The Department for Transport (DfT) has teamed up with key players in the road maintenance and data services sector to fund a £2m 'machine learning' project to survey the state of the nation's roads and develop 'the most thorough understanding ever of Britain’s road markings'.
Through analysis of nearly 100,000 miles of road and 150 million high definition (HD) images, the department will be able to advise councils 'where they could invest in areas that may need it most'.
Footways and cycleways will also receive a national 'stocktake' under the scheme.
The DfT will undertake the work in partnership with the Local Council Roads Innovation Group (LCRIG), which will be working with data experts Gaist to use machine learning AI technology to analyse the imagery.
Transport secretary Chris Grayling said: 'Road markings play a vital role in keeping everyone who is using the road safe, so making sure they’re up to standard is imperative.
'This funding will allow for advanced AI learning technology to assess the condition of the markings to improve the safety of our roads for all users.'
Paula Claytonsmith, managing director, Gaist, said: 'We are using over 146 million HD road images from our national databank and cutting-edge AI technology to assess over 96,000 miles of classified roads as part of this project. This is the largest exercise in assessing road marking readiness ever undertaken in England. Gaist are proud to have the AI capability that puts an SME UK business at the forefront of technological advances.'
Road markings are one of the most cost effective areas of road safety investment, according to the experts who designed the iRAP safety methodology, and can help major improvements in user satisfaction, parking and traffic flow.
The funding will also go towards a survey of councils around pavement and footway conditions, which will help outline where funding could be targeted.
The department is planning to assess sections of the National Cycle Network, building on the audit undertaken by cycling and walking charity Sustrans, to better understand its condition.
