The Effectiveness of Commercial Driver Training: A Times Series Modeling Approach

• Activity Number: 172
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Transportation Statistics Interest Group
• Author(s): Xingwei Wu* and Huizhong Guo and Linda Boyle
• Companies: University of Washington and University of Washington and University of Washington
• Keywords: ARIMA ; commercial drivers ; time series model ; driver training

Abstract

Crashes involving commercial drivers can be quite severe and the need to regulate training has become acute. In Washington State, laws requiring Commercial Driver License (CDL) training has been in effect since Jan 2008. This paper uses a time series model to examine the effectiveness of the intervention program to reduce moving violations. Crashes and moving violations are examined for drivers issued a CDL in Washington State for the years 2003 to 2012. An autoregressive integrated moving average (ARIMA) mode was used with the findings showing that violation rates could be substantially reduced within 6 months of licensure. However, after 1 year of licensure, the reduction was not as prominent. Although the outcomes appear promising, extraneous factors may have contributed to the reduction in moving violations. The findings have implications for future educational programs targeted toward commercial drivers.