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Image courtesy: Pixabay In an interconnected world, using a cloud based infrastructure is highly likely for most smart cities. However, conforming to common industry standards and the applicable law, also apply to these structures, whether they are a cloud platform or within a data space. Failure in either area could result in penalties based on security issues and reputational damage, in addition to unauthorised access and harm to individuals. To provide guidance for smart cities, D1.7 provides recommendations for navigating the European Cloud Infrastructure including the below takeaways. Recommendations 1. Location of the cloud and dataspaces As cloud providers operate globally and may be regulated outside European legal frameworks, smart cities should be sure to duly assess if European based personal data is protected by assessing if the GDPR applies to the service provider. GDPR applies if the service provider is based within the EU, regardless of whether the processing takes place in the EU or not, or if the service provider is not based in the EU but personal data of data subjects who are in the EU are processed in certain cases. If the service provider does not offer a data center within the EU, it is recommended to carry out a Transfer Risk Assessment addressing the flow of data and the receiving location’s data regulations. This should include whether such country is covered by a current EU Adequacy Decision, and if data can be processed with safeguards such as Standard data protection Contractual Clauses (SCCs) adopted or approved by the European Commission, or Binding Corporate Rules (BCRs), codes of conduct to govern the processing of data in the third country. Ultimately however, smart cities should consider if an alternative service can be used with a data center situated in the EU. 2. Reconciling existing regulation with the shift to dataspaces It is evident that current legal frameworks are curated for an infrastructure focused on cloud infrastructures, rather than data and information focused data spaces. In practice, utilising data spaces would allow for data to be shared across nine industries: Health, Industrial, Agriculture, Finance, Mobility, Green Deal, Energy, Public Administration, and Skills. For smart cities, it will be important to define data sovereignty principles and clear ownership of data to allow for protection of personal data. In practice this means anonymising data and ensuing there is a clear owner of data points who can respond to GDPR orientated data subject requests when they arise. In particular, smart cities may lead to the blurring of the processor and controller rule within data spaces. Smart cities should therefore ask: Is the chosen cloud environment providing any additional services, such as cloud-provided AI tools? If so, does the Smart City own all the outputs from this service? If cloud provided tools have access to personal data, additional clauses should be agreed that processing shall only occur on the Smart City's instructions and no rights in the outputs shall be transferred to the cloud provider. Does the cloud provider/data space have access to data stored? Will the cloud provider use the data to improve their own services and products? If cloud provided tools have access to personal data, additional clauses should be agreed that processing shall only occur on the Smart City's instructions and the outputs will only be used by the Smart City. If data is shared in a data space, who has ownership over the imputed data and takes the role as a controller? Clear delineation of roles and responsibilities should be defined within the data space before the Smart City shares data. If data is shared in a data space, are there mechanisms in place to ensure the security and protection of personal data whether through technical or organisational security measures? 3. A middle way could be achieved with ‘Data Altruism’ The concept of Data Altruism under the Data Governance Act (the ‘DGA’), where entities can agree to their data being used for the public good, may provide an alternative for smart cities while respecting data subject rights. Under the DGA Article 2, Data Altruism is “the consent by data subjects to process personal data pertaining to them, or permissions of other data holders to allow the use of their non-personal data without seeking a reward, for purposes of general interest, such as scientific research purposes or improving public services…”. In practice, smart cities must operate on a not-for-profit basis and be independent from any entity that operates on a for-profit basis and perform the activities related to data altruism through a legally independent structure, separate from other activities undertaken to be eligible, in addition to notifying data subjects that data collected is for the general interest and gaining their consent for processing. [Read Full Deliverable]

Key Points Local Digital Twins (LDTs) use personal and non-personal data in order to improve urban operations, the environment and economic outcomes within cities. Despite allowing for increased efficiency and forward city planning, the use of personal data can give rise to exploitation of the rights and interests of individuals, resulting in harm on a personal level. Therefore, with the support of DUET LDT pilots (Athens, Pilsen, Flanders) who provided a range of findings and real-life data-supported policy case studies, the legal (GSL), management (AIV/DV) and a technical consortium partner (IMEC) drafted a guide on ethical principles for using data-driven decisions in the cloud. The aim of this guide is to provide future LDTs support in making ethically aware, compliant and legal decisions in regards to data processing. Image courtesy: Pixabay Abstract This deliverable seeks to provide the final version of and Ethical Code of Conduct tailored to assist in any data-based decision-making process. DUET LDT pilots undertook interviews with GSL to discuss their usage of the first iteration of the deliverable (D1.5), improvements in the structure and user experience of the guidance, and the evolving nature of use cases. As the second and final iteration, the guidance also includes new user types and the ethical considerations to discuss, including entrepreneur/founder, smart city provider roles, and an extension of a public servant/city official role. In particular, the