Describing Airspace Complexity: Airspace Response to Disturbances

Lee, Keumjin; Féron, Éric; Pritchett, Amy R.

doi:10.2514/1.36308

Cited by 87 publications

(62 citation statements)

References 41 publications

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“…However, this index is feasible for the computation of long-term tracking and is not suited to predict traffic situations in real-time operational settings. Regarding the impacts of abrupt disturbances in traffic flow caused by the entrance of another aircraft into the airspace, Lee et al proposed a complexity model based on traffic flow disturbance, defined the complexity degree as a measure of control activities faced by a controller in response to emergency, and validated this method via simulation [10]. As an important traffic structure, aircraft clusters (groups of proximate aircraft) were originally studied to improve the performance of conflict resolution and were later used to measure sector-independent airspace congestion in future air traffic operations [11][12][13].…”

Section: Journal Of Advanced Transportationmentioning

confidence: 99%

Modeling Air Traffic Situation Complexity with a Dynamic Weighted Network Approach

Wang

Song

Ruiying

2018

Journal of Advanced Transportation

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In order to address the flight delays and risks associated with the forecasted increase in air traffic, there is a need to increase the capacity of air traffic management systems. This should be based on objective measurements of traffic situation complexity. In current air traffic complexity research, no simple means is available to integrate airspace and traffic flow characteristics. In this paper, we propose a new approach for the measurement of air traffic situation complexity. This approach considers the effects of both airspace and traffic flow and objectively quantifies air traffic situation complexity. Considering the aircraft, waypoints, and airways as nodes, and the complexity relationships among these nodes as edges, a dynamic weighted network is constructed. Air traffic situation complexity is defined as the sum of the weights of all edges in the network, and the relationships of complexity with some commonly used indices are statistically analyzed. The results indicate that the new complexity index is more accurate than traffic count and reflects the number of trajectory changes as well as the high-risk situations. Additionally, analysis of potential applications reveals that this new index contributes to achieving complexity-based management, which represents an efficient method for increasing airspace system capacity.

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Section: Journal Of Advanced Transportationmentioning

confidence: 99%

Modeling Air Traffic Situation Complexity with a Dynamic Weighted Network Approach

Wang

Song

Ruiying

2018

Journal of Advanced Transportation

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“…In order to better assess airspace complexity, methods such as 'complexity maps' and the 'solution space' have been proposed in Lee et. al [17] and Hermes et al [18]. Both solutions act as an airspace complexity measure method, where a complexity map details the control activity as a function of the parameters describing the disturbances, and the solution space details the two-dimensional speed and heading possibilities of one controlled aircraft that will not induce separation violations.…”

Section: Previous Research On Complexity Factorsmentioning

confidence: 99%

Measuring Sector Complexity: Solution Space-Based Method

Rahman¹,

Borst²,

Mulder³

et al. 2012

Advances in Air Navigation Services

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“…In this light, complexity maps, as have been developed by Lee et al, 16 and the Solution Space, as was introduced by Hermes et al, 17 were proposed as complexity metrics.…”

Section: Figure 1 Forbidden Beam Zonesmentioning

confidence: 99%

The Use of the Dynamic Solution Space to Assess Air Traffic Controller Workload

d'Engelbronner

Mulder

Stigter³

et al. 2010

AIAA Guidance, Navigation, and Control Conference

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Air traffic capacity is mainly bound by air traffic controller workload. In order to effectively find solutions for this problem, off-line pre-experimental workload assessment methods are desirable. In order to better understand the workload associated with air traffic control, previous research introduced the static Solution Space as a possible workload metric. The Solution Space Diagram is a mapping of intruding aircraft trajectories to the velocity/heading plane in the form of Conflict Zones and safe areas. Choosing a velocity vector in either one will provide an unsafe or a safe solution, respectively. In this paper an improved, dynamic Solution Space will be tested for correlations with air traffic controller workload, measured experimentally. A two dimensional experiment has been conducted, where subjects were required to line up all aircraft in a sector towards a certain waypoint, while continuously providing subjective workload ratings. High correlations were found between several Solution Space parameters and the subjective workload. Even though a conventional workload metric shows also to be highly correlated to the measured workload, the Solution Space could be the scenario independent workload metric that is currently missing in air traffic controller workload determination.

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Describing Airspace Complexity: Airspace Response to Disturbances

Abstract: The evolution of the complexity map for Sector B by adding aircraft. 34 Complexity map for the traffic situation in Sectors A and B. The plot contours indicate the number of heading changes required by aircraft for all combinations of entering aircraft bearing and position angles. 36

Cited by 87 publications

References 41 publications

Modeling Air Traffic Situation Complexity with a Dynamic Weighted Network Approach

Modeling Air Traffic Situation Complexity with a Dynamic Weighted Network Approach

Measuring Sector Complexity: Solution Space-Based Method

The Use of the Dynamic Solution Space to Assess Air Traffic Controller Workload

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