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AAIB认定2023年9月14日VSR Ventures Learjet 45XR在孟买机场坠毁是由于飞行员在恶劣天气下继续不稳定进近并不当操作，导致低空失速。事故中所有8名乘客幸存但受伤，飞机损毁。调查显示无技术故障，但指出VSR Ventures维护记录、内部审计和飞行员培训存在不足。
推荐理由：事故调查报告揭示了飞行员在不利天气条件下未正确处理不稳定进近的问题，对eVTOL运营方和飞行员培训机构具有警示意义，需加强训练和操作规范。
Accurate demand forecasting is a prerequisite for eVTOL vertiport dispatch, pad staffing, battery service planning, and access management, yet short-horizon demand is affected by confounded interactions among weather, road access, queue feedback, aircraft availability, and pricing interventions. This study evaluates the proposed model on a synthetic but engineering-constrained eight-vertiport network, so the reported results should be interpreted as controlled methodological evidence rather than direct commercial demand measurement. The paper proposes a Causal Aware Feature Fusion DeepONet Mamba model that learns a function-to-function mapping from urban mobility states to vertiport demand fields. Causal gates separate contextual variables, network states, and intervention-sensitive variables before a Mamba branch encoder and coordinate-aware DeepONet trunk produce interval demand forecasts. Experiments with 184 simulated operating days show that the proposed model achieves an MAE of 18.7 requests per 15-minute interval, reducing error by 15.6% relative to the strongest non-causal baseline. Peak interval error decreases to 54 requests, 0.9-quantile pinball loss decreases to 11.3, and scenario ranking error is reduced by 32.8% compared with Mamba without causal fusion. Spatial transfer tests, missing-sensor experiments, and capacity-restriction scenarios indicate that causal feature control improves robustness when operating conditions deviate from ordinary correlations. The results show that combining causal fusion, neural operator learning, and selective state-space memory
Advanced air mobility operations hold the potential to enhance and expand regional transportation of both people and goods in populated areas. However, hazardous flight conditions arising from interactions between wind and the built environment remain a significant challenge for aerial vehicles in urban settings. This work proposes a novel framework towards safe flight planning of aerial vehicles in windy urban environments. A learning-based surrogate model is trained to rapidly predict flow fields from readily available information such as building geometry and incident wind. This surrogate prediction is used to calculate a volumetric flight challenge scalar field based on critical flow parameters and proximity to structures. A safe, flow-informed flight trajectory is then identified through a cost-minimizing pathfinder. The complete system is demonstrated experimentally through flight tests of a micro aerial vehicle through a model urban geometry placed in a large fan-array wind tunnel. Comparing this approach to trajectories generated without knowledge of the wind field, we find the flow-informed approach reduces undesired vehicle displacement and improves flight stability. This work is among the first practical demonstrations of safe, wind-aware methodologies for advanced air mobility in urban environments.（预印本；同行评审状态请核对原文。）
