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

10 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)562-578
Number of pages17
JournalJournal of Transportation Safety and Security
Volume11
Issue number5
DOIs
StatePublished - Sep 3 2019

Keywords

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

ASJC Scopus subject areas

  • Transportation
  • Safety Research

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