The 2012 Olympic Games: Will Data Save the Day?

Data analysis is being used during the Olympic Games for everything from ensuring a smooth flow of commuter traffic to generating a multi-colored light show on the London Eye each night based on Twitter feed sentiments.

Image courtesy of Flickr user Mr G’s Travels.

Maybe big data could have saved Mitt Romney from his now infamous diplomatic blunder in London this past week, when the U.S. presidential candidate questioned whether The Big Smoke was ready to stage a successful Olympic Games.

The fact is, data analytics are in use everywhere in London during the Games — on the Tube, in the airwaves and on the London Eye, the enormous Ferris wheel that is now staged as a giant emoticon swirling over the city — providing a second-by-second read on everything from public transport to crowd movements to citizen sentiments.

Behind the scenes, Transport for London (TfL) is employing a massive, multifaceted data analysis program to ensure that London travelers stay informed — and moving along their merry way — during the Olympics. TfL Director of Games transport Mark Evers said in a recent interview with tech news outlet V3.co.uk that TfL is aggregating data generated from its commuter network, video cameras, smart phones and social media to ensure a smooth flow of commuter traffic — as well as crowd control.

“We’re doing an enormous amount of work to make sure we keep London transport ready to play its part in both hosting great games, but also to make sure we keep London open for business,” Evers said.

According to a recent article in The Economist, TfL operates one of the largest metro telecoms networks in the world, “just to cope with the huge amounts of data generated by its 270 stations and 530 trains.” During the Games, TfL is continuously monitoring that data to determine where hotspots are, and to act accordingly, be that by alerting travelers to delays or altering traffic flows.

TfL is also monitoring ticket sales from its Oyster card prepay system. In partnership with the Centre for Advanced Spatial Analysis at University College London, TfL is determining, for example, what time of day people are traveling and where the most heavily trafficked stations are.