The success of advanced air mobility (AAM) operations is largely contingent on its effective integration with other ground transport modes. Under many use cases, AAM operators have to work with ride-hailing operators to create a seamless air taxi travel experience with adequate first and last-mile access. In investigating this multimodal coalition, this study proposes a distance-based subsidy rate design for AAM operators to incentivize ride-hail access to AAM hubs, incorporating air mobility operators' profitability considerations and travelers' route choices jointly. Using New York City (NYC) airport access as a case study, this study integrates high-volume for-hire vehicle (HVFHV) data from NYC taxi zones to consider real-world spatial demand distributions while considering passenger groups with different values of time (VOT) to derive insights on distinctive customer bases. Overall, the results show that AAM operators would need to subsidize the ride-hailing operators on vertiport access trips when air taxi operating costs exceed $12/mi. The analysis of ridership at AAM hubs indicates that ridership and profit contributions differ across different candidate vertiports in Manhattan, reflecting spatial demand heterogeneity. Additionally, having the airport access system in place, the taxi zones that generate the highest passenger demand to all three major NYC airports are identified under lower air taxi fare scenarios. These findings highlight how a distance-based subsidy rate design is beneficial in facilitating better access to vertiports and to foster high air taxi rid（预印本；同行评审状态请核对原文。）
机场接入用例是城市空中交通（UAM）早期阶段具有前景的应用。理解UAM在机场的运营模式对于制定公平且有效的监管和管理决策至关重要。一个核心的开放问题是UAM是否会像地铁和铁路这样的常规定期运输服务一样被整合进机场交通网络，还是像交通运输网络公司（TNC）那样以动态供需匹配为特征。在本文中，我们提出一个两阶段框架，用于开展UAM网络的经济可行性分析。在第一阶段，我们引入了一个联合供需变量定价问题，以评估动态定价对UAM运营的影响。该模型使用二元Logit公式来捕捉出行时间优势与票价水平之间的权衡。在第二阶段，确定的需求作为输入用于非线性充电时间的电动城市空中交通车辆路径问题（eUAMVRP-NL），以优化车队调度和充电决策，从而得出运营收入和成本估算。我们将该框架应用于洛杉矶国际机场（LAX）接入市场的案例研究，该案例包含一个八辐状垂直港口网络。我们的结果表明，UAM运营显著受益于类似TNC的管理；相比固定定价方案，可变定价政策可使运营利润增加超过100%。此外，我们识别出较长UAM飞行中的阶段长度经济性。（预印本；同行评审状态请核对原文。）
Electric vertical takeoff and landing (eVTOL) aircraft operating in high-density urban airspace must maintain safe separation through tactical conflict resolution, yet the energy cost of such maneuvers has not been systematically quantified. This paper investigates how conflict-resolution maneuvers under the Modified Voltage Potential (MVP) algorithm affect eVTOL energy consumption. Using a physics-based power model integrated within a traffic simulation, we analyze approximately 71,767 en route sections within a sector, across traffic densities of 10-60 simultaneous aircraft. The main finding is that MVP-based deconfliction is energy-efficient: median energy overhead remains below 1.5% across all density levels, and the majority of en route flights within the sector incur negligible penalty. However, the distribution exhibits pronounced right-skewness, with tail cases reaching 44% overhead at the highest densities due to sustained multi-aircraft conflicts. The 95th percentile ranges from 3.84% to 5.3%, suggesting that a 4-5% reserve margin accommodates the vast majority of tactical