Omitted variable bias in crash reduction factors.

Details

• Creators:
  Noland, Robert B. ; Adediji, Yemi
• Corporate Creators:
  Rutgers University
• Corporate Contributors:
  United States. Dept. of Transportation. Research and Innovative Technology Administration
• Subject/TRT Terms:
  • Accident data
  • Bias (Statistics)
  • Highway design
  • Geometric design
  • Highway safety
• Publication/ Report Number:
  49997-38-25
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  University Transportation Research Center
• Abstract:
  Transportation planners and traffic engineers are increasingly turning to crash reduction factors to evaluate changes in road ; geometric and design features in order to reduce crashes. Crash reduction factors are typically estimated based on segmenting a ; highway and associating crashes with geometric features; this allows statistical methods to be applied to the data. Concurrently ; there is a stream of research that relies on spatial units of analysis to examine crashes; these typically use broad features of the ; road network combined with socio-economic and demographic factors that are associated with crashes. In this paper, we ; examine whether omission of these spatial factors in a link-based geometric model results in omitted variable bias. Our results ; suggest that there is no change in coefficient signs, but that there is a reduction in the magnitude of estimates. The sign of spatial ; variables, however, is quite different when combined into a link-based model. We also find substantial variability in coefficient ; estimates, and discuss the implications of these results for the use of crash reduction factors.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29707/dot_29707_DS1.pdf
• File Type:
  [PDF - 2.41 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:7c04377e207bf26dac7735657647625f0514445aff7adf09a0a12835a47d71a9