United States Department of Transportation
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Knowledge Discovery in Massive Transportation Datasets: Merging Information from Disparate Sources to Enhance Traffic Safety: [fact sheet]
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2018-02-01
[PDF-428.72 KB]
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Abstract:Broad adoption of engineering and policy advances, including air bags, highway safety barriers, and distracted driving laws, contributes to increased vehicle safety on our Nation’s roadways. Although the number of deaths and injuries from crashes decreased slightly in 2017, the number of 2017 deaths is still at a level not seen since 2007 (National Safety Council 2017). One promising avenue for reducing crashes lies in extracting and analyzing safety-related information from vast and expanding datasets related to driver behavior, vehicle performance, traffic patterns, weather, and infrastructure characteristics. Identifying and making sense of this information will require new techniques. The Federal Highway Administration (FHWA) Exploratory Advanced Research (EAR) Program is supporting research projects that can process massive amounts of transportation-related data from structured, semistructured, and unstructured datasets using open-source tools and technology. The Palo Alto Research Center, Inc. (PARC) is developing automated methods to integrate information from large unrelated datasets. CUBRC, a Buffalo, New York-based systems integration research organization, is developing a layered infrastructure to ingest, store, analyze, and display information.
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