A snapshot of DUET's digital twin simulation
Implementing digital twins for cities is a complex process. There is no one-size-fits-all solution, as cities are at different stages of development, not just economically but also in terms of data governance and innovation capacity. A digital twin solution for a big European capital with a buoyant smart-city ecosystem is likely to differ radically from a medium-sized urban area where the smart-city concept took hold only recently.
DUET’s Digital Twin Maturity Model was developed with these differences in mind. The idea behind it is to provide an easy-to-understand pathway for the digital twin implementation journey. It's a high level diagnostic tool that helps determine where a city is in its digital twin journey by identifying the people, governance and technology enablers. Once the ‘as-is’ state is identified, then cities can discuss and explore where they want ‘to be’ so they can better plan and prepare for how to achieve their vision and create their own strategy and roadmap for change.
After exploring existing digital twin models around soft values (Gemini principles) and harder technical abilities (Arup) the DUET team decided a more inclusive model was needed as a starting point for local digital twin conversations that could link to the other existing models as well as DUET’s own deliverables and outputs. DUET envisioned a model that started from strategy and aligned with overarching public sector digital transformation models so digital twins could be embedded in existing approaches.
The Digital Twin Maturity Model envisages three types of digital twins - experimental, insightful, intelligent - with strategy development as a starting point.
An overview of the Digital Twin Maturity Model
1. Strategy Phase (Awareness)
This phase is the starting point for many smaller to medium sized cities. It is characterised by the fact that the city already has a digital transformation strategy along with the political will to create local digital twins for enhancing its decision making and urban planning processes. Enablers to help the city move forward with this phase include the ability to source funding for getting started, being able to leverage existing digital twin models, and the availability of urban open data sources.
Questions to ask at this stage are: What is a digital twin? How can a digital twin support better evidence based decisions? Who else has implemented digital twins? What case studies are available?
2. Experimental Twins (Exploratory)
The next step on the journey is the creation of digital twin capabilities around a defined use case which involves a small number of open data sets across 2 domains (e.g. transport volume and air quality). This approach allows a first foray into the world of predictive modelling, enabling the city to test interoperability.
Key enablers for this stage include digital capabilities to model and understand data, funding to enable the innovation pilot and the ability to test the digital twin play.
Questions to address at this stage are: What outcomes/use case needs to be achieved? Is there buy-in from relevant stakeholders? Is the data needed available? What existing analytics models can be leveraged? What contractual/legal obligations and/or restrictions should be observed? How much data processing, cleaning, and formatting is required?
3. Insightful Twins (Predictive)
The third phase sees cities moving from exploring digital twin use to a more structured use of the twins to predict policy impact. Thanks to earlier experimenting with data models, the city can now integrate larger numbers of urban data sets, and use more advanced simulations to generate actionable insights which cover multiple domains e.g. impact of creating a low emission zone on travel times, air quality, noise, amount of freight, traffic accidents. Enablers here include the adoption of easy-to-understand interfaces which centre differing stakeholders around a common view for more strategic and evidence-based discussions/consultation.
Questions to address at this stage are: What kind of visuals are needed for the use case? What is the frequency of the data? What kind of prediction models are needed? What security and access controls are needed? Should the twin interface be 2D or 3D? How will the digital twin outputs be integrated into existing public sector processes?
4. Intelligent Twins (Future-Ready)
The fourth phase of the model is the achievement of intelligent, AI enabled twins that are ready to tackle a range of policy challenges to create a more resilient and sustainable future. At this stage the twins can ingest and use both structured and unstructured data for cross-domain impact modelling and prediction in (near) real-time. As a result, cities can use the resulting insights to make real-time decisions and longer-term policies with a strong multi-dimensional alignment. Enablers include the fact that digital twin supported information is embedded as an official channel in city-leadership decision processes. Furthermore, ethical processes and standards are adopted for AI use and the digital twins can leverage High Performance Computing (HPC) for advanced modelling, analytics and predictions. At this stage, the AI enabled twins should be deemed ‘future ready’.
Questions to address at this stage are: What ethics principles are guiding the use of AI? How are Intelligent Twins helping the city achieve its vision of becoming smart and sustainable in the next 5/10/15 years?
Follow the model on our website here, where you can explore additional content relevant to each stage.
Comments