Search Results (97)

The rapid growth of Unmanned Aircraft Systems (UASs) and Advanced Air Mobility (AAM) presents significant integration and safety challenges for the National Airspace System (NAS), often relying on disconnected Air Traffic Management (ATM) and Unmanned Aircraft System Traffic Management (UTM) practices that contribute to airspace incidents. This study evaluates Geographic Information Systems (GISs) as a unified, data-driven framework to enhance shared airspace safety and efficiency. A comprehensive, multi-phase methodology was developed using GIS (specifically Esri ArcGIS Pro) to integrate heterogeneous aviation data, including FAA aeronautical data, Automatic Dependent Surveillance–Broadcast (ADS-B) for crewed aircraft, and UAS Flight Records, necessitating detailed spatial–temporal data preprocessing for harmonization. The effectiveness of this GIS-based approach was demonstrated through a case study analyzing a critical interaction between a University UAS (Da-Jiang Innovations (DJI) M300) and a crewed Piper PA-28-181 near Purdue University Airport (KLAF). The resulting two-dimensional (2D) and three-dimensional (3D) models successfully enabled the visualization, quantitative measurement, and analysis of aircraft trajectories, confirming a minimum separation of approximately 459 feet laterally and 339 feet vertically. The findings confirm that a GIS offers a centralized, scalable platform for collating, analyzing, modeling, and visualizing air traffic operations, directly addressing ATM/UTM integration deficiencies. This GIS framework, especially when combined with advancements in sensor technologies and Artificial Intelligence (AI) for anomaly detection, is critical for modernizing NAS oversight, improving situational awareness, and establishing a foundation for real-time risk prediction and dynamic airspace management. Full article

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and industry. This review examines the conceptual foundations, and state-of-the-art developments of AIoT, with a particular emphasis on its applications in smart cities and its relevance to climate change management. AIoT integrates sensing, connectivity, and intelligent analytics to provide optimized solutions in transportation systems, energy management, waste collection, and environmental monitoring, directly influencing urban sustainability. Beyond urban efficiency, AIoT can play a critical role in addressing the global challenges and management of climate change by (a) precise measurements and autonomously remote monitoring; (b) real-time optimization in renewable energy distribution; and (c) developing prediction models for early warning of climate disasters. This paper performs a literature review and bibliometric analysis to identify the current landscape of AIoT research in smart city contexts. Over 1885 articles from Web of Sciences and over 1854 from Scopus databases, published between 1993 and January 2026, were analyzed. The results reveal a strong and accelerating growth in research activity, with publication output doubling in the most recent two years compared to 2023. Waste management and air quality monitoring have emerged as leading application domains, where AIoT-based optimization and predictive models demonstrate measurable improvements in operational efficiency and environmental impact. Altogether, these support faster and more effective decisions for reducing greenhouse gas emissions and ensuring the sustainable use of resources. The reviewed studies reveal rapid advancements in edge intelligence, federated learning, and secure data sharing through the integration of AIoT with blockchain technologies. However, significant challenges remain regarding scalability, interoperability, privacy, ethical governance, and the effective translation of research outcomes into policy and citizen-oriented tools such as climate applications, insurance models, and disaster alert systems. By synthesizing current research trends, this article highlights the potential of AIoT to support sustainable, resilient, and citizen-centric smart city ecosystems while identifying both critical gaps and promising directions for future investigations. Full article

