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09Road transportation is responsible for a significant amount of global and urban greenhouse gas emissions. It is especially problematic at city intersections where pollution can be 29 times higher than on open roads.¹ About half of the emissions at intersections comes from traffic stopping and starting², and we found that by leveraging AI we can reduce these emissions by optimizing traffic lights.

Green Light, a Google Research initiative, uses AI and Google Maps driving trends to model traffic patterns and make recommendations for optimizing the existing traffic light plans. City engineers can implement these in as little as five minutes, using existing infrastructure. By optimizing not just one intersection, but coordinating across several adjacent intersections to create waves of green lights, cities can improve traffic flow and further reduce stop-and-go emissions.

Several cities have already participated in Project Green Light — and city officials are welcome to sign up for our waiting list.

We’re excited to share that early numbers indicate a potential for up to 30% reduction in stops and up to 10% reduction in emissions at intersections.³ Green Light is now live in 70 intersections in 12 cities from Haifa to Rio de Janeiro to Bangalore. In the intersections where Green Light is already live, this can save fuel and lower emissions for up to 30M car rides monthly.

Green Light is already live and helping to reduce emissions at intersections in 12 cities: Abu Dhabi, Bali, Bangalore, Budapest, Haifa, Hamburg, Hyderabad, Jakarta, Kolkata, Manchester, Rio de Janeiro and Seattle.

Applying AI to optimize traffic lights

For many city traffic engineers, it is hard and expensive to get access to reliable data for traffic light optimization, which means that many traffic lights rely on outdated configurations. Our city partners tell us that prior to Green Light, they would try to optimize traffic lights using expensive sensors or time-consuming manual vehicle counts — and these solutions do not provide complete information on key parameters they need.