Air traffic controller performance in a human-in-the-loop virtual tower simulation

2010 ◽  
Author(s):  
Daniel J. Hannon
Keyword(s):  
Air Traffic ◽  
Air Traffic Controller ◽  
Human In The Loop ◽  
Controller Performance

scholarly journals May I Interrupt? The effect of SPAM Probe Questions on Air Traffic Controller Performance

2015 ◽  
Vol 3 ◽  
pp. 2998-3004 ◽  
Author(s):  
Jillian Keeler ◽  
Henri Battiste ◽  
Elyse C. Hallett ◽  
Zach Roberts ◽  
Alice Winter ◽  
...  
Keyword(s):  
Air Traffic ◽  
Air Traffic Controller ◽  
Controller Performance

Another Look at air Traffic Controller Performance Evaluation

1996 ◽  
Vol 40 (12) ◽  
pp. 574-578
Author(s):  
Earl S. Stein ◽  
Randy L. Sollenberger
Keyword(s):  
Rating Scales ◽  
Rating Scale ◽  
Evaluation Criteria ◽  
Air Traffic ◽  
Group Discussions ◽  
Air Traffic Controller ◽  
Rater Reliability ◽  
Controller Performance ◽  
Performance Area ◽  
Mutual Evaluation

This paper describes a study that evaluated the reliability of a recently developed rating form designed to assess air traffic controller performance. Six supervisors from different radar approach control facilities nationwide viewed 20 video tapes of controllers working traffic from a previously recorded simulation study. The observer/raters used a new evaluation form that consisted of 24 different rating scales measuring specific areas of controller performance. An important part of this study was observer training. The training consisted of practice rating sessions followed by group discussions. In discussion, observers established mutual evaluation criteria for each performance area. Inter-rater reliability was assessed using intraclass correlations, and intra-rater reliability was assessed using Pearson product-moment correlations on repeated video tapes. In general, the reliability of the form was quite good, however, a few rating scales were much less reliable than the others. Reasons for the differences in rating scale reliability are discussed.

Prediction of subjective ratings of air traffic controller performance by computer-derived measures and behavioral observations.

2000 ◽  
Author(s):  
Carol A. Manning ◽  
Scott H. Mills ◽  
Henry J. Mogilka ◽  
Jerry W. Hedge ◽  
Kenneth W. Bruskiewicz ◽  
...  
Keyword(s):  
Air Traffic ◽  
Subjective Ratings ◽  
Air Traffic Controller ◽  
Behavioral Observations ◽  
Controller Performance

scholarly journals An Air Traffic Controller Action Extraction-Prediction Model Using Machine Learning Approach

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Duc-Thinh Pham ◽  
Sameer Alam ◽  
Vu Duong
Keyword(s):  
Traffic Control ◽  
Air Traffic ◽  
Learning Problem ◽  
Air Traffic Controller ◽  
Explanatory Variables ◽  
Human In The Loop ◽  
Machine Learning Approach ◽  
Traffic Scenario ◽  
Trajectory Features ◽  
Future Work

In air traffic control, the airspace is divided into several smaller sectors for better management of air traffic and air traffic controller workload. Such sectors are usually managed by a team of two air traffic controllers: planning controller (D-side) and executive controller (R-side). D-side controller is responsible for processing flight-plan information to plan and organize the flow of traffic entering the sector. R-side controller deals with ensuring safety of flights in their sector. A better understanding and predictability of D-side controller actions, for a given traffic scenario, may help in automating some of its tasks and hence reduce workload. In this paper, we propose a learning model to predict D-side controller actions. The learning problem is modeled as a supervised learning problem, where the target variables are D-side controller actions and the explanatory variables are the aircraft 4D trajectory features. The model is trained on six months of ADS-B data over an en-route sector, and its generalization performance was assessed, using crossvalidation, on the same sector. Results indicate that the model for vertical maneuver actions provides highest prediction accuracy (99%). Besides, the model for speed change and course change action provides predictability accuracy of 80% and 87%, respectively. The model to predict the set of all the actions (altitude, speed, and course change) for each flight achieves an accuracy of 70% implying for 70% of flights; D-side controller’s action can be predicted from trajectory information at sector entry position. In terms of operational validation, the proposed approach is envisioned as ATCO assisting tool, not an autonomous tool. Thus, there is always ATCO discretion element, and as more ATCO actions are collected, the models can be further trained for better accuracy. For future work, we will consider expanding the feature set by including parameters such as weather and wind. Moreover, human in the loop simulation will be performed to measure the effectiveness of the proposed approach.

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