Incorporating User Preferences in Collaborative Traffic Flow Management

Sheth, Kapil; Gutierrez-Nolasco, Sebastian

doi:10.2514/6.2008-7398

Cited by 13 publications

(11 citation statements)

References 9 publications

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“…An example is the use of Bayesian Networks for predictive modeling [39]. Similarly, HLIF issues include safety and cost in routing and scheduling aircraft [40,41], conflict scenarios [42], and user preferences [43]. Finally, a key aspect of ACES is the use of weather assessment for ATM [44].…”

Section: Airspace Concept Evaluation Systemmentioning

confidence: 99%

Enhanced air operations using JView for an air-ground fused situation awareness udop

Blasch

2013

2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC)

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Successful management of air assets coordinates many different types of information sources from the air and on the ground for air transportation safety. An example of a combined air-ground operation is air traffic control (ATC), which requires effective visualization of information for users. In this paper, we demonstrate the use of different visualization techniques to support air-ground coordination to enhance situation awareness. Situation awareness is afforded through strategic collection, fusion and presentation of air, ground, and space data. Visual analytics examples are presented using the Java Viewer (JView) User Defined Operating Picture (UDOP) that utilizes the Airspace Concepts Evaluation System (ACES) for civilian aviation applications.

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Section: Airspace Concept Evaluation Systemmentioning

confidence: 99%

Enhanced air operations using JView for an air-ground fused situation awareness udop

Blasch

2013

2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC)

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“…Previous ACES efforts include visualization [56] and integration of flight physics, airspace configurations, airport layouts, weather modeling, and scheduling in the ACES system [57]. As related, the IEEE Aerospace and Electronics Systems Society deemed air traffic control (ATC) as a key next decade system engineering need [58].…”

Section: Airspace Concept Evaluation Systemmentioning

confidence: 99%

Ontologies for nextgen avionics systems

Blasch¹

2015

2015 IEEE/AIAA 34th Digital Avionics Systems Conference (DASC)

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The Federal Aviation Administration (FAA) Next Generation Air Transportation System (NextGen) system incorporates many updates to aerospace technology including coordination of air traffic management (ATM), flight deck control, and avionics architectures. One such consideration is the need for ontologies. An ontology is a formal naming and definition of the types, properties, and interrelationships of the entities that exist (and persist) for a particular domain. In aviation, an ontology is needed to organize the variables to afford computations, instructions, and the relationships between parameters. For example Triples are used as Universal Resource Identifiers (URIs) in the case of {subject, predicate (verb), object}. The subject and object entities can be connected with the relationship predicate. Together, they are an event such as an ontology coordinating Notice to Airmen (NOTAM) flight weather information. In this paper, we explore the concepts of ontologies for applications to aerospace avionics as motivated by the NextGen and Single European Sky ATM Research (SESAR) standards.

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“…User preference into the System Planning task can subsequently be implemented using the credit-based concept proposed in Ref. 45 (see arrow [10] in Fig. 1).…”

Section: Atsp Planningmentioning

confidence: 99%

Integrated Traffic Flow Management Decision Making

Grabbe

Sridhar

Mukherjee

2009

AIAA Guidance, Navigation, and Control Conference

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A generalized approach is proposed to support integrated traffic flow management decision making studies at both the U.S. national and regional levels. It can consider tradeoffs between alternative optimization and heuristic based models, strategic versus tactical flight controls, and system versus fleet preferences. Preliminary testing was accomplished by implementing thirteen unique traffic flow management models, which included all of the key components of the system and conducting 85, six-hour fast-time simulation experiments. These experiments considered variations in the strategic planning look-ahead times, the replanning intervals, and the types of traffic flow management control strategies. Initial testing indicates that longer strategic planning look-ahead times and re-planning intervals result in steadily decreasing levels of sector congestion for a fixed delay level. This applies when accurate estimates of the air traffic demand, airport capacities and airspace capacities are available. In general, the distribution of the delays amongst the users was found to be most equitable when scheduling flights using a heuristic scheduling algorithm, such as ration-by-distance. On the other hand, equity was the worst when using scheduling algorithms that took into account the num ber of seats aboard each flight. Though the scheduling algorithms were effective at alleviating sector congestion, the tactical rerouting algorithm was the primary control for avoiding en route weather hazards. Finally, the modeled levels of sector congestion, the number of weather incursions, and the total system delays, were found to be in fair agreement with the values that were operationally observed on both good and bad weather days.

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Incorporating User Preferences in Collaborative Traffic Flow Management

Cited by 13 publications

References 9 publications

Enhanced air operations using JView for an air-ground fused situation awareness udop

Enhanced air operations using JView for an air-ground fused situation awareness udop

Ontologies for nextgen avionics systems

Integrated Traffic Flow Management Decision Making

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