“Green Light” is an innovative solution offered by Google, aimed at improving urban mobility and reducing greenhouse gas emissions in cities. Utilizing advanced artificial intelligence and data from Google Maps, the system optimizes the operation of traffic lights, contributing to smoother traffic flow and fewer stops. In today’s article, I will introduce you to the workings of this solution, its benefits for cities and residents, and how technology can contribute to sustainable urban development.
Traffic is responsible for a significant part of global and urban greenhouse gas emissions. This problem is especially acute at intersections, where pollution levels can be up to 29 times higher than on open roads. Moreover, at intersections, half of these emissions come from cars accelerating after stopping. Although some amount of “stop-and-go” traffic is inevitable, some can be prevented by optimizing traffic light configurations. To improve their performance, cities must either install expensive devices or manually count vehicles. Both solutions are costly and do not provide all the necessary information.
The “Green Light” project leverages artificial intelligence and driving trends from Google Maps, which possess one of the most extensive global road networks, to model traffic patterns and create intelligent recommendations for urban traffic engineers to optimize vehicle flow. Preliminary data indicates the possibility of reducing stops by up to 30% and reducing greenhouse gas emissions by 10%. By optimizing each intersection and coordinating between neighboring intersections, engineers want to create waves of green lights, helping cities reduce “stop-and-go” traffic. Currently, “Green Light” is in operation at 70 intersections in 12 cities across 4 continents – from Haifa in Israel, through Bangalore in India, to Hamburg in Germany. At these intersections, fuel savings and emission reductions have been achieved for up to 30 million trips per month.
How Google Greenlight Works
For many years, Google has been meticulously mapping cities around the world (read the article on, how Google Maps works), which has allowed the collection of a large amount of data and a deep understanding of traffic dynamics. This has given engineers knowledge of key traffic light parameters, such as their cycle length, crossing time, and the way they are coordinated. The analysis also took into account information about the operation of various sensors and systems related to the lights.
To further improve traffic flow, engineers created a special model that analyzes how vehicles move through an intersection. This allows for a better understanding of typical driver behaviors, wait times at lights, and how different traffic light plans adapt to changing conditions throughout the day.
AI-Based Recommendations
Passing this data through artificial intelligence can yield a series of suggestions for modifying the operation of existing traffic lights at selected locations. Such suggestions are then passed on to cities, and local traffic engineers can then assess these proposals and, if necessary, implement them in a very short time, using the tools available to them.
However, it’s not just about delivering recommendations, but also measuring the effects. Among the available data are information on how modifications have affected road traffic and how many vehicle stops have been saved. The data also show how changes have affected the natural environment.
