Anthony J. Masalonis

AIR TRAFFIC CONTROLLER PERFORMANCE AND WORKLOAD UNDER MATURE FREE FLIGHT: CONFLICT DETECTION AND RESOLUTION OF AIRCRAFT SELF-SEPARATION

The effects of conflict detection and self-separating aircraft resolution on the mental workload and performance of en-route air traffic controllers were examined. An air traffic control simulator was used to manipulate traffic loads and traffic complexity. A mature stage of free flight was simulated by having controllers monitor self-separating aircraft. Four 30-min scenarios were created to combine moderate (11 aircraft) and heavy traffic loads (17 aircraft) in a 50-mile radius sector with the presence or absence of self-separating and conflicting aircraft. Conflicts (defined as a loss of separation of 5 nm laterally and 1,000 ft vertically) were indicated to the controller by the appearance of a red circle around each of the aircraft involved. A self-separation event was defined as an evasive maneuver (either altitude or speed change) made by 1 aircraft to avoid a potential conflict with another aircraft. Performance and workload measurements indicated that controllers had difficulty both in detecting conflicts and in recognizing self-separating events in a timely manner in saturated airspace. Controller mental workload also increased, as indexed both by subjective and secondary task measures. Implications for the design of automated tools to support controllers under free flight environments are discussed.

On the theory of fuzzy signal detection: Theoretical and practical considerations

This work examines the foundations for and explores the implications of fuzzy signal detection (Fuzzy SDT), a theory that represents the marriage of two powerful extant theories, fuzzy set theory and signal detection theory. Fuzzy SDT permits the modelling and prediction of human, machine, and human-machine performance in a wide range of settings. Fuzzy SDT exploits the strengths of each theory to provide new and dynamic insights into performance. Fuzzy SDT explicitly recognizes that the binary decision states of classic signal detection represent two ends of a single continuum whose uncertainty decreases towards such end states and is maximized in its centre. It is shown how Fuzzy SDT has its origins in some more general concepts of human performance, and companion works are referenced which provide the mathematical foundation for Fuzzy SDT and its application in a specific domain. The present work examines the wider implications of Fuzzy SDT by illustrating the relevance of fuzzification in the larger cycle of design, configuration, and use of technology. It also examines the broader concerns of the temporal relationship between signal and response, showing time to be a crucial, if neglected, dimension of action, the exploration and exploitation of which can produce a deeper understanding of human behaviour in psychology, machine behaviour in engineering and human-machine behaviour in ergonomics.

Effects of training operators on situation-specific automation reliability

Decision support automations reliability can vary predictably according to situation, resulting in inappropriate trust in automation, and possible performance decrements. Training might ameliorate these problems. In a realtime simulation, air traffic controllers detected aircraft conflicts, assisted by an automated tool which was very reliable in normal situations, but less so in Free Flight (FF) scenarios where certain aircraft deviated from their flight plans. One group of participants was given a small amount of pre-experiment training, being informed that the automation reliability would suffer in these scenarios. As hypothesized, subjective trust was lower in FF for the trained participants. Overall performance did not differ, but the trained group were more likely to detect both real and perceived conflicts (bias shift). Also, they were more likely in general to unquestioningly accept the automations judgments (which in general was appropriate), as assessed by a new experimental scale of self-reported use-of-information. Only the non-trained group showed a relationship between subjective trust and unquestioning acceptance of the automations judgments on the new scale.

Effects of Situation-Specific Reliability on Trust and Usage of Automated Air Traffic Control Decision Aids

Automation trust was explored in a human-in-the-loop Air Traffic Control simulation. Controllers were asked to detect closely approaching aircraft pairs, assisted by an automated tool whose reliability varied according to whether the scenario contained Free Flight (aircraft deviating from flight plans at will). Training on the causes of variable reliability, given to half the controllers, enabled them to appropriately trust the automation less (assessed subjectively) during Free Flight. The trained group also had higher Hit and False Alarm rates than the other group, and were more likely overall to unquestioningly accept the tools judgments; this behavior sometimes aided task performance, but the training did not affect overall performance. A new System Confidence rating scale showed promise as a supplement to established Trust and Self Confidence ratings, as did a scale enabling participant reports of the degree of automation information use in making each decision. Implications for decision support design and training are discussed.

Designing Automated Alerting Systems: Standard and Fuzzy Signal Detection Theory and Bayesian Analysis

Quantitative models of human-automation interaction can aid in the design of automation for effective human use. The application of signal detection theory (SDT), Bayesian analysis, and fuzzy SDT to the design of automated alerting and warning systems is discussed. SDT and Bayesian analysis can be used to design automated systems with high posterior probabilities of correct response to hazards. Fuzzy SDT can provide estimates of performance that better capture the temporal and contextual variability inherent in real-world hazards that need to be detected by automated warning systems.