 0.23 vs. 0.59 ± 0.24; P =.0025).

Conclusions: Differences exist in driving patterns of older and younger drivers as measured by reaction times and driving simulator outcomes. These results are the first to compare these 2 specific adult age groups' driving performance as measured by a standardized driving simulator scenario. Identifying these differences is essential in addressing them and preventing future traffic injuries.     

Understanding the context of alcohol impaired driving for fatal crash–involved drivers: A descriptive case analysis

Objective: The objective of this study was to understand the social context and circumstances surrounding alcohol-impaired driving prior to fatal crash involvement for drivers with an illegal blood alcohol concentration (BAC ≥ 0.05 g/100 ml or 0.00 g/100 ml for restricted license holders).

Methods: Coroners' case reports investigating fatal crashes in South Australia over a 3-year period (2008–2010) were examined. The personal and crash characteristics of drivers with an illegal BAC were compared with those who had a legal BAC. For each driver with an illegal BAC, information was recorded including characteristics of last trip, location and social context of alcohol consumption, quantity and type of alcohol consumed, BAC level, presence of drugs, perceived alcohol intoxication, and alcohol dependence. Official traffic offense records were also obtained.

Results: Of the 284 fatal crashes included in the study, 34% (n = 95) involved a driver or rider with an illegal BAC. Prior to the crash, alcohol was most frequently consumed by drivers in rural areas, within private homes, and was part of normal social activities. Drivers recorded a high level of alcohol impairment, with a mean BAC of 0.173 g/100 ml and a level of alcohol dependence that was above the Australian national average (7.4 vs. 3.9%). In addition, 23% of drivers were known to be experiencing psychological stress at the time of the crash. The results also confirm that drink driving recidivism continues to be a significant problem, with 44% of drivers recording at least one prior alcohol driving offense.

Conclusions: Alcohol-impaired driving continues to be a leading cause of fatal crashes. The popularity of drinking at home, particularly in rural areas, has implications for police enforcement strategies and suggests that drink driving interventions that focus on community values and looking after friends might be beneficial. Importantly, the study highlights the need for a broader holistic approach to reduce the high levels of alcohol consumption and alcohol dependence underlying drink driving behavior.     

Identifying risky drivers with simulated driving

Objective: The present study aimed to examine whether high-risk drivers differ from low-risk drivers in driving behavior in a simulated environment.

Method: The 2 risk groups including 36 drivers (18 males and 18 females) performed driving tasks in a simulated environment. The simulated driving behaviors are compared between the 2 risk groups.

Results: The high-risk drivers drove much faster and exhibited larger offsets of the steering wheel than did the low-risk drivers in events without incidents. Additionally, the high-risk drivers used turn signals and horns less frequently than the low-risk drivers.

Conclusions: The present study revealed that the high-risk group differed from the low-risk group in driving behavior in a simulated environment. These results also suggest that simulated driving tasks might be useful tools for the evaluation of drivers’ potential risks.     

Injury prevention programs against distracted driving: Are they effective?

Introduction: Distracted driving (talking and/or texting) is a growing public safety problem, with increasing incidence among adult drivers. The aim of this study was to identify the incidence of distracted driving (DD) among health care providers and to create awareness against DD. We hypothesized that distracted driving is prevalent among health care providers and a preventive campaign against distracted driving would effectively decrease distracted driving among health care providers.

Methods: We performed a 4-phase prospective interventional study of all health care providers at our level 1 trauma center. Phase 1: one week of pre-intervention observation; phase 2: one week of intervention; phase 3: one week of postintervention observation; and phase 4: one week of 6 months of postintervention observation. Observations were performed outside employee parking garage at the following time intervals: 6:30–8:30 a.m., 4:40–5:30 p.m., and 6:30–7:30 p.m. Intervention included an e-mail survey, pamphlets and banners in the hospital cafeteria, and a postintervention survey. Hospital employees were identified with badges and scrubs, employees exiting through employee gate, and parking pass on the car. Outcome measure was incidence of DD pre, post, and 6 months postintervention.

Results: A total of 15,416 observations (pre: 6,639, post: 4,220, 6 months post: 4,557) and 520 survey responses were collected. The incident of DD was 11.8% among health care providers. There was a significant reduction in DD in each time interval of observation between pre- and postintervention. On subanalysis, there was a significant decrease in talking (P = .0001) and texting (P = .01) while driving postintervention compared to pre-intervention. In the survey, 35.5% of respondents admitted to DD and 4.5% respondents were involved in an accident due to DD. We found that 77% respondents felt more informed after the survey and 91% respondents supported a state legislation against DD. The reduction in the incidence of DD postintervention was sustained even at 6-month follow-up.