As global airspace operations grow increasingly complex, the risk of near-mid-air collisions (NMACs) poses a persistent and critical challenge to aviation safety. Traditional collision-avoidance systems, while effective in many scenarios, are limited by rule-based logic and reliance on transponder data, particularly in environments featuring diverse aircraft types, unmanned aerial systems (UAS), and evolving urban air mobility platforms. This paper introduces a novel, integrative machine learning framework designed to analyze NMAC incidents using the rich, contextual information contained within the NASA Aviation Safety Reporting System (ASRS) database. The methodology is structured around three pillars: (1) natural language processing (NLP) techniques are applied to extract latent topics and semantic features from pilot and crew incident narratives; (2) cluster analysis is conducted on both textual and structured incident features to empirically define distinct typologies of NMAC events; and (3) supervised machine learning models are developed to predict pilot decision outcomes (evasive action vs. no action) based on integrated data sources. The analysis reveals seven operationally coherent topics that reflect communication demands, pattern geometry, visibility challenges, airspace transitions, and advisory-driven interactions. A four-cluster solution further distinguishes incident contexts ranging from tower-directed approaches to general aviation pattern and cruise operations. The Random Forest model produces the strongest predictive performance, with topic-based indicators, miss distance, altitude, and operating rule emerging as influential features. The results show that narrative semantics provide measurable signals of coordination load and acquisition difficulty, and that integrating text with structured variables enhances the prediction of maneuvering decisions in NMAC situations. These findings highlight opportunities to strengthen radio practice, manage pattern spacing, improve mixed equipage awareness, and refine alerting in short-range airport area encounters. Full article

Applications of Innovative Air Mobility (IAM) place high demands on the safe coordination of multiple UAVs and UAV-tailored takeoff and landing pads to mitigate unforeseen adverse effects. However, existing modeling approaches for multi-UAV flight operation often provide neither formal correctness guarantees nor effective mechanisms for maintaining cyber–physical consistency. To address these limitations, this paper proposes a bigraph-based digital twin framework that unifies modeling, execution, and synchronization for the management of landing operations involving multiple UAVs. Leveraging Bigraphical Reactive Systems (BRS), the framework employs a bigrid-based spatial model to formally represent UAV–pad occupancy constraints and to enforce one-to-one pad assignments via reaction rules, supporting formal proofs of safety properties. The model is linked to physical execution through modular APIs and a state-machine-based control service, enabling runtime cyber–physical synchronization. The formal specification is verified through model checking, which exhaustively explores the solution space (i.e., UAV behaviors in abstracted environments) to identify bigraph-algebraic solutions that guarantee conflict-free landings across different pad configurations. The framework is instantiated on the Crazyflie platform, demonstrating its ability to bridge formal modeling and physical execution while maintaining safety, scalability, and robustness in operational scenarios involving multiple UAVs. Full article

This study investigates the impact of high-resolution spatiotemporal profiles of road transport emissions on urban air quality simulations for Thessaloniki, Greece. Dynamic spatiotemporal emission profiles were developed based on real vehicle speed data and implemented in an integrated air quality modeling system to improve the representation of temporal and spatial traffic activity patterns. The new profiles captured the variability of emissions across hours, days, and months, reflecting differences in congestion intensity and seasonal mobility behavior. Zero-out air quality simulations, in which road transport emissions were entirely removed from the model domain, revealed that road transport is a dominant source of urban air pollution, contributing by up to 47 μg/m³ to daily NO₂ and up to 15 μg/m³ to daily PM_2.5 concentrations during winter, while remaining significant in summer. The speed-based spatiotemporal profiles affected NO and NO₂ concentrations by up to +20 μg/m³ and +3.8 μg/m³, respectively, during the rush hours in winter. The use of dynamic spatiotemporal profiles improved model performance with a maximum daily BIAS reduction of –5 μg/m³ for NO and an increase in the index of agreement of up to 0.13 during the warm period, demonstrating a more accurate representation of traffic-related air pollution dynamics. Improvements for PM_2.5 were smaller but consistent across most monitoring sites. Overall, the study demonstrated that incorporating detailed traffic-derived spatiotemporal profiles enhances the accuracy of urban air quality simulations. The proposed approach provides valuable input for municipal action plans, supporting the design of effective traffic management and emission reduction strategies tailored to local conditions. Full article

