统一三大AI治理标准，实现智能交通系统合规自动化。

Organizations deploying AI-enabled Intelligent Transportation Systems face fragmented governance: ISO/IEC~42001 demands a certifiable management system, the EU AI Act imposes binding high-risk obligations from August~2026, and the NIST AI Risk Management Framework structures voluntary practice. This paper introduces UGAF-ITS, a standards harmonization framework that consolidates 154 source obligations from the three instruments into 12 unified controls across eight governance domains through a reproducible five-phase crosswalk methodology. A three-tier operating model allocates each control to the vehicle, edge, or cloud tier where enforcement and defensible evidence production are feasible. An evidence backbone of 20 versioned artifacts supports a single audit package across all three frameworks without duplicating content. We evaluate UGAF-ITS through an open-source governance engine applied to four architecturally distinct ITS deployment scenarios. The engine encodes the complete crosswalk catalog and executes eight compliance computations. Three-tier deployments achieve 91.7\% average scoped framework coverage with 45.9\% evidence reduction, complete bidirectional traceability, and 80\% of artifacts serving all three frameworks simultaneously. Partial deployments contract predictably, with reduction holding between 25\% and 50\% across archetypes. The siloed baseline is enumerated document by document and computed by the tool at run time. These results establish structural properties of the harmonized catalog under architectural variation. They do not establish operational compliance readiness, which external assessment with practitioners has yet to test. The tool, scenarios, and all reported results are publicly available for independent replication.