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.     

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.     

Slow eyelid closure as a measure of driver drowsiness and its relationship to performance

Objective: Slow eyelid closure is recognized as an indicator of sleepiness in sleep-deprived individuals, although automated ocular devices are not well validated. This study aimed to determine whether changes in eyelid closure are evident following acute sleep deprivation as assessed by an automated device and how ocular parameters relate to performance after sleep deprivation.

Methods: Twelve healthy professional drivers (45.58 ± 10.93 years) completed 2 randomized sessions: After a normal night of sleep and after 24 h of total sleep deprivation. Slow eye closure (PERCLOS) was measured while drivers performed a simulated driving task.

Results: Following sleep deprivation, drivers displayed significantly more eyelid closure (P < .05), greater variation in lane position (P < .01) and more attentional lapses (P < .05) compared to after normal sleep. PERCLOS was moderately associated with variability in both vigilance performance (r = 0.68, P < .05) and variation in lane position on the driving task (r = 0.61, P < .05).

Conclusions: Automated ocular measurement appears to be an effective means of detecting impairment due to sleep loss in the laboratory.     

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).     

Experimental investigation of the effect of occupant characteristics on contemporary seat belt payout behavior in frontal impacts

Objective: The goal of this study was to investigate the influence of the occupant characteristics on seat belt force vs. payout behavior based on experiment data from different configurations in frontal impacts.

Methods: The data set reviewed consists of 58 frontal sled tests using several anthropomorphic test devices (ATDs) and postmortem human subjects (PMHS), restrained by different belt systems (standard belt, SB; force-limiting belt, FLB) at 2 impact severities (48 and 29 km/h). The seat belt behavior was characterized in terms of the shoulder belt force vs. belt payout behavior. A univariate linear regression was used to assess the factor significance of the occupant body mass or stature on the peak tension force and gross belt payout.

Results: With the SB, the seat belt behavior obtained by the ATDs exhibited similar force slopes regardless of the occupant size and impact severities, whereas those obtained by the PMHS were varied. Under the 48 km/h impact, the peak tension force and gross belt payout obtained by ATDs was highly correlated to the occupant stature (P =.03, P =.02) and body mass (P =.05, P =.04), though no statistical difference with the stature or body mass were noticed for the PMHS (peak force: P =.09, P =.42; gross payout: P =.40, P =.48). With the FLB under the 48 km/h impact, highly linear relationships were noticed between the occupant body mass and the peak tension force (R² = 0.9782) and between the gross payout and stature (R² = 0.9232) regardless of the occupant types.

Conclusions: The analysis indicated that the PMHS characteristics showed a significant influence on the belt response, whereas the belt response obtained with the ATDs was more reproducible. The potential cause included the occupant anthropometry, body mass distribution, and relative motion among body segments specific to the population variance. This study provided a primary data source to understand the biomechanical interaction of the occupant with the restraint system. Further research is necessary to consider these effects in the computational studies and optimized design of the restraint system in a more realistic manner.     

Driving violations and health promotion behaviors among undergraduate students: Self-report of on-road behavior

Introduction: The purposes of this study are to characterize Israeli undergraduate students' driving violations in the terms of problem behavior theory and to identify whether there is any relationship between driving violations and health risk behaviors, daring behaviors, excitement seeking, and health promotion behaviors.

Methods: This study is based on a structured self-reported anonymous questionnaire distributed to undergraduate students in an academic institution. The sample included 533 undergraduate students (374 females and 159 males). The mean age was 23.4 (SD = 1.4, range = 5).

Results: A higher prevalence of self-reported driving violations was found among males in comparison to females. All substance use measures were positively related to driving violations; for example, use of cigarettes (OR = 4.287, P <.001) and water pipes (odds ratio [OR] = 3.000, P <.001) as well as binge drinking (OR = 5.707, P <.001) and regular cannabis smoking (OR = 5.667, P <.001) raise the probability of committing rare driving violations. The strongest predictive factors for the frequent driving violations group were alcohol consumption–related variables: binge drinking (OR = 2.560, P <.01) and drunkenness (OR = 2.284, P <.05). Strong odd ratios were also found between the frequent driving violations group and selling or dealing drugs (12.143, P <.001), and stealing something valuable (13.680, P <.001). The strongest predicted variable for the rare driving violations group was physical confrontation due to verbal disagreement (3.439, P <.05) and the concept that selling or dealing drugs is socially acceptable (2.521, P <.05). The probability of executing rare driving violations was higher for subjects who reported intense physical workout regimens (OR = 1.638, P <.05).

Conclusions: Problem behavior theory succeeded in explaining health risk behavior and driving violations. This study shows that bachelors tend to be more involved in risk behaviors, such as substance use, excitement-seeking behaviors, and daring behaviors and are active physically and thus constitute a risk group for driving violations. As such, intervention resources should be directed toward this group.