A Note on Assessment of Transportation Infrastructure Data Management Practices

Abstract

The amount of data being maintained by state departments of transportation (DOTs) and local transportation agencies is increasing steadily. Although data provide opportunities to facilitate decision making at transportation agencies, there are challenges involved in managing large and diverse data. This article provides an assessment of the maturity of three data management practice (stewardship, storage and warehousing, and integration) for 16 transportation data groups based on a survey of 43 DOTs in the United States. The assessment results show that data management practices at the monitoring and operations phases of transportation infrastructure life cycle are likely more mature than those at other phases. Inventory data, in particular, has the most mature data management practices. On the other end, real estate data and travel modeling data have the least mature data management practices. A comparison of the practices indicates that data stewardship is more mature than data integration and storage and warehousing practices.

Challenges and Solutions in Integrating AI with Legacy Inventory Systems

“…This limitation can hinder the effective use of AI in large-scale inventory management scenarios [14]. Additionally, data processing challenges, such as the handling and analysis of large datasets required for AI, can overwhelm the limited capabilities of older systems [15].…”