Air Traffic Volume and Air Traffic Control Human Errors

Data were obtained in an operational air traffic control (ATC) setting from seven (7) FAA Air Route Traffic Control Centers (ARTCCs) for 65 air space sectors within those centers. Measures used for study analysis were obtained during 90 minute data collection sessions and included: (a) ATC system inputs in the form of time of day, facility location, air traffic volume and air traffic configurational complexity, (b) the presence of a single radar controller or a two-person controller team, (c) work activity parameters obtained as frequencies of occurrence for tasks involving communications, computer interactions, flight strip activities and handoffs, and (d) controller performance outcomes based on observed performance pressures exhibited by the controller. A hypothesized path (causal) model incorporating the above variables was constructed and tested for its explanatory value. Computations of direct effects within the model showed generally significant linkages between work activities and traffic volume, level of traffic complexity, and controller configuration (e.g. higher levels of traffic volume and complexity, and the presence of a controller team were associated with higher levels of task activity). Most consistently significant, however, in its causal linkages to work activity was air traffic complexity. This was the dominant predictor, by far, of rated controller performance pressure when all other variables - including traffic volume - were accounted for by the analysis method. The time of day and facility location variables were erratic in their effect and difficult to interpret. On the basis of the study results, it was recommended that further research focus on development of the application of air traffic complexity as a measurement construct of potentially unique importance; one which seems to have been little understood and generally neglected in the research literature.

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A Predictor-Compensator Design to Assist Human Decision-Making Process in an Air-Traffic-Control Simulator

Volume 1: Aerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications; Bio-Medical and Bio-Mechanical Systems; Biomedical Robots and Rehab; Bipeds and Locomotion; Control Design Methods for Adv. Powertrain Systems and Components; Control of Adv. Combustion Engines, Building Energy Systems, Mechanical Systems; Control, Monitoring, and Energy Harvesting of Vibratory Systems ◽

10.1115/dscc2013-4026 ◽

2013 ◽

Cited By ~ 1

Author(s):

Keivan Sadeghzadeh ◽

Rifat Sipahi

Keyword(s):

Traffic Control ◽

Air Traffic Control ◽

Human Subjects ◽

Air Traffic ◽

Human Errors ◽

Compensator Design ◽

Human Decision ◽

Machine Interface ◽

Smart Machines ◽

Human Operators

Air traffic control is a demanding task for human operators, as this task requires tracking multiple events, managing the events, and taking actions in the presence of multiple and possibly competing objectives. In such critical tasks, human intelligence is extremely crucial however human decisions also become more prone to errors, which could cause tragic events. One idea to prevent such errors is to design smart machines that can assist human subjects in making decisions whenever human errors become more likely. In this article, we present a simulation model that captures the essence of how a human subject model would interact with a simplified version of an air traffic control simulator, and show how we design a predictor-compensator in order to regulate and possibly improve this interaction, such that overall human-machine interface can be optimized, and human workload is reduced on average.

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Workload Perception of Air Traffic Control Officers and Pilots During Continuous Descent Operations Approach Procedures

Aviation Psychology and Applied Human Factors ◽

10.1027/2192-0923/a000154 ◽

2019 ◽

Vol 9 (1) ◽

pp. 2-11

Author(s):

Marina Efthymiou ◽

Frank Fichert ◽

Olaf Lantzsch

Keyword(s):

Fuel Consumption ◽

Traffic Control ◽

Air Traffic Control ◽

Negative Impact ◽

Air Traffic ◽

Reduce Fuel Consumption ◽

Total Load ◽

Open Path

Abstract. The paper examines the workload perceived by air traffic control officers (ATCOs) and pilots during continuous descent operations (CDOs), applying closed- and open-path procedures. CDOs reduce fuel consumption and noise emissions. Therefore, they are supported by airports as well as airlines. However, their use often depends on pilots asking for CDOs and controllers giving approval and directions. An adapted NASA Total Load Index (TLX) was used to measure the workload perception of ATCOs and pilots when applying CDOs at selected European airports. The main finding is that ATCOs’ workload increased when giving both closed- and open-path CDOs, which may have a negative impact on their willingness to apply CDOs. The main problem reported by pilots was insufficient distance-to-go information provided by ATCOs. The workload change is important when considering the use of CDOs.

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How to Evaluate Remote Tower Metrics in Connection With Weather Observations

Aviation Psychology and Applied Human Factors ◽

10.1027/2192-0923/a000142 ◽

2018 ◽

Vol 8 (2) ◽

pp. 100-111 ◽

Cited By ~ 1

Author(s):

Maik Friedrich ◽

Christoph Möhlenbrink

Keyword(s):

Traffic Control ◽

Air Traffic Control ◽

Influential Factors ◽

Air Traffic ◽

Weather Event ◽

Evaluation Of Performance ◽

Weather Events ◽

Weather Observations ◽

Validation Experiment ◽

Remote Controller

Abstract. Owing to the different approaches for remote tower operation, a standardized set of indicators is needed to evaluate the technical implementations at a task performance level. One of the most influential factors for air traffic control is weather. This article describes the influence of weather metrics on remote tower operations and how to validate them against each other. Weather metrics are essential to the evaluation of different remote controller working positions. Therefore, weather metrics were identified as part of a validation at the Erfurt-Weimar Airport. Air traffic control officers observed weather events at the tower control working position and the remote control working position. The eight participating air traffic control officers answered time-synchronized questionnaires at both workplaces. The questionnaires addressed operationally relevant weather events in the aerodrome. The validation experiment targeted the air traffic control officer’s ability to categorize and judge the same weather event at different workplaces. The results show the potential of standardized indicators for the evaluation of performance and the importance of weather metrics in relation to other evaluation metrics.

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