ACCIDENT REDUCTION FACTORS AND CAUSAL INFERENCE IN TRAFFIC SAFETY STUDIES: A REVIEW

Accident reduction factors are used to predict the change in accident occurrence which a countermeasure can be expected to cause. Since ethical and legal obstacles preclude the use of randomized experiments when evaluating traffic safety improvements, empirical support for the causal effectiveness of accident countermeasures comes entirely from observational studies. Drawing on developments in causal inference initiated by Donald Rubin, it is argued here that the mechanism by which sites are selected for application of a countermeasure should be included as part of a study's data model, and that when important features of the selection mechanism are neglected, existing methods for estimating accident reduction factors become inconsistent. A promising, but neglected, way out of these difficulties lies in developing rational countermeasure selection methods which also support valid causal inference of countermeasure effects.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Corporate Authors:

  Elsevier

  The Boulevard, Langford Lane
  Kidlington, Oxford  United Kingdom  OX5 1GB
• Authors:
  • Davis, G A
• Publication Date: 2000-1

Language

• English

Media Info

• Features: Appendices; References; Tables;
• Pagination: p. 95-109
• Serial:
  • Accident Analysis & Prevention
  • Volume: 32
  • Issue Number: 1
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Before and after studies; Countermeasures; Traffic crashes; Traffic safety
• Uncontrolled Terms: Causal inference
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 00796966
• Record Type: Publication
• Source Agency: National Highway Traffic Safety Administration
• Report/Paper Numbers: HS-042 956
• Files: HSL, TRIS, ATRI, USDOT
• Created Date: Aug 18 2000 12:00AM