Junfeng Zhang

Precise trajectory prediction in high-density terminal maneuvering areas is a fundamental prerequisite for the realization of next-generation trajectory-based operations. However, the practical deployment of deep learning models in this domain is often hindered by the technical challenges of effectively integrating heterogeneous environmental data and the inherent drift associated with recursive error accumulation. This study proposes a context-aware multiflow Informer framework that synergistically integrates target aircraft kinematics with operational traffic context and atmospheric perturbations. The architecture employs a cascaded gating mechanism to autonomously align internal flight dynamics with external influences, while utilizing a non-autoregressive generative decoder to achieve one-shot trajectory synthesis, thereby mitigating the cumulative error propagation characteristic of traditional recursive models. Experimental results using actual trajectory data from Guangzhou Baiyun International Airport demonstrate that the proposed model consistently outperforms recurrent baseline models, yielding architectural improvements ranging from 3.8 to 8.1% across multiple metrics and expanding to between 10.4 and 13.8% upon integrating multimodal environmental data. Furthermore, interpretability analysis suggests that the model has the potential to learn motion inertia and relevant operational logic, while cross-airport validation underscores its portability, offering a reliable predictive foundation for intelligent air traffic management systems.

Adverse weather conditions, particularly thunderstorms, are the primary cause of flight delays and safety threats, accounting for approximately 58.7% of irregular flights in 2025. Traditional static rerouting methods often fail to adapt to the non-linear evolution of convective weather. This paper proposes a high-fidelity dynamic rerouting framework to enhance flight safety and efficiency. In the perception layer, a RainNet deep learning model is employed for short-term recursive nowcasting of radar reflectivity, which is subsequently transformed into Dynamic Avoidance Zones (DAZ) via clustering and convex hull algorithms. In the decision layer, a two-stage improved Genetic Algorithm (GA) is developed to solve the rerouting path. The first stage generates initial collaborative solutions under a receding-horizon framework, while the second stage applies a “path-straightening” module to reduce cumulative turning angles and curvature fluctuations. The comparative results in actual scenarios demonstrate a distinct dual-advantage over baseline methodologies. Compared to sampling-based strategies, the proposed model reduces the path length by 14.79%. Furthermore, when compared to heuristic algorithms, it actively trades a negligible 1% distance margin to achieve a massive 92.7% reduction in the cumulative turning angle. With a maximum single turn of only 32.51°, the trajectory completely eliminates sawtooth jitter and redundant detours. Ultimately, this research provides essential technical support for improving air traffic management efficiency and reducing controller workload during severe weather events.

Trajectory optimization is essential for realizing trajectory-based operation and has become a hot topic in air traffic management research in recent years. Although existing optimization models have significantly succeeded in their applications, most still need controllers to intervene during rush hours. To further improve the trajectory optimization performance, this paper proposed a data-driven-based trajectory optimization model that learns human controllers’ experience from historical data and embeds this knowledge into the trajectory optimization model to enhance its performance. Case studies at Guangzhou Baiyun International Airport are conducted to validate the proposed model. Compared with a conventional genetic-algorithm-based trajectory optimization model, the proposed approach—by incorporating real historical trajectory data—demonstrates significantly improved performance in managing arrival flows during peak hours, increasing airspace capacity by approximately 30% under trajectory-based operation conditions. Besides, the optimized trajectories embedded with more actual operation experience are familiar to controllers and pilots; therefore, the airspace user training cost would be relatively low.

The emergence of surface trajectory-based operation (STBO) has promoted the development of taxi automation systems to plan conflict-free aircraft trajectories for efficient airport operations. Unlike traditional airport ground movement, aircraft guided by the taxi automation system possess a significant degree of freedom during taxiing, requiring the system to coordinate aircraft movement on the surface with timely responses to uncertainty. This study proposes a dynamic routing and scheduling approach with an adaptive surface situation characterization mechanism. A link-level unimpeded taxi time estimation method is designed to fine-grain characterize airport operation. A comprehensive evaluation system is then developed to identify similar situation patterns. Finally, a dynamic routing and scheduling approach is proposed to optimize airport ground movement with an adaptive and refined surface situation mechanism. The case study of actual operational data collected from a hub airport reveals that the proposed approach achieves efficient movement plans and collaborative control for all aircraft, as well as a more balanced utilization of the runway. The embedded adaptive situation mechanism also makes a better tradeoff between computational efficiency and optimal solutions. The study provides a promising approach to generating conflict-free four-dimensional trajectories for airport operations in future STBO scenarios.

