Search Results (588)

The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents an Internet of Things (IoT)-based architecture integrating heterogeneous sensing with edge–cloud orchestration and AI-driven control for green routing and coordinated Electric Vehicle (EV) charging. The framework supports adaptive traffic management, energy-aware charging, and multimodal integration through standards-aware interfaces and auditable Key Performance Indicators (KPIs). We hypothesize that, relative to a static shortest-path baseline, the integrated green routing and EV-charging coordination reduce (H1) mean travel time per trip by ≥7%, (H2) CO₂ intensity (g/km) by ≥6%, and (H3) station peak load by ≥20% under moderate-to-high demand conditions. These hypotheses are tested in Simulation of Urban MObility (SUMO) with Handbook Emission Factors for Road Transport (HBEFA) emission classes, using 10 independent random seeds and reporting means with 95% confidence intervals and formal significance testing. The results confirm the hypotheses: average travel time decreases by approximately 9.8%, CO₂ intensity by approximately 8%, and peak load by approximately 25% under demand multipliers ≥1.2 and EV shares ≥20%. Gains are attenuated under light demand, where congestion effects are weaker. We further discuss scalability, interoperability, privacy/security, and the simulation-to-deployment gap, and outline priorities for reproducible field pilots. In summary, a pragmatic edge–cloud IoT stack has the potential to lower congestion, reduce per-kilometer emissions, and smooth charging demand, provided it is supported by reliable data integration, resilient edge services, and standards-compliant interoperability, thereby contributing to sustainable urban mobility in line with the objectives of SDG 11 (Sustainable Cities and Communities). Full article

Over several decades, childhood asthma has emerged as a significant global public health concern, with the highest prevalence reported in industrialized countries. The rapid rise in asthma incidence and loss of control when the diagnosis is established can be related to environmental and lifestyle changes, especially during early infancy. Current evidence indicates a potential link to an imbalance in immune system responses, influenced by tobacco smoke, traffic-related air pollution, outdoor and indoor allergens, gut microbiome, viral infection, obesity, sedentary lifestyle and dietary patterns. This narrative review aims to explore the landscape of contemporary environmental risk factors for childhood asthma, with a focus on their interplay and the relative importance. Full article

Indoor environmental quality in offices, comprising thermal, acoustic, lighting, and air quality domains, is known to influence human comfort, yet the relative importance of their sub-factors—particularly in severe cold regions—remains unclear. This study addresses this gap by integrating objective (Criteria Importance Through Intercriteria Correlation, CRITIC) and subjective (Analytic Hierarchy Process, AHP) weighting methods, supported by field measurements and questionnaire surveys in open-plan offices in three provinces in northeastern China. Cluster analysis categorized acoustic sub-factors into outdoor traffic, outdoor entertainment, people conversation, burst sound, and people movement. Results show that temperature is the dominant thermal comfort driver (39.7% CRITIC; 45.5% AHP), exceeding air velocity and humidity, which had nearly equal influence. Indoor sound exerted greater impact than outdoor sound, with people conversation ranked highest among indoor noise sources, and burst sound and movement showing similar but slightly lower weights. Natural light outweighed artificial light in importance (54.2% CRITIC; 61.0% AHP), while air freshness and pollution were nearly equally influential. Compared to CRITIC, AHP produced more dispersed weights, reflecting subjective bias toward pronounced differences. These findings provide a quantitative basis for prioritizing environmental design interventions—such as controlling indoor conversational noise, optimizing natural lighting, and stabilizing temperature—to enhance comfort in offices in severe cold regions. Full article

The complexity and magnitude of airspace operations are ever increasing, which creates new challenges for air traffic controllers. With the increase in the volume of operations, the size of available data is also increasing. Data-driven AI solutions can provide actionable information for complex decision-making processes that controllers face and assist them in improving the efficiency and safety of operations. However, for such solutions to be trusted by the users and stakeholders, they need to undergo a comprehensive validation process. In this paper, the literature in the development of responsible AI is studied and a subset of the framework is applied to an AI tool proposed for airport runway configuration management. The focus of this study is tackle two main challenges: (1) detection and mitigation of existing bias in the training data and the trained AI tool; and (2) quantification and improvement of the AI tool’s robustness to potential sources of noise in the data. We validate several responsible AI techniques using historical data and simulation studies on three major US airports and quantify their effectiveness in reducing the detected bias and also improving the robustness of the model to adversarial noise in the input data. Full article

