Center for Injury Research and Prevention

Improve Teen Driver Behaviors Research

improve teen driver behaviors-SHRP2
CIRP researchers are using SHRP2 data in studies to help improve teen driver behaviors.

Through the analysis of naturalistic and simulated driving data, the Teen Driving Safety Research team at the Children's Hospital of Philadelphia Research Institute is working to reduce the frequency and severity of crashes involving teen drivers. The researchers are conducting a number of studies to assess the driving skills of newly licensed teens, how various driving scenarios affect teen driver behaviors and emotions, and how interventions affect teen driver behaviors and skill levels. The following projects are currently underway:

Driving Analytics: Comparison of Teen and Adult Naturalistic Car-following Patterns

This study identified the specific predictors of crashes and near crashes among young drivers and determined if these predictors vary across age and skill level using the Strategic Highway Research Program 2 (SHRP2) naturalistic dataset.

Read more about the research.

Principal Investigators: Helen Loeb, PhD; Thomas Seacrist, MBEFunding: Center for Child Injury Prevention Studies (CChIPS)

Machine Learning Techniques: Online Prediction of Driving Behavior and Generation of Customized Feedback via Machine Learning Models

Machine learning is an analytic technique that shows promise in providing algorithms to help predict and manage certain teen driver behaviors that can contribute to crash risk, including speed management. In this multi-year study, researchers successfully created a mechanism to receive real-time data from a driving simulator to train machine learning models using this data to make predictions of teen driver behaviors. They were also able to provide feedback about speed management to teen drivers while in the simulator.

The researchers are continuing to analyze the data to help inform the development of interventions to help teen drivers develop crucial speed management skills. The mechanism they created also shows great promise in preventing teens from engaging in other dangerous driving behaviors via personalized feedback models.

Read the study abstract

Read a blog post about the research.

Principal Investigators: Yi-Ching Lee, PhD; Santiago Ontañón, PhDFunding: Center for Child Injury Prevention Studies (CChIPS)