Ordnance Survey Involved in Project to Reduce Traffic Jams and Air Pollution
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Ordnance Survey Involved in Project to Reduce Traffic Jams and Air Pollution

A partnership between Ordnance Survey, the Department for Transport, the British Parking Association, and GeoPlace, could dramatically reduce traffic congestion on UK roads by making crucial data on planned changes to the road network available to tech companies to build artificial intelligence routing platforms.

Through the Department for Transport’s review of legislation around Traffic Regulation Orders (TROs), the data could soon be made available to tech firms to develop and enhance navigational apps. It is hoped the apps could warn drivers up to months in advance of planned disruption to routes and offer alternatives, potentially saving them time and money.

The initiative aims to give drivers the confidence to plan important trips without the fear of being stuck in traffic, and has the potential to reduce congestion, delays and air pollution.

Development of proposals to make it easier for authorities, private app developers and companies to access data around the predicted 50,000 yearly road closures and use it to its full potential, contributes to Government’s commitment to make travelling cleaner and greener, safer, easier and more reliable.

Location and data for positive change

Minister of State, George Freeman, said: “When councils and utilities plan work months in advance, why don’t we tell drivers so that they can avoid roadworks? Today’s announcement to work towards sharing Government data will reduce congestion, pollution and frustration.”

Ordnance Survey Director, David Henderson, said: “This review supports the Government’s Future of Mobility Grand Challenge, which aims to make journeys in the UK greener, safer, easier and more reliable. It’s very pleasing that once again Ordnance Survey is able to use location and data in aiding positive change.”

Evidence suggests that opening TRO data up will also help with the development of route planning systems for self-driving vehicles.

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