The recent solar storm – or coronal mass ejection – which hit the earth’s atmosphere on October 8 was recorded by researchers at the Svalbard observatory in artic Norway, as having created a GPS error in the region of 10 metres. At the same time, the US National Oceanographic and Atmospheric Administration predicts that we are in an increasing cycle of solar activity.

“Innovations that rely upon GPS and other satellite navigation technologies can clearly be influenced by solar weather events, as the experience of the recent solar storm clearly shows,” commented innovITS CEO Phil Pettitt. “At the innovITS Advance site we are able to monitor the precise accuracy of satellite navigation signals and can replicate the effects of any past solar weather incident – or simulate even more extreme conditions – so that system robustness can be fully tested and validated. With the increasing reliance upon navigation technologies in telematics and intelligent transport systems – and the safety criticality of many applications – the ability to prove robustness under extremes of signal disruption due to solar weather, or man- made intervention, is an important development capability.”

Download the press release here.

The edition of the BBC’s prime time evening magazine programme broadcast on Thursday August 23rd, included a feature filmed at innovITS Advance, highlighting research carried out by the University of Southampton into the application of ‘machine learning’ approaches to the optimization of traffic signal control.

The research carried out by the team from the University of Southampton has been using computer games and simulations to investigate what makes good traffic control. This work has shown that – given the right conditions – humans are excellent at controlling the traffic but that it is difficult to get a computer programme to perform at the same level. To demonstrate this finding, the traffic lights at one of the main junctions of the innovITS Advance circuit were controlled first by a computer and then by a human, while 30 volunteer drivers tried to negotiate the junction. The result was a win for human based control of the signals, with longer delays for computer based control. The researchers from University of Southampton have now developed "machine learning" traffic control computers that can learn how to control the lights like a human would and even learn their own improved strategies through experience.

"In transport research we are always looking ahead, and we can consider a future where all vehicles are equipped with WiFi and GPS and can transmit their positions to signalized junctions,” explains Dr Simon Box of the University of Southampton Transport Research Group. “This opens the way to the use of artificial intelligence approaches to traffic control such as machine learning. The demonstration carried out at innovITS Advance for BBC’s The One Show, indicates that the human brain, carefully employed, can be an extremely effective traffic control computer. In our research we aim to be able to emulate this approach in a new kind of software that can provide significant benefits in improving the efficiency of traffic flow, hence improving road space utilization, reducing journey times and potentially, improving fuel efficiency.”

“We were pleased to be able to host this demonstration by the team from Southampton University,” said innovITS Advance operations manager Catherine Ferris. “The development of artificial intelligence based approaches to junction control is one of many new and promising technologies that can help us make better use of existing urban and road capacity while reducing the environmental impacts of road traffic. The highly controllable innovITS Advance circuit – where roads, WiFi mesh, mobile phone networks, and even GPS reception, can be controlled in a manner that would be impossible on the public roads – provides an excellent environment in which such new technologies can be developed and perfected before their commercial implementation.”

View ADVANCE on the One Show (BBC iPlayer available 'til 30th August)

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