guidance discusses the building blocks of the ethical discourse around data-based decision making, and suggests an ethical code of conduct (ethical principles) for LDTs in such a context. Recommendations Ethical considerations should be read alongside data protection and privacy aspects. In particular, LDT pilots should be aware of steps to take to ensure a privacy-by design approach, including the data minimisation principle. Anonymisation or avoidance of personal data is preferable unless it is strictly necessary for your task and proportionate to meeting the pre-defined purpose of your activity. In cases where personal data must be used and anonymisation would make the purpose of the use case futile, the guidance highlights techniques and procedures to be avoided in such a case such as singling out data, aggregated records and personal data by inference. Consider the interconnected nature of data storage. Despite the focus of DUET on LDTs, use cases should consider the possibility and necessary safeguards if personal data must be stored outside of Europe. The final thirteen ethical principles can be found below. For the complete context and further guidance for smart cities, reference to the full deliverable is advisable and can be found below. [Read Full Deliverable] 1. Accountability and data sovereignty Know the origin of the data, its lawful and ethical uses, and any limitations on their sharing or publication. This includes understanding the origin of data when working with private/public data sources as expressed by the European Union Agency for Cybersecurity (ENISA). This also includes understanding all possible locations of processing of personal data and the different data regulations this may be subject to. 2. Transparency You should know what data you collect and for what purposes. The data subjects (e.g. the citizens) should know what data you collect about them and for what purposes. Be transparent about the scope and source of the data, as well as the limitations of the data. Explain what information the data contains, how (and where) it was collected, whether it is static data, updated regularly, or real-time. If the data is publicly available, provide a link to the origin data repository/source url. Make sure that decision makers are aware of the deficiencies/limitations of the data. Promote knowledge of utilisable data/models within your organisation so that employees are aware that helpful data or applications may be available to carry out their tasks. 3. Data quality Get the best data as you can for your purposes. Best may mean: data most suited for your purpose; most complete, correct, and up-to-date data (clean data); data with a transparent track record of their collection, storage, and the log of previous processing; data with a clear licence to (further) use. Take active steps to ensure and maximise the quality, objectivity, usefulness, integrity and security of data. 4. Data quality for publication If the data is sufficient for an internal use (within the services of the city), it is typically equally good for making the data publicly accessible (open). Use open standards and open licences. Publish / share data only after you have cleared the applicable legal requirements. 5. Data security The integrity and security of data should be maximised. Use trusted third-party services providers (e.g., approved by the future European Union Cybersecurity Certification Scheme on Cloud Services (EUCS)). 6. Data everywhere Promote the use of data in public interest, be active in seeking out data that may be (re)used in public interest. Actively explore the ways in which data can be obtained from partners (private or public) with whom you engaged in a joint activity (e.g., public procurement). 7. Transparent and fair use of AI and computer models. Fighting the “opacity” problem. Cities should strive to develop the officials’ ability to understand, interpret and use automated decision-making systems. They should understand at least the basics of the underlying algorithms and the data used. This can be achieved by a targeted education and training, for example. Data subjects (citizens) should be informed about the fact that automated decisions are being taken about them and with the help of their data. To the extent possible, cities should strive to make sure that data subjects also understand the underlying algorithms, to the extent practicable. Algorithms and automated decisions should be fair and proportional. They should not prejudice the data subjects. Even though some bias may be inherent in data, the algorithms and the data they use (or train on) should not create or perpetuate material biases (racial, ethnical, sexual, political, religious, etc.) Ensure an element of human control over the AI: Individuals to whom human oversight is assigned should fully understand the capacities and limitations of the AI system and should be able to duly monitor its operation, so that signs of anomalies, dysfunctions and unexpected performance can be detected and addressed as soon as possible. Data subjects should be granted the right to appeal relating to data processing and the automated decisions that affect them. 8. Presentation of data or results The way data or data-based decisions are presented should avoid creating or perpetuating bias (e.g., the use of red and green color coding for visualisations). 9. Data ownership and management Data ownership typically goes hand in hand with the responsibility for data management. Third parties contracted out for city data management should be chosen responsibly, adequate data processing agreements should be put in place. Smart cities should understand if their data is public or private when acquiring a data set from a third party source, and the limitations on usage. 10. Privacy-by-design Comply with all legal requirements when acquiring, using, or publishing personal data. (see also D1.2 Cities Guide to Legal Compliance for Data-Driven Decision Making). If you come across a personal data breach, report it to your Data Protection Officer. Minimise the amount of personal data obtained, used and stored. 