With population aging and life expectancy increasing, elderly drivers have been
increasing quickly in the United States and the heterogeneity among them with
age is also increasingly non-ignorable. Based on traffic crash data of
Pennsylvania from 2011 to 2019, this study was designed to identify this
heterogeneity by quantifying the relationship between age and crash
characteristics using linear regression. It is found that for elderly
driver-involved crashes, the proportion leading to casualties significantly
increases with age. Meanwhile, the proportions at night, on rainy days, on snowy
days, and involving driving under the influence (DUI) decrease linearly with
age, implying that elderly drivers tend to avoid traveling in risky
scenarios.
Regarding collision types, elderly driver-involved crashes are mainly composed of
angle, rear-end, and hit-fixed-object collisions, proportions of which increase
linearly, decrease linearly, and keep consistent with age, respectively. The
increase in angle collisions is primarily attributed to more crashes at
stop-controlled intersections. The findings suggest that it may be inappropriate
to take elderly drivers as homogeneous or simply categorize them into several
age groups. Instead, regarding elderly drivers, age should be taken as
continuous in future studies to display their linearly changing trends. This is
one of the pioneering studies exploring the heterogeneity across elderly drivers
with age with solid data analysis. The findings are expected to provide new
insights for agencies to develop customized countermeasures regarding elderly
traffic safety in the aging society.
