State and Local Government Data Analysis Tools for Roadway Safety - Notice of Funding Opportunity
In November 2019, USDOT released a NOFO titled “State and Local Government Data Analysis Tools for Roadway Safety ,” which focused on building the capacity of State, local, and tribal governments to use innovative data tools and information to improve roadway safety. The NOFO made available over $3 million for up to 12 awards. Eligible applicants included State or local governments, metropolitan planning organizations (MPO) and regional governments, other political subdivisions of a State or local government, and tribal governments. USDOT received 40 eligible applications requesting almost $15 million in Federal funds. In June 2020, nine State, local, and tribal governments were selected for an award:
The City of New Orleans in Louisiana was selected to receive $402,791 to refine and expand USDOT’s existing Pedestrian Fatality Risk Map to include risk to bicyclists, which will help the City make defined, targeted decisions around small-area and corridor-level investments with the greatest potential to prevent serious injuries and fatalities for vulnerable road users.
The Confederated Tribes and Bands of the Yakama Nation Department of Natural Resources in Washington State was selected to receive $430,000 to build on an existing roadway data analysis tool developed by the University of Washington’s STAR Lab, and develop a comprehensive roadway safety data visualization and evaluation platform to support decision-making about where to invest in roadway safety countermeasures.
The Connecticut Department of Transportation was selected to receive $453,000 to develop a tool to improve the State’s behavioral safety decision making by integrating crash and roadway information with data on citations, toxicology, and hospital injury data, and it will quantify the costs and benefits of behavioral safety countermeasures to inform decision making.
The Maryland Department of Transportation State Highway Administration was selected to receive $358,500 to develop and implement a data analytics and visualization dashboard using mobile device location data and electric scooter trip data available from the City of Baltimore to better understand pedestrian, bicycle, and electric scooter travel volumes and their exposure to risk.
The Massachusetts Department of Transportation was selected to receive $429,100 to expand an existing crash data portal to help regional transportation planners and law enforcement identify higher risk roadways and risk factors to target roadway safety improvements, and develop publicly available analytic tools and data visualizations.
MetroPlan Orlando, the MPO for the Orlando, Florida metropolitan area, was selected to receive $294,942 to build upon the University of Central Florida’s (UCF) safety data visualization tool (winner of USDOT’s Solving for Safety Visualization Challenge), which uses real-time traffic conditions to estimate the likelihood of a crash at specific locations and help system operators target monitoring of video feeds to identify crashes, deploy first responders, and clear crash scenes more quickly, reducing the probability of secondary crashes occurring at those locations.
The North Carolina Department of Transportation was selected to receive $384,500 to develop an AI tool for automated analysis of existing videolog data that would extract roadside hazards – such as trees, embankments, and steep slopes – on all rural roads in the state, to help identify roadway segments in need of infrastructure safety improvements.
The Regional Transportation Commission of Washoe County, the MPO for the Reno, Nevada metropolitan area, was selected to receive $298,600 to automatically extract highly accurate road geometric features from mobile light-detection-and-ranging (LiDAR) data collected on area roadways, and use AI to create a dataset that would be incorporated into GIS software for roadway safety analysis.
The Virginia Department of Transportation was selected to receive $232,500 to develop a systemic safety analysis tool, which would identify and visualize locations with higher levels of risk that would benefit from eight low-cost roadway safety countermeasures, allowing for the implementation of these countermeasures at many sites with similar roadway features.