In recent years, the application prospect of urban logistics unmanned aerial vehicles has attracted extensive attention. The high-density operation of UAVs requires autonomous separation maintenance capability. To achieve autonomous separation maintenance, it is necessary to conduct autonomous track prediction and formulate the required separation accordingly. Based on the target level of safety requirements for UAV operation, aiming at the autonomous separation maintenance ability of UAVs and considering the accuracy of track prediction, a method to calculate the required separation between UAVs is proposed. This study consists of two parts. Firstly, based on historical data, the position prediction error of the flight track is investigated. Using a machine learning model, a two-stage track prediction method, which involves classification followed by prediction, is proposed for urban logistics UAV track data. Subsequently, based on the track prediction error distribution, by designing a gas model and a position error probability model, a separation-formulating model for urban logistics UAVs in free flight is proposed in which UAV maneuverability is considered. By applying this model, the required separation is formulated for UAVs. When the required separation is set to 48.5 m, the overall collision risk meets the TLS requirements. The research provides a feasible method for establishing autonomous separation for urban logistics UAVs.

The rapid advancement of unmanned aerial vehicles (UAVs) has introduced new challenges in overseeing and managing their flight operations due to their diverse flight dynamics and performance metrics. To address these complexities, this study introduces a concept of trajectory conformity aimed at enhancing the supervision and control of UAV flights. Trajectory conformity, from a regulatory perspective, is defined as the distribution of deviations between a UAV’s actual flight path and its intended trajectory, offering a measure of system-wide operational error. The concept of conformity is hoped to simplify and strengthen the monitoring process to ensure conflict-free drone flying. The present work is also concerned with the development of a comprehensive UAV collision risk model grounded in trajectory conformity analysis. The normality and homogeneity of UAV trajectory deviations are validated by evaluating the trajectory data obtained from real-world UAV flights. Well clear thresholds between two UAVs operating in three orthogonal directions within the same airspace have been established by the developed model. The results obtained demonstrate the effectiveness in omni-encounter scenarios, underscoring the potential to strengthen safety measures. The present work is expected to enhance UAV safety systems, such as detect and avoid (DAA) and unmanned aircraft system traffic management (UTM), by enabling real-time collision warnings within predefined safety thresholds, supporting proactive risk mitigation. Furthermore, the model’s versatility allows it to be applied to various UAV operational aspects in future works, including route planning, flight procedure design, airspace capacity assessments, and establishment of separation minima.

Over the past forty years, air traffic flow management (ATFM) has garnered significant attention since the initial approach was introduced to address single-airport ground delay issues. Traditional methods for solving both single- and multi-airport ground delay problems primarily rely on operations research techniques and are typically formulated as mixed-integer problems (MIPs), with solvers employed to approximate optimal solutions. Despite their effectiveness in smaller-scale problems, these approaches struggle with the complexity and scalability required for large-scale, multi-sector ATFM, leading to suboptimal performance in real-time scenarios. To overcome these limitations, we propose a novel neural network-based demand and capacity balancing (NN-DCB) method that leverages neural branching and neural diving to efficiently solve the ATFM problem. Using data from 15,927 flight trajectories across 287 airspace sectors on a typical day in February 2024, our method re-allocates trajectory entry and exit times in each sector. The results demonstrate that large-scale ATFM problems can be solved within 15 min, offering a significant performance improvement over the state-of-the-art methods. This study confirms that neural network-based approaches are more effective for large-scale ATFM problem-solving.

With the rapid development of the low-altitude economy, the intensive take-offs and landings of Unmanned Aerial Vehicles (UAVs) performing logistics transport tasks in urban areas have introduced significant safety risks. To reduce the likelihood of collisions, logistics operators—such as Meituan, Antwork, and Fengyi—have established fixed departure and arrival air routes above vertiports and designed fixed flight air routes between vertiports to guide UAVs to fly along predefined paths. In the complex and constrained low-altitude urban environment, the design of safe and efficient air routes has undoubtedly become a key enabler for successful operations. This research, grounded in both current theoretical research and real-world logistics UAV operations, defines the concept of UAV logistics air routes and presents a comprehensive description of their structure. A parametric model for one-way round-trip logistics air routes is proposed, along with an air route evaluation model and optimization method. Based on this framework, the research identifies four basic configurations that are commonly adopted for one-way round-trip operations. These configurations can be further improved into two optimized configurations with more balanced performance across multiple metrics. Simulation results reveal that Configuration 1 is only suitable for small-scale transport; as the number of delivery tasks increases, delays grow linearly. When the task volume exceeds 100 operations per 30 min, Configurations 2, 3, and 4 reduce average delay by 88.9%, 89.2%, and 93.3%, respectively, compared with Configuration 1. The research also finds that flight speed along segments and the cruise segment capacity have the most significant influence on delays. Properly increasing these two parameters can lead to a 28.4% reduction in the average delay. The two optimized configurations, derived through further refinement, show better trade-offs between average delay and flight time than any of the fundamental configurations. This research not only provides practical guidance for the planning and design of UAV logistics air routes but also lays a methodological foundation for future developments in UAV scheduling and air route network design.

As Urban Air Mobility (UAM) moves toward implementation, managing high-density, high-volume flights in urban airspaces becomes increasingly critical. In such environments, the design of vertiport airspace structures plays a key role in determining how many UAVs can operate safely and efficiently within a specific airspace. Existing studies have not fully explored the complex interdependencies between airspace structure parameters and fleet operation capacity, particularly regarding how various structural components and their configurations affect UAV fleet performance. This paper addresses these gaps by proposing a multi-layered funnel-shaped airspace structure for vertiports, along with an adjustable parameter model to assess factors affecting landing capacity. The proposed design includes the assembly layer, upper layer, lower layer, and approach point, forming the basis for fleet operations, divided into three phases: arrival, approach, and landing. By modeling fleet operations with various constraints and time-based algorithms, simulations have been conducted to analyze the impact of changing airspace structure parametric dimensions on UAV fleet operation capacity. The results reveal that fleet capacity is closely influenced by two limitations: the distance traveled in each phase and the availability of holding points at each layer. These findings provide valuable insights and contribute to future airspace design efforts for UAM vertiports.

Air traffic is increasingly complicated, and with the expansion of the aviation industry, a growing emphasis on the safety of flight is being driven. According to flight experience and safety regulation standards, flight abnormal behavior is typically manifested through trajectories as well as other behavioral characteristics. Trajectory anomaly detection is a critical component for ensuring flight safety. This paper presents a comprehensive review that covers flight abnormal behavior analysis and trajectory anomaly detection. The definition of flight abnormal behavior and trajectory is clarified at first. Then, this paper proposes a framework of anomaly detection in flight trajectory. On this basis, the review expounds upon the methodologies that have been employed in three primary types of trajectory anomaly detection: speed anomalies, altitude anomalies, and heading deviations. The main applications in this field consist of anomaly warning, online real-time anomaly detection, and the quantitative evaluation of flight abnormal behavior. Future research should encompass studies on the classification of flight traffic behavior classification, the integration of flight trajectory, and other data sources to identify flight abnormal behaviors. This study contributes to furnish more actionable insights for the advancement of trajectory anomaly detection technologies, offering significant implications for an in-depth comprehension of flight abnormal behavior.