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Title	Mobility And Transport Data For City Digital Twin Modeling And Exploitation
ID_Doc	37342
Authors	Bellini P.; Bilotta S.; Collini E.; Fanfani M.; Nesi P.
Year	2023
Published	Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023
DOI	http://dx.doi.org/10.1109/ISC257844.2023.10293300
Abstract	With the continuous increasing of urban growth, the definition and application of innovative, sustainable, smart, and green mobility solutions is nowadays of paramount importance. However, what-if analysis for planning and mitigating problems require huge amount of data and precise representation in order to be effective as decision support tools. For this reason, Big Data and IoT/IoE technologies are exploited to collect and manage a large range of mobility and transport information, e.g., traffic to people flow, heatmaps, origin destination, sensor data, and their complex relationships. In this paper a clear model for collecting and producing them in the context of what-if analysis is presented. Analytic processes, based on advanced statistics and artificial intelligence, can provide predictions and simulations to show the impact of specific scenarios on the urban mobility and that have to be represented on the user interface in seamless manner. In addition, in this paper, data handling and analytics to perform 3D City Digital Twin for What-If analysis on the Snap4City platform is described, offering an immediate and realistic visualization to help decision-makers. © 2023 IEEE.
Author Keywords	big data; digital twin; mobility; smart cities

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