Well, the traffic light revolution is already underway. It is all part of the promise of connected and self-driving cars, which allow data about individual journeys, routes and vehicles to be centrally monitored, controlled and systematized.
Once computers are in full control of our cars, do we even need traffic lights at intersections? That’s the idea behind AIM – autonomous intersection management – at the artificial intelligence laboratory at the University of Texas at Austin. Rather than stop at red lights, self-driving cars would schedule a slot through an intersection in real-time, speeding up or slowing down to ensure they’re in the right place at the right time – and not smashing into another car.
In black and white text, that seems eminently sensible. But it won’t be for the fainthearted – at least not until passengers have learnt to entirely trust their automated pilots:
For the idea to work, it would require roads to be mostly full of autonomous cars, says project leader Professor Peter Stone – and then it wouldn’t seem so terrifying.
“When I show people that video, I tell people to not envision themselves with their foot hovering over the brake or with their white knuckles on the steering wheel, but rather they’re in the back seat with the windows dark, doing a crossword puzzle or reading the newspaper, talking to family or whatever,” he says. “Once the driving is not a human task and people grow to trust the software controllers, people will also get used to the idea of cars going through the intersections.”
That said, he stressed that driving is now and will likely always remain a risk and reward equation, but he predicted that with AIM, “the efficiency gains will be so high that they’ll offset the perceived risk”.
How much faster will careening through intersections be compared to carefully stopping? The researchers compared AIM to heavy traffic on a major road, saying it would reduce delay by as much as 100 times – though that’s only at intersections, not total driving time.
“Intersections are already quite dangerous. When a computer’s doing the driving, even with all the cars going through without stopping, it’s going to be a lot safer than it is today.”
TomTom collects swaths of traffic data from its satnav devices but also used anonymised data from third party navigation apps, including smartphone maps. “We have agreements with a number of smartphone manufacturers, so they provide us with real time GPS feeds wherever their smartphones are,” says Nick Cohn, senior traffic expert at TomTom.
It also gathers data from telematics units installed in fleet vehicles as well as in-dash systems, giving TomTom a comprehensive overview of traffic flows. The resulting information on near real-time congestion is shared with customers, which includes road authorities who use it to plan traffic management as well as consumers.
“Most have camera data that doesn’t cover the whole network, so they use our data to supplement that and for deciding whether they need to switch to a different traffic signal scheme,” Cohn says.
When a driver hits a patch of congestion – a red zone of a smartphone or satnav map – it may be because of data that was collected, aggregated and distributed from connected cars in weeks or months past. Before ubiquitous connectivity, Cohn said the travel times seen by TomTom were very different than that given by road authorities such as the AA. As data improves, the numbers are merging, suggesting travel advice has become more accurate.
As cars become more connected – whether it’s through satnav or simply the smartphones in our pockets – better data in means we get better data out on the road.
“On its own, [each] is of low value, but when merged together in the internet of things’ cloud processing platform, [we] can make sense of them and make actionable insights,” he says. “It might be to turn some traffic lights green quicker or send a text message to a car, or alert satnavs in the car to quietly change the routing so they’re now going somewhere else.”