title: Bayesian multiple testing procedures for hotspot identification.
abstract: Ranking a group of candidate sites and selecting from it the high-risk locations or hotspots for detailed engineering study and countermeasure evaluation is the first step in a transport safety improvement program. Past studies have however mainly focused on the task of applying appropriate methods for ranking locations, with few focusing on the issue of how to define selection methods or threshold rules for hotspot identification. The primary goal of this paper is to introduce a multiple testing-based approach to the problem of selecting hotspots. Following the recent developments in the literature, two testing procedures are studied under a Bayesian framework: Bayesian test with weights (BTW) and a Bayesian test controlling for the posterior false discovery rate (FDR) or false negative rate (FNR). The hypotheses tests are implemented on the basis of two random effect or Bayesian models, namely, the hierarchical Poisson/Gamma or Negative Binomial model and the hierarchical Poisson/Lognormal model. A dataset of highway-railway grade crossings is used as an application example to illustrate the proposed procedures incorporating both the posterior distribution of accident frequency and the posterior distribution of ranks. Results on the effects of various decision parameters used in hotspot identification procedures are discussed.
authors.affiliation: Centre for Data and Analysis in Transportation, Economics Department, Université Laval, Québec G1K7P4, Canada. luis-f.miranda-moreno.1@ulaval.ca
authors.forename: Luis F
authors.initials: LF
authors.lastname: Miranda-Moreno
authors.forename: Aurélie
authors.initials: A
authors.lastname: Labbe
authors.forename: Liping
authors.initials: L
authors.lastname: Fu
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D002170:Canada; D003661:Decision Support Techniques; D006801:Humans; D011890:Railroads; D018570:Risk Assessment / Q000706:statistics & numerical data