11. Anonymised data preference Do not use personal data unless it is strictly necessary for your task and proportionate to meeting the pre-defined purpose of your activity. If anonymous data is not available, but personal data is, ensure that the data is anonymised before its further use, if possible. Ask the upstream data provider, who best understands the data, to anonymise the data before it is supplied. Non-anonymised data should in no case be made public (or open data), unless strictly required for carrying out the task in question, and unless cleared by the Data Protection Officer for publication. Where data is anonymised, do not proactively take any steps in the direction to re-identify the data (link the data to individual persons). The following techniques and procedures, for example, should be avoided unless the goal is actually to re-identify otherwise anonymous or pseudonymised data: Singling out, which corresponds to the possibility to isolate some or all records which identify an individual in the dataset; Linkability, which is the ability to link, at least, two records concerning the same data subject or a group of data subjects (either in the same database or in two different databases). If an attacker can establish (e.g. by means of correlation analysis) that two records are assigned to a same group of individuals but cannot single out individuals in this group, the technique provides resistance against “singling out” but not against linkability; or Inference, which is the possibility to deduce, with significant probability, the value of an attribute from the values of a set of other attributes. If the risk of re-identification materialises on a given dataset, take all reasonable steps, seek appropriate expert advice and apply all relevant professional standards in order to mitigate the risk of a privacy breach and further unlawful personal data processing. 12. Training and sufficient data usage information Ensure to provide sufficient information about the application including how it works and the data the model is sourcing from. If applicable, provide a contact to the application administrator for possible troubleshooting. Ensure all people involved have an understanding of these ethical principles.

​ Key Points Local Digital Twins (LDTs) offer public administrations a cost-effective and strategic way to improve urban operations, the environment and economic outcomes through real-time decision support modelling and impact prediction. The LDT business case is its ability to be a central repository for data across a whole city or region, providing an inherent understanding of complex systems for all stakeholder communities to use in order to address common goals which transcend across multiple domains. Despite a range of benefits, important challenges in LDT adoption include issues of trust and transparency especially around data quality. Therefore starting with a single use case and focusing on data and modeling standardisation is the key to successful and sustainable growth of an LDT. DUET LDT pilots - Athens, Pilsen, Flanders - provide a range of findings, resources, and real-life data-supported policy case studies which can help cities on their digital twin journeys independently of their starting point. Abstract This policy brief explores the potential of local digital twins for data-supported decision making in a time when cities are under pressure to deliver more sustainable policies, optimise service performance and grow local economies, all while keeping people safe, reducing budget spend and managing a wide range of socio economic challenges. Digital twins already play an important role in industrial transformations as they help users better understand and have control over their assets. For example, in an engineering context, by connecting engineers to the right data and right processes, they can derive greater end-to-end insights and intelligence about their resources. As a result engineers can quickly determine the best actions needed to deliver sustainable system performance improvements. DUET (Digital Urban European Twins), a European innovation initiative, transferred the concept of digital twins from an industrial setting to the public sector domain creating a technology called Local Digital Twins (LDT), also known as urban digital twins. Replicating a city's physical assets, processes and systems using data, analytics and machine learning, DUET created virtual replicas of the cities of Athens, Pilsen and the Region of Flanders that automatically updated and changed in real-time as the physical cities themselves changed. Through a 2D and 3D interface policy makers, city managers and stakeholders were able to simulate, model and explore the predicted impact of different policy options on their city, before making better informed decisions. This policy brief features a quick-start digital twin maturity model, alongside three key policy recommendations using the experience of DUET to inspire and encourage European cities to start their own digital twin journeys. Recommendations Understand that Local Digital Twins are a method and a journey, not an end goal: Cities should start from a relevant policy issue and use it to define a policy case. This will ensure that their journey is driven by real needs instead of just a focus on having perfect 3D models which have little to offer beyond eye-catching visualisations. As a city’s confidence in developing and using LDT’s grows, more use cases may be added to the mix to take advantage of the tool’s cross-domain policy simulation capabilities. Take advantage of existing city data to get started and focus on ensuring data quality: LDTs are data hungry. The insights you get are as good as the data used to feed LDTs. Having the right data will make a difference between an experimental policy case and one that can have a real-life application. With the data issue settled, think about the kind of predictive analysis you want to perform using a LDT. Collaborate and break down silos to drive the LDT business case: LDTs reach their full potential not when they are used behind closed doors by a single department but when they are deployed as a collaborative tool to engage internal and external stakeholders, including citizens. To achieve that, a multi-actor governance approach is needed, one that promotes cooperation and knowledge sharing between different administrative units, and at the same time keeps the wider community informed and engaged in local policy processes. Acknowledgements: DUET received funding from the EU Horizon 2020 research and innovation programme under grant agreement No. 870697. This policy brief represents the views of the DUET project only.

