The Department of Transportation is expanding its ongoing traffic safety data initiative by launching a prize competition on Challenge.gov.
The challenge, called Solving for Safety, seeks new data visualization tools that can “reveal insights into serious crashes and improve understanding of transportation safety.” DOT is calling on “solvers” from tech companies, research firms, academic institutions and more to build tools that can both analyze data for unseen patterns and visualize model simulations in service of the creation of better policy.
“Recent innovations in data analytics and visualization tools give us the potential to understand risk at the system level, and to develop tools and discover insights that will lead to new, life-saving strategies that address injuries and fatalities on our roadways,” Under Secretary for Policy Derek Kan said in a statement.
In addition to solvers, the challenge calls for participation from “challenge innovation agents” — companies and organizations in the transportation safety space who can provide “real-world knowledge, guidance, insight, issues, and data to Solvers.”
Potential focus points for challenge solvers include protecting the safety of non-motorized road users like pedestrians and cyclists; educating particularly risky road users, like young drivers or drowsy drivers; and better understanding what differentiates a near-miss from a crash at “conflict points” like intersections and railroad crossings.
The challenge will be run in three stages, with prizes totaling $350,000.
This competition is the newest piece of DOT’s Safety Data Initiative, launched in January, which set out a department strategy of using big data to make American highways safer. There were 37,461 motor vehicle crash fatalities in 2016, a 5.6 percent increase over the previous year. The Safety Data Initiative also includes two data pilots, including one with the navigation app Waze where DOT crash data is being integrated with crowdsourced hazard data in the app to see if this information can be used to predict likely future accidents.