TfL to use artificial intelligence to help fuel London’s cycling boom

A UK-first trial of artificial intelligence (AI) technology could make it much easier to plan and operate new cycle routes in the capital.

Until now, Transport for London (TfL) has relied mainly on manual traffic counts to record how many people are cycling on any given road, with this information used to assess demand for new cycle routes and improve the operation of the road network for cyclists.

Since 2018, TfL has worked with London tech firm Vivacity Labs to trial using sensors at two busy locations along Millbank. The sensors, which are up to 98% accurate, use artificial intelligence to detect road users and decipher what mode of transport they are using.

Gathering data around the clock, the sensors provide a significantly more detailed account of how the capital’s roads are being used 24/7, simultaneously processing and discarding data video within seconds to ensure no personal data is stored.

TfL is in the process of introducing 43 more Vivacity sensors at 20 central London locations to gather more data and test the technology’s full capabilities. According to TfL, as London’s cycling network grows it will use information from the sensors to work out where investment in new infrastructure can be best targeted.

Implementing the sensors is part of a wider programme of modernisation of TfL’s current road network systems to provide data in real time, which it hopes will enable it to better balance road demand and manage congestion.

Hopefully, the identification of where cycling infrastructure is needed in the capital and beyond, will go some way to achieving the government’s cycling target of doubling cycling ‘stages’ from 800 million in 2013 to 1.6 billion in 2025, despite it revealing that funding per head must double in order to do so.

Will Norman, London’s Walking and Cycling Commissioner, said: “By getting more people cycling and walking, we can help to tackle congestion and pollution in London and improve our health. Our healthy streets approach is based on evidence and data and we welcome new technology that supports this.”

Hayley Everett

Multimedia Reporter

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