Partners from the DUET project took to the stage at the Smart City Expo and World Congress in Barcelona this November to showcase its award-winning digital twin technology with accompanying policy use cases from the cities of Athens, Pilsen and the region of Flanders. Fig: DUET Partners participate in main Congress roundtable discussion on disruptive tech for cities Digital Vlaanderen with the DUET digital twin project was awarded the best enabling technology prize at the World Smart City awards last year. The awards are presented annually to the acknowledge the most outstanding initiatives and projects in the innovation and urban transformation industry. One year later the DUET team were invited back to participate in a special panel at the Congress entitled ‘disruptive tech empowering digital services’. Chaired by Cecilla Tham, founder and director at Futurity Systems, members of the panel included DUET partners Lieven Raes, smart cities manager at Digitaal Vlanderen and Susie Ruston McAleer, founder and director of 21c, alongside Mahel Abaab-Fournial, director at Dassault Systèmes and Cynthia Curry, senior director from the smart cities and cleantech eosystem in Atlanta. The session posed a number of interesting questions around how new technologies , including digital twins, are starting to be used for shaping urbanism and decision-making in cities. “How have these technologies disrupted urban developments and transformed cities life so far? Do cities have the sufficient skills in place to use these technologies? How do we balance the impact of new digital interactions while ensuring the sustainability of the whole urban ecosystem?” asked Tham. “Cities have passion and drive to modernise and improve life for the people they serve” said Ruston McAleer. ” We witnessed an accelerated pace of change over the last three years, but now at the tail end of the pandemic, as barriers to innovation have come back up, cities are struggling to continue the momentum. Digital twins provide an important vehicle or continued and sustainable transformation”. “These real-time, virtual replica’s of cities,” continued Raes... “interconnect various urban data sources, simulation models and algorithms, making them an ideal sandbox for policy experimentation. Cities can save time, cost and resources by modelling the predicted impact of different decision choices on city systems before deciding upon the best course of action.” Curry stressed the importance of the human side to disruptive technology, ensuring that no-one is left behind in the course of the digital transition. Abaab-Fournial provided case studies from the Paris Region where cloud, data and AI are supporting regional traffic challenges. The conversation then circled to discussions around barriers to change. “All of us in this room, working with smart cities have a collective responsibility to ensure disruptive tech does not cause harm, whether intentionally or unintentionally. Those driving urban change need ways to unpack the black box of AI, make algorithms more transparent in order to encourage its use and drive urban innovation further” said Ruston McAleer. Raes continued the theme, “digital twin simulations such as DUET show why a particular policy or urban planning decision is made and can be a good starting point for transparent democracy”. The session ended with all panellists encouraging the audience to get started with their transformation journey by leveraging partnerships and focusing on the use cases that matter most to them. "It's a misconception that digitalTwins should always be 3D and cities must start big. Its best to start this journey with a single 'right' use case, such as traffic which can be visualised and analysed equally well in 2D" reminded Raes. Fig: DUET speaking at the Green Agora on digital twins for local decision makers SCEW’s 24,000 attendees who could not attend the congress session also had the opportunity to meet DUET partners and see the twins in action at numerous points across the Expo. Fig: Sharing DUET lessons at the European Smart Cities and Communities stand From presence at the European Commission’s prominent smart city initiatives stand, to participation in the EC's panel on 'Digital twins for local decision makers' as well as a private presentation to the Tokoyo delegation from Japan, DUET was visible across the conference floor. Fig: DUET presented to the delegation from Tokyo at the Japan Pavilion Jurgen Silence of Digital Vlaanderen, who delivered the presentation at the Japan Pavilion, shared his thoughts on the knowledge exchange afterwards: "As an EU project manager, it is inspiring to see and learn about the approach of smart city solutions on the other side of the world. We build comparable solutions, and face similar obstacles to tackle. Especially the sometimes diverging technical approaches are intriguing, offering opportunities to improve solution performance and fine-tuning in both directions. DUET officially comes to an end at the start of 2022, but is seeking new opportunities to take its technology to market ready status.

