Development of a Crash Prediction Model for Older Drivers

Older drivers are more likely to crash and to be fatally injured when involved in a crash. (IIHS, 2001; Li, 2003) As the driving population ages, jurisdictions are struggling to find ways to re-assess older driver competency in an equitable, cost effective manner. Because it will allow drivers to be identified as "high risk" based on objective criteria - recent driving performance - crash prediction modeling is a logical mechanism for identifying high-risk older drivers. In addition, the factors contributing the greatest weight to the prediction model will likely identify potential areas for focused retraining. We propose to use historical data from Massachusetts' statewide crash, driver licensing, and citation datasets to derive and validate a crash prediction model that will identify a subgroup of older drivers at high risk for a near term injury causing crash. First, a crash prediction model will be derived using information from Massachusetts' crash, citation, and driver history datasets. These data will be deterministically linked using unique identifiers at the driver level for each of the more than 670,000 licensed drivers aged over 64 in 2004. The primary outcome of interest will be driver participation in an injury crash in 2004. Secondary analysis will evaluate for significant differences in the model if moderate or serious injury/fatal crashes are the primary outcome. We intend to use Poisson or negative binomial regression modeling, depending on data characteristics.


    • English


    • Status: Active
    • Funding: $162771.00
    • Contract Numbers:



    • Sponsor Organizations:

      Research and Innovative Technology Administration

      Department of Transportation
      1200 New Jersey Avneue, SE
      Washington, DC  United States  20590
    • Performing Organizations:

      University of Massachusetts Transportation Center

      University of Massachusetts
      Amherst, MA  United States  01003
    • Principal Investigators:

      Collura, John

      Knodler, Michael

    • Start Date: 20080901
    • Expected Completion Date: 0
    • Actual Completion Date: 20090831
    • Source Data: RiP Project 19938

    Subject/Index Terms

    Filing Info

    • Accession Number: 01480780
    • Record Type: Research project
    • Source Agency: New England University Transportation Center
    • Contract Numbers: DTRT07-G-0001, UMAR20-9
    • Files: UTC, RiP
    • Created Date: May 7 2013 1:02AM