Search Results (293)

Autonomous driving systems rely on vast and diverse datasets for robust object recognition. However, acquiring real-world data, especially for rare and hazardous scenarios, is prohibitively expensive and risky. While purely synthetic data offers flexibility, it often suffers from a significant reality gap due to discrepancies in visual fidelity and physics. To address these challenges, this paper proposes a novel real–virtual fusion framework for efficiently generating highly realistic augmented image datasets for autonomous driving. Our methodology leverages real-world driving data from South Korea’s K-City, synchronizing it with a digital twin environment in Morai Sim (v24.R2) through a robust look-up table and fine-tuned localization approach. We then seamlessly inject diverse virtual objects (e.g., pedestrians, vehicles, traffic lights) into real image backgrounds. A critical contribution is our focus on inconsistency mitigation, employing advanced techniques such as illumination matching during virtual object injection to minimize visual discrepancies. We evaluate the proposed approach through experiments. Our results show that this real–virtual fusion strategy significantly bridges the reality gap, providing a cost-effective and safe solution for enriching autonomous driving datasets and improving the generalization capabilities of perception models. Full article

This article proposes a strategic framework for Kyiv’s post-conflict sustainability and resilience under brittle, anxious, non-linear, and incomprehensible (BANI) conditions. We integrate adaptive governance, circular-economy reconstruction, and city-scale digital capabilities, including AI-enabled analytics, IoT sensing, and urban digital twins. Building on recent assessments of Ukraine’s reconstruction needs, we outline a socio-technical model that links sustainability and resilience objectives under shock risk and budget constraints and show how an illustrative five-year optimisation can rebalance investments toward distributed renewables and early-warning infrastructure. The example portfolio achieves an end-horizon composite performance of F_optimized(5) = 0.65 (on a 0–1 normalised index where 1 indicates achieving the policy-defined targets; 0.65 indicates ~65% progress toward those targets at year 5, improving on the baseline allocation under shocks), indicating improved robustness relative to a baseline allocation. We emphasise that effective implementation depends on secure-by-design digital architecture, participatory prioritisation of indicators and weights, and iterative monitoring that supports rapid adaptation as conditions evolve. The framework provides a pragmatic roadmap for Kyiv and similarly vulnerable cities seeking a low-carbon recovery while reducing systemic brittleness and mitigating anxiety-driven decision-making delays. Full article

Urban Digital Twins (UDTs) represent a recent application of Digital Twins (DTs), with the objective of replicating cities and providing a framework for urban planning. The utilization of UDTs provides a structured approach for the modeling and analysis of urban environments, incorporating a range of geospatial data presented in both two-dimensional (2D) and three-dimensional (3D) formats. This article details the process of processing, modeling, and integrating urban geospatial data into a Digital Twin. Two integrations for end-user platforms were demonstrated using a Geographic Information System (GIS) and an Unreal Engine (UE5) game platform. GIS-based dashboard systems provide professionals with the tools necessary to monitor, analyze, and create scenarios, thereby promoting collaboration between authorities and citizens. Game engines have the potential to play a pivotal role in the development of future UDTs by facilitating the creation of immersive experiences that aid users in comprehending their environment and promoting citizen engagement. Full article

Digital twins (DTs) are increasingly adopted to enhance the monitoring, management, and planning of urban infrastructure. While DT development for buildings is well established, applications to urban road networks remain limited, particularly in integrating heterogeneous geospatial datasets into semantically rich, multi-scale representations. This study presents a methodology for developing a BIM-based DT of urban roads by integrating geospatial data from Mobile Mapping System (MMS) surveys with semantic information from municipal geodatabases. The approach follows a multi-modal (point clouds, imagery, vector data), multi-scale and multi-level framework, where ‘multi-level’ refers to modeling at different scopes—from a city-wide level, offering a generalized representation of the entire road network, to asset-level detail, capturing parametric BIM elements for individual road segments or specific components such as road sign and road marker, lamp posts and traffic light. MMS-derived LiDAR point clouds allow accurate 3D reconstruction of road surfaces, curbs, and ancillary infrastructure, while municipal geodatabases enrich the model with thematic layers including pavement condition, road classification, and street furniture. The resulting DT framework supports multi-scale visualization, asset management, and predictive maintenance. By combining geometric precision with semantic richness, the proposed methodology delivers an interoperable and scalable framework for sustainable urban road management, providing a foundation for AI-ready applications such as automated defect detection, traffic simulation, and predictive maintenance planning. The resulting DT achieved a geometric accuracy of ±3 cm and integrated more than 45 km of urban road network, enabling multi-scale analyses and AI-ready data fusion. Full article

