Replacement of distractions with other distractions : a propensity-based approach to estimating realistic crash odds ratios for driver engagement in secondary tasks.
20200501 ST [electronic version only]
Flannagan, C. Bärgman, J. & Bálint, A.
Transportation Research Part F - Traffic Psychology and Behaviour, Vol. 63 (May 2019), p. 186-192, 15 ref.
|Samenvatting||As Automated Vehicles (AVs) enter the fleet at lower levels of automated (SAE, 2018), the need for human drivers to remain engaged in the driving task will continue. Thus, understanding driver distraction and estimating the reduction in risk associated with removing distractions is important as AV technology develops. While previous research (e.g., Dingus et al., 2016) has estimated large odds ratios (i.e., 3–4) for using cell-phones while driving, countermeasures directed at reducing cell-phone use have not realized large crash reductions. One reason may be that drivers may replace cell-phone use with other risky activities and that odds ratios (ORs) have often compared cell-phone use to ideal driving rather than a realistic reference. Using data from the second Strategic Highway Research Program (SHRP2), we developed two cell-phone propensity models, one with age and one without, to develop weights for events without cell phone use. Using these weights, we estimated the probability of engagement in a variety of tasks in place of cell-phone use. We also estimated weighted odds ratios for cell-phone use (all uses) and cell-phone talking only. Weighted ORs are lower than unweighted ORs and much lower than ORs compared to ideal driving. This is consistent with the idea that in practice, even if cell-phone bans are effective at reducing cell-phone use, they may not greatly reduce risk because drivers may replace cell-phone use with other distracting activities in the same situations in which they normally use cell phones while driving. We also discuss the influence of young drivers on our results. Younger drivers in the dataset are more likely to use cell phones and thus are influential in the propensity model results. (Author/publisher)|
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