Abstract
A main objective of Departments of Transportation is to improve the safety of the roadways over which they have jurisdiction. Safety projects, such as cable barriers and raised medians, are utilized to reduce both crash frequency and crash severity. The efficacy of these projects must be evaluated in order to use resources in the best way possible. Five models are proposed for the evaluation of traffic projects: (1) a Bayesian Poisson regression model; (2) a hierarchical Poisson regression model building on model (1) by adding hyperpriors; (3) a similar model correcting for overdispersion; (4) a dynamic linear model; and (5) a traditional before-after study model. Evaluation of these models is discussed using various metrics including DIC. Using the models selected for analysis, it was determined that cable barriers are quite effective at reducing severe crashes and cross-median crashes on Utah highways. Raised medians are also largely effective at reducing severe crashes. The results of before and after analyses are highly valuable to Departments of Transportation in identifying effective projects and in determining which roadway segments will benefit most from their implementation.
Degree
MS
College and Department
Physical and Mathematical Sciences; Statistics
Rights
http://lib.byu.edu/about/copyright/
BYU ScholarsArchive Citation
Olsen, Andrew Nolan, "Hierarchical Bayesian Methods for Evaluation of Traffic Project Efficacy" (2011). Theses and Dissertations. 2922.
https://scholarsarchive.byu.edu/etd/2922
Date Submitted
2011-03-07
Document Type
Selected Project
Handle
http://hdl.lib.byu.edu/1877/etd4219
Keywords
hierarchical model, Poisson regression, dynamic linear model
Language
English