Search Results (2,643)

Airfield Ground Lighting Power Systems (AGLPS) are critical for ensuring safe aircraft operations, particularly under low-visibility conditions. Conventional systems are based on series circuits supplied by constant current regulators, which impose limitations in terms of flexibility, scalability, and maintenance. This work investigates an alternative AGLPS architecture based on a low-voltage parallel distribution network enabled by LED luminaires, distributed power electronics, and Power Line Communication (PLC) for control and monitoring. A theoretical and conceptual approach is adopted, including electrical modelling of the power distribution system, verification of conductor sizing under high admissible voltage drops, and evaluation of communication performance using PLC and Modbus protocols. The results demonstrate that the proposed architecture can operate with significantly higher voltage drops without affecting luminous output, allowing for the use of standard low-voltage cabling. In addition, communication analysis shows that control and monitoring operations can be executed within a few milliseconds, meeting operational requirements. An economic assessment indicates a reduction in system complexity and overall costs compared to conventional series systems. The findings confirm that parallel AGLPS architectures constitute a technically feasible and advantageous alternative to traditional systems, enabling enhanced flexibility, improved maintainability, and the integration of advanced digital functionalities. Full article

The atmospheric boundary layer (ABL) has strong diurnal variability, but routine radiosonde launches at 00:00 and 12:00 UTC cannot fully resolve its daily evolution. This study develops and evaluates a 13-year (2007–2019) hourly ABL profile dataset using Aircraft Meteorological Data Relay (AMDAR) observations from 42 selected European airports, and applies it to characterize airport-scale diurnal, seasonal, and regional variations in ABL structure. AMDAR-derived temperature and wind profiles were validated against collocated radiosonde observations by season, pressure layer, and airport–radiosonde distance. Errors decrease for shorter separation distances and lower-tropospheric layers. For separations < 50 km and pressures > 850 hPa, spring, summer, autumn, and winter RMSEs are 0.9/1.0/1.4/1.2 K for temperature, 1.7/2.0/1.9/1.9 m/s for zonal wind, and 1.4/1.6/1.9/1.6 m/s for meridional wind. Hourly AMDAR profiles reveal distinct diurnal ABL evolution at airport scale. Seasonal ABL height (ABLH) composites are mainly 250–900 m, with available nighttime and early-morning values of about 300–450 m and spring–summer afternoon maxima of 800–900 m at far-inland airports. Coastal airports show weaker daytime growth, mostly below 600–650 m. These results demonstrate AMDAR’s value as a supplementary profile dataset for characterizing European airport-scale ABL structure and diurnal variability. Full article

The proliferation of 3D-printed firearms poses a growing challenge for civil security, particularly in controlled public environments such as airports, train stations, and other transit hubs. These objects are often manufactured from polymer materials, exhibit high design variability, and are difficult to detect using conventional inspection systems. With over 20,000 weapon designs freely available online, traditional dataset creation methods cannot match the pace of design evolution. To address this challenge, we present LEOPARD, a pipeline designed to support civil security applications by converting CAD (computer-aided design) models of illicit firearm components into large-scale, photorealistic synthetic datasets. The pipeline incorporates procedural geometric variations, material imperfections, and physics-based rendering to realistically model 3D-printed objects as they may appear during security screening. Using this pipeline, we introduce LEOPARD-Zero, a dataset of 75,000 fully annotated synthetic images focused on the detection of illegal 3D-printed firearm components, with potential applications in civil transportation security contexts. Object detection models trained exclusively on our synthetic data achieve high performance on real 3D-printed components, with mAP@50 exceeding 83% and precision reaching up to 91.9%, demonstrating viable performance without requiring extensive real-world data collection. To encourage further research in automated inspection and public safety, we have released LEOPARD-Zero. Full article

