Citation

Lack CD, Berkow KS, Gao Y. Accid. Anal. Prev. 2021; 155: e106105.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.aap.2021.106105

PMID

unavailable

Abstract

Many researchers have developed predictive models of crashes based on the safety scores of commercial truck companies, but these studies have been based on aggregated data at the truck company level-evaluating the total crashes and violations per company over a period of time. This level of aggregation obscures critical information. Here, a new approach to organizing non-aggregated data is taken, and logistic regression and random forest models are applied to non-aggregated FMCSA roadside inspection, violation, and crash data at the specific vehicle level. Resampling methods are used to improve model performance where there are relatively few events of interest-crashes. These results point not to specific "unsafe" drivers, but rather, patterns of unsafe behaviors or conditions that predict roadway crashes. Working toward reducing these behaviors systematically could save lives on US highways.

Language: en

Keywords

Safety culture; Crash prediction; FMCSA crash data