In the face of growing demand for air traffic, there is a clear need to measure how difficult a given air traffic situation looks. Currently, the concept of air traffic complexity is usually used to describe the air traffic situation. As yet, not enough work has been done on the between-aircraft proximity from the perspective of structure. This paper proposed a method for description of structural characteristics of air traffic situation based on the theory of complex network, which provides a new clue to precisely describe the air traffic situation complexity. The routinely recorded radar data are collected to construct air traffic situation network with aircraft regarded as node, and the between-aircraft proximity relation as edge. The air traffic situation network under three thresholds were statistically analyzed using network topology indices including degree, edge, connection rate, and network structure entropy. The results show that the inherent structure characteristics can provide an intuitionistic and accessible metrics to measure air traffic situation. For example, node degree can distinguish the key aircraft in the sector; the network connection rate reflects the proximity of aircraft; the network structure entropy reflects the homogeneity of aircraft node degrees.

In order to address the flight delays and risks associated with the forecasted increase in air traffic, there is a need to increase the capacity of air traffic management systems. This should be based on objective measurements of traffic situation complexity. In current air traffic complexity research, no simple means is available to integrate airspace and traffic flow characteristics. In this paper, we propose a new approach for the measurement of air traffic situation complexity. This approach considers the effects of both airspace and traffic flow and objectively quantifies air traffic situation complexity. Considering the aircraft, waypoints, and airways as nodes, and the complexity relationships among these nodes as edges, a dynamic weighted network is constructed. Air traffic situation complexity is defined as the sum of the weights of all edges in the network, and the relationships of complexity with some commonly used indices are statistically analyzed. The results indicate that the new complexity index is more accurate than traffic count and reflects the number of trajectory changes as well as the high-risk situations. Additionally, analysis of potential applications reveals that this new index contributes to achieving complexity-based management, which represents an efficient method for increasing airspace system capacity.

With the rapid growth of civil aviation, the increasing expansion of air traffic flow has brought serious challenges to the service capacity of the current airspace system, making the operation of the control sector increasingly complex. The accurate quantification of sector situational complexity is the basis for improving the service capability of airspace systems. The existing research on complexity ignores the resilience of the air traffic system in case of flight change, which cannot fully describe the dynamic characteristics of the air traffic situation. For this reason, a new air traffic complexity evaluation algorithm based on system resilience is proposed. Firstly, an air traffic situation network based on between-flight interaction is established. Then an overall sector complexity index based on network efficiency, average network failure rate, and average network recovery rate is built. Then, the complexity index is verified by analyzing the real radar number of ZSSSAR01 (sector 1 of Shanghai). By establishing a sector complexity optimization model, the complexity of sector air traffic and its volatility can be greatly reduced by changing the departure time of some flights. Finally, by optimizing the complexity of the sector, the workload of controllers is reduced, and the safety and efficiency of air traffic operations are improved.

Air traffic complexity, an essential attribute of air traffic situation, is the main driving force of workload for air-traffic controllers and is the key to achieving refined air traffic control. The existing air traffic complexity studies are based on static network, ignoring the dynamic evolution of between-aircraft proximity relations. Research on such evolution course and propagation characteristics will help to comprehensively explore the mechanisms of complexity formation. Herein, an air traffic complexity propagation research method based on temporal networking and disease propagation modeling is proposed. First, a temporal network is built with aircraft as nodes and between-aircraft proximity relations as edges. Second, the disease propagation model is introduced to simulate the evolution course of between-aircraft proximity relations, and the propagation model is solved using Runge–Kutta algorithm and particle swarm optimization. Third, based on the solved results of the propagation model, the aircraft are divided into three groups with high, medium, and low propagation capability, respectively. Finally, the effects of different factors on the propagation course are analyzed using multivariate linear regression. Real data validation shows the propagation of high-propagation capability aircraft is significantly affected by duration, and the temporal-correlation coefficient. The propagation of medium-propagation capability aircraft is significantly affected by duration and the clustering degree. By adjusting the influencing factors, the air traffic complexity propagation process can be effectively controlled.

Flight turbulence poses a severe threat to aviation safety, and achieving its effective early warning is a critical challenge to be addressed. To overcome the limitations of traditional methods reliant on single information sources or advanced remote sensing equipment, this study constructs a multiscale information fusion framework for aircraft turbulence early warning. The framework initially screens key forecast indices from meteorological reanalysis data. Subsequently, dynamic modes strongly associated with turbulence are extracted via advanced signal decomposition to drive a large‐scale background risk assessment model that integrates Gaussian process classifiers with an entropy‐based weighting method. Concurrently, a small‐scale turbulence life cycle model for vertical wind speed is developed through the analysis of high‐frequency quick access recorder (QAR) data to capture real‐time local variations and critical turbulence precursor characteristics. The core innovation of this framework lies in the utilization of an ensemble Kalman filter (EnKF) for dynamic fusion and state optimization of large‐scale background risk, small‐scale life cycle patterns (particularly precursor features), and high‐frequency vertical wind speed observations. This fusion process exhibits a sensitive, trigger‐like response to identified turbulence precursor signals, generating high‐precision ensemble predictions of vertical wind speed, from which Eddy dissipation rate (EDR) is ultimately estimated for early warning. Experimental results, based on nearly 200 min of real flight data from various flights, demonstrate that the model not only achieves high‐precision prediction of EDR evolutionary trends but also showcases superior warning performance, with an average precision of 95.5% being attained and an average effective early warning lead time of approximately 4 s being provided. This research validates the effectiveness of the proposed multiscale fusion strategy in aircraft turbulence early warning, offering advanced technological support for enhancing aviation safety.