Collecting Wi-Fi data on pedestrians as they move around can provide analysis on infrastructure, to a depth that’s never been seen before, think scientists.
Collecting breadcrumb data, as people go about their daily business can be used to discover human motivations, predict how individuals react to change, and where to locate simple resources, such as automated teller machines, the researchers from Swiss university Ecole Polytechnique Fédérale de Lausanne (EPFL) believe.
“We have statistics and numbers on people who drive and take the train, but pedestrian behavior is often a mystery,” says Antonin Danalet of the school in a university website article. “Understanding the use of pedestrian infrastructure at music festivals, museums and hospitals” could be useful too, he says.
What Danalet means, is that if planners can get data on walking departure points and destinations, and exactly how people move around, the planners, or conceivably marketers too, can tailor infrastructure, like sidewalks or restaurant locations. That’s the kind of data that the tracing of Wi-Fi movements can provide—you get to know how many people move through a zone.
Insight is also possible, Danalet says. In his tests, at the university campus, he says that by collecting breadcrumb data as students were handed off from access point to access point, he was able to figure out why individuals chose one campus restaurant over another.
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Usually it was related to proximity and price, he found. The breadcrumbs created by the Wi-Fi traces showed that students didn’t want to travel distances for a particular type of cuisine.
Don’t go out of your way creating unique menus; feed them in many places; and keep it cheap, could be valuable advice for franchisees, discovered by the analysis.
Amusingly, Danalet was able to catch-out a lying faculty, where many said they arrived at campus earlier in the morning than they actually did. The Wi-Fi traces indicated that many were fibbing when they had said, in an earlier survey, that they got to work earlier than what actually transpired. A bit like the fascist autocrat Mussolini—who would keep a light on in his office during the night so people would think he was working—the students were nowhere near the campus when they said they were. This kind of behavioral data can provide cost savings—an organization doesn’t have to turn the heat on so early.
And Wi-Fi trace analysis doesn’t cost much to do, because you already have the access point data. There’s no new equipment to install, such as a turnstile. Danalet says it’s cheaper than video, or physical surveillance.
The EPFL university project isn’t the only Wi-Fi breadcrumb one out there. I’ve written about crowdsourcing MAC addresses from Wi-Fi signals in order to judge real-time public transit ridership.
And earlier this year I wrote about a law enforcement expert who says that MAC addresses from Wi-Fi signals should be used to catch criminals at the scene. That researcher says he wants to use router log-in attempts.
Danalet’s experiment captured over 2 million data points “picked up by the campus’s 789 WiFi antennas,” the article explains. The scientist says it wasn’t the data itself that provided the information, but with the help of the IT department, he was able to “link the points he collected with 2,000 individuals,” the university’s article says.
“The data was anonymized,” Danalet explains in the story. “I only knew if the traces came from an EPFL student or staff member. If it was a student, I also knew the section and year,” he qualifies. Whether that will keep the privacy aficionados at bay, if this takes off, is another story.
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