ENHANCING STATE DOTS ASSET DATA MATURITY EVALUATION: DEVELOPING A DIGITAL TWIN-BASED FRAMEWORK

State Departments of Transportation (DOTs) are confronted with growing challenges in managing infrastructure assets due to fragmented data systems, variable quality standards, and limited real-time capabilities as needed.This study suggests a comprehensive, layered, and futured-oriented framework for measuring and improving asset data maturity, specifically tailored to state DOTs.Through synthesizing five established data maturity assessment methodologies including UK Data Maturity Assessment, American Association of State Highway and Transportation Officials (AASHTO) Transportation Asset Management (TAM) Data Assistant tool, Federal Transit Administration (FTA) TAM Self-Assessment, DCAM, and MMADQ and conceptually integrating digital twin, the proposed framework fills gaps in governance, data quality, and asset lifecycle management.It provides a fivelevel scale of maturity across three interdependent modules and facilitates a dynamic, data-enabled approach towards decision-making.Validation is conducted through theoretical conformity with global standards, application with a state DOT's Transportation Asset Management Plan, and comparative analysis with existing frameworks.The findings show the robustness, usability, and added value of the framework, especially in supporting future self-assessment tools and intelligent infrastructure management systems.This research provides a strategic roadmap for public infrastructure digital transformation, which enables state DOTs to enhance asset performance, optimize resource utilization, and facilitate long-term resilience.