Google’s Green Light Project Utilizes AI to Reduce Traffic Pollution

Google has discussed some early results from its Green Light project, launched two years ago, and announced a new wave of expansions.

The project aims to address street-level pollution caused by vehicles idling at traffic signals.

Green Light employs machine learning systems to analyze map data to calculate the level of traffic congestion at specific intersections, along with the average wait times for vehicles there.

This information is used to train AI models capable of independently optimizing traffic flow at these intersections, reducing idle times as well as the braking and acceleration necessary for vehicles passing through.

This is part of Google’s goal to collectively help its partners reduce their carbon emissions by one gigaton by 2030. Google first announced the project in 2021 and trialed it in four intersections.

During those tests, the company observed a reduction in fuel consumption and intersection delay times ranging from 10% to 20%.

Since then, the pilot program has grown and expanded to dozens of partner cities around the world, including Rio de Janeiro in Brazil, Manchester in England, and Jakarta in Indonesia.

In 2024, Google plans to expand further into more cities. Initial figures suggest the company could potentially see a 30% reduction in stoppage points.

The company stated, “We believe the project is unique because it is scalable and cost-effective for cities compared to alternative options. Ideally, cities deploy the models across networks of residential buildings to create waves of Green Light deployment.”

Google’s AI recommendations work with existing infrastructure and traffic systems, and city engineers can monitor the impact and see results within weeks.

The Manchester trial witnessed improvements in emission levels and a 18% increase in air quality.

Google has also advocated for the effectiveness of its map routing in reducing emissions, claiming to have prevented over 2.4 million metric tons of carbon emissions, equivalent to removing approximately 500,000 fuel-powered cars from the road for an entire year.


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