Job Analysis for Air Traffic Control

This article presents a review and categorization of job analyses on the role of air traffic controllers (ATCO). There are three parts – how the role has been conceptualized, why it was conceptualized in this manner, and what we can conclude from developments in ATCO job analysis. The article includes a history of job analysis in air traffic control and two tables summarizing task and worker analyses. A large amount of information is available on tasks and attributes and we conclude that ATCO job analyses have been carried out in a varied and disunited manner. While there is no universally accepted analysis for the role of ATCO, previous analyses could nonetheless be used as a foundation for future analytic work.

A study on radiotelephony English test design

Radiotelephony English is taught in college for the learners whose future professions are mainly pilots and air traffic controllers. The present study is to analyse the radiotelephony English test design in a university to see the extent of which it evaluates learners’ communicative competence in aviation scope. Theoretical frameworks on communicative competence, modern test theory and ICAO language proficiency requirements for the learners of radiotelephony communication are presented. The study reveals that learners’ communicative competence which includes both radiotelephony and everyday communication skills are important components in radiotelephony test design. The study points out that the application of modern test theory in designing radiotelephony test in college is vital in meeting the validity and reliability of the test and the students’ individual needs in English language learning for future career needs to be reflected in the test design.

Mental Readiness for Frontline Workers in Homelessness Services in Ottawa, Canada

This study investigated factors related to competency by assessing the mental readiness among highly recognized frontline workers in homelessness services (FWHSs) by means of self-completed questionnaires. A total of 35 highly respected FWHSs in Ottawa, Canada were identified by their peers and supervisors as “exceptional” for various specialty areas: addictions, mental health, hoarding, trauma and post-traumatic stress disorder (PTSD). An Operational Readiness Framework was used to examine how FWHSs perform at their best in challenging situations. A series of questionnaires were completed at a Think Tank to determine their mental readiness before, during and after challenging situations. Quantitative and qualitative analyses of mental readiness were performed to prioritize identified challenges. The study findings were then compared to the “Wheel of Excellence” based on results from elite athletes and other high performers such as surgeons, police, and air traffic controllers. The analysis revealed that mental readiness is required to achieve peak performance in addressing the challenges of homelessness. The balance between readiness (physical, technical and mental) and performance contributed to their competency and resiliency. Common elements of success were found: commitment, self-belief, positive imagery, mental preparation, full focus, distraction control and constructive evaluation. This investigation confirmed many similarities in mental readiness practices engaged by excellent FWHSs and other top professionals. This study offered, for the first time, a comprehensive understanding of specific high-performance readiness practices through a streetwise, frontline-worker perspective. Practical recommendations for training and assessment were provided relevant to excellence in homelessness services.

CLARITY AND PRONUNCIATION OF AB-INITIO AIR TRAFFIC CONTROLLER

The increasing number of aircraft flying around the world has led to the requirement for air traffic controllers to improve their communication skills to face high demand traffic in the future. The paper examines the communication errors in the pilot-controller communication of six ab-initio air traffic controllers during simulation training. More than three hours of conversation were collected and analyzed qualitatively using conversational analysis. The transcribed data yielded a total of 62 instances of communication errors. The data revealed that clarity and pronunciation of ab-initio controllers contributed to problematic communication and reduced the efficiency of the air traffic controllers in communicating. In contrast, pronunciation errors rarely diminished comprehension amongst the controllers and pilots who share a similar first language and are familiar with the use of English in a lingua franca setting. The study also describes other instances of communication errors in pilot-controller communication. The results indicate that ab-initio air traffic controllers need to be proficient in three main areas in pilot controller communication to improve their performance: aviation phraseology, aviation English, and aviation knowledge. The findings suggest that pilots and air traffic controllers should achieve level 4 (operational) in aviation language proficiency test, before proceeding to aviation training that requires them to be proficient in their language skills.

Controller Fatigue State Detection Based on ES-DFNN

The fatiguing work of air traffic controllers inevitably threatens air traffic safety. Determining whether eyes are in an open or closed state is currently the main method for detecting fatigue in air traffic controllers. Here, an eye state recognition model based on deep-fusion neural networks is proposed for determination of the fatigue state of controllers. This method uses transfer learning strategies to pre-train deep neural networks and deep convolutional neural networks and performs network fusion at the decision-making layer. The fused network demonstrated an improved ability to classify the target domain dataset. First, a deep-cascaded neural network algorithm was used to realize face detection and eye positioning. Second, according to the eye selection mechanism, the pictures of the eyes to be tested were cropped and passed into the deep-fusion neural network to determine the eye state. Finally, the PERCLOS indicator was combined to detect the fatigue state of the controller. On the ZJU, CEW and ATCE datasets, the accuracy, F1 score and AUC values of different networks were compared, and, on the ZJU and CEW datasets, the recognition accuracy and AUC values among different methods were evaluated based on a comparative experiment. The experimental results show that the deep-fusion neural network model demonstrated better performance than the other assessed network models. When applied to the controller eye dataset, the recognition accuracy was 98.44%, and the recognition accuracy for the test video was 97.30%.

Spatiotemporal Graph Indicators for Air Traffic Complexity Analysis

There has been extensive research in formalising air traffic complexity, but existing works focus mainly on a metric to tie down the peak air traffic controllers workload rather than a dynamic approach to complexity that could guide both strategical, pre-tactical and tactical actions for a smooth flow of aircraft. In this paper, aircraft interdependencies are formalized using graph theory and four complexity indicators are described, which combine spatiotemporal topological information with the severity of the interdependencies. These indicators can be used to predict the dynamic evolution of complexity, by not giving one single score, but measuring complexity in a time window. Results show that these indicators can capture complex spatiotemporal areas in a sector and give a detailed and nuanced view of sector complexity.