Search Results (184)

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 $R^2$ 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. Full article

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

Advancements in biosensing technologies have introduced opportunities for non-invasive, real-time monitoring of salivary biomarkers, enabling progress in fields ranging from personalized medicine to public health. Identifying and prioritizing the most critical analytes to measure in saliva is essential for estimating physiological status and forecasting performance in applied contexts. This study examined the value of 12 salivary analytes, including hormones, metabolites, and enzymes, for predicting cognitive and physical performance outcomes in military personnel (N = 115) engaged in stressful laboratory and field tasks. We calculated a series of features to quantify time-series analyte data and applied multiple regression techniques, including Elastic Net, Partial Least Squares, and Random Forest regression, to evaluate their predictive utility for five outcomes of interest: the ability to move, shoot, communicate, navigate, and sustain performance under stress. Predictive performance was poor across all models, with R-squared values near zero and limited evidence that salivary analytes provided stable or meaningful performance predictions. While certain features (e.g., post-peak slopes and variance metrics) appeared more frequently than others, no individual analyte emerged as a reliable predictor. These results suggest that salivary biomarkers alone are unlikely to provide robust insights into cognitive and physical performance outcomes. Future research may benefit from combining salivary and other biosensor data with contextual variables to improve predictive accuracy in real-world settings. Full article

Traffic congestion constitutes a substantial global issue, adversely impacting economic productivity and quality of life, with associated costs estimated at approximately 2% of GDP in various nations. This systematic review investigates the application of machine learning (ML) in traffic congestion forecasting from 2010 to 2024, adhering to the PRISMA 2020 guidelines. A comprehensive search of three major databases (IEEE Xplore, SpringerLink, and ScienceDirect) yielded 9695 initial records, with 115 studies meeting the inclusion criteria following rigorous screening. Data extraction encompassed methodological approaches, ML techniques, traffic characteristics, and forecasting periods, with quality assessment achieving near-perfect inter-rater reliability (Cohen’s $\kappa$ = 0.89). Deep Neural Networks were the predominant technical approach (47%), with supervised learning being the most prevalent (57%). Classification tasks were the most common (42%), primarily addressing recurrent congestion scenarios (76%) and passenger vehicles (90%). The quality of publications was notably high, with 85% appearing in Q1-ranked journals, demonstrating exponential growth from minimal activity in 2010 to 18 studies in 2022. Significant research gaps persist: reinforcement learning is underutilized (8%), rural road networks are underrepresented (2%), and industry–academia collaboration is limited (3%). Future research should prioritize multimodal transportation systems, real-time adaptation mechanisms, and enhanced practical implementation to advance intelligent transportation systems (ITSs). This review was not registered because it focused on mapping the research landscape rather than intervention effects. Full article

Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the $\tau$-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the $\tau$-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments. Full article

Ocean forecasting is critical for various applications and is essential for understanding air–sea interactions, which contribute to mitigating the impacts of extreme events. While data-driven forecasting models have demonstrated considerable potential and speed, they often primarily focus on spatial variations while neglecting temporal dynamics. This paper presents the TSformer, a novel non-autoregressive spatio-temporal transformer designed for medium-range ocean eddy-resolving forecasting, enabling forecasts of up to 30 days in advance. We introduce an innovative hierarchical U-Net encoder–decoder architecture based on 3D Swin Transformer blocks, which extends the scope of local attention computation from spatial to spatio-temporal contexts to reduce accumulation errors. The TSformer is trained on 28 years of homogeneous, high-dimensional 3D ocean reanalysis datasets, supplemented by three 2D remote sensing datasets for surface forcing. Based on the near-real-time operational forecast results from 2023, comparative performance assessments against in situ profiles and satellite observation data indicate that the TSformer exhibits forecast performance comparable to leading numerical ocean forecasting models while being orders of magnitude faster. Unlike autoregressive models, the TSformer maintains 3D consistency in physical motion, ensuring long-term coherence and stability. Furthermore, the TSformer model, which incorporates surface auxiliary observational data, effectively simulates the vertical cooling and mixing effects induced by Super Typhoon Saola. Full article

