Search Results (697)

Complex urban scenarios impose high demands on the environmental perception capabilities of unmanned systems, which serve as a prerequisite for executing autonomous missions such as disaster response, infrastructure inspection, and smart city operations. UAVs, leveraging their high mobility, can provide accurate prior maps and wide-area aerial observation for unmanned ground vehicles. However, their long-range perception accuracy is limited. Conversely, UGVs can achieve high-precision environmental perception along their navigation paths using prior maps, but suffer from a constrained field of view. The collaboration between the two platforms complements their respective strengths, thereby enhancing 3D object perception and mapping accuracy in complex scenarios. To address the aforementioned challenges, this study proposes a cross-platform feature fusion method for 3D object perception and an incremental map updating approach for UAVs and UGVs. First, a dynamic SLAM method that integrates an optimized YOLOv8 with ORB-SLAM3 is employed to mitigate map blurring caused by dynamic noise, providing prior map information for UGVs. Second, a multimodal fusion perception model is constructed for UGVs, utilizing attention mechanisms to achieve deep fusion of multimodal Bird’s-Eye-View (BEV) features. This overcomes issues such as diminishing complementarity between modalities and weak temporal feature associations. Finally, an air ground fusion model based on a cross-attention mechanism is developed to fuse aerial view features with ground-based fused BEV features across platforms, yielding a unified feature representation for 3D object detection and generating a fused high-precision map. Experimental results demonstrate that under complex occlusion scenarios in a simulated dataset, the proposed collaborative perception system improves the mean Average Precision (mAP) by 12.7% and 15.7% compared to using a single UAV or a single UGV, respectively, while increasing the map accuracy F1-score by 0.21. This study provides technical support for achieving real-time and accurate air ground collaborative perception in complex dynamic environments. Full article

Air quality forecasting and environmental health research at urban and regional scales depend on the combination of measurements from heterogeneous sensor networks, yet the construction of integrated multi-source datasets is rarely described or released as a self-contained deliverable. This paper presents an open dataset that combines four sensor-derived sources covering the whole of Spain over the period from 2022 to 2024: hourly air quality observations from the 588 stations of the national network operated by the Ministerio para la Transición Ecológica y el Reto Demográfico (MITECO), daily meteorological records from the Agencia Estatal de Meteorología (AEMET), daily mobility indicators derived from anonymised mobile telephony events published by the Ministerio de Transportes y Movilidad Sostenible (MITMA) at the municipality level, and a calendar of national and Autonomous Community public holidays. The processing pipeline harmonises sources that differ in temporal resolution, spatial codification and quality regime into a tidy hourly table indexed by station and timestamp, with a fixed feature schema of 56 variables per record. Air quality stations are paired with their nearest AEMET station through a three-tier distance rule, and the daily exogenous features are aligned to the air quality time axis through a two-variant temporal-alignment scheme (lag-and-expand to the hourly grid for the hourly release, same-calendar-day join for the daily release). A complementary daily resolution variant of the dataset is also released, with 72 columns and the same feature schema except for the air quality block, which is aggregated to daily mean, minimum and maximum. The integrated dataset contains approximately 15 million hourly records across the 588 stations and is released on Zenodo (DOI 10.5281/zenodo.20196221) under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. It is intended as a substrate for research on air quality forecasting, environmental epidemiology and multi-source data fusion at the nationwide scale. Full article

Continuous population growth will lead to further expansion and densification of urban environments. In this context, Urban Air Mobility (UAM) has emerged as a new transportation solution through the use of Vertical Take-Off and Landing (VTOL) aircraft, more precisely, configurations such as lift-plus-cruise tilt-rotors. During the conceptual design phase, propeller design methodologies commonly reported in the literature rely on vortex-based approaches or actuator disk theory. However, the accuracy of these methods strongly depends on the inflow angle and operating conditions. This paper introduces an analytical model to predict propeller thrust at a 90° inflow angle (edgewise flight), based on a correction of the thrust under axial flight conditions and the propeller geometry evaluated at 75% span. The approach relies on local velocity and angle of attack estimations derived from classical Blade Element Momentum Theory (BEMT) with an additional correction to account for stall effects at high angles of attack. This capability is particularly relevant for modeling lift-plus-cruise tilt-rotor configurations cruise phase during early design stages while maintaining minimal computational cost. The proposed model is validated against wind tunnel measurements for several propellers tested at different global pitch angles, varying from 0 m/s to 9.1 m/s of windspeed and 1300 to 6200 rpms, demonstrating the applicability of the developed formulation for blades with twist angles up to 16°. Full article

