Data - and Knowledge-Driven Urban Safety Risk Evolution, Forecasting, and Early Warning

Digital Twins are becoming central enablers of Europe’s digital and green transitions, yet their data-intensive and autonomous nature exposes them to one of the most complex regulatory environments in the world. This article presents a comprehensive scoping review of how six principal European digital laws—the General Data Protection Regulation, Data Governance Act, Data Act, Artificial Intelligence Act, NIS2 Directive, and Cyber Resilience Act—jointly govern the design, deployment, and operation of Digital Twin systems. Building on the PRISMA-ScR methodology, the study constructs a Unified Digital Twin Compliance Framework (UDTCF) that consolidates overlapping obligations across data governance, privacy, cybersecurity, transparency, interoperability, and ethical responsibility. The framework is operationalised through a Digital Twin Compliance Evaluation Matrix (DTCEM) that enables qualitative assessment of compliance maturity in research and innovation projects. Applying these tools to representative European cases in Smart Cities, Industrial Manufacturing, Transportation, and Energy Systems reveals strong convergence in data governance, security, and interoperability, but also persistent gaps in the transparency, explainability, and accountability of AI-driven components. The findings demonstrate that European digital legislation forms a coherent yet fragmented ecosystem that increasingly requires integration through compliance-by-design methodologies. The article concludes that Digital Twins can act not only as regulated technologies but also as compliance infrastructures themselves, embedding legal, ethical, and technical safeguards that reinforce Europe’s vision for trustworthy, resilient, and human-centric digital transformation.

The increasing urban traffic problems have made the transportation system require a large amount of data. Aiming at the current problems of data types redundancy and low coordination rate of intelligent transportation systems (ITS), this paper proposes an improved digital twin architecture applicable to ITS. Based on the improved digital twin architecture, a framework for dynamic and static data collaboration in ITS is constructed. For various collaboration methods, this paper specifically describes the collaboration methods and scopes, and designs the framework and interfaces for data mapping. Finally, the effectiveness of the framework is verified by case studies to mine the spatiotemporal distribution characteristics of data, capture human travel characteristics, and visualize intersections using digital twins. This paper provides a new data fusion idea for digital twin systems in ITS, and the framework covers all data types in digital twin systems for cross-integration analysis.

Accurate prediction of drone motion within structured urban air corridors is essential for ensuring safe and efficient operations in Urban Air Mobility (UAM) systems. Although real-world Remote Identification (Remote ID) regulations require drones to broadcast critical flight information such as velocity, access to large-scale, high-quality broadcast data remains limited. To address this, this study leverages a Digital Twin (DT) framework to augment Remote ID spatio-temporal broadcasts, emulating the sensing environment of dense urban airspace. Using Remote ID data, we propose BiDGCNLLM, a hybrid prediction framework that integrates a Bidirectional Graph Convolutional Network (BiGCN) with Dynamic Edge Weighting and a reprogrammed Large Language Model (LLM, Qwen2.5–0.5B) to capture spatial dependencies and temporal patterns in drone speed trajectories. The model forecasts near-future speed variations in surrounding drones, supporting proactive conflict avoidance in constrained air corridors. Results from the AirSUMO co-simulation platform and a DT replica of the Cranfield University campus show that BiDGCNLLM outperforms state-of-the-art time series models in short-term velocity prediction. Compared to Transformer-LSTM, BiDGCNLLM marginally improves the R2 by 11.59%. This study introduces the integration of LLMs into dynamic graph-based drone prediction. It shows the potential of Remote ID broadcasts to enable scalable, real-time airspace safety solutions in UAM.

The expansion of Urban Air Mobility (UAM) has led to diverse aircraft designs, with piloted systems expected to evolve into remotely piloted and automated operations. Future advancements in Intelligent Transportation Systems (ITSs) will further improve automation capabilities, promising significant benefits to the environment and overall efficiency of UAM aircraft. However, UAM aircraft face unique operational conditions that need to be accounted for when assessing safety risks, such as lower operating altitudes and hazards present in urban settings, thus leading to a potential increased risk of collisions with foreign objects, particularly birds and drones. This paper reviews historical safety data with an aim to better assess the potential risks of UAM aircraft. A survey was conducted to gather quantitative and qualitative insights from subject matter experts, reinforcing findings from existing studies. The results highlight the need for a comprehensive risk assessment framework to guide design improvements and regulatory strategies, ensuring safer UAM operations.

