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Messina Urban Digital Twin (GDU) – AI-enabled Urban Intelligence Platform (Subnational: Municipality of Messina)


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Added by:   OECD analyst
Added on:   17 Jul 2026
Updated by:   OECD analyst
Updated on:   17 Jul 2026

The Messina Urban Digital Twin (GDU) is an AI-enabled platform that integrates data from IoT sensors, administrative sources and urban systems to support decision-making and scenario simulation. It transforms fragmented urban data into a dynamic and interactive model of the city. The initiative aims to improve planning, resource management and public services through data-driven insights.

Name in original language

Gemello Digitale Urbano di Messina (GDU)

Initiative overview

The Messina Urban Digital Twin (GDU) was developed to address the fragmentation of urban data across multiple systems and the lack of integrated tools for evidence-based decision-making in local government. Cities generate large volumes of data through IoT sensors, administrative processes and external systems, but these data are often siloed and underutilised.

The initiative integrates heterogeneous data sources, including IoT infrastructure, environmental monitoring, mobility systems and administrative datasets, into a unified digital representation of the urban environment. AI techniques are used to analyse data patterns, support predictive insights and enable simulation of complex urban scenarios, such as traffic flows, environmental impacts and resource allocation. The system enables public officials to explore “what-if” scenarios and supports strategic planning, operational management and policy evaluation. It is designed as a modular and scalable architecture, allowing progressive integration of new data sources and services.

The initiative is part of the broader investment framework of the PN METRO Plus 2021–2027 programme, ensuring alignment with national and European urban innovation priorities and supporting its long-term sustainability and scalability. The GDU is also aligned with national and European digital strategies and integrates with the municipality’s Open Data ecosystem, promoting transparency and enabling future reuse by developers, researchers and other public administrations.
 

Other relevant details

The initiative has enabled the integration of heterogeneous urban data sources into a unified framework and the development of initial simulation models for key domains such as mobility and environmental monitoring. Early impacts include improved data availability, better situational awareness for decision-makers, and reduced fragmentation of information systems. Impact is assessed through system integration metrics, data coverage, and usability for planning processes. Future impacts include predictive analytics capabilities, improved urban resilience, and more efficient resource allocation. Key challenges include integrating heterogeneous data sources, ensuring data quality and interoperability, and designing scalable architectures capable of supporting real-time and predictive analytics. Additional challenges relate to governance, including data ownership, cross-department coordination and alignment with regulatory frameworks. These challenges have been addressed through modular architecture design, standardisation efforts and strong institutional coordination. A key lesson learned is that urban AI systems require not only technological integration but also organisational transformation and clear governance models.