Reliable spatial georeferencing of mobile mapping platforms is a fundamental prerequisite for high-fidelity urban remote sensing products such as 3D point clouds and digital twins. However, in deep urban canyons, severe signal occlusion and multipath effects reduce visible GNSS satellites, causing ambiguity resolution (AR) failure and degraded observation geometry for UGV-borne systems. Conventional Vehicle-to-Vehicle (V2V) cooperation offers limited improvement due to symmetric ground-level occlusion. To overcome this, we propose an asymmetric GNSS/UWB fusion method that introduces Unmanned Aerial Vehicles (UAVs) as high-altitude dynamic spatial anchors to reconstruct the 3D observation geometry. Two contributions are presented: (i) an asymmetric heterogeneous stochastic model coupling carrier-to-noise ratio (C/N0) and elevation angle to handle the quality disparity between air and ground sensor links, preventing multipath contamination of high-fidelity UAV observations; and (ii) a dynamic baseline constrained least-squares algorithm integrating Ultra-Wideband (UWB) ranging to stabilize GNSS positioning under high-dynamic relative motion. Validated through high-fidelity simulations and field experiments, the method achieves a 98.2% AR success rate and sub-decimeter 3D accuracy under extreme occlusion (≤3 visible satellites), while urban-canyon tests demonstrate 100% positioning availability across all evaluated epochs and reduce the 95th-percentile 3D error from 7.25 m to 0.19 m under the tested single-UAV/single-UGV configuration. The framework supports smart city modeling, 3D reconstruction, and infrastructure monitoring. Full article

Urban air-quality monitoring networks are often sparse, leaving coverage gaps where particulate matter (PM) concentrations cannot be directly observed. This paper extends the CityAirQ pollution tracking platform and its mobile air-quality device prototype by introducing an AI-based benchmark for two Bucharest station networks across three deployment-oriented tasks: multi-station temporal forecasting (Task A), leave-one-station-out same-day spatial estimation (Task B), and a preliminary mobile-site prediction pilot at an uncalibrated location (Task C). The benchmark compares machine-learning models, including ensemble tree methods, recurrent neural networks, and lightweight graph-inspired architectures, evaluated under a unified time-aware rolling protocol. In Task A, the proposed Advanced Stage 0–3 pipeline achieves the best overall MAE (7.12 $\mathsf{\mu }$g/m³), a 4.7% reduction relative to Random Forest (7.47 $\mathsf{\mu }$g/m³), while the Seasonal naïve (10.41 $\mathsf{\mu }$g/m³), Persistence (11.51 $\mathsf{\mu }$g/m³), neural, and graph-inspired references perform worse under recursive forecasting. In Task B, the neighbour-only Random Forest reaches a mean $R^2$ of 0.873 on the classic four-station network and a median $R^2$ of 0.734 on the ten-station city-scale extension. Task C is reported as an exploratory six-day prediction pilot, not as deployment-grade validation: no co-located EPA FRM/FEM or equivalent reference monitor was available at the mobile location ℓ. The historical-transfer Random Forest retained a sample-limited positive PM_2.5 association with the raw mobile readings ($r=0.432$, $n=6$), while a strict one-day-ahead online persistence predictor reduced PM_2.5 MAE from 40.58 to 20.00 $\mathsf{\mu }$g/m³ on the five forecastable mobile days. Ultimately, accurate PM monitoring empowers sustainable urban planning, helping to mitigate exposure risks and supporting long-term public health and environmental sustainability initiatives. Full article

Existing unmanned aerial vehicle (UAV) urban logistics planning follows a sequential paradigm—depot siting first, routing second—that embeds a structural information loss. Straight-line distance screening systematically overestimates the feasible service radius of candidate depots, creating a blindzone of depot–demand pairs that appear reachable but prove operationally infeasible under road network distances. We term this range-feasibility blindness and derive its analytical radius $\Delta =R_{max}(\alpha -1)/\left(2\alpha \right)$, where $\alpha$ is the road-to-straight-line distance ratio. Empirical measurement across three Chinese urban districts confirms $\alpha \in [1.40,1.52]$ and blindzone radii exceeding 2.8 km, establishing the phenomenon as a systemic property of high-density urban road geometry. To eliminate this failure by construction, we formulate a feasibility-embedded location–routing mixed-integer linear programme (MILP) that enforces road network range constraints simultaneously with depot opening decisions, making blindzone configurations implicitly inadmissible. A structure-aware Adaptive Large Neighbourhood Search (ALNS) solves the model at practical scales. Benchmark experiments on Dongli District (Tianjin) show cost reductions of 20.6–28.2% over greedy sequential baselines across three demand scenarios, with gains increasing monotonically with instance scale; cross-city experiments in Beijing and Shanghai confirm consistent improvement averaging 11.4% (Chaoyang, Beijing) and 10.2% (Pudong, Shanghai) over greedy initialisation across diverse urban morphologies. These results position joint optimisation as a necessary methodological shift for city-scale UAV infrastructure planning. Full article

