The complexity and resource-sharing nature of traffic within multi-airport regions present significant challenges for air traffic management. This paper aims to develop a mesoscopic traffic model for exploring the traffic dynamics under coupled operations, and thus to conduct capacity decoupling analysis. We propose an integrated surface–airspace model. In the surface model, we utilize linear regression and random forest regression to model unimpeded taxiing time and taxiway network delays due to sparsity of ground traffic. In the airspace model, a dualized queuing network topology is constructed including a runway system, where the $G(t)/GI/s(t)$ fluid queuing model is applied, and an inter-node traffic flow transmission mechanism is introduced to simulate airspace network traffic. Based on the hybrid and efficient model, we employ a Monte Carlo approach and use a quantile regression envelope model for capacity decoupling analysis. Using the Shanghai multi-airport region as a case study, the model’s performance is validated from the perspectives of operation time and traffic throughput. The results show that our model accurately represents traffic dynamics and estimates delays within an acceptable margin of error. The capacity decoupling analysis effectively captures the interdependence in traffic flow caused by resource sharing, both within a single airport and between airports.

An airport slot is the core resource in the air transportation system. In most busy airports in China, airline demand significantly exceeds the available slot capacity. Scientific and reasonable slot allocation techniques and methods can improve the operational efficiency and benefits of multi-airport systems. Existing research has predominantly addressed slot allocation optimization for individual airports; however, there are differences in the functional positioning and resource allocation during multi-airport slot optimization, which makes cooperative optimization in the context of multi-airport slot allocation difficult. The dynamic sharing of airspace capacity in multi-airport systems is crucial for optimizing airport slot allocation and improving resource utilization efficiency. This study develops a multi-objective optimization model incorporating coupled airspace capacity relationships within multi-airport systems and the fairness of airlines and airports in order to realize the optimal utilization of multi-airport system resources, considering specialized 24 h airport slot coordination parameter patterns and slot firebreaks in China. Finally, the validity and scalability of the model are verified using real flight data from three airports in the Beijing airport terminal area, and simulations are conducted to verify the model. The findings provide a solid reference for the optimization of airport slot timetables in multi-airport systems, having both important theoretical value and practical significance.

Over time, the rapid expansion of civil aviation infrastructure has led to the establishment of multi-airport systems (MASs) or Metroplex, where airports situated in close proximity form interconnected networks. Given that individual airport capacities often fall short of meeting flight scheduling demands, devising effective multi-airport flight scheduling methods becomes imperative. This article introduces a novel MAS slot expansion configuration framework centered on coupling terminal areas. In contrast to conventional airport capacity slot expansion approaches, this framework demonstrates superior configurational efficacy within respective airport terminal environments. The model outlined in this research identifies the terminal control sector as the pivotal resource node within the interconnected terminal area, aiming to maximize the total expanded slots while minimizing the overall unfairness among airports within the terminal airspace. Employing the ε-constraint method facilitates the transformation of the minimization objective into solvable constraint conditions. Subsequently, leveraging Beijing Metroplex as a case study, the research devises benchmark, single-airport, multi-airport minimum, and multi-airport maximum scenarios to compare and analyze configuration outcomes in terms of key resource allocation impacts and coupled resource utilization efficiencies. Ultimately, employing the AirTOp fast-time simulation model validates each scenario, demonstrating that the proposed configuration method yields reduced delay levels and fewer conflicts in simulation environments.