A Digital Twin Decision Support System for the Urban Facility Management Process

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 24; p. 8460
• Main Authors: Bujari, Armir, Calvio, Alessandro, Foschini, Luca, Sabbioni, Andrea, Corradi, Antonio
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.12.2021; MDPI
• Subjects: Apache Spark; Big Data; Data processing; Decision making; Decision support systems; Digital Twin; geographic information system; Literature reviews; Smart cities; smart city; Taxonomy; Urban Facility Management; geographic information system; big data; Urban Facility Management; Digital Twin; Apache Spark; smart city
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21248460

The ever increasing pace of IoT deployment is opening the door to concrete implementations of smart city applications, enabling the large-scale sensing and modeling of (near-)real-time digital replicas of physical processes and environments. This digital replica could serve as the basis of a decision support system, providing insights into possible optimizations of resources in a smart city scenario. In this article, we discuss an extension of a prior work, presenting a detailed proof-of-concept implementation of a Digital Twin solution for the Urban Facility Management (UFM) process. The Interactive Planning Platform for City District Adaptive Maintenance Operations (IPPODAMO) is a distributed geographical system, fed with and ingesting heterogeneous data sources originating from different urban data providers. The data are subject to continuous refinements and algorithmic processes, used to quantify and build synthetic indexes measuring the activity level inside an area of interest. IPPODAMO takes into account potential interference from other stakeholders in the urban environment, enabling the informed scheduling of operations, aimed at minimizing interference and the costs of operations.