Abstract
According to the United Nations' prediction, the world is expected to have 43 megacities that host more than 10 million inhabitants by 2030. City logistics for freight distribution is a challenging and crucial problem for these cities. Recent disruptive approaches such as Smart City provide us a new perspective to investigate the problem as well as for decision making. It creates a pervasive and mobile computing environment that allows the city itself to be overlaid with sensing and actuation, embedded with “smart things” to develop an “ambient intelligence.” By choosing this angle, this work investigates the problem of freight parking management for last-mile delivery in smart city, called Smart City Logistics Parking (SCLP). A use case is conceptualized and modeled via a bottom-up approach. The bottom-layer aims to represent the structure of the SCLP, i.e., the physical elements constituting the SCLP physical infrastructure into a digital representation, a.k.a a Digital Twin of the SCLP. Property Graph modeling is applied at this step as a meta-model to formulate the object relationships with properties. The upper-layer makes use of associated ontologies to add semantics to the structural description of the SCLP. The built model is then implemented into a Digital Twin experimental platform, namely Thing in the Future (Thing'in in short) from Orange Labs. The modeling work presented in this paper encourages future works on simulation of the decision-making processes based on the Digital Twin platform.
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Acknowledgements
This work is supported by the Physical Internet Chair at Mines ParisTech - PSL University, and the project ASAP (Awaken Sleeping Assets Project) which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.
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Liu, Y. et al. (2021). Digital Twin-Driven Approach for Smart City Logistics: The Case of Freight Parking Management. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-030-85910-7_25
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DOI: https://doi.org/10.1007/978-3-030-85910-7_25
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