deconfliction scenarios. To support operational planning, we develop a machine learning model that estimates energy overhead at mission initiation. Because conflict outcomes depend on future traffic interactions that cannot be known in advance, the model provides both point estimates and uncertainty bounds. These bounds are conservative; actual outcomes fall within the predicted range more often than the stated confidence level, making them suitable for safety-critical reserve planning. Together（预印本；同行评审状态请核对原文。）
The Low-Altitude Economy (LAE) is rapidly emerging as a new technological and industrial frontier, with unmanned aerial vehicles (UAVs), electric vertical takeoff and landing (eVTOL) aircraft, and aerial swarms increasingly deployed in logistics, infrastructure inspection, security, and emergency response. However, the large-scale development of the LAE demands a reliable aerial foundation that ensures not only real-time connectivity and computational support, but also navigation integrity and safe airspace management for safety-critical operations. High-Altitude Platforms (HAPs), positioned at around 20 km, provide a unique balance between wide-area coverage and low-latency responsiveness. Compared with low earth orbit (LEO) satellites, HAPs are closer to end users and thus capable of delivering millisecond-level connectivity, fine-grained regulatory oversight, and powerful onboard computing and caching resources. Beyond connectivity and computation, HAPs-assisted sensing and regulation further enable navigation integrity and airspace trust, which are essential for safety-critical UAV and eVTOL operations in the LAE. This article proposes a five-stage evolutionary roadmap for HAPs in the LAE: from serving as aerial infrastructure bases, to becoming super back-ends for UAV, to acting as frontline support for ground users, further enabling swarm-scale UAV coordination, and ultimately advancing toward edge-air-cloud closed-loop autonomy. In parallel, HAPs complement LEO satellites and cloud infrastructures to form a global-regional-local three-tier architecture. Looking forwa（预印本；同行评审状态请核对原文。）
Advanced Air Mobility (AAM) operations are planned to utilize strategic flight planning services that predict temporal uncertainties to validate flight plans against hazards such as weather cells, restricted airspaces, and CNS disruption areas. This paper presents a Kalman Filter-based uncertainty propagation method that models Flight Management System (FMS) correction behavior through a sigmoid-blended measurement noise covariance. The sigmoid formulation generalizes existing discrete FMS activation thresholds into a continuous, tunable function that smoothly transitions the filter's measurement noise based on progress toward each waypoint. When the measurement noise is high, due to an inverse relationship, the Kalman gain is small and thus uncertainty grows; as the aircraft nears a waypoint, measurement noise decreases as a function of progress, the Kalman gain increases, and state covariance contracts which models the FMS progressively correcting toward the planned trajectory. The approach is computationally efficient (up to two orders of magnitude faster