From Smart Cities to Responsive Cities

Welcome to DUET, a brand new innovation initiative which leverages the advanced capabilities of cloud and high-performance computing (HPC), in the form of Digital Twins, to help public sector decision-making become more democratic and effective. By creating digital replica's of a city, people, no matter their background, can use the Digital Twins 3D and 2D interfaces for easy policy impact exploration and experimentation across entire cities and regions.


DUET's use of Digital Twins  truly changes the policy game, disrupting the field of Smart Cities and transitioning to a new age of Responsive Cities. With Responsive Cities, solutions are not designed around citizens, they are designed with the citizen placed firmly at the center of the action. Where Smart Cities are technology driven and produce large amounts of data from fixed or centrally controlled sensors, Responsive Cities recognise that citizens are also a major player in data generation which helps to shape real-time city decisions.

As a cooperative endeavour, involving 15 different partners from across Europe, we are always on the look out for new collaboration opportunities. If you are interested in Digital Twins, have an initiative to share, or want to connect for any reason at all just drop us a line using the contact form below. 


To help educate our audience on different aspects of the project we have created the following video series


The three DUET city test beds cover a range of scales for testing different scenarios related to the field of mobility, health and environment. These policy domains were chosen for testing purposes due to immediate policy needs in all areas and the fact that these issues often intersect and impact each other, providing a perfect opportunity for robustly testing the Digital Twins with multidisciplinary input and multi-sectoral output. 

Starting from a similar data cloud concept, three city test beds - each committed to digital transformation - have been chosen to cover different geographical scopes, spatial challenges, and policy needs and will be rolled out in a cascading order to (a) have more efficient use of resources (b) demonstrate transferability towards other cities and regions, (c) ensure lessons learned are captured and used during the project.  The piloting will end with the cities using each others data/models/APIs to demonstrate interoperability and potential for Policy Ready Data-as-a-Service. 

Image by Elijah G


The Flanders DUET Digital Twin will concentrate on the design of new measures, implementation of actions and evaluation of the success of actions as foreseen in for example Flanders Regional Mobility Plan, and the Flanders Environment Plan20 which aims both for smoother mobility through actions that are kinder to the environment and reduces the impact on human health.

Athens City


Athens sees data informed decision making as a key pillar for city and business transformation. In an era of exponential and constant change, Athens wishes to embrace Digital Twin use to understand city relationships and overcome engagement barriers with stakeholders, co-create new innovative digital services and deliver new business value.

City of Pilsen


The Pilsen Digital Twin will focus on the interrelation between transport and noise pollution in a 3D environment. Noise pollution in the city environment is influenced by many factors. One of the factors is the local road transport - especially the traffic volumes that pass by the built-up environment, types of vehicles and traffic conditions such as speed limits.

Traffic Jam


January 2021

The paper describes Traffic Modeller (www.trafficmodeller.com) - a web map application for monitoring, analysis and prediction of traffic in a metropolitan area.  Traffic Modeller outputs  play a crucial role in city policy-making. The resulting maps allow city planners to study the traffic behaviour while modelling various road network parameters (e.g. road closures, road capacity) and explore the effects of the changes in traffic flows in near real-time. 


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