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.     

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.     

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.     

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.     

Physiological signal analysis for fatigue level of experienced and inexperienced drivers

Objective: We studied the changes in driving fatigue levels of experienced and inexperienced drivers at 3 periods of the day: 9:00 a.m.–12:00 p.m., 12:00 p.m.–2:00 p.m., and 4:00 p.m.–6:00 p.m.

Methods: Thirty drivers were involved in 120-min real-car driving, and sleepiness ratings (Stanford Sleepiness Scale, SSS; Hoddes et al. 1973 Hoddes E, Zarcone V, Smythe H, Phillips R, Dement WC. Quantification of sleepiness: a new approach. Psychophysiology. 1973;10:431–436.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]), electroencephalogram (EEG) signals, and heart rates (HRs) were recorded. Together with principal component analysis, the relationship between EEG signals and HR was explored and used to determine a comprehensive indicator of driving fatigue. Then the comprehensive indicator was assessed via paired t test.

Results: Experienced and inexperienced drivers behaved significantly differently in terms of subjective fatigue during preliminary trials. At the beginning of trials and after termination, subjective fatigue level was aggravated with prolonged continuous driving. Moreover, we discussed the changing rules of EEG signals and HR and found that with prolonged time, the ratios of δ and β waves significantly declined, whereas that of the θ wave significantly rose. The ratio of (α + θ)/β significantly rose both before trials and after termination, but HR dropped significantly. However, one-factor analysis of variance shows that driving experience significantly affects the θ wave, (α + θ)/β ratio, and HR.

Conclusions: We found that in a monotonous road environment, fatigue symptoms occurred in inexperienced drivers and experienced drivers after about 60 and 80 min of continuous driving, respectively. Therefore, as for drivers with different experiences, restriction on continuous driving time would avoid fatigued driving and thereby eliminate traffic accidents. We find that the comprehensive indicator changes significantly with fatigue level. The integration of different indicators improves the recognition accuracy of different driving fatigue levels.     

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.