TfL Begins Collecting Data On Tube Passengers This Week

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The anonymous tracking data is to be used to help passengers avoid congestion and improve transport policy

Transport for London said on Monday it was officially launching a data analytics programme tracking passengers’ movements around the London Underground network via their mobile phones’ Wi-Fi signals, a scheme it believes could have wide-ranging benefits.

The launch follows a four-week pilot in December 2016 and includes appropriate privacy safeguards developed in cooperation with the Information Commissioner’s Office, TfL said.

The system records devices’ unique MAC addresses, in an anonymised form, and is limited to those that have alrady been authenticated on the Tube’s Virgin Media-operated Wi-Fi network.

It then tracks the devices in real time as users move around the network, giving TfL insight into traffic flows and the ways that factos such as overcrowding and disruption affect the network.

telecomsCommercial impact

TfL said it would use the data in part to boost advertising revenues by showing footfall past key spots in a quantifiable form.

The 2016 pilot collected data on some 5.6 million devices.

The transport authority said the real-time data would allow it to deploy staff to busy areas as needed, as well as helping with transport planning and seeing which lines need capacity upgrades.

It could also help passengers plan their routes more effectively, with an app that could, for instance, advise passengers of the best route to avoid crowds or to avoid a certain busy train and get on the next one.

Open data

TfL previously found that transfer times between the Northern Line and the Victoria Line at Euston took from one to five minutes depending on which route the passenger took.

The data could be provided to third-party app makers via TfL’s existing open data API.

“The benefits this new depersonalised dataset could unlock across our network – from providing customers with better alerts about overcrowding to helping station staff have a better understanding of the network in near-real time – are enormous,” said Lauren Sager Weinstein, TfL’s chief data officer.

“By better understanding overall patterns and flows, we can provide better information to our customers and help us to plan and operate our transport network more effectively for all.”

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