Novel use of a virtual driving assessment to classify driver skill at the time of licensure |
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| Affiliation: | 1. Children’s Hospital of Philadelphia, Philadelphia, PA, USA;2. University of Michigan School of Public Health, MI, USA;3. University of Pennsylvania, Philadelphia, PA, USA;4. Diagnostic Driving, Inc., Philadelphia, PA, USA;5. Computer Science Department, Drexel University, Philadelphia, PA, USA;1. Department of Psychology, University of Oslo, Oslo, Norway;2. Institute of Transport Economics, Gaustadalleen 21, 0349 Oslo, Norway;1. TS2-LESCOT, Univ Gustave Eiffel, IFSTTAR, Univ Lyon, F-69675 Lyon, France;2. Equipe de recherche PsyCAP, Cerema, France;3. TS2-UMRESTTE, Univ Gustave Eiffel, IFSTTAR, Univ Lyon, F-69675 Lyon, France;1. Faculty of Medicine and Health Sciences, Université de Sherbrooke, Longueuil, Quebec, Canada;2. Charles Lemoyne Hospital Research Centre, Longueuil, Quebec, Canada;3. University of Michigan Transportation Research Institute, Ann Arbor, Michigan;4. Department of Community and Behavioral Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado;5. Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland;1. University of Pennsylvania, School of Nursing, Center for Global Women''s Health, Center for Health Equity Research, Claire Fagin Hall, 418 Curie Boulevard, Philadelphia, PA 19104-4217, USA;2. Center for Injury Research and Prevention, The Children''s Hospital of Philadelphia, 3535 Market Street, Suite 1150, Philadelphia, PA 19104, USA;3. Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA;4. Division of General Pediatrics, Perelman School of Medicine at the University of Pennsylvania, 295 John Morgan Building, 3620 Hamilton Walk, Philadelphia, PA 19104, USA;5. National Science Foundation Center for Child Injury Prevention Studies, The Children''s Hospital of Philadelphia, 3535 Market Street, Suite 1150, Philadelphia, PA 19104, USA;1. Virginia Tech Transportation Institute, Blacksburg, Virginia;2. Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland;3. Public Policy Center, University of Iowa, Iowa City, Iowa;4. Texas A&M Transportation Institute, College Station, Texas |
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| Abstract: | Motor vehicle crash rates are highest immediately after licensure, and driver error is one of the leading causes. Yet, few studies have quantified driving skills at the time of licensure, making it difficult to identify at-risk drivers before independent driving. Using data from a virtual driving assessment implemented into the licensing workflow in Ohio, this study presents the first population-level study classifying degree of skill at the time of licensure and validating these against a measure of on-road performance: license exam outcomes. Principal component and cluster analysis of 33,249 virtual driving assessments identified 20 Skill Clusters that were then grouped into 4 major summary “Driving Classes”; i) No Issues (i.e. careful and skilled drivers); ii) Minor Issues (i.e. an average new driver with minor vehicle control skill deficits); iii) Major Issues (i.e. drivers with more control issues and who take more risks); and iv) Major Issues with Aggression (i.e. drivers with even more control issues and more reckless and risk-taking behavior). Category labels were determined based on patterns of VDA skill deficits alone (i.e. agnostic of the license examination outcome). These Skill Clusters and Driving Classes had different distributions by sex and age, reflecting age-related licensing policies (i.e. those under 18 and subject to GDL and driver education and training), and were differentially associated with subsequent performance on the on-road licensing examination (showing criterion validity). The No Issues and Minor Issues classes had lower than average odds of failing, and the other two more problematic Driving Classes had higher odds of failing. Thus, this study showed that license applicants can be classified based on their driving skills at the time of licensure. Future studies will validate these Skill Cluster classes in relation to their prediction of post-licensure crash outcomes. |
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| Keywords: | New Driver Skills License Examination Virtual Driving Assessment Novice Drivers Driver Safety VDA" },{" #name" :" keyword" ," $" :{" id" :" pc_KtGW4YYZk1" }," $$" :[{" #name" :" text" ," _" :" Virtual Driving Assessment GLD" },{" #name" :" keyword" ," $" :{" id" :" pc_UzrRw1itfm" }," $$" :[{" #name" :" text" ," _" :" Graduated Driver Licensing |
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