Google’s AI-Powered ‘Project Green Light’ Speeds Traffic, Reduces Fuel Consumption and Carbon Emissions

Google’s “Project Green Light” uses machine learning on Maps data to optimize the length of green lights, reports Engadget, “reducing idle times as well as the amount of braking and accelerating vehicles have to do there.”

When the program was first announced in 2021, it had only been pilot tested in four intersections in Israel in partnership with the Israel National Roads Company but Google had reportedly observed a “10 to 20% reduction in fuel and intersection delay time” during those tests. The pilot program has grown since then, spreading to a dozen partner cities around the world, including Rio de Janeiro, Brazil; Manchester, England and Jakarta, Indonesia. “Today we’re happy to share that… we plan to scale to more cities in 2024,” Yael Maguire, Google VP of Geo Sustainability, told reporters during a pre-brief event last week. “Early numbers indicate a potential for us to see a 30% reduction in stops….”

Maguire also noted that the Manchester test reportedly saw improvements to emission levels and air quality rise by as much as 18%. The company also touted the efficacy of its Maps routing in reducing emissions, with Maguire pointing out at it had “helped prevent more than 2.4 million metric tons of carbon emissions — the equivalent of taking about 500,000 fuel-based cars off the road for an entire year.”