Conclusion: There was a 32% reduction in the incidence of distracted driving postintervention, which remained low even at 6-month follow-up. Implementation of an effective injury prevention campaign could reduce the incidence of distracted driving nationally.     

Are you okay to drive? Commuting behavior and blood alcohol concentrations among restaurant diners

Objective: Drink driving is widely recognized as a major road safety problem. In Australia, health promotion messages encourage monitoring the number of standard drinks consumed prior to driving. This pilot research aimed to investigate commuting behavior and blood alcohol concentration (BAC) of diners, including intended drivers, at Sunshine Coast restaurants.

Methods: Five hundred and forty-four diners (n = 260 males) consented to participate in a brief interview and to use a breathalyzer device to measure their BAC.

Results: Forty percent of participants advised they don't drink and drive (34% of males, 45% of females; 67.25% of <17–20 years, 30.5% of 50–59 years), and of the remaining participants, 75% advised they count the number of their drinks (69% of males, 84% of females; 32% of <17–20 years, 82% of 50–59 years), while 10% of participants monitored their BAC by how they were feeling (12% of males, 6% of females). Thirty-seven percent of participants said it was easy/very easy to estimate their BAC (41% of males; 33% of females; 21% of <17–20 years, 43% of 50–59 years). The actual BAC was less than expected for 56% of participants, with one-third underestimating BAC and some intended drivers having an actual BAC in excess of the 0.05 limit.

Conclusions: Given the proportion of diners who reported they count the number of drinks, or use feelings as a way to gauge BAC, coupled with the considerable proportion who underestimated their BAC, a safer public health message is to avoid driving if you intend to drink. In addition, targeted intervention for experienced drivers (and, arguably, drinkers) appears warranted, as every participant aged less than 21 years who stated he or she would drive home indeed had a zero BAC. Interestingly every female driver who stated she would be driving home also had a legal BAC, suggesting gender-specific intervention.     

Driving in mild cognitive impairment: The role of depressive symptoms

Objectives: Previous studies indicate a negative association between depression and driving fitness in the general population. Our goal was to cover a gap in the literature and to explore the link between depressive symptoms and driving behavior in individuals with mild cognitive impairment (MCI) through the use of a driving simulator experiment.

Methods: Twenty-four individuals with MCI (mean age = 67.42, SD = 7.13) and 23 cognitively healthy individuals (mean age = 65.13, SD = 7.21) were introduced in the study. A valid driving license and regular car use served as main inclusion criteria. Data collection included a neurological/neuropsychological assessment and a driving simulator evaluation. Depressive symptomatology was assessed with the Patient Health Questionnaire (PHQ-9).

Results: Significant interaction effects indicating a greater negative impact of depressive symptoms in drivers with MCI than in cognitively healthy drivers were observed in the case of various driving indexes, namely, average speed, accident risk, side bar hits, headway distance, headway distance variation, and lateral position variation. The associations between depressive symptoms and driving behavior remained significant after controlling for daytime sleepiness and cognition.

Conclusions: Depressive symptoms could be a factor explaining why certain patients with MCI present altered driving skills. Therefore, interventions for treating the depressive symptoms of individuals with MCI could prove to be beneficial regarding their driving performance.     

Crash risk perception of sleepy driving and its comparisons with drink driving and speeding: Which behavior is perceived as the riskiest?

Objective: Driver sleepiness is a major crash risk factor but may be underrecognized as a risky driving behavior. Sleepy driving is usually rated as less of a road safety issue than more well-known risky driving behaviors, such as drink driving and speeding. The objective of this study was to compare perception of crash risk of sleepy driving, drink driving, and speeding.

Methods: Three hundred Australian drivers completed a questionnaire that assessed crash risk perceptions for sleepy driving, drink driving, and speeding. Additionally, the participants' perceptions of crash risk were assessed for 5 different contextual scenarios that included different levels of sleepiness (low, high), driving duration (short, long), and time of day/circadian influences (afternoon, nighttime) of driving.

Results: The analysis confirmed that sleepy driving was considered a risky driving behavior but not as risky as high levels of speeding (P < .05). Yet, the risk of crashing at 4 a.m. was considered as equally risky as low levels of speeding (10 km over the limit). The comparisons of the contextual scenarios revealed driving scenarios that would arguably be perceived as quite risky because time of day/circadian influences were not reported as high risk.

