A new study found adaptive traffic signals powered by big data reduced peak-hour travel times by 11% in China’s 100 most congested cities – saving 31.73 million tonnes of CO₂ annually.
A new study found adaptive traffic signals powered by big data reduced peak-hour travel times by 11% in China’s 100 most congested cities – saving 31.73 million tonnes of CO₂ annually.
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The original was posted on /r/science by /u/calliope_kekule on 2025-03-01 05:53:17+00:00.
"big data" is not generative AI. They're different things. Just in case anyone read that as "AI fixes things".
It's weird cause technically adaptive traffic patterns are trained using tools like reinforcement learning, which is technically AI, however it's the broad term AI and not GenAI.
I mean, this is also an area where neural networks will improve things. Neural networks are excellent for optimizing data with an extremely large amount of input variables, as is the case here. You don't need language models, you don't need to steal all the content on the internet for training. You have analysis tools that will easily validate any solution, so you're not going to deal with mystery hallucinations.
It's not an extremely large amount of data at all, you can get perfect efficiency by having lights act on completely local, real-time, sensor data, as in "how many cars are in which direction". AI is useful to recognise who wants to use the light but that's the end of it. You don't need to predict traffic patters as you don't need them to see what's the state of the streets right now, worse, such predictions are a source of BS. Lots of patterns happen all the time that have no precedence as construction sites shift, sportsball games get cancelled or not, whatnot.
It's a confusing situation, because big data is what it sounds like. Large amounts of data on actual events. But it doesn't mean they didn't use AI to help interpret the data, or to come up with the adaptive traffic signaling.