than Monte Carlo methods), scales with control inputs, and is parametrically tunable for different classes of aircraft. The measurement noise covariance is calibrated using real Automatic Dependent Surveillance-Broadcast (ADS-B) data from commercial Instrument Flight Rules (IFR) flights serving as surrogates for future AAM operations, achieving coverage probability conservative relative to theoretical Gaussian predictions at the 1-sigma confidence level on a hold out verification dataset (N = 36). Parameter sensitivity （预印本；同行评审状态请核对原文。）
With rapid urbanization and increasing population density, urban traffic congestion has become a critical issue, and traditional ground transportation methods are no longer sufficient to address it effectively. To tackle this challenge, the concept of Advanced Air Mobility (AAM) has emerged, aiming to utilize low-altitude airspace to establish a three-dimensional transportation system. Among various components of the AAM system, electric vertical take-off and landing (eVTOL) aircraft plays a pivotal role due to their flexibility and efficiency. However, the immaturity of Ultra Reliable Low Latency Communication (URLLC) technologies poses significant challenges to safety-critical AAM operations. Specifically, existing Stacked Intelligent Metasurfaces (SIM)-based eVTOL systems lack rigorous mathematical frameworks to quantify probabilistic delay bounds under dynamic air traffic patterns, a prerequisite for collision avoidance and airspace management. To bridge this gap, we employ network calculus tools to derive the probabilistic upper bound on communication delay in the AAM system for the first time. Furthermore, we formulate a complex non-convex optimization problem that jointly minimizes the probabilistic delay bound and the propagation delay. To solve this problem efficiently, we propose a solution based on the Block Coordinate Descent (BCD) algorithm and Semidefinite Relaxation (SDR) method. In addition, we conduct a comprehensive analysis of how various factors impact regret and transmission rate, and explore the influence of varying load intensity and total delay on th（预印本；同行评审状态请核对原文。）
Electric Vertical Take-off and Landing vehicles (eVTOLs) are driving Advanced Air Mobility (AAM) toward transforming urban transportation by extending travel from congested ground networks to low-altitude airspace. This transition promises to reduce traffic congestion and significantly shorten commute times. To ensure aviation safety, eVTOLs must fly within prescribed flight corridors. These corridors are managed by ground-based Air Traffic Control (ATCo) stations, which oversee air-ground communication and flight scheduling. However, one critical challenge remains: the lack of high rate air-ground communication and safe flight planning within these corridors. The introduction of 6G-oriented Stacked Intelligent Metasurface (SIM) technology presents a high rate communication solution. With advanced phase-shifting capabilities, SIM enables precise wireless signal control and supports beam-tracking communication with eVTOLs. Leveraging this technology, we propose a Composite Potential Field (CPF) approach. This method dynamically integrates target, separation, and communication fields to optimize both SIM communication efficiency and flight safety. Simulation results validate the effectiveness of this DT-based approach. Compared to the potential field flight control benchmark, it improves the transmission rate by 8.3\%. Additionally, it reduces flight distance deviation from the prescribed corridor by 10\% compared to predetermined optimization methods.（预印本；同行评审状态请核对原文。）
