What does the Empirical Bayes method improve upon compared to traditional methods?

The Empirical Bayes method enhances traditional methods by incorporating regression to the mean, which addresses the tendency for extreme events, such as a high number of collisions at a specific site, to revert to more average levels over time. Traditional methods might rely solely on observed data without accounting for this statistical phenomenon, potentially leading to inaccurate predictions of future collision rates. By using Empirical Bayes, analysts can combine prior information (from historical data) with new data, leading to more reliable estimates and better-informed safety interventions.

This approach also acknowledges that sites with very high or low collision counts may not maintain those levels, thereby allowing for a more nuanced understanding of roadway safety. It balances the noise inherent in collision data with the underlying trends, resulting in a stronger foundation for decision-making regarding road safety measures and resource allocation.