Using Probabilistic Demand Predictions for Traffic Flow Management Decision Support

Masalonis, Anthony J.; Mulgund, Sandeep; Song, Lixia; Wan, Yan; Zobell, Stephen

doi:10.2514/6.2004-5231

Cited by 20 publications

(21 citation statements)

References 4 publications

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“…Numerous methods have been proposed integrating uncertainty into data for visualization [31], and some have been evaluated for specific tasks [15,26]. However there has been less research into how well these provide decision support.…”

Section: Visual Support For Uncertainty In Reasoningmentioning

confidence: 99%

Visualization of Uncertainty and Reasoning

Zuk

Carpendale

Smart Graphics

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Abstract. This article gathers and consolidates the issues involved in uncertainty relating to reasoning and analyzes how uncertainty visualizations can support cognitive and meta-cognitive processes. Uncertainty in data is paralleled by uncertainty in reasoning processes, and while uncertainty in data is starting to get some of the visualization research attention it deserves, the uncertainty in the reasoning process is thus far often overlooked. While concurring with the importance of incorporating data uncertainty visualizations, we suggest also developing closely integrated visualizations that provide support for uncertainty in reasoning.

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Section: Visual Support For Uncertainty In Reasoningmentioning

confidence: 99%

Visualization of Uncertainty and Reasoning

Zuk

Carpendale

Smart Graphics

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“…Probabilistic demand prediction for traffic flow decision support [28] Masalonis, Mulgund, Song, Wanke and Zobell in [28] present visualization concepts and requirements for the display of uncertainty in decision support systems for air traffic flow management (TFM). Interviews with expert operators provide guidance for the development of a human machine interface prototype shown in figure 9 and 10.…”

Section: Information Fusionmentioning

confidence: 99%

“…A key element in the representation of uncertain information is the identification of sources and degree of uncertainty [28]. A general model is described in [10].…”

Section: A Sources Of Uncertaintymentioning

confidence: 99%

Evaluation of uncertainty visualization techniques for information fusion

Riveiro

2007

2007 10th International Conference on Information Fusion

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This paper highlights the importance of uncertainty visualization in information fusion, reviews general methods of representing uncertainty and presents perceptual and cognitive principles from Tufte, Chambers and Bertin as well as users experiments documented in the literature. Examples of uncertainty representations in information fusion are analyzed using these general theories. These principles can be used in future theoretical evaluations of existing or newly developed uncertainty visualization techniques before usability testing with actual users.

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“…Air traffic management research and development has provided a substantial collection of decision support tools that provide automated conflict detection and resolution [3,4,5],trial planning [6], controller advisories for metering and sequencing [7,8],traffic load forecasting [9,10], weather impact assessment [11,12,13]. The ability to properly forecast future aircraft trajectories is central in many of those decision support tools.…”

Section: Trajectory Predictionmentioning

confidence: 99%

4D Trajectories: A functional data perspective

Puechmorel

Delahaye

2007

2007 IEEE/AIAA 26th Digital Avionics Systems Conference

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Abstract3D or 4D trajectories are fundamental objects within the frame of ATM and can be defined as mappings from a bounded interval to the space 3 R .However, this functional aspect is almost never used in applications. A great improvement over existing procedures for statistical analysis of trajectories can be obtained by explicitly considering trajectory data as functional data. Since raw functional data is by essence infinite dimensional, no computation can be made unless a finite representation has been found: all the complexity of functional data analysis is hidden in this stage. The present paper will first analyze the functional aspect of trajectories in order to extract operationally relevant quantities then will give a way of representing optimally aircraft trajectories with a finite number of parameters, allowing further processing. Applications to trajectory prediction and classification will be briefly discussed at the end.

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Using Probabilistic Demand Predictions for Traffic Flow Management Decision Support

Cited by 20 publications

References 4 publications

Visualization of Uncertainty and Reasoning

Visualization of Uncertainty and Reasoning

Evaluation of uncertainty visualization techniques for information fusion

4D Trajectories: A functional data perspective

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