The civil aviation industry is experiencing significant growth in air traffic density within terminal areas, necessitating improved air traffic efficiency. In China’s pursuit of world-class airport clusters, operational complexities arise due to the co-location of these airports in the same terminal area airspace, resulting in lower operational efficiency. To mitigate congestion and flight delays, this study proposes an integrated model that considers multiple runways and route selections, accounting for actual route point restrictions. Utilizing actual operational data from Shanghai metroplex, the proposed model is validated. The study focuses on the airport metroplex system and presents a comprehensive mixed-integer programming (MIP) model for arrival sequencing, considering multiple airports, runways, and routes. The maximum landing efficiency is adopted as the objective function, optimizing arrival scheduling while considering time intervals, route selection, and landing constraints. The Multi-waypoint Rolling Horizon Control (MWRHC) algorithm is employed to tackle time-efficiency challenges, ensuring flight safety by continuous monitoring of flights in the terminal area. Comparative analysis reveals the algorithm’s superior optimization performance for single-runway airports compared to dual-runway airports. Overall, the proposed model and algorithm effectively improve the efficiency of multi-airport arrival scheduling in airport metroplex systems.

We introduce a novel data analytics framework, using a multiplex recurrence network (MRN) to analyze flight tracking flow fluctuations in multi-airport terminal areas, with a case study on Shanghai’s Pudong (PVG) and Hongqiao (SHA) airports. It fills the gap in existing research by focusing on daily traffic flow fluctuations and exploring the operational dynamics within multi-airport systems. The MRN method effectively captures the synchronicity and similarity of traffic flows, offering novel insights into the management of airport terminal areas. Empirical analysis based on actual flight operation data validates the MRN’s efficacy, revealing distinct patterns of traffic flow synchronicity across four different scenarios—All Arrivals of PVG and SHA (AllArr), All Departures of PVG and SHA (AllDep), All Arrivals and Departures of PVG (AllPVG), and All Arrivals and Departures of SHA (AllSHA). These findings highlight the potential for enhancing operational efficiency by improving the synchronicity of traffic flows, especially during peak periods. This novel framework for understanding and optimizing air traffic flows in terminal areas offers both academic insights and practical implications for air traffic management, and provides a foundation for targeted interventions to improve the efficiency of airport operations in multi-airport systems.

A multilayer network approach to model and analyze air traffic networks is proposed. These networks are viewed as complex systems with interactions between airports, airspaces, procedures, and air traffic flows (ATFs). A topology-based airport-airspace network and a flight trajectory network are developed to represent critical physical and operational characteristics. A multilayer traffic flow network and an interrelated traffic congestion propagation network are also formulated to represent the ATF connection and congestion propagation dynamics, respectively. Furthermore, a set of analytical metrics, including those of airport surface (AS), terminal controlled airspace (TCA), and area-controlled airspace (ACA), is introduced and applied to a case study in central and south-eastern China. The empirical results show the existence of a fundamental diagram of the airport, terminal, and intersections of air routes. Moreover, the dynamics and underlying mechanisms of congestion propagation through the AS-TCA-ACA network are revealed and interpreted using the classical susceptible-infectious-removed model in a hierarchical network. Finally, a high propagation probability among adjacent terminals and a high recovery probability are identified at the network system level. This study provides analytical tools for comprehending the complex interactions among air traffic systems and identifies future developments and automation of layered coupled air traffic management systems.

To address the insufficient evaluation of scenario adaptability in the coordinated control of shared waypoints within multi-airport systems, this study proposes two optimization strategies: the Multi-Waypoint Rolling Horizon Control (MWRHC) and the Multi-Waypoint Ant Colony Optimization (MWACO) algorithms. A systematic comparison of their applicability and control performance is conducted. Using empirical data from peak-hour operations in the Yangtze River Delta multi-airport system, the applicability and optimization effectiveness of both algorithms in arrival–departure sequencing are evaluated. The metric “Average Flight Time Improvement” is introduced to quantify and compare the performance of different airports during the optimization process, thereby revealing the operational characteristics of MWRHC and MWACO under varying traffic conditions. The results demonstrate that the MWACO algorithm exhibits superior global optimization capability in high-traffic airport environments, whereas the MWRHC algorithm performs better in local optimization and real-time scheduling under moderate traffic conditions.