Fully Bayesian inference techniques for traffic safety treatment before-and-after study

The importance of improving traffic safety is often understated, partially because it often takes a retrospective approach, garnering little public attention. Nonetheless, from both an economical and societal point of view, traffic safety presents severe and significant problems despite the sizeable benefits that advancements in transportation have brought to society. To further complicate the matter, the net results of most traffic safety interventions are not always straightforward or intuitive. This illustrates the need for sound engineering evaluation of traffic safety interventions that is grounded in statistical analysis. It should be noted that these engineering evaluations can be applied not only to location-specific safety treatments, but can also be used to test the effectiveness of traffic safety-targeted policies such as changes in BAC level or seat belt laws. Previously, a prominent and effective methodology for conducting traffic safety intervention evaluations was known as the Empirical Bayes inference techniques. It was effective in accounting for a number of confounding factors, which threaten the validity of any claims made by simply looking at raw collision data. However, several key drawbacks have been identified, including difficulties to obtain the necessary amount of input data and the statistical discontinuity in the steps where the uncertainties around the input data are not entirely carried through to the final estimates. In theory, the recently-developed Full Bayes technique fully addresses the weaknesses of the Empirical Bayes method; however, there have been hesitations to adopt the methodology because of the increased level of complexity and the previous lack of adequate computational power. The purpose of this thesis to perform a thorough literature on methodologies for conducting traffic safety intervention models particularly with regards to Bayesian inference, devise a standardized methodology using the findings, apply the methodology on a real-world case study in Edmonton, Alberta, and summarize the results to demonstrate the strengths and the feasibility of the Full Bayes methodology. The results indicated that the treatment program was effective in reducing right-turn collisions by 39%. A standardized practical guideline was also developed using the literature review and the results and includes various provisions for flexibility and alterations.Applied Science, Faculty ofCivil Engineering, Department ofGraduat