A Method for Calculating Air Traffic Controller Communication Complexity

2015 ◽  
pp. 25-32
Author(s):  
Zach Roberts ◽  
Blake Arnsdorff ◽  
James Cunningham ◽  
Dan Chiappe
Keyword(s):  
Communication Complexity ◽  
Air Traffic ◽  
Air Traffic Controller

Predictive Validity of a Selection Procedure for Air Traffic Controller Trainees

2013 ◽  
Vol 3 (1) ◽  
pp. 19-27 ◽  
Author(s):  
Yvonne Pecena ◽  
Doris Keye ◽  
Kristin Conzelmann ◽  
Dietrich Grasshoff ◽  
Peter Maschke ◽  
...  
Keyword(s):  
Predictive Validity ◽  
Selection Procedure ◽  
Air Traffic ◽  
Performance Criteria ◽  
Training Phase ◽  
Air Traffic Controller ◽  
Training Performance ◽  
Navigation Service ◽  
German Aerospace ◽  
Training Success

The job of an air traffic controller (ATCO) is very specific and demanding. The assessment of potential suitable candidates requires a customized and efficient selection procedure. The German Aerospace Center DLR conducts a highly selective, multiple-stage selection procedure for ab initio ATCO applicants for the German Air Navigation Service Provider DFS. Successful applicants start their training with a training phase at the DFS Academy and then continue with a unit training phase in live traffic. ATCO validity studies are scarcely reported in the international scientific literature and have mainly been conducted in a military context with only small and male samples. This validation study encompasses the data from 430 DFS ATCO trainees, starting with candidate selection and extending to the completion of their training. Validity analyses involved the prediction of training success and several training performance criteria derived from initial training. The final training success rate of about 79% was highly satisfactory and higher than that of other countries. The findings demonstrated that all stages of the selection procedure showed predictive validity toward training performance. Among the best predictors were scores measuring attention and multitasking ability, and ratings on general motivation from the interview.

Air Traffic Controller Situation Awareness Is Embodied

2014 ◽  
Author(s):  
Dan Chiappe ◽  
Thomas Strybel ◽  
Kim-Phuong Vu ◽  
Lindsay Sturre
Keyword(s):  
Situation Awareness ◽  
Air Traffic ◽  
Air Traffic Controller

Determinants of enlisted air traffic controller success

1998 ◽  
Author(s):  
Frederick M. Siem ◽  
Thomas R. Carretta
Keyword(s):  
Air Traffic ◽  
Air Traffic Controller

Estimating Fatigue From Simulated Air Traffic Controller Communication

2011 ◽  
Author(s):  
Jarek Krajewski ◽  
David Sommer ◽  
Sebastian Schnieder ◽  
Martin Golz
Keyword(s):  
Air Traffic ◽  
Air Traffic Controller

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

2010 ◽  
Author(s):  
Jurriaan d'Engelbronner ◽  
Max Mulder ◽  
Marinus Van Paassen ◽  
Sebastiaan De Stigter ◽  
Hans Huisman
Keyword(s):  
Solution Space ◽  
Air Traffic ◽  
Air Traffic Controller ◽  
Dynamic Solution

scholarly journals 1F4-2　Analysis and Modeling of Situation Awareness of Air Traffic Controller

2018 ◽  
Vol 54 (Supplement) ◽  
pp. 1F4-2-1F4-2
Author(s):  
Hisae AOYAMA ◽  
Daisuke KARIKAWA
Keyword(s):  
Situation Awareness ◽  
Air Traffic ◽  
Air Traffic Controller

scholarly journals Hybrid of Hill Climbing and SAT Solving for Air Traffic Controller Shift Scheduling

2016 ◽  
Vol 10 (2) ◽  
Author(s):  
Mirko Stojadinović
Keyword(s):  
Heuristic Method ◽  
Optimal Solution ◽  
Air Traffic ◽  
Hill Climbing ◽  
Hybrid Technique ◽  
Sat Solving ◽  
Shift Scheduling ◽  
Exact Methods ◽  
Air Traffic Controller ◽  
Three Phases

Modern computers solve many problems by using exact methods, heuristic methods and very often by using their combination. Air Traffic Controller Shift Scheduling Problem has been successfully solved by using SAT technology (reduction to logical formulas) and several models of the problem exist. We present a technique for solving this problem that is a combination of SAT solving and meta-heuristic method hill climbing, and consists of three phases. First, SAT solver is used to generate feasible solution. Then, the hill climbing is used to improve this solution, in terms of number of satisfied wishes of controllers. Finally, SAT solving is used to further improve the found solution by fixing some parts of the solution. Three phases are repeated until optimal solution is found. Usage of exact method (SAT solving) guarantees that the found solution is optimal; usage of meta-heuristic (hill climbing) increases the efficiency in finding good solutions. By using these essentially different ways of solving, we aim to use the best from both worlds. Results indicate that this hybrid technique outperforms previously most efficient developed techniques.

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