A New Model to Improve Aggregate Air Traffic Demand Predictions

Modeling Air Traffic Demand for a Real-Time Queuing Network Model of the National Airspace System

“…Predicting demand volume given the route is a common need for TFM decision support tools, and it has been studied extensively in the context of tactical congestion management tools. [6][7][8][9][10] Past work suggests that a reasonable model of aggregate demand for some NAS resources can be constructed for 15 minute prediction intervals by considering predicted filed and scheduled flights for the interval of interest, as well as those for the immediately preceding and subsequent intervals. 9 This work was done for airport arrival and en route sector counts.…”

“…[6][7][8][9][10] Past work suggests that a reasonable model of aggregate demand for some NAS resources can be constructed for 15 minute prediction intervals by considering predicted filed and scheduled flights for the interval of interest, as well as those for the immediately preceding and subsequent intervals. 9 This work was done for airport arrival and en route sector counts. In the context of sector counts, the previous method was extended to predict the statistical form and moments of demand distributions.…”

A network‐based dynamic air traffic flow model for short‐term en route traffic prediction

“…Thus, in practice, it is difficult to make sound online air traffic control strategies based on the trajectory-based model due to the short forecast horizon and the expensive computational cost.Efficient ATFM requires reasonable prediction of the whole traffic flow situations in the specified airspace, rather than the temporal-spatial information of individual aircraft. Therefore, the aggregate air traffic flow models are introduced recently, which focus on the overall distribution of the air traffic flow in the airspace volumes of interest [5][6][7][8][9][10][11][12][13][18][19][20][21][22][23][24]. Because the aircrafts in the airspace volumes are spatially aggregated, the dimension of the aggregated model depends solely on the number of airspace volumes rather than the total number of aircrafts in the airspace, which will reduce the computational cost significantly.…”

“…Because the aircrafts in the airspace volumes are spatially aggregated, the dimension of the aggregated model depends solely on the number of airspace volumes rather than the total number of aircrafts in the airspace, which will reduce the computational cost significantly. In addition, because the behaviour of the individual aircraft is not taken into account in the aggregated model, it is less sensitive to the uncertainty factors related to individual aircraft, such as the departure delay and the weather, and thus, a longer forecast time horizon with less prediction errors can be achieved.Recently, the aggregated approach is widely discussed in the literatures [5][6][7][8][9][10][11][12][13][18][19][20][21][22][23][24]. A stochastic framework with linear dynamic system model was developed by Sridhar et al, where the dimension of the model depends on the number of control volumes by introducing split parameters to describe the air traffic flow distribution in neighbouring airspace.…”

“…Recently, the aggregated approach is widely discussed in the literatures [5][6][7][8][9][10][11][12][13][18][19][20][21][22][23][24]. A stochastic framework with linear dynamic system model was developed by Sridhar et al, where the dimension of the model depends on the number of control volumes by introducing split parameters to describe the air traffic flow distribution in neighbouring airspace.…”