Use of codes data to improve estimates of at-fault risk for elderly drivers

Abstract

The fastest-growing demographic in the United States is people aged 65 and over. Because elderly drivers may experience decline in the physical and mental faculties required for driving (which could lead to unsafe driving behaviors), it is critical to determine whether elderly drivers are more likely than younger drivers to be at fault in a crash. This study uses Kentucky crash data and linked hospital and emergency department records to evaluate whether linked data can more accurately estimate the crash propensity of elderly drivers to be at-fault in injury crashes. The Kentucky crash data is edited to conform to the General Use Model (GUM), with crash propensities for linked data compared to propensities developed using the GUM dataset alone. The quasi-induced exposure method is used to determine crash exposure. Factors such as age, gender, and crash location are explored to assess their influence on the risk of a driver being at fault in an injury crash. The overall findings are consistent with previous research — elderly drivers are more likely than younger drivers to be at fault in a crash. Linking crash with hospital and emergency department records could also establish a clearer understanding of the injury crash propensity of all age groups. Equipped with this knowledge, transportation practitioners can design more targeted and effective countermeasures and safety programs to improve the safety of all motorists.