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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Design exception in-service monitoring program development

Stahley, Laura Margot 13 January 2014 (has links)
When project sites consist of substandard design elements according to standards set by the Federal Highway Administration (FHWA), design exceptions are implemented. The goal of this thesis is to analyze a sample set of 18 design exceptions taken from a total of 467 design exceptions approved in Georgia from 1995 – 2012. Crash data were obtained at the locations of each of these design exceptions three years before the let date and three years after the construction end date. To compensate for causal factors other than the design exception on the roadway, similar information from a range of control sites were also obtained. These control sites consisted of projects without design exceptions that occurred within the same time constraints as the design exception projects, were of the same work type, and were either located on the same route or within the same district. The potential safety impacts of the design exceptions were evaluated by comparing the before and after crash rates of projects before and after crash rates at these control sites Based on these data, no statistically significant relationship between the existence of a design exception and crash rates was identified. The sample set in this study was too small and the number of crashes found at both projects with design exceptions and control sites without design exceptions was too low to perform a significant Empirical Bayesian (EB) analysis. When additional data is available, an EB before and after analysis is recommended to compensate for any potential regression to the mean bias.

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