The imbalance between air traffic capacity and demand, especially in the terminal maneuvering area, constrains the development of the civil aviation industry. To enhance the capacity, providing aircraft with optimal trajectories is the way forward. The key challenge of building a trajectory optimization model lies in embedding air traffic controllers’ operation experience into the optimization process. Previous efforts proposed a data-driven trajectory optimization method to learn operation experience from historical data for trajectory optimization use, which is limited by the issues of insufficient quantity and diversity of trajectory data. To solve those issues, an improved model is proposed to further improve the trajectory optimization performance. Firstly, this paper exploited a connecting-based trajectory generation model to generate massive synthetic trajectories. Then, this paper used a data-mining-based valuable trajectory identification technique to find valuable trajectories that can be used to enrich its operation experience. A case study on Guangzhou Baiyun International Airport was conducted to verify the proposed method. The results show that the proposed model successfully incorporates more operation experience during the optimization process. Compared with the benchmark model, the proposed model can adapt to more complex air traffic and provide optimal trajectories for more incoming aircraft.

Data mining has achieved great success in air traffic management as a technology for learning knowledge from historical data that benefits people. However, data mining can rarely be embedded into the trajectory optimization process since regular optimization algorithms cannot utilize the functional and implicit knowledge extracted from historical data in a general paradigm. To tackle this issue, this research proposes a novel data mining-based trajectory generation method that is compatible with existing optimization algorithms. Firstly, the proposed method generates trajectories by combining various maneuvers learned from operation data instead of reconstructing trajectories with generative models. In such a manner, data mining-based trajectory optimization can be achieved by solving a combinatorial optimization problem. Secondly, the proposed method introduces a majorization–minimization-based adversarial training paradigm to train the generation model with more general loss functions, including non-differentiable flight performance constraints. A case study on Guangzhou Baiyun International Airport was conducted to validate the proposed method. The results illustrate that the trajectory generation model can generate trajectories with high fidelity, diversity, and flyability.

Collaboration between terminal airspace and airport surface operation shows an increasing significance for the best efficiency of both parts of the air traffic management domain. Runways play a critical role in connecting the two parts for departure and arrival aircraft. Suppose the gate and the entry fix of an aircraft are predetermined according to the flight plan, and they are on the opposite side of the airport terminal. The aircraft will either spend more time (i.e., delay in the air) landing on a runway close to its gate or take a longer distance (i.e., detour on the ground) taxiing to its gate if a runway close to its entry fix is assigned. This paper proposes a runway assignment model considering terminal airspace operation and airport surface movement simultaneously to discover how runway assignments can affect integrated operations. Four different runway assignment schemes are applied in this model. Subsequently, a metaheuristic method is proposed to solve the model. Furthermore, the historical taxiing and flight time data are analyzed to demonstrate the potential benefits of runway reassignment. Finally, the results show that the free assignment of the runway stands out among the four schemes, not only in the performance of terminal airspace operation (lower flight time) but also in airport surface movement (lower pushback delay, taxi time).

Temporal–spatial resource optimization within the terminal maneuvering area has become an important research direction to meet the growing demand for air traffic. Accurate departure flight time prediction from taking off to the metering fixes is critical for departure management, connecting the surface operations, and overhead stream insertion. This paper employs ensemble learning methods (including bagging, boosting, and stacking) to predict departure flight times via different metering fixes based on four feature categories: initial states, operating situation, traffic demand, and wind velocity. The stacking method employs a linear regressor, a support vector regressor, and a tree-based ensemble regressor as base learners. The Guangzhou Baiyun International Airport case study shows that the stacking method proposed in this work outperforms other methods and could achieve satisfactory performance in departure flight time prediction, with a high prediction accuracy of up to 89% within a 1 min absolute error and 98% within a 2 min absolute error. Besides, the affecting factors analysis indicates that the operation direction, flight distance, and traffic demand in different areas significantly improve prediction accuracy.