Electric vertical takeoff and landing (eVTOL) aircraft manufacturers await numerous pre-orders for eVTOLs and expect demand for such advanced air mobility (AAM) aircraft to rise dramatically soon. However, eVTOL manufacturers (EMs) cannot commence mass production of commercial eVTOLs due to a lack of supply chain planning for eVTOL manufacturing. The eVTOL supply chain differs from traditional ones due to stringent quality standards and limited suppliers for eVTOL parts, shortages in skilled labor and machinery, and contract renegotiations with major aerospace suppliers. The emerging AAM aircraft market introduces uncertainties in supplier pricing and capacities, eVTOL manufacturing costs, and eVTOL demand, further compounding the supply chain planning challenges for EMs. Despite this critical need, no study has been conducted to develop a comprehensive supply chain planning model for EMs. To address this research gap, we propose a stochastic optimization model for integrated supply chain planning of EMs while maximizing their operating profits under the abovementioned uncertainties. We conduct various numerical cases to analyze the impact of 1) endogenous eVTOL demand influenced by the quality of eVTOLs, 2) supply chain disruptions caused by geopolitical conflicts and resource scarcity, and 3) high-volume eVTOL demand similar to that experienced by automotive manufacturers, on EM supply chain planning. The results indicate that our proposed model is adaptable in all cases and outperforms established benchmark stochastic models. The findings suggest that EMs can commence ma（预印本；同行评审状态请核对原文。）
Urban air mobility is an innovative mode of transportation in which electric vertical takeoff and landing (eVTOL) vehicles operate between nodes called vertiports. We outline a self-organized vertiport arrival system based on deep reinforcement learning. The airspace around the vertiport is assumed to be circular, and the vehicles can freely operate inside. Each aircraft is considered an individual agent and follows a shared policy, resulting in decentralized actions that are based on local information. We investigate the development of the reinforcement learning policy during training and illustrate how the algorithm moves from suboptimal local holding patterns to a safe and efficient final policy. The latter is validated in simulation-based scenarios, including robustness analyses against sensor noise and a changing distribution of inbound traffic. Lastly, we deploy the final policy on small-scale unmanned aerial vehicles to showcase its real-world usability.（预印本；同行评审状态请核对原文。）
全球eVTOL通信系统市场分析报告发布，涵盖2026-2032年行业份额、销售预测与需求展望。报告指出，空地通信、V2X连接及低延迟通信是推动市场增长的关键因素。企业需关注技术发展、供应链条件及客户需求以优化战略决策。