Ethiopian pastoralist communities are facing a recurrent drought crisis that significantly affects the availability of water and pasture resources for communities dependent on livestock. The increasing intensity, duration and frequency of droughts in the pastoral community in Ethiopia have drawn the attention of multiple stakeholders and increased stakeholder debates on the role of early warning systems (EWSs) for anticipatory action to build climate resilience in the pastoral community. The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT), in collaboration with various partners, has developed an interactive web-based digital EWS to provide near real-time information on water and pasture conditions in pastoral and agro-pastoral regions of Ethiopia. In this study, a stakeholder analysis was conducted to identify key stakeholders, understand stakeholder needs, and facilitate collaboration towards sustaining the EWS. The stakeholder analysis revealed the roles and information needs of key actors engaged in livestock water and pasture monitoring and early warning systems aimed at improving the pastoral communities’ resilience. The analysis showed a pressing need for access to real-time information on water and pasture availability and seasonal climate forecasts by local communities for effective and optimal resources management. Local and national governments need similar data for evidence-based decision-making in resource allocation and policy development. International and non-governmental organizations (INGOs) require the same information for efficient humanitarian responses and targeted development interventions. The private sector seeks insights into market dynamics to better align production strategies with community needs. An EWS serves as a vital tool for development partners, facilitating improved planning, coordination, and impact assessment. It also emphasizes the importance of proactive collaboration among stakeholders, including local communities, government bodies, INGOs, and academic and research institutions. Enhanced communication strategies, such as partnerships with local media, are essential for timely information dissemination. Ultimately, sustained collaboration and adaptive strategies are crucial for optimizing the impact of an EWS towards improving the livelihoods and resilience of pastoral communities amid climate variability. Full article

As society evolves and technology advances, increasing transportation demands have heightened safety risks near schools and on mixed-traffic roads. While traditional studies on pedestrian evasive behavior have mainly focused on general traffic environments and used image-based features to predict trajectories, few have specifically addressed the behavior of pedestrians in school zones. This study fills that gap by analyzing pedestrian evasive actions near school zones in Pudong New Area, Shanghai, using real-time video data. In contrast to previous approaches, our research leverages key traffic variables—such as vehicle speed, pedestrian proximity, and traffic density—to predict whether pedestrians will engage in evasive behavior. We independently apply three predictive models: the traditional BP (Backpropagation) neural network, an improved GA-BP(genetic algorithm–backpropagation) neural network, and the XGBoost (Extreme Gradient Boosting) ensemble learning method. Our findings show that the improved GA-BP model outperforms the others, achieving an accuracy of over 79%. Furthermore, this study identifies crucial traffic factors influencing pedestrian behavior, offering valuable insights for road safety decision-making in school zones. This research demonstrates the potential of advanced predictive models for forecasting pedestrian evasive behavior. It enhances safety in school zones by highlighting the key traffic variables affecting pedestrians. Full article

Long-term continuous and reliable real-time ocean wave height data are important for climatologists, offshore industries, leisure craft users, and marine forecasters. However, maintaining data continuity and reliability is challenging due to offshore equipment failures and sparse in situ observations. Opposing interactions between wind-driven ocean waves generate acoustic waves near the ocean surface, which can convert to seismic waves at the seafloor and travel through the Earth’s solid structure. These low-frequency seismic waves, known as secondary microseisms, are clearly recorded on terrestrial seismometers offering land-based access to ocean wave states via seismic ground vibrations. Here, we demonstrate the potential of this by estimating ocean Significant Wave Heights (SWHs) in the Northeast Atlantic using continuous recordings from a land-based seismic network in Ireland. Our method involves connecting secondary microseism amplitudes with the ocean waves that generate them, using an Artificial Neural Network (ANN) to quantify the relationship. Time series data of secondary microseism amplitudes together with buoy-derived and numerical model ocean significant wave heights are used to train and test the ANN. Application of the ANN to previously unseen data yields SWH estimates that closely match in situ buoy observations, located approximately 200 km offshore, Northwest of Ireland. Terrestrial seismic data are relatively cheap to acquire, with reliable weather-independent data streams. This suggests a pathway to a complementary, exceptionally cost-effective, data-driven approach for future operational applications in real-time SWH determination. Full article

