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.

This paper introduces the concept of safety culture and focuses on the construction of a safety culture in civil aviation air traffic control (ATC). From the perspective of the impact of human factors on aviation operation and the development of safety management systems, the necessity of cultivating a safety culture in ATC is emphasized. Three methods are provided to achieve this: holding more culture activities, paying more attention to the psychological health of air traffic controllers, and implementing new technologies to protect air traffic controllers.

This study asserts that paired aircraft can withstand specific wake turbulence levels and explores the longitudinal collision risk in closely spaced parallel runway approaches. The goal is to enhance the safety margin of the paired approach and allow for more flexible implementation. Based on QAR data, a theoretical spacing model for paired aircraft and a probability distribution of acceleration error are established to facilitate the analysis of the actual spacing of paired aircraft. Wake turbulence attenuation is modelled using large eddy simulation, creating a vortex attenuation model. Drawing inspiration from the Hallock-Burnham vortex model, new models for induced velocity and vortex core motion are proposed. The study assumes that trailing aircraft can handle certain wake intensities, leading to a new model for calculating wake turbulence safety intervals, limiting the trailing aircraft’s maximum roll angle to its critical limit. Using probability theory, a model for longitudinal collision risk is formulated, combining wake turbulence safety separation and the actual separation of paired aircraft. The study also examines various factors influencing longitudinal collision risk, emphasising the significant impact of crosswind conditions. It concludes that a stronger crosswind component reduces the wake turbulence safety separation, thereby increasing the risk of longitudinal collisions, particularly during the final stage of the approach. Notably, collision risk is directly proportional to the crosswind component and initial longitudinal separation, but inversely proportional to runway spacing.