EUROCAE 通过视频系列揭示 ED-12 标准在航空软件适航认证中的关键作用。该标准被全球认证机构用于支持航空软件的适航审批。ED-12/RTCA DO-178 是确保航空软件行为符合预期的核心依据。
马尔加莱将举办VERT-IGO项目活动，验证eVTOL和货运无人机在空域整合的可行性；计划在马尔加莱机场至马拉贝拉和马尔加莱港至阿尔梅里亚两条走廊进行演示飞行；项目旨在安全整合电动空中出租车和大型货运无人机进入现有空域。
eVTOL Insights 2026年10月数字杂志发布，聚焦eVTOL领域领导力、自主飞行及先进空中交通下一步发展。Joby Aviation展示其自主飞行技术，一架改装塞斯纳飞机完成3,199英里无操控飞行。Horizon Aircraft CEO分享创业与航空业务建设经验，探讨如何将项目转化为商业可行服务。
日本宇宙航空研究开发机构（JAXA）的一项研究提出，通过将VFR飞行限制在精心设计的四维操作体积内，可在低空空域实现VFR与无人机、eVTOL等的协同运行。研究基于实际飞行测试，验证了操作体积约束的有效性，为低空空域整合提供了技术路径。
推荐理由：该研究提出通过操作体积约束VFR飞行以实现低空空域整合，为eVTOL和无人机在复杂空域中的协同运行提供了技术路径，对空管系统设计和适航审定标准具有参考价值。
FAA 已定义 ACAS X 新一代避撞系统以适应混合空域，eVTOL 运营商应确保空域信息一致并建立自动避让机制。TCAS 在高密度空域中提供独立安全层，但需与运行程序结合以提升整体安全性。行业需关注早期冲突预警而非仅依赖 RA 干预。
一项研究指出，eVTOL 和无人机在急救医疗中的应用已取得进展，但政策框架滞后。研究整合了文献计量分析、系统综述和政策文本分析，发现 eVTOL 技术与健康政策之间关联较弱，且政策工具侧重于环境和供应侧，需求侧工具不足。研究建议构建双轮驱动、四链整合框架，以促进 eVTOL 急救服务的可持续发展。
推荐理由：研究揭示了 eVTOL 在急救医疗中的潜力，但政策滞后，需关注技术成熟度与政策框架的同步性，对医疗急救系统升级和政策制定有参考价值。
ANRA Technologies 推出 VISTA 空域感知应用，帮助无人机运营商获取附近飞行器意图和位置信息。该应用不连接 UTM 服务，仅接收数据，提升共享空域下的飞行意识。已有超过 20 个组织使用其可视化能力监控无人机活动。
一项研究提出基于多候选飞行路径的UAM调度模型，通过优化飞行路径、巡航高度和时间安排，降低噪声暴露并提升运营效率。该模型在多个 vertiport 对间引入四条候选平面路径，使目标函数值下降6.71%，噪声暴露减少15.14%。研究采用定制分支定界算法，显著提升求解效率。
阿曼交通、通信和信息技术部联合民航局宣布启动“先进空中交通演示与实测战略计划”，邀请国内外企业参与国家监管沙盒测试；演示活动定于2026年12月6日至10日举行，重点测试物流、医疗等领域的创新技术。
在先进空中交通（AAM）环境中进行战略飞行计划验证需要可靠的方法来预测飞行器状态的不确定性并检测与动态空域危害的潜在冲突。本文提出了一种结合不确定性条件轨迹预测和多面体危害表示的新型框架，用于飞行前冲突检测。我们引入了一种闭式不确定性估计方法，该方法将非均匀有理B样条（NURBS）曲线拟合用于运动轨迹生成，并与卡尔曼滤波器结合用于状态协方差传播。借鉴光传播算法（LPA）范式，我们采用了一个sigmoid混合测量噪声模型，以捕捉飞行管理系统在接近所需到达时间（RTA）时的不确定性减少行为。通过速度到时间方差转换，得出的时间不确定性界限，使飞行路径上到达时间偏差的概率评估成为可能。对于危害表示，我们开发了一个操作条件分类方案，将网格化的环境数据，特别是天气现象，转换为具有强度分层的三维多面体体积。这些危害多面体结合了基于飞行器性能特性的特定安全缓冲区。通过在均值轨迹周围的空域不确定性管与危害多面体和时间重叠上运行的网格交集算法进行冲突检测。该框架（预印本；同行评审状态请核对原文。）
变革性的电动垂直起降（eVTOL）飞行器在过去十年中因其高效的翼载巡航能力和对地面基础设施的依赖减少而受到广泛关注。然而，这些飞行器的控制系统设计仍然具有挑战性，因为它们必须在多个飞行阶段中运行，每个阶段都有不同的主导动态。如果未加以解决，这种复杂性将显著增加飞行员的工作负担，因此推动了多阶段飞行操作的飞行员控制系统开发。简化的飞行操作概念为减少飞行员工作负担提供了一种有前景的策略。本研究提出了eVTOL飞机的一种新型飞行员控制系统设计与实现，并通过配备主动力反馈侧杆的全动模拟器上的串联倾斜翼飞机模拟进行验证。该系统在特定飞行阶段为飞行员提供触觉反馈，支持直观控制。所提出的方案通过利用可用的自由度实现了无缝过渡和多阶段飞行操作。此外，还提出了一种基于最优控制的方法作为评估指令滤波器引起的性能损失和操纵器活动的指标。结果表明，所提出的指令滤波器与闭环系统相比不会显著增加任务持续时间，而主动侧杆有助于减少操纵器活动。（预印本；同行评审状态请核对原文。）
先进空中交通（AAM）的快速扩展使得基于走廊的结构空域成为高密度无人机（UAV）交通的有前景的基础设施。编队飞行可以通过抑制激波传播来提高走廊容量，但在坡道分支、合并和拥堵期间，刚性编队会变得低效或不安全。为了解决这个问题，本文提出了一种任务驱动的分支-合并控制框架，用于结构空域中的无人机编队。首先，建立了一个走廊-坡道分支结构空域模型，以表征交通动态和空间约束。在此基础上，一种快速任务驱动的聚类机制整合了空间连通性、飞行意图和空中任务交互，以实现坡道分支和交通重塑的实时分支和合并。为了使分支-合并重新配置在编队的媒体访问控制（MAC）层上可执行，进一步设计了一种集群感知的分布式时分多址（CAD-TDMA）协议。它在保护集群内控制同步的同时，保守地重复使用低风险的集群间时隙。仿真结果表明，所提出的分支-合并算法在严重物理重叠和拥堵下保持接近零的几何误分类。通过编队分支-合并轨迹，CAD-TDMA在固定TDMA和WiFi MAC上实现了最佳的延迟-损失-吞吐量权衡。这表明所提出的编队控制框架可以同时支持实时编队重新配置和可靠的通信。（预印本；同行评审状态请核对原文。）
电动垂直起降（eVTOL）飞行器的城市空中交通（UAM）有助于缓解地面交通拥堵。设计一个安全且能减少旅行时间的eVTOL飞行轨迹对于UAM的采用至关重要。现有的轨迹设计研究可能未能充分考虑城市环境中密集的障碍物、复杂的eVTOL飞行动力学或一个或多个飞行阶段。不考虑这些因素可能导致低质量，甚至不可行的轨迹。我们开发了一个混合框架，可以整合建筑物障碍物数据、风速数据、eVTOL飞行动力学和其他现实操作约束，以估算上升、巡航和下降阶段的四维eVTOL飞行轨迹，旨在最小化旅行时间。我们的框架首先用相交的凸多边形填充无障碍区域，然后使用基于凸集的路径规划器识别这些多边形的潜在低旅行时间候选序列，最后使用最优控制程序生成最终轨迹，该轨迹按之前识别的顺序穿过这些多边形。我们在纽约市内的路线中评估了我们的框架。我们的框架可以在存在多达250个建筑物障碍物的情况下设计符合上述约束的轨迹。我们展示了不包括上述约束可能导致飞行时间低估高达20%。（预印本；同行评审状态请核对原文。）
