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Analyzing Raised Median Safety Impacts Using Bayesian Methods

Because traffic safety studies are not performed in a controlled environment such as a laboratory, but rather in an uncontrolled real-world setting, traditional analysis methods often lack the capability to evaluate the effectiveness of roadway safety measures adequately. In recent years, however, advanced statistical methods have been used in traffic safety studies to determine the effectiveness of such measures more accurately. These methods, especially Bayesian statistical techniques, can account for the shortcomings of traditional methods. Hierarchical Bayesian modeling is a powerful tool for expressing rich statistical models that more fully reflect a given problem than traditional safety evaluation methods could. This paper uses a hierarchical Bayesian model to analyze the effectiveness of raised medians on overall and severe crash frequency in Utah by determining the effect each newly installed median has on crash frequency and frequency of severe crashes at study sites before and after installation of the medians. Several sites at which raised medians have been installed in the past 10 years were evaluated with available crash data. The results show that the installation of a raised median is an effective technique to reduce the overall crash frequency and frequency of severe crashes on Utah roadways, with results showing a reduction in overall crash frequency of 39% and frequency of severe crashes of 44% along corridors where raised medians were installed. The results also show that hierarchical Bayesian modeling is a useful method for evaluating effectiveness of roadway safety measures.