Privacy-Preserving Information Fusion of Heterogeneous Cross-Jurisdictional Sources for Traffic Accident Severity Prediction

Road safety authorities each hold accident records that, when combined, could train stronger severity prediction models, yet these records can be neither centralized, for privacy and governance reasons, nor naively merged, because jurisdictions encode severity under incompatible ontologies. This paper recasts that impasse as an information fusion problem and fuses model updates from multiple road safety data silos into a single severity model while every raw record stays at its source. Three components act together: model-level fusion under differential privacy, a reliability-weighted aggregation rule that trusts each source based on its measured quality rather than its size, and a per-source centered logit adjustment layer that reconciles mismatched label priors without double-correcting the shared class imbalance. The primary evaluation is a clean cross-silo setting: five United States state datasets (US Accidents) that share one severity ontology but are held by distinct custodians. Here, over five seeds with 95% confidence intervals, effect sizes, and Holm–Bonferroni correction, private fusion recovers most of a centralized upper bound while keeping data local (0.599 balanced accuracy versus 0.624 when centralized and 0.544 when local-only), and reliability-weighted fusion attains the highest macro F1 of all methods (0.567). Reliability weighting yields a small but consistent robustness advantage under privacy noise; in leave-one-state-out transfer, its improvement over uniform averaging is large on every held-out state but, after Holm correction, survives in two out of five cases. Crucially, we also report a boundary honestly; a United Kingdom source that encodes injury severity—an ontologically different target from the US traffic impact scale—is used as a deliberate out-of-ontology transfer stress test, and a transfer to it collapses to chance (0.50, p=0.62). Two further honest results are reported: alignment raises accuracy everywhere but does not close the across-source gap, and a membership inference attack reveals no measurable leakage for differential privacy to remove.