城市空中交通（UAM）需要可靠地评估低空风的危害，因为风、阵风和建筑物引起的湍流已被认为是影响车辆稳定性、路线可行性、垂直港选址和空域管理的关键因素。尽管风洞实验、计算流体力学（CFD）、多尺度降尺度、降阶模型和无人机规划数据集已推动了风感知分析的发展，但用于数据驱动、城市规模UAM规划的公共资源在地理覆盖范围、场景多样性、垂直范围、建筑真实性以及任务导向基准测试方面仍有限。为了解决这一差距，我们引入了U3DWind，这是一个使用我们GPU加速的格子玻尔兹曼方法-大涡模拟（LBM-LES）框架生成的建筑物分辨率低空风场数据集，用于快速城市流模拟。U3DWind涵盖了中国五个超大城市：北京、上海、广州、深圳和香港。该数据集包含720次模拟，每座城市有16个入流方向、三个参考风速和三个季节性大气场景（年、夏季和冬季）。以10米网格分辨率，该数据集提供了三维三分量（3D3C）速度、湍流动能（TKE）、流密度和流-固掩膜。为了支持操作相关的评估，我们进一步定义了五个基准任务：风场预测、稀疏传感器风场重建、站点风暴露排名、适航性风合规性风险评分和噪声传播建模。作为多城市、建筑物分辨率的3D城市风场（预印本；同行评审状态请核对原文。）
随着城市空中交通（UAM）向高密度运行扩展，在复杂的3D城市环境中生成无碰撞轨迹是一项关键安全要求。本文提出一种可扩展的序列二次规划（SQP）框架，将几何环境约束、运行限制和飞行器动力学整合到单一在线轨迹优化过程中。我们的方法不会预先计算无障碍通道，而是将避障编码为实时分离超平面约束，并在求解器的每次迭代中重新生成，从而随着参考轨迹和环境的变化，在线联合求解密集城市几何环境与完整自由度的飞行器动力学。可变尺度四叉树分解将计算量控制在有限范围内，使该框架能够扩展到全市范围的环境，同时保持高速飞行所需的实时性能。我们在五个真实城市中心的飞行任务中，将该框架与传统序列二次规划、迭代线性二次调节器和微分动态规划进行了验证；在仅使用 CPU 的硬件上，成功率和净空率均达到100%。（预印本；同行评审状态请核对原文。）
航空交通增长、先进空中交通和日益自主的操作正在推动对可扩展和适应性空域设计方法的需求。这一挑战的核心在于量化交通流结构和需求，部分由空域几何形状决定，如何影响冲突生成和运营复杂性。本文提出一个分析框架，利用随机流量模型计算结构化空域中的冲突率和冲突概率。交通流被建模为具有规定到达间隔时间分布的更新过程，而流之间的相互作用则通过几何依赖的最小间距约束在合并和交叉处进行捕捉。在此公式中，推导出冲突率和冲突概率的预期值的闭合形式上界，作为流量配置和需求的函数。这些指标被解释为空域复杂性的互补度量，反映了管制员工作量和每架飞机的操作风险。该方法被应用于具有不同路由结构和流量分布的代表性六边形单元几何形状。结果揭示了路由灵活性、容量和冲突生成之间的非单调权衡，中间流量配置优于高度受限和高度分布的案例。所提出的框架为评估空域设计替代方案和基于复杂性的交通管理策略提供了一个可行的工具。（预印本；同行评审状态请核对原文。）
先进空中交通（AAM）利用电动垂直起降（eVTOL）飞行器应对城市拥堵和排放问题。然而，走廊设计、运营管理及分离标准在高密度运行安全方面仍缺乏充分研究。本文应用系统综述和元分析的首选报告项目（PRISMA）指南，系统地回顾IEEE Xplore和Web of Science的相关文献，重点关注2010年至2024年的出版物。采用背景、干预、机制和结果（CIMO）框架指导研究问题的开发。在筛选2,039篇期刊和会议论文后，有62篇文章符合纳入标准。研究结果揭示了缺乏集成的走廊设计方法、有限的操作策略以及依赖于传统航空设计的标准。提出统一的走廊设计和分离定义框架及分类法，以解决这些不足，为未来研究和操作框架提供信息，确保在城市环境中安全、高效地部署eVTOL飞行器。（预印本；同行评审状态请核对原文。）
电动垂直起降（eVTOL）飞机因其高效率、环保性和低噪音等优势，被视为缓解城市交通拥堵的有效手段。尽管eVTOL在全球范围内具有政策支持、产业基础和市场前景，但其商业化过程仍面临障碍。纯电动eVTOL面临包括电池能量密度有限、高运营功率需求以及快速能量补充的挑战，这些因素共同限制了其飞行续航能力和应用场景。为解决这些问题，通过调查电池能量补充策略，提出了一种基于闭环供应链的eVTOL电池充电和换电优化框架。首先，采用时空网络技术解决充电站和换电站之间高效灵活电池运输系统的建模挑战。随后，旨在最大化模型的运营收益，优化电池换电、运输和充电过程的管理，促进eVTOL、换电站和充电站之间的协调运行。在此基础上，通过Gurobi求解模型，验证其可行性。最后，敏感性分析结果表明，该模型在电池换电服务质量、系统可持续性和运营韧性等方面表现出色。（预印本；同行评审状态请核对原文。）
先进的空中交通运营预计会显著增加城市空域的空中交通量，需要自主交通管理系统以确保在高度拥堵环境中无碰撞运行。在本文中，我们提出了一种多智能体协调框架，该框架使用最小到达时间（TTR）作为优先级分配、时间分离和安全过滤的统一指标。我们专注于协调多个空中车辆合并进入空中走廊的问题，同时保持车辆之间的安全分离。车辆根据TTR分配到达一致的优先级，目标TTR值用于强制时间间隔，从而诱导空间分离。基于哈密顿-雅可比可达性值函数的优先级一致的安全过滤层促进了碰撞避免，同时最小地修改参考引导。在高度拥堵的走廊合并场景的仿真结果表明，所提出的方法相比时间最优引导和无优先级安全过滤，在安全、公平性和效率方面均有提升。（预印本；同行评审状态请核对原文。）
确保先进空中交通（AAM）在低空空域中的安全高效运行，需要一个可靠、强大且具有韧性的监视系统，能够持续检测、识别和跟踪飞机，无论是在正常还是非正常条件下。为满足这一需求，本研究开发了一个全面的3R建模框架，即可靠性、鲁棒性和韧性，用于先进空中交通（SAM）系统的监视，重点在于多类型传感器网络的最优设计和运行。在正常运行条件下，可靠性模型确定满足监视覆盖和检测要求所需的传感器类型、数量和位置。为应对外部扰动，如恶劣天气条件或AAM交通需求的突然增加，鲁棒性模型识别出维持系统性能所需的额外传感器需求。此外，对于由主传感器故障引起的监视中断，韧性模型开发了备用传感器部署和调度策略，以提供临时监视覆盖，减少运营中断，并支持AAM操作的安全继续。（预印本；同行评审状态请核对原文。）
先进空中交通（AAM）已成为下一代交通系统的关键支柱，涵盖了广泛的无人驾驶航空器（UAV）应用。为了实现AAM，保持UAV与控制中心之间可靠且高效的通信链路至关重要。同时，无线网络的高度动态性，结合UAV有限的机载能源，使得高效的轨迹规划和网络关联变得至关重要。现有的地面网络由于频繁的切换和覆盖空白，往往无法提供普遍的覆盖。为了解决这些挑战，地球静止轨道（GEO）卫星提供了一种有前景的补充解决方案，以扩展UAV的连接范围，超越地面边界。本文提出了一种集成的GEO地面网络架构，以确保无缝的UAV连接。利用人工智能（AI），开发了一种基于深度Q网络（DQN）的算法，用于联合UAV轨迹和关联规划（JUTAP），旨在最小化能耗、切换频率和断连性。仿真结果验证了该算法在集成的GEO地面框架内的有效性。（预印本；同行评审状态请核对原文。）
本调查回顾了现有和设想中的安全漏洞及防御机制，这些与先进空中交通（AAM）系统相关，重点是电动垂直起降（eVTOL）飞机。借鉴商业航空中航电系统的漏洞和自动化无人机系统（UAS）的漏洞，本文提出了攻击分类，分析了缓解策略，并提出了针对未来AAM生态系统的安全系统架构。本文还强调了关键威胁向量，包括全球定位系统（GPS）干扰/欺骗、空中交通管制（ATC）射频误用、对TCAS和ADS-B的攻击、通过电子飞行包（EFB）的可能后门、由飞机自动化和连接性引入的新漏洞，以及飞行管理系统（FMS）软件、数据库和云服务的风险。最后，本文描述了针对这些攻击的新兴防御技术，并指出需要解决的开放性技术问题以实现更好的防御机制。（预印本；同行评审状态请核对原文。）
