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.
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