Abstract
Digital Twins of Smart Cities are fundamental tools for decision makers since they can provide interactive 3D visualizations of the city enriched with real-time information and connected to actual complete digital model of the entities with all their heterogeneous data/info. Such a technology can be exploited to observe the status of the city, and to perform analysis and simulations, and thus to develop strategies. Indeed, such solutions must satisfy a series of requirements spanning from the 3D construction to the interactive functionality of user interface for the decision makers. In this paper, a Smart City Digital Twin model and tools are presented, which satisfy a wide range of requirements. The principles at the basis of the design and development are reported and discussed. The solution has been developed on top of Snap4City platform and validated on Florence City case (Italy), in CN Mobility of Ministry. Finally, a comparison among several different Smart City Digital Twin solutions is offered.
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16 December 2023
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References
Mylonas, G., Kalogers, A., Kkalogeras, G., Anagnostopoulos, C., Alexakos, C., Muñoz, L.: Digital twins from smart manufacturing to smart cities: a survey. IEEE Access 9, 143222–143249 (2021). https://doi.org/10.1109/ACCESS.2021.3120843
Shahat, E., Hyun, C.T., Yeom, C.: City digital twin potentials: a review and research agenda. MDPI, p. 3 (2021)
Gröger, G., Plümer, L.: “CityGML Interoperable semantic 3D city models. ISPRS J. Photogrammetry Remote Sens., 16–21 (2012)
Jovanovic, D., et al.: Building virtual 3d city model for smart cities applications: a case study on campus area of the university of novi sad. ISPRS Int. J. Geo-Information, 16–21 (2020)
Adreani, L., et al.: Digital twin framework for smart city solutions. In: Proceedings of the 28th International DMS Conference on Visualization and Visual Languages (DMSVIVA 2022) (2022)
Han, Q., Nesi, P., Pantaleo, G., Paoli, I.: Smart city dashboards: design, development and evaluation. In: Proceedings of the IEEE ICHMS 2020, International Conference on Human Machine Systems, September 2020. http://ichms.dimes.unical.it/
Garau, C., Nesi, P., Paoli, I., Paolucci, M., Zamperlin, P.: A big data platform for smart and sustainable cities: environmental monitoring case studies in europe. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12255, pp. 393–406. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58820-5_30
Bilotta, S., Nesi, P.: Traffic flow reconstruction by solving indeterminacy on traffic distribution at junctions. Futur. Gener. Comput. Syst. 114, 649–660 (2021)
Bilotta, S., Collini, E., Nesi, P., Pantaleo, G.: Short-term prediction of city traffic flow via convolutional deep learning. IEEE Access 10, 113086–113099 (2022). https://doi.org/10.1109/ACCESS.2022.3217240
Girard, N., Charpiat, G., Tarabalka, Y.: Aligning and updating cadaster maps with aerial images by multi-task, multi-resolution deep learning. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11365, pp. 675–690. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20873-8_43
Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recogn. 26(9), 1277–1278 (1993). https://doi.org/10.1016/0031-3203(93)90135-J
Campello, R.J.G.B., Moulavi, D., Sander, J.: Density-based clustering based on hierarchical density estimates. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 160–172. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37456-2_14
Toldo, R., Fusiello, A.: Robust multiple structures estimation with j-linkage. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 537–547. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88682-2_41
Badii, C., Bellini, P., Cenni, D., Difino, A., Nesi, P., Paolucci, M.: Analysis and assessment of a knowledge based smart city architecture providing service APIs. Futur. Gener. Comput. Syst. 75, 14–29 (2017)
Nesi, P., et al.: An integrated smart city platform. Semantic Keyword-based Search on Structured Data Sources. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-74497-1_17
Bellini, P., Bugli, F., Nesi, P., Pantaleo, G., Paolucci, M., Zaza, I.: Data flow management and visual analytic for big data smart City/IOT. In: 19th IEEE Int. Conf. on Scalable Computing and Communication, IEEE SCALCOM 2019, Leicester, UK. https://www.slideshare.net/paolonesi/data-flow-management-and-visual-analytic-for-big-data-smart-cityiot
Bellini, P., Bilotta, S., Palesi, A.L.I., Nesi, P., Pantaleo, G.: Vehicular traffic flow reconstruction analysis to mitigate scenarios with large city changes. IEEE ACCESS 10, 131061–131075 (2022). https://doi.org/10.1109/ACCESS.2022.3229183
Helsinki 3D city model. https://kartta.hel.fi/3d/#/
Rotterdam 3D. https://www.3drotterdam.nl
Berlin 3D, 3dcitydb. https://www.3dcitydb.org/3dcitydb-web-map/1.7/3dwebclient/index.html?title=Berlin_Demo&batchSize=1&latitude=52.517479728958044&longitude=13.411141287558161&height=534.3099172951087&heading=345.2992773976952&pitch=-44.26228062802528&roll=359.933888621294&layer_0=url%3Dhttps%253A%252F%252Fwww.3dcitydb.org%252F3dcitydb%252Ffileadmin%252Fmydata%252FBerlin_Demo%252FBerlin_Buildings_rgbTexture_ScaleFactor_0.3%252FBerlin_Buildings_rgbTexture_collada_MasterJSON.json%26name%3DBrlin_Buildings_rgbTexture%26active%3Dtrue%26spreadsheetUrl%3Dhttps%253A%252F%252Fwww.google.com%252Ffusiontables%252FDataSource%253Fdocid%253D19cuclDgIHMqrRQyBwLEztMLeGzP83IBWfEtKQA3B%2526pli%253D1%2523rows%253Aid%253D1%26cityobjectsJsonUrl%3D%26minLodPixels%3D100%26maxLodPixels%3D1.7976931348623157e%252B308%26maxSizeOfCachedTiles%3D200%26maxCountOfVisibleTiles%3D200
Stockholm Opencities Planner. https://eu.opencitiesplanner.bentley.com/stockholm/stockholmvaxer
Wellington DT. https://buildmedia.com/work/wellington-digital-twin
DUET Project. https://www.digitalurbantwins.com/
Acknowledgement
The authors would like to thank the MIUR, the University of Florence and the companies involved for co-founding the national Center on Sustainable Mobility, MOST. A thanks to the many developers on snap4city platforms. Snap4City (https://www.snap4city.org) is open technologies of DISIT Lab.
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Adreani, L., Bellini, P., Fanfani, M., Nesi, P., Pantaleo, G. (2023). Design and Develop of a Smart City Digital Twin with 3D Representation and User Interface for What-If Analysis. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14111. Springer, Cham. https://doi.org/10.1007/978-3-031-37126-4_34
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