What is generally considered the best practice to ensure the accuracy of output from a Safety Performance Function (SPF)?

The best practice to ensure the accuracy of output from a Safety Performance Function (SPF) is to calibrate the SPF with local information on crashes, traffic volumes, and roadway characteristics. This approach enhances the reliability of the predictions made by the SPF by integrating specific data that reflects the unique conditions and factors influencing safety in a particular area.

Calibration takes into account local variations that might affect crash rates, such as differences in driver behavior, roadway geometry, or vehicle types, which may not be captured in the generalized models used to construct the SPF. By adjusting the SPF based on empirical local data, practitioners can achieve a more accurate and relevant assessment of safety performance for specific locations.

This method stands out compared to the other options, which may not provide the same level of context or specificity. For instance, averaging outputs from different SPFs may lead to a diluted result that loses the advantages of tailored calibration. Conducting surveys of existing intersection types, although informative, does not directly address the nuances of data calibration necessary for accurate predictive modeling. Lastly, the selection of an SPF based solely on the number of citations could be misleading as it does not guarantee that the SPF is appropriate for local conditions or that it has been verified for use in the specific context where it