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- Urban Digital Twins (UDTs)
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- models 
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- distributed digital twin of the public bus service of the city of Malaga and the users who travel on it.
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- simulations and inferences
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- he goal of smart cities is to use technology for providing useful services to their citizens and to solve urban problems
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- hindering reusability and interoperability
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- owerful information processing technology.
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- digital representation of a system, employing models and data for representing its properties, conditions, and actions
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- two-way feedback
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- real-time monitoring, adopting adaptation policies, and enhancing decision-making processes
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- citizens 
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- must be also taken into consideration
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- advocate for integrating citizens in the UDT picture, exploiting their information, habits and preferences, and allowing them to have full control over their own data, as well as benefiting from better services from the organisations with whom they share them
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- Digital Avatars
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- Smart cities of the future,
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- digital avatars:
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- advocate for a distributed, hierarchical, and low-coupled architecture deployed over the Cloud-to-Thing Continuum (the Continuum, in short) in which the digital counterpart of each element of the system is deployed on the device which is closest to it, improving the scalability, response time, and privacy of the sensed data
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- we have applied it to a case study of smart city transportation in Malaga (Spain), building a UDT which models both its public bus system, and the individuals that make use of it.
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- oosely coupled and hierarchical architecture of the UDT.
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- UDT using highlevel UML models
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- The design has been made trying to be scalable in both directions, up and down, so that the system can regulate the workloads that occur at different times
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- [:span]
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- Digital Avatar (D A ).
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- e have distributed these models according to the computational capacity of the layers of the Continuum
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- located at the fog layer
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- we have deployed the user model on the edge layer.
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- his involves some interaction between the edge and fog layers as shown in Figure 1
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- public transportation networks in smart cities
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- particularly in Malaga (Spain)
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- We have at our disposal information updated every minute regarding the city’s bus utilisation, including data about routes, stops, timetables, and the GPS location of buses
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- This information is provided by the City Council as open data1, and it plays a critical role within the model. The bus routes and stops build the primary infrastructure of the network, acting as the fundamental pillar that sustains the entire system. The GPS locations of the buses, updated every minute, introduce dynamism to the system, offering updates on the buses’ positions and movements throughout the city
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- or instance, the company does not know where its passengers will get off, a detail our UDT can infer from the habits stored in the DA.
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- , 
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- Estimating precisely the stop time is challenging due to the minute-by-minute snapshots of the buses and the fact that a bus does not stop at a station if there are no passengers to get on or off. This variability in travel times can affect the accuracy of the model’s predictions, so it is something the model needs to take into account
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- In this paper we have described how UML models and their simulation in the USE tool are suitable for the specification of UDTs in order to verify their expected behavior with a very low computational cost.
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- distributed architecture where data processing is performed close to the node that produces the data
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- We are currently completing the validation of our model as far as buses are concerned and analyzing the causes that lead to deviations between estimated and actual behavio
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- hen, we will be able to predict data such as the arrival time at a destination or the future occupancy of the buses. Finally, we plan to use the information provided by users’ DAs to refine predictions and provide personalized recommendations.
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