Conventional optimization-based metering depends on strict adherence to precomputed schedules, which limits the flexibility required for the stochastic operations of Advanced Air Mobility (AAM). In contrast, multi-agent reinforcement learning (MARL) offers a decentralized, adaptive framework that can better handle uncertainty, required for safe aircraft separation assurance. Despite this advantage, current MARL approaches often overfit to specific airspace structures, limiting their adaptability to new configurations. To improve generalization, we recast the MARL problem in a relative polar state space and train a transformer encoder model across diverse traffic patterns and intersection angles. The learned model provides speed advisories to resolve conflicts while maintaining aircraft near their desired cruising speeds. In our experiments, we evaluated encoder depths of 1, 2, and 3 layers in both structured and unstructured airspaces, and found that a single encoder configuration outperformed deeper variants, yielding near-zero near mid-air collision rates and shorter loss-of-separation infringements than the deeper configurations. Additionally, we showed that the same configuration outperforms a baseline model designed purely with attention. Together, our results suggest that the newly formulated state representation, novel design of neural network architecture, and proposed training strategy provide an adaptable and scalable decentralized solution for aircraft separation assurance in both structured and unstructured airspaces.（预印本；同行评审状态请核对原文。）
城市空中交通（UAM）是一种无人机的新兴应用，有望减少出行时间并缓解城市交通系统的拥堵。随着无人机密度的增加，UAM交通预计会经历与地面交通类似的拥堵。然而，UAM交通的基本特性，特别是在真实世界操作条件下，仍 largely unexplored。本研究提出了一种构建UAM交通基本图（FD）的通用框架，通过将理论分析与物理实验相结合。据我们所知，这是首次利用真实世界物理实验数据推导UAM基本图的研究。在理论方面，我们设计了两种无人机控制律以实现避撞，并开发了基于仿真的交通生成方法，以产生多样化的UAM交通场景。基于Edie的定义，我们应用了交通流理论，并采用近静态交通条件过滤方法来构建基本图。为了考虑真实世界干扰和建模不确定性，我们进一步在缩小比例的测试平台上使用Bitcraze Crazyflie无人机进行了物理实验。收集并整理了仿真和物理实验轨迹数据，形成了UAMTra2Flow数据集，该数据集使用所提出的框架进行分析。初步结果表明，经典地面交通的基本图结构，特别是Underwood模型，适用于UAM系统。值得注意的是，从物理实验获得的基本图曲线与基于仿真的结果存在偏差（预印本；同行评审状态请核对原文。）
城市空中交通（UAM）带来了前所未有的交通协调挑战，尤其是在密集城区走廊中无人机密度不断增加的情况下。本文介绍了一种数学模型，利用控制算法优化一种基于边缘人工智能的分布式蜂群架构，以实现智能冲突解决，使系统能够在低延迟下进行实时决策。通过使用轻量级神经网络，该系统利用边缘节点执行分布式冲突检测与解决。开发了一个仿真平台，以评估在不同无人机密度下的该方案。结果表明，冲突解决时间缩短了多达3.8倍，且准确性相比传统集中式控制模型有所提升。所提出的架构对于未来UAM系统的可扩展、高效和安全的空中交通管理具有很高的前景。（预印本；同行评审状态请核对原文。）
城市空中交通由于近期在电动垂直起降（eVTOL）飞行器方面的技术进步而获得快速发展，提供更快的点对点空中出租车服务，有助于缓解长期拥堵的城市交通。研究评估了城市空中交通（UAM）在旧金山湾区的可行性及系统级采用潜力，通过比较旧金山、奥克兰、圣何塞和帕洛阿尔托机场等关键区域节点的乘客出发、等待、旅行和到达时间与传统地面交通的差异。在MATLAB中开发了多智能体仿真，以评估机队运营并利用泊松过程建模需求到达，考虑随机乘客流量和周转限制。结果表明，在高峰需求期间利用UAM可将整个地区的总旅行时间减少多达百分之八十。本文的研究结果突出了机队调度优化的关键运营因素。特别是机队规模、乘客请求量和周转时间如何直接影响等待时间、运营成本和整体用户接受度。（预印本；同行评审状态请核对原文。）
我们介绍了Multi-Agent gatekeeper，这是一个框架，为密集3D环境中的领航-跟随编队控制提供可证明的安全保证。现有方法面临权衡：在线规划器和控制器缺乏正式的安全保证，而离线规划器则缺乏对代理数量或期望编队变化的适应性。为了解决这一差距，我们提出了一种混合架构，其中单个领航者跟踪一个预先计算的安全轨迹，该轨迹作为所有跟随代理的共享轨迹备份集。跟随者执行一个名义上的编队保持跟踪控制器，并通过始终拥有已知安全的备份机动沿领航者的路径保持安全。我们正式证明了该方法可确保与静态障碍物和其他代理的避碰。主要贡献包括：（1）Multi-Agent gatekeeper算法，该算法将我们的单代理gatekeeper框架扩展到多代理系统；（2）轨迹备份集，用于可证明安全的代理间协调，用于领航-跟随编队控制；以及（3）首次在3D环境中应用gatekeeper框架。我们在模拟的3D城市环境中展示了我们的方法，其中在100次随机试验中实现了100%的避碰成功率，显著优于基线CBF和NMPC方法。最后，我们在一组四旋翼飞行器上展示了由此产生的轨迹的物理可行性。（预印本；同行评审状态请核对原文。）