The Use of the Dynamic Solution Space to Assess Air Traffic Controller Workload

Air traffic capacity is mainly bound by air traffic controller workload. In order to effectively find solutions for this problem, off-line pre-experimental workload assessment methods are desirable. In order to better understand the workload associated with air traffic control, previous research introduced the static Solution Space as a possible workload metric. The Solution Space Diagram is a mapping of intruding aircraft trajectories to the velocity/heading plane in the form of Conflict Zones and safe areas. Choosing a velocity vector in either one will provide an unsafe or a safe solution, respectively. In this paper an improved, dynamic Solution Space will be tested for correlations with air traffic controller workload, measured experimentally. A two dimensional experiment has been conducted, where subjects were required to line up all aircraft in a sector towards a certain waypoint, while continuously providing subjective workload ratings. High correlations were found between several Solution Space parameters and the subjective workload. Even though a conventional workload metric shows also to be highly correlated to the measured workload, the Solution Space could be the scenario independent workload metric that is currently missing in air traffic controller workload determination.

[1]  Banavar Sridhar,et al.  Airspace Complexity and its Application in Air Traffic Management , 1998 .

[2]  Brian Hilburn,et al.  COGNITIVE COMPLEXITY IN AIR TRAFFIC CONTROL: A LITERATURE REVIEW , 2004 .

[3]  Paul U. Lee,et al.  Examining the Moderating Effect of Workload on Controller Task Distribution , 2007, HCI.

[4]  S. Magyarits,et al.  Dynamic density: measuring and predicting sector complexity [ATC] , 2002, Proceedings. The 21st Digital Avionics Systems Conference.

[5]  Milan Janic.  A model of air traffic control sector capacity based on air traffic controller workload , 1997 .

[6]  A J Tattersall,et al.  An experimental evaluation of instantaneous self-assessment as a measure of workload. , 1996, Ergonomics.

[7]  André Dittmar,et al.  Mental workload in air traffic control: an index constructed from field tests. , 2004, Aviation, space, and environmental medicine.

[8]  Amy R. Pritchett,et al.  Describing Airspace Complexity: Airspace Response to Disturbances , 2009 .

[9]  Parimal Kopardehr.  DYNAMIC DENSITY : MEASURING AND PREDICTING SECTOR COMPLEXITY , 2004 .

[10]  Paul U. Lee,et al.  A Non-Linear Relationship between Controller Workload and Traffic Count , 2005 .

[11]  P. Averty,et al.  Evaluating a new index of mental workload in real ATC situation using psychophysiological measures , 2002, Proceedings. The 21st Digital Avionics Systems Conference.

[12]  Henk G. Stassen,et al.  Internal representation, internal model, human performance model and mental workload , 1988, Autom..

[13]  Max Mulder,et al.  Solution-Space-Based Complexity Analysis of the Difficulty of Aircraft Merging Tasks , 2009 .

[14]  René van Paassen,et al.  Ecological Interface Design of a Tactical Airborne Separation Assistance Tool , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[15]  Andy P. Field,et al.  Discovering Statistics Using SPSS , 2000 .

[16]  Max Mulder,et al.  Solution Space Based Analysis of the Difficulty of Aircraft Merging Tasks , 2009 .

[17]  Anthony J. Masalonis,et al.  DYNAMIC DENSITY AND COMPLEXITY METRICS FOR REALTIME TRAFFIC FLOW MANAGEMENT , 2003 .