Spatial–temporal dynamics of airside congestion: A Macroscopic Fundamental Diagram perspective

Airport airside congestion, driven by the growing imbalance between air traffic demand and constrained capacity, presents significant operational challenges that affect efficiency, safety, and environmental impact. Effectively addressing this requires models that capture the complex interactions within the airside network (taxiways, runways, gates) to provide insights into traffic flow dynamics and mechanisms of congestion formation, spread, and dissipation. Traditional approaches – such as microsimulation methods and queuing models – are often either computationally demanding or focus on specific components (like runways), limiting their ability to capture broader network interactions and reducing their operational feasibility. This study proposes an alternative approach, adapting the Macroscopic Fundamental Diagram (MFD) to model airside traffic using three-dimensional aircraft trajectory data. By analyzing aggregate traffic variables – flow, density, and speed – the MFD provides a computationally efficient means of understanding airside congestion patterns and supports 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. Additionally, this study analyzes the impact of weather conditions on the airside network flow, highlighting the effects of variable wind and adverse weather, such as rain and thunderstorm, on airside 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.