Developing a taxonomy of human errors and violations that lead to crashes

Human errors and violations are highly relevant to the safe systems approach as human error tends to dominate crash occurrence, contributing to 80%-90% of crashes. A better understanding of “critical reasons for the critical pre-crash events” has significant potential in reducing deadly behaviors on roadways. A key gap in the literature relates to the origin of the different types of human errors, e.g., whether they begin with intentional actions or unintentional actions, and how they relate to the built environment. The project will be organized into the following tasks: Task A: Review relevant work that will be integrated into a taxonomy -- as part of identifying gaps in the relevant literature, the research team will identify data limitations that may pose a threat to study results. After the team develops a taxonomy based on Naturalistic Driving Study (NDS) data, the team will match the potential countermeasures to the crash contributory factors at hand, based on insights from the literature. Task B: Process and prepare the naturalistic driving study database and develop a framework -- in addition to theoretical developments, new data analytic methods will be used to extract valuable information about errors and violations and their respective mechanisms from driving and crash data. While a substantial amount of the work will be conceptual, data from the SHRP NDS will be used. Task C: Develop a methodology to classify crash-contributing errors -- the project will explore the role of human cognition, information acquisition, processing and use, and rational behavior as they relate to human errors in crashes. A classification of the full range of errors and violations can help focus on predominant error types and facilitate in the design of effective prevention and mitigation tools and strategies. Task D: Develop a “safety matrix” that quantifies the contributions of different factors for different scenarios -- in this task, the team will develop safety matrices to quantify the proportions of different crash-contributing factors. The relationships between error-producing environments and errors recorded in crashes will add a new dimension to the existing understanding of errors. Task E: Apply a modeling approach for analyzing relationships -- the overall framework would be a Structural Equation Modeling (SEM) approach. Correlations between human errors and various factors will be quantified. Task F: Use the safety matrix to explore implications for road safety in the future -- in this task, the team will explore the implications of the results for improving current and future road safety. The aim is to expand the knowledge-base for implementation of more informed decisions about safe vehicles, safe people, safe speeds, and safe environments. Task G: Final report and dissemination -- after completion of the project, the research team will document the results so they can be used by engineers and planners in the future.


  • English


  • Status: Active
  • Funding: $79,722
  • 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:

    Collaborative Sciences Center for Road Safety

    University of North Carolina, Chapel Hill
    Chapel Hill, NC  United States  27514
  • Project Managers:

    Sandt, Laura

  • Performing Organizations:

    University of Tennessee, Knoxville

    Center for Transportation Research
    Conference Center Building
    Knoxville, TN  United States  37996-4133

    Florida Atlantic University, Boca Raton

    Boca Raton, FL  United States  33431
  • Principal Investigators:

    Dumbaugh, Eric

    Khattak, Asad

  • Start Date: 20180501
  • Expected Completion Date: 20210331
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01668385
  • Record Type: Research project
  • Source Agency: Collaborative Sciences Center for Road Safety
  • Contract Numbers: 69A3551747113
  • Files: UTC, RIP
  • Created Date: May 1 2018 1:31PM