With the continuous growth of air transportation demand, air traffic congestion in the Terminal Area has become increasingly serious. In order to assist controllers in efficiently alleviating the traffic congestion situation in the Terminal Area, this paper takes aircraft trajectory adjustment and flow control from the perspective of the Terminal Area as a starting point and proposes a congestion relief strategy based on a complex network and multi-objective optimization theory. First, a Terminal Area traffic network model is established with the approach point, departure point, waypoint, and navigation station as nodes and the flight path as edges. Next, a multi-objective optimization model that takes into account both congestion relief and reduced operating costs is constructed. Finally, an improved ant colony optimization is proposed to solve this optimization model and provide a unified approach to path planning for multiple aircraft. Finally, simulation experiments were conducted based on the airspace structure and operation of the Beijing Terminal Area. At the same time, ablation experiments were designed to compare the method in this paper with other ant colony optimizations. The experimental results show that the path planning results of the improved ant colony optimization can better alleviate the traffic congestion situation in the Terminal Area, converge faster, and reduce the risk of falling into a local optimum. Full article

An analysis of air traffic complexity in multi-airport terminal areas can assist air traffic controllers in accurately assessing the air traffic situation and collaboratively managing air traffic flows, thereby enhancing the utilization of airspace resources and reducing flight delays. This paper proposes an air traffic complexity analysis method for multi-airport terminal areas based on a route segment–flight state interdependent network. The interdependent network model consists of an upper-layer flight state network, a lower-layer route segment network, and inter-layer coupling edges. The upper-layer network is constructed with aircraft as nodes and flight conflicts between aircraft as edges. The lower-layer network uses route segments as nodes and the connectivity between route segments as edges. The inter-layer coupling edges are determined by evaluating the relationship between aircraft and route segments—if an aircraft is on a specific route segment, a coupling edge exists between the corresponding aircraft node and route segment node. Based on this model, node-level complexity metrics are established to analyze the importance and complexity of individual route segments. Additionally, network-level complexity metrics are introduced to assess the overall air traffic complexity in multi-airport terminal areas. Finally, the method proposed in this paper is validated using flight scenarios in the Guangdong–Hong Kong–Macao Greater Bay Area. By comparing and analyzing the results with the actual situation, it is shown that the proposed method can accurately assess the air traffic complexity in multi-airport terminal areas. Full article

This study investigates the atmospheric formation and sinks of HONO and HNO₃ and their contribution to secondary PM_2.5 formation in Daejeon (urban) and Iksan (suburban), South Korea. Continuous observations revealed distinct concentration patterns: Iksan exhibited elevated ammonia and nitrate levels associated with agricultural activities and biomass burning, while Daejeon showed higher NOx concentrations driven by traffic and industrial sources. Positive Matrix Factorization (PMF) analysis indicated that secondary formation was the dominant contributor to PM_2.5 at both sites, with biomass burning exerting an additional influence in Iksan. Among observed precursors, HNO₃ showed the highest conversion to aerosol nitrate, highlighting aerosol-phase reactions as its primary sink, followed by dry deposition. Seasonal analysis demonstrated that HONO loss was largely controlled by photolysis in summer. Externally transported aerosols contributed more than locally formed particles at both sites, emphasizing the role of regional background pollution. These findings provide a scientific basis for region-specific air quality strategies that combine local precursor control with the management of long-range transport. Full article

Aircraft trajectory prediction (ATP) is a critical technology for air traffic control (ATC), safeguarding aviation safety and airspace resource management. To address the limitations of existing methods—kinetic models’ susceptibility to environmental disturbances and machine learning’s lack of physical interpretability—this paper proposes a Spatial–Temporal Physics-Constrained Multilayer Perceptron (STPC-MLP) model. The model employs a spatiotemporal attention encoder to decouple timestamps and spatial coordinates (longitude, latitude, altitude), eliminating feature ambiguity caused by mixed representations. By fusing temporal and spatial attention features, it effectively extracts trajectory degradation patterns. Furthermore, a Hidden Physics-Constrained Multilayer Perceptron (HPC-MLP) integrates kinematic equations (e.g., maximum acceleration and minimum turning radius constraints) as physical regularization terms in the loss function, ensuring predictions strictly adhere to aircraft maneuvering principles. Experiments demonstrate that STPC-MLP reduces the trajectory point prediction error (RMSE) by 7.13% compared to a conventional optimal Informer model. In ablation studies, the absence of the HPC-MLP module, attention mechanism, and physical constraint loss terms significantly increased prediction errors, unequivocally validating the efficacy of the STPC-MLP architecture for trajectory prediction. Full article