Increasing urbanisation and the intensification of environmental and climate challenges require a review of governance models and tools supporting urban and territorial planning. The Twin Transition concept (green and digital) requires the integration of advanced monitoring and simulation systems. In this context, Digital Twins (DTs) have evolved from static virtual replicas to dynamic urban intelligence systems. Thanks to the integration of IoT sensors and artificial intelligence algorithms, DT enables the transition from a descriptive to a prescriptive approach, supporting climate uncertainty management and real-time territorial governance. The ability to integrate multi-source data and provide high-resolution site-specific representations makes these tools strategic for planning, resource management, and the assessment of urban and peri-urban resilience. The contribution comparatively analyses different digital twin frameworks, with particular attention to their applicability in highly complex environmental contexts, such as the city of Taranto. As a Site of National Interest, Taranto requires models capable of integrating industrial pollutant monitoring with urban regeneration and biodiversity protection strategies. The study assesses the potential of DT as predictive models to support governance for more sustainable, adaptive, and resilient cities. Full article

Recent projections by the Intergovernmental Panel on Climate Change indicate that global warming will turn permanent and further intensify the severity and frequency of extreme weather events (heat waves, rain, and intense droughts), with coastal regions being the most vulnerable to extreme events. Therefore, the risk of natural disasters and the associated regional impacts on water, food, energy, social, and health security represents one of the world’s greatest challenges of this century. However, conventional methodologies for monitoring these regions during extreme events are usually not available to managers and decision-makers with the necessary urgency. The aim of this study was to present a framework concept for assessing extreme flood event impacts in coastal zones using a suite of field data combined with numerical (hydrological, meteorological, and hydrodynamic) and computational (flooding) models in a virtual environment that provides a replica of a natural environment—the Patos Lagoon Digital Twin. The study case was the extreme flood event that occurred in the southernmost region of Brazil in May 2024, considered the largest flooding event in 125 years of data. The hydrodynamic model calculated the water levels around Rio Grande City (MAE ± 0.18 m). These results fed the flooding model, which projected the water over the digital elevation model of the city and produced predictions of flooding conditions on every street (ranging from a few centimeters up to 1.5 m) days before the flooding happened. The results were further customized to attend specific demands from the security forces and municipal civil defense, who evaluated the best alternatives for evacuation strategies and infrastructure safety during the May 2024 extreme flood event. Flood Safety Maps were also generated for all the terminals in the Port of Rio Grande, indicating that the terminals were 0.05 to 2.5 m above the flood level. Overall, this study contributes to a better understanding of the strengths of digital twin models in simulating the impacts of extreme flood events in coastal areas and provides valuable insights into the potential impacts of future climate change in coastal regions, particularly in southern Brazil. This knowledge is crucial for developing targeted strategies to increase regional resilience and sustainability, ensuring that adaptation measures are effectively tailored to anticipated climate impacts. Full article

Urban Digital Twins (UDTs) are emerging as a new paradigm in smart city strategies, enabling real-time interaction with urban environments and supporting data-driven decision-making. By expanding beyond traditional smart functions, UDTs facilitate the analysis and simulation of urban resilience and sustainability indicators within a virtual city ecosystem, addressing both immediate urban challenges and long-term planning goals. This paper introduces TwinCity, a city-scale Urban Digital Twin framework developed and validated through a case study of the Green City of Benguerir, Morocco. The framework incorporates a technical architecture based on semantic 3D city models, data integration, and simulation scenarios to analyse the solar energy potential of the rooftop, the energy consumption of the building and the morphological indicators. A user-friendly web interface was developed to visualise and interact with the UDT, ensuring its accessibility. By bridging the gap between technical challenges (such as data scarcity) and practical applications, this work offers a replicable model for cities in the Global South. Full article

This paper proposes a comprehensive framework for integrating Digital Twins (DT) with real-time traffic optimization systems to enhance urban mobility management in Smart Cities. Using the Pobitno Roundabout in Rzeszów as a case study, we established a calibrated microsimulation model (validated via the GEH statistic) that serves as the core of the proposed Digital Twin. The study goes beyond static scenario analysis by introducing an Adaptive Inflow Metering (AIM) logic designed to interact with IoT sensor data. While traditional geometrical upgrades (e.g., turbo-roundabouts) were analyzed, simulation results revealed that geometrical changes alone—without dynamic control—may fail under peak load conditions (resulting in LOS F). Consequently, the research demonstrates how the DT framework allows for the testing of “Software-in-the-Loop” (SiL) solutions where Python-based algorithms dynamically adjust inflow parameters to prevent gridlock. The findings confirm that combining physical infrastructure changes with digital, real-time optimization algorithms is essential for achieving sustainable “green transport” goals and reducing emissions in congested urban nodes. Full article

