Aircraft sequencing has the potential to decrease flight delays and improve operational efficiency at airports. This paper presents the aircraft sequencing problem (ASP) on multiple runways with complex interactions by allocating flights on runways and optimizing landing times, take-off times, and crossing times simultaneously in a uniform framework. The problem was formulated as a mixed-integer program considering realistic operational constraints, including runway assignment preferences based on the entry/exit fixes of the terminal maneuvering area (TMA), minimum runway separation, time window, and arrival crossing rules. Variable-fixing strategies were applied, to strengthen the formulation. A first-come-first-served (FCFS) heuristic was proposed for comparison. Various instances from the literature and from realistic data sets were tested. Our computational study showed that the solution approach optimizes runway schedules, to achieve significantly fewer flight delays, taking runway assignment preferences and arrival crossings into account.

This paper presents an integrated approach for terminal airspace management which is limited by route topology and sector capacity constraints to improve arrival and departure operations. The study evaluates various arrival route topologies, including standard terminal arrival route, point merge system, vector for space, and trombone route, while departures are operated via standard instrument departure. A conflict detection and resolution mechanism is implemented for waypoint, link, and mixed-mode runway conflicts. A maximum capacity of the number of simultaneous flights within each approach sector is predefined, and sector congestion is monitored by precise entry/exit time tracking. An optimization framework is used to adjust speed, entry times, departure times and routing to minimize sector congestion, flight delays and extra flight distances. A tailored selective simulated annealing heuristic with local search is proposed to solve the corresponding problem. Computational experiments at Urumqi Tianshan International Airport show that the mixture of all topologies achieves the lowest average entry delay in terminal airspace (1.3 minutes), while standard and vectoring routes yield the lowest average landing delays (1.7 minutes). Point merge and trombone routes increase extra flight distances, while the mixture of three topologies achieves reasonable extra flight distances primarily through vectoring. Under nominal sector capacity, the mixture of three strategies continues to yield a lower average entry delay in terminal airspace. When sector capacity is severely restricted, the use of standard routes results in reduced in-sector flight times, though it requires significant deviations from scheduled time slots.

Air traffic controllers benefit from referencing historical dates with similar complex air traffic conditions to identify potential management measures and their effects, which is critical for understanding air transportation system laws and optimizing decisions. This study conducted data mining using flight timetables. It first explored airport congestion mechanisms and quantified congestion as time series, then proposed a higher-order cumulants based time series feature extraction method. This method was fused with other features to build a high-dimensional airport congestion feature vector, and finally K-means clustering was applied to extract and analyze congestion patterns. The clustering method was empirically validated with 2023 flight data from Guangzhou Baiyun International Airport and it accurately classified airport operational states. To verify universality, the same framework was applied to 6 airports under the "one-city, two-airports" layout in Beijing, Shanghai and Chengdu. Results showed significant congestion pattern differences between existing and newly constructed airports. Conclusions confirm the proposed feature extraction and clustering framework is effective and universal, and it can accurately capture airport congestion dynamics. Under the "one-city, two-airports" layout, existing and newly constructed airports differ significantly in operational modes, and most single-airport city airports have operational modes highly consistent with existing airports. This study provides valuable decision-making references for airport managers and air traffic controllers. It helps them deepen understanding of air traffic dynamics and airport congestion patterns, thereby optimizing traffic management strategies and improving airport operational efficiency.

The precise identification of terminal area arrival procedures is crucial for airspace planning, traffic management, and safety analysis. Traditional methods are limited in automatically detecting specific procedural maneuvers from large amounts of trajectory data. This paper proposes a methodology with knowledge-based filtering to automatically identify three common air traffic control arrival procedures, namely Point Merge System, Vector for Space, and Trombone, from historical trajectory data. After clustering the landing trajectories in the terminal area, we identify the predominant flight patterns. Then, a knowledge-based filtering algorithm, designed based on knowledge of the procedure and geometry criteria, is employed to precisely extract trajectories with different procedure patterns. Experimental results demonstrate that this method effectively identifies the distinct procedural trajectories. An in-depth analysis of the extracted trajectories reveals significant characteristics and differences in their spatial distribution, trajectory structure, and operational efficiency. This work provides data-driven decision support for evaluating terminal area operational performance and arrival procedures.