2018•Chalmers Research (Chalmers University of Technology)Requires access

What Are Drivers Doing When They Aren't on the Cell Phone?

Carol A. C. Flannagan, Jonas Bärgman, András Ferenc Bálint

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Abstract

Cell-phone bans have been motivated by previous research estimating large odds ratios (i.e., 3-4) for using cell-phones while driving. However, large crash reductions have not been realized. One reason may be that drivers may replace cell-phone use with other risky activities and that ORs have often compared cell-phone use to ideal driving rather than a realistic reference. Using SHRP2 data, 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 ORs 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.

About this research paper

What this paper is about

Cell-phone bans have been motivated by previous research estimating large odds ratios (i.e., 3-4) for using cell-phones while driving. However, large crash reductions have not been realized. One reason may be that drivers may replace cell-phone use with other risky activities and that ORs have often compared cell-phone use to ideal driving rather than a realistic reference. Using SHRP2 data, 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 ORs 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.

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Available abstract

Cell-phone bans have been motivated by previous research estimating large odds ratios (i.e., 3-4) for using cell-phones while driving. However, large crash reductions have not been realized. One reason may be that drivers may replace cell-phone use with other risky activities and that ORs have often compared cell-phone use to ideal driving rather than a realistic reference. Using SHRP2 data, 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 ORs 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.

Key concepts: Phone, Odds, Computer science, Advertising, Computer security, Internet privacy, Business, Logistic regression

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