The efficient scheduling of airport logistics vehicles is crucial for ensuring timely and cost-effective ground operations, particularly under dynamic disturbances such as flight delays, cancellations, and new task arrivals. With the increasing deployment of Internet of Things (IoT) technologies in airport environments, real-time data from sensors and connected devices enables efficient and adaptive scheduling. This paper considers a dynamic Airport Logistics Vehicle Scheduling (ALVS) problem that aims to minimize both vehicle usage and total task waiting time while satisfying task precedence and time window constraints. To address this problem, we propose a hybrid optimization framework, termed Periodic and Event-Driven Rolling Horizon Optimization (PERHO), which integrates periodic updates with event-driven rescheduling to adapt to real-time task variations in airport ground operations. Within PERHO, an Order-aware Adaptive Strategy Selection (OASS) algorithm is developed to dynamically select the most appropriate task sequencing heuristic from a candidate set based on recent performance and order relationships. Extensive experiments across various instance scales and dynamic scenarios demonstrate the effectiveness of the proposed PERHO-OASS approach. In experiments considering dynamic events, PERHO-OASS reduces vehicle usage and task waiting time by an average of 23.55% and 61.95%, respectively, over fixed heuristic algorithms, and by an average of 3.77% and 17.30% over adaptive selection methods, demonstrating strong robustness under uncertainty. The proposed approach can support airport operators in improving the efficiency and reliability of ground logistics operations. Full article

Runway microtexture is a key parameter governing pavement friction. In recent years, several microtexture assessment methods have been developed; however, understanding of microtexture evolution under operational conditions, as well as the effects of maintenance techniques, remains limited. In this study, a runway at an Australian airport was investigated using laser profilometry. Measurements were conducted across multiple transverse sections, including aircraft touchdown and mid-runway zones. Microtexture deterioration rates were evaluated based on the estimated number of tire–pavement contacts, and aggregate polishing was assessed at different locations. Measurements were also performed after rubber contamination removal and rejuvenation treatments. The results indicate that approximately 25% of total microtexture reduction can be attributed to surface polishing, with a lower contribution in touchdown zones due to the protective effect of rubber deposits. A non-linear degradation trend was observed in touchdown zones, where approximately 1100 tire contacts reduced average microtexture roughness from 18 μm to 11 μm. Rubber removal effectively restored microtexture close to its original levels across the runway width. A rejuvenation treatment with a covering of fine sand initially improved microtexture; however, rapid deterioration occurred due to loss of the sand coating. These findings improve the understanding of microtexture evolution under operational runway conditions, albeit only at a case study level, and support more effective runway maintenance planning and intervention strategies. Full article

With the increasing frequency of extreme weather and rapid growth of civil aviation, severe convective weather (thunderstorms, short-term heavy precipitation, and strong winds) poses growing threats to flight safety. This study proposes a multi-label CNN-ConvLSTM framework that fuses airport Doppler radar echoes, Himawari-8 satellite imagery, surface observations, and radar optical flow features to nowcast multiple severe convective events within the next 30 min. The model uses 2D-CNN for spatial extraction, ConvLSTM for temporal dynamics, and a weighted joint loss (Focal Loss and Dice Loss) to address class imbalance. Trained on 396 samples (positive-to-negative ratio 1:2.5) from 83 events at Guanghan Airport (2021–2024), incorporating optical flow features significantly boosted performance: macro-F1 increased from 0.719 to 0.792, and Threat Score (TS) from 0.567 to 0.705. Notably, false negatives for minority classes dropped sharply, with strong winds F1-score rising from 0.15 to 1.00. Ablation analysis showed optical flow as the top contributor (Mean Decrease in TS ≈ 0.5). Through multi-modal fusion and motion enhancement, this interpretable model provides high-precision nowcasting for airport severe convective weather, offering substantial value for aviation safety. Full article

Digital transformation has become one of the key drivers of airport sustainability development; however, existing digital maturity frameworks are not fully tailored to the aviation context, particularly within Australia. This study built a conceptual digital maturity model for Australian airports by integrating ISO/IEC maturity framework with the Airport 1.0–5.0 concept. A structured literature review informed the dimension formulation, and the model was validated through case studies of Australia’s Big 4 airports and one regional airport. The findings show that the Big 4 airports have largely achieved Airport 4.0 maturity, while Cairns Airport demonstrates maturity between Airport 2.5 and 3.0. These results confirm the model’s applicability and discriminative capability across diverse operational scales. The proposed model offers a practical, context-specific framework for benchmarking, planning, and guiding digital transformation initiatives across Australian airports. Full article

