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Large-Scale Traffic Experiment Uses AI Against ‘Phantom’ Jam

December 1, 2022
UC Berkeley-affiliated CIRCLES members pose in front of signed I-24 MOTION and CIRCLES banners at the end of testing week. (Photo & Caption Credit: Berkeley News)
UC Berkeley-affiliated CIRCLES members pose in front of signed I-24 MOTION and CIRCLES banners at the end of testing week. (Photo & Caption Credit: Berkeley News)

CEE Professors Maria Laura Delle Monache and Alexandre M. Bayen discuss their research on the impact of AI-equipped vehicles on traffic jams and fuel consumption in a large-scale traffic experiment outside of Nashville through the Circles Consortium with Berkeley News

“By conducting the experiment at this large of a scale, we hope to show that our results can be reproduced at the societal level,” said CIRCLES co-PI Maria Laura Delle Monache, an assistant professor of civil and environmental engineering at UC Berkeley. “Even when only a few vehicles behave differently, the overall system can be impacted, making it better for everyone on the road and not only for those with AI-equipped vehicles.”

“Driving is very intuitive. If there’s a gap in front of you, you accelerate. If someone brakes, you slow down. But it turns out that this very normal reaction can lead to stop-and-go traffic and energy inefficiency,” said Alexandre Bayen, associate provost and Liao-Cho Professor of Engineering at the University of California, Berkeley. “That’s precisely what AI technology can fix — it can direct the vehicle to things that are not intuitive to humans but are overall more efficient.”

Massive field test showing how AI smooths traffic flow