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

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.