The mixed operation of large unmanned aerial vehicles (UAVs) and manned aircraft in complex airspace poses a high collision risk, where the accuracy and real-time performance of risk assessment directly affect airspace safety supervision and operational decision-making. To address the limitations of traditional collision probability models, which inadequately characterize aircraft navigation errors and struggle to adapt to complex dynamic operational environments, this paper proposes a real-time collision probability calculation model based on historical trajectory data and nonparametric error modeling. To address the local anomaly problem caused by the discontinuity of trajectory data, a navigation error calculation method based on B-spline clustering fitting was constructed. The absolute errors under different smoothing factors were compared, and the optimal smoothing factor was determined through the Wilcoxon test to resolve local anomalies caused by overfitting. Kernel density estimation was employed for nonparametric modeling of navigation errors, breaking through the inherent Laplace distribution assumption of traditional models, and an analytical model capable of estimating collision probability at any given time was established. To verify the reliability of the proposed model, Monte Carlo simulations were conducted using operational data from real-world scenarios, and the model calculation results were systematically compared with ground-truth values. The results show that, compared with the double-exponential distribution model, the kernel density estimation model improves the calculation accuracy by 36.85%, with smaller error fluctuations across different scenarios. Furthermore, sensitivity analysis was conducted using the random forest algorithm, identifying relative distance as the dominant factor influencing risk. The research findings can provide theoretical support and methodological reference for risk monitoring and operational strategy optimization in complex airspace.

With the rapid development of emerging aerial vehicles such as Unmanned Aircraft Systems (UASs) and electric Vertical Take-Off and Landing (eVTOL) aircraft, new concepts and systems for low-altitude operations, represented by Advanced Air Mobility (AAM) and UAS Traffic Management (UTM), pose unprecedented challenges to the low-altitude safety management capabilities of existing aviation systems. The future of low-altitude operations is expected to evolve towards high-density, on-demand responsiveness, and collaborative operations between manned and unmanned vehicles. In this context, traditional management approaches within the human-in-the-loop framework are inadequate to meet the airspace demands of low-altitude operations, necessitating development of a more timely and intelligent risk management capability supported by low-altitude air navigation systems. This paper first elucidates the fundamental concepts of low-altitude safety and proposes a safety management framework for future low-altitude operations, clearly defining the roles and responsibilities of various stakeholders. In response to the in-time and intelligent capability requirements for risk management in aviation systems driven by the development of the low-altitude economy, this paper reviews the evolution of risk management technologies and capabilities in aviation systems, synthesizes the current developmental demands, and outlines the representative research, current status, and limitations of key technologies. Based on existing gaps and present demands, this paper identifies core challenges faced by low-altitude aviation systems and proposes future directions for developing real-time risk management capabilities. Finally, to promote the safe and orderly development of low-altitude operation, this paper concludes by outlining the core challenges faced by low-altitude air navigation systems and providing directions for future development in constructing real-time risk management capabilities, offering guidance for research and construction of low-altitude air navigation system risk management capabilities.

The successful application of new technologies such as remotely piloted aircraft systems, distributed electric propulsion systems, and automatic control systems on electric vertical take-off and landing(eVTOL) aircraft has prompted Urban Air Mobility (UAM) to be mentioned frequently. UAM is a newly raised transport mode of using eVTOL aircraft to transport people and cargo in urban areas, which is thought to share some of the traffic on the ground. One of the prerequisites for UAM to operate on a regular basis is that its demand can support the operating costs, so forecasting UAM demand is necessary. We conduct UAM demand forecasting based on the four-step method, focusing on improving the third-step modal split, and propose a demand forecasting model based on the logit model. The model combines a nested logit (NL) model with a multinomial logit (MNL) model to solve the problem of non-existent UAM sharing rates. We use Chengdu, China as an example, and focus on forecasting the UAM traffic demand in 2030 with the help of the four-step method. The results show that UAM is suitable for shared operation during the early stages. With a fully shared operation, the UAM share rate increases by 0.73% for every kilometer increase in distance. Moreover, UAM is more competitive than other modes for delivery distances exceeding 15 ​km. Finally, using the distributions of the share rate and traffic flow pattern from the simulation, we propose the routes that can be prioritized for UAM operations in Chengdu.