cchips@email.chop.edu
Roberts Center for Pediatric Research 2716 South Street, 13th Floor Philadelphia, PA 19146
RESEARCH PORTFOLIO
Principal Investigator: Rachel Myers, PhD
The goal of this study is to create a unique source of epidemiologic crash data that enables novel description of use and installation patterns of child restraints among crash-involved children and— following linkage with hospital discharge and death certificate data—novel description of child occupant injury outcomes based on restraint use and installation (rear- vs. forward-facing).
2013-2014
- Understanding and Predicting Human Driving Behaviors via Machine Learning Models (Multiple Year Project)
Principal Investigator: Yi-Ching Lee, PhD
Poor speed management is a key factor in teen driver crashes. In order to inform new training and technology to reduce teen crash risk due to poor speed management, a more complete understanding of this complex driving behavior is needed. Early results from our current CChIPS work indicate that machine learning techniques can be used to model drivers’ speed management behaviors. These techniques have the potential to become part of in-vehicle monitoring…- Exploration of the Effect of Positive Reinforcement on Teen Driving Behavior(2013-2014)
Principal Investigator: Yi-Ching Lee, PhD
Principal Investigator: Yi-Ching Lee, PhD, The Children's Hospital of Philadelphia
2015-2016
- Understanding and Predicting Human Driving Behaviors via Machine Learning Models (Multiple Year Project)
Principal Investigator: Yi-Ching Lee, PhD
Poor speed management is a key factor in teen driver crashes. In order to inform new training and technology to reduce teen crash risk due to poor speed management, a more complete understanding of this complex driving behavior is needed. Early results from our current CChIPS work indicate that machine learning techniques can be used to model drivers’ speed management behaviors. These techniques have the potential to become part of in-vehicle monitoring…
2014-2015
- Understanding and Predicting Human Driving Behaviors via Machine Learning Models (Multiple Year Project)
Principal Investigator: Yi-Ching Lee, PhD
Poor speed management is a key factor in teen driver crashes. In order to inform new training and technology to reduce teen crash risk due to poor speed management, a more complete understanding of this complex driving behavior is needed. Early results from our current CChIPS work indicate that machine learning techniques can be used to model drivers’ speed management behaviors. These techniques have the potential to become part of in-vehicle monitoring…- Exploration of the Effect of Positive Reinforcement on Teen Driving Behavior
Principal Investigator: Yi-Ching Lee, PhD
New technologies such as in-vehicle monitoring systems offer the potential to improve safety by generating alerts and positive feedback when certain driving practices are detected. With the combination of positive feedback and motivational incentives, behaviors from risk-taking-prone teen and young drivers may be changed to be more positive and less risky. This study aims to understand the effect of positive reinforcement on the shaping of teen and youth…
2010-2011
- Effect of Distraction on Teen Driving Performance in an Emotionally Realistic Driving Simulator
Principal Investigator: Yi-Ching Lee, PhD
The objectives of this research are to: (1) create in-vehicle stressful tasks to distract teen drivers while measuring teens' abilities to handle environmental stressful events, and (2) measure teens' beliefs and behaviors about in-vehicle distractions by measuring their risk-taking and decision-making characteristics.