Do Elevated Gravitational-Force Events While Driving Predict Crashes and Near Crashes?

Division of Epidemiology, Statistics, and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, 6100 Executive Boulevard, Room 7B13M, Bethesda, MD 20892-7510, USA.
American journal of epidemiology (Impact Factor: 5.23). 01/2012; 175(10):1075-9. DOI: 10.1093/aje/kwr440
Source: PubMed


The purpose of this research was to determine the extent to which elevated gravitational-force event rates predict crashes and near crashes. Accelerometers, global positioning systems, cameras, and other technology were installed in vehicles driven by 42 newly licensed Virginia teenage drivers for a period of 18 months between 2006 and 2009. Elevated gravitational force and crash and near-crash events were identified, and rates per miles driven were calculated. (One mile = 1.6 km.) The correlation between crashes and near crashes and elevated gravitational-force event rates was 0.60. Analyses were done by using generalized estimating equations with logistic regression. Higher elevated gravitational-force event rates in the past month substantially increased the risk of a crash in the subsequent month (odds ratio = 1.07, 95% confidence interval: 1.02, 1.12). Although the difference in this relation did not vary significantly by time, it was highest in the first 6 months compared with the second and third 6-month periods. With a receiver operating characteristic curve, the risk models showed relatively high predictive accuracy with an area under the curve of 0.76. The authors conclude that elevated gravitational-force event rates can be used to assess risk and to show high predictive accuracy of a near-future crash.

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    • "KRD rate was highly correlated with CNC rate (r = 0.60). In receiver operating curve analyses, the area under the curve was 0.76, showing high predictive validity with CNC rates (Simons-Morton et al., 2012). Analyses were conducted using the counts of the elevated g-force, accounting for the number of miles driven by each teenage driver. "
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    Preview · Article · Sep 2015 · Journal of Safety Research
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    • "The Cronbach's alpha for the composite measure was 0.78 (Simons- Morton et al., 2011b), and was highly correlated with crashes and near crashes (r = 0.60). In receiver operating curve analyses the area under the curve was 0.76, showing high predictive validity (Simons-Morton et al., 2012). Analyses were conducted using the counts of the elevated g-force, accounting for the number of miles driven by each teenage and adult driver. "
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    ABSTRACT: This study examined the driving behavior of 42 parent–teenager dyads for 18 months, under naturalistic driving conditions. At baseline participants’ personality characteristics were assessed. Objective risky driving measures (kinematic risky driving) were captured by accelerometers for the duration of the study. To estimate teenage and parent correlations in kinematic risky driving, separate Poisson regression models were fit for teenagers and parents. Standardized residuals were computed for each trip for each individual. Correlations were obtained by estimating the Spearman rank correlations of the individual average residuals across teenagers and parents. The bootstrap technique was used to estimate the standard errors associated with the parent–teenager correlations. The overall correlation between teenage and parent kinematic risky driving for the 18-month study period was positive, but weak (r = 0.18). When the association between parent and teenagers’ risky driving was adjusted for shared personality characteristics, the correlation reduced to 0.09. Although interesting, the 95% confidence intervals on the difference between these two estimates overlapped zero. We conclude that the weak similarity in parent–teen kinematic risky driving was partly explained by shared personality characteristics.
    Full-text · Article · Aug 2014 · Accident; analysis and prevention
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    • "Simons-Morton et al. (2012) conducted a naturalistic teenage driving study to explore whether the observed events of interest , referred to as " elevated gravitational-force " events in the study, could be used to predict at-fault crashes and near crashes for teenage drivers. The elevated gravitational-force events were events satisfying either longitudinal acceleration greater than 0.35 g, deceleration greater than 0.45 g, lateral acceleration greater than 0.05 g, or yaw rate change greater than 6 • in 3 s (Table 1, Simons-Morton et al., 2012). Simons-Morton and colleagues reviewed video to determine whether a crash or near crash is the subject driver's fault. "
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    ABSTRACT: There has been considerable research conducted over the last 40 years using traffic safety-related events to support road safety analyses. Dating back to traffic conflict studies from the 1960s these observational studies of driver behavior have been criticized due to: poor quality data; lack of available and useful exposure measures linked to the observations; the incomparability of self-reported safety-related events; and, the difficulty in assessing culpability for safety-related events. This study seeks to explore the relationships between driver characteristics and traffic safety-related events, and between traffic safety-related events and crash involvement while mitigating some of those limitations. The Virginia Tech Transportation Institute 100-Car Naturalistic Driving Study dataset, in which the participants' vehicles were instrumented with various cameras and sensors during the study period, was used for this study. The study data set includes 90 drivers observed for 12-13 months driving. This study focuses on single vehicle run-off-road safety-related events only, including 14 crashes and 182 safety-related events (30 near crashes, and 152 crash-relevant incidents). Among the findings are: (1) drivers under age 25 are significantly more likely to be involved in safety-related events and crashes; and (2) significantly positive correlations exist between crashes, near crashes, and crash-relevant incidents. Although there is still much to learn about the factors affecting the positive correlation between safety-related events and crashes, a Bayesian multivariate Poisson log-normal model is shown to be useful to quantify the associations between safety-related events and crash risk while controlling for driver characteristics.
    Full-text · Article · Jul 2014 · Accident Analysis & Prevention
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