A low-code assessment platform for urban digital twins

Abstract

Context:

Digital twins, as virtual models reflecting physical entities, serve multiple purposes, supporting the understanding, design, and management of systems, across various domains. The concept of digital twins has been also applied to smart cities, coining the term “Urban Digital Twin”. Indeed, smart cities continuously produce data that can serve as a real-time feed for their digital twin representations. However, developing urban digital twins is challenging as the domain is complex and evolving rapidly. Moreover, existing digital twin platforms are mostly generic platforms, limiting their adoption for a specific context such as smart cities.

Objectives:

The aim of this study is to overcome the limitation of existing general-purpose digital twin platforms, particularly in the context of smart cities, by supporting the development and evolution of urban digital twins.

Method:

To this aim, we propose a low-code assessment platform for smart cities represented as urban digital twins by leveraging distributed runtime models to implement and deploy service-based quality evaluation systems. The innovative platform and its architecture enable the creation of efficient urban digital twins, allowing for their continuous evolution without requiring redeployment whenever the digital twin definitions are modified.

Results:

Experimental evaluations demonstrate the effectiveness and efficiency of the proposed platform, highlighting its potential for evolving urban digital twins in smart cities quality assessment.

Conclusions:

Results show evidence that combining distributed runtime models with the advantages of low-code platforms is beneficial within the smart cities domain. Moreover, this study underscores the significance of specialized platforms tailored to the smart cities domain.