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• W14516063 abstract "This paper seeks to identify road segments with elevated weather-related crash risk, using historical crash and traffic information for Pennsylvania. PennDOT has expressed an interest in using the identified sites as part of a system of Highway Advisory Radio, web sites and Changeable Message Signs to warn travelers of potentially significant weather events in their area. The ranking of sites is performed using Full Bayesian Hierarchical Models with random effects and it is based on expected excess crash frequency and Relative Risk for the site compared with the expected frequency and risk of a group of similar segments. The use of random effects made possible the estimation of Relative Risk and excess crash frequency for each segment. The precision of the estimates in the Full Bayes approach is not established in terms of distributional assumptions, but drawn from the posterior distribution in the form of a credible set which increases the certainty in terms of potential for safety improvement. The number of segments with a significant excess crash frequency of weather-related crashes is different for each road type and it varies from 2 to 9% (at 95% confidence). This methodology is applicable to any type of crash and facility, and it is particularly helpful when analyzing relatively infrequent crash types." @default.
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• W14516063 date "2007-01-01" @default.
• W14516063 modified "2023-09-27" @default.
• W14516063 title "Identifying Road Segments with High Risk of Weather-Related Crashes Using Full Bayesian Hierarchical Models" @default.
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