A Methodology and Results for Comparative Assessment of the Prediction Performance of the Collaborative Routing Coordination Tools (CRCT)

Lindsay, Kenneth S.

doi:10.2514/6.2003-5630

Cited by 3 publications

(5 citation statements)

References 2 publications

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“…23 Studies of the predictive performance of CRCT produced a set of trajectory metrics for traffic flow management applications. 8,9,24 These studies documented that CRCT did not achieve significant predictive accuracy improvement over the operational parametric model in the ETMS. This equivalent performance is attributed to similarities in the parametric methods employed in both systems, though the software was developed separately for each.…”

Section: A Tfm Software Systems and Modelsmentioning

confidence: 96%

“…29 The metric selected for evaluating performance was the reduction of error for meter-fix crossing time prediction. 5,15,24 A meter fix is typically a point at which arriving flights are handed off from the ARTCC to the TRACON. The metric is defined as the difference between the predicted and actual meter fix crossing times: !…”

Section: E Air Traffic Metrics and Databasesmentioning

confidence: 99%

“…The 'Sector hit rate' is a measurement of the fraction of flights predicted to enter a sector that actually do enter the sector. 24 The formal definition of this metric is: As illustrated in Fig. 5, the parametric model performed approximately 10% better than the kinetic model in sector hit rate metrics.…”

Section: Descent Profile Modeling Errorsmentioning

confidence: 99%

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Modeling Tactical Trajectory Accuracy Effects on Traffic Flow Management Operations

Reisman¹,

Murphy²,

Krolak³

et al. 2007

7th AIAA ATIO Conf, 2nd CEIAT Int'l Conf on Innov and Integr in Aero Sciences,17th LTA Systems Tech Conf; Followed by 2nd TEOS

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Software, databases, and methods from previous predictive model studies were used to assess benefits of augmenting parametric methods with kinetic model trajectory predictions. Those results were consistent with other studies of models for aircraft position prediction accuracy during climbs and descents. A kinetic model reduced error for average arrival meter fix crossing time predictions across a one-hour predictive range by 0.6 minutes (39% improvement) when compared to the parametric model used in an experimental traffic flow management system. These results are comparable to the kinetic model's 1.1 minute (47% improvement) error reduction over the operational traffic flow management's parametric implementation. Data also show national convective weather conditions do not appear to affect performance differentials. Kinetic systems do not necessarily ensure performance superior to parametric systems in all areas. Parametric models of air traffic management procedural effects on altitude profiles demonstrated an average 10% error reduction for selected sector entry and occupancy metrics. The sources of these particular errors are not inherent to kinetic methods and could be addressed by procedural modeling improvements. The errors reductions achievable by kinetic models do not ameliorate pre-departure uncertainties, though traffic flow management functions a variety of air traffic management teams and functions may benefit from these levels of increased accuracy at their information exchange boundaries, particularly at Center, sector, and meter-fix crossings.

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Section: A Tfm Software Systems and Modelsmentioning

confidence: 96%

Section: E Air Traffic Metrics and Databasesmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Tactical Trajectory Accuracy Effects on Traffic Flow Management Operations

Reisman¹,

Murphy²,

Krolak³

et al. 2007

7th AIAA ATIO Conf, 2nd CEIAT Int'l Conf on Innov and Integr in Aero Sciences,17th LTA Systems Tech Conf; Followed by 2nd TEOS

View full text Add to dashboard Cite

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“…Measures of this type have been used to identify CRCT capabilities that would improve the predictive accuracy of ETMS. 4 Similar measures were again used to perform the predeparture uncertainty analysis that is reported herein.…”

Section: Introductionmentioning

confidence: 99%

“…5 Because DST decisions are based on these trajectories, NAS uncertainty ultimately causes DST prediction errors. Using CRCT and established prediction performance metrics, 4 CAASD assessed the impact of predeparture uncertainty on CRCT sector load prediction error. The contribution of en route uncertainty was also assessed.…”

Section: Introductionmentioning

confidence: 99%

Predeparture Uncertainty and Prediction Performance in Collaborative Routing Coordination Tools

Lindsay

Greenbaum

Wan

2005

Journal of Guidance, Control, and Dynamics

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The collaborative routing coordination tools concept demonstration and evaluation prototype is a tool to help the Federal Aviation Administration detect traffic flow problems in advance, generate reroute-based problem resolutions, and evaluate the resolution strategies. The tool does this by modeling four-dimensional aircraft trajectories and using them to predict demand for sector usage. The predictions are affected by various sources of uncertainty in the national airspace system. Therefore, it is useful to understand the contribution of this uncertainty to the tool's predictive error. A methodology was developed and used to assess the impact of predeparture uncertainty on the prototype tool's prediction performance. The tool's trajectory modeling and sector load prediction are described, the methodology used to assess the tool's prediction performance is presented, and the results of the predeparture uncertainty analysis are discussed. The analysis is based on tool predictions made for two air route traffic control centers. Tool runs were made against recordings of actual air traffic data on two good weather and two bad weather days. Analysis of run data showed that predeparture uncertainty is a greater contributor to sector prediction error than en route uncertainty. Because predeparture uncertainty is reflected in departure time estimate, research is recommended to identify methods to improve the accuracy of this estimate. Kenneth S. Lindsay is a Senior Information Systems Engineer at the MITRE Corporation's Center for Advanced Aviation System Development (CAASD). His areas of expertise include aircraft trajectory modeling, algorithmic performance analysis, and optimization. He holds B.A. and M.S. degrees in computer mathematics and secondary mathematics education, respectively, from the University of Pennsylvania, an M.A. in applied mathematics from the University of Maryland, and a D.Sc. in operations research from George Washington University. Daniel P. Greenbaum is a Lead Software Systems Engineer at MITRE CAASD where he has been developing prototypes of operational systems and models related to traffic flow management of aircraft since 1998. Previously, he worked as a labor law attorney in Washington, D.C. He received a B.A. in American history from the University of Pennsylvania in 1989, a J.D. from Cornell Law School in 1992, an M.E. in systems engineering from the University of Virginia in 1998, and an M.S. in computer science from George Washington University (expected 2005). Craig R. Wanke is a Senior Principal Engineer at MITRE CAASD. He has worked for the past 10 years on advanced air traffic management decision support systems and is currently focusing on strategic traffic management applications. He holds S.B., S.M., and Ph.D. degrees in aeronautical engineering from the Massachusetts Institute of Technology. He is a Senior Member of AIAA.

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Impact of Pre-Departure Uncertainty on Collaborative Routing Coordination Tools (CRCT) Prediction Performance

Lindsay

Greenbaum

Wan

2003

AIAA's 3rd Annual Aviation Technology, Integration, and Operations (ATIO) Forum

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A Methodology and Results for Comparative Assessment of the Prediction Performance of the Collaborative Routing Coordination Tools (CRCT)

Cited by 3 publications

References 2 publications

Modeling Tactical Trajectory Accuracy Effects on Traffic Flow Management Operations

Modeling Tactical Trajectory Accuracy Effects on Traffic Flow Management Operations

Predeparture Uncertainty and Prediction Performance in Collaborative Routing Coordination Tools

Impact of Pre-Departure Uncertainty on Collaborative Routing Coordination Tools (CRCT) Prediction Performance

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