Google's Project Green Light Aims for Cleaner Streets
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Two years ago, Google introduced Project Green Light, an innovative initiative to combat pollution from vehicles idling at traffic signals. At the recent Sustainability ‘23 event, Google shared initial insights from this initiative and unveiled plans for its expansion.
The Green Light project employs machine learning to analyze Maps data, determining traffic congestion and average wait times at specific traffic signals. This data trains AI models to autonomously refine traffic light timings, decreasing idle durations and the subsequent braking and accelerating by vehicles. This aligns with Google's ambition to aid its collaborators in lowering their carbon emissions by a gigaton by the end of this decade.
Maguire, a representative from Google, highlighted, “Our AI recommendations seamlessly integrate with current infrastructure and traffic setups.” He explained that urban engineers can observe tangible results in mere weeks. For instance, a trial in Manchester witnessed up to an 18% enhancement in emission levels and air quality. Additionally, the company emphasized how their Maps routing effectively cuts down emissions. Maguire added that it "has curtailed over 2.4 million metric tons of carbon emissions, equating to removing approximately 500,000 gasoline cars from the streets for a year."
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