Short Term National Airspace System Delay Prediction Using Weather Impacted Traffic Index

Sridhar, Banavar; Chen, Neil

doi:10.2514/6.2008-7395

Cited by 6 publications

(3 citation statements)

References 6 publications

(13 reference statements)

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“…The authors acknowledge that the purpose of the model is not to reproduce the exact delays, but only the trends and behaviors that are seen (Pyrgiotis et al 2013). Other models focus on estimating, not predicting, weather impacts (Klein et al 2007, Sridhar and Chen 2009 or airline operational factors (Yao et al 2010).…”

Section: Implications Of Resultsmentioning

confidence: 99%

“…Recently, Pyrgiotis et al (2013) have considered delay propagation in a network of airports using a queuing model. Other prediction models (Klein et al 2007, Sridhar and Chen 2009) have focused on weather-related delays, and the development of a Weather Impacted Traffic Index (WITI). Xu et al (2005) proposed a Bayesian network approach to estimating delay propagation.…”

Section: Introductionmentioning

confidence: 99%

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Characterization and prediction of air traffic delays

Rebollo

Balakrishnan

2014

Transportation Research Part C: Emerging Technologies

228

136

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This paper presents a new class of models for predicting air traffic delays. The proposed models consider both temporal and spatial (that is, network) delay states as explanatory variables, and use Random Forest algorithms to predict departure delays 2-24 hours in the future. In addition to local delay variables that describe the arrival or departure delay states of the most influential airports and links (origin-destination pairs) in the network, new network delay variables that characterize the global delay state of the entire National Airspace System at the time of prediction are proposed. The paper analyzes the performance of the proposed prediction models in both classifying delays as above or below a certain threshold, as well as predicting delay values. The models are trained and validated on operational data from 2007 and 2008, and are evaluated using the 100 most-delayed links in the system. The results show that for a 2-hour forecast horizon, the average test error over these 100 links is 19% when classifying delays as above or below 60 minutes. Similarly, the average over these 100 links of the median test error is found to be 21 minutes when predicting departure delays for a 2-hour forecast horizon. The effects of changes in the classification threshold and forecast horizon on prediction performance are studied.

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Section: Implications Of Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterization and prediction of air traffic delays

Rebollo

Balakrishnan

2014

Transportation Research Part C: Emerging Technologies

228

136

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“…Reference [3] presents aggregate statistics on the implementation of various TMIs (including GDPs and AFPs), while [4] presents aggregate statistics on various types of delays as well as other NAS data of interest. Some other aspects of flight delays are analyzed in [5] and [6]. An analysis of double delays, specifically the interaction between GDPs and arrival metering, affecting short-haul flights bound for Newark airport is presented in [7].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Additional Delays Experienced by Flights Subject to Ground Holding

Bilimoria

2016

16th AIAA Aviation Technology, Integration, and Operations Conference

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Flights subject to Ground Delay Programs and Airspace Flow Programs are held on the ground, taking specified delays at their departure airports, to address capacity constraints at their arrival airports and regions of en route airspace, respectively. These ground holds, ranging from tens of minutes to a few hours, affect a large number of flights and can disrupt air traffic operations on a national scale. It is therefore of interest to minimize additional delays for such flights. This paper analyzes the magnitude and nature of additional delays experienced by flights subject to ground holding, utilizing historical flight operations data for five airports whose arrivals experienced the most pre-departure ground holding in 2015. At four of these airports, arrivals subject to ground holding incurred additional gate-arrival delays substantially larger (about two to three times on average) than for arrivals that were not subject to ground holding. These inequities in additional gate-arrival delays were caused primarily by taxi-out delays at the departure airports. Airborne delays were a secondary cause of these delay inequities, and there was a minor contribution from taxi-in delays. There were mixed trends in the role of gate-departure delays not caused by ground holding. Overall, these results motivate a recommendation to expedite surface movements at departure airports for those flights already delayed by ground holding.

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Short-Term National Airspace System Delay Prediction Using Weather Impacted Traffic Index

Sridhar

Chen

2009

Journal of Guidance, Control, and Dynamics

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This paper describes a method to predict delay in the National Airspace System for durations of up to two hours. Various linear autoregressive model structures with exogenous inputs were implemented to perform the delay prediction. Current and forecast weather impacted traffic indices and air traffic volume were used as inputs to the system while the air traffic delay is the predicted output of the model. The refined methodology for generating the weather impacted traffic indices, together with the high-update-rate of Corridor Integrated Weather System is well suited for real-time delay prediction. An adaptive scheme was implemented to provide both weather-related and non-weather-related delay predictions depending on the weather and air traffic condition. The method will benefit air traffic management by facilitating the development of strategies to reduce delays, cancellations, and other costs during the day of operations in various weather conditions.

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Short Term National Airspace System Delay Prediction Using Weather Impacted Traffic Index

Cited by 6 publications

References 6 publications

Characterization and prediction of air traffic delays

Characterization and prediction of air traffic delays

Analysis of Additional Delays Experienced by Flights Subject to Ground Holding

Short-Term National Airspace System Delay Prediction Using Weather Impacted Traffic Index

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