At the start of 2020, a team within Google Research was asked to explore new ideas for research projects that focused on accelerating climate mitigation. “We were looking into all kinds of big ideas, from cultivated meat to energy to air pollution,” Dotan Emanuel, a software engineer on the team, says.

At the dinner table one evening, Dotan shared some of those big ideas with his family — and the conversation soon pivoted to a frustration familiar to many of us: “My wife Osnat said, ‘Why don’t you do something about traffic lights? We stand at them for no good reason,’” he recalls.

Road transportation is responsible for significant global and urban greenhouse gas emissions. It’s especially problematic at city intersections where pollution can be 29 times higher than on open roads, and about half of these emissions come from traffic accelerating after stopping. With millions of traffic lights across the world, the scale of the problem was huge — and if Google could do something to address it, so was the opportunity.

“My initial thought was that we can’t do anything about traffic lights,” Dotan says. “But when it comes to research, the most fascinating challenges lie in the unknown.”

With their curiosity sufficiently piqued, Dotan and his team dug into the mechanics of traffic engineering. They found that while some amount of stop-and-go traffic is unavoidable, a portion can be prevented by optimizing traffic light timing. To do that, cities traditionally needed to either install expensive hardware or run time-consuming manual vehicle counts, neither of which provide complete information on key parameters they need.

“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,” Dotan says. “And a few weeks later, we had a project proposal ready.”

That proposal was for Project Green Light, an initiative that uses AI to make recommendations for city engineers to optimize existing traffic lights and reduce stop-and-go emissions. After evaluating dozens of other great ideas, Green Light was chosen for its simplicity, scalability and potential for impact.