Intelligent driving cabins operated by artificial intelligence technology are evolving into the third living space. They aim to integrate perception, analysis, decision making, and intervention. By using multimodal biosignal acquisition technologies (flexible sensors and non-contact sensing), it is possible to monitor the physiological indicators of heart rate and blood pressure in real time. Leveraging the benefits of domain controllers in the vehicle and edge computing helps the AI platform reduce data latency and enhance real-time processing capabilities, as well as integrate the cabin’s internal and external data through machine learning. Its aim is to build tailored health baselines and high-precision risk prediction models (e.g., CNN, LSTM). This system can initiate multi-level interventions such as adjustments to the environment, health recommendations, and ADAS-assisted emergency parking with telemedicine help. Current issues consist of sensor precision, AI model interpretation, security of data privacy, and whom to attribute legal liability to. Future development will mainly focus on cognitive digital twin construction, L4/L5 autonomous driving integration, new biomedical sensor applications, and smart city medical ecosystems. Full article

This article examines the spatial and temporal unevenness of the transport complex operation in a large city with a population of about 0.9 million people and without off-street transport. The patterns of changes in the number of passengers transported in the city are described by a harmonic model, and seasonal unevenness with different numbers of peak values is noted. All routes can be divided into three groups based on the trend in passenger volume. The largest number of routes exhibited a downward trend in passenger volume. A downward trend in passenger volume is observed in the total number of passengers on all routes, despite an increase in the city’s population. Parking occupancy rates also show seasonal fluctuations. A downward trend in paid parking occupancy rates is emerging in the city’s central administrative and business district. The results of the study are relevant for choosing methods for managing the transport behavior model. Analysis of uneven passenger numbers on individual routes is necessary for improving the route network and determining the optimal number and passenger capacity of buses. Analyzing uneven occupancy rates in paid parking lots allows for the development of differentiated rates. The methods used in this article can be integrated into a city’s digital twin to improve forecasting accuracy. Full article

Cities increasingly utilise digital technologies to tackle climate risks and urban shocks, yet their real impact on resilience remains uncertain. This paper systematically reviews 115 peer-reviewed studies (2012–2024) to explore how smart city technologies engage with planning instruments, governance arrangements, and social processes, following PRISMA 2020 and combining bibliometric co-occurrence mapping with a qualitative synthesis of full texts. Three themes organise the findings: (i) urban planning and design, (ii) smart technologies in resilience, and (iii) strategic planning and policy integration. Across these themes, Internet of Things (IoT) and geographic information system (GIS) applications have the strongest empirical support for enhancing absorptive and adaptive capacities through risk mapping, early warning systems, and infrastructure operations, while artificial intelligence, digital twins, and blockchain remain largely at pilot or conceptual stages. The review also highlights significant geographical and hazard biases: most cases come from high-income cities and concentrate on floods and earthquakes, while slow stresses (such as heat, housing insecurity, and inequality) and cities in the Global South are under-represented. Overall, the study promotes a “smart–resilience co-production” perspective, demonstrating that resilience improvements rely less on technology alone and more on how digital systems are integrated into governance and participatory practices. Full article

The paper introduces a real-time digital-twin controller that manages evacuation routes while operating GEEM for emergency energy management during building fires. The system consists of three interconnected parts which include (i) a physics-based hazard surrogate for short-term smoke and temperature field prediction from sensor data (ii), a router system that manages path updates for individual users and controls exposure and network congestion (iii), and an energy management system that regulates the exchange between PV power and battery storage and diesel fuel and grid electricity to preserve vital life-safety operations while reducing both power usage and environmental carbon output. The system operates through independent modules that function autonomously to preserve operational stability when sensors face delays or communication failures, and it meets Industry 5.0 requirements through its implementation of auditable policy controls for hazard penalties, fairness weight, and battery reserve floor settings. We evaluate the controller in co-simulation across multiple building layouts and feeder constraints. The proposed method achieves superior performance to existing AI/RL baselines because it reduces near-worst-case egress time ($T_{95}$ and worst-case exposure) and decreases both event energy $E_{\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{t}}$ and CO₂-equivalent ${{CO}_2}_{\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{t}}$ while upholding all capacity, exposure cap, and grid import limit constraints. A high-VRE, tight-feeder stress test shows how reserve management, flexible-load shedding, and PV curtailment can achieve trade-offs between unserved critical load $U_{\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}$ and emissions. The team delivers implementation details together with reporting templates to assist researchers in reaching reproducibility goals. The research shows that emergency energy systems, which integrate evacuation systems, achieve better safety results and environmental advantages that enable smart-city integration through digital thread operations throughout design, commissioning, and operational stages. Full article