Conclusions: The results suggest a lack of awareness or appreciation of circadian rhythm functioning, particularly the descending phase of circadian rhythm that promotes increased sleepiness in the afternoon and during the early hours of the morning. Yet, the results suggested an appreciation of the danger associated with long-distance driving and driver sleepiness. Further efforts are required to improve the community's awareness of the impairing effects from sleepiness and, in particular, knowledge regarding the human circadian rhythm and the increased sleep propensity during the circadian nadir.     

The useful field of view assessment predicts simulated commercial motor vehicle driving safety

Objective: The Useful Field of View (UFOV) assessment, a measure of visual speed of processing, has been shown to be a predictive measure of motor vehicle collision (MVC) involvement in an older adult population, but it remains unknown whether UFOV predicts commercial motor vehicle (CMV) driving safety during secondary task engagement. The purpose of this study is to determine whether the UFOV assessment predicts simulated MVCs in long-haul CMV drivers.

Method: Fifty licensed CMV drivers (M_age = 39.80, SD = 8.38, 98% male, 56% Caucasian) were administered the 3-subtest version of the UFOV assessment, where lower scores measured in milliseconds indicated better performance. CMV drivers completed 4 simulated drives, each spanning approximately a 22.50-mile distance. Four secondary tasks were presented to participants in a counterbalanced order during the drives: (a) no secondary task, (b) cell phone conversation, (c) text messaging interaction, and (d) e-mailing interaction with an on-board dispatch device.

Results: The selective attention subtest significantly predicted simulated MVCs regardless of secondary task. Each 20 ms slower on subtest 3 was associated with a 25% increase in the risk of an MVC in the simulated drive. The e-mail interaction secondary task significantly predicted simulated MVCs with a 4.14 times greater risk of an MVC compared to the no secondary task condition. Subtest 3, a measure of visual speed of processing, significantly predicted MVCs in the email interaction task. Each 20 ms slower on subtest 3 was associated with a 25% increase in the risk of an MVC during the email interaction task.

Conclusions: The UFOV subtest 3 may be a promising measure to identify CMV drivers who may be at risk for MVCs or in need of cognitive training aimed at improving speed of processing. Subtest 3 may also identify CMV drivers who are particularly at risk when engaged in secondary tasks while driving.     

Driving self-regulation and ride service utilization in a multicommunity,multistate sample of U.S. older adults

Objectives: This study examined a multicommunity alternative transportation program available 24 hours a day, 7 days a week, for any purpose, offering door-through-door service in private automobiles to members who either do not drive or are transitioning away from driving. Specific aims were to describe the characteristics of members by driving status and ride service usage of these members.

Methods: Data came from administrative records maintained by a nonprofit ride service program and include 2,661 individuals aged 65+ residing in 14 states who joined the program between April 1, 2010, and November 8, 2013. Latent class analysis was used to group current drivers into 3 classes of driving status of low, medium, and high self-regulation, based on their self-reported avoidance of certain driving situations and weekly driving frequency. Demographics and ride service use rate for rides taken through March 31, 2014, by type of ride (e.g., medical, social, etc.) were calculated for nondrivers and drivers in each driving status class.

Results: The majority of ride service users were female (77%) and aged 65–74 years (82%). The primary method of getting around when enrolling for the transportation service was by riding with a friend or family member (60%). Among the 67,883 rides given, nondrivers took the majority (69%) of rides. Medical rides were the most common, accounting for 40% of all rides.

Conclusions: Reported ride usage suggests that older adults are willing to use such ride services for a variety of trips when these services are not limited to specific types (e.g., medical). Further research can help tailor strategies to encourage both nondrivers and drivers to make better use of alternative transportation that meets the special needs of older people.     

Prevalence and characteristics of road traffic injuries among young drivers in Oman, 2009–2011

Objectives: Studies from different parts of the world have indicated that the impact of road traffic incidents disproportionally affects young adults. Few known studies have been forthcoming from Arabian Gulf countries. Within Oman, a high proportion of the population is under the age of 20. Coupled with the drastic increase in motorization in recent years, there is a need to understand the state of road safety among young people in Oman. The current research aimed to explore the prevalence and characteristics of road traffic injuries among young drivers aged 17–25 years.

Methods: Crash data from 2009 to 2011 were extracted from the Directorate General of Traffic, Royal Oman Police (ROP) database in Oman. The data were analyzed to explore the impact of road crashes on young people (17–25 years), the characteristics of young driver crashes, and how these differ from older drivers and to identify key predictors of fatalities in young driver crashes.