Achieving sustainable air quality improvements in rapidly industrializing regions requires a clear understanding of the emission sources that drive the formation of PM_2.5 pollution. This study identified the sources of PM_2.5 and its organic carbon (OC) in Zibo, a typical industrial city in Northern China Plain, using the Positive Matrix Factorization (PMF) model during five pollution episodes (P1–P5) from 26 November 2022 to 9 February 2023. A high-temporal-resolution online observation of 61 organic molecular tracers was conducted using an Aerodyne TAG stand-alone system combined with a gas chromatograph–mass spectrometer (TAG-GC/MS) system. The results indicate that during pollution episodes, PM_2.5 was contributed by 32.4% from coal combustion and 27.1% from inorganic secondary sources. Moreover, fireworks contributed 13.1% of PM_2.5, primarily due to the extensive fireworks during the Gregorian and Lunar New Year celebrations. Similarly, coal combustion was the largest contributor to OC, followed by mobile sources and secondary organic aerosol (SOA) sources, accounting for 16.2% and 15.3%, respectively. Although fireworks contributed significantly to PM_2.5 concentrations (31.6% in P4 of 20–24 January 2023), their impact on OC was negligible. Overall, a combination of local and regional industrial combustion emissions, mobile sources, extensive residential heating during cold weather, and unfavorable meteorological conditions led to elevated secondary aerosol concentrations and the occurrence of this haze episode. The high-temporal-resolution measurements obtained using the TAG-GC/MS system, which provided more information on source-indicating organic molecules (tracers), significantly enhanced the source apportionment capability of PM_2.5 and OC. The findings provide science-based evidence for designing more sustainable emission control strategies, highlighting that the coordinated management of coal combustion, mobile emissions, and wintertime heating is essential for long-term air quality and public health benefits. Full article

The rapid expansion of Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) poses significant challenges for the integration of Unmanned Aircraft Systems (UAS) into dense urban environments, where safety risks and population exposure are particularly high. This study proposes and applies a methodology based on probabilistic assessment of both ground and air risk, grounded in the principles of safety management and the use of geospatial data from OpenStreetMap (OSM), official aeronautical charts, and digital urban models. The urban area is discretized into a spatial grid on which independent risks are calculated per cell and later combined through a cumulative probabilistic fusion model. The resulting risk estimates enable the construction of cost matrices compatible with path-search algorithms. The methodology is applied to a case study in Medellín, Colombia, connecting the Oviedo and San Diego shopping centers through Beyond Visual Line of Sight (BVLOS) operations of a DJI FlyCart 30 drone. Results show that planning with the A* algorithm produces safe routes that minimize exposure to critical areas such as hospitals and restricted air corridors, while maintaining operational efficiency metrics. This approach demonstrates a practical bridge between regulatory theory and operational practice in UAM corridor design, offering a replicable solution for risk management in urban scenarios. Full article

Unmanned vertical takeoff and landing (VTOL) aircraft are increasingly deployed for logistics, surveillance, and urban air mobility (UAM) applications. However, the limitations of full-electric (FE) and internal combustion engine (ICE) systems in meeting diverse mission requirements have motivated the development of hybrid-electric (HE) propulsion systems. The design of HE powertrains remains challenging due to configuration flexibility and the lack of unified criteria for performance trade-offs among FE, ICE-powered, and HE configurations. This study proposes an integrated propulsion co-design framework coupling power allocation, energy management, and component capacity constraints through parametric system modeling. These interdependencies are represented by three key matching parameters: the power ratio (Φ), energy ratio (Ω), and maximum continuous discharge rate (rc). Through Pareto-optimal design space exploration, trade-off analysis results and optimization principles are derived for diverse mission scenarios such as UAM, remote sensing, and military surveillance. Different technological conditions are considered to guide component-level technological advancements. The method was applied to the power system retrofit of the Great White eVTOL. Subsystem steady-state tests provided accurate design inputs, and a simulation model was developed to reproduce the full flight mission. By comparing the simulation with flight-test measurements, mean absolute percentage errors of 8.91% for instantaneous fuel consumption and 0.26% for battery voltage were obtained. Based on these error magnitudes, a dynamic design margin was defined and then incorporated into a subsequent re-optimization, which achieved the 1.5 h endurance target with a 10.49% increase in cost per ton-kilometer relative to the initial design. These results demonstrate that the proposed co-design methodology offers a scalable, data-driven foundation for early-stage hybrid-electric VTOL powertrain design, enabling iterative performance correction and supporting system optimization in subsequent design stages. Full article

