Carnegie Mellon University researchers will collaborate with Fujitsu Limited, a leading Japanese information and communication technology company, to develop social digital twin technology, with an emphasis on exploring practical applications for the work.
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. This research is the first attempt between Fujitsu and CMU to explore future applications of social digital twins in global communities.
A project through CMU's Mobility Data Analytics Center(opens in new window) (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(opens in new window) 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.
CMU's efforts will be led by Laszlo Jeni(opens in new window), director of the Computational Behavior Lab, and Sean Qian(opens in new window), director of the MAC. Fujitsu and CMU will draw on the findings of these projects to create foundational technologies for social digital twins that will simulate traffic networks and movement patterns of people in real-time. That work will build on the deployment of the researchers' projects with CMU's transportation research institute, Traffic21(opens in new window).
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.
Within this project, Fujitsu and CMU will leverage so-called "converging technologies," advanced technologies that combine computer sciences and knowledge from the humanities and social sciences. These technologies aim to solve diverse and complex issues faced by cities working toward the realization of a sustainable society.
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 to analyze and predict the behavior of people and movements of vehicles, the effects and potential risks of proposed interventions can be reflected in advance to optimize outcomes of urban planning and policy.
Fujitsu and CMU's 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.
In addition to the analysis of traffic congestion and ways to deliver economic efficiency, Fujitsu and CMU will further leverage the social digital twin platform to promote the verification of detailed measures to solve environmental issues, including the reduction of carbon dioxide emissions and improving urban transportation networks.
The researchers will additionally continue their efforts to contribute to the realization of safe and sustainable next-generation smart cities by promoting measures to mitigate pandemics and ensure the flexible, efficient allocation of medical resources while driving economic growth.