Modeling the effects of drive error and impairment on crash injury severity
DOI:
https://doi.org/10.55329/obon4340Keywords:
compounding effect, driver errors, error categorization, impairment, injury severity modelAbstract
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.
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Author Biographies
Mohammad Razaur Rahman Shaon, University of Connecticut, the United States of America
Mohammad Razaur Rahman Shaon, Ph.D., PE, PTOE, RSP1 is an Associate Research Scientist at Connecticut Transportation Institute. He received his Ph.D. degree from the University of Wisconsin-Milwaukee with a focus in Transportation Engineering. His research work examines the fundamental nature of road user behavior, particularly how traffic safety is influenced by driver behaviors and proposed statistical and econometric models to account for driver behavior variables into crash occurrences. He has authored more than 25 peer-reviewed research articles and conference proceedings.
CRediT contribution: Conceptualization, Formal analysis, Investigation, Methodology, Visualization, Writing—original draft, Writing—review & editing.
Xiao Qin, University of Wisconsin–Milwaukee, the United States of America
Xiao Qin, Ph.D., PE is a professor in civil & environmental engineering at the University of Wisconsin Milwaukee. He is also the director of UWM’s Institute for Physical Infrastructure & Transportation (IPIT). He received his PhD from the University of Connecticut. His research work focused on transportation data analytics and highway safety.
CRediT contribution: Conceptualization, Methodology, Writing—review & editing.
Eric Jackson, University of Connecticut, the United States of America
Eric Jackson, PhD is an Associate Research Professor at UConn. He also serves as currently the Executive Director of the Connecticut Transportation Institute and the Director of the Connecticut Transportation Safety Research Center (CTSRC) at UConn. He completed his B.S. in civil engineering from the University of Kentucky in 2002 and his Masters (2004) and PhD (2008) at the University of Connecticut. His research efforts include conducting research on driver behavior and vehicle dynamics impacts on vehicle emissions. He assisted in the complete overhaul and modernization of crash data and safety analysis in the state. Dr. Jackson’s current research has focused on improving the crash data collection process in Connecticut as well as providing public access to crash data and transportation safety analysis tools.
CRediT contribution: Conceptualization, Supervision, Writing—review & editing.
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Copyright (c) 2025 Mohammad Razaur Rahman Shaon, Xiao Qin, Eric Jackson
This work is licensed under a Creative Commons Attribution 4.0 International License.
