With the widespread application of unmanned aerial vehicles (UAVs) in civilian and military fields, how to effectively detect and resolve conflicts of large-volume and high-density UAV flights in local airspace has become an important issue. This paper proposes a method for UAV conflict detection and resolution based on tensor operation and an improved differential algorithm. Firstly, the UAV protection zone model and airspace rasterization model are constructed, and the rapid detection of flight conflicts is achieved by using the properties of tensor Hadamard product operations and prime factorization. Then, for the detected conflicts, a hybrid improved differential evolution algorithm is used for resolution. This algorithm improves the solution speed and quality by using an adaptive mutation operator and introducing a redundant evaluation mechanism and a confidence-based selection strategy. Simulation results show that this method can quickly and accurately detect and resolve flight conflicts in high-density UAV scenarios, with high timeliness and conflict resolution capability.

The vigorous development of urban air mobility (UAM) is reshaping the urban travel landscape, but it also poses severe challenges to the safe and efficient operation of dense and complex airspace. Potential conflicts between flight plans have become a core bottleneck restricting its development. Traditional flight plan adjustment and management methods often rely on deterministic trajectory predictions, ignoring the inherent temporal uncertainties in actual operations, which may lead to the underestimation of potential risks. Meanwhile, existing global optimization strategies often face issues of inefficiency and overly broad adjustment scopes when dealing with large-scale plan conflicts. To address these challenges, this study proposes an innovative flight plan conflict management framework. First, by introducing a probabilistic model of flight time errors, a new conflict detection mechanism based on confidence intervals is constructed, significantly enhancing the ability to foresee non-obvious conflict risks. Furthermore, based on complex network theory, the framework accurately identifies a small number of “critical flight plans” that play a core role in the conflict network, revealing their key impact on chain reactions of conflicts. On this basis, a phased optimization strategy is adopted, prioritizing the adjustment of spatiotemporal parameters (departure time and speed) for these critical plans to systematically resolve most conflicts. Subsequently, only fine-tuning the speeds of non-critical plans is required to address remaining local conflicts, thereby minimizing interference with the overall operational order. Simulation results demonstrate that this framework not only significantly improves the comprehensiveness of conflict detection but also effectively reduces the total number of conflicts. Additionally, the proposed phased artificial lemming algorithm (ALA) outperforms traditional optimization algorithms in terms of solution quality. This work provides an important theoretical foundation and a practically valuable solution for developing robust and efficient UAM dynamic scheduling systems, holding promise to support the safe and orderly operation of large-scale urban air traffic in the future.

An analysis of air traffic complexity in multi-airport terminal areas can assist air traffic controllers in accurately assessing the air traffic situation and collaboratively managing air traffic flows, thereby enhancing the utilization of airspace resources and reducing flight delays. This paper proposes an air traffic complexity analysis method for multi-airport terminal areas based on a route segment–flight state interdependent network. The interdependent network model consists of an upper-layer flight state network, a lower-layer route segment network, and inter-layer coupling edges. The upper-layer network is constructed with aircraft as nodes and flight conflicts between aircraft as edges. The lower-layer network uses route segments as nodes and the connectivity between route segments as edges. The inter-layer coupling edges are determined by evaluating the relationship between aircraft and route segments—if an aircraft is on a specific route segment, a coupling edge exists between the corresponding aircraft node and route segment node. Based on this model, node-level complexity metrics are established to analyze the importance and complexity of individual route segments. Additionally, network-level complexity metrics are introduced to assess the overall air traffic complexity in multi-airport terminal areas. Finally, the method proposed in this paper is validated using flight scenarios in the Guangdong–Hong Kong–Macao Greater Bay Area. By comparing and analyzing the results with the actual situation, it is shown that the proposed method can accurately assess the air traffic complexity in multi-airport terminal areas.