Optimally and equitably distributing delays with the aggregate flow model

Abstract

The aggregate flow model is used to determine how to distribute predeparture delays among air traffic control Centers and across time to optimally satisfy constraints on airspace capacity and departure rates. To do so, a quadratic cost on cumulative departure delays is introduced, resulting in an optimization problem that can be quickly solved using convex optimization tools. Simulations using the model demonstrate the behavior of the National Airspace System (NAS) when implementing optimal departure delays for a particular constraint scenario. These results show that capacity-constrained air traffic control Centers suffer the highest delays. Three approaches for increasing the equity of the distribution of delays across the NAS are investigated. The first involves setting an upper bound on the Gini coefficient, a quasi-convex measure of inequality. Another is to make delays in some Centers more costly than in others. The last approach is to put an upper bound on the delay per departure for each Center. Simulation results demonstrate that bounding delay per departure effectively reduces the delays for the constrained Center. Enforcing an upper bound on the Gini coefficient and increasing the weight on delays in some Centers may impose large delays on other Centers when reducing the delays in the constrained Center.

Risk-Hedged Approach for Re-routing Air Traffic Under Weather Uncertainty

“…[5][6][7][8]. Works that include weather among the uncertainties affecting traffic management and congestion include [3] and [9][10][11][12][13][14][15][16]. The combinatorial nature of routing problems is manifest in most of these works through the use of discrete optimization models.…”

Traffic Flow Management Using Aggregate Flow Models and the Development of Disaggregation Methods

“…The Disaggregated profile is the output from the disaggregation algorithm, which generates flightspecific action. Table 1 summarizes the costs defined by equation (16) for the real, simulated, optimized and disaggregated data. Clearly, the optimized solution has the minimal cost among the four, while the disaggregated profile obtained with flight-specific information has a larger cost.…”

“…The Optimized profile is an output from the optimization with the objective function of equation (16). Notice that the optimized center counts are below 200, which is the user-defined center capacity of ZOB in the example.…”

“…16 The objective function can be formed as follows, are the weights for en route delay and ground delay in center i, respectively. The objective functions above are subject to the same constraints described in the previous subsection.…”

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.…”