Offshore wind farms play a pivotal role in the global transition to clean energy but remain susceptible to diverse meteorological hazards—ranging from highly variable wind speeds and temperature anomalies to severe oceanic disturbances—that can jeopardize both turbine safety and overall power output. Although Hidden Markov Models (HMMs) have a longstanding track record in operational forecasting, this study leverages and extends their capabilities by introducing a dynamic HMM framework tailored specifically for multi-risk offshore wind applications. Building upon historical datasets and expert assessments, the proposed model begins with initial transition and observation probabilities and then refines them adaptively through periodic or event-triggered recalibrations (e.g., Baum–Welch), thus capturing evolving weather patterns in near-real-time. Compared to static Markov chains, naive Bayes classifiers, and RNN (LSTM) baselines, our approach demonstrates notable accuracy gains, with improvements of up to 10% in severe weather conditions across three industrial-scale wind farms. Additionally, the model’s minutes-level computational overhead for parameter updates and state decoding proves feasible for real-time deployment, thereby supporting proactive scheduling and maintenance decisions. While this work focuses on the core dynamic HMM method, future expansions may incorporate hierarchical structures, Bayesian uncertainty quantification, and GAN-based synthetic data to further enhance robustness under high-dimensional measurements and rare, long-tail meteorological events. In sum, the multi-risk forecasting methodology presented here—though built on an established HMM concept—offers a practical, adaptive solution that significantly bolsters safety margins and operational reliability in offshore wind power systems. Full article

This study examines restroom cleanliness in public facilities, department stores, supermarkets, and schools by using water droplet volumes around washbowls as an indicator of usage. Rising cleaning costs due to labour shortages necessitate more efficient restroom maintenance. Quantifying water droplet accumulation and predicting cleaning schedules can help optimise cleaning frequency. To achieve this, water droplet volumes were measured at specific time intervals, with significant variations indicating increased restroom usage and potential dirt buildup. For real-world assessment, acrylic plates were placed on both sides of washbowls in public restrooms. These plates were collected every hour over five days and analysed using near-infrared photography to track changes in water droplet areas. The collected data informed the development of a prediction system based on the decision tree method, implemented via the LightGBM framework. This paper presents the developed prediction system, which utilises in situ water droplet volume measurements, and evaluates its accuracy in forecasting restroom cleaning needs. Full article

This paper explores the application of parallel algorithms and high-performance computing (HPC) in the processing and forecasting of large-scale water demand data. Building upon prior work, which identified the need for more robust and scalable forecasting models, this study integrates parallel computing frameworks such as Apache Spark for distributed data processing, Message Passing Interface (MPI) for fine-grained parallel execution, and CUDA-enabled GPUs for deep learning acceleration. These advancements significantly improve model training and deployment speed, enabling near-real-time data processing. Apache Spark’s in-memory computing and distributed data handling optimize data preprocessing and model execution, while MPI provides enhanced control over custom parallel algorithms, ensuring high performance in complex simulations. By leveraging these techniques, urban water utilities can implement scalable, efficient, and reliable forecasting solutions critical for sustainable water resource management in increasingly complex environments. Additionally, expanding these models to larger datasets and diverse regional contexts will be essential for validating their robustness and applicability in different urban settings. Addressing these challenges will help bridge the gap between theoretical advancements and practical implementation, ensuring that HPC-driven forecasting models provide actionable insights for real-world water management decision-making. Full article

