Search Results (970)

To address peak-period congestion in high-density air route networks and the high cost and limited precision of traditional global control methods, this study proposes a congestion mitigation method based on pinning control theory. First, a comprehensive evaluation index system for critical waypoints is constructed from complex-network structural characteristics, traffic flow characteristics, and congestion-state information. Pearson correlation analysis is used to examine redundancy among candidate indicators, and the entropy-weighted TOPSIS method is then employed to evaluate waypoint importance and identify critical pinning nodes. Second, a GA-PID pinning control optimization model is established to realize closed-loop optimization of network congestion by dynamically regulating a small number of critical nodes. Finally, simulation experiments are conducted using actual operational trajectory data from the Yangtze River Delta airspace. The results show that the proposed method reduces the network congestion coefficient from 176 to 137, representing a decrease of 22.16%, and increases airspace resource utilization from 70.76% to 84.41%, representing an improvement of 19.29%. Compared with the baseline GA method, the proposed method achieves better optimization performance and requires adjustments at only 13 waypoints, whereas the baseline GA method requires adjustments at 25 waypoints, demonstrating lower control costs and higher regulation efficiency. Full article

Receptor modeling of ambient pollutant concentrations plays a central role in urban air quality assessments. This study proposes an integrated framework combining Self-Organizing Maps (SOM), Positive Matrix Factorization (PMF), and Time-Series Analysis (TSA) for a comprehensive evaluation of urban air pollution patterns and source dynamics. The methodology was applied to multi-annual air quality and meteorological datasets (2009–2018) from two major Bulgarian cities, Plovdiv and Varna. The SOM was used for assessing the overall parameter patterns of the cities, leading to a clear clustering of the site samples on the map. Thus, PMF was run separately for the two sites, identifying a different number of sources (three and four, respectively). Traffic-related and sulfur-rich combustion sources were identified in both cities, while a crustal/resuspended dust factor was observed only in Varna. TSA revealed distinct temporal behaviors among source types. Traffic-related aerosol contributions decreased in both cities (−5.14% yr⁻¹ in Plovdiv; −9.30% yr⁻¹ in Varna), whereas sulfur-rich combustion factors showed increasing trends (+4.64% yr⁻¹ and +2.97% yr⁻¹, respectively). Traffic fresh exhaust factors exhibited pronounced seasonal variability and significant weekday–weekend differences in both cities. The integrated SOM–PMF–TSA framework enhanced source interpretability and temporal characterization, providing a robust approach for urban air quality assessment and supporting targeted air pollution management strategies. Full article

Periodic time-series forecasting is central to 6G-enabled urban digital twins, where both cellular traffic management and environmental sensing demand accurate predictions over recurring diurnal and weekly regimes. Contrastive self-supervised learning has emerged as a promising approach for learning temporal representations, yet when applied to such periodic data, it suffers from class collision: temporally distant but semantically similar recurrent patterns are pushed apart as false negatives. We propose Clustering-Enhanced Contrastive Learning (CECL), which couples temporal contrastive learning with a clustering regularizer that maintains a Gaussian mixture structure over the latent space, preserving global periodic structure while retaining local discriminability. We evaluate CECL on five datasets across three tracks spanning two domains: cellular traffic forecasting (Milan CDR, 20 cells), multi-site hourly air-quality forecasting (Beijing Multi-Site, 12 stations; QUANT, 3 cities), and daily air-quality forecasting (Haikou and Taizhou). Across all tracks, CECL consistently outperforms supervised and contrastive baselines, reducing RMSE by 3–$10\%$ relative to the strongest contrastive competitor (CoST). These results demonstrate that clustering-guided contrastive regularization yields robust gains for periodic forecasting in both 6G network management and environmental sensing scenarios. Full article

Traditional DEA models can neither effectively handle fuzzy random variables nor achieve a complete ranking of decision-making units (DMUs). Based on the conventional fuzzy stochastic DEA model, this study introduces an exponential distribution extension. By incorporating fuzzy random variables, it significantly simplifies the deterministic transformation of chance-constrained models. Moreover, most existing DEA ranking methods only consider the relative efficiencies among DMUs while ignoring their internal structural characteristics. To address this issue, we develop a deterministic model for the exponentially extended fuzzy stochastic DEA and design a weight formula that reflects the internal input–output structure of each DMU. This approach makes the complete ranking of DMUs more reasonable and better aligned with practical situations. Finally, the rationality and effectiveness of the proposed model are verified through a comparative analysis of rankings obtained from different DEA models. The results indicate that the input–output structure within a decision-making unit plays a significant role in its efficiency ranking. Full article