It has become somewhat of a habit in DUET to attend the Major Cities of Europe (MCE), a conference that dates back to 1982 and that has become a place where experts from policy and innovation communities get together to discuss local challenges and their solutions. After a successful participation in two previous MCE editions, and considering that DUET is coming to a close in just a few months, the decision to go to #MCELarissa2022 was a non-brainer. With over 200 participants representing some of Europe’s most innovative projects and communities working on digital transformation, this year’s conference was a perfect opportunity for DUET to showcase its digital twins solution to potential adopters in local and regional administrations. This was achieved in three ways: dissemination at the booth, a plenary speech, and a workshop. Booth DUET was among 20 or so initiatives represented at the event’s ‘EU project corner.’ Members of the DUET consortium who travelled to Larissa (Digital Vlaanderen, DAEM, 21c, GFOSS) looked after the booth from the start of the conference through to the end (2-4 November) to make sure every passer-by was engaged and received all the necessary information about the project. Our booth featured a big 42” screen that played video recordings of the digital twin platform (see Figure 1). This helped to attract people’s attention and pique their interest in DUET’s technology. More than a dozen city reps, most of them from Greece and Italy, left their business cards with a view to continuing a conversation about possible adoption after the conference. Figure 1. DUET booth Plenary speech On the first day of the conference DUET was presented to the entire MCE audience by Lieven Raes of Digital Vlaanderen. He started with an introduction to Local Digital Twins, and then explained how DUET can help public sector decision-making become more democratic and effective, both in the short and long term, through the development and use of Local Digital Twins for policy impact exploration and experimentation in entire cities and regions. These digital replicas of a city’s system will ultimately enhance day-to-day city management by helping city managers react quickly to real-time events through rapid experimentation of different decision impacts ensure longer term policy decisions are more effective and trusted by enabling city managers from different units, to explore and discuss with citizens and businesses city issues in a visual, easy-to-digest way via a common view Here's a snippet of Lieven's speech. Workshop On the second day of the conference, DUET partners held a workshop in which almost 50 participants had a chance to see and play with the citytwin.eu platform. We had a truly international audience in the room as can be seen from the word cloud below (Figure 2). Figure 2. Geographic spread of participants The vast majority were working at a local administration, while the distribution of other stakeholders was more or less equal. See Figure 3. Figure 3. Professional background of participants An interesting finding was that the Digital Twins concept was new to most people in the room. Only a few considered themselves to be experts on the subject or had previously worked on a Digital Twin project (Figure 4). Figure 4. Participants' familiarity with Digital Twins The use case that was selected for the experiment was the ‘Athens partial reduction of traffic’. During a demo, those with a laptop were able to login and cast themselves as an urban planner with a powerful tool under their fingertips. At the end of this session, participants were asked whether they would like to have this tool for their city. Overwhelmingly people said yes (Figure 5). Figure 5. Participants' interest in the DUET solution After the demo, the audience split into three groups to explore various aspects of Digital Twins. The first group discussed the use of a Digital Twin for policy making on a neighbourhood level, the integration of local sensors in a Digital Twin, the pros and cons of a Digital Twin solution, and the link with open data and citizen science initiatives. The second group discussed the barriers to adoption of Digital Twins, focusing mainly on data availability and quality. The group also explored various requirements linked to the interoperability of systems and skilled personnel. The discussion in the third group centered around several topics, including the need for qualitative data and models, the indisputable extra value of real-time data streams and dataset combinations, the rising demand for scalability and target-group customisation, and how historic datasets can trigger self-learning in an optimal Digital Twin setting. In addition, this group explored the increasing security challenges and the volatility of visualisations and interpretations in a fast-changing world. Here's a sneak peak into the group discussions held at the workshop. Conclusion Europe’s cities create vast amounts of data, most of which isn’t used by urban planners and policymakers in their decision-making. Participation at #MCELarissa2022 revealed yet again a strong need by policy makers to harness this information and transform cities for the better. During the project and at the conference DUET has demonstrated how Local Digital Twins can support co-creative cities with planning and management. A Local Digital Twin has the potential to go beyond an organisation’s internal planning and simulation tool. It contributes to greater transparency, and the DUET Local Digital Twin already offers tools for policy scenario comparison and storytelling. Visit http://citytwin.eu/ to learn more.