Accurate identification of dust emission sources is crucial for simulating dust aerosols in atmospheric chemical models. Therefore, a dynamically updated dust source function (DSF) was developed within the dust emission scheme of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) to simulate an East Asian dust storm event from 13 to 16 March 2021. Utilizing satellite-derived input of vegetation cover, snow cover, soil texture, and land use, the DSF was updated to better identify dust source areas over bare soils and sparsely vegetated regions in western China and central-western Mongolia. With the updated DSF, simulated dust emissions increase significantly over western China and Mongolia. The dust aerosol simulations demonstrate substantial improvements in near-surface PM₁₀ concentrations, a better agreement with remotely sensed dust aerosol optical depth (DOD), and a more accurate representation of the vertical distribution of dust extinction coefficients compared to observations. This study highlights the importance of integrating real-time data to accurately characterize dust emission sources, thereby improving atmospheric environment simulations. Full article

The present research activity carried out demonstrated how simulation tools developed through WebGIS 3D digital platforms are capable of producing approximate forecasts of the effects of potentially catastrophic meteorological phenomena that may affect riverbeds in the territories observed. This work presents an analysis and simulation platform with graphic representation of the results in the form of three-dimensional animation. This methodology may represent a useful tool for all bodies and organizations that need to create hypothetical scenarios for the management of emergencies related to flooding events in watercourses, especially in areas of maximum hydrogeological vulnerability in the Italian territory. These scenarios are particularly useful in cases where watercourses are located near inhabited centers, industrial areas or strategic infrastructures, where the risk of material damage and danger to the population is greater. The simulation is based on the morphology of the land adjacent to the bed of an affected watercourse, taking elevation into account to determine the direction of the expansion of the water mass. An important aspect of the platform is the extreme speed of simulation resolution, which allows the tool to be used even in real time. This real-time forecasting approach is crucial for making quick and informed decisions, thus reducing reaction times and improving emergency management on the ground, with a potential positive impact on the safety of the population and the protection of infrastructure. Full article

This study investigates the climatic background of winter PM2.5 (particulate matter with a diameter of 2.5 micrometers or smaller) concentrations in Hubei Province (DJF-HBPMC) and evaluates its predictability. The key findings are as follows: (1) Elevated DJF-HBPMC levels are associated with an upper-tropospheric northerly anomaly, a deepened southern branch trough (SBT) that facilitates southwesterly flow into central and eastern China, and a weakened East Asian winter monsoon (EAWM), which reduces the frequency and intensity of cold air intrusions. Near-surface easterlies and an anomalous anticyclonic circulation over Hubei contribute to reduced precipitation, thereby decreasing the dispersion of pollutants and leading to higher PM2.5 concentrations. (2) Significant correlations are observed between DJF-HBPMC and sea surface temperature (SST) anomalies in specific oceanic regions, as well as sea-ice concentration (SIC) anomalies near the Antarctic. For the atmospheric pattern anomalies over Hubei Province, the North Atlantic SST mode (NA) promotes the southward intrusion of northerlies, while the Northwest Pacific (NWP) and South Pacific (SPC) SST modes enhance wet deposition through increased precipitation, showing a negative correlation with DJF-HBPMC. Conversely, the South Atlantic–Southwest Indian Ocean SST mode (SAIO) and the Ross Sea sea-ice mode (ROSIC) contribute to more stable local atmospheric conditions, which reduce pollutant dispersion and increase PM2.5 accumulation, thus exhibiting a positive correlation with DJF-HBPMC. (3) A multiple linear regression (MLR) model, using selected seasonal SST and SIC indices, effectively predicts DJF-HBPMC, showing high correlation coefficients (CORR) and anomaly sign consistency rates (AS) compared to real-time values. (4) In daily HBPMC forecasting, both the Reversed Unrestricted Mixed-Frequency Data Sampling (RU-MIDAS) and Reversed Restricted-MIDAS (RR-MIDAS) models exhibit superior skill using only monthly precipitation, and the RR-MIDAS offers the best balance in prediction accuracy and trend consistency when incorporating monthly precipitation along with monthly SST and SIC indices. Full article