Proposing a Digital Twin DataDOSE Framework for Asset Management in State Departments of Transportation

The economic competitiveness, quality of life, and travel safety of a state hinge on the effective management of its transportation assets as overseen by the state and local jurisdictional Departments of Transportation (DOTs). To operate, maintain, upgrade, and expand assets, Transportation Asset Management (TAM) was developed as a strategic and systematic data-driven decision-making process that relies on quality asset data to guide decision-making. However, within state DOTs, every division that interacts with an asset documents parts of its history, resulting in data fragmentation. This shifted the focus of state DOTs to digital project delivery and Digital Twins to close the gap in capturing data and leverage the effectiveness of TAM. The following paper contributes to the data discourse withing state DOTs and proposes the DataDOSE framework that is data- and user-centered to support successful data-driven asset management decision-making in state DOTs. The framework is a middle-out approach that does not rely solely on top-down directives, but rather fosters a collaborative culture where ideas flow both upward and downward in the agency, empowering all stakeholders to contribute their valuable insights. DataDOSE encompasses four steps: Defining assets through the lens of data, Organizing asset data into the Asset Data Structure, Strategizing with a Data Governance Plan, and Executing with a Data Management Plan. This paper discusses the elements that state DOTs need to consider in answering why data is important, what data is needed, and how to govern and manage data. This paper builds the foundation for multiple future directions including developing a user-friendly digital tool based on the DataDOSE framework, establishing appropriate Key Performance Indicators (KPIs) to measure the framework's effectiveness, and ultimately creating guidance documents and training materials to facilitate the adoption of the DataDOSE framework across state DOTs.