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.

Today, our societies must respond quickly and inventively to new challenges and crises, be it a health crisis, the energy shift or a crisis of a geopolitical nature. Governments, companies and citizens need to become more and more agile and resilient. That was the central theme of the Digital Flanders Trefdag in 2022: how can we as a society leverage digital transformation to make ourselves more agile and resilient? DUET weighed in on this important topic through a presentation by Lieven Raes, Advisor at Digital Flanders and DUET project coordinator. In his speech, Lieven highlighted the many potentials of urban Digital Twins: as a decision support tool for policy makers and urban planners, as an enabler of digital ecosystems that allows them to better respond to a crisis, as a stakeholder engagement tool that promotes citizen participation in urban decision making. The added value of this pioneering technology was then illustrated with specific use cases developed within DUET for Flanders, such as simulation of a bridge closure, pedestrian flows, shadow mapping, to give just a few examples. All these are available on our Digital Twin platform. Visit https://citytwin.eu/ to see the power of Digital Twins in action!

DUET is conducting the final round of local testing activities with a dual purpose of demonstrating the benefits of its digital twin solution to local stakeholders (urban planners, city managers, citizens) and collecting their feedback on digital twin's UX and other functionalities that can be further improved and enhanced. One such activity took place in Pilsen (CZ) recently. In total, almost 40 participants - most of them from the local administration - gathered on the premises of SITMP, the city's IT administration, to test two scenarios in a digital twin environment. In one scenario, they had to simulate the impact of a large-scale construction project in Nepomuchka street, in another - conduct an emergency exercise at a local football stadium to see how it would affect traffic in nearby streets. "I'm excited to see so many civil servants using our Local Digital Twin in today's exercise", said Stanislav Štangl, Head of GIS at SITMP. "Many admitted DUET's technology is a valuable decision support tool that can make urban planning in Pilsen more agile and data driven. We also received a lot of useful feedback on what can be improved, so we'll be working on this in the coming weeks." The project will be sharing updates from other pilots in due course. In the meantime, do check out the Pilsen's testing session as it happen (see below), and let us know you if you have any questions about the Pilsen twin or DUET in general. Contact us by email.

When it comes to Local Digital Twins (LDTs), stakeholder engagement plays a role during solution development and its subsequent deployment. At the development stage, stakeholder input is needed to ensure that a LDT meets the needs of its primary user group, which is usually policymakers. They must articulate their needs to the technical team, specifying what they want to do and see using a LDT. Some cities will have climate neutrality high on the political agenda, so the use cases that focus on trends in and simulations of energy consumption and carbon emissions from traffic, built environment, land use, industrial processes, and so on would be a priority. Other cities might see LDT more as a decision support tool for emergency response, therefore prioritising real-time monitoring of water levels and the need for multi-model simulations involving waterways, urban transport, pedestrian flows and buildings. Whatever the use case, the involvement of end-users in the solution design over a series of testing and development cycles helps prevent a LDT from becoming yet another data platform that is capable of producing great visuals but, in reality, hardly ever used to solve real-life policy challenges. Once a LDT becomes operational, stakeholder participation can move up a gear, but only in certain cases. This largely depends on the nature of LDT a city has. Some LDTs are closed solutions used solely by governments. Some are open, and it this type of ‘network oriented’ LDTs that extends the traditional user base beyond policymakers. You can read more about different LDT types here. DUET’s platform, for example, can be accessed by anyone to see the impact of road closures on traffic and pollution in nearby streets (Pilsen), to find green routes for recreation and walking (Athens), or to understand how pollution levels, both air and noise, change according to traffic volumes (Ghent). Other cities that opted for a network-oriented solution allow companies to improve their service offering based on climate data provided through the platform (Helsinki). And in Rotterdam, the ambition is to allow citizens to design urban spaces in a digital twin environment and then see the proposed changes in real life using an Augmented Reality app. All these examples show that stakeholder engagement in the context of network-oriented LDT allows wider groups of urban stakeholders to become part of the vibrant, smart city ecosystem. Thanks to this LDT type, citizens can have a much greater influence over policy processes, instead of just being on the receiving end of policy decisions. As they review, tweak and propose alternatives to original plans using a LDT, not only does this help improve public policies, the actual link between government and civil society becomes stronger as a result, leading to better governance and democratic outcomes for everyone.

