What strategy can improve a basic before/after analysis for safety effectiveness evaluation?

The Empirical Bayes method is a valuable strategy that improves a basic before/after analysis in safety effectiveness evaluation by addressing the inherent variations in crash data and accounting for regression to the mean. In traditional before/after studies, the evaluation of a safety intervention can be skewed due to random fluctuations in crash occurrence, meaning that an increase or decrease in crashes may not accurately reflect the true impact of the intervention. The Empirical Bayes method mitigates this issue by incorporating historical data and adjusting the observed crash data based on expected crash rates had the safety measure not been implemented.

This statistical approach enhances the reliability of the analysis by providing a more predictive modelling framework, utilizing prior crash data to establish a baseline for comparison. Consequently, it allows for a more nuanced understanding of the safety intervention's effectiveness, leading to more informed decision-making regarding road safety policies and measures.

Other strategies mentioned have their respective merits; for instance, the network screening method identifies high-crash locations to prioritize safety improvements, while roadway safety audits evaluate specific locations for safety issues. The modified absolute risk assessment method analyzes crash risks in a relative manner, but none of these approaches directly enhance the robustness of the before/after analysis as effectively as the Empirical Bayes method.