Tapping into the power grid could predict the morning traffic

Why is there traffic? This eternal question haunts civic planners, fluid dynamics professors, and car manufacturers alike. But just counting the cars on the road won’t give you a sufficient answer: you need to look at the data behind the data. In this case, CMU researchers show that electricity usage may be key to understanding movement around the city.

The idea that traffic and electricity use might be related makes sense: when you turn the lights and stereo on and off indicates when you’re home to stay, when you’re sleeping, when you’re likely to leave for work or return, and so on.

“Our results show that morning peak congestion times are clearly related to particular types of electricity-use patterns,” explained Sean Qian, who led the study.

They looked at electricity usage from 322 households over 79 days, training a machine learning model on that usage and the patterns within it. The model learned to associate certain patterns with increases in traffic — so for instance, when a large number of households has a dip in power use earlier than usual, it might mean that the next day will see more traffic when all those early-to-bed people are also early to rise.

The researchers report that their predictions of morning traffic patterns were more accurate using this model than predictions using actual traffic data.

Notably, all that’s needed is the electricity usage, Qian said, nothing like demographics: “It requires no personally identifiable information from households. All we need to know is when and how much someone uses electricity.”

Interestingly, the correlation goes the other way as well, and traffic patterns could be used to predict electricity demand. A few less brownouts would be welcome during a heat wave like this summer’s, so I say the more data the better.

There are many factors like this that indicate the dynamics of a living city — not just electricity use but water use, mobile phone connections, the response to different kinds of weather, and more. Traffic is only one result of a city struggling to operate at maximum capacity, and all these data feed into each other.

The current study was limited to a single electricity provider and apparently other providers are loath to share their data — so there’s still a lot of room to grow here as the value of that data becomes more apparent.

Qian et al published their research in the journal Transportation Research.

Knitting machines power up with computer-generated patterns for 3D shapes

At last, a use for that industrial knitting machine you bought at a yard sale! Carnegie Mellon researchers have created a method that generates knitting patterns for arbitrary 3D shapes, opening the possibility of “on-demand knitting.” Think 3D printing, but softer.

The idea is actually quite compelling for those of us who are picky about their knitwear. How often have we picked up a knit cap, glove, or scarf only to find it too long, too short, too tight, too loose, etc?

If you fed your sartorial requirements (a 3D mesh) into this system from James McCann and students at CMU’s Textiles Lab, it could quickly spit out a pattern that a knitting machine could follow easily yet is perfectly suited for your purposes.

This has to be done carefully — the machines aren’t the same as human knitters, obviously, and a poorly configured pattern might lead to yarn breaking or jamming the machine. But it’s a lot better than having to build that pattern purl by purl.

With a little more work, “Knitting machines could become as easy to use as 3D printers,” McCann said in a CMU news release.

Of course, it’s unlikely you’ll have one of your own. But maker spaces and designer ateliers (I believe that’s the term) will be more likely to if it’s this easy to create new and perfectly sized garments with them.

McCann and his team will be presenting their research at SIGGRAPH this summer.