Abstract

PildoLabs is developing a UTM system to coordinate manned and unmanned aircraft operations for emergency operations. The system needs a mechanism to (1) validate and publish flight plans involved in the emergency operations and (2) provide a mechanism for third party systems to query published flight plans. SWIM is an aeronautical information exchange standard that is being developed in the frame of SESAR aimed at facilitating standardized information sharing. EUROCONTROL already provides some SWIM based services, including a flight plan management sets. The objec

Abstract

traffic controllers (ATCos) monitor flight operations and resolve predicted aircraft conflicts to ensure safe flights, making them one of the essential human operators in air traffic control systems. Researchers have been studying ATCos with human subjective approaches to understand their tasks and air traffic managing processes. As a result, models were developed to predict ATCo actions. However, there is a gap between our knowledge and the real-world. The developed models have never been validated against the real-world, which creates uncertainties in our understanding of how ATCos detect and resolve predicted aircraft conflicts. Moreover, we do not know how information from air traffic control systems affects their actions. This Ph.D. dissertation work introduces methods to evaluate existing ATCo action prediction models. It develops a prediction model based on flight contextual information (information describing flight operations) to explain the relationship between ATCo actions and information. Unlike conventional approaches, this work takes data-driven approaches that collect large-scale flight tracking data. From the collected real-world data, ATCo actions and corresponding predicted aircraft conflicts were identified by developed algorithms. Comparison methods were developed to measure both qualitative and quantitative differences between solutions from the existing prediction models and ATCo actions on the same aircraft conflicts. The collected data is further utilized to develop an ATCo action prediction model. A hierarchical structure found from analyzing the collected ATCo actions was applied to build a structure for the model. The flight contextual information generated from the collected data was used to predict the actions. Results from this work found that the collected ATCo actions do not show any preferences on the methods to resolve aircraft conflicts. Results found that the evaluated existing prediction model does not reflect the real-world. Also, a large portion of the real conflicts was to be solved by the model both physically and operationally. Lastly, the developed prediction model showed a clear relationship between ATCo actions and applied flight contextual information. These results suggest the following takeaways. First, human actions can be identified from closed-loop data. It could be an alternative approach to collect human subjective data. Second, the importance of evaluating models before implications. Third, potentials to utilize the flight contextual information to conduct high-end prediction models.

Abstract

Introduction: This paper presents two studies that explore the implications of NextGen technologies for pilot/controller information exchange and coordinated decision making. Method: In Study 1 five participative focus groups were conducted with human factors experts. In Study 2 fifteen interviews were conducted with air traffic controllers. Both studies employed a thematic analysis. Results and Discussion: Results from Study 1 suggest that changes to pilot and controller information acquisition will alter rather than reduce breakdowns in coordinated decision making. Study 2 identified some basic issues in information exchange with NextGen technologies and suggests that some important non-operational information will be lost. Conclusion: The two studies highlight some important challenges that need to be carefully considered as the NextGen technologies move towards maturity. Language: en

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International audience; This work proposes a methodology for developing an airport arrival and departure manager tool. This methodology employs optimization together with simulation techniques for improving the robustness of the solution. An arrival and departure manager tool is intended to help air traffic controllers in managing the inbound and outbound traffic without incurring in conflicts or delays. In this context, air traffic controllers need to have a tool able to help them to make the right decisions in a short time horizon. The maindecisions taken in the present methodology for each aircraft are: entry time and entry speed in the airspace and push back time at the gate. The objective of this methodology is to have a smooth flow of aircraft both in the airspace and on the ground. Preliminary tests were made using Paris Charles de Gaulle Airport as case study, and the results show that conflicts were sensibly reduced.

Abstract

Students in complex visual domains must acquire visual problem solving strategies that allow them to make fast decisions and come up with good solutions to real-time problems. In this study, 31 air traffic controllers at different levels of expertise (novice, intermediate, expert) were confronted with 9 problem situations depicted on a radar screen. Participants were asked to provide the optimal order of arrival of all depicted aircrafts. Eye-movements, time-on-task, perceived mental effort, and task performance were recorded. Eye-tracking data revealed that novices use inefficient means-end visual problem solving strategies in which they primarily focus on the destination of aircraft. Higher levels of expertise yield visual problem solving strategies characterized by more efficient retrieval of relevant information and more efficient scan paths. Furthermore, experts’ solutions were more similar than intermediates’ solutions and intermediates’ solutions were more similar than novices’ solutions. Performance measures showed that experts and intermediates reached better solutions than novices, and that experts were faster and invested less mental effort than intermediates and novices. These findings may help creating eye-movement modeling examples for the teaching of visual problem solving strategies in complex visual domains.

Abstract

RESUMO Objetivo Investigar o perfil das habilidades auditivas de fechamento e figura-fundo e fatores relacionados à atenção, comunicação e saúde de controladores de tráfego aéreo (CTA) de aproximação de aeronaves e comparar com os de outros profissionais civis e militares. Método Participaram 60 adultos com limiares audiométricos normais reunidos em dois grupos, pareados por idade e gênero: o grupo estudo formado por 30 profissionais do Controle de Aproximação de aeronaves e o controle, por 30 outros servidores civis e militares. Todos foram submetidos a perguntas relacionadas à atenção, comunicação e saúde e ao Teste de Fala com Ruído e de Identificação de Sentenças Sintéticas em escuta monótica (SSI) para avaliar fechamento e figura-fundo, respectivamente. Foram usados testes estatísticos não paramétricos e análise de regressão logística. Resultados O CTA autorreferiu maior cansaço/esgotamento e estresse e obteve melhor desempenho do