Modeling Airside Congestion Dynamics Using Macroscopic Fundamental Diagram: Insights and Applications

Airport airside congestion, driven by the growing imbalance between air traffic demand and constrained capacity, presents significant operational challenges. To address this, there is a critical need for effective models that capture the complex interactions within the airside (taxiway-runway-gate) system and provide insights into traffic flow dynamics and congestion mechanisms. Traditional approaches, such as microsimulation and queuing models, although detailed, tend to focus on individual components without capturing the broader interactions within sub-systems. This limitation, combined with high computational demands, restricts their effectiveness for real-time applications. This study proposes adapting the Macroscopic Fundamental Diagram (MFD) to model airside traffic using three-dimensional aircraft trajectory data. By focusing on aggregate traffic variables—flow, density, and speed—the MFD offers a computationally efficient approach to understanding airside congestion patterns and informed decision-making. This paper presents a novel methodology for constructing airside MFDs using A-SMGCS data from Singapore Changi Airport. The study also investigates the spatial and temporal factors contributing to congestion, offering insights into how congestion patterns develop and evolve under varying operational conditions. In the temporal domain, even during low-demand periods, departure and arrival banks contribute to congestion. In the spatial domain, traffic inhomogeneity—an uneven distribution of traffic on the airside network—reduces overall flow, particularly during congestion. These findings highlight the potential to improve airside capacity utilization and mitigate congestion by distributing traffic more evenly across both temporal and spatial domains, i.e., minimizing schedule banks and ensuring a balanced allocation of taxi routes.