Mobility & Transportation
With support from the Toyota Mobility Foundation, a set of innovative machine learning technologies help traffic planners study the movement of pedestrians and bicycles, maximizing safety for everyone on the road.
Key Impact
Instituted multi-channel technology for traffic planning
Developing and testing machine learning technology to make getting around L.A. safer.
Building on the use of smart systems by the Los Angeles Department of Transportation (LADOT) to monitor vehicular traffic, this project developed machine learning technologies to monitor the movement of pedestrians and bicycles through critical intersections.
MFLA partnered with the City’s Data Science Federation and the California State University Los Angeles Data Science Research Group on this effort, with key support from the Toyota Mobility Foundation.
The goal was to develop a mature algorithm to increase safety and traffic flow through better traffic management and planning, incorporating key data on pedestrian and bicycle movement. After undergoing tests in the Arts District, the technology can be further developed to serve as a model for other cities and municipalities.
Issue Area
Partners
Data Science Federation
California State University Los Angeles Data Science Research Group
Toyota Mobility Foundation
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Embedded with LADOT, the one-year fellowship developed an action plan for Los Angeles to take advantage of technology as it looks to develop transportation solutions.