Big Data Methods for Simplifying Traffic Safety Analyses

Data used for safety analyses have unique characteristics that are not found in other areas of research. For example, one important characteristic that has been recently documented in the literature is related to datasets that contained a large amount of zero responses. For such datasets, the number of sites where no crash is observed is so large that traditional distributions and regression models cannot be used efficiently. In another example, naturalistic data that are now available for transportation and safety analyses usually include terabytes of information, in which traditional statistical tools cannot be used efficiently for establishing relationships. So far, very little research has been devoted about how to handle such datasets and when specific analysis tools or statistical models could be used based on the characteristics of the data. The primary objectives of this research are to evaluate statistical and other related methods that could simplify the analysis of the unique attributes related to safety and transportation-related big data. The secondary objective is to present guidelines that can be used by researchers and practitioners for simplifying data analyses. The project can have a significant impact in safety and research related to big data.


  • English


  • Status: Active
  • Funding: $293890
  • Contract Numbers:


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Safety through Disruption University Transportation Center

    Virginia Tech Transportation Institute
    Blacksburg, VA  United States  24060
  • Project Managers:

    Harwood, Leslie

  • Performing Organizations:

    Texas A&M Transportation Institute

    Texas A&M University System
    3135 TAMU
    College Station, TX  United States  77843-3135

    Virginia Tech Transportation Institute

    3500 Transportation Research Plaza
    Blacksburg, Virginia  United States  24061

    San Diego State University

    5500 Campanile Dr
    San Diego, CA  United States  92182
  • Principal Investigators:

    Lord, Dominique

  • Start Date: 20170501
  • Expected Completion Date: 20181231
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01632139
  • Record Type: Research project
  • Source Agency: Safety through Disruption University Transportation Center
  • Contract Numbers: 69A3551747115
  • Files: UTC, RiP
  • Created Date: Apr 4 2017 12:05PM