Gaming out the future: How digital twins can help cities prep for any scenario

In a convention hall in Santa Clara, Calif., 1,000 people braced themselves for an earthquake. The lights flickered, they heard a massive rumble, and a screen at the front of the room showed exactly what was happening. Power was going out across the city, just as it had in 1989, when a devastating 6.9 magnitude quake upheaved the Bay Area and took 63 lives. But this time, the disaster — a simulation created by Edmonton-based tech company RUNWITHIT Synthetics (RWI) — played out differently.

RWI had created a digital model of Santa Clara, a synthetic twin that showed the city’s streets in detail — houses, roads, pedestrian paths, power lines, even the local football stadium — and as the roomful of people watched, the model demonstrated how such an earthquake might unfold with new emergency measures in place. Graphics on the screen showed what would happen if motion sensors automatically shut down the power at felled transmission lines and sent emergency notifications to gas crews, who could handle methane leaks. Moving green dots, each one representing a resident, illustrated how people would respond if they received digital alerts outlining the emergency and directing them toward navigable streets. Instead of the gridlock that had paralyzed traffic during the 1989 disaster, cars were moving. Within 15 minutes, 87 per cent of the people affected had moved to safety.

“Digital twin” models like RWI’s, which was featured at the 2019 Internet of Things World conference in Santa Clara, have become go-to tools for utility operators and city planners faced with making costly decisions in complex environments. RWI’s digital twins use machine learning and massive sets of publicly available data, such as census figures, municipal records and national surveys, to simulate cities, layering in psychographic and behavioural modelling as well as demographic forecasts to predict how people are likely to respond in different situations. CEO Myrna Bittner refers to the models as digital “sandboxes” that can be used for a range of purposes, from exploring what-if disaster scenarios — how a punishing heat wave might affect a community, for instance — to playing out the consequences of planning decisions like densification, infrastructure investment and greenbelt preservation. They’re like “SimCity,” the once-popular virtual game, Bittner says, “but for real.”

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