An analysis of relational complexity in an air traffic control conflict detection task

Theoretical analyses of air traffic complexity were carried out using the Method for the Analysis of Relational Complexity. Twenty-two air traffic controllers examined static air traffic displays and were required to detect and resolve conflicts. Objective measures of performance included conflict detection time and accuracy. Subjective perceptions of mental workload were assessed by a complexity-sorting task and subjective ratings of the difficulty of different aspects of the task. A metric quantifying the complexity of pair-wise relations among aircraft was able to account for a substantial portion of the variance in the perceived complexity and difficulty of conflict detection problems, as well as reaction time. Other variables that influenced performance included the mean minimum separation between aircraft pairs and the amount of time that aircraft spent in conflict.

ATC-lab: An air traffic control simulator for the laboratory

Air Traffic Control Laboratory Simulator (ATC-lab) is a new low- and medium-fidelity task environment that simulates air traffic control. ATC-lab allows the researcher to study human performance of tasks under tightly controlled experimental conditions in a dynamic, spatial environment. The researcher can create standardized air traffic scenarios by manipulating a wide variety of parameters. These include temporal and spatial variables. There are two main versions of ATC-lab. The medium-fidelity simulator provides a simplified version of en route air traffic control, requiring participants to visually search a screen and both recognize and resolve conflicts so that adequate separation is maintained between all aircraft. The low-fidelity simulator presents pairs of aircraft in isolation, controlling the participant's focus of attention, which provides a more systematic measurement of conflict recognition and resolution performance. Preliminary studies have demonstrated that ATC-lab is a flexible tool for applied cognition research.

The influence of memory for prior instances on performance in a conflict detection task

In 3 experiments, the authors examined the role of memory for prior instances for making relative judgments in conflict detection. Participants saw pairs of aircraft either repeatedly conflict with each other or pass safely before being tested on new aircraft pairs, which varied in similarity to the training pairs. Performance was influenced by the similarity between aircraft pairs. Detection time was faster when a conflict pair resembled a pair that had repeatedly conflicted. Detection time was slower, and participants missed conflicts, when a conflict pair resembled a pair that had repeatedly passed safely. The findings identify aircraft features that are used as inputs into the memory decision process and provide an indication of the processes involved in the use of memory for prior instances to make relative judgments.

Simulation of air traffic controllers' behaviour using the operator choice model

The Operator Choice Model (OCM) was developed to model the behaviour of operators attending to complex tasks involving interdependent concurrent activities, such as in Air Traffic Control (ATC). The purpose of the OCM is to provide a flexible framework for modelling and simulation that can be used for quantitative analyses in human reliability assessment, comparison between human computer interaction (HCI) designs, and analysis of operator workload. The OCM virtual operator is essentially a cycle of four processes: Scan Classify Decide Action Perform Action. Once a cycle is complete, the operator will return to the Scan process. It is also possible to truncate a cycle and return to Scan after each of the processes. These processes are described using Continuous Time Probabilistic Automata (CTPA). The details of the probability and timing models are specific to the domain of application, and need to be specified using domain experts. We are building an application of the OCM for use in ATC. In order to develop a realistic model we are calibrating the probability and timing models that comprise each process using experimental data from a series of experiments conducted with student subjects. These experiments have identified the factors that influence perception and decision making in simplified conflict detection and resolution tasks. This paper presents an application of the OCM approach to a simple ATC conflict detection experiment. The aim is to calibrate the OCM so that its behaviour resembles that of the experimental subjects when it is challenged with the same task. Its behaviour should also interpolate when challenged with scenarios similar to those used to calibrate it. The approach illustrated here uses logistic regression to model the classifications made by the subjects. This model is fitted to the calibration data, and provides an extrapolation to classifications in scenarios outside of the calibration data. A simple strategy is used to calibrate the timing component of the model, and the results for reaction times are compared between the OCM and the student subjects. While this approach to timing does not capture the full complexity of the reaction time distribution seen in the data from the student subjects, the mean and the tail of the distributions are similar.

Managing the airspace: A task analysis of Australian air traffic control

The aim of this study was to examine the way Australian air traffic controllers manage their airspace. Fourteen controllers ranging from 7 to 30 years experience were sampled from the Brisbane air traffic control centre. All had previously been endorsed for en route radar sectors. Five static pictures varying in workload level (low, medium and high) were presented to participants. Controllers were asked to work through the scenarios and describe aloud how they would resolve any potential conflicts between the aircraft. Following this controllers were asked a set of probe questions based on the critical decision method, to extract further information about the way they manage their airspace. A content analysis was used to assess patterns in the way controllers scan, strategies used in conflict detection and conflict resolution and the effect of workload on strategy choice. Findings revealed that controllers use specific strategies (such as working in a left to right scan or prioritising levels) when managing their airspace. Further analyses are still planned however a model based on the processes controllers used to resolve conflicts has been developed and will be presented as a summary of the results.