Will Big Data Be the Solution to the London Tube Strike?
Several times a year, London will experience a Tube strike in which thousands are unable to ride the rails. As a result, Transport for London (TFL) has been working round the clock to provide extra transportation routes and networks to accommodate those people. Because people have a harder time getting to work, businesses experience the backlash of the strike, requiring them to implement more flexibility in their workplace practices than ever before, whether that means working from home or working different hours in order to accommodate the commuters. All the effort put into working around the strike in August cost the city nearly £300 million.
When tube strikes occur, the lost revenue and congested streets leave London government and TFL scrambling for a solution, and they may have found it in big data. Strikes cause them to rely more on big data for improved consumer experience as the solution.
How It Works
As they rely on the vast streaming of data gathered from ticket kiosks, traffic reports, and more, TFL can determine where the greatest need for transportation is during a strike. It will be handled very similarly to the way TFL handled the Putney Bridge closure during the summer of 2014. When the bridge closed for repairs, it put a wrench in the flow of London’s traffic system. The street was still open to pedestrians and cyclists, but no vehicular traffic could cross while it was under construction.
This caused massive delays and traffic jams in all of the surrounding areas, and it virtually stopped bus routes at that point. TFL was able to use their data collections to determine in a matter of minutes that the solution to the problem was to allow passengers to take the bus up to the bridge, walk across it, and begin their route on another bus on the other side. They did not have to pay twice, but they were still able to use public transport to reach their destination without too much of a hiccup.
Furthermore, TFL was able to send out alerts to app subscribers and via the news and text messages to tell bus passenger regulars that the buses in that area were to be delayed and they should take another route. They also provided the fastest route around the congestion, which involved longer bus routes and taking the London Underground.
Strikes are being handled the same way. TFL officials will process data to alert passengers of the least congested and alternate routes. Millions will be seeking an alternate route for transportation, and thanks to the quick data processing that comes with big data, they’ll be able to provide that, sending out extra buses and creating alternate commuting routes.
Obviously, this solution will not eliminate congestion and disgruntled customers. In situations like this, there will always be those, but it is a prime example of how to significantly increase efficiencies in a tight spot. It’s also a clear picture of what a smart city infrastructure is capable of as far as solving problems goes. With even more smart city data, the problems could be significantly smaller. Though there will still not be enough buses or road space to go around, TFL is able to anticipate the problems and do their best to ease the solution, determining which routes have more congestion and allocating resources where they’re needed most.
One of the most beneficial aspects of this whole situation is the ability to anticipate problems and their solutions before they happen. Big data has been collected for years, ever since the invention of the internet, which means that TFL can track past patterns and use them to project future ones. They can look at how harmful a tube strike is and how often it happens and use that information to equip the city for the next one.
Larry Alton is a Freelance Writer