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• Urban Digital Twins (UDTs) ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64846df7-2222-4332-adb7-dfc169f4f6c2
• models ls-type:: annotation hl-page:: 1 hl-color:: yellow id:: 64846e09-5fd8-4671-9e8e-54ffd572b2e4
• distributed digital twin of the public bus service of the city of Malaga and the users who travel on it. ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64846e25-8c5a-4f51-9cde-327cf0e1395a
• simulations and inferences ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64846e3a-e5c9-4033-9a4c-5bc3a5d09daa
• he goal of smart cities is to use technology for providing useful services to their citizens and to solve urban problems ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 64846e50-fc32-4d74-b4e5-d4089cd7ce3d
• hindering reusability and interoperability ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 64846e5f-b424-4078-b516-e2528a303f97
• owerful information processing technology. ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64846e73-99fb-4d00-839e-cc677aca2982
• digital representation of a system, employing models and data for representing its properties, conditions, and actions ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64846e7e-2f54-404e-9085-90cd41b6d592
• two-way feedback ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64846e97-c5e4-4c0f-9199-51370827967f
• real-time monitoring, adopting adaptation policies, and enhancing decision-making processes ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64846ea1-7333-4f3c-a593-2b1af3bb5e48
• citizens ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64846eed-3852-4cec-85eb-3f4516779d08
• must be also taken into consideration ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64846ef1-b3b6-4815-a6c7-039069a79a13
• 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 ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 64846f07-cedf-4a4a-83ef-9f51568fb830
• Digital Avatars ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64846f0d-4c06-452a-bd80-34856a259152
• Smart cities of the future, ls-type:: annotation hl-page:: 5 hl-color:: green id:: 64846f2a-04b2-47ef-b754-f81d6b3d5a62
• digital avatars: ls-type:: annotation hl-page:: 5 hl-color:: green id:: 64846f4c-db62-42c1-bd36-4a7188c8c3f1
• 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 ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 64846f84-4e0b-4905-a1e3-88842abbfc4c
• 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. ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 64846fb8-6942-4de4-ae81-c4299093faba
• oosely coupled and hierarchical architecture of the UDT. ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 64846fcf-5621-43dc-b171-5b508669a811
• UDT using highlevel UML models ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64846fed-23dd-4eb9-9541-f81d671562f5
• 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 ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64847830-d71c-43db-8ab4-3b012856f0ef
• [:span] ls-type:: annotation hl-page:: 3 hl-color:: green id:: 6484783d-2ba0-4e31-a90a-2def854237b8 hl-type:: area hl-stamp:: 1686403131587
• Digital Avatar (D A ). ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64847873-c1c7-4fdf-a1d6-79a4487772b2
• e have distributed these models according to the computational capacity of the layers of the Continuum ls-type:: annotation hl-page:: 3 hl-color:: yellow id:: 648478f1-3e1b-4c3e-9874-8bd11c6d09d7
• located at the fog layer ls-type:: annotation hl-page:: 3 hl-color:: green id:: 64847927-a0e5-4aab-8bc0-306fd7c6e422
• we have deployed the user model on the edge layer. ls-type:: annotation hl-page:: 3 hl-color:: green id:: 6484795d-d3f5-4d31-8837-bab7ff738f19
• his involves some interaction between the edge and fog layers as shown in Figure 1 ls-type:: annotation hl-page:: 3 hl-color:: yellow id:: 648479d0-3303-41dd-af3f-02f242aef44a
• public transportation networks in smart cities ls-type:: annotation hl-page:: 4 hl-color:: purple id:: 64847a0c-9860-41a7-81d1-c7952a2a9fae
• particularly in Malaga (Spain) ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 64847a10-ece8-4f39-85ec-f0523dc354d7
• 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 ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 64847a47-046a-4e25-aa52-a7afa25d5b1e
• 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 ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 64847b07-33ea-4186-862f-81cca422b239 hl-stamp:: 1686403852837
• 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. ls-type:: annotation hl-page:: 4 hl-color:: green id:: 64847b64-d7d6-418d-9f52-f8a2583a290f
• , ls-type:: annotation hl-page:: 4 hl-color:: green id:: 64847d4a-336b-4ada-8928-94d4c90b3f31
• 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 ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 64847e39-01fb-438d-916a-0ae7f33d5a3f hl-stamp:: 1686404667605
• 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. ls-type:: annotation hl-page:: 5 hl-color:: yellow id:: 64847ea3-3526-4737-9e13-609d07e7795e
• distributed architecture where data processing is performed close to the node that produces the data ls-type:: annotation hl-page:: 5 hl-color:: yellow id:: 64847f37-e14f-48c6-96e0-10db95d20a59
• 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 ls-type:: annotation hl-page:: 5 hl-color:: green id:: 64847f6d-60c8-44e2-a748-aaabc0d25476
• 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. ls-type:: annotation hl-page:: 5 hl-color:: yellow id:: 64847f81-ac62-43df-8c64-cfa561240341