PHASE I Task 1. Review current research, technologies, and tools that apply AI to data quality assessment, and identify relevant ones applicable to transportation and business data management and governance. Summarize lessons learned and gaps in current practices to inform subsequent tasks. Task 2. In collaboration with NCHRP, identify at least six datasets from various transportation agency domains that exhibit known data quality issues. Define representative use cases to evaluate AI-based techniques for detecting, correcting, and preventing data errors. Document data characteristics, quality challenges, and evaluation criteria. Ensure that data permissions and privacy considerations are addressed early. Task 3. Develop preliminary AI methods for automatically identifying business and data quality rules by analyzing documentation (e.g., standards, manuals, policies, and training materials), inferring patterns from related datasets, and applying reinforcement learning techniques. Create prototype AI-driven frameworks capable of detecting data inconsistencies and generating recommendations for integration with existing data quality tools and workflows. Task 4. At the conclusion of Phase I, prepare an interim report summarizing Phase I findings, datasets, preliminary methods, and recommended refinements to the Phase II plan for NCHRP review. Note: Following a 1-month review of Interim Report No. 1 by the NCHRP, the research team will be required to meet with the project panel to discuss the interim report. Work on Phase II of the project will not begin until authorized by the NCHRP. PHASE II Task 5. Apply the AI methods developed in Phase I to the datasets identified earlier. Assess the feasibility, accuracy, performance, and implementation challenges of each AI method tested. Define and apply quantitative and qualitative performance metrics—such as accuracy, precision, recall, and processing efficiency. Document success rates, limitations, data dependencies, and resource requirements to inform practical deployment. Task 6. Develop strategies for transportation agencies at varying levels of data maturity to prepare for and implement AI-based data quality improvement. Summarize findings on current capabilities, emerging opportunities, and key limitations. Task 7. Deliver a conduct of research report that documents the entire research effort, and a guide to help state DOTs apply AI effectively to improve data quality.
