Classification and Computation of Aggregate Delay Using Center-Based Weather Impacted Traffic Index

Sridhar, Banavar; Swei, Sean Shan‐Min

doi:10.2514/6.2007-7890

Cited by 13 publications

(15 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This inequity in the distribution of delays among air traffic control Centers is portrayed graphically in the histogram of delays in Figure 4. Such inequality has been observed in real delay data [4] and motivates the analysis of equity constraints presented in the following sub-section. …”

Section: Optimal System Behaviormentioning

confidence: 88%

See 1 more Smart Citation

Optimally and equitably distributing delays with the aggregate flow model

Bloem

Sridhar

2008

2008 IEEE/AIAA 27th Digital Avionics Systems Conference

Self Cite

View full text Add to dashboard Cite

The aggregate flow model is used to determine how to distribute predeparture delays among air traffic control Centers and across time to optimally satisfy constraints on airspace capacity and departure rates. To do so, a quadratic cost on cumulative departure delays is introduced, resulting in an optimization problem that can be quickly solved using convex optimization tools. Simulations using the model demonstrate the behavior of the National Airspace System (NAS) when implementing optimal departure delays for a particular constraint scenario. These results show that capacity-constrained air traffic control Centers suffer the highest delays. Three approaches for increasing the equity of the distribution of delays across the NAS are investigated. The first involves setting an upper bound on the Gini coefficient, a quasi-convex measure of inequality. Another is to make delays in some Centers more costly than in others. The last approach is to put an upper bound on the delay per departure for each Center. Simulation results demonstrate that bounding delay per departure effectively reduces the delays for the constrained Center. Enforcing an upper bound on the Gini coefficient and increasing the weight on delays in some Centers may impose large delays on other Centers when reducing the delays in the constrained Center.

show abstract

Section: Optimal System Behaviormentioning

confidence: 88%

“…Indeed, research on the impact of weather on delays has found that weather-induced delays are not distributed evenly across air traffic control Centers [4]. This distribution of delays may not be desirable when high-delay or high-priority areas are assigned large amounts of delay.…”

Section: Introductionmentioning

confidence: 98%

Optimally and equitably distributing delays with the aggregate flow model

Bloem

Sridhar

2008

2008 IEEE/AIAA 27th Digital Avionics Systems Conference

Self Cite

View full text Add to dashboard Cite

show abstract

“…A 2007 Airspace Project paper by Sridhar and Swei, provides a good example of the desired traceability. 21 In their studies, the authors demonstrate that a "Weather Impacted Traffic Index" (WITI) is a strong indicator of overall system delay. The WITI metric provides a means to superimpose traffic and weather conditions into a delay estimation tool.…”

Section: Metricsmentioning

confidence: 99%

Development and Application of an Integrated Approach toward NASA Airspace Systems Research

Barhydt¹,

Fong²,

Abramson³

et al. 2008

The 26th Congress of ICAS and 8th AIAA ATIO

View full text Add to dashboard Cite

The National Aeronautics and Space Administration's (NASA) Airspace Systems Program is contributing air traffic management research in support of the 2025 Next Generation Air Transportation System (NextGen). Contributions support research and development needs provided by the interagency Joint Planning and Development Office (JPDO). These needs generally call for integrated technical solutions that improve systemlevel performance and work effectively across multiple domains and planning time horizons. In response, the Airspace Systems Program is pursuing an integrated research approach and has adapted systems engineering best practices for application in a research environment. Systems engineering methods aim to enable researchers to methodically compare different technical approaches, consider system-level performance, and develop compatible solutions. Systems engineering activities are performed iteratively as the research matures. Products of this approach include a demand and needs analysis, system-level descriptions focusing on NASA research contributions, system assessment and design studies, and common systemlevel metrics, scenarios, and assumptions. Results from the first systems engineering iteration include a preliminary demand and needs analysis; a functional modeling tool; and initial system-level metrics, scenario characteristics, and assumptions. Demand and needs analysis results suggest that several advanced concepts can mitigate demand/capacity imbalances for NextGen, but fall short of enabling three-times current-day capacity at the nation's busiest airports and airspace. Current activities are focusing on standardizing metrics, scenarios, and assumptions, conducting system-level performance assessments of integrated research solutions, and exploring key system design interfaces.

show abstract

“…Currently, most WITI computations are done using the NOWRAD weather data [3][4][5] . NOWRAD weather data consists of 16 radar data levels (called color levels) reported in the weather grid.…”

Section: A Computation Of Witimentioning

confidence: 99%

“…The short-term delay prediction system uses WITI, predicted WITI, and air traffic demand as inputs and provides estimates of NAS delay as outputs. The methodology used for WITI computation [3][4][5] is refined to generate both WITI and predicted WITI based on the Corridor Integrated Weather System (CIWS) 9 using the Future ATM Concepts Evaluation Tool (FACET) 10,11 . The predicted CIWS-WITI, which is updated every 5 minutes with 5-minute forecast time-steps, is more suitable for short-term delay prediction compared to the WITI based on CCFP, 1 Senior Scientist for Air Transportation Systems, Aviation Systems Division, Fellow.…”

Section: Introductionmentioning

confidence: 99%

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

Sridhar

Chen

2008

AIAA Guidance, Navigation and Control Conference and Exhibit

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Classification and Computation of Aggregate Delay Using Center-Based Weather Impacted Traffic Index

Cited by 13 publications

References 5 publications

Optimally and equitably distributing delays with the aggregate flow model

Optimally and equitably distributing delays with the aggregate flow model

Development and Application of an Integrated Approach toward NASA Airspace Systems Research

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

Contact Info

Product

Resources

About