An Aggregate Flow Model for Air Traffic Management

Sridhar, Banavar; Soni, Tarun Kumar; Sheth, Kapil; Chatterji, Gano B.

doi:10.2514/6.2004-5316

Cited by 41 publications

(55 citation statements)

References 3 publications

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“…Equation (16) characterizes the dynamics of the traffic flow on the air route i. Equation 17ensures that the total inflow of the air route is not greater than the scheduled inflow at each time step, and Equation (18) ensures that all the scheduled inflow will fly into the air route during the optimization time horizon K. Equation (19) ensures that the control variable of each control volume does not exceed the traffic flow intended to exit if no control measure is taken, where the left part represents the traffic flow that will not enter the next control volume and remain in the current one during the k-th time interval for optimization purpose, and the right part represents the traffic flow which will flow into the next control volume if no optimal strategy implemented.…”

Section: Optimization At the Air Route Levelmentioning

confidence: 99%

“…The aggregate models are widely discussed in the recent literatures [15][16][17][18][19][20][21][22]. A stochastic framework with linear dynamic system model is developed by Sridhar et al, where aircraft are aggregated according to their geographic location, e.g., control center.…”

Section: Introductionmentioning

confidence: 99%

“…Poisson distributions and split parameters, which are estimated from historical traffic data, are introduced to characterize the inflow quantity and the traffic transmission ratio between neighboring control centers, respectively. The established time-invariant model further develops into a time-variant one, where the split parameters are updated each one hour based on mining of historical air traffic operational data [15] [16]. Then the stochastic linear dynamic system model is applied to predict air traffic flow at both sector and center levels, which facilitates the identification of capacity-demand imbalances in advance [17] [18].…”

Section: Introductionmentioning

confidence: 99%

“…When air traffic mainly concentrates on one air route, the imbalance problem can be resolved by taking control strategies for the traffic flow on only one air route. While in other cases, the control strategies for the whole network traffic flow should be taken.The MILP problem of the optimization at the air route level (i.e., Equation(15)(16)(17)(18)(19)(20)(21)) can be directly solved by using the mathematical programming solver, CPLEX. The optimization problem at the en route network level can hardly be solved efficiently for the increased dimensions of the model variables.…”

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confidence: 99%

See 3 more Smart Citations

A network based dynamic air traffic flow model for en route airspace system traffic flow optimization

Chen

Zhang

et al. 2017

Transportation Research Part E: Logistics and Transportation Re

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Section: Optimization At the Air Route Levelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

A network based dynamic air traffic flow model for en route airspace system traffic flow optimization

Chen

Zhang

et al. 2017

Transportation Research Part E: Logistics and Transportation Re

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“…Models of this sort are categorized as either Lagrangian, in which flights are treated discretely, 1 or Eulerian, in which flights are aggregated. 2,3 Other models attempt to optimize aspects of the NAS through more traditional operations research methods by modeling the system, or some part of it, as linear or nonlinear programs. 4,5,6 Still other models use other tools such as constraint programming 7 or genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Delay Optimization for Airspace Capacity Management with Runtime and Equity Considerations

Rios

Ross

2007

AIAA Guidance, Navigation and Control Conference and Exhibit

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En route air traffic management is difficult and can benefit greatly from decision support tools. This paper presents a study of the efficiency and effectiveness of two practical approaches to real-time scheduling algorithms: a simple greedy scheduler and a well-studied optimal scheduler. A subset (region) of the National Airspace System is isolated to perform optimization on a manageable portion of the airspace. The schedulers are tested on realistic data sets representing traffic and conditions in the corridor between Chicago and New York area airports. In particular, the optimal scheduling of flights with both origin and destination in that corridor is considered, while reserving sufficient airspace for other air traffic. In a majority of cases, the greedy method provides sufficient (often optimal) results, while under difficult traffic and weather conditions, the optimal scheduler is worth the runtime requirements due to the inability of the greedy version to find satisfactory solutions. Further benefits of an optimal scheduler are demonstrated by incorporating the concept of equity or 'fairness' into the scheduling decision. Design choices in implementing equity amongst the various airlines are discussed and results demonstrating the utility of these choices are provided. Equity is easily implemented for the optimal scheduler but not the greedy, and does not require significant additional run time. Ultimately, it is shown that an equity-aware decision support tool for delay optimization can be developed to run in real-time and can benefit from incorporating more than one approach depending on the complexity of the scenario.

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

Chen

et al. 2016

J of Advced Transportation

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This paper presents a dynamic network-based approach for short-term air traffic flow prediction in en route airspace. A dynamic network characterizing both the topological structure of airspace and the dynamics of air traffic flow is developed, based on which the continuity equation in fluid mechanics is adopted to describe the continuous behaviour of the en route traffic. Building on the network-based continuity equation, the space division concept in cell transmission model is introduced to discretize the proposed model both in space and time. The model parameters are sequentially updated based on the statistical properties of the recent radar data and the new predicting results. The proposed method is applied to a real data set from Shanghai Area Control Center for the short-term air traffic flow prediction both at flight path and en route sector level. The analysis of the case study shows that the developed method can characterize well the dynamics of the en route traffic flow, thereby providing satisfactory prediction results with appropriate uncertainty limits. The mean relative prediction errors are less than 0.10 and 0.14, and the absolute errors fall in the range of 0 to 1 and 0 to 3 in more than 95% time intervals respectively, for the flight path and en route sector level. airport conditions, airline operation and human factors. [4]. Furthermore, the dimension of the model depends on the number of aircraft under consideration, which demands a huge computational cost in real context to resolve. 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. 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 d...

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An Aggregate Flow Model for Air Traffic Management

Cited by 41 publications

References 3 publications

A network based dynamic air traffic flow model for en route airspace system traffic flow optimization

A network based dynamic air traffic flow model for en route airspace system traffic flow optimization

Delay Optimization for Airspace Capacity Management with Runtime and Equity Considerations

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

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