Determining the Value of Information for Minimizing Controller Taskload

In the future, air traffic controllers will most likely come to rely on decision-support tools and increased levels of automation to help manage and separate aircraft. Conflict detection and conflict resolution are examples of two key areas where increased automation and improved accuracy are considered imperatives to the future efficiency of airspace systems. The inclusion of decision-support tools for conflict-detection and resolution is expected to reduce controller workload by decreasing the mental stress associated with identifying potential conflicts and maintaining aircraft separation. Despite the benefits of such systems, there has been little study into the best methods to implement conflict-detection and resolution algorithms in practice, and what is the resulting controller taskload. In this paper, we examine how the capabilities and implementation strategy of conflict-detection and resolution tools affect controller taskload. Our goal is to understand how conflict-detection and resolution decision-support tools can best be designed and implemented to support human-based control of aircraft.

[1]  John Lygeros,et al.  Ground Assisted Conflict Resolution in Self-Separation Airspace , 2008 .

[2]  Nicolas Durand,et al.  A statistical analysis of the influence of vertical and ground speed errors on conflict probe , 2001 .

[3]  N. Durand,et al.  Scheduling heuristics for on-board sequential air conflict solving , 2004, The 23rd Digital Avionics Systems Conference (IEEE Cat. No.04CH37576).

[4]  Int Roduction,et al.  AIRCRAFT CONFLICT RE SOLUTION TAKING INTO ACCOUNT CONTROLLER WORKLOAD USING MIXE D INTEGER LINEAR PRO GRAMMING , 2003 .

[5]  Nicolas Durand,et al.  A traffic complexity approach through cluster analysis , 2003 .

[6]  Jean-Marc Alliot,et al.  OPTIMAL RESOLUTION OF EN ROUTE CONFLICTS. , 1995 .

[7]  Heinz Erzberger,et al.  Concept for Next Generation Air Traffic Control System , 2002 .

[8]  Peter Brooker,et al.  Controller workload, airspace capacity and future systems. , 2003 .

[9]  G. Gawinowski,et al.  ERASMUS: a new path for 4d trajectory-based enablers to reduce the traffic complexity , 2007, 2007 IEEE/AIAA 26th Digital Avionics Systems Conference.

[10]  John Lygeros,et al.  Subliminal air traffic control: Human friendly control of a multi-agent system , 2007, 2007 American Control Conference.

[11]  James K. Kuchar,et al.  A review of conflict detection and resolution modeling methods , 2000, IEEE Trans. Intell. Transp. Syst..

[12]  Earl S Stein,et al.  HUMAN OPERATOR WORKLOAD IN AIR TRAFFIC CONTROL. , 1998 .

[13]  M. Mongeau,et al.  A new method for generating optimal conflict free 4D trajectory , 2010 .

[14]  Shayne Loft,et al.  Modeling and Predicting Mental Workload in En Route Air Traffic Control: Critical Review and Broader Implications , 2007, Hum. Factors.

[15]  William Singhose,et al.  A fuel optimal and reduced controller workload optimization model for conflict resolution , 2009, 2009 IEEE/AIAA 28th Digital Avionics Systems Conference.

[17]  R. H. Mogford,et al.  The Complexity Construct in Air Traffic Control: A Review and Synthesis of the Literature. , 1995 .

[18]  William P. Niedringhaus.  Maneuver option manager: automated simplification of complex air traffic control problems , 1992, IEEE Trans. Syst. Man Cybern..

[19]  Paul U. Lee,et al.  Identification and Characterization of Key Human Performance Issues and Research in the Next Generation Air Transportation System (NextGen) , 2010 .