Continuous estimation of oxygen uptake (VO₂) using wearable inertial sensors offers a practical alternative to laboratory-based metabolic testing but remains challenging due to the indirect relationship between kinematics and physiological demand. This study presents a lightweight two-stage pipeline for simultaneous heel-strike (HS) detection and VO₂ estimation using a single calf-mounted IMU. In Stage 1, an Extreme Learning Machine (ELM) + Random Forest (RF) ensemble achieves the highest HS detection F1-score (0.818) under leave-one-subject-out (LOSO) validation, outperforming a temporal convolutional network (TCN) deep learning baseline (F1 = 0.674), which exhibited higher variability across subjects. In Stage 2, kinematic and gait-derived features from 30 s windows are used to estimate normalized VO₂ via RF and ensemble regression under LOSO cross-validation across 24 participants. The RF model achieves a median R² of 0.687 using predicted HS (Pred-HS) events and 0.679 using ground-truth (GT) annotations, with the ensemble showing similar performance (median R² ≈ 0.675–0.691). No statistically significant difference was observed between GT-HS and Pred-HS conditions (p > 0.05). SHAP analysis identifies accelerometer variability (acc_std) and gyroscope-derived features as dominant predictors, with demographic variables contributing minimally. Overall, the results suggest that VO₂ estimation may be achieved using automatically detected gait events without manual annotation. The proposed pipeline is computationally efficient and indicates feasibility under controlled conditions, subject to further validation. Full article

Landslides are widespread and highly destructive geological hazards that pose serious threats to infrastructure and densely populated areas. Conducting scientific and accurate predictions of landslide spatial distribution is therefore of great practical importance for supporting landslide prevention, risk management, and the reduction in casualties and economic losses. Landslides are driven by multiple variables, including elevation, road distance, river distance, slope and land use, with complex nonlinear interactions that traditional linear models cannot accurately capture. This study adopts a Categorical Boosting model (CatBoost) as the base prediction model, which demonstrates strong performance in capturing interactions among multiple variables and achieves relatively robust landslide spatial distribution predictions without complex feature engineering. However, CatBoost is highly sensitive to hyperparameters and difficult to manually optimize. Based on the Nutcracker Optimization Algorithm (NOA), which features an efficient search strategy, a multi-level improved Nutcracker Optimization Algorithm (COLNOA) is proposed to optimize its hyperparameters. The proposed algorithm integrates Circle Chaotic Mapping into the initial population construction of the NOA to generate two distinct populations and enables information exchange between them during the evolutionary process, thereby enhancing global search capability. In addition, Opposition-Based Learning and lateral mutation strategies are introduced to update inferior individuals in each iteration, improving their search capability. Based on these improvements, a COLNOA-CatBoost prediction model is developed. The proposed model is applied to a case study in Wanzhou District, Chongqing, China. The results show that the proposed model achieves a recall of 0.863, an F1-score of 0.860, and an accuracy of 0.865, outperforming baseline models such as decision trees. Compared with the original CatBoost model, recall, F1-score, and accuracy are improved by 34.8%, 35.0%, and 35.1%, respectively. The spatial prediction results indicate that high-risk landslide areas in Wanzhou District are mainly concentrated in regions such as Zouma Town, medium-risk areas in Xintian Town, low-risk areas in Fenshui Town, and very low-risk areas in Longju Town. Further analysis of terrain and landforms indicates that the high-risk areas for landslides in Wanzhou District are mainly related to steep slopes, deep river valleys, exposed or cut slopes at the foot of the slope, runoff convergence, and road excavation slopes. The extremely low and low-risk areas are mostly distributed in the middle and low mountain and hilly areas with relatively flat terrain, weak river cutting and engineering disturbance. This is consistent with the previous correlation analysis that the number of landslides increases with increasing slope and decreases with increasing elevation, distance from rivers, and distance from roads. Overall, the proposed model provides an effective approach for landslide spatial distribution prediction. 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

Reliable short-term traffic flow prediction is crucial for intelligent transportation systems to enable real-time control, mitigate congestion, and improve urban mobility. However, traffic dynamics are inherently uncertain, temporally dependent, and subject to pronounced intraday variability, making accurate forecasting challenging. To address these issues, this study introduces a Functional AutoRegressive (FAR) model that represents daily traffic profiles as continuous stochastic functions rather than discrete observations, thereby preserving temporal continuity and capturing underlying symmetric structures. The model is developed using high-frequency traffic data collected at 15-min intervals from the Dublin Airport Link Road, Ireland, covering January 2022 to December 2024; data from 2022–2023 are used for model estimation, while 2024 data are reserved for one-day-ahead out-of-sample evaluation. A moving-window filtering technique is incorporated to enhance robustness by probabilistically identifying outliers and reducing noise. The proposed FAR approach is benchmarked against conventional models, including autoregressive (AR), autoregressive moving average (ARMA), nonparametric autoregressive (NPAR), and vector autoregressive (VAR) models. Empirical results demonstrate that the FAR model consistently achieves superior forecasting performance across all traffic conditions, yielding a full-day MAPE of 9.160% compared to 11.623% for the VAR model, along with lower MAE (76.772) and RMSE (131.767). It also performs best on both workdays and weekends, with MAPEs of 8.129% and 10.438%, respectively. Moreover, the model remains robust across peak and off-peak periods, effectively capturing both symmetric and asymmetric traffic variations while offering a more interpretable representation of intraday patterns. These findings suggest that functional time series modeling provides an effective and computationally efficient framework for traffic forecasting, with strong potential for application in next-generation intelligent transportation systems. Full article

