Correction: Caprari et al. Digital Twin for Urban Planning in the Green Deal Era: A State of the Art and Future Perspectives. Sustainability 2022, 14, 6263

Caprari, Giorgio; Castelli, Giordana; Montuori, M.; Camardelli, Marialucia; Malvezzi, Roberto

doi:10.3390/su142214893

Cited by 3 publications

(1 citation statement)

References 1 publication

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“…A DT framework, consisting of a digital model, a knowledge base, and an analytics component, can accurately monitor and predict the city's state in realtime [19,20]. In the context of a smart city platform, a DT is a digital representation of real-world environments brought to life with real-time data from sensors and other data sources [21,22,23,24]. Singapore has already implemented DT for applications such as water reclamation plants [25].…”

Section: Introductionmentioning

confidence: 99%

A Digital Twin Architecture for Intelligent Traffic Management Based on Artificial Intelligence

Aloupogianni,

Doctor,

Karyotis

et al. 2024

Intelligent Environments 2024: Combined Proceedings of Workshops and Demos &Amp; Videos Session

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This work presents a concept for an innovative Digital Twin (DT) framework for urban traffic monitoring and management, tailored for the city of Singapore. The proposed architecture leverages real-time traffic and weather data integration, AI processing, and modular design to offer adaptive and versatile traffic insights. By incorporating live information from various sources and integrating real-time weather data, the framework enables proactive traffic management and enhances safety during adverse weather conditions. The paper discusses the implementation of the framework, and its potential impact on urban mobility, and suggests future directions for research and development to facilitate the framework’s implementation.

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Section: Introductionmentioning

confidence: 99%

A Digital Twin Architecture for Intelligent Traffic Management Based on Artificial Intelligence

Aloupogianni,

Doctor,

Karyotis

et al. 2024

Intelligent Environments 2024: Combined Proceedings of Workshops and Demos &Amp; Videos Session

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Developing an assessment governance framework for urban digital twins: Insights from smart cities

Diaz-Sarachaga

2025

Cities

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An AI-Based Digital Twin Framework for Intelligent Traffic Management in Singapore

Aloupogianni,

Doctor,

Karyotis

et al. 2024

2024 International Conference on Electrical, Computer and Energy Technologies (ICECET

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Urban centers worldwide grapple with the intricate challenge of traffic congestion, necessitating sophisticated solutions grounded in real-time data analytics. This paper presents a cutting-edge Digital Twin (DT) framework tailored for urban traffic management, with a focus on the context of Singapore's technologically advanced landscape. By seamlessly integrating live weather data and on-road camera information, the proposed framework offers insights into traffic dynamics, enabling adaptive decision-making. Leveraging a modular architecture and advanced artificial intelligence (AI) algorithms, the framework aims to optimize traffic flow, mitigate accidents, and ensure resilient commuting experiences, even amidst adverse weather conditions. Evaluation of individual components showcases promising performance metrics, albeit contingent upon data availability and user engagement. Future research endeavors will explore scalability, user-centric design enhancements, and the longitudinal efficacy of the proposed framework, positioning it as a novel solution for urban traffic management.

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Correction: Caprari et al. Digital Twin for Urban Planning in the Green Deal Era: A State of the Art and Future Perspectives. Sustainability 2022, 14, 6263

Abstract: The authors would like to make the following corrections to the published paper [...]

Cited by 3 publications

References 1 publication

A Digital Twin Architecture for Intelligent Traffic Management Based on Artificial Intelligence

A Digital Twin Architecture for Intelligent Traffic Management Based on Artificial Intelligence

Developing an assessment governance framework for urban digital twins: Insights from smart cities

An AI-Based Digital Twin Framework for Intelligent Traffic Management in Singapore

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