Development of a risk map analysis tool (RiskGrid) to support road safety impact assessment

Traditional Mobility Impact Assessments frequently overlook road safety, focusing primarily on traffic efficiency. To address the growing need for proactive, quantitative Road Safety Impact Assessments, this study introduces RiskGrid, a novel, data-driven framework that integrates safety analytics into conventional transport planning. Applied to an expanding suburban case study in Rome, the methodology utilizes a three-stage approach encompassing macrosimulation, microsimulation, and predictive safety modeling. RiskGrid transforms vehicle trajectories into continuous spatial risk maps by calculating directional interaction potential, representing a conceptual shift from discrete conflict counting to continuous risk mapping. The study evaluates RiskGrid against the established Surrogate Safety Assessment Model (SSAM3) using real-world crash data. Results demonstrate that RiskGrid significantly outperforms SSAM3 in both computational efficiency and predictive validity. While SSAM3 required over 12 min per scenario and correctly identified only two of 13 official crash hotspots, RiskGrid’s automated workflow processed the data in under one minute and successfully detected 12 out of 13 hotspots. By combining mobility performance with proactive safety analytics, RiskGrid provides an easy-to-use tool that aligns with the Safe System Approach, empowering planners to anticipate and mitigate safety risks in future infrastructure developments.