This study revises the odds ratios (ORs) of secondary tasks estimated by Virginia Tech Transportation Institute (VTTI), who conducted the 100-Car naturalistic driving study. An independent and objective re-counting and re-analysis of all secondary tasks observed in the 100-Car databases removed misclassification errors and epidemiological biases. The corrected estimates of secondary task crude OR and Population Attributable Risk Percent (PAR%) for crashes and near-crashes vs. a random baseline were substantially lower for almost every secondary task, compared to the VTTI estimates previously reported. These corrected estimates were then adjusted for confounding from demographics, time of day, weekday-weekend, and closeness to junction by employing secondary task counts from a matched baseline from a later VTTI 100-Car analysis. This matched baseline caused most OR estimates to decline even further. The final revised OR estimates for many secondary tasks even reversed from a causal to a preventive effect. The OR estimate for “Not Distracted” (i.e., not performing any secondary task) was a surprising 2.3 (95% confidence interval 2.0 to 2.7), with a PAR% of 29% (95% confidence interval 24% to 34). Hence, crashes and near-crashes in the population would increase in a range from 24% to 34% if all secondary tasks were eliminated while driving. A further new finding was that illegal, improper, aggressive, reckless, and impaired driver behaviors likely confounded and falsely elevated even these final revised OR estimates. These findings reject the common but erroneous belief that every secondary task performed while driving is a “distraction” that automatically increases crash risk.
Revised Odds Ratio Estimates of Secondary Tasks: A Re-Analysis of the 100-Car Naturalistic Driving Study Data
Sae Technical Papers
SAE 2015 World Congress & Exhibition ; 2015
2015-04-14
Aufsatz (Konferenz)
Englisch
British Library Conference Proceedings | 2015
|Creation of the Naturalistic Engagement in Secondary Tasks (NEST) distracted driving dataset
Online Contents | 2015
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