Multi-Airport Capacity Decoupling Analysis Using Hybrid and Integrated Surface–Airspace Traffic Modeling

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.