Driver crash risk factors and prevalence evaluation using naturalistic driving data

Abstract

The accurate evaluation of crash causal factors can provide fundamental information for effective transportation policy, vehicle design, and driver education. Naturalistic driving (ND) data collected with multiple onboard video cameras and sensors provide a unique opportunity to evaluate risk factors during the seconds leading up to a crash. This paper uses a National Academy of Sciences-sponsored ND dataset comprising 905 injurious and property damage crash events, the magnitude of which allows the first direct analysis (to our knowledge) of causal factors using crashes only. The results show that crash causation has shifted dramatically in recent years, with driver-related factors (i.e., error, impairment, fatigue, and distraction) present in almost 90% of crashes. The results also definitively show that distraction is detrimental to driver safety, with handheld electronic devices having high use rates and risk.D uring recent years, the percentage of crashes involving some type of driver error or impairment before the crash was thought to be as high as 94% (1). Factors such as vehicle failures, roadway design or condition, or environment composed lower crash percentages. Naturalistic driving studies (NDSs) offer a unique opportunity to study driver performance and behavior experienced in the real world with actual consequences and risks (2-4). The NDS research method developed at the Virginia Tech Transportation Institute (VTTI) involves equipping volunteer participants' vehicles with advanced, unobtrusive instrumentation (e.g., cameras, sensors, radar) that automatically and continuously collects driving parameters-including speed, time to collision, global positioning system (GPS) location, acceleration, and eye glance behavior-from key-on to key-off (2, 5). The recently completed Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS), sponsored by the Transportation Research Board (TRB) of the National Academy of Sciences (NAS), is the largest NDS of its kind, capturing more than 35 million miles of continuous naturalistic driving data and 2 petabytes (PB) of video, kinematic, and audio data from more than 3,500 participants (5).NDSs provide insight into the factors that cause crashes, giving researchers the opportunity to observe actual driver behavior and to accurately understand drivers' performance during the minutes or seconds leading up to a crash (6, 7). However, previous NDSs captured a relatively small number of crashes (2,8). To obtain a statistically valid sample of crash factors in earlier NDSs, surrogate measures (e.g., near-crash events) were integrated into analyses. Near-crashes are operationally defined as having the observable factors that could lead to a crash, with one difference present: the performance of a successful evasive maneuver. Although previous studies used near-crashes as a surrogate for estimating crash risk, the accuracy and validity of combining crashes and near-crashes to estimate driver risk are just beginning to be understood (9). With the com...

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