Data Management Life Cycle, Final report
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Summary
This report, produced by the Texas A&M Transportation Institute, addresses the growing complexity of transportation data management in an era of rapid technological advancement and expanding data volumes. The primary motivation is to provide policymakers and researchers with a structured framework to understand the value, nature, and policy implications of transportation data. As data becomes a critical asset for decision-making, safety, and efficiency, the report aims to organize the broad topic of data into a digestible life cycle model to guide future research and high-level prioritization. The researchers developed a "Data Management Life Cycle" framework through a cyclical, iterative process that categorizes data topics into seven distinct phases: Collection, Process, Store and Secure, Use, Share and Communicate, Archive, and Destroy or Re-use. The report details each phase, examining specific challenges and opportunities. For instance, the Collection phase discusses techniques, public-private partnerships, and the impact of Internet of Things (IoT) technologies. The Process phase focuses on data quality metrics and advanced processing tools like cloud computing. The Store and Secure phase addresses storage costs and security risks, while the Use and Share phases explore data application in planning and the balance between transparency and privacy. Additionally, the report identifies seven cross-cutting issues that affect all phases: Purpose and Value, Privacy, Data Ownership, Liability, Public Perception, Security, and Standards and Data Quality. Key findings include the identification of significant barriers to effective data management, such as technical limitations, institutional silos, and financial constraints. The report highlights that while big data sources like probe data and automated license plate readers offer valuable insights, they introduce complex privacy and liability concerns. It notes that data archiving is increasingly costly, with estimates ranging from $300,000 to over $4 million annually for regional systems, yet remains essential for benchmarking and Intelligent Transportation Systems (ITS). The framework reveals that data management is not merely a technical issue but involves substantial policy implications regarding ownership, security, and public trust. The significance of this work lies in its provision of a comprehensive roadmap for transportation agencies and policymakers. By treating data as a managed asset similar to physical infrastructure, the report encourages a more strategic approach to data procurement, retention, and utilization. It underscores the need for clear policies on data ownership and privacy, particularly as connected and automated vehicles proliferate. Ultimately, the report serves as a foundational guide for prioritizing future research efforts and improving the efficiency, safety, and effectiveness of transportation systems through better data management practices.
Key finding
The report defines a seven-phase data management life cycle and identifies seven cross-cutting issues that influence transportation data policy and management.
Methodology
review
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 24 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Methodological Resource: dataset resource