Liquid hydrogen-powered aircraft (LH₂ aircraft) offer the potential for a zero-carbon footprint when hydrogen is produced from renewable sources. However, integrating LH₂ aircraft into the air transport system is complex due to differences in LH₂ supply availability and varying levels of airport readiness. To address these disparities and comply with anticipated sustainability regulations, hydrogen tankering can serve as a temporary strategy by carrying additional hydrogen to avoid refueling at destinations lacking LH₂ capabilities. This study presents a novel model that evaluates the potential of tankering while accounting for its interaction with strategic LH₂ infrastructure placement, tactical flight scheduling, and operational aircraft routing. Applying the framework to a real-world case in the Baltic Sea region reveals trade-offs between system costs and environmental benefits under different regulatory measures. Full article

Airports face persistent capacity constraints and increasing delays. This study introduces a behavioural framework for demand management that integrates airport and airline preferences with principles from Prospect Theory. By incorporating concepts from behavioural economics—such as loss aversion, reference dependence, and non-linear probability weighting—into choice architectures, we explore how adaptive decision environments can influence airline scheduling and demand distribution. A practical example illustrates the applicability of the proposed methodology. Results suggest that behavioural interventions can sustain economically viable schedules while maximising total prospect value. This approach provides policymakers and operators with innovative tools to address complex capacity challenges in air transport systems. Full article

Rising air temperatures are expected to increasingly affect aircraft take-off performance, potentially causing disruption in airport operations. This study develops an airport climate-risk assessment framework combining aircraft performance modeling with the IPCC hazard–exposure–vulnerability approach, using publicly available data. The Take-Off Distance Required (TODR) was simulated for an A320-231 aircraft under varying temperature conditions, and threshold temperatures, above which fully-laden aircraft cannot depart for a given runway length, were derived for six European airports. Climate projections for 2050 were used to forecast frequency of threshold exceedance (hazard), while exposure and vulnerability were estimated through traffic volume and infrastructure-related factors. Results show that mid-century warming will raise the number of days when temperature is so high that the TODR is longer than the available runway length. Airports with shorter runways, frequent departures, and infrastructure constraints exhibit the highest projected risk levels. The findings indicate that increasing temperatures may impose growing operational constraints. The proposed framework provides an accessible preliminary tool for screening climate-related operational risks, supporting early identification of airports that may require targeted adaptation measures. Full article

This study examines passengers’ perceptions of environmental sustainability practices at Al-Ahsa International Airport and investigates whether these practices are reflected in passenger satisfaction, within the broader policy context of Saudi Arabia’s Vision 2030. It contributes to the emerging literature on perceived environmental sustainability in airport service environments, particularly in regional and developing aviation contexts. The analysis draws on a structured questionnaire administered to 302 passengers, supported by relevant secondary data, and combines descriptive statistics, a SWOT analysis and an ordinal logistic regression model to explore three practical dimensions of environmental performance, namely energy and climate initiatives, waste management practices, and environmentally supportive infrastructure. The results indicate that passengers are generally satisfied with the airport’s environmental performance, with waste management and sustainability-oriented infrastructure showing a statistically significant and positive association with passengers’ satisfaction. Energy and climate practices also exhibit a statistically significant positive effect; however, their impact is comparatively weaker than that of waste management and infrastructure. The findings therefore point to the need to expand clean and renewable energy investments while also making such efforts more visible through targeted awareness activities for passengers and staff, alongside continued improvements in infrastructure that support environmentally responsible behaviour, as part of the airport’s transition towards a greener and more tourism-supportive facility. Full article