Purpose Simulation of urban air mobility (UAM) is a growing challenge – especially considering the risks of aircraft collisions. This study shows the importance of a simulation of a large number of vertical take-off and landing (VTOL) vehicles operating in one airspace. The purpose of this study is to investigate the proper modelling and implementation of the collision algorithm between the VTOLs. This study shows an approach based on which the tool could be used for a further detailed analysis of risk assessment. Design/methodology/approach This study uses the UAM Traffic Analysis Tool, which facilitates the simulation of various VTOL traffic scenarios and the analysis of their dynamics. The approach includes input data modelling, scenario generation and the application of collision detection algorithms, such as the Foster method. Findings The results demonstrate that using metrics that account for VTOL vehicles “existence” provides more accurate estimates of collision counts and risk-prone areas. The findings also indicate that decentralised control methods may offer greater efficiency in managing large volumes of drone traffic. Originality/value This research introduces a novel simulation tool for UAM, offering valuable insights into risk management and collision prevention. The findings contribute to the advancement of safety regulations and operational strategies, paving the way for more effective air traffic management solutions in urban environments.

Intelligent Transportation Systems (ITS) have progressively incorporated machine learning to optimize traffic efficiency, enhance safety, and improve real-time decision-making. However, the traditional centralized machine learning (ML) paradigm faces critical limitations regarding data privacy, scalability, and single-point vulnerabilities. This study explores FL as a decentralized alternative that preserves privacy by training local models without transferring raw data. Based on a systematic literature review encompassing 39 ITS-related studies, this work classifies applications according to their architectural detail—distinguishing systems from models—and identifies three families of federated learning (FL) frameworks: privacy-focused, integrable, and advanced infrastructure. Three representative frameworks—Federated Learning-based Gated Recurrent Unit (FedGRU), Digital Twin + Hierarchical Federated Learning (DT + HFL), and Transfer Learning with Convolutional Neural Networks (TFL-CNN)—were comparatively analyzed against a client–server baseline to assess their suitability for ITS adaptation. Our qualitative, architecture-level comparison suggests that DT + HFL and TFL-CNN, characterized by hierarchical aggregation and edge-level coordination, are conceptually better aligned with scalability and stability requirements in vehicular and traffic deployments than pure client–server baselines. FedGRU, while conceptually relevant as a meta-framework for coordinating multiple organizational models, is primarily intended as a complementary reference rather than as a standalone architecture for large-scale ITS deployment. Through application-level evaluations—including traffic prediction, accident detection, transport-mode identification, and driver profiling—this study demonstrates that FL can be effectively integrated into ITS with moderate architectural adjustments. This work does not introduce new experimental results; instead, it provides a qualitative, architecture-level comparison and adaptation guideline to support the migration of ITS applications toward federated learning. Overall, the results establish a solid methodological foundation for migrating centralized ITS architectures toward federated, privacy-preserving intelligence, in alignment with the evolution of edge and 6G infrastructures.

Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.

Urban Air Mobility (UAM) has the potential to revolutionize urban transportation, largely with the deployment of Unmanned Aerial Vehicles (UAVs), commonly known as drones. After an initial stage focused on technology requirements, research is now shifting toward investigating operational requirements, which are unavoidably affected by urban characteristics. This study aims to explore the implementation of UAM services within urban environments by mapping the current scientific landscape from a city-focused perspective. Following a systematic search procedure, a bibliometric analysis was conducted on studies published between 2010 and 2024, examining over 350 articles that address UAM and urban-related topics. Trends in publication volume and scientific impact were analysed, along with influential manuscripts, collaborations, and leading countries in the field. Through a keyword co-occurrence analysis, five main research themes were identified: air traffic management, risk assessment, environmental factors (wind and noise), and vertiport location. These themes were further explored through a scoping review to assess current research and emerging directions. The findings highlight that urban characteristics are not just operational constraints but also fundamental elements that shape UAM strategies, influencing UAV path planning, safety, environmental constraints, and infrastructure design. Future research directions include the development of urban digital twins, comprehensive urban spatial databases, and multi-objective optimization frameworks to support the effective implementation of UAM into cities.

Digital Twins are emerging as a transformative technology within Cyber-Physical Systems (CPS), offering enhanced optimization, predictive maintenance, and real-time monitoring. However, their integration also introduces significant security challenges. These include vulnerabilities such as data breaches, unauthorized access, and cyber-attacks that disrupt real-time data flow between their physical and digital components. The involvement of IoT devices, sensors, and complex networked environments expands the attack surface, making Digital Twins susceptible to threats like Distributed Denial-of-Service (DDoS) attacks, malware infiltration, and insider sabotage. Effective risk management and assessment are crucial in identifying vulnerabilities, evaluating risks, and implementing mitigation strategies. Securing Digital Twins ensures data integrity, system reliability, and the continued functionality of the physical assets they represent. This paper aims to classify the various security threats associated with Digital Twins and propose structured risk management approaches to enhance their security within CPS. By addressing these challenges, organizations can ensure the dependability and trustworthiness of Digital Twin implementations across industries such as manufacturing, healthcare, smart cities, and IoT ecosystems.