China’s low-altitude economy (LAE) is moving from policy experimentation to coordinated industrial deployment, yet existing assessments often treat the LAE as a homogeneous sector or equate aircraft capability with deployment readiness. This study develops a scenario-gated sustainability readiness framework for six representative LAE and urban air mobility (UAM) scenarios in China: emergency medical logistics and disaster response, infrastructure inspection and public-service monitoring, urban instant logistics, airport shuttle and intermodal passenger transfer, urban air taxi, and low-altitude tourism. The proposed framework consists of a scenario layer, an eight-dimensional readiness layer, and a decision layer integrating 0–4 ordinal scoring, evidence-confidence tagging, non-compensatory gate conditions, and readiness classification. The eight dimensions cover mission and demand fit; airspace and traffic controllability; infrastructure and site readiness; digital communication, navigation, surveillance, and data security; vehicle, energy, and environmental performance; weather and route-environment robustness; workforce and organizational readiness; and social acceptance and legal legitimacy. The illustrative application indicates that infrastructure inspection is the only routine scaling candidate; emergency medical logistics and urban instant logistics are suitable for bounded routine operation; airport shuttle and tourism should remain controlled pilot candidates; and open-network urban air taxi is still at the pre-pilot stage. The study contributes a scenario-based deployment logic for sustainable aviation and UAM governance. Full article

The rapid growth of the low-altitude economy and urban air mobility (UAM) is reshaping urban transport and infrastructure systems. However, current planning practices still tend to treat green spaces, stormwater facilities, and drone infrastructure as separate subsystems. This paper proposes a Garden Hydrology UAV collaborative infrastructure framework for resilient urban low-altitude logistics and inspection. Pocket parks and sponge city facilities (rain gardens, detention basins) are redesigned as multi-functional UAV bases that integrate take-off/landing and charging with stormwater retention and recreation. A SWMM-based hydrological model provides time-varying inundation and storage states, which are mapped into dynamic node availability constraints for UAV operations, using EPA SWMM 5.2. A multi-objective optimization model is formulated to minimize logistics operation cost, hydrological risk exposure and noise impact on sensitive receptors, while respecting airspace and battery constraints. A stylized 4 km² high-density district is used to evaluate three scenarios: depot-only operations, garden–UAV integration without hydrological coupling, and the full collaborative framework with SWMM-based node availability and high-precision navigation. Simulation results show that the integrated design reduces makespan by up to 19.7%, energy use by 22.3%, and hydrological risk exposure by 63.4%, while lowering noise exposure by 21.3%, relative to the baseline. The study suggests that garden and sponge city infrastructures can become key physical supports of smart low-altitude networks under the low-altitude economy. Full article

This article discusses a novel aircraft coordination algorithm for automated vertiport operation. New applications of Innovative Air Mobility (IAM) including inspection, logistics and security UAVs, Urban Air Mobility (UAM) or Regional Air Mobility (RAM) present a coordination challenge, especially near vertiports, as large numbers of vehicles with different characteristics share the airspace, and so avoiding collisions, optimizing resource usage and operating with low human intervention is important.In this paper, this problem is addressed by proposing a new formulation of the aircraft coordination problem that makes use of a discretized airspace to detect potential conflicts and collisions between cooperative and non-cooperative aircraft in the surroundings of a vertiport. The proposed algorithm not only considers the cells traversed by the aircraft, but also the set of adjacent cells, making the algorithm more conservative and robust than other algorithms found in the literature, and achieving a 100% conflict-detection rate. A mathematical model of aircraft dynamics is employed to turn high-level flight plans into detailed aircraft trajectories, using those trajectories to detect potential collisions. The deconfliction problem is formulated as a mixed-integer optimization program that computes orders of pass for every conflict while minimizing the divergence between requested time of arrival (RTA) and estimated time of arrival (ETA). This problem is implemented in OR-Tools to be solved by means of the CP-SAT solver. The validity of the solution is tested by extensive simulation, showing tactical coordination of up to 25 aircraft landing on a vertiport. Full article

