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Bayesian multiple testing procedures for hotspot identification.

Luis F Miranda-Moreno1, Aurélie Labbe, Liping Fu

  • 1Centre for Data and Analysis in Transportation, Economics Department, Université Laval, Québec G1K7P4, Canada. luis-f.miranda-moreno.1@ulaval.ca

Accident; Analysis and Prevention
|October 9, 2007
PubMed
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This study introduces a novel Bayesian approach for identifying high-risk transport locations, improving safety analysis. It uses multiple testing procedures to select critical hotspots for further engineering study.

Area of Science:

  • Transportation Safety
  • Statistical Modeling
  • Risk Assessment

Background:

  • Effective transport safety programs require identifying high-risk locations (hotspots) for detailed study.
  • Existing research primarily focuses on ranking methods, with less attention on defining selection criteria for hotspots.

Purpose of the Study:

  • To introduce a multiple testing-based approach for selecting transport safety hotspots.
  • To address the gap in defining selection methods and threshold rules for hotspot identification.

Main Methods:

  • Utilized two Bayesian testing procedures: Bayesian test with weights (BTW) and a Bayesian test controlling for posterior false discovery rate (FDR) or false negative rate (FNR).
  • Implemented hypothesis tests using hierarchical Poisson/Gamma (Negative Binomial) and Poisson/Lognormal models.

Related Experiment Videos

  • Applied the methods to a dataset of highway-railway grade crossings, considering posterior distributions of accident frequency and ranks.

    • Main Results:

    • Demonstrated the application of Bayesian hotspot identification procedures using real-world data.
    • Analyzed the impact of various decision parameters on hotspot identification outcomes.
    • Incorporated both accident frequency and rank distributions into the selection process.

    Conclusions:

    • The proposed multiple testing-based Bayesian framework offers a robust method for selecting transport safety hotspots.
    • The study provides insights into defining selection rules and decision parameters for effective safety improvement programs.
    • This approach enhances the identification of critical locations for targeted engineering interventions and countermeasure evaluation.