Emerging technologies such as the Internet of Things (IoT), big data, mobile devices, high-performance computing, and advanced modeling are reshaping urban management. When integrated with conventional tools, these innovations enable smarter governance—particularly in air quality control—improving public health and quality of life. Yet, urban expansion driven by economic growth continues to worsen pollution and its health impacts. This study presents AERQ, a decision support system (DSS) designed to address urban air quality challenges through real-time sensor data and the AERMOD dispersion model. Applied to Cagliari (Italy), AERQ is used to evaluate key traffic-related pollutants (CO, PM, NO₂) and simulate mitigation scenarios. Results are delivered via a user-friendly web-based platform for policymakers, technicians, and citizens. AERQ supports data-driven planning and near real-time responses, demonstrating the potential of integrated digital tools for sustainable urban governance. In the case study, it achieved 10 m spatial and 1 h temporal resolution, while reducing simulation time by 99%—delivering detailed five-year scenarios in just 15 h. Full article

Efficient grain storage is critical for ensuring food security, particularly in regions with hot and humid climates where environmental fluctuations can accelerate spoilage. This study presents the development of a low-cost, Arduino-based microcontroller platform Smart Grain Monitoring System designed to continuously monitor key storage parameters. The system integrates sensors to measure temperature, relative humidity, air quality, and the weight of stored grains—factors essential for the early detection of microbial activity, fermentation, or structural degradation. Data is transmitted wirelessly in real time to a mobile application via the Blynk Internet of Things (IoT) platform, allowing for remote access, alerts, and trend analysis. The system is designed to be affordable, scalable, and easy to deploy in agricultural settings with limited infrastructure. To enhance mechanical performance and usability, the sensor system is housed in a reflective glass silo enclosure that provides both thermal insulation and visual grain access. A three-dimensional computer-aided design (3D CAD)model was developed to optimize the placement of electronics and ensure structural integrity. Key features include custom mounts for sensors and electronics, a top lid for grain refill and hygiene, and a stable base for load cell installation. This integrated framework offers a reliable, real-time monitoring solution that supports proactive grain management and reduces post-harvest losses in rural storage environments. Full article

The low-altitude economy (LAE), encompassing urban air mobility, drone logistics and sub 3000 m aerial surveillance, demands secure, intelligent infrastructures to manage increasingly complex, multi-stakeholder operations. This survey evaluates the integration of Internet of Things (IoT) networks, artificial intelligence (AI) decision-making and blockchain trust mechanisms as foundational enablers for next-generation LAE ecosystems. IoT sensor arrays deployed at ground stations, unmanned aerial vehicles (UAVs) and vertiports form a real-time data fabric that records variables from air traffic density to environmental parameters. These continuous data streams empower AI models ranging from predictive analytics and computer vision (CV) to multi-agent reinforcement learning (MARL) and large language model (LLM) reasoning to optimize flight paths, identify anomalies and coordinate swarm behaviors autonomously. In parallel, blockchain architectures furnish immutable audit trails for regulatory compliance, support secure device authentication via decentralized identifiers (DIDs) and automate contractual exchanges for services such as airspace leasing or payload delivery. By examining current research and practical deployments, this review demonstrates how the synergistic application of IoT, AI and blockchain can bolster operational efficiency, resilience and trustworthiness across the LAE landscape. Full article

