A stochastic weather-impact simulator for strategic air traffic management

Xue, Mengran; Roy, Sandip; Taylor, Christine; Zobell, Stephen; Wan, Yan; Wan, Yan

doi:10.3233/aop-170065

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…[1][2][3][4][5][6][7] To account for forecast uncertainty, [4] and [8] leveraged an ensemble forecast product to capture the wide range of capacity outcomes persistent in the planning horizon. Using a prototype simulation and evaluation capability, these works further highlighted the resulting capacity variation in developing traffic management strategies that arise as a result of the inherent differences among ensemble members.…”

Section: Introductionmentioning

confidence: 99%

Using Ensemble Weather Forecasts for Predicting Airport Runway Configuration and Capacity

Tien

Taylor

Wan

2016

16th AIAA Aviation Technology, Integration, and Operations Conference

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Strategic air traffic flow management requires predictions of airport departure and arrival capacities several hours into the future. It is difficult to do this well because of weather forecast uncertainty. However, if the measurement of prediction uncertainty is provided, strategies can be developed to account for it. In this paper, we examine the potential for using ensemble weather products to quantify uncertainty in airport capacity prediction. An improved prediction model from previous studies that utilizes weather forecast variables to predict runway configuration and capacity is validated and then applied to each ensemble member, so that the distribution of possible capacity outcomes and prediction confidence can be obtained. Our results suggest that the performance of the prediction model has been improved, and that the forecast variation among ensemble members effectively captures the possible variations in future arrival capacity for certain airports.

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Section: Introductionmentioning

confidence: 99%

Using Ensemble Weather Forecasts for Predicting Airport Runway Configuration and Capacity

Tien

Taylor

Wan

2016

16th AIAA Aviation Technology, Integration, and Operations Conference

Self Cite

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“…The influence model, which describes a class of networked discrete-state Markov chains with quasi-linear interactions [2,3], has proved useful for representing social processes in human groups [1], environmental phenomena (e.g. convective weather propagation) [4,5], and decision-making algorithms [6], among other stochastic network dynamics. The model is appealing for myriad applications because of: 1) the tractability enabled by its quasi-linear structure, and 2) the model's ability to represent high-dimensional stochastic network processes with a terse parameterization.…”

Section: Introductionmentioning

confidence: 99%

“…With this motivation, several studies (e.g. [1,4,5]) have proposed methods for estimating influence model parameters from observed data. However, these various studies have provided procedures and heuristics for parameter estimation, rather than formal guarantees about estimability of the parameters.…”

Section: Introductionmentioning

confidence: 99%

Comments Regarding `On the Identifiability of the Influence Model for Stochastic Spatiotemporal Spread Processes'

Roy

2018

Preprint

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The identifiability analysis of a networked Markov chain model known as the influence model, as described in [7], is examined. Two errors in the identifiability analysis -one related to the unidentifiability of the partially-observed influence model, the second related to an omission of an additional recurrence criterion for identifiability -are noted. In addition, some concerns about the formulation of the identifiability problem and the proposed estimation approach are noted.

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“…To address the aforementioned issues, flow-based models have been investigated to manage air traffic at the strategic time frame, and probabilistic weather information has been exploited to design TMIs that are robust to weather uncertainties. [12][13][14][15][16][17][18] Managing traffic at an aggregated-flow level significantly reduces the computational costs, and permits planning at a broader scale. Article 18 introduces a dynamic rerouting model which considers both GDP and rerouting to reduce total expected ground and airborne delays under uncertain weather.…”

Section: Introductionmentioning

confidence: 99%

A Network Condition-Centric Flow Selection and Rerouting Strategy to Mitigate Air<br /> Traffic Congestion under Uncertainties

Xie

Wan

2017

17th AIAA Aviation Technology, Integration, and Operations Conference

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Rerouting is an effective air traffic flow management strategy to reduce en-route traffic congestion caused by convective weather or other off-nominal situations. In this paper, we introduce a network condition-centric flow selection and rerouting method to improve the efficiency of rerouting in an uncertain and dynamically changing airspace environment. The method adjusts the flows to be rerouted according to different network conditions to maximally reduce traffic congestion. To address the challenges such as high-dimensional demands and weather uncertainties, we adopt a scalable sampling-based control method that enables an efficient and robust optimal rerouting design. A series of analysis and simulation studies motivate and illustrate good performance of the proposed method.

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A stochastic weather-impact simulator for strategic air traffic management

Cited by 3 publications

References 14 publications

Using Ensemble Weather Forecasts for Predicting Airport Runway Configuration and Capacity

Using Ensemble Weather Forecasts for Predicting Airport Runway Configuration and Capacity

Comments Regarding `On the Identifiability of the Influence Model for Stochastic Spatiotemporal Spread Processes'

A Network Condition-Centric Flow Selection and Rerouting Strategy to Mitigate Air<br /> Traffic Congestion under Uncertainties

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