As cities grapple with increasing congestion, sustainability concerns, and the need for efficient mobility systems, Mobility Digital Twins (MoDTs) have emerged as promising technology for improving urban transportation. However, the development of MoDTs remains hindered by challenges such as structural complexity, data heterogeneity, lack of interoperability, and limited support for scalability, maintainability, and adaptability. This work aims to address these barriers by introducing a structured and systematic engineering framework that supports the design development of MoDT, reducing technical debt, development costs and human errors, while promoting long-term evolution. We propose a Model-Driven Engineering (MDE) approach that organizes the development of MoDTs through models at different levels of abstraction and adopts automated transformations from high-level specifications to executable code artifacts, supporting MoDT life-cycle. The proposed approach is validated through its application in developing a MoDT for the city of Bologna, Italy. To support this, we introduce the M2DT tool, which automates the workflow from high-level models to software code artifacts. The resulting BoMoDT platform is built using open-source technologies and real mobility data. This case study demonstrates the feasibility and effectiveness of our approach, which, to our knowledge, is the first to apply a model-driven strategy for the entire MoDT development. A qualitative evaluation confirms that our framework addresses key challenges in MoDT development. Quantitative experiments further validate BoMoDT’s ability to accurately reproduce and monitor real urban mobility conditions. The proposed approach offers a solid foundation for addressing MoDT development challenges. By combining automation with structured abstraction, it improves adaptability and maintainability while enabling scalable integration, helping make MoDTs more accessible for future urban system design. • Six challenges in Mobility Digital Twins are identified from literature analysis. • A model-driven approach is introduced to structure their development. • The approach is applied to real world urban mobility case study. • BoMoDT is presented as DT platform for simulation and monitoring of Bologna mobility. • Fidelity and responsiveness are quantitatively evaluated.

In recent years, the emerging Digital Twin (DT) technology is playing a key role in fostering the transition towards the Industry 4.0. DTs, representing virtual replicas of physical objects, products or processes established thanks to a bidirectional continuous flow of information between the physical and the virtual world, are currently adopted in multiple domains such as manufacturing, aerospace, automotive, energy, construction, smart cities and smart mobility, etc.. DTs live together with the physical system they replicate, receiving the same data and often triggering specific control actions that directly impact on the real system status. When dealing with DTs of complex Cyber-Physical Systems (CPSs), the data sources may be heterogeneous and untrustworthy, which requires the DT to be able to address security issues related to data in transit from/to the physical twin and data at rest. A possible way to address these data security issues consists in adopting Distributed Ledger Technologies (DLTs) which provide several security guarantees on data through cryptographic hashing techniques. In this work, we present a three-fold contribution: (i) we discuss the results of a Systematic Literature Review (SLR) on the state-of-the-art of the research related to the integration between DLT and Digital Twins, aimed at clarifying relevant aspects such as, among others, what is the favourite DLT choice in existing proposals or what is the type of DT-related information to store on-chain; (ii) leveraging the SLR results and other related research, we propose an architectural framework for the integration of DT and DLTs (more specifically, blockchains); (iii) we validate the proposed architecture by means of two proof-of-concept implementations leveraging different technological stacks and taking into account two different operational scenarios. Finally, we conduct a requirements coverage analysis and compare the PoCs through a coverage matrix.

Digital Twins (DTs) are the novel paradigm for the development of Cyber-Physical systems. The state of art presents several use cases in different domains, and one of the most complex examples is represented by the Urban Digital Twin (UDT), which aims to virtualize urban assets (e.g., buildings, mobility infrastructures, energy grids, waste management facilities, etc.) and build advanced analysis and prediction services upon a city’s digital representation. UDTs represent a formidable example of system of systems, as they are structured into a hierarchy of interconnected DT instances that process and share a huge amount of data subject to different access and usage policies.Regarding data management in complex distributed scenarios, Data Spaces are an emerging paradigm that aims at building a secure and privacy-preserving infrastructure to pool, access, share, process and use data. As a matter of fact, existing DTs solutions (not only in the urban domain) do not present a clear software architecture characterization and, moreover, they pay little attention to the data management aspects.To bridge this gap, in this paper we present the Digital Twin Space, an architectural proposal that aims to instantiate Data Spaces into DTs according to the guidelines set by relevant international projects. We use the UDT as a running example and we validate the model in the case of a smartPV panel, showing that the model matches the real cyber-physical system.

Modern cyber-physical systems based on the Industrial Internet of Things (IIoT) can be highly distributed and heterogeneous, and that increases the risk of failures due to misbehavior of interconnected components, or other interaction anomalies. In this paper, we introduce a conceptual architecture for IIoT anomaly detection based on the paradigms of Digital Twins (DT) and Autonomic Computing (AC), and we test it through a proof-of-concept of industrial relevance. The architecture is derived from the current state-of-the-art in DT research and leverages on the MAPE-K feedback loop of AC in order to monitor, analyze, plan, and execute appropriate reconfiguration or mitigation strategies based on the detected deviation from prescriptive behavior stored as shared knowledge. We demonstrate the approach and discuss results by using a reference operational scenario of adequate complexity and criticality within the European Railway Traffic Management System.

The digital transformation process fostered by the development of Industry 4.0 technologies has largely affected the health sector, increasing diagnostic capabilities and improving drug effectiveness and treatment delivery. The Digital Twin (DT) technology, based on the virtualization of physical assets/processes and on a bidirectional communication between the digital and physical space for data exchange, is considered a game changer in modern health systems. Digital Twin applications in healthcare are various, ranging from virtualization of hospitals' physical spaces/organizational processes to individuals' physiological/genetic/lifestyle characteristics replication, and include the modeling of public health-related processes for monitoring, optimization and planning purposes. In this paper, motivated by the current COVID-19 pandemic, we focus on the application of the Digital Twin technology for virus containment on the workplace through social distancing. The contribution of this paper is three-fold: i) we review the existing literature on the adoption of the Digital Twin technology in the healthcare domain, and propose a classification of DT applications into four categories; ii) we propose a generalized Digital Twin architecture that can be used as reference to identify the main functional components of a Digital Twin system; iii) we present CanTwin, a real-life industrial case study developed by Hitachi and representing the Digital Twin of a canteen service serving 1100 workers, set up for social distancing monitoring, queue inspection, people counting and tracking, table occupancy supervision.