Predicting separation errors of air traffic controllers through integrated sequence analysis of multimodal behaviour indicators

Xiong, Ruoxin; Wang, Yanyu; Tang, Pingbo; Cooke, Nancy J.; Ligda, Sarah V.; Lieber, Christopher S.; Liu, Yongming

doi:10.1016/j.aei.2023.101894

Cited by 12 publications

(2 citation statements)

References 53 publications

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“…The study in [19] presented framework for prediction of separation error which include long leading time and a multi-facto approach characterizing the inter-relationships between task complexity, behavioral activity, cognitive load, and operational performance. The authors applied synchronization on raw data collected from multiple sources at different sampling rates depending on up-date period of the Metacraft simulation system which extracts information about air traffic.…”

Section: Detailed Literature Reviewmentioning

confidence: 99%

Machine Learning-based Aircraft Conflict Prediction: A Systematic Literature Review

Jamal,

ashraf,

Jhanjhi

2023

Preprint

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Over the last few decades, there is a remarkable increase in air traffic worldwide; however, despite of rapid technological development and improved airport infrastructure, there is still an increasing number of aviation accidents and fatalities. In order to meet safety demands of Air Traffic Control (ATC), Machine Learning (ML) based aircraft conflict detection techniques are focused to predict events before they come to reality. This research presents a systematic literature review (SLR) about the applications of ML techniques in safety prediction of ATC system. Initially, the existing aircraft conflict prediction models are classified into three broad categories; logical, geometric and probabilistic. A formal process of SLR is then conducted which is initiated with preprocessing and effective searching. A series of filtering called title, abstract and objective-based are then applied to extract the most relevant articles. Detailed and critical reviews of selected articles are performed to identify challenges which are segregated into dataset, model and evaluation techniques, furthermore, the optimal solutions for each are proposed. Finally the matrices to map the identified challenged with their proposed solutions are constructed. This research serves as a foundation for future research and towards integration of ML based safety prediction techniques into modern ATC system.

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Section: Detailed Literature Reviewmentioning

confidence: 99%

Machine Learning-based Aircraft Conflict Prediction: A Systematic Literature Review

Jamal,

ashraf,

Jhanjhi

2023

Preprint

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“…Being an air traffic controller (ATCO) has been considered as one of the most stressful and difficult professions because of the significant responsibilities involved [1]. Air traffic controllers (ATCOs) prevent the collision of aircraft and ensure the required separation intervals between aircraft [2,3]. Hundreds of passengers' lives are at risk if ATCOs make incorrect decisions.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Effects of Fatigue and Tension on the Physical Characteristics and Abilities of Young Air Traffic Controllers

Zhang,

Liu,

Bai

et al. 2023

Applied Sciences

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The purpose of this study was to analyze the effects of fatigue and tension on the physical characteristics and abilities of air traffic controllers (ATCOs) and determine their influence mechanisms. A simulated experiment was designed to evaluate the responses of ATCOs in four states: alertness, fatigue, tension, and fatigue and tension. Thirty young male ATCOs participated in the experiment. Fifteen parameters of their physical characteristics and abilities were collected and analyzed to estimate the effects and the decreasing order of influence of fatigue and tension on the indicators. The results showed that most of the parameters of the ATCOs were significantly affected by fatigue and tension. The attention, perception, reaction time, decision-making ability, and comprehensive performance of the ATCOs were adversely affected by fatigue, and tension had negative effects on their attention, decision-making ability, and comprehensive performance. Fatigue and tension impair the physical characteristics and abilities of ATCOs. Both states initially affected the physical characteristics of the ATCOs and then impaired their abilities. However, the influence mechanisms involved were different. The primary effect of the fatigue state was slowing down, whereas the effect of the tense state was instability. These results provide a reference for the evaluation and management of fatigue and tension states in ATCOs.

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Fabric image retrieval based on multi-modal feature fusion

Zhang,

Liu,

et al. 2024

SIViP

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Predicting separation errors of air traffic controllers through integrated sequence analysis of multimodal behaviour indicators

Cited by 12 publications

References 53 publications

Machine Learning-based Aircraft Conflict Prediction: A Systematic Literature Review

Machine Learning-based Aircraft Conflict Prediction: A Systematic Literature Review

Effects of Fatigue and Tension on the Physical Characteristics and Abilities of Young Air Traffic Controllers

Fabric image retrieval based on multi-modal feature fusion

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