High-Speed Prediction of Air Traffic for Real-Time Decision Support

Tandale, Monish; Wiraatmadja, Sandy; Menon, P. K.; Rios, Joseph

doi:10.2514/6.2011-6660

Cited by 8 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, an alternate method tested in the development of this work involved using an established departure uncertainty distribution 20 to perturb the departure time of all flights. This perturbation was repetitively simulated using the Computational Appliance for Rapid Prediction of Aircraft Trajectories (CARPAT) 21 to develop sector demand statistics. This method includes additional, historically-based uncertainties such as aircraft speed uncertainty.…”

Section: Iiic Alternate Methods Of Uncertainty Data Generationmentioning

confidence: 99%

The Value of Reduced Uncertainty in Air Traffic Flow Management

Rios

Morando

2011

AIAA Guidance, Navigation, and Control Conference

Self Cite

View full text Add to dashboard Cite

Given the numerous sources of uncertainty inherent in the National Airspace System, plans to alleviate demand-capacity imbalances are sometimes untrustworthy. In addition, differing solutions to demand-capacity imbalances are difficult to compare in terms of their potential quality in the face of weather and schedule uncertainties. In this work, a method for evaluating the robustness of a traffic scheduling solution to various uncertainties is presented. By converting solutions to predicted demands and capacities of sectors and airports together with measures of uncertainty on those predictions, any given plan can be evaluated based on the number of expected capacity violations along with distributions on their severity. With such measures, the most robust of a set of potential plans might be chosen by a traffic manager. As a side-effect of this approach, the value of reduced uncertainties can be measured in terms of reduced expected violations. The value of reduced uncertainty is demonstrated using a deterministic, minimum-delay approach to managing demand-capacity imbalances. When the deterministic solution is measured in terms of expected violations given models of demand and capacity uncertainty, results indicate that if the calculated uncertainty measures are reduced by 50%, then the number of expected violations will decrease by just over 40%.

show abstract

Section: Iiic Alternate Methods Of Uncertainty Data Generationmentioning

confidence: 99%

The Value of Reduced Uncertainty in Air Traffic Flow Management

Rios

Morando

2011

AIAA Guidance, Navigation, and Control Conference

Self Cite

View full text Add to dashboard Cite

show abstract

“…We use kinematic models of aircraft navigation with simplified dynamics and control, similar to the models developed by others. 13,14,15 The aircraft state vector is defined as…”

Section: B Modelingmentioning

confidence: 99%

Predicting Real-Time Safety of the National Airspace System

Roychoudhury

Spirkovska

Daigle

et al. 2016

AIAA Infotech @ Aerospace

View full text Add to dashboard Cite

Situation awareness is necessary for operators to make informed decisions regarding avoidance of airspace hazards. To this end, each operator must consolidate operationsrelevant information from disparate sources and apply extensive domain knowledge to correctly interpret the current state of the NAS as well as forecast its (combined) evolution over the duration of the NAS operation. This time-and workload-intensive process is periodically repeated throughout the operation so that changes can be managed in a timely manner. The imprecision, inaccuracy, inconsistency, and incompleteness of the incoming data further challenges the process. To facilitate informed decision making, this paper presents a model-based framework for the automated real-time monitoring and prediction of possible effects of airspace hazards on the safety of the National Airspace System (NAS). First, hazards to flight are identified and transformed into safety metrics, that is, quantities of interest that could be evaluated based on available data and are predictive of an unsafe event. The safety metrics and associated thresholds that specify when an event transitions from safe to unsafe are combined with models of airspace operations and aircraft dynamics. The framework can include any hazard to flight that can be modeled quantitatively. Models can be detailed and complex, or they can be considerably simplifed, as appropriate to the application. Real-time NAS safety monitoring and prediction begins with an estimate of the state of the NAS using the dynamic models. Given the state estimate and a probability distribution of future inputs to the NAS, we can then predict the evolution of the NAS-the future state-and the occurrence of hazards and unsafe events. The entire probability distribution of airspace safety metrics is computed, not just point estimates, without significant assumptions regarding the distribution type and/or parameters. We demonstrate our overall approach through a simulated scenario in which we predict the occurrence of some unsafe events and show how these predictions evolve in time as flight operations progress. Predictions accounting for common sources of uncertainty are included and it is shown how the predictions improve in time, become more confident, and change dynamically as new information is made available to the prediction algorithm.

show abstract

“…We use kinematic models of aircraft navigation with simplified dynamics and control, similar to the models developed by others (Bilmoria, Banavar, Chatterji, Sheth, & Grabbe, 2000;Chatterji, Sridhar, & Bilimoria, 1996;Tandale, Wiraatmadja, Menon, & Rios, 2011). In the following description, we present differential equations in continuous time t; for implementation purposes, they are converted to difference equations using a sampling time of 10 s. The aircraft state vector is defined as…”

Section: Aircraft Modelingmentioning

confidence: 99%

System-level Prognostics for the National Airspace

2016

View full text Add to dashboard Cite

In the National Airspace System (NAS), safety is assured through a set of rules, regulations, and procedures to respond to unsafe events. However, safety stands to benefit immensely from the introduction of tools and methodologies from Prognostics and Health Management (PHM). PHM will enable the NAS to stochastically predict unsafe states within the NAS, enabling a proactive preventative response strategy, as opposed to a reactive mitigative one. However, current PHM methods do not directly apply to the NAS for several reasons: they typically apply only at the component level, are implemented in a centralized manner, and are focused only on predicting remaining useful life. In this paper, we extend the model-based prognostics approach to PHM in order to provide a framework that can be applied to the NAS. We offer a system-level approach that supports a distributed implementation, and provide algorithms to predict the probability of an unsafe state, either at a specific time or within a time interval,and to predict the time of an unsafe state. Experimental results in simulation demonstrate the new approach.

show abstract

High-Speed Prediction of Air Traffic for Real-Time Decision Support

Cited by 8 publications

References 3 publications

The Value of Reduced Uncertainty in Air Traffic Flow Management

The Value of Reduced Uncertainty in Air Traffic Flow Management

Predicting Real-Time Safety of the National Airspace System

System-level Prognostics for the National Airspace

Contact Info

Product

Resources

About