Sustainable transport management (STM) has become an increasingly important issue in recent years, as cities have faced growing traffic congestion, air pollution, and other transport-related challenges. Travel time (TT) represents one of the critical determinants of Quality of Service (QoS) and user satisfaction in public passenger transport (PPT). TT extends beyond in-vehicle duration and encompasses a sequence of temporal components, including access, waiting, transfer, and egress times. TT reflects the complexity of an integrated passenger transport system (IPTS), where users experience transport services as a door-to-door journey rather than isolated trips. This article analyses the TT within IPTSs through the lens of European quality standards EN 13816 and EN 15140 for PPT. Standard EN 13816 provides a normative framework for defining TT as a key QoS criterion reflecting user expectations and a user-oriented perspective, while standard EN 15140 operationalises this framework by specifying methodological requirements for the measurement and evaluation of the delivered TT quality at system-level performance objectives. This research highlights a structural gap between the conceptualisation of TT as a door-to-door journey, a user-oriented phenomenon, and its measurement through fragmented, mode-specific performance metrics. It limits the ability of transport authorities and operators to accurately evaluate the QoS and to design efficient urban mobility (UM) systems. Full article

Artificial Intelligence (AI) is emerging as a transformative enabler in aviation, with applications spanning Guidance, Navigation and Control (GNC), Air Traffic Management (ATM), and predictive maintenance. However, the adoption of AI in safety-critical domains remains constrained by the absence of established certification guidance. Traditional standards such as Aerospace Recommended Practice (ARP), ARP4754B, ARP4761A, DO-178C, and DO-254 assume deterministic behaviour and verifiable logic, whereas AI exhibits adaptive and non-deterministic characteristics. Regulatory initiatives, including the European Union Artificial Intelligence Act, the European Union Aviation Safety Agency (EASA) AI Roadmap 2.0, the Federal Aviation Administration (FAA) AI Safety Assurance Roadmap, and ISO/IEC Technical Report (TR) 5469:2024, signal progress but remain fragmented, exploratory, and often limited to low-level autonomous use cases. This study adopts a qualitative approach combining literature and standards analysis with expert interviews to identify gaps in post-deployment assurance, data governance, explainability, and accountability. A conceptual life cycle-oriented framework is proposed that embeds AI-specific assurance activities such as dataset validation, iterative verification, drift detection, and retraining oversight into established certification processes. The framework extends classical and emerging verification and validation models into operational service, linking machine learning constituents to system-level safety arguments and regulatory expectations to support the development of trustworthy and certifiable AI-enabled aviation systems. Full article

Increased flight volumes necessitate urgent reforms in Airspace Management (ASM) to mitigate risks of fatalities and near-misses. In order to enhance aviation system safety, the International Civil Aviation Organization (ICAO) mandates that state parties must conduct the Universal Safety Oversight Audit Program (USOAP) to continuously monitor civil aviation. This research aims to identify critical factors influencing Thailand’s ASM by employing experimental design and Structural Equation Modeling (SEM) to analyze influences and relationships among communication, surveillance, navigation, Air Traffic Management (ATM), and ASM. The methodology includes stimulation and a questionnaire-based survey conducted with aviation professionals and mapping out their answers to find the influences, relationships, and importance of the different factors. The results were validated using various statistical tools. The findings indicate signi1ficant direct and indirect effects on ASM, emphasizing that effective communication and robust surveillance are essential for safety and operational efficiency. This study highlights the need to increase the ASM framework, providing actionable insights for optimizing air traffic control in response to the growing air traffic demand. Furthermore, SEM for Airspace optimization can be applied internationally to significantly reduce accidents and incidents in the future. Full article

Runway incursions (RIs) have emerged as a major threat to airport surface safety, driven by the coupled influence of human, equipment, environmental, and management factors. Conventional assessment methods struggle to simultaneously capture the fuzziness of expert linguistic judgment and the randomness of operational conditions. This study proposes an integrated DEMATEL–Cloud–TOPSIS framework for runway incursion risk assessment and validates it at Chengdu Tianfu International Airport. A hierarchical indicator system comprising 24 indicators across four dimensions—Human (H), Equipment (M), Environment (E), and Management (G)—was constructed from 90 RI cases collected between 2018 and 2023. DEMATEL quantified inter-indicator causal dependencies and DEMATEL-derived weights; the Cloud model translated linguistic expert judgments into digital characteristics (Ex, En, He); and TOPSIS produced relative closeness coefficients for risk ranking. Human, equipment, and environmental risks are all at a medium-risk, while management risk is at a low-risk, but significant differences still exist. Management achieved the highest closeness ($C_i$ = 0.6322) and Environment the lowest ($C_i$ = 0.5096). At the indicator level, ATC Instruction Accuracy (H1) exhibited the greatest operational maturity ($C_i$ = 0.9119), whereas Unclear Crew Coordination (H6) showed the lowest relative closeness ($C_i$ = 0.0156), followed by Aircraft Equipment (M5) ($C_i$ = 0.0195). Meanwhile, Runway Configuration Complexity (E2) remained a weak structural factor within the Environmental dimension ($C_i$ = 0.1502). The framework provides an interpretable, quantitative basis for targeted safety management at complex hub airports. Full article

