確率モデルによる到着交通流の制御性能の解析と予測

抄録

The performance of incoming air traffic flow control to Tokyo International Airport is studied using surveillance data for future automatic arrival management system development. A probabilistic model that simulates the queueing mechanism of air traffic flow control is built from the time data of all flights that cross the boundaries of appropriately assumed traffic control area. The probabilistic model presents the nature of daily changes in air traffic flow control. Monte Carlo simulations using the model predict variations in flight time delay due to various conditions, such as spacing accuracy and incoming traffic density fluctuation. This paper quantitatively evaluates the importance of spacing accuracy to reduce delay time due to increasing air traffic volume. It demonstrates the effectiveness of a practical method of controlling spacing more appropriately to comply with the minimum spacing regulation of the wake turbulence category depending on the weight of aircraft.