Accident reduction factors and causal inference in traffic safety studies: A review

Research output: Contribution to journalShort surveypeer-review

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Abstract

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.

Original languageEnglish (US)
Pages (from-to)95-109
Number of pages15
JournalAccident Analysis and Prevention
Volume32
Issue number1
DOIs
StatePublished - Jan 2000

Bibliographical note

Funding Information:
The author would like to thank two referees, as well as several individuals attending the 1999 meeting of the Transportation Research Board, for helpful comments on earlier drafts of this paper. This research was supported by the Minnesota Department of Transportation. However, this paper represents results of research conducted by the author and does not necessarily represent the views or policies of the Minnesota Department of Transportation.

Keywords

  • Accident countermeasures
  • Accident reduction factor
  • Before-after studies
  • Rubin causal model

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