Abstract
Network screening is the most important step in the highway safety management process. Screening criteria based on the empirical Bayes (EB) approach are considered to be most reliable as it accounts for the regression-to-the-mean bias. However, the EB approach requires safety performance functions (SPFs), preferably calibrated to local conditions, which are often unavailable. The SafetyAnalyst software, developed by the Federal Highway Administration, automates the EB approach using the default SPFs which were developed using multiple states’ data. Local agencies are encouraged to develop jurisdiction-specific SPFs to better reflect local conditions. However, the benefits of developing local SPFs for rural and urban two-lane and multi-lane highway facilities are unclear and may vary from state to state. This research compares the performance of Florida-specific SPFs with SafetyAnalyst-default SPFs calibrated to Florida data using mean absolute deviation, mean squared predicted error, and Freeman-Tukey R-square goodness-of-fit measures. The results showed that Florida-specific SPFs generally produced better-fitted models than the calibrated SafetyAnalyst-default SPFs. In contrast, when the crash prediction capabilities of the already-available local SPFs, calibrated to the latest time period for which they will be applied, are compared with the calibrated SafetyAnalyst-default SPFs, the calibrated SafetyAnalyst-default SPFs in general were found to better predict crash frequencies compared with the existing Florida-specific SPFs calibrated to the latest data. Therefore, the local SPFs are recommended when developed using present data; however, the calibrated SafetyAnalyst-default SPFs could be used if local SPFs developed from present data are not available.
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