Digital twin technology has been enabling organizations, especially those in the manufacturing, healthcare, and oil and gas industry improve their operations as it enables them to monitor their infrastructure remotely.
In smart cities, digital twin technologies are also considered crucial especially in having visibility over the infrastructure. For enterprises, using digital twin in smart cities will give them better opportunities to not just make data-driven decisions but also understand social behaviors.
This is why Fujitsu Limited and Carnegie Mellon University (CMU) researchers are collaborating on research projects focused on the development of social digital twin technology, with an emphasis on exploring practical applications for their joint research and technology.
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 (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 the 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.
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 off of the deployment of the researchers’ projects with CMU’s transportation research institute, Traffic21.
As it is still early days, 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 realize more sustainable and safe cities in the future.
Converging Technologies for digital twin
To get the best out of the social digital twin, Fujitsu and CMU leveraged so-called “converging technologies,” advanced technologies that combine computer sciences and knowledge from the humanities and social sciences, aiming 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 and the ability to visualize and predict future actions and possible risks based on human behavioral characteristics. 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 interventions can be reflected in advance to optimize outcomes of urban planning and policy.
The initial research will 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 are able to dynamically understand the daily changing traffic demand of the city. Researchers will be able to use digital models to test urban traffic solutions to adjust traffic regulations and toll systems in accordance with the efficient 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. This includes the reduction of CO2 emissions and improving urban transportation networks as well as promoting measures to mitigate pandemics and ensure the flexible, efficient allocation of medical resources while driving economic growth.
