Digital Twin Models Under Development for Smart City Applications

Digital Twin Models Under Development for Smart City Applications

Photo illus. smart city by Pxfuel
Connected Smart City

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Cities are challenged by diverse and complex urban planning issues on the way to the realization of a sustainable society in a smart city environment. Carnegie Mellon University researchers will collaborate with Fujitsu to develop social digital twin technology for promoting smart city technologies.

A social digital twin digitally reproduces the relationships and connections between people, goods, the economy and society to offer a simulation, prediction and decision-making environment for solving diverse and complex social issues. 

A project through CMU’s Mobility Data Analytics Center (MAC) will leverage real-world data, including input of traffic regulations and the movement of vehicles, to evaluate the effectiveness of measures designed to dynamically estimate and control traffic flow. 

Another project with CMU’s Computational Behavior Lab in the School of Computer Science’s Robotics Institute will extend current capabilities in 3D modeling of pedestrians and forecasting their behavior over time in urban environments. This technology can be used to monitor activity on streets and determine where issues or accidents may be taking place.

The findings of these projects will help simulate traffic networks and movement patterns of people in real-time. 

The researchers anticipate that the social digital twin technology will play an active role in improving efforts to ease congestion, positively influence travel behavior, and ultimately help to create safer, more sustainable cities in the future.

The researchers aim to develop a new platform that delivers a broad set of solutions for a variety of social issues based on highly accurate simulations of the movements of people and vehicles, which will help them visualize and predict future actions and possible risks based on human behavior. 

By using the newly developed social digital twin platform, the effects and potential risks of proposed interventions can be reflected in advance to optimize outcomes of urban planning and policy.

The research will initially focus on developing advanced sensing technology to better understand people’s movements; improve behavior forecasting through artificial intelligence; and create social digital twin models to simulate how people interact with goods, the economy and society.

The research will include a social digital twin model based on real-time traffic data from road networks that can dynamically understand a city’s daily-changing traffic demand. Researchers can then use the digital models to test solutions to adjust traffic regulations and toll systems to improve traffic flow, according to