The IEEE Internet Computing Magazine published its long-awaited issue on digital twins. The latest edition features an article by DUET on how to build interoperable and trusted digital twins of smart cities. GHENT, 18 July 2022 - Digital twins became a buzzword fairly recently despite being in existence for at least 50 years in one form or the other. What started as a useful tool for engineers is now fast-establishing a foothold in areas as diverse as health, last-mile logistics, and urban planning. In Europe and internationally, several dozen cities already have local digital twins (LDTs) at different maturity levels, with many more expected to follow suit in the coming years. To accelerate LDT adoption, DUET is proposing a reference architecture based on common principles, building blocks, and standards. The architecture acts as a central broker onto which different data sources, models, visualisations, interaction clients, and other components connect. Dynamic correspondence linking the architecture with models and data makes it possible to monitor and synchronise the state and behaviour of the digital twin with the physical environment being mirrored. Individual models are integrated through APIs to form a cloud of models that can be called upon to perform various what-if analyses related to traffic, air quality and noise pollution. “The three uses were selected by DUET pilot cities to achieve their local policy objectives. One of the strengths of the DUET framework is that it can be easily adapted to the needs of new cities with different priorities e.g. energy efficiency of buildings, risk of flooding,” said DUET coordinator Lieven Raes of Digital Vlaanderen. “The framework can easily accommodate new monitoring and simulation requirements thanks to the modular nature of its architecture. But arguably the framework’s biggest strength is its function as a change management tool, one that facilitates new ways of working among urban stakeholders, who can use digital twin technology to explore urban dynamics holistically, gradually building a shared understanding of reality, and based on that co-creating solutions with a more lasting impact.” Read the full paper DUET: A Framework for Building Interoperable and Trusted Digital Twins of Smart Cities.

The DUET project was first presented to the Flemish cities and municipalities on 24 June, 2022, during the user day "open data and geodata" of Digital Flanders. Lieven Raes and Gert Vervaet showed how different models of traffic, air quality and noise interact in Ghent (Belgium), Pilzen (Czech Republic) and Athens (Greece). They pointed out that to get good interactions and reliable simulations, the quality of data is vital. There was a great deal of interest in the architecture and reusability of the DUET components.

The hEART Symposium celebrated its 10th anniversary in early June 2022, providing an opportunity for experts in transport modelling, transport economics, transport engineering and transport policy to exchange ideas on latest advances in technologies driving innovation in the field. The event was hosted in Leuven by Chris Tampère of KU Leuven in conjunction with Francesco Viti of University of Luxembourg. Attending the event was also a team of DUET experts from KU Leuven (Paul Ortmann, Chris Tampere, Lotte Notelaers) who presented their research and some innovative results part-funded by the project, such as dyntapy and poidpy. In a nutshell, dyntapy is a python package for researchers working on or with macroscopic vehicular dynamic and static traffic assignments. Most publications in transport modelling still rely on closed source software for macroscopic assignments. This makes it difficult to reproduce, critically assess and build on others' work. dyntapy fills this gap by making assignment algorithms accessible and providing supplementary functionality such as network parsing and visualisation. The poidpy package estimates car traffic demand between zones in a region purely from open data (points of interest in OpenStreetMap). It assists in quickly setting up a new transport model for any region of interest, or to convert existing traffic demand data into a different zoning system e.g. refinement into smaller zones of a coarsely-zoned demand matrix. Interested in testing dyntapy? Here is the link to run the tutorial straight from your browser. Test of poidpy comes in the form of a Jupyter tutorial through this link. For any questions about dyntapy feel free to contact Paul Ortman or Lotte Notelaers directly.