Investigating potential ways to improve fuel efficiency of aircraft operations is crucial for the development of the global air traffic management (ATM) performance target. The implementation of trajectory-based operations (TBOs) will play a major role in enhancing the predictability of air traffic and flight efficiency. TBO also provides new means for aircraft to save energy and reduce emissions. By comprehensively considering aircraft dynamics, available route limitations, sector capacity constraints, and air traffic control restrictions on altitude and speed, a “runway-to-runway” four-dimensional trajectory multi-objective planning method under loose-to-tight heterogeneous constraints is proposed in this paper. Taking the Shanghai–Beijing city pair as an example, the upper bounds of the Pareto front describing potential fuel consumption reduction under the influence of flight time were determined under different airspace rigidities, such as different ideal and realistic operating environments, as well as fixed and optional routes. In the congestion-free scenario with fixed route, the upper bounds on fuel consumption reduction range from 3.36% to 13.38% under different benchmarks. In the capacity-constrained scenario, the trade-off solutions of trajectory optimization are compressed due to limited available entry time slots of congested sectors. The results show that more flexible route options improve fuel-saving potentials up to 8.99%. In addition, the sensitivity analysis further illustrated the pattern of how optimal solutions evolved with congested locations and severity. The outcome of this paper would provide a preliminary framework for predicting and evaluating fuel efficiency improvement potentials in TBOs, which is meaningful for setting performance targets of green ATM systems.

Aiming at the problems of the imprecise safety distance standards for low-altitude UAVs within complex low-altitude environments and the difficulty of managing heterogeneous vehicles, a UAV safety interval calibration method based on random heading is proposed. Firstly, a UAV clustering model based on K-Means++ was established for the performance characteristics of UAVs, using evaluation indexes such as contour coefficient, sum of squares of errors, Davidson–Bourdain index, etc. Then, combining this with the characteristics of UAVs with random heading, a UAV safety interval calibration model based on random heading was constructed, and the conflict probability and airspace utilization rate were determined and metered for UAV safety interval calibration. The experimental results showed that the profile coefficient, sum of squares of errors, and Davidson–Berding index of the iteratively optimized UAV clustering were optimized by 53.9%, 55.6%, and 46.6%, respectively, compared with the initial clustering results, and that the safe intervals calibrated in the experimental environment of a single category of UAVs were also applicable to the fusion airspace environment after validation. The research results can provide a theoretical basis and methodological support for the safety interval calibration of UAVs in low-altitude fusion operations.

Air-to-air (A2A) and air-to-ground (A2G) communication links are typical link types for unmanned aerial vehicle (UAV) communication networks, where radio propagation channels are fundamental for the design and optimization of corresponding communication systems. In this paper, a UAV channel measurement system based on two unmanned aerial vehicles (UAVs) is developed, which is capable of simultaneous A2A and A2G measurements. This system adopts an integrated hardware and signal processing architecture that ensures time and frequency synchronization among multiple aerial and ground nodes. Several data postprocessing steps, including the back-to-back calibration, sliding-correlation-based channel impulse response (CIR) extraction, and constant false alarm rate (CFAR)-based multi-path extraction, are performed to achieve accurate channel data. A channel emulator is used to validate the accuracy of the developed system. Finally, the developed channel measurement system is applied to conduct field channel measurements in a campus scenario. Measured channel characteristics, including path loss (PL), shadow fading (SF), Rician K-factor, root mean square delay spread (RMS-DS), and small-scale fading (SSF) are analyzed, which reveal distinct propagation behaviors between the A2A and A2G channels. These results provide valuable experimental insights and channel measurement data for modeling UAV channels.

With China’s low-altitude economy becoming a strategic emerging industry, the rapid growth of UAV applications demands higher efficiency in low-altitude airspace utilization and safety management. However, existing studies lack unified grid division standards and refined methods to evaluate capacity for complex urban low-altitude airspace. This study is devoted to developing a methodology for determining safe distances and assessing the throughput capacity of transport systems. The work is based on a multi-criteria assessment that takes into account four significant indicators. The application of the Pareto optimization principle made it possible to identify the most effective compromise solutions. A collision probability model with random UAV(Unmanned Aerial Vehicle) headings was proposed to determine safety separations, and a grid capacity simulation model with saturation judgment and convergence verification was established. The optimal grid granularity was identified as 20 m. Safety separations for DJI M300RTK, Mavic 3Pro, and Air 3S were 104 m, 86 m, and 47 m, respectively. Saturated capacity stabilized within 106–116 s, with stable values of 1.022, 0.961, and 1.023 drones/min for the three UAV models. The results of the study contain key conclusions about traffic capacity and suggest ways to optimize it. Conclusions: This study provides a theoretical framework for airspace resource optimization and UAV path planning, offering quantifiable benchmarks to evaluate and manage urban low-altitude airspace.

This study investigates the construction of a topological network for resilient low-altitude vertiports. Addressing the issue of excessive network redundancy often caused by maximizing algebraic connectivity in traditional topology optimization problems, we employ algebraic connectivity—a key spectral metric—as a measure of network topology resilience. The objective function employs normalized algebraic connectivity that simultaneously considers total network distance, achieving an effective trade-off between global fault tolerance and construction costs at the model level. To address this challenging combinatorial optimization problem, the Gray Wolf Optimizer (GWO) algorithm is mechanistically enhanced. Experiments demonstrate that the proposed method achieves superior performance in key metrics such as objective function value and total network distance, significantly enhancing network resilience while controlling construction costs. For the optimized network topology solutions, simulations of six failure modes for nodes and edges analyze the response characteristics of the vertiport network’s maximum connected subgraph proportion and global efficiency during the gradual removal of nodes and edges. Results demonstrate that the designed vertiport topology network exhibits robust resilience. It maintains high connectivity and global efficiency under both random attacks and degree-based targeted node attacks, showcasing strong engineering applicability.