The remote tower operation mode has changed how controllers acquire, integrate, and interpret operational information, making Situation Awareness (SA) prediction more challenging because of the coupling of multiple heterogeneous information sources. To address the limitations of existing physiological-data-based studies in modeling cross-modal relationships and deep multimodal interactions, this study proposes MGRF-Net, a multimodal physiological data-driven model for predicting remote tower controllers’ SA. The model first encodes eye-tracking, electroencephalography, electrocardiography, and electrodermal activity signals independently to obtain high-level temporal representations. A graph attention-enhanced relational learning module is then introduced to capture interactive dependencies among modalities, followed by a dual-branch gated fusion mechanism to adaptively integrate multimodal information and improve prediction stability. Using multimodal physiological data collected from a remote tower simulation experiment and evaluated with 12-fold cross-validation, MGRF-Net achieved 0.0658 RMSE, 0.0461 MAE, 0.8579 R², and 0.9308 PCC, outperforming LightGBM, MLP, PatchTST, iTransformer, and TimeMixer. Ablation experiments and SHAP analysis further confirmed the effectiveness and interpretability of the proposed model. The results indicate that MGRF-Net can effectively capture cross-modal coupling patterns in the formation of controllers’ SA and provides a promising approach for complex cognitive state monitoring and intelligent assistance in remote tower operations. Full article

The aircraft landing problem (ALP) poses significant challenges for traditional Monte Carlo Tree Search (MCTS) due to its vast search space and reliance on inefficient random simulations. To overcome these limitations, this paper proposes a novel Transformer-Augmented Monte Carlo Tree Search (TMCTS) algorithm. Our approach integrates a reinforcement learning framework that incorporates key operational constraints, including wake turbulence separation and time windows, and employs a cost function aimed at minimizing both delay time and fuel consumption. A core innovation is the replacement of the conventional random simulation phase in MCTS with a Transformer-based value predictor. This leverages the Transformer’s superior ability to model sequences and capture global dependencies among flights, thereby dramatically accelerating search convergence. Specifically, we designed a two-head Transformer network (comprising policy and value heads) to provide informed prior knowledge, which effectively guides the selection and expansion steps of the MCTS tree. The model is trained within an Actor–Critic framework, utilizing behavior cloning for pre-training followed by reinforcement learning for fine-tuning. Experimental evaluations on the standard OR-Library benchmark demonstrate that our TMCTS method significantly reduces scheduling deviation compared to state-of-the-art baselines (including FCFS, DPALO+GA, DPALO+PSO, and CPLEX). Moreover, it achieves a 93.7% reduction in computation time relative to the CPLEX method, highlighting its superior efficiency and practical applicability for real-time scheduling. Full article

To detect the fatigue status of air traffic controllers before duty and better manage on-the-job fatigue risk, this study proposes a convenient and effective pre-shift fatigue assessment method. A cohort of controller cadets was examined, using average reaction time, the standard deviation of reaction time, and the fastest 10% reaction time from a psychomotor vigilance test (PVT) as indicators of fatigue. These indicators were combined with self-reported MFI-16 fatigue scale scores to establish a quantitative relationship between fatigue level and each metric, allowing calculation of a comprehensive fatigue index for each cadet. This quantified fatigue index was then fitted against control-aptitude test scores to develop a regression model. An experimental condition involving 24 h of sleep deprivation was used to generate data for model development and validation. Results showed strong correlations between PVT metrics (average reaction time, reaction time variability, and fastest 10% reaction time) and fatigue scale scores. The resulting fatigue index model demonstrated good agreement between predicted and measured control-aptitude test scores. This study provides a theoretical foundation for a practical fatigue detection and early-warning method for air traffic controllers, offering significant value for reducing safety risks and enhancing civil aviation safety. Full article

Ensuring the safety of sustainably managed medicinal plants is closely linked to the quality of plant raw materials, including the presence of heavy metals within safe limits. Sustainable management in the context of herbal raw materials therefore entails responsible management of herbal plant resources, integrating environmental protection with ensuring long-term economic profitability. The aim of this study was to analyze selected biochemical parameters and to determine metal concentrations in soils and leaves of Plantago lanceolata L. collected from natural habitats at increasing distances from traffic routes. The content of Zn, Cu, Ni, and Pb was determined in the soils and leaves of Plantago lanceolata L. Assessing the content of these elements in plant raw materials allows for: the prevention of harmful substances in final products, adaptation of raw materials to applicable safety standards (avoiding toxicity), and protection of consumer health. This promotes sustainable development by building a safe supply chain. The leaves of Plantago lanceolata L. were also tested for biochemical enzymatic (catalase (CAT) and superoxide dismutase (SOD)) and non-enzymatic (chlorophyll a and b (Chl a and b), carotenoids (Car), ascorbic acid (AAC)), and mechanisms regulating the activity of reactive oxygen species (ROS) were determined in the leaves of Plantago lanceolata L. Based on the results of leaf pH, relative water content (RWC), ascorbic acid content, and total chlorophyll content, the air pollution tolerance index (APTI) was calculated. The distance from the road has a significant impact on the concentration of the heavy metals analyzed. The soils were found to be free of Zn, Cu, Pb, and Ni contamination. However, analysis of Plantago lanceolata L. leaves revealed exceedances of acceptable lead limits for herbal plants. The content of pigments, the ratio of Chl a/b, and Chl (a + b)/Car in the leaves of Plantago lanceolata L. was significantly dependent on the distance from the road. The activity of CAT and SOD in the leaves of Plantago lanceolata L. growing closest to the road was significantly higher compared to the others. APTI values suggest that Plantago lanceolata L. exhibits sensitivity to pollution, independent of its distance from the emission source. Full article

Urban air pollution in Skopje, a city with complex topography, is strongly influenced by traffic emissions, household heating, industrial activities, and meteorological conditions, leading to pronounced spatial and seasonal variability. The objective of this study is to assess the contribution of major urban emission sources to air quality in Skopje, with a focus on traffic pollution, and to quantify their seasonal influence on NO₂, PM₁₀, and PM_2.5 concentrations using a high-resolution urban dispersion modelling approach. The methodology is based on the ADMS-Urban dispersion modelling system, integrating traffic activity data as line sources, together with area sources representing household heating, point sources representing industrial facilities, and seasonally representative meteorological data. Model performance was evaluated through comparison with measurements from official urban monitoring stations. The results show that the model successfully reproduces the observed spatial gradients and seasonal trends of NO₂, PM₁₀, and PM_2.5 concentrations across the urban area. Source contribution analysis indicates that household heating dominates particulate matter pollution throughout the year, while traffic and industrial combustion are the main contributors to NO₂. The isolated traffic contribution exhibits clear seasonal variability, with the highest concentrations occurring during winter due to reduced atmospheric dispersion and increased traffic-related emissions. The model is primarily suitable for assessing spatial patterns and relative source contributions rather than accurate prediction of absolute concentration levels, due to the use of aggregated Tier 1 emission factors. The study confirms that physically based urban dispersion modelling provides a robust framework for identifying pollution hotspots, quantifying traffic contributions, and supporting targeted air quality management strategies in Skopje. Full article

This paper offers a critical review of cutting-edge research on multi-energy microgrids (MEMs), with a novel exploration of their potential role in supporting urban air mobility (UAM), specifically electric vertical takeoff and landing (eVTOL) aircraft. While extensive research has focused on improving the economic performance and emission reductions of MEMs, particularly in the context of electric vehicle (EV) charging, there remains a significant gap in understanding how microgrids can support the decarbonization of UAM. The paper examines the opportunities and challenges of integrating microgrids with UAM operations, highlighting the need for more research to optimize energy management systems that balance renewable energy use with the growing demand for aerial transport. Thermal energy storage systems are emphasized as a critical component for addressing transportation energy needs, offering a promising solution to reduce carbon emissions while enhancing system efficiency. This review aims to provide new insights into how the coupling of microgrids and UAM can contribute to the development of economically and environmentally sustainable smart cities. Full article

Sensor data are increasingly used in monitoring spatiotemporal phenomena for diverse applications such as flood management, urban traffic, air quality control, forest fire management, etc. Real-time modelling and representation of such evolving phenomena is fundamental for efficient and near-real-time decision-making processes. In addition to simple and local alerts about occurring changes over time at a given location, as is the case in Sensor Event Service (SES), the decision-making process may require more global spatial information, such as knowing if the monitored phenomenon is expanding or contracting around a given spot or if it is moving from one spot to another, especially for non-punctual spatial features. For such cases, spatiotemporal information should be computed over the whole set of distributed data from which the geometry of monitored phenomena can be assessed. This paper proposes an event-driven fuzzy rule-based decentralized spatial reasoning approach to compute spatiotemporal changes occurring in vague shape phenomena from distributed sensor data streams. Inferring local and partial spatial changes from individual nodes over the sensor network is prior to the computation of developing changes that the monitored phenomenon undergoes over the whole area covered by the sensor network. In this approach, we suggest a Fuzzy-Extended Spatiotemporal Change Pattern (FESTCP) to compute spatiotemporal changes about fuzzy regions. To evaluate our method, simulated case studies of ambient air pollution in Quebec City are carried out. The results reveal that the proposed method could provide satisfactory information about spatiotemporal changes in real-world phenomena monitored by a sensor network for a real-time decision-making process. Full article