Disaggregation Method for an Aggregate Traffic Flow Management Model

Sun, Dong; Sridhar, Banavar; Grabbe, Shon

doi:10.2514/1.47469

Cited by 19 publications

(15 citation statements)

References 17 publications

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“…Airspace capacity in the absence of weather is limited mostly by air traffic controller workload considerations. When traffic demand is expected to exceed capacity, traffic flow management techniques, [1][2][3][4][5][6][7] such as delaying fights on the ground, spacing them in the air and changing their routes of flight, are used to curtail demand. The delays cost the airlines and the flying public.…”

Section: Introductionmentioning

confidence: 99%

Interaction of Airspace Partitions and Traffic Flow Management Delay

Palopo

Chatterji

Lee

2010

10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference

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To ensure that air traffic demand does not exceed airport and airspace capacities, traffic management restrictions, such as delaying aircraft on the ground, assigning them different routes and metering them in the airspace, are implemented. To reduce the delays resulting from these restrictions, revising the partitioning of airspace has been proposed to distribute capacity to yield a more efficient airspace configuration. The capacity of an airspace partition, commonly referred to as a sector, is limited by the number of flights that an air traffic controller can safely manage within the sector. Where viable, re-partitioning of the airspace distributes the flights over more efficient sectors and reduces individual sector demand. This increases the overall airspace efficiency, but requires additional resources in some sectors in terms of controllers and equipment, which is undesirable. This study examines the tradeoff of the number of sectors designed for a specified amount of traffic in a clear-weather day and the delays needed for accommodating the traffic demand. Results show that most of the delays are caused by airport arrival and departure capacity constraints. Some delays caused by airspace capacity constraints can be eliminated by re-partitioning the airspace. Analyses show that about 360 high-altitude sectors, which are approximately today's operational number of sectors of 373, are adequate for delays to be driven solely by airport capacity constraints for the current daily air traffic demand. For a marginal increase of 15 seconds of average delay, the number of sectors can be reduced to 283. In addition, simulations of traffic growths of 15% and 20% with forecasted airport capacities in the years 2018 and 2025 show that delays will continue to be governed by airport capacities. In clear-weather days, for small increases in traffic demand, increasing sector capacities will have almost no effect on delays.

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Section: Introductionmentioning

confidence: 99%

Interaction of Airspace Partitions and Traffic Flow Management Delay

Palopo

Chatterji

Lee

2010

10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference

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“…Since the vector x k 1 t; x k 2 t; · · · ; x k n k t sets globally optimal states for route k, these states can be used as constraints for scheduling the flights on route k where variables are defined as ground delays and airborne delays associated with individual flights. The disaggregation process is discussed in detail in [19,22]. The outputs of this process are delays imposed on individual flights in each sector.…”

Section: Link Transmission Model and Traffic Flow Management Problemmentioning

confidence: 99%

Migrating Large-Scale Air Traffic Modeling to the Cloud

Cao

Sun

2015

Journal of Aerospace Information Systems

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Coordinating nationwide air traffic flow is a large-scale problem. The modeling process generally involves analysis of massive flight data, and its optimization involves computationally expensive algorithms. This paper uses Hadoop MapReduce, a big data processing model, to facilitate air traffic flow modeling and optimization, where computationally intensive tasks are automatically spread to Hadoop clusters for concurrent executions. The overall wall-clock time of computation is reduced. A nationwide traffic flow management problem that has been previously studied was restructured under the MapReduce framework. The problem aims at minimizing flight delays while respecting system capacities. Due to its temporal and spatial scope, the size of this problem grows to an extent where it is too big to be solved on standalone computers. Lagrangian relaxation was applied to decompose the original problem into a collection of solvable subproblems. The optimization proceeds in two iterative stages: solving subproblems and Lagrange multiplier updates. These two processes are encapsulated in the mapper and reducer functions, respectively. As a result, the optimization is automatically scheduled to run in parallel tasks. The cloud-based air traffic modeling and optimization were validated through running nationwide air traffic optimization instances on a small Hadoop cluster with six nodes. The modeling processing is eight times faster and the optimization is 16 times faster than that running on standalone computers. Nomenclature A arr , A dep = set of links connecting origin or destination airport C s t = maximum number of aircraft allowed in sector s at time t C arr t, C dep t = airport arrival and departure capacity d k λ s t = objective of the kth subproblem f k t = departures into route k at time t K = set of routes in simulation and optimization n k = number of links on route k Q s i = set of links inside sector s i q k i t = outflow of link i at time t S = set of sectors in the National Airspace System s i = sector that link i lies in T = planning time horizon of air traffic optimization T k i = traversal time of link i on route k, min t = time step λ s t = Lagrange multiplier for sector s at time t Subscripts i = index of a link j = index of iteration s = index of a sector Superscript k = index of a route

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“…They also allow powerful controlled queuing techniques to be leveraged. While it is not trivial to translate control actions proposed by such techniques into implementable directives to aircraft [15], controlled queuing algorithms show promise for air traffic management [5], [16].…”

Section: A Air Traffic and Airspace Modelmentioning

confidence: 99%

Coordinated tactical air traffic and airspace management

Bloem

Bambos

2011

IEEE Conference on Decision and Control and European Control Conference

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Air traffic management and airspace management reduce air traffic congestion to maintain safety. Managing traffic induces costs on airspace users and managing airspace causes additional work for air traffic controllers. This paper proposes and simulates algorithms for tactically reducing airspace congestion with coordinated air traffic and airspace management. A modified version of the Projective Cone Scheduling algorithm performs tactical air traffic management. An algorithm based on approximate dynamic programming accomplishes tactical airspace management. Three types of coordination between these air traffic and airspace management algorithms are investigated. Monte Carlo simulations of a simple problem instance involving severe congestion indicate that increased coordination between air traffic and airspace management can lead to lower costs with no increase in algorithm computation time.

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Disaggregation Method for an Aggregate Traffic Flow Management Model

Cited by 19 publications

References 17 publications

Interaction of Airspace Partitions and Traffic Flow Management Delay

Interaction of Airspace Partitions and Traffic Flow Management Delay

Migrating Large-Scale Air Traffic Modeling to the Cloud

Coordinated tactical air traffic and airspace management

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