Evaluating the Influence of Crashes on Driving Behavior using Naturalistic Driving Study Data

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Date
2015-07-16
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Publisher
National Surface Transportation Safety Center for Excellence
Abstract

It is hypothesized that intense events such as crashes could influence driver behavior and driving risk. This study evaluated the influences of crash events on driver behavior and driving risk using data from the 100-Car Naturalistic Driving Study, which included 51 crashes from primary drivers. Two metrics were used to measure driver behavior and risk: the proportion of baselines where the drivers were engaged in complex and moderate secondary tasks and the intensity of the near-crashes (NCs) and safety-critical incidents (SCIs). For the distraction analysis, we sampled 882 6-second baseline epochs within 15-hour windows before and after crashes. Results from a mixed binomial regression model indicated that the percentage of baselines where drivers engaged in complex secondary tasks dropped after crashes (odds ratio = 0.54; 95% CI [0.32, 0.93]). The driving risk analysis used the intensity of SCIs and NCs to measure the driving risk. Since there are typically more than one SCI and NC events before and after a crash, we developed four alternative recurrent event models to evaluate the influence of crashes based on actual driving time. The driving period was divided into several phases based on the relationship to crashes, and the intensities of these periods were compared. Results show a reduction in SCI intensity after the first crash (intensity rate ratio = 0.82; 95% CI [0.693, 0.971]) and the second crash (intensity rate ratio = 0.47; 95% CI [0.377, 0.59]) for male drivers. Females were observed to have a nonsignificant response to the first crash, but SCI intensity decreased after the second crash (intensity rate ratio = 0.43; 95% CI [0.342, 0.547]). This study indicated that crashes do have a positive effect on drivers’ behavior in terms of distraction and driving risk.

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Keywords
naturalistic driving studies, transportation safety, Recurrent event models
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