Detecting the Change Points of Driving Risk for Novice Teenage Drivers Using Recurrent Event Models

• Activity Number: 23
• Type: Topic Contributed
• Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Government Statistics Section
• Author(s): Qing Li*+ and Feng Guo and Simons-Morton Bruce
• Companies: Virginia Tech and Virginia Tech and NICHD
• Keywords: Change point ; recurrent event ; NTDS ; Bayesian framework

Abstract

Studies have shown that driving risk for teenagers tends to be high in the early period after licensure but drops quickly. To better understand their driving behaviors, driving data in the first 18 months of 42 newly licensed teenager drivers aged 16-17 were collected using video monitoring techniques in the Naturalistic Teenage Driving Study (NTDS). This paper focuses on the time of change for occurrence rate of Crash and Near-Crash (CNC) of these teenager drivers. A driver may encounter multiple CNC events over lifetime or during driving-learning period, therefore, the CNC were treated as recurrent events. The differences among the drivers are incorporated into the Poisson process model as random coefficient rates. Constant baseline intensity function with one or two change points is considered. Analysis of the NTDS data is carried out with the combination of frequentist approach and Bayesian framework. The paper advanced the application of the change-point detection method to multiple drivers with recurrent events.