In a network as large as the London Underground, signal failures and other unplanned problems are inevitable, says Ricardo Silva, a statistician at University College London. Transport for London (TfL) has strategies in place for planned disruptions, but unplanned disruptions often result in total chaos, with overcrowding on bus services and other trains, as well as huge changes in foot traffic.

Silva and a team of other researchers set out to improve TfL's response to unplanned disruptions. They realised that if they examined past disruptions to learn how passengers had responded, they could use that information to predict how people might respond to unplanned disruptions in the future. Their research was published in a recent PNAS paper.

To build their predictions, the research team used data from passengers' fare cards, called Oyster cards. Until now, Oyster card data had only been used to obtain very general information, like knowing how many people can be expected to enter or exit a station at a given time. What hasn’t been done before is to delve into a deeper level of detail, tracking individual commuters’ routes.

This is exactly what Silva's team did. Using data provided by TfL, they tracked commuter movements for 70 weekdays spread out between February 2011 and February 2012. The data was anonymized, but the researchers were able to see exactly when and where a passenger entered the system, and when and where they exited.

The data allowed the team to see what normal commuting patterns are like. Once they knew what “normal” looked like, they were able to see how this changed when there was an unplanned disruption.

Someone facing a disruption on their normal line might switch to another line, if the option is available, or they might be forced to leave the station and walk or catch a bus. The research team gathered data on when people chose to leave the station and when they used another Underground line. This allowed them to understand the contrast between what would have happened with no disruption, and what actually happened Silva explains.

With enough information about past events, the team could now anticipate how future disruptions might look. They tested their system on a real unplanned disruption on the Victoria line one evening, predicting a much higher rate of people leaving the station than normal. Their prediction was very close to the real-world number of exits.

Silva is quick to point out that their system isn’t perfectly accurate: given a rate of 80 people leaving a station every minute, they might wrongly predict movements of roughly 12 of those people. This means an error margin of approximately 15 percent. “There’s a lot of variability from event to event,” says Silva.

While the scale of the research is definitely a new step, the paper falls short in not giving details on how this information can actually be used to minimize disruption, says Richard Mounce, who researches transport systems and was not involved in this paper.

However, Silva has a number of ideas for how the data could be used. The principal change, he says, should be to bus schedules. If there’s disruption on an Underground line, this means that more buses are needed in that part of the system to pick up the slack. Providing extra buses is a normal part of the response to planned disruptions, but this predictive system could allow TfL to react faster to unplanned disruptions by diverting buses to where they’re most needed.

In the longer term, it could also be possible to use the data to provide individualised information to passengers. Automated e-mails could be sent to commuters affected by a disruption, advising them on the most efficient changes to their normal routes. The data could also be used in smartphone apps, such as Citymapper, to provide more comprehensive guidance to commuters. However, says Silva, this is all a long way off: while in principle the data is already usable, implementation is likely to be slow and expensive.

The long-term plan, he says, is to continue to gather data on passenger behavior and use it to generate more accurate and detailed predictions. The system could also be transferred to other transport networks, Silva adds. Not all transport systems use cards to swipe out as well as to swipe in, but those that do could use a similar analysis to improve their contingency plans.

PNAS, 2015. DOI: doi: 10.1073/pnas.1412908112 (About DOIs).