In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

As organizations increasingly adopt cloud computing, ensuring data compliance has become a critical priority. Cloud environments pose unique challenges in meeting regulatory requirements due to their distributed and often multi-tenant nature. This review presents a comprehensive model for achieving data compliance in cloud computing, addressing essential aspects such as data privacy, security, sovereignty, and governance. The model emphasizes a structured, layered approach to compliance, integrating risk assessment, data governance, continuous monitoring, and incident management to ensure compliance throughout the data lifecycle. Key components of the proposed model include robust data privacy and protection mechanisms aligned with global regulations such as GDPR and CCPA, data security protocols for safeguarding confidentiality and access control, and data sovereignty policies to handle jurisdictional requirements and cross-border data flows. Additionally, continuous monitoring and automated auditing tools enhance real-time compliance management, while incident management procedures prepare organizations to respond promptly to breaches or policy violations. The framework also incorporates advanced technological solutions, including compliance automation, data loss prevention, and blockchain for traceability, to streamline compliance tasks and improve transparency. By addressing both the technical and governance aspects of data compliance, this model supports organizations in navigating complex regulatory landscapes across multi-cloud and hybrid environments. The review highlights best practices for ensuring compliance, such as regular policy updates, employee training, and third-party compliance management, to sustain long-term adherence. As the regulatory landscape evolves and new privacy-preserving technologies emerge, this model offers a scalable and adaptive approach, positioning organizations to manage data compliance effectively within dynamic cloud infrastructures. This comprehensive approach ensures secure, compliant data operations, fostering trust and accountability in cloud computing environments.

General Background: The financial performance of Sharia commercial banks is crucial for Indonesia's economic growth. Specific Background: This study explores the impact of Good Corporate Governance (GCG), Internal Audit, and Risk Management on the financial performance of Sharia banks, particularly after the enactment of Law No. 21 of 2008. Knowledge Gap: Existing literature shows mixed results regarding the effects of these governance factors, indicating a need for comprehensive analysis in the context of Sharia banking. Aims: The research aims to assess how GCG, Internal Audit, and Risk Management affect financial performance from 2018 to 2021. Results: Analysis of 44 Sharia banks reveals that while Internal Audit negatively affects performance, GCG and Risk Management have significant positive impacts. Novelty: This study introduces a thorough examination of Risk Management alongside GCG and Internal Audit, enhancing the understanding of their roles in Sharia banking. Implications: Findings highlight the importance of strengthening governance frameworks in Sharia banks to improve financial performance, providing insights for investors and policymakers. Highlights: Good Corporate Governance and Risk Management significantly enhance financial performance in Sharia banks. Internal Audit shows a negative impact on the financial performance, emphasizing the need for effective implementation. The study addresses a gap in literature by examining the interplay of GCG, Internal Audit, and Risk Management in the Sharia banking sector. Keywords: Good Corporate Governance, Internal Audit, Risk Management, Financial Performance, Sharia Banks

In the VUCA era, supply chain disruptions are increasingly frequent and severe, posing significant challenges to global manufacturing industries. This study investigates the application and challenges of emerging technologies, particularly digital twin (DT) technology, in supply chain risk management through a qualitative case study approach. Focusing on six manufacturing enterprises, three of which have deployed DT and three still rely on traditional models, this research aims to reveal the practical effectiveness and implementation obstacles of DT technology in risk identification, assessment, and response stages. Traditional risk management methods, often based on periodic assessments and static contingency plans, have proven inadequate in addressing sudden global crises, as exemplified by the 2023 Red Sea crisis's impact on the European automotive industry. This study employs document analysis of enterprise risk reports, audit records, and emergency response plans to demonstrate how DT technology can transform risk management into a proactive, predictive strategy. The findings show that DT technology enhances supply chain resilience by enabling real-time risk perception, dynamic simulation, and automated response. However, the adoption of DT technology also faces challenges such as organizational division, financial barriers, and human resistance to change. This research provides actionable guidelines for enterprises to navigate the complex path of digital transformation and offers a low-tech threshold risk management upgrade path, especially for small and medium-sized manufacturers.

Data Fusion for ITS: Techniques and Research Needs

IntroductionDigital twins can accelerate sustainable development by leveraging big data and artificial intelligence to simulate state, reactions and potential developments of physical systems. In doing so, they can create a comprehensive basis for data-driven policy decisions. One of the purposes of digital twins is to facilitate the implementation of the EU's Green Deal-in line with internationally binding climate and environmental targets. One prerequisite for the success of digital twins is a comprehensive, high-quality database. This requires a suitable legal framework that ensures access to such data.MethodsApplying a qualitative governance analysis, the following article examines if the EU's strategies and legal acts on data governance are paving the way for digital twin projects which promote sustainability.ResultsResults show important starting points for open and fair data use within the growing field of EU digital law. However, there is still a lot of progress to be made to legally link the use of digital twins with binding sustainability objectives.