Objective: Rapid vertical manoeuvres and intermittent vibration in autonomous electric vertical take-off and landing (eVTOL) aircraft can provoke pronounced psychological anxiety in passengers. To address this, we propose a closed-loop adaptive system that integrates an in-ear wearable sensor with dynamic regulation of the cabin microenvironment, enabling real-time monitoring of each passenger’s autonomic state and delivering individualised mitigation through a continuous sense–analyse–intervene–feedback loop. Methods: The system is built around a pair of custom in-ear modules that integrate dual-wavelength photoplethysmography (PPG; 525 nm green and 940 nm infrared), galvanic skin response (GSR), and a six-axis inertial measurement unit (IMU) sampled at 200 Hz. To suppress the 20–80 Hz vibration generated by the distributed electric propulsion system, a compliant silicone damping sleeve attenuates high-frequency components at the hardware level, while a Kalman filter fuses the IMU and PPG streams and an adaptive notch filter removes residual rotor harmonics. The pipeline raises the heart-rate-variability (HRV) signal-to-noise ratio (SNR) to 24.1 dB, with a Pearson correlation of 0.96 against a medical-grade chest strap. A hybrid CNN–LSTM network—two convolutional layers (32 filters each) followed by two LSTM layers (128 hidden units)—predicts impending anxiety from HRV time-domain features (RMSSD, pNN50) and frequency-domain features (LF/HF ratio), triggering intervention 8.2 s in advance on average. According to the predicted anxiety level (mild/moderate/severe), a fuzzy controller modulates transcutaneous auricular vagus nerve stimulation (1–5 mA), the binaural-beat frequency (4–8 Hz, theta band), and the cabin lighting colour temperature (2700–6500 K) in real time. The intervention parameters are continuously refined by SPSA-based stochastic optimisation of the HRV recovery rate (step size 0.01; updated every 30 s). Results: In a randomised controlled experiment conducted in a simulated flight environment (N = 50; aged 22–45 years; 1:1 sex ratio), the active group reached physiological recovery in 52.3 s on average, compared with 98.6 s for the sham-controlled group—a 47% reduction (Cohen’s d = 1.24, p < 0.001). User acceptance reached 94%. Conclusions: The proposed in-ear platform enables closed-loop adaptive regulation of anxiety in the eVTOL cabin and overcomes the limitations of conventional passive mitigation strategies. By combining vibration-tolerant physiological sensing with multimodal environmental control, the work offers a practical pathway for improving passenger experience in urban air mobility and provides a useful reference for human-factors standards governing autonomous aircraft. Full article

Traffic simulation models are widely used in transportation analysis, often oriented toward keeping urban systems sustainable from various points of view, ranging from energy consumption to air quality. However, their accuracy depends on the quality of the data used to represent both the road network and travel demand. Although open-source datasets can be used to develop simulation networks and observed traffic information is available from big-data platforms, integrating these heterogeneous datasets remains challenging. Indeed, different road segmentation schemes may be used across different platforms, and common identifiers are often not adopted. This study proposes a GIS-based framework for spatially joining traffic data from big-data platforms with road networks used in traffic simulation models. The methodology integrates a microscopic simulation network derived from OpenStreetMap and implemented in SUMO with traffic data obtained from the TomTom Traffic Stats service. The workflow is implemented in QGIS (3.34 prizren) and combines spatial buffering, directional filtering, overlap analysis, and hierarchical match cleaning to associate traffic segments with the corresponding simulation network edges. The framework is applied to an urban case study in the city of Biella, Italy. Results show that more than 80% of the simulation network edges can be successfully linked with traffic segments, enabling the integration of hourly traffic indicators such as travel time and speed. The resulting dataset supports several applications, including network calibration, simulation validation, detector placement, and traffic demand estimation, contributing to the development of more reliable traffic simulation models for comparing and selecting sustainable urban mobility actions within the transportation planning process. Full article

