The Federal Highway Administration’s (FHWA’s) Office of Research, Development, and Technology (HRT) is located at the Turner-Fairbank Highway Research Center (TFHRC), which houses 15 laboratories, support facilities, and data sets; and conducts applied and exploratory advanced research in vehicle-highway interaction, nanotechnology, and a host of other types of transportation research in safety, pavements, highway structures and bridges, human-centered systems, operations and intelligent transportation systems, and materials. The laboratories at the center provide a vital resource for advancing the body of knowledge that has been created and developed by our researchers.

TFHRC is poised to provide leadership in the research and development of Advanced Data Science in a manner that is interdisciplinary and that can work across the stovepipes of information stores around the country. Leadership includes exploring and solving difficult transportation questions using emerging analytical tools and approaches at TFHRC and in partnership with others through coordination and cooperation. To this aim, TFHRC is seeking to establish a new Pathway to Advancing Novel Data Analytics (PANDA) laboratory.

The PANDA is envisioned as both a physical laboratory in TFHRC, to support Secure Data Enclave operations that protect sensitive data types, and as a virtual laboratory that others in FHWA and USDOT can access. One goal of PANDA is to demonstrate the potential benefits of data science in highway transportation for programs with FHWA and for state and local partners. Federal and State research generates large volumes of data, whose useful life is often limited to the duration of the relevant project. The growth of data science and AI within HRT will tease new relationships and insights from data and set examples and alliances for more localized data-driven policy, regulatory, and specification-related decisions.

To support PANDA, SD Solutions developed a data ecosystem inventory that applies objective criteria and provides insight and indicators such as star rating, ranking, priority rating etc., on the suitability of various data sets for use in researching FHWA-domain areas through applied AI, Machine Learning, and other related technologies. SD Solutions also developed a draft metadata template for the inventory.