A spatially structured empirical Bayes framework for the evaluation of network-wide safety countermeasures

• Proposes a network-structured EB framework with network process convolution. • Improves counterfactual collision prediction via spatial uncertainty modeling. • Enhances uncertainty quantification of network-wide countermeasure safety effects. • Shows when proposed approach excels under correlated network structures. • Delivers spatial risk surfaces for network-wide safety planning. This study proposes a two-step, spatially structured Empirical Bayes (EB) framework for evaluating the safety effectiveness of network-wide countermeasures, leveraging the Network Process Convolution (NPC) model. A central challenge in road safety evaluation is not only estimating treatment effects but also accurately quantifying uncertainty, particularly when interventions generate local and spillover effects. The NPC uses a network-based Gaussian Process with reweighted kernel convolution to capture spatial correlations of collisions along road networks, enabling robust estimation of both site-specific and network-wide effects. The two-step procedure ensures an unbiased prior structure for generating counterfactual outcomes. We conducted a simulation study under varying spatial correlation scenarios and applied the method to the City of Edmonton’s Driver Feedback Sign (DFS) program using 10 years of collision data across 1,366 road segments. Performance was benchmarked against the traditional EB Poisson-Gamma (EB-PG) method. Simulations show that while both methods accurately recover counterfactual collisions and reduction ratios, EB-NPC provides more reliable and well-calibrated uncertainty quantification, particularly under moderate to strong spatial correlation. In the Edmonton case study, EB-NPC mostly produced slightly higher estimated reductions and more informative predictive uncertainty, whereas EB-PG remained more robust in areas with weak spatial structure. Beyond numerical estimation, EB-NPC generates continuous spatial risk surfaces, allowing practitioners to visualize network-wide safety patterns and prioritize high-risk segments. Overall, the proposed approach improves recovery of counterfactual outcomes and delivers accurate, interpretable uncertainty characterization, offering a powerful tool for data-driven transportation safety management.