Using WiFi To Measure Crowds, Estimate Waits

October 27, 2014 by Dave Haynes


We’re starting to see technology emerge that can establish patterns in retail and public environments just by picking up the MAC addresses on smartphones that have their WiFi on and triangulating roughly where they are.

That can establish heat maps of where people go and how long they dwell, and in some cases, start to spit out things like retail sales conversion ratios (look up AisleLabs).

In tech-centric Austin, Texas, the city’s airport is using WiFi analytics (as reported in Engadget) to estimate the wait times in TSA screening lines, based on months of detecting passengers’ WiFi-equipped phones as they make their way through the take off your shoes, empty your pockets process.

The airport is working with public WiFi reseller Boingo. The setup doesn’t require signing in to Boingo, and like AisleLabs, the set-up also uses beacons to add more data.

Why this is interesting in the context of digital signage is that the output can shape messaging on screens, so people will have a sense of how much time they need to budget to get through and to their gate. It could also, at larger airports, relate on screens the wait times at different TSA screening areas. I know at Charlotte, NC’s airport they had TSA people walking up and telling people at a busy checkpoint they could get through faster by walking down to the next one. That had me thinking there must be a more efficient way, like crowd analysis and dynamic screens.

The WiFi analysis tech is the sort of thing you could apply to all kinds of busy venues like stadiums, arenas, event centers, mass transit stations and amusement parks.

There are going to be people freaked out about their phone’s MAC address being picked up and their movements tracked, but it’s anonymous data used to establish patterns, not foll0w you. In a highly secure place like an airport, you’re undoubtedly on any number of cameras anyway.

I suppose the simple foil to that is to turn off your WiFi. if you care that much.

photo credit: hyku via photopin cc

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