Meanwhile, Arnaud Grignard and his colleagues at the MIT Media Lab are using agent-based simulation to explore possible designs for busy public spaces, including a regenerated Champs-Elysees in Paris. And New York startup Topos is using AI to help understand how the layout of a city affects those living in it. In one project it used a range of AI approaches, including image recognition and natural-language processing, to learn how different areas in New York were used by the people living there. It then redrew the boundaries of New York’s five boroughs on the basis of similarities between neighborhoods, such as whether they are residential or commercial, leafy or urban. The resulting map arrays the boroughs as more or less concentric rings around a central Manhattan.
Jasper Wijnands, at the University of Melbourne in Australia, is also convinced that AI has a place in future urban design. He and his colleagues have started exploring the use of generative adversarial networks (GANs) to do style transfer on images from Google Street View.
Style transfer is typically used to reproduce one image in the style of another, such as making a selfie look like as if it were painted by Van Gogh. But instead of a visual style, Wijnands got his AI to learn a “style” that reflected the public health data in different city blocks. He then asked it to reproduce Street View images in the style of neighborhoods where public health was good. In other words, his AI can touch up images of bad neighborhoods so that they look like good ones. City planners could then use these tweaks–a green space here, a wider street there–as a guide for urban improvements.
The AI was not taught what sorts of things planners think make cities better, but it hit upon common ideas by itself. “It’s interesting to see that the GAN output is consistent with our scientific understanding of the impact of green space on health,” says Wijnands.
His team now has a $1.2 million grant to develop the approach, and he is introducing it to his urban-planning students.
Design impacts
One of the more immediate uses for AI in city planning is to understand the impact of urban design at a global scale. In January Wijnands and his colleagues published a study in The Lancet Planetary Health in which they looked at 1,692 cities, home to a third of the world’s population. They used convolutional neural networks, typically used for image recognition, to classify different urban layouts according to the number of serious road accidents that had happened in them. Cities with more high-transit rail networks and denser street layouts arranged around small blocks were shown to be safer than more sprawling layouts arranged around cul-de-sacs.
Those results may not be too surprising, but the data could not have been analyzed at all without automation.
Visions of utopian living are always based on presuppositions about what kinds of urban spaces make people happier or healthier. But these are hard to test, and ambitious regeneration projects can fail. AI city planners could help in a number of ways, revealing the hidden impacts of certain existing layouts or simulating thousands of potential designs. Salge is now working with planners in the US on how future competitions might incorporate more realistic data about how people use cities, such as how they move about or where they go shopping. That could make the artificial creations even more lifelike–and potentially more useful.
But don’t expect AI to take over planning completely. Cities are a lot more than an arrangement of objects on the ground: they are lived in. And that means they are the result of many trade-offs, says Dave Amos, an urban planner who has a popular YouTube channel called City Beautiful. As Amos puts it in a video reviewing the winning entry to the GDMC competition in 2018: “Planning is inherently a political process. You need people to butt heads about what the development is going to be like.”
