Developing a Taxonomy of Human Errors and Violations That Lead to Crashes

Details

• Creators:
  Khattak, Asad ; Ahmad, Numan ; Wali, Behram ; Dumbaugh, Eric
• Corporate Creators:
  University of Tennessee, Knoxville. Center for Transportation Research ; Florida Atlantic University
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; Collaborative Sciences Center for Road Safety
• Subject/TRT Terms:
  Built Environment Built Environment And Roadway Characteristics Crash Analysis Crash Causes Driver Errors Driver Performance Human Error Safety Naturalistic Driving Study Taxonomy Traffic Crashes Traffic Violations

  More +
• Publication/ Report Number:
  CSCRS-R19
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report (May 2018 – Nov. 2019)
• Corporate Publisher:
  Collaborative Sciences Center for Road Safety
• Abstract:
  Driver errors and violations are highly relevant to the safe systems approach as human errors tend to be a predominant cause of crash occurrence. This study develops a deeper understanding of critical pre-crash driver errors and violations that have significant potential in reducing dangerous behaviors on roadways. A driver error and violation taxonomy (TDEV) is developed to understand the factors that contribute to crashes and it is applied using naturalistic data from detailed real-world driving and monitoring ecosystems. Specifically, data from the Naturalistic Driving study (NDS) is analyzed to understand the origin of different types of human errors, especially focusing on how they relate to roadway and built environments. Different types of human errors and violations are categorized using a perception, recognition, decision, and reaction framework and we explore how errors and violations contribute to safety-critical events in naturalistic settings. For the NDS data available to the research team, human errors and violations contributed to 93% of the observed crashes, while roadway factors contributed to 17%, vehicle factors contributed in 1%, and 4% of crashes contained unknown factors. The most common human errors were recognition and decision errors, which occurred in 39% and 34% of crashes, respectively. These two error types occurred more frequently (each contributing about 39% of crashes) when business or industrial structures were present. While the most prevalent errors in crashes and near crashes were recognition errors, performance errors such as weak judgement (8%) were strongly correlated with crash occurrence. Path analysis uncovered direct and indirect relationships between key built-environment factors, errors and violations, and crash propensity. Possibly due to their complexity for drivers, urban environments are associated with higher chances of crashes and they can induce more recognition errors, which associate with even higher chances of crashes. Finally, this report discusses implications for crash investigations.
• Format:
  PDF
• Funding:
  69A3551747113
• Collection(s):
  University Transportation Centers US Transportation Collection
• Main Document Checksum:
  urn:sha-512:007ffd6da4707fb680b8506d4947b38b8e7e7f443e2b3aabf036ebd80ee687b0dc48880548e4363f374e179f02b1e00a8531ae2f01ea52b88d337592aafae130
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/56829/dot_56829_DS1.pdf
• File Type:
  [PDF - 3.74 MB ]