Tapping the power of Big Data to manage the Tube Strike
London is experiencing its worst Tube strike in more than a decade. TfL have laid on extra buses to try and cope with the added demand – but how do they decide which routes need more capacity?
Managers across the capital have braced themselves for the effects of the worst London Underground strike since 2002. Whether it’s working from home or allowing staff greater flexibility with their working hours, the industrial action is set to cost the capital £300m. Transport for London (TfL) is running extra services on its bus and river networks throughout the disruption to try to cope with the surge in demand.
TfL head of analytics Lauren Sager Weinstein deals with a myriad of data sources on a daily basis – from Oyster Card journey histories to bus and tube timetabling and everything in between. Her mission: “How can we run our services better and serve our customers better using data?”
She recalls how TfL dealt with the closure of Putney Bridge in Summer 2014 for urgent maintenance. “We were able to work out that half of the journeys started or ended very close to Putney Bridge,” she said. “The bridge was still open to pedestrians and cyclists, so we knew those people would be able to cross and either reach their destination or continue their journey on the other side.”
As for the other half of the journeys, Weinstein said that in order to serve the needs of those passengers who used the bridge as the mid-point of their journey, TfL “increased bus services on alternate routes [and also sent customers] personalised messages about how their journey was likely to be affected.”
It is a perfect example of how Big Data is enhancing modern life.
TfL will be using similar methods – right now – to contend with the tube strike but, given that millions of people will be seeking an alternative mode of transport to commute, it is unlikely there will be enough buses to go round. Even with the old Routemasters forced into service.
Managers everywhere should take note of how TfL uses data to increase efficiency on its service and keep the capital moving. Clearly, investing in new infrastructure – such as Crossrail – is the obvious solution to increasing network capacity, but that’s both expensive and time-consuming. That is where data can provide a more immediate answer: low productivity has become an endemic problem in the UK – something that simply shouldn’t be the case when you consider the range of metrics available.
Lauren Sager Weinstein was talking at the recent MT Live event, hosted by Management Today in partnership with CMI
Image courtesy of William Perugini / Shutterstock.com (note: @InsightsCMI Twitter image courtesy of CristinaMuraca)