A conceptual digital twin framework for city logistics

Belfadel, Abdelhadi; Hörl, Sebastian; Tapia, Rodrigo Javier; Politaki, Dimitra; Kureshi, Ibad; Tavasszy, Lóránt; Puchinger, Jakob

doi:10.1016/j.compenvurbsys.2023.101989

Cited by 7 publications

(1 citation statement)

References 17 publications

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“…Urban logistics can benefit from the integration of sensors and actuators within the infrastructure. This can be achieved through a platform architecture for digital twins that informs policy making via interactive dashboards [ 56 ]. This approach allows for real-time sensor data and logistics system documentation to drive simulation models that can pinpoint gaps and opportunities for transformation within city ecosystems.…”

Section: Resultsmentioning

confidence: 99%

Navigating the Evolution of Digital Twins Research through Keyword Co-Occurence Network Analysis

Li,

Zhou,

et al. 2024

Sensors

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Digital twin technology has become increasingly popular and has revolutionized data integration and system modeling across various industries, such as manufacturing, energy, and healthcare. This study aims to explore the evolving research landscape of digital twins using Keyword Co-occurrence Network (KCN) analysis. We analyze metadata from 9639 peer-reviewed articles published between 2000 and 2023. The results unfold in two parts. The first part examines trends and keyword interconnection over time, and the second part maps sensing technology keywords to six application areas. This study reveals that research on digital twins is rapidly diversifying, with focused themes such as predictive and decision-making functions. Additionally, there is an emphasis on real-time data and point cloud technologies. The advent of federated learning and edge computing also highlights a shift toward distributed computation, prioritizing data privacy. This study confirms that digital twins have evolved into complex systems that can conduct predictive operations through advanced sensing technologies. The discussion also identifies challenges in sensor selection and empirical knowledge integration.

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Section: Resultsmentioning

confidence: 99%