Near Crashes as Crash Surrogate for Naturalistic Driving Studies

Abstract

Naturalistic driving is an innovative method for investigating driver behavior and traffic safety. However, as the number of crashes observed in naturalistic driving studies is typically small, crash surrogates are needed. A study evaluated the use of near crashes as a surrogate measure for assessment of the safety impact of driver behaviors and other risk factors. Two metrics, the precision and bias of risk estimation, were used to assess whether near crashes could be combined with crashes. The principles and exact conditions for improved precision and unbiased estimation were proposed and applied to data from the 100-Car Naturalistic Driving Study. The analyses indicated that a positive relationship exists between the frequencies of contributing factors for crashes and for near crashes. The study also indicated that analyses based on combined crash and near-crash data consistently underestimate the risk of contributing factors compared to use of crash data alone. At the same time, the precision of the estimation will increase. This consistent pattern allows investigators to identify true high-risk behaviors while qualitatively assessing potential bias. In summary, the study concluded that the use of near crashes as a crash surrogate provides definite benefit when naturalistic studies are not large enough to generate sufficient numbers of crashes for statistical analysis.

Driver crash risk factors and prevalence evaluation using naturalistic driving data

“…The SHRP 2 NDS results estimate the risk of talking on a handheld cell phone to be 2.2 times higher than model driving, or slightly higher than the overall distraction risk. This result seems consistent with other naturalistic and epidemiological studies when one considers the following: (i) the comparison made within the SHRP 2 NDS uses model driving, as defined previously, which is not true of crash database analyses; and (ii) a recent study (9) showed that odds ratios are somewhat underestimated when minor collisions and near-crashes are used as surrogates for crashes in the estimates obtained in previous NDSs. Because this analysis is (to our knowledge) the first of its kind to use only crashes, the determined odds ratio of 2.2 for talking on a handheld cell phone seems consistent and accurate.…”

“…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 completion of the SHRP 2 NDS, however, researchers now have access to an order-of-magnitude larger sample size that allows the sole use of crash events to determine the safety outcome for risk factor evaluation.…”

The impact of sleep disorders on driving safety—findings from the Second Strategic Highway Research Program naturalistic driving study

“…Second, empirical and epidemiological studies investigated crashes but omitted nearcrashes. Near-crashes share similar causal factors with crashes; therefore, both should be considered as safety critical events (Guo, Klauer, Hankey, & Dingus, 2010). Third, most driving studies on sleep disorders were limited in their participant samples, preventing investigation into any interactions between demographic factors and sleep disorders on crash risks (Barger et al, 2005;Ellen et al, 2006;Lerman et al, 1995).…”

Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas from the Shanghai Naturalistic Driving Study

“…To overcome the restriction of driving simulation and field experiment such as short test horizon and limited controlled settings [21], the 100-car Naturalistic Driving Study (NDS) was the first large-scale NDS conducted in the US [22,23], followed by the 60-Taxi NDS in Japan [24]. The UDRIVE Naturalistic Driving Study was conducted from 2012 to 2016 in seven countries in Europe [25].…”