One researcher thinks we can mine data from utilities to predict traffic jams hours in advance. Joshua Roberts/Reuters

The way we consume power after midnight can reveal how we bad the morning rush hour will be.

Commuters check Google Maps for traffic updates the same way they check the weather app for rain predictions. And for good reasons: By pooling information from millions of drivers already on the road, Google can paint an impressively accurate real-time portrait of congestion. Meanwhile, historical numbers can roughly predict when your morning commutes may be particularly bad.

But “the information we extract from traffic data has been exhausted,” said Zhen (Sean) Qian, who directs the Mobility Data Analytics Center at Carnegie Mellon University. He thinks that to more accurately predict how gridlock varies from day to day, there’s a whole other set of data that cities haven’t mined yet: electricity use.

“Essentially we all use the urban system—the electricity, water, the sewage system and gas—and when people use them and how heavily they do is correlated to the way they use the transportation system,” he said. How we use electricity at night, it turns out, can reveal when we leave for work the next day. “So we might be able to get new information that helps explain travel time one or two hours in advance by having a better understanding of human activity.”

In a recent study in the journal Transportation Research Part C, Qian and his student Pinchao Zhang used 2014 data to demonstrate how electricity usage patterns can predict when peak congestion begins on various segments of a major highway in Austin, Texas—the 14th most congested city in the U.S. They crunched 79 days worth of electricity usage data for 322 households (stripped of all private information, including location), feeding it into a machine learning algorithm that then categorized the households into 10 groups according to the time and amount of electricity use between midnight and 6 a.m. By extrapolating the most critical traffic-related information about each group for each day, the model then predicted what the commute may look like that morning.

When compared with 2014 traffic data, they found that 8 out of the 10 patterns had an impact on highway traffic. Households that show a spike of electricity use from midnight to 2 a.m., for example, may be night owls who sleep in, leave late, and likely won’t contribute to the early morning congestion. In contrast, households that report low electricity use from midnight to 5 a.m., followed by a rise after 5:30 a.m., could be early risers who will be on the road during rush hour. If the researchers’ model detects more households falling into the former group, it might predict that peak congestion will start closer to, say, 7:45 a.m. rather than the usual 7:30.

“In addition to using a plain machine learning model to make the connection between two systems, we also try to use our intuition to explain [the human activity] behind the patterns of electricity use,” said Qian.

He also acknowledged that the sample size is small. Making a more accurate prediction model will not only call for more numbers, but also more variety of data. Ideally, the predictions would factor in historic and real-time traffic data, along with weather updates, public transit information, and insights into how other utilities are used. (A spike in pre-dawn toilet flushing? Prepare for a jammed-up morning rush.) Knowing more detail about the households themselves would only improve the model—but Qian said it’s not necessary.

“For the manager of roadway systems, what they’re interested to know are the ‘what if’ scenarios,” he said. “If there is an accident, or if there is extreme weather conditions, or if the city adds one more lane or change the parking prices [downtown]—things you can’t learn from the past.” They also want to better understand driver’s behavior and proactively manipulate it to reduce congestion. There’s no doubt that Google Maps is already helpful to drivers, he added, “but it was never designed from the city manager’s perspective.”

Right now, this study is only a “proof of concept,” according to Qian, to show that it’s worth the effort for cities to explore this kind of data—and make it publicly available. In fact, he said, the hardest part is accessing the information, which is usually spread across public agencies without any sort of coordination or guarded by private utility companies.

“One paper won’t address all the issues, but it’s a starting point,” he said. “If more people realize the importance of analyzing data, it's a good win-win for the public and private industries.”

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