Strategies for Developing and Using Data Ontologies for Data-Driven Decision-Making
State departments of transportation (DOTs) have long collected and used data to inform decisions and to manage assets and programs. Much of the data are managed using legacy systems that lack interoperability and are difficult to access and update. These systems continue to be used because they are highly relevant for their intended use and well understood by those who have years of experience using them. However, they may be less useful for decisions that cut across functional areas, involve multiple modes, engage with external partners, or require integration with modern data technologies. In many decision-making contexts, legacy systems can hamper cross-cutting analysis and require extensive investigation of metadata to ensure that the results of an analysis are meaningful for the decision at hand. A common approach to making legacy systems more amenable for cross-functional decisions is to develop a ‘data lake' or ‘data warehouse', moving all agency data to a single, enterprise-level platform. While this approach can provide agency-wide access, it does not address differences in data models or the need for a cross-functional, shared understanding of the meaning of the data. The shift to performance-based management and the need to respond quickly in emergent conditions has made data-driven decision-making imperative for state DOTs. This requires a data governance and management perspective that elevates data as an asset, with the same priority as traditional physical transportation assets. This perspective requires data representations that include data ontologies, glossaries, taxonomies, registries, catalogs, metadata, and models. Data ontologies are a key element in data representation. A data ontology describes the core information concepts represented in the data that, in turn, drive the business processes and the relationships among the concepts. An ontology ensures not only consistent naming across disparate disciplines, organizations, and information technology systems, but consistent meaning across users. For example, data about a highway bridge would not only include bridge components—superstructure, substructure, and deck—but also traffic volumes carried, noise propagation to nearby receptors, hydrologic conditions below the bridge, stormwater runoff, use of the bridge by birds or bats, maintenance history, construction and maintenance costs, and cultural or historic value of the bridge to a community. Taken together, these interrelated data provide a holistic view of the bridge and its context; a view that can better inform decision-making. A well-designed ontology allows these data to be discovered, integrated, analyzed, and understood by all users without resorting to ad hoc methods that may produce unreliable results and different interpretations. This common understanding of the meaning of the data also supports nimble, multidisciplinary teams that are prepared to collaborate to analyze the data and provide leadership with the best available information to formulate a response. Research is needed to identify effective practices for developing robust data ontologies and for building agency capacity to use them in transportation decision-making. The objective of this project is to develop a guide for state DOTs on strategies for implementing data ontologies that support nimble and efficient data-driven decision-making.
Language
- English
Project
- Status: Active
- Funding: $300000
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Contract Numbers:
Project 23-27
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Sponsor Organizations:
National Cooperative Highway Research Program
Transportation Research Board
500 Fifth Street, NW
Washington, DC United States 20001American Association of State Highway and Transportation Officials (AASHTO)
444 North Capitol Street, NW
Washington, DC United States 20001Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC United States 20590 -
Project Managers:
Brooks, Mike
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Performing Organizations:
One Penn Plaza
250 W 34th Street
New York, New York United States 10119 -
Principal Investigators:
Boadi, Richard
- Start Date: 20231103
- Expected Completion Date: 20250902
- Actual Completion Date: 0
Subject/Index Terms
- TRT Terms: Business practices; Data collection; Data management; Decision making; Guidelines; State departments of transportation
- Subject Areas: Administration and Management; Data and Information Technology; Highways; Planning and Forecasting;
Filing Info
- Accession Number: 01779274
- Record Type: Research project
- Source Agency: Transportation Research Board
- Contract Numbers: Project 23-27
- Files: TRB, RIP
- Created Date: Aug 23 2021 4:58PM