An understanding of the complexity factors that affect controller workload under higher levels of automation for conflict detection and resolution and under higher traffic densities is critical for future operations. This paper examines traffic complexity variables under higher levels of automation where the human controller is still in the loop, but is being supported by advanced conflict detection and resolution automation. The study involved two conflict resolution automation modes (i.e., trial-planning automation and advisory automation) and three traffic densities (i.e., 1X, 2X and 3X). The results indicate that under the 1X traffic condition, controller workload was the lowest with advanced levels of automation. The complexity and workload increased progressively for the 2X and 3X traffic conditions. Results also showed that several variables such as horizontal proximity, aircraft density, separation criticality index, and two degrees of freedom indices appear to be relevant complexity measures for higher traffic densities. The degrees of freedom index for aircraft in conflict appears to be a relevant measure for higher levels of automation. Regression results show that automation resolution mode, number of aircraft, number of conflicts, separation criticality index, and degrees of freedom for aircraft in conflict represent complexity and correlate with controller workload under higher densities.C ontroller workload is the main factor limiting en route airspace capacity. One of the key factors contributing to controller workload is conflict detection and resolution activity. Higher levels of automation for conflict detection and resolution are being investigated to reduce the controller workload and increase en route capacity. These levels include conflicts being detected by automation and three levels of automation for conflict resolution. Under the first level of automation for conflict resolution, the controller resolves conflicts using an automated trial planning capability. [1][2][3] Under the second level, the automation suggests resolution to the controller, and under the third level the automation also resolves the conflicts. It is anticipated that the controller workload will reduce under the higher levels of automation. However, there have been no studies thus far to identify the complexity variables that will contribute to the controller workload under the first and second automation options. Under the third level, the role of controller is somewhat unclear and largely reduced to monitoring. The complexity factors applicable under the first and second automation levels are not understood. The study reported in this paper focuses on the first two automation options.Multiple studies have been conducted to measure and predict controller workload under current operations. Controller workload is subjective and is an effect of air traffic complexity. A number of complexity factors affect
The importance of managing air and space traffic interactions will increase as the frequency of commercial space operations increases in the future. It is desirable that commercial operators of both aircraft and spacecraft receive equitable access to the shared resource of the National Airspace System while maintaining a high level of safety by protecting air traffic from possible spacecraft malfunctions. Current operational practice is conservative, reserving large volumes of airspace over a substantial time window. Space transition corridors are 4-dimensional envelopes, tailored to the trajectories of spacecraft during their launch and reentry flight phases, that provide a safety buffer without imposing excessive re-routing/delay costs on air traffic. Corridors with various spatial and temporal parameters were modeled in a simulation study, using air traffic rerouting distance as a performance metric. It was found that distance penalty contours can provide a basis for conducting tradeoffs within a corridor's temporal design space (time window duration vs. window midpoint time). A tool based on these contours could be useful for launch and reentry planning to reduce re-routing/delay costs for aircraft flying in the vicinity of spaceports while maintaining safety.
This article describes a method for defining route structure from flight tracks. Individual merge and diverge intersections between pairs of flights are identified, clustered, and grouped into nodes of a route structure network. Links are placed between nodes to represent major traffic flows. A parametric analysis determined the algorithm input parameters producing route structures of current day flight plans that are closest to today's airway structure. These parameters are then used to define and analyse the dynamic route structure over the course of a day for current day flight paths. Route structures are also compared between current day flight paths and more user-preferred paths such as great circle and weather avoidance routing.
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