Air traffic control is widely recognized as one of the most psychologically demanding occupations, where safety and operational performance are tightly interwoven. This study explores the relationship between occupational health and safety (OHS) systems and the psychological well-being of Air Traffic Controllers (ATCOs), focusing on factors such as perceived safety, job satisfaction, stress, and mental strain. Conducted within the Hellenic Air Navigation Services Provider, the research adopts a cross-sectional design using a structured questionnaire distributed nationwide. The study draws upon the Job Demands–Resources (JD-R) theoretical framework to examine how organizational resources, such as training, clarity of role, supervisor support, and employee autonomy, interact with job demands to shape occupational outcomes. Special emphasis is placed on understanding how the presence and awareness of structured OHS systems influence ATCOs’ perceptions of safety and well-being. Our analysis indicated that participants who reported the presence of an OHS system perceived their workplace as significantly safer than those who reported no such system. Hence, the existence of structured measures allows ATCOs to feel confident in their work environment. By assessing a range of psychosocial and organizational variables, this study seeks to contribute to both the academic discourse and practical strategies aimed at improving safety culture and psychological resilience in high-risk work environments. The findings are intended to inform policy development and organizational practices within the air traffic control sector and other safety-critical domains. Full article

PM_2.5 pollution is still serious in densely populated cities with frequent traffic activities, and it continues to threaten public health. Therefore, it is urgent that we obtain ultrahigh-resolution data that can reveal its complex spatiotemporal variation characteristics, supporting more refined environmental governance and health risk prevention and control. This study first carried out ground monitoring based on low-cost sensors combined with observation results, which were corrected with the national environmental monitoring station data. This study also introduced multi-source auxiliary variables and constructed a machine learning model through the stacking ensemble learning method. The model combines corrected low-cost sensor data with high-resolution prediction factors to achieve ultrahigh-spatiotemporal-resolution prediction of PM_2.5 at 100 m × 100 m spatial resolution and hourly temporal resolution. The results show that the constructed model shows good prediction ability in 5-fold cross validation, with an overall R² of 0.93 and a root mean square error (RMSE) of 3.09 μg/m³. The spatiotemporal analysis based on the prediction results further revealed that the PM_2.5 concentration in the city showed significant variation characteristics at both the ultra-local scale and the short-term scale, reflecting the high heterogeneity of urban air pollution. In addition, by comparing and analyzing the monitoring data of a national environmental monitoring station that were not used in the correction, it was found that the corrected low-cost sensor data significantly reduced the prediction uncertainty, reducing the RMSE from 72.068 μg/m³ to 16.759 μg/m³, verifying its effectiveness in high spatiotemporal resolution air quality monitoring. This shows that low-cost sensors are expected to make up for the problem of insufficient spatial coverage in traditional national environmental monitoring stations, supporting the successful assessment of urban-level air pollution and health risk management, and therefore having broad application prospects. Full article

Unsafe behavior among air traffic controllers is a significant causal factor in civil aviation safety incidents. To explore the risks and pathways associated with controller-induced aviation accidents, this study develops an analytical model of controller unsafe behavior based on association rules and fault tree Bayesian networks. First, the Human Factors Analysis and Classification System (HFACS) was applied to identify and categorize aviation incident reports attributed to controller errors. Next, association rule algorithms were employed to uncover potential associations between controller unsafe behaviors and related risk factors, and a fault tree Bayesian network (FT-BN) model of controller unsafe behaviors was constructed based on these associations. The results revealed that the most likely unsafe behaviors were: improper allocation of aircraft spacing (30.5%), failure to take necessary intervention measures (28.4%), and improper transfer of control (27.8%). Backward analysis of the FT-BN indicated that improper allocation of aircraft spacing was most likely triggered by failure to provide adequate controller training, failure to take necessary intervention measures was most often caused by forgotten information, and improper transfer of control was most frequently associated with controller fatigue and failure to put risk management efforts in place. This study provides an important framework for the analysis and evaluation of controller behavior management and offers key insights for improving air traffic safety. Full article

Background/Objectives: Following the recent publication of reassuring outcomes from the ARA MED 330 protocol regarding long-term insulin use in pilots, combined with continuous advancements in diabetes technology, European aeromedical examiners are increasingly optimistic about establishing more flexible medical requirements for insulin-treated aviation professionals. These professionals have historically been considered unfit for duty due to hypoglycemic risks. According to current research, hypoglycemia, the primary incapacitation risk for flight crew, is considered virtually non-existent among air traffic controllers (ATCOs). Additionally, stress-induced hyperglycemia also represents a low-frequency risk in these professionals, who are experienced in managing highly stressful operational environments. This study presents a narrative review examining stress and its metabolic effects in healthy individuals, ATCOs, and people with diabetes (PwD). Methods: This narrative review was conducted based on a comprehensive PubMed search performed by two independent reviewers (GAR and AM) spanning January 2023 to January 2025. The search strategy focused on English-language, peer-reviewed studies involving human participants and addressed stress, glucose regulation, and occupational factors in ATCOs and people with diabetes. Additional relevant articles were identified through reference screening. A total of 33 studies met the inclusion criteria. Studies focusing solely on oxidative or molecular mechanisms were excluded from the analysis. Results: Stressful events consistently triggered the expected hyperglycemic reaction in both healthy individuals and PwD. However, the literature indicates ATCOs demonstrate remarkable stress resilience and adaptation to the demanding conditions of their work environment, suggesting a unique occupational profile regarding metabolic stress responses. Conclusions: These findings contribute valuable insights to ongoing discussions regarding aeromedical fitness standards. The evidence suggests that ATCOs may not face the same metabolic risks as flight crews, indicating that current medical certification processes for insulin-treated aviation professionals warrant reconsideration in light of this emerging evidence. This research supports the potential for more individualized, occupation-specific aeromedical standards that better reflect the actual risk profiles of different aviation roles. Full article

