Air Traffic Control Workstation Mock-Up for Free Flight Experimentation: Lab Development and Capabilities

Masalonis, Anthony J.; Le, Michael A.; Klinge, James C.; Galster, Scott M.; Duley, Jacqueline A.; Hancock, Peter A.; Hilburn, Brian; Parasuraman, Raja

doi:10.1177/1071181397041002166

Cited by 13 publications

(8 citation statements)

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“…The study was conducted using a PC-based ATC simulator (Masalonis et al 1997). Eleven experienced controllers were presented with 45 brief traffic scenarios and asked to assess the situation, and to either issue the clearance(s) they deemed necessary to resolve the situation, or to report 'no problem'.…”

Section: Resultsmentioning

confidence: 99%

Fuzzy signal detection theory: analysis of human and machine performance in air traffic control, and analytic considerations

Masalonis

Parasuraman

2003

Ergonomics

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This paper applies fuzzy SDT (signal detection theory) techniques, which combine fuzzy logic and conventional SDT, to empirical data. Two studies involving detection of aircraft conflicts in air traffic control (ATC) were analysed using both conventional and fuzzy SDT. Study 1 used data from a preliminary field evaluation of an automated conflict probe system, the User Request Evaluation Tool (URET). The second study used data from a laboratory controller-in-the-loop simulation of Free Flight conditions. Instead of assigning each potential conflict event as a signal (conflict) or non-signal, each event was defined as a signal (conflict) to some fuzzy degree between 0 and 1 by mapping distance into the range [0, 1]. Each event was also given a fuzzy membership, [0, 1], in the set 'response', based on the perceived probability of a conflict or on the colour-coded alert severity. Fuzzy SDT generally reduced the computed false alarm rate for both the human and machine conflict systems, partly because conflicts just outside the conflict criterion used in conventional SDT, were defined by fuzzy SDT as a signal worthy of some attention. The results illustrate the potential of fuzzy SDT to provide, especially in exploratory data analysis, a more complete picture of performance in aircraft conflict detection and many other applications. Alternative analytic methods also using fuzzy SDT concepts are discussed.

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Section: Resultsmentioning

confidence: 99%

Fuzzy signal detection theory: analysis of human and machine performance in air traffic control, and analytic considerations

Masalonis

Parasuraman

2003

Ergonomics

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“…ATC simulator. A PC-based medium-fidelity ATC simulator (Masalonis et al, 1997) was used to simulate en route airspace. The program was written in C code for a Pentium-II processor with two 21-in.…”

Section: Apparatusmentioning

confidence: 99%

Transitioning to Future Air Traffic Management: Effects of Imperfect Automation on Controller Attention and Performance

Rovira

Parasuraman

2010

Hum Factors

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“…A PC-based medium-fidelity ATC simulator (Masalonis, Le, Klinge, Galster, Duley, Hancock, Hilburn, & Parasuraman, 1997) was used to simulate a generic airspace. The simulation consisted of a primary visual display (PVD) and a datalink display, which were presented on a 21-and a 15-inch monitor, respectively.…”

Section: Apparatusmentioning

confidence: 99%

The Role of the Air Traffic Controller in Future Air Traffic Management: An Empirical Study of Active Control versus Passive Monitoring

2001

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Proposals for air traffic management such as Free Flight call for a transfer of responsibility for separation between aircraft from air traffic controllers (ATCos) to pilots. Under many proposals, the role of the ATCo will change from one of active control to passive monitoring. The present study directly compared these types of control with respect to ATCo mental workload, conflict detection, and memory. Eighteen ATCos participated in an air traffic control simulation of Free Flight procedures under moderate and high traffic load. Dependent variables included accuracy and timeliness in detecting potential conflicts, accepting and handing off aircraft, mental workload (as assessed by a secondary task, heart rate variability, and subjective ratings), and memory for aircraft location. High traffic density and passive control both degraded conflict detection performance. Actual or potential applications of this research include the recommendation that designs for future air traffic management keep authority for separation of aircraft with the controller.

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Air Traffic Control Workstation Mock-Up for Free Flight Experimentation: Lab Development and Capabilities

Cited by 13 publications

References 0 publications

Fuzzy signal detection theory: analysis of human and machine performance in air traffic control, and analytic considerations

Fuzzy signal detection theory: analysis of human and machine performance in air traffic control, and analytic considerations

Transitioning to Future Air Traffic Management: Effects of Imperfect Automation on Controller Attention and Performance

The Role of the Air Traffic Controller in Future Air Traffic Management: An Empirical Study of Active Control versus Passive Monitoring

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