Data-Driven Simulation for Evaluating the Impact of Lower Arrival Aircraft Separation on Available Airspace and Runway Capacity at Tokyo International Airport

Abstract

Although the application of new wake turbulence categories, the so-called “RECAT (wake turbulence category re-categorization)”, will realize lower aircraft separation minima and directly increase runway throughput, the impacts of increasing arrival traffic on the surrounding airspace and arrival traffic flow as a whole have not yet been discussed. This paper proposes a data-driven simulation approach and evaluates the effectiveness of the lower aircraft separation in the arrival traffic at the target airport. The maximum runway capacity was clarified using statistics on aircraft types, stochastic distributions of inter-aircraft time and runway occupancy time, and the levels of the automation systems that supported air traffic controllers’ separation work. Based on the estimated available runway capacity, simulation models were proposed by analyzing actual radar track and flight plan data during the 6 months between September 2019 and February 2020, under actual operational constraints and weather conditions. The simulation results showed that the application of RECAT would reduce vectoring time in the terminal area by 7% to 10% under the current airspace and runway capacity when following a first-come first-served arrival sequence. In addition, increasing airspace capacity by 10% in the terminal area could dramatically reduce en-route and takeoff delay times while keeping vectoring time the same as under the current operation in the terminal area. These findings clarified that applying RECAT would contribute to mitigating air traffic congestion close to the airport, and to reducing delay times in arrival traffic as a whole while increasing runway throughput. The simulation results demonstrated the relevance of the theoretical results given by queue-based approaches in the authors’ past studies.

Analyzing Stochastic Features in Airport Surface Traffic Flow Using Cellular Automaton: Tokyo International Airport

“…It is the buffer time for safe take-offs and landings. Accurate prediction and optimization of the ROT can improve take-off/landing throughput [12], [30]- [32]. In a previous study of Tokyo International Airport, the ROT was set at 95 s for consecutive takeoff and 115 s for consecutive landing from the entry of the preceding aircraft onto the runway [33].…”

Evolutionary Fleet Development Considering Airport Capacity Limitations and their Mitigation

“…The results show significant capacity benefits up to about 30 %, while also accounting for limitations such as limited benefits of a too high number of wake turbulence categories or the increasing relevance of runway occupancy times [21]. Further studies in this field examine the impact of single measures on specific airports, such as Sekine et al [22] for the benefits of RECAT for runway capacity at Tokyo Haneda airport. However, the projected high, long-term air transport demand growth mostly surpasses these measures' capabilities to enhance airport capacities [4].…”

Aircraft Wake Evolution Prediction Based on Parallel Hybrid Neural Network Model

“…Under clear sky conditions, classical numerical simulation techniques primarily involve Large Eddy Simulation (LES) methods and Reynolds-Averaged Navier-Stokes approaches. Holzäpfel and others [3,4] used aircraft wake velocity models as initial conditions for numerical simulations, significantly enhancing computational efficiency and accuracy. In terms of wake velocity models, research institutions in Europe and America, including the German Aerospace Center (DLR), Office National d'Études et de Recherches Aérospatiales (ONERA), and the National Aeronautics and Space Administration (NASA), have combined theoretical analysis, numerical simulation, and extensive experimental measurements to obtain the tangential velocity distribution of aircraft wake on cross-sections [5], with representative models including the Lamb-Oseen, Hallock-Burnham, Proctor, and Winckelmans models.…”