Accurate simulation modeling of the danger zone and real-time visualization of the toxic cloud spread during a fire and explosion at an industrial facility in a nearby urban area are in demand by rescue services conducting evacuation. Using a cellular automaton method allows us to create an optimal predictive model of the danger zone spread, combine modeling accuracy with computational speed, and consider multiple input variables and the cascading nature of an accident during visualization. The objective of this study was to develop a mobile application for calculating the parameters of the danger zone during an accident at an industrial facility caused by a toxic cloud spreading into an urban area, based on the selection of a cellular automaton algorithm. The primary objective of the study was a highly detailed visualization of the danger zone with several predicted values of toxic substance concentrations in the air. The authors developed a cellular automaton-based model, which forms the basis of the mobile application. It takes into account several variables characterizing chemicals in the explosion and fire zone, climate factors, occupancy, building parameters, and the availability of respiratory protection. The FireSoft Mobile app was developed using the Visual Studio 2022 development environment, C# 10.0, and .NET MAUI, adapted for Android 8.0 and higher. The mobile app was tested to visualize a cloud of toxic pollutants forming a hazardous zone in an urban agglomeration for cases involving an ammonia tank explosion and a large fire involving a large amount of polyvinyl chloride. The results demonstrate the app’s feasibility and effectiveness in predicting, planning, and managing evacuation measures during accidents at an industrial facility. Full article

The increasing use of Unmanned Aerial Systems (UAS) in civil applications has accelerated the development of new Air Traffic Management (ATM) frameworks to ensure the safe and efficient operation. Onboard technology, such as Detect and Avoid (DAA) systems, have been proposed as an alternative to reduce operational risk to acceptable levels. However, these technologies require preliminary validation to meet current regulatory standards, which define the Minimum Operational Performance (MOP). In this work, we propose the architecture of two DAA systems based on frequency-modulated continuous-wave (FMCW) radars operating in the radiolocalization bands at 9.5 GHz and 24 GHz. The performance of both onboard systems was validated through the probability of detection $P_d$ for different intruder categories, meeting the MOP in accordance with the RTCA DO-366A, DO-396, and ASTM F3442 standards. Full article

Benzene is a major carcinogenic urban pollutant whose variability reflects interactions between emission sources, human activity, and atmospheric conditions. Although COVID-19 restrictions generally reduced traffic-related emissions, the combined effects of mobility changes, residential activity, and policy interventions on urban benzene dynamics remain insufficiently understood. This study investigated benzene variability in Belgrade, Serbia, during the COVID-19 state of emergency, from 15 March to 6 May 2020. A multi-source dataset was used, integrating high-resolution VOC measurements by PTR-quad-MS, meteorological variables, regulatory air-quality indicators, epidemiological data, mobility proxies, and quantified government-response measures. Tree-based ensemble machine-learning models, metaheuristic hyperparameter optimization, and explainable artificial intelligence methods, including SAGE and SHAP, were applied to examine non-linear and time-lagged relationships within the urban atmospheric system. The results showed that benzene variability was primarily associated with co-measured non-target VOCs, reflecting shared urban emission-source structures. Mobility and policy-related predictors contributed through short delayed responses, with an estimated response window of approximately 48–72 h. Sustained mobility reductions were associated with lower benzene concentrations, whereas increased residential activity partially offset traffic-related reductions. Within the Belgrade case study, these findings demonstrate the potential of interpretable machine learning to extract robust patterns from heterogeneous urban environmental datasets, while emphasizing the need for validation across additional cities and non-pandemic conditions before broader generalization. Full article

Traffic congestion and air pollution caused by rapid urbanization have emerged as critical challenges in metropolitan areas worldwide. Urban air mobility (UAM), particularly electric propulsion-based systems, has gained attention as a promising solution. For the successful commercialization of UAM, a lightweight airframe design with ensured structural integrity is essential. This study proposes an optimized lightweight design process that integrates automated composite manufacturing with artificial intelligence (AI)-based material property prediction. Finite-element analysis (FEA) was performed on glass fiber-, basalt fiber-, and carbon fiber-reinforced polymers under identical deformation conditions to derive design material properties in terms of elastic modulus and weight reduction. A large-scale dataset of fiber-reinforced plastics was established through an automated manufacturing process, and a deep learning regression model was developed using Altair AI Studio to predict mechanical properties under untested material and process conditions. The predicted properties were applied to a UAM fuselage model, and FEA results demonstrated that composite structures achieved equivalent or superior stiffness with up to 50% weight reduction compared to aluminum. In addition, inverse reserve factor (IRF) analysis confirmed structural safety, with all configurations maintaining IRF values below 1. The proposed AI-driven framework provides a scalable and data-driven lightweight design methodology applicable to next-generation UAM and advanced air mobility structures. Full article