Modeling the effects of drive error and impairment on crash injury severity

Under the safe system approach, drivers will inevitably make mistakes and errors that can contribute to crashes. Driver errors are widely cited as one of the critical reasons for crash occurrence in safety literature. Despite universal acceptance, the discussion of their effects on crash injury outcomes is limited. The primary objective of this study is to quantify the effects of driver errors in the crash injury severity model at urban intersections. To obtain research objectives, driver errors were categorized as sequential events in a driving task. Combinations of driver error categories were created and ranked based on their odds-ratios with injury severity levels. Furthermore, driver impairment was considered in combination with the driver error categories to explore the compounding effects on crash consequences. Multiple ordered logit models were estimated to quantify the effect of driver errors and their interactions with driver impairment on the crash injury levels at uncontrolled, sign-controlled, and signal-controlled intersections. Improved model performance was observed when driver error combinations were modeled along with traditional crash variables. The exploration of multiple model formulations indicated that including driver impairment as an error category can yield informative inferences from both theoretical and modeling perspectives. Appropriate countermeasures were recommended for major contributing factors to improve intersection safety based on modeling results. It is expected that this study can offer specific insights into explanatory variables and help safety professionals to develop effective countermeasures.