Urban building energy modeling (UBEM) is crucial for assessing energy consumption patterns at the city-scale and for supporting data driven planning and decarbonization strategies. However, its practical deployment is often hindered by the need to balance detailed physics-based simulations with acceptable computation times when thousands of buildings are involved. This work presents a large-scale real world UBEM case study and proposes a workflow that combines EnergyPlus simulations, high-performance computing (HPC), and open urban datasets to model the energy consumption of the building stock in the Municipality of Bologna, Italy. Geometric data such as building footprints and heights were acquired from the Bologna Open Data portal and complemented by aerial light detection and ranging (LiDAR) measurements to refine elevations and roof geometries. Non-geometrical building characteristics, including wall materials, insulation levels, and window properties, were derived from local building regulations and the European TABULA project, enabling the assignment of archetypes in contexts where granular information about building materials is not available. The pipeline’s modular design allows us to analyze different combinations of retrofitting scenarios, making it possible to identify the groups of buildings that would benefit the most. A key feature of the workflow is the use of Leonardo, the supercomputer hosted and managed by Cineca, which made it possible to simulate the energy consumption of approximately 25,000 buildings in less than 30 min. In contrast to approaches that mainly reduce computation time by simplifying the physical model or aggregating representative buildings, the HPC-based workflow allows the entire building stock to be individually simulated (within the intrinsic simplifications of UBEM) without introducing further compromises in model detail. Overall, this case study demonstrates that the combination of open data and HPC-accelerated UBEM can deliver city-scale energy simulations that are both computationally tractable and sufficiently detailed to inform municipal decision-making and future digital twin applications. Full article

In the context of Industry 4.0, digital transformation is reshaping global energy systems. Among the key enabling technologies, Digital Twin (DT)—a dynamic, virtual replica of physical systems—has emerged as a critical tool for improving the performance, reliability, and safety of clean energy infrastructure. In line with the United Nations Sustainable Development Goals (SDGs)—particularly SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities)—the integration of DTs presents unprecedented opportunities to enhance operational efficiency and support proactive decision making. This state-of-the-art review, focused on studies published in 2020–2025, summarizes applications of DTs across the energy value chain, encompassing a broad spectrum of sectors—including solar, wind, hydropower, hydrogen, geothermal, bioenergy, nuclear, and tidal energy—and their critical role in building-to-grid integration. It synthesizes foundational concepts, assesses the evolution of the DT from a predictive tool to a system-level risk-management platform, and provides a critical analysis of its impact. Furthermore, this review discusses the key challenges hindering widespread adoption, including the critical need for interoperability across systems, ensuring the cybersecurity of socio-technical infrastructure, and addressing the complexities of the human-in-the-loop problem. Key research gaps are identified to guide future innovation. Ultimately, this study underscores the transformative potential of DTs as essential tools for accelerating the digital transformation of the energy sector, offering a robust framework for both methodological development and practical deployment. Full article

The accelerated growth of e-commerce and ongoing urban expansion have intensified the challenges associated with last-mile delivery, making it a critical issue in sustainable urban logistics. Therefore, our paper presents a scoping review to systematically delineate the current state of research on smart and sustainable last-mile delivery systems. We explore both innovative technologies—such as artificial intelligence, autonomous vehicles, the Internet of Things, and digital twins—and human-centered dimensions, including urban design, policy development, and collaborative stakeholder engagement. Using the PRISMA-ScR-based methodology, 140 peer-reviewed articles (2015–2025) have been analyzed to highlight key trends, gaps, and prospective directions. The study underlines how the technologies of Industry 4.0 have improved visibility and operational efficiency, but holistic thinking that incorporates environmental, human, and policy factors remains undeveloped. Based on these findings, this article provides a conceptual framework for smart and sustainable last-mile delivery, focusing on the intersection of digital and simulation tools and human-centric governance to achieve optimized efficiency, environmental performance, and equity. This framework helps both academics and decision-makers to advance data-driven, resilient, and integrative city logistic ecosystems. Full article