Traffic Complexity Measurement under Higher Levels of Automation and Higher Traffic Densities

Abstract

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

Formulation of Reduced-Taskload Optimization Models for Conflict Resolution

“…In human-in-the-loop studies with AAC acting as an advisory decision-support tool, even with automated communications and datalink systems, the taskload associated with the conflict-resolution process was problematic [27]. As a side note, the authors of [27] suggest that high traffic volumes, the airspace management taskload, and the rate of conflict-resolution advisories proposed by the system became too large for controllers to process, so controllers accepted almost all advisories due to time pressure.…”

“…As a side note, the authors of [27] suggest that high traffic volumes, the airspace management taskload, and the rate of conflict-resolution advisories proposed by the system became too large for controllers to process, so controllers accepted almost all advisories due to time pressure. Even with automated communication and data channels, we can expect time pressures and loss of situation awareness to be exacerbated.…”

“…To prevent issues surrounding conflict-resolution taskload, similar to that noted in [27], the parametric methodology presented here seeks to embed the consideration of conflict-resolution taskload into decision-support tools, thereby creating a class of reduced-taskload conflict-resolution algorithms. While the underlying conflictresolution program used for simulations in this paper is specified, the parameterized modeling procedure for considering conflict-resolution taskload can be included as part of any algorithm.…”

A Perspective on NASA Ames Air Traffic Management Research

“…Simulation results correlated variations in traffic structure with controller workload measurements. 49 These measurements of complexity are more multifaceted than simply setting a limit on the number of aircraft a controller can manage. Using the complexity measure, and perhaps controlling flow to that measure, may offer a meaningful connection between flow management and automated separation assurance.…”

Development and Application of an Integrated Approach toward NASA Airspace Systems Research

“…Airspace project research has determined several complexity-related variables that are applicable to automation-assisted conflict resolution. 23 Variables such as the available maneuvering degrees of freedom may also be applicable to completely automated conflict resolution concepts. Concept-appropriate complexity metrics should provide traceability to system-level capacity predictions and instantaneous safety assessments.…”