Principal Investigator: Elizabeth Walshe, PhD, Children's Hospital of Philadelphia

The CHOP-developed virtual driving assessment (VDA) can safely and reliably expose young drivers to common crash scenarios. This study seeks to mine the richness of the dynamic VDA data that may provide better prediction of crash risk than previous approaches. Specifically, we will explore a new dynamic approach, Time Intervals-Related Patterns (TIRP) analysis, which transforms time-based data into meaningful intervals and makes patterns easier to analyze. The utility of applying these advanced methods to driving data in order to diagnostically predict which young drivers crash early in licensure will be explored.