Machine Learning Times
Machine Learning Times
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2 years ago
Massive Traffic Experiment Pits Machine Learning Against ‘Phantom’ Jams

Originally published in Berkeley News, Nov 22, 2022.

Many traffic jams are caused by human behavior: a slight tap on the brakes can ripple through a line of cars, triggering a slowdown — or complete gridlock — for no apparent reason.

But in a massive traffic experiment that occurred outside of Nashville last week, scientists tested whether introducing just a few AI-equipped vehicles to the road can help ease these “phantom” jams and reduce fuel consumption for everyone. The answer seems to be yes.

Over the course of five days, researchers conducted one of the largest traffic experiments of its kind in the world, deploying a fleet of 100 Nissan Rogue, Toyota RAV4 and Cadillac XT5 vehicles onto a busy stretch of Nashville’s I-24 during the morning commute. Each vehicle was equipped with an AI-powered cruise control system designed to automatically adjust the speed of the vehicle to improve the overall flow of traffic — essentially turning each car into its own “robot traffic manager.”

“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 is able to fix — it can direct the vehicle to things that are not intuitive to humans, but are overall more efficient.”

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