Urban air pollution caused by vehicular emissions remains one of the most pressing environmental challenges, negatively affecting both public health and climate processes. In Kokshetau, Kazakhstan, where electric vehicle (EV) adoption accounts for only 0.019% of the total fleet and charging infrastructure is nearly absent, reducing transport-related emissions requires short-term and cost-effective solutions. This study proposes an integrated approach combining urban ecology principles with computational modeling to optimize traffic signal control for emission reduction. An artificial neural network (ANN) was trained using intersection-specific traffic data to predict emissions of carbon monoxide (CO), nitrogen oxides (NO_x), sulfur dioxide (SO₂), and particulate matter (PM_2.5). The ANN was incorporated into a nonlinear optimization framework to determine traffic signal timings that minimize total emissions without increasing traffic delays. The results demonstrate reductions in emissions of CO by 12.4%, NO_x by 9.8%, SO₂ by 7.6%, and PM_2.5 by 10.3% at major congestion hotspots. These findings highlight the potential of the proposed framework to improve urban air quality, reduce ecological risks, and support sustainable transport planning. The method is scalable and adaptable to other cities with similar urban and environmental characteristics, facilitating the transition toward eco-friendly mobility and integrating data-driven traffic management into broader climate and public health policies. 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 monitoring and prediction of air quality (AQ) is key to minimizing the negative impact of air pollution, as it enables the implementation of corrective measures. An IoT-based multi-purpose monitoring system has therefore been designed. To develop a reliable remote system, this study addresses three challenges: (1) design of a low-cost compact, robust, multi-sensor system, (2) model validation over several months to ensure accurate detection, and (3) the application of machine learning (ML) techniques to classify and predict AQ. The developed system demonstrates a significant cost reduction for regular monitoring, including effective data management under harsh environmental conditions. The prototype integrates pollutant sensors, as well as the detection of liquified petroleum gas, humidity, and temperature. A dataset with more than 30,000 entries per month (data recorded approximately every minute) was saved on the platform. Results identified the three highest pollution categories, highlighting the urgency of addressing AQ in densely populated regions. The ML algorithms allowed us to predict AQ trends with 99.97% accuracy. To summarize, by reducing monitoring costs and enabling large-scale data management, this system offers an effective solution for real-time environmental monitoring. It also highlights the potential of artificial intelligence-based AQ predictions in supporting public health initiatives. This is particularly interesting for developing countries, where pollution control is limited. Future research will develop the models to include data from different environments and seasons, exploring its integration into mobile apps and cloud platforms for real-time monitoring. Full article

The shift toward sustainable aviation has accelerated research into hybrid electric aircraft, particularly in the context of regional air mobility. To support this transition, we introduce the Soft Fixed Route Hybrid Electric Aircraft Charging Problem with Variable Speed (S-FRHACP-VS), a novel optimization problem for managing hybrid electric aircraft operations that considers variable speed. The objective is to minimize total costs by determining charging strategies, refueling decisions, hybridization ratios, and speed decisions while adhering to a soft schedule. This paper introduces an iterative variable-based fixation heuristic, named Iterative Two-Stage Mixed-Integer Programming Heuristic (ITS-MIP-H), that alternatively optimizes speed and hybridization ratios while considering the soft schedule constraints, nonlinear charging, and nonlinear energy consumption functions. In addition, a metaheuristic genetic algorithm is proposed as an alternative optimization approach. Experiments on ten realistic flight instances demonstrate that optimizing speed leads to an average cost reduction of 7.64% compared to the best non-speed-optimized model, with reductions of up to 18.64% compared to an all-fuel-based heuristic. Although genetic algorithm provides a viable alternative that performs better than the best non-speed-optimized model, the proposed iterative variable-based fixation heuristic approach consistently outperforms the metaheuristic, achieving the best solutions within seconds. These results provide new insights into the integration of hybrid electric aircraft within transportation networks, contributing to advancements in aircraft routing optimization, energy-efficient operations, and sustainable aviation policy development. Full article