@article{ref1,
title="Investigating the interaction between age and liability for crashes at stop-sign-controlled intersections",
journal="Transportation research interdisciplinary perspectives",
year="2022",
author="Alhomaidat, Fadi and Abushattal, Mousa and Morgan Kwayu, Keneth and Kwigizile, Valerian",
volume="14",
number="",
pages="e100612-e100612",
abstract="The present study examined State of Michigan Traffic Crash Reports filed between 2015 and 2019 to explore the interaction between age and liability for crashes at stop-sign-controlled intersections. A driver's liability for a crash was derived from the &quot;Hazardous Action&quot; field in each crash report. The likelihood of assigning liability to an elderly driver was examined in light of pre-crash actions defined in each report's &quot;Actions Prior to Crash&quot; field. Logistic regression was applied to calculate odds ratios used to explain the likelihood. Furthermore, Random Forest machine learning technique was used to predict driver liability based on pre-crash actions. Distraction, number of travel lanes, and driving under the influence of alcohol or drugs were significant predictors of the likelihood that an elderly driver was at-fault in a crash. A &quot;going straight&quot; pre-crash action by an elderly driver was the best indicator of liability, regardless of the pre-crash action by a young driver. For interaction scenarios, an elderly driver going straight at a stop-sign-controlled intersection was associated with a lower likelihood of being liable for a crash. Turning actions increased the likelihood of the elderly driver being liable for a crash. The results can be used to appraise countermeasures that improve the safety of elderly drivers at stop-controlled intersections.<p /> <p>Language: en</p>",
language="en",
issn="2590-1982",
doi="10.1016/j.trip.2022.100612",
url="http://dx.doi.org/10.1016/j.trip.2022.100612"
}