Results: Overall, young people were overrepresented in injuries and fatalities within the sample time period. Though it is true that many young people in crashes were driving at the time, it was also evident that young people were often victims in a crash caused by someone else. Thus, to reduce the impact of road crashes on young people, there is a need to generally address road safety within Oman. When young drivers were involved in crashes they were predominantly male. The types of crashes these drivers have can be broadly attributed to risk taking and inexperience. Speeding and nighttime driving were the key risk factors for fatalities.

Conclusion: The results highlight the need to address young driver safety in Oman. From these findings, the introduction of a graduated driver licensing system with nighttime driving restrictions could significantly improve young driver safety.     

The use and safety of medications known to affect driving in Jordan: A cross-sectional study

Objectives: The aim of this study was to estimate the main driving-impairing medications used by drivers in Jordan, the reported frequency of medication side effects, the frequency of motor vehicle crashes (MVCs) while using driving-impairing medicines, as well as factors associated with MVCs.

Methods: A cross-sectional study involving 1,049 individuals (age 18–75 years) who are actively driving vehicles and taking at least one medication known to affect driving (anxiolytics, antidepressants, hypnotics, antiepileptics, opioids, sedating antihistamines, hypoglycemic agents, antihypertensives, central nervous system [CNS] stimulants, and herbals with CNS-related effects) was conducted in Amman, Jordan, over a period of 8 months (September 2013–May 2014) using a structured validated questionnaire.

Results: Sixty-three percent of participants noticed a link between a medicine taken and feeling sleepy and 57% stated that they experience at least one adverse effect other than sleepiness from their medication. About 22% of the participants reported having a MVC while on medication. Multiple logistic regression analysis showed that among the participants who reported having a crash while taking a driving-impairing medication, the odds ratios were significantly higher for the use of inhalant substance (odds ratio [OR] = 2.787, P = .014), having chronic conditions (OR = 1.869, P = .001), and use of antiepileptic medications (OR = 2.348, P = .008) and significantly lower for the use of antihypertensives (OR = 0.533, P = .008).

Conclusion: The study results show high prevalence of adverse effects of medications with potential for driving impairment, including involvement in MVCs. Our findings highlight the types of patient-related and medication-related factors associated with MVCs in Jordan, such as inhalant use, presence of chronic conditions, and use of antiepileptics.     

The characteristics of young Indigenous drink drivers in Queensland,Australia

Objective: This study investigates the prevalence and characteristics of first drink driving convictions among young Aboriginal and Torres Strait Islander (Indigenous) Australians (aged from 14 to 24) and considers some of the risk factors associated with recidivism.

Methods: Convictions recorded between 2006 and 2013 were extracted from the Queensland Department of Justice and Attorney General database. Convictions were regrouped by gender, age, Accessibility/Remoteness Index of Australia classification, and sentence severity. Chi-square analyses and logistic regression were conducted to identify group differences in offense characteristics for gender and recidivism (recidivists versus nonrecidivists).

Results: The sample consisted of 1,583 individuals (74.1% males) convicted in the 8-year period. Gender comparisons showed that there was no significant difference in age at time of first offense, blood alcohol concentration (BAC) level at apprehension, or type of penalty received between males and females. However, males received larger fines and longer periods of license disqualification. Comparisons for reoffending and non-reoffending revealed that males, drivers aged 14–17 years of age and 18 to 20 years of age, and inner regional drivers were more likely to reoffend.

Conclusions: There were limited differences between females and males or recidivists and nonrecidivists at first conviction. Convictions for drink driving may provide an opportunity for early alcohol intervention with Indigenous young drivers (<20 years) because it is likely to be an individual's first alcohol-related conviction.     

The use of navigation systems in naturalistic driving

Objective: In this study, we assessed the use of portable navigation systems in everyday driving by applying in-vehicle naturalistic driving.

Method: Experienced users of navigation systems, 7 females and 14 males, were provided with a specially equipped vehicle for approximately 1 month. Their trips were recorded using 4 cameras, Global Positioning System (GPS) data, and other sensor data. The drivers’ navigation system use data were coded from the video recordings, which showed how often and for how long the system was activated and how often and for how long a driver operated the system.

Results: The system was activated for 23% of trips, predominantly on longer and unique trips. Analyses of the percentage of time for which the speed limit was exceeded showed no evidence of differences between trips for which the navigation system was used or not used. On trips for which the navigation system was activated, participants spent about 5% of trip time interacting with the device. About 40% of interacting behavior took place in the first 10% of the trip time, and about 35% took place while the car was standing still or moving at a very low speed; that is, 0–10 km/h.

Conclusion: These results shed light on how and when drivers use navigation systems. They suggest that although drivers regulate their use of such systems to some extent, they often perform risky tasks while driving.