The introduction of autonomous vehicles will create major challenges for urban planners, and heighten the need for smart city technology that can manage traffic flows more intelligently, according to an expert in the field.

While much media hype is heralding the potential for autonomous vehicles to revolutionise urban transit usage, municipal planners and systems designers should remain keenly aware of the negative implications that the technology could have for traffic levels in cities, according to Daniel Hobohm, Global Head of Product Lifecycle Management, Siemens AG.

Hobohm said one of the primary impacts of the introduction of autonomous cars will be a sharp increase in vehicle usage and attendant worsening of congestion.

“For cities, autonomous vehicles are going to increase congestion,” he said. “This is simply because we’re going to use these vehicles more frequently and ubiquitously for far more trivial things than we do today.”

Hobohm points out that the introduction of full automation to road vehicles will completely transform the ways in which they’re used, particularly as part of taxi or logistics systems, given that the technology dramatically reduces the cost of operation per car by removing the need for human operation.

“The head of Uber said that the most expensive part of a taxi ride is the driver – the car itself and fuel may cost some money, but the driver costs the most,” he said. “If we can replace them, the cost of a taxi drops significantly, and it begins to really compete with public transit like buses or light rail. This means you have a form of transportation that can be used for all sorts of things that you haven’t used cars for previously – you can work and sleep in an autonomous vehicle, and you can also use it for other more trivial tasks.

“You can send it to grab a pizza, or go fetch you kids from school, or use it to deliver a wedding photo album to your grandmother via an autonomous logistics vehicle that roams the streets. Because of this rise in vehicle usage urban congestion is bound to increase.

“This means cities will have to handle traffic more efficiently, and find smarter ways to manage transit.”

Fortunately for city planners as well as commuters and municipal residents, other technologies are emerging alongside autonomous vehicles that will serve to improve the efficiency of urban transit systems. Chief amongst these technologies are the standard planks of the smart city concept – enhanced sensor and detection technology that can amass vast troves of data on urban environments, and heightened connectivity that can channel this wealth of information to big data centres where it can be processed and analysed.

“It’s intelligent infrastructure that can manage transportation – today we can try to manage traffic with inputs – we have detectors so we know roughly whether there’s going to be congestion or gridlock,” said Hobohm.

“You’d be amazed at how much detection you can have – at an intersection you can have inductive loops in the ground, you can have radar detectors, laser detectors, video detectors, all for the purpose of detecting traffic and differentiating between individual cars, trucks or bicycles.”

This data can then be applied to the development of models and algorithms for optimising the handling of vehicle traffic.

“You increase the amount of data entering and can have more algorithms that try to balance the city in a much smarter way, or devise a better model of that city and how it should be most efficiently managed,” Hobohm said.

Once a smart model of urban traffic is created, it can be used as the basis for influencing traffic flows via the flexible and extemporised introduction of restrictions for specific roads, districts or even vehicles.

This is made possible by the enhanced connectivity that is fast becoming a fundamental aspect of urban centres, and permits the delivery of tailored cues or information to the display panels of individual vehicles.

“By virtue of connectivity, you can now connect and communicate a lot faster and a lot more to vehicles on the road,” said Hobohm. “Today, we exercise control via limits and communicate them via road signs – so you see a sign saying drive 50 here, or brake if it’s a red light. But let’s imagine you could reach any car anywhere in the city, and tell them ‘okay in this area the speed limit is now 30’ or ‘trucks are not allowed here, only bikes or cars are.’

“Now we can create a much smarter model of the city, and then use it to provide far more intelligent limits. We can even differentiate by user, as well as achieve far higher data granularity, and in that way manage the city more efficiently.

For automated cars, information relayed to them from traffic management centres can be incorporated into their decision-making processes in order to optimise both safety and efficiency.

“I don’t think city authorities will go so far as to assume control of automated vehicles, as it would impinge on their individual autonomy,” said Hobohm. “The city can simply impose limits, and then the algorithms in the car will find the most efficient route to its destination given those restrictions.

“In the future for example, you could just section off a part of the map while students are leaving school, and say that autonomous cars cannot enter, or they’re only allowed to drive 10 to 15 kilometres an hour and proceed very very slowly.

“And then the car can make the decision – if it really needs to go through that area it’s going to drive a 10 kilometres an hour, or decide that given this information it will be much better to take a detour.”