Files and links (1)
Accepted (AM)Open Access (License Unspecified), Open
Journal article
Novel use of a virtual driving assessment to classify driver skill at the time of licensure
TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, v 87
May 2022
PMID: 36267629
Featured in Collection : UN Sustainable Development Goals @ Drexel
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 clas-sified 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.
Metrics
Details
- Title
- Novel use of a virtual driving assessment to classify driver skill at the time of licensure
- Publication Details
- TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, v 87
- Publisher
- ELSEVIER SCI LTD; OXFORD
- Grant note
- Research reported in this publication was supported by the Eunice Kennedy Shriver Institute of Child Health and Human Development of the National Institutes of Health (NIH) under award number 1R21HD099635-01 (NICHD) ; by the US National Highway Traffic Safety Administration through the Ohio Traffic Safety Office (NHTSA) ; and by the State of Ohio's Department of Administrative Services, managed by the Ohio Bureau of Motor Vehicles; by the Annenberg Public Policy Center at the university of Pennsylvania; by The Distinguished Chair in the Department of Pediatrics at Children's Hospital of Philadelphia (CHOP) ; as well as a gift from New Jersey Manufacturers (NJM) Insurance Group to CHOP. Data collection was financed by NHTSA and the Ohio Bureau for Motor vehicles. Data management and analyses were supported jointly by NICHD, Ohio Bureau of Motor Vehicles, NHTSA, and the Annenberg Public Policy Center at the University of Pennsylvania. The preparation of this manuscript was supported jointly by NICHD, NHTSA, the Annenberg Public Policy Center and NJM.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000797737700006
- Scopus ID
- 2-s2.0-85129498614
- Other Identifier
- 991021861311404721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Psychology, Applied
- Transportation