Probabilistic Airport Capacity Prediction Incorporating the Impact of Terminal Weather

Kicinger, Rafal; Cross, Carolyn; Myers, Tim; Krozel, Jimmy; Mauro, Carl A.; Kierstead, David

doi:10.2514/6.2011-6691

Cited by 10 publications

(12 citation statements)

References 6 publications

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“…Following recent analysis of crosswind and tailwind impacts reported in [28], crosswind and tailwind thresholds for determining runway usability were assumed equal to 30 and 20 kt, respectively. Wake vortex separations were assumed the same as in previous work [10], i.e., consistent with current operational requirements defined in the Air Traffic Control (ATC) handbook [20].…”

Section: Hartsfield-jackson Atlanta International Airportmentioning

confidence: 99%

“…It uses the convective weather avoidance model [21], an extension of the Maximum Flow, Minimum Cut Theory [22], and computational geometry algorithms [22,23] to identify airspace blockages or no-fly zones for departing and arriving flights in the terminal airspace. The no-fly zones determined by weather avoidance fields are then used to determine if a particular arrival or departure fix is deemed to be "open" or "closed" because of weather blockage [10]. The ACM computes capacity estimates at the airport surface as a function of projected demand mix and forecasted weather conditions, specifically surface winds, cloud ceiling height, and visibility.…”

Section: Airport Capacity Predictionmentioning

confidence: 99%

“…Examples of analytical airport capacity models include the Federal Aviation Administration (FAA) Airfield Capacity Model [9], the Logistics Management Institute Airport Capacity Model [7,19], the Boeing Airport Capacity Constraints Model [6], a class of integrated analytical airport capacity and delay models called MANTEA Airfield Capacity And Delays (MACAD) [8], and the IACM [10].…”

Section: Airport Capacity Predictionmentioning

confidence: 99%

“…The IACM explicitly integrates convective storm, C&V, and surface wind forecasts to produce probabilistic arrival and departure capacity predictions for each runway configuration at an airport. Initially, the IACM incorporated deterministic weather forecasts and associated forecast error models derived using historical weather data [10]. The IACM was subsequently extended by Kicinger et al [11] to use ensemble forecasts, an emerging class of weather prediction methods that aims to generate a representative sample of the possible future states of the atmosphere [12].…”

Section: Introductionmentioning

confidence: 99%

“…The integrated airport capacity model (IACM) [10] was developed specifically to address this gap. The IACM explicitly integrates convective storm, C&V, and surface wind forecasts to produce probabilistic arrival and departure capacity predictions for each runway configuration at an airport.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Airport Capacity Prediction with Explicit Consideration of Weather Forecast Uncertainty

Kicinger¹,

Chen²,

Steiner³

et al. 2016

Journal of Air Transportation

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This paper describes a stochastic analytical model for predicting airport capacity with a look-ahead horizon suitable for strategic traffic flow management. The model extends previous research on airport capacity estimation by explicitly integrating the impact of terminal weather and its uncertainty. Different types of weather forecast inputs are explored, including deterministic forecasts, deterministic forecasts with forecast error models, and ensemble forecasts, to produce distributions of predicted arrival and departure capacity for each runway configuration at an airport. The paper introduces a mathematical capacity prediction model and weather data sources supported by a proof-of-concept prototype implementation, including results of validation studies at Hartsfield-Jackson Atlanta International Airport. These results are compared with standard airport benchmark capacities and actual observed throughputs. Results show that our analytical airport capacity model accurately predicts maximum available airport capacity for HartsfieldJackson Atlanta International Airport for different weather conditions. The validation studies reveal a limited impact of forecast uncertainty representation on the accuracy of airport capacity predictions. Nomenclature A i= actual hourly traffic count b DEP = separation buffer for consecutive departure release, s b MIT = final approach separation buffer, n mile b REL = separation buffer for departure release between two successive arrivals, n mile D = length of common approach path, n mile P i = predicted hourly traffic rate computed by Integrated Airport Capacity Model S i;j = minimum separation requirements between leading aircraft of type i and following aircraft of type j, n mile T = Theil inequality coefficient T c = Theil inequality coefficient incomplete covariation T m = Theil inequality coefficient inequality proportion T s = Theil inequality coefficient error in trend V i = speed of aircraft i, kt μ = minimum time separation between two consecutive arrivals, s

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Section: Hartsfield-jackson Atlanta International Airportmentioning

confidence: 99%

Section: Airport Capacity Predictionmentioning

confidence: 99%

Section: Airport Capacity Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Airport Capacity Prediction with Explicit Consideration of Weather Forecast Uncertainty

Kicinger¹,

Chen²,

Steiner³

et al. 2016

Journal of Air Transportation

Self Cite

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Airport Capacity Prediction With Multisource Features: A Temporal Deep Learning Approach

Chen

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Predicting Peak Service Rate Based On Weather Impacts Using Machine Learning Techniques

2018

Proceedings of 2018 the 8th International Workshop on Computer Science and Engineering

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As the air traffic congestion and large-scale flight delays become more and more serious, it is particularly important to predict the highest sustainable throughput grades of terminal which improved the effect of TFM. Current research has focused on predicting the impact of runway configurations on airport capacity. However, the selection of runway configuration does not take into account all the weather conditions that affect the terminal zone operation, and the transition from runway configuration to airport capacity is also a complex study. This article describes a methodology for predicting peak service rate based on the meteorological conditions directly. Two machine learning algorithms were introduced and evaluated for use in the process of developing the model. K-means algorithm is one of the unsupervised learning algorithms in machine learning filed which can be used for clustering. Random forest is a non-traditional machine learning algorithm, composed of many decision trees, which can be used for classification and regression. First, a k-means algorithm is applied to all days in 2016 of Guangzhou airport, resulting in the identification of 3 clusters that represent unique classifications of peak service rate that were historically implemented. Second, a forecast model based on the weather is developed by the application of random forest algorithm. The model provided 7 input features that describe the weather and evaluated the importance of each. Finally, after calculating the 5301 data from Guangzhou airport, the predict accuracy of model indicates that this methodology is feasible but still needs some improvements.

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Probabilistic Airport Capacity Prediction Incorporating the Impact of Terminal Weather

Cited by 10 publications

References 6 publications

Airport Capacity Prediction with Explicit Consideration of Weather Forecast Uncertainty

Airport Capacity Prediction with Explicit Consideration of Weather Forecast Uncertainty

Airport Capacity Prediction With Multisource Features: A Temporal Deep Learning Approach

Predicting Peak Service Rate Based On Weather Impacts Using Machine Learning Techniques

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