A comparative Full Bayesian before-and-after analysis and application to urban road safety countermeasures in New Jersey

Ozlem Yanmaz-Tuzel, Kaan Ozbay

Research output: Contribution to journalArticle

Abstract

This paper develops a step-by-step methodology for the application of Full Bayes (FB) approach for before-and-after analysis of road safety countermeasures. As part of this methodology, it studies the posterior prediction capability of Bayesian approaches and their use in crash reduction factor (CRF) estimation. A collection of candidate models are developed to investigate the impacts of different countermeasures on road safety when limited data are available. The candidate models include traditional, random effects, non-hierarchical and hierarchical Poisson-Gamma and Poisson-Lognormal (P-LN) distributions. The use of random effects and hierarchical model structures allows treatment of the data in a time-series cross-section panel, and deal with the spatial and temporal effects in the data. Next, the proposed FB estimation methodology is applied to urban roads in New Jersey to investigate the impacts of different treatment measures on the safety of "urban collectors and arterial roads" with speed limits less than 45 mph. The treatment types include (1) increase in lane width, (2) installation of median barriers, (3) vertical and horizontal improvements in the road alignment; and (4) installation of guide rails. The safety performance functions developed via different model structures show that random effects hierarchical P-LN models with informative hyper-priors perform better compared with other model structures for each treatment type. The individual CRF values are also found to be consistent across the road sections, with all showing a decrease in crash rates after the specific treatment except guide rail installation treatment. The highest decrease in the crash rate is observed after the improvement in vertical and horizontal alignment followed by increase in lane width and installation of median barriers. Overall statistical analyses of the results obtained from different candidate models show that when limited data are available, P-LN model structure combined with higher levels of hierarchy and informative priors may reduce the biases in model parameters resulting in more robust estimates.

Original languageEnglish (US)
Pages (from-to)2099-2107
Number of pages9
JournalAccident Analysis and Prevention
Volume42
Issue number6
DOIs
StatePublished - Nov 2010

Fingerprint

Model structures
road
Safety
Poisson Distribution
Rails
Bayes Theorem
candidacy
Poisson distribution
methodology
Time series
speed limit
time series
trend
performance
Values

Keywords

  • Bayesian methods
  • Before-and-after analysis
  • Hierarchical models
  • Safety performance functions

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Safety, Risk, Reliability and Quality
  • Human Factors and Ergonomics
  • Law

Cite this

A comparative Full Bayesian before-and-after analysis and application to urban road safety countermeasures in New Jersey. / Yanmaz-Tuzel, Ozlem; Ozbay, Kaan.

In: Accident Analysis and Prevention, Vol. 42, No. 6, 11.2010, p. 2099-2107.

Research output: Contribution to journalArticle

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