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Google Green Light: AI for smarter and greener traffic lights
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Traffic jams are not only frustrating but also harmful to the environment. According to a study, road transportation accounts for a large share of global and urban greenhouse gas emissions, and the situation is worse at city intersections, where pollution can be 29 times higher than on open roads. The main reason is vehicles' frequent stopping and starting, which consumes more fuel and emits more carbon dioxide.
But what if we could use artificial intelligence (AI) to optimize traffic lights and reduce these emissions? That is the idea behind Green Light, a Google Research initiative that aims to make traffic lights smarter and greener. Green Light uses AI, and Google Maps driving trends to model traffic patterns and make recommendations for improving the existing traffic light plans. City engineers can implement these recommendations using existing infrastructure in as little as five minutes.
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The critical innovation of Green Light is that it does not optimize just one intersection but coordinates across several adjacent intersections to create waves of green lights. This way, 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.
A blog update said that early numbers indicate a potential for up to 30% reduction in stops and up to 10% reduction in emissions at intersections1. Green Light lives 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 monthly emissions for up to 30M car rides.
Applying AI to optimize traffic lights
For many city traffic engineers, getting access to reliable data for traffic light optimization is complex and expensive, which means that many traffic lights rely on outdated configurations. Green Light's city partners have said that before 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 critical parameters they need.
Google Research teams have been exploring the use of AI and driving trends from Google Maps to model intersections and traffic flow. They have built an AI-based model of each intersection, including its structure, traffic patterns (such as patterns of starting and stopping), light scheduling, and how traffic and light schedules interact.
Then, it also built a model of the interaction between traffic lights. Based on this model, the team developed AI-based optimizations and provided recommendations to city engineers via the Green Light interface.
For example, it might identify an opportunity to coordinate between intersections that are not yet synced and recommend the timing of the traffic lights so that traffic flows more effectively along a stretch of road.
Green Light can analyze thousands of intersections simultaneously, improving the flow through multiple intersections in the city. The AI-based recommendations work with existing infrastructure and traffic systems, and city engineers can monitor the impact and see results within weeks.
Google says that working with cities globally can help people and the planet. The technology has shown success in cities like Seattle and Hamburg — and the potential for impact is even more significant in cities with less access to advanced technology. The team is working closely with its partner cities to expand to more intersections in each city or town. It aims to develop more locations next year so that people can experience cleaner, greener cities worldwide.
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