A Route-Based Queuing Network Model for Air Traffic Flow Contingency Management

Tien, Shin-Lai; Taylor, Christine; Zhou, Yi; Wan, Yan; Wan, Yan

doi:10.2514/6.2011-6863

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…Using the above defined factors, we propose a regression model to determine the coefficients in Equation 5. As such, we modify Equation 5 to represent the definition of the regression model for each day of data, defined as data set , as shown in Equation 11.…”

Section: Regression Model For Coefficientsmentioning

confidence: 99%

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

Wan

Taylor

Maşek

et al. 2012

AIAA Modeling and Simulation Technologies Conference

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A predictive model for departure traffic demand and its route distribution at look-ahead times of 2-15 hours is proposed, for use in a queuing-network-based tool for strategic Traffic Flow Management (TFM). The proposed model uses a combination of operational data (filed flight plans, schedules), historical statistics of demand, and timeof-operation-specific factors to generate statistical predictions of traffic demand for particular routes between pairs of airports or airport clusters. Specifically, a two-stage predictor for demand is proposed. First, traffic demand for an origin-destination (O-D) pair is modeled as the summation of a known demand which captures filed and scheduled traffic, and an unknown demand which is modeled as non-homogeneous Poisson process. Second, the fraction of this O-D traffic demand on each route is modeled using a linear regression, with the historical route fractions, known (filed) route fractions, and wind-adjusted transit times for the routes serving as regressors. Historical data on demands and actual traffic volumes are used to evaluate aspects of the model, including the Poisson-process assumption and the regression model for route distributions.

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Section: Regression Model For Coefficientsmentioning

confidence: 99%

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

Wan

Taylor

Maşek

et al. 2012

AIAA Modeling and Simulation Technologies Conference

Self Cite

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“…The size of the problem must be reduced in order to proceed. All constraints are defined by route, except for inequality (3). Routes passing through the same sector are coupled together by the sector capacity.…”

Section: Link Transmission Model and Traffic Flow Management Problemmentioning

confidence: 99%

“…En route traffic is generally modeled as a multicommodity network. Paradigms involve a queuing network model [3], a Bertsimas-Stock-Paterson (BSP) model [4], and an aggregate flow model [5]. Despite the variations in network abstraction, the underlying formulations are similar, characterized by linear objective functions and integral decision variables.…”

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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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“…In this paper, we will draw on a multi-resolution flow-network and queueing model for the NAS, which was developed for strategic traffic management under weather uncertainty [3,12], see Figure 1. This flow-and queueing-network model is the main analysis engine for the Flow Contingency Management (FCM) Tool, which allows evaluation and design of traffic management initiatives for the entire NAS over a full day, across severe weather futures derived from an ensemble weather forecast [3,4,12,14]. Several studies have validated that this flow-network model permits effective forecasting of NAS-wide traffic flows, and evaluation/design of TMIs within a region of interest [15].…”

Section: Modeling For Evaluation Proactive and Reactive Designmentioning

confidence: 99%

“…These regional flow-design tasks naturally require models for air traffic at the resolution of major traffic flows; several such models have been developed in recent years, to allow strategizing of traffic management in the presence of weather uncertainty. Here, we will explore via an illustrative example and basic formal analysis how a flow-level model of the NAS---specifically, a queueing-network model for flow contingency planning [3,4]---can be used for proactive and reactive management under non-weather disturbances to the NAS.…”

mentioning

confidence: 99%

Proactive and Reactive Management of Non-Weather Capacity Disruption Events in the National Airspace System: A Flow Modeling and Design Approach

Roy

Wan

2015

15th AIAA Aviation Technology, Integration, and Operations Conference

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I. Introduction A primary function of air traffic flow management is to strategically shape traffic demand to match capacities (e.g., airport arrival/departure rate limits and Sector capacities), without imposing excessive delay. In the current United States National Airspace System (NAS), the disruptions modulating capacities and hence traffic management are predominantly weather events, including convection, winter weather, and high winds. However, during the next 15 years and beyond, it is likely that the air traffic system will be increasingly subject to man-made disruptions that impact traffic management, including 1) a growing frequency of cyber-and physical-world security incidents, 2) commercial space operations, and 3) integration of high-altitude unmanned aircraft. Disruptions of these types have already begun to impact traffic control and management: for instance, the insider attack on the Chicago Air Route Traffic Control Center (ZAU) communication equipment during October 2014, and several recent commercial space launches and UAS-integration test scenarios. To date, such non-weather disruptions have had relatively contained and limited impact on air traffic system operations, but they will undoubtedly incur greater impact and cost in the near future as the airspace system becomes increasingly heterogeneous and cyber-enabled. In consequence, paradigms for traffic management that account for non-weather disruptions will be needed in the near future. The management of air traffic flows in the face of severe weather alone is a hard problem. However, because of the predominance of weather impact, traffic managers are experienced in characterizing the impact of weather on capacities, and in shaping flows to match capacity requirements. Also, the managers have available a number of tools which assist in decision-making at several time/spatial scales, and additional decision-support capabilities are under development [1,2]. Addressing non-weather disruptions in air traffic management entails several new challenges: 1) These disruptions may cause drastic recurring long-duration and/or recurring reduction in airspace capacity. For instance, space operations can require complete closure of multiple Sectors over the countdown/launch duration, and will have regular impact when commercial space operations become common. Likewise, security incidents may cause closure of major airports or even Center offices. Because of their severity, the disturbances can have NAS-wide impact on traffic. 2) Predicting capacity reduction profiles due to non-weather disruptions is challenging. On one hand, these disruptions may be pre-planned and have highly structured impact which allow for nearly deterministic models. On the other hand, however, operators have limited experience with the potential impacts, and formal models for capacity-reduction impacts may be unavailable. Also, the events themselves are often rare, and may involve uncertainties that do not easily admit statistical models (e.g., the possible delay in a launch coun...

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A Route-Based Queuing Network Model for Air Traffic Flow Contingency Management

Cited by 11 publications

References 5 publications

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

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

Migrating Large-Scale Air Traffic Modeling to the Cloud

Proactive and Reactive Management of Non-Weather Capacity Disruption Events in the National Airspace System: A Flow Modeling and Design Approach

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