Applications of a Macroscopic Model for En Route Sector Capacity

Welch, Jerry D.; Andrews, J.W.; Martin, Brian; Shank, Eric M.

doi:10.2514/6.2008-7221

Cited by 22 publications

(30 citation statements)

References 3 publications

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“…For dynamic density and other airspace complexity measures [4][5][6][7] to be useful as traffic management tools, it is necessary to predict them for durations of 30 to 120 minutes. Since dynamic density is a function of the position and velocity of all aircraft in a sector, a trajectory prediction algorithm can be used to predict dynamic density [8].…”

Section: Introductionmentioning

confidence: 99%

Impact of uncertainty on the prediction of airspace complexity of congested sectors

Sridhar

Kulkarni

2009

2009 IEEE/AIAA 28th Digital Avionics Systems Conference

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The ability of traffic controllers to separate aircraft determines the capacity of the region of airspace under their control, referred to as a sector. Complexity metrics, specifically dynamic density, is used as an estimate for controller workload. The prediction of dynamic density is required for the development of efficient long-term air traffic plans. This paper explores the influence of trajectory errors on the prediction of dynamic density and uses a worstcase analysis to describe the conditions under which forecast errors may lead to excessive complexity. Although the approach has general applicability, it is described using one definition of complexity. Depending on the sector and the complexity function, when a sector is highly congested, the method identifies aircraft entering the sector at certain locations, boundaries and altitudes, whose errors in prediction contribute significantly to the increase in workload. If these errors cannot be reduced, it may be necessary to limit the traffic approaching the sector from these altitudes and boundaries.

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Section: Introductionmentioning

confidence: 99%

Impact of uncertainty on the prediction of airspace complexity of congested sectors

Sridhar

Kulkarni

2009

2009 IEEE/AIAA 28th Digital Avionics Systems Conference

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“…We have developed a more complete analytical model for en route sector capacity which addresses these MAP deficiencies. 10,11 The model can be dynamically adjusted for traffic flow changes, loss of airspace due to weather, and splitting or combining of sectors. Each en route air traffic sector has an inherent capacity determined by the aggregated workload intensity of inter-sector coordination (transit tasks), aircraft separation assurance (conflict tasks), repetitive (recurring tasks) such as traffic scanning, and activities unrelated to traffic count (background tasks).…”

Section: Introductionmentioning

confidence: 99%

Macroscopic Capacity Model with Individual Sector Closing Speed Estimates

Welch

Andrews

2009

9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)

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Reliable airspace capacity estimates are important both for operational air traffic management and for airspace design. Air traffic management relies on manual procedures. Hence, controller workload determines the capacity of most sectors. Yet the current operational model for estimating capacity in United States airspace does not account for workload from conflict avoidance tasks. Aircraft closing speeds and airspace separation standards determine the rate of aircraft conflicts. Numerically, conflict workload intensity is the product of the conflict rate and the mean controller time required to service a conflict. As workload intensity approaches unity, the sector reaches capacity. We determine unknown model parameters by fitting capacity calculations against peak traffic observations for en route sectors. The result is an analytical model for capacity that is more accurate than the current operational model. The mean closing speed of all pairs of aircraft in a volume of airspace determines the conflict rate. This paper reports an effort to refine the conflict component of the model by replacing its original global closing speed estimate with local traffic-based closing speed estimates for individual sectors. Exact calculation of mean closing speed requires full position and velocity information for all flight tracks. The database that we used to obtain the peak traffic counts includes initial heading, speed, and altitude fields for all traffic entering a sector. Without positional coordinates and intersector track information, these data fields provide only crude closing speed estimates. We examined these estimates as possible indicators of sector route and altitude complexity in the New York Center. Individual sector closing speed estimates based on these observations did not improve the model fit for the 30 New York sectors.

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“…Sector capacities for each configuration were assigned using the method presented in Welch et al [24]. This capacity estimation method validated well with respect to historical data using a simple quadratic model based on sector volume and the average flight transit time through the sector during the peak traffic period.…”

Section: Figure 2 Reconfiguration Scenario Designmentioning

confidence: 99%

Comparing methods for dynamic airspace configuration

Zelinski

Lai

2011

2011 IEEE/AIAA 30th Digital Avionics Systems Conference

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This paper compares airspace design solutions for dynamically reconfiguring airspace in response to nominal daily traffic volume fluctuation. Airspace designs from seven algorithmic methods and a representation of current day operations in Kansas City Center were simulated with two times today's demand traffic. A three-configuration scenario was used to represent current day operations. Algorithms used projected unimpeded flight tracks to design initial 24-hour plans to switch between three configurations at predetermined reconfiguration times. At each reconfiguration time, algorithms used updated projected flight tracks to update the subsequent planned configurations. Compared to the baseline, most airspace design methods reduced delay and increased reconfiguration complexity, with similar traffic pattern complexity results. Design updates enabled several methods to as much as half the delay from their original designs. Freeform design methods reduced delay and increased reconfiguration complexity the most.

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Applications of a Macroscopic Model for En Route Sector Capacity

Cited by 22 publications

References 3 publications

Impact of uncertainty on the prediction of airspace complexity of congested sectors

Impact of uncertainty on the prediction of airspace complexity of congested sectors

Macroscopic Capacity Model with Individual Sector Closing Speed Estimates

Comparing methods for dynamic airspace configuration

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