Digital Twin Framework for Smart City Solutions

Adreani, Lorenzo; Bellini, Pierfrancesco; Colombo, Carlo; Fanfani, Marco; Nesi, Paolo; Pantaleo, Gianni; Pisanu, Riccardo

doi:10.18293/dmsviva2022-012

Cited by 10 publications

(6 citation statements)

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“…It is noteworthy that, in an urban context, the infrastructure representation together with its real-time status, if augmented with 3D representations, defines the so-called City Information Modeling (CIM) [18], an extension applied to the city of the concept of Building Information Modeling (BIM), the fundamental building block of smart city Digital Twins [19,20].…”

Section: Mobility Transport Data Overviewmentioning

confidence: 99%

“…Such data results are at the basis of the what-if analysis approaches (orange blocks) to assess and set up different strategies for sustainable mobility, which can solve specific critical or hypothetical conditions in order to improve mobility and transport scenarios. Finally, all the data, results and information are presented to operators (and maybe to final users) by means of operative dashboards [122] as well as aggregated and integrated into smart city Digital Twins representations [123,124] exploiting client-side business logic [125] to guarantee seamless interactivity. Such data results and evaluations can be used to perform and assess different mobility and transport scenarios.…”

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confidence: 99%

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Data Sources and Models for Integrated Mobility and Transport Solutions

Bellini,

Bilotta,

Collini

et al. 2024

Sensors

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The number of data sources and models in the mobility and transport domain strongly proliferated in the last decade. Most formats have been created to enable specific and innovative applications. On the other hand, the available data models present a certain degree of complexity in terms of their integration and management due to partial overlaps, and in most cases, they could be exploited alternatively to implement the same smart and latest innovative solutions. This paper offers an overview of data models, standards and their relationships. A second contribution highlights any possible exploitation of data models for implementing operational processes for city transportation management and for the feeding of simulation and optimization processes that produce other data results in other data models. The final goal in most cases is the monitoring and control of city transport conditions, as well as the tactic and strategic planning of city infrastructure. This work was developed in the context of the CN MOST, a national center of sustainable mobility in Italy, and it is based on exploiting the Snap4City platform.

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Section: Mobility Transport Data Overviewmentioning

confidence: 99%

mentioning

confidence: 99%

Data Sources and Models for Integrated Mobility and Transport Solutions

Bellini,

Bilotta,

Collini

et al. 2024

Sensors

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“…Adreani et al [96] proposed an automatic approach for generating 3D city models from diverse data sets and seamlessly integrating them into the open-source Smart City framework, Snap4City. Their solution enables the creation of comprehensive visualizations of 3D city elements combined with a wide range of Smart City data, including time-series data from IoT devices, heatmaps, traffic flows, bus routes, and cycling paths.…”

Section: A Smart Cities and Urban Planningmentioning

confidence: 99%

A Comprehensive Survey on Revolutionizing Connectivity Through Artificial Intelligence-Enabled Digital Twin Network in 6G

Sheraz,

Chuah,

Lee

et al. 2024

IEEE Access

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The deployment of 5G has exposed capacity constraints in realizing the key vision of the Internet of Everything (IoE). Therefore, the researchers are exploring potentials of Digital Twin Network (DTN) in wireless networks. DTN is a novel technology to create virtual replicas of physical environment for testing, optimizing, and managing wireless networks. The integration of Artificial Intelligence (AI) and DTN appears to be a promising approach to address communication systems by providing an efficient environment for testing and improving AI models before deployment in real networks for effective network management, optimal resource allocation, and precise behavior prediction. Therefore, AI-enabled DTN in 6G represents a compelling avenue to address multifaceted challenges faced by wireless networks. In this comprehensive work, we offer a holistic survey that delves into the state-of-the-art approaches for AIenabled DTNs in 6G. Firstly, we discuss the evolution of wireless networks and concept of AI-enabled DTN in 6G. Secondly, we discuss the role of AI-enabled DTN in 6G and driving advancements in fundamental components of 6G including resource allocation, caching, data offloading, and data security. Thirdly, we conduct a detailed discussion on key enabling technologies for realizing the capabilities of AI-enabled DTN in 6G. Fourthly, several applications of AI-enabled DTN in 6G are discussed for the practical relevance and significance in various industries such as smart cities, healthcare, and transportation etc. Finally, we provide lessons learned and highlight existing challenges and research directions to embark on further research efforts in the realm of AI-enabled DTN in 6G.INDEX TERMS Digital twin networks (DTNs), 6G, artificial intelligence (AI), caching, resource allocation, data offloading, security, enabling technologies, unmanned aerial vehicle (UAV), mmWave, THz.

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“…Regarding roof and façade patterns, they are respectively extracted from orthomaps and street level images. To obtain an accurate orthomap segmentation, so as to extract the roof texture, a deep net was used [70] to find any similarity transformation required to locally warp the orthomaps and make them accurately fit the building plant shapes [71], [72]. On the other hand, façade patterns are extracted by segmenting the building façade and then rectifying it using planar homographies.…”

Section: ) Façade and Roof Pattern Extractionmentioning

confidence: 99%

Smart City Digital Twin Framework for Real-Time Multi-Data Integration and Wide Public Distribution

Adreani,

Bellini,

Fanfani

et al. 2024

IEEE Access

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Digital Twins are becoming fundamental tools to monitor the status of entities, predict their future evolution and simulate alternative scenarios to understand the impact of possible changes for planning and design. More recently, Digital Twin solutions have been applied in the context of Smart Cities. Thanks to the large deployment of sensors, together with the increasing amount of information available for municipalities and governmental organizations, it is possible to build wide virtual reproductions of urban environments including structural data and real-time information that can undoubtfully help decision makers to face future challenges in urban development and improve the citizens' quality of life. In this paper, the Snap4City Smart City Digital Twin framework is presented, which can respond to the requirements identified in recent literature and by international forums. The proposed architecture provides an integrated solution for data gathering, indexing, computing, and information distribution, thus realizing a continuously updated digital twin of the urban environment at global and local scales for monitoring operation and planning. It addresses 3D building models, road networks, Internet of Things entities, points of interest, paths, as well as results from analytical processes for traffic density reconstruction, pollutant dispersion, predictions, and what-if analysis for assessing impact of changes, all integrated into a freely accessible interactive 3D web interface, enabling stakeholder and citizen participation to city decision processes. As case study, the digital twin of the city of Florence (Italy) is presented, including what-if analysis. The solution is released on top of the Snap4City platform as open-source and made available through our GitHub repository (https://github.com/disit) and as Docker compose.

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Digital Twin Framework for Smart City Solutions

Cited by 10 publications

References 0 publications

Data Sources and Models for Integrated Mobility and Transport Solutions

Data Sources and Models for Integrated Mobility and Transport Solutions

A Comprehensive Survey on Revolutionizing Connectivity Through Artificial Intelligence-Enabled Digital Twin Network in 6G

Smart City Digital Twin Framework for Real-Time Multi-Data Integration and Wide Public Distribution

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