A probabilistic airport capacity model for improved ground delay program planning

Provan, Christopher; Cook, Lara; Cunningham, Jon

doi:10.1109/dasc.2011.6095990

Cited by 19 publications

(13 citation statements)

References 2 publications

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“…The AAR selected by traffic managers depends on factors like runway configurations, weather conditions, and the type of aircraft that are scheduled to arrive at the airport. Other researchers have developed AAR prediction models [16][17][18][19][20][21][22][23], and we implemented a model similar to the bagged decision tree model proposed by Provan et al [21] and Cunningham et al [22]. This type of model worked well not only for Provan et al [21] and Cunningham et al [22] but also for Wang [18,20].…”

Section: E Predictions Of Future Aarmentioning

confidence: 98%

“…Other researchers have developed AAR prediction models [16][17][18][19][20][21][22][23], and we implemented a model similar to the bagged decision tree model proposed by Provan et al [21] and Cunningham et al [22]. This type of model worked well not only for Provan et al [21] and Cunningham et al [22] but also for Wang [18,20]. The AAR predictions from this model for the hour starting 1 h from the current time through the hour starting 10 h from the current time are provided to the GDP models (10 features).…”

Section: E Predictions Of Future Aarmentioning

confidence: 99%

“…Random forest models were selected because they typically perform well with minimal tuning of the algorithms that train the models, they generally do not overfit to the training data even when provided with many features, and other researchers have found that related models predict AARs better than alternative models [18,20,21,28].…”

Section: Bc: Random Forest Classifiers For Cancellation and Initializmentioning

confidence: 99%

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Ground Delay Program Analytics with Behavioral Cloning and Inverse Reinforcement Learning

Bloem

Bambos

2015

Journal of Aerospace Information Systems

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Historical data are used to build two types of models that predict Ground Delay Program implementation decisions and produce insights into how and why those decisions are made. More specifically, behavioral cloning and inverse reinforcement learning models are built that predict hourly Ground Delay Program implementation at Newark Liberty International and San Francisco International airports. Data available to the models include actual and scheduled air traffic metrics and observed and forecasted weather conditions. The developed random forest models are substantially better at predicting hourly Ground Delay Program implementation for these airports than the developed inverse reinforcement learning models. However, all of the models struggle to predict the initialization and cancellation of Ground Delay Programs. The structure of the models are also investigated in order to gain insights into Ground Delay Program implementation decision making. Notably, characteristics of both types of model suggest that Ground Delay Program implementation decisions are more tactical than strategic: they are made primarily based on conditions now or conditions anticipated in only the next couple of hours.

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Section: E Predictions Of Future Aarmentioning

confidence: 98%

Section: E Predictions Of Future Aarmentioning

confidence: 99%

Section: Bc: Random Forest Classifiers For Cancellation and Initializmentioning

confidence: 99%

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Ground Delay Program Analytics with Behavioral Cloning and Inverse Reinforcement Learning

Bloem

Bambos

2015

Journal of Aerospace Information Systems

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“…Buxi and Hansen 9 generated probabilistic capacity profiles for a full day, by correlating current Terminal Aerodrome Forecasts (TAF) with historical forecasts. Provan et al 10 derived probability distributions for airport capacity given weather forecasts and the airport state (runway configurations, demand, operational standards, procedures, etc.). Kicinger et al 11 explored the usage of time-lagged ensemble forecasts on airport capacity prediction.…”

Section: Airport Capacity Predictionmentioning

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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“…While a few terminal weather forecast product accuracy analyses have been conducted, such as METAR and Terminal Area Forecast (TAF), the role of surface weather analyzed forecast accuracy has not been sufficiently well quantified with different modeling approaches [1][2][3][4]. Some weather forecast products, such as Rapid Updated Cycle (RUC) weather forecasts, have not yet been utilized to model airport capacity directly.…”

Section: C3-2mentioning

confidence: 99%

Prediction of weather impacted airport capacity using RUC-2 forecast

Wang

2012

2012 IEEE/AIAA 31st Digital Avionics Systems Conference (DASC)

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Weather induced airborne delays have caused significant problems at the major commercial airports in the United States. Using Rapid Updated Cycle forecast data (RUC-2), this paper presents real case studies for assessing the impact of weather on airport capacities by Quadratic Response Surface (QRS) linear regression models and ensemble Bagging Decision Tree regression (BDT) models. Three highdemand major airports: Newark Liberty International Airport (EWR), Chicago O'Hare International Airport (ORD), and Atlanta International Airport (ATL) were selected for the analysis. Both QRS and BDT models were developed and evaluated using the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) data and RUC -2 weather forecasts for these airports. The performances of the two weather impacted airport capacity models were compared and the errors between estimates and observations of airport arrival rates (AAR) were evaluated.The accuracy of AAR predictions in several hour look-ahead times using RUC-2 forecast weather was examined. The experimental results show that the accuracies of AAR estimations by nonlinear BDT regression models are much better than that from QRS multiple linear regression models. With airport AAR BDT models, the errors on weather corresponding AAR estimates using RUC weather data are smaller than that by METAR surface weather observations. Considering RUC includes much more weather information than METAR does, these findings strongly suggest that the adverse weather impact on airport capacity is complicated, relies on many predicting variables carried by RUC and it is inherently nonlinear. As a consequence, by using RUC weather forecasts, root mean squared error (RMSE) of the airport AAR BDT model predictions at one-hour look-ahead time are less than one and two arrival aircrafts per quarterly-hour for EWR and ORD/ATL airport, respectively. The ratio between AAR RMSE and average AAR for AAR predictions at one to eight hour look-ahead time is less than 10% for all three airports with nonlinear BDT models using RUC-2 weather forecasts. These results appear to be reasonably good.

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A probabilistic airport capacity model for improved ground delay program planning

Cited by 19 publications

References 2 publications

Ground Delay Program Analytics with Behavioral Cloning and Inverse Reinforcement Learning

Ground Delay Program Analytics with Behavioral Cloning and Inverse Reinforcement Learning

Using Ensemble Weather Forecasts for Predicting Airport Runway Configuration and Capacity

Prediction of weather impacted airport capacity using RUC-2 forecast

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