Digital twin: a future of applying urban data
by Ziyang Huang
Let me start with a simulation computer game Cities: Skylines, where people play as an urban planner and policy maker, create and manage cities in a huge area with a maximum of 324 km2. Not just a game, it’s kind of an urban simulator for me. Several months ago, I read an article explaining why most roads in Manhattan are one-way, and I was curious about its efficiency, so I turned to this game. I built a smaller version of Manhattan with 6 avenues and 19 streets, set different areas for residential, commercial and industrial uses, and most importantly, every road remained two-direction. After a few minutes running, as what shows in the upper part of figure 1, the two middle avenues became very congested. By modifying these two avenues to one-way roads, the traffic condition got much better soon as the lower part of figure 1.
Figure 1 Traffic congestion and control in Cities: Skylines
The upper part shows the original traffic congestion on virtual road, the lower part shows the traffic only after the two horizontal road have been made one-way as the yellow arrows. We can see even the other unchanged horizontal road become less congested.
Extremely similar with the physical reality and process, easy to be modified and can be observed visually, these are the ideal characteristics of a perfect urban system model.
Digital Twin in Urban Science
In strict terms, the Digital Twin is a set of virtual information constructs that fully describes a potential or actual physical manufactured product from the micro atomic level to the macro geometrical level (Grieves & Vickers, 2017). However, it is not necessary and also impossible by now to copy almost everything of the city into the model due to the storage and computing limit. Generally, simplification should be applied, but how? Currently, Digital twin is used to monitor and manage resources, energy, communication, transportation and logistics, and the geographic information systems scale down to the level of buildings. Center for Advanced Spatial Analysis from UCL has explored the vision of Digital Twin and built a virtual London platform (Dawkins, Hudson-Smith & Dennett, 2018).
Figure 2 ViLo: The Virtual London Platform by CASA
Advantage of Digital Twin
Due to the increase of data volume and data type, especially the increase of data update frequency, traditional planning analysis has been difficult to keep up with the speed of data update. It is necessary to establish a systematic model, which updates data and applies analysis model as automatically as possible. Digital twin, as a suitable approach to handle the high frequency data, allows managers to find public problem faster and react in time for emergencies (Batty, 2018). In addition, Digital Twin is also an effective means of low-cost testing, like what I did in Cities: Skylines, the results of policies can be valued in Digital Twin before implementation, timely virtual feedback allow us to do detailed modification and optimization. Applying digital twin, combining with virtual reality technology, governors and planners can better introduce their policy to the public, which would prompt the interaction and communication between them, increasing the efficiency of law and policy making.
Future of Digital Twin
With development of data center and cloud computing, we may cope with the aforementioned limits of Digital Twin, high resolution models would come into being in the future. Maybe we would witness the day when human beings become a parameter of a Digital Twin model someday in the future. But the next question is how to digitalize ourselves and what personal information could be used for public purpose. Apparently, further discussions are required.
Ziyang Huang is a Master’s Candidate for the Class of 2021 at NYU’s Center for Urban Science and Progress.
Dawkins, O., Hudson-Smith, A., & Dennett, A. Living with a Digital Twin: Operational Management and Engagement Using IoT and Mixed Realities at UCL’s Here East Campus on the Queen Elizabeth Olympic Park. In Proceedings of the 26th annual GIScience Research UK, Leicester, UK, 17–20 April 2018
Grieves M., Vickers J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. Transdiscipl. Perspect. Complex Syst., Cham: Springer International Publishing; 2017, p. 85–113.
Batty, M. (2018). Digital twins. Environment and Planning B: Urban Analytics and City Science, 45 (5), 817-819. doi:10.1177/2399808318796416