Fatigue in air traffic controllers (ATCOs), particularly within remote tower operations, poses a substantial risk to aviation safety due to its detrimental effects on vigilance, decision-making, and situational awareness. While electroencephalography (EEG) provides a promising avenue for objective fatigue monitoring, existing models often fail to adequately capture both the spatial dependencies across brain regions and the temporal dynamics of cognitive states. To address this challenge, we propose a novel EEG-based fatigue detection framework, Spatio-Temporal Graph with Center Loss Network (STG-CLNet), which jointly models topological brain connectivity and temporal EEG evolution. The model leverages a multi-stage graph convolutional network to encode spatial dependencies and a triple-layer LSTM module to capture temporal progression, while incorporating center loss to enhance feature discriminability in the embedding space. We constructed a domain-specific EEG dataset involving 34 ATCO participants operating in high- and low-traffic remote tower simulations, with fatigue labels derived from three validated subjective metrics. Experimental results demonstrate that STG-CLNet achieves superior classification performance (accuracy = 96.73%, recall = 92.01%, F1-score = 87.15%), outperforming several strong baselines, including LSTM and EEGNet. These findings underscore the potential of STG-CLNet for integration into real-time cognitive monitoring systems in air traffic control, contributing to both theoretical advancement and operational safety enhancement. Full article

The metropolitan agglomeration in and around Bucharest, Romania’s capital and largest city, has experienced significant growth in recent decades, both economically and demographically. With over two million residents in its metropolitan area, Bucharest faces urban mobility challenges characterized by congested roads, overcrowded public transport routes, limited parking, and air pollution. This study evaluates the potential of AI-driven adaptive traffic signal control to address these challenges using an agent-based simulation approach. The authors focus on Bucharest’s north-western part, a critical congestion area. A detailed road network was derived from OpenStreetMap and calibrated with empirical traffic data from TomTom Junction Analytics and Route Monitoring (corridor-level speeds and junction-level turn ratios). Using the MATSim framework, the authors implemented and compared fixed-time and adaptive signal control scenarios. The adaptive approach uses a decentralized, demand-responsive algorithm to minimize delays and queue spillback in real time. Simulation results indicate that adaptive signal control significantly improves network-wide average speeds, reduces congestion peaks, and flattens the number of en-route agents throughout the day, compared to fixed-time plans. While simplifications remain in the model, such as generalized signal timings and the exclusion of pedestrian movements, these findings suggest that deploying adaptive traffic management systems could deliver substantial operational benefits in Bucharest’s urban context. This work demonstrates a scalable methodology combining open geospatial data, commercial traffic analytics, and agent-based simulation to rigorously evaluate AI-based traffic management strategies, offering evidence-based guidance for urban mobility planning and policy decisions. Full article

The global shortage of air traffic controllers (ATCOs) has led to significant challenges. One of them is the high workload of ATCOs, often resulting in flight delays. This makes it essential to develop solutions that reduce ATCOs’ workload in order to increase capacity. One promising approach is the integration of decision-support systems. A typical task for which these systems are used for is the resolution of aircraft conflicts in the upper airspaces. A key challenge in implementing these support systems is to ensure a high acceptance and adoption rate of the proposed advisories. One potential solution to this problem is to personalize the advisories, aligning them with individual ATCOs’ preferences and conflict resolution strategies. As this approach offers many promising research directions, this literature review aims to provide a comprehensive overview of existing research in this domain and highlight potential opportunities and open challenges. Overall, 16 papers are discussed in detail to examine the diversity of conflict resolution strategies among ATCOs, the impact of personalization on the acceptance rate of advisories, the technical feasibility of implementing personalization, and the balance between personalized advisories and operational efficiency. Additionally, this paper highlights the opportunities such personalization presents, along with the unresolved challenges that should be addressed in future research. Full article