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

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Area of Science:

  • Urban planning and management
  • Digital twin technology
  • Internet of Things (IoT) applications

Background:

  • Increasing IoT deployment enables smart city applications.
  • Digital replicas can optimize urban resource management.
  • Urban Facility Management (UFM) requires efficient operational planning.

Purpose of the Study:

  • Present a proof-of-concept for a Digital Twin solution in UFM.
  • Detail the Interactive Planning Platform for City District Adaptive Maintenance Operations (IPPODAMO).
  • Demonstrate optimized scheduling for urban maintenance operations.

Main Methods:

  • Developed a distributed geographical system (IPPODAMO).
  • Ingested and refined heterogeneous data from urban providers.
  • Quantified urban activity levels using synthetic indexes.
  • Modeled stakeholder interference for informed scheduling.

Main Results:

  • IPPODAMO provides a detailed proof-of-concept for UFM Digital Twin.
  • The system quantifies urban activity and stakeholder interference.
  • Enables informed scheduling to minimize operational interference and costs.

Conclusions:

  • Digital Twin solutions like IPPODAMO can significantly enhance UFM.
  • Integration of heterogeneous data is key to effective urban modeling.
  • Optimized scheduling reduces costs and improves smart city operations.