This innovation applies sheaf theory from algebraic topology to model roadway sensor networks as topological spaces, enabling a local-to-global inference architecture that certifies the consistency and reliability of heterogeneous sensor data. By overcoming limitations of conventional black-box fusion methods, it ensures globally valid predictions through cohomological certification and structured data integration. The system addresses data fragmentation, missing information, and certifiability challenges, organized around research thrusts targeting identifiability, stability, active sensing, and sheaf-theoretic learning. The process includes multi-sensor ingestion, topological construction, sheaf assignment, and diagnostic remediation, aiming to provide certified safety outputs for intelligent transportation applications.