Google’s Project Green Light leverages AI to optimize and improve traffic flow. How so? AI is used to make recommendations for city engineers to optimize existing traffic lights and reduce stop-and-go emissions.
Project Green Light utilized Google Maps’ driving trends to create an AI model that measures how traffic flows through an intersection, including patterns of starting and stopping, average wait times at a traffic light, as well as coordination between adjacent intersections. It identifies possible improvements, like reducing the wait time of red traffic lights during off-peak hours or an opportunity to coordinate between intersections that aren’t yet synced. These recommendations can be implemented by engineers in as little as five minutes, using their city’s existing infrastructure.
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My initial thought was that we can’t do anything about traffic lights. But when it comes to research, the most fascinating challenges lie in the unknown. We quickly understood we have a strong advantage that cities could benefit from — over a decade of Google Maps driving trends from across the globe. And a few weeks later, we had a project proposal ready,” said Dotan Emanuel, a software engineer on Google’s Project Green Light team.
