An enhanced empirical bayesian method for identifying road hotspots and predicting number of crashes

Alexander S. Lee, Wei Hua Lin, Gurdiljot Singh Gill, Wen Cheng

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The Empirical Bayesian (EB) method has been widely used for traffic safety analysis. It is well known that the EB method is powerful in handling the regression-to-the-mean bias that would often arise in traffic safety analysis. A prerequisite for applying the EB method for the estimation of the safety of a road segment is to identify a group of similar road segments. In this article, the authors intend to enhance the EB method by incorporating a similarity measure based on the Proportion Discordance Ratio (PDR) into the procedure to identify similar road segments safety wise. Specifically, a methodology to assess and objectively quantify similarity among road segments based on crash patterns is developed, where each crash pattern contains a unique combination of selected crash-related features. Improvement in predicting the number of crashes that would occur in road segments by applying the EB method enhanced by the PDR is demonstrated through a case study.

Original languageEnglish (US)
Pages (from-to)1-17
Number of pages17
JournalJournal of Transportation Safety and Security
DOIs
StateAccepted/In press - May 26 2018

Keywords

  • Empirical Bayesian
  • feature space
  • hotspot prediction
  • Proportion Discordance Ratio
  • similarity
  • Traffic crash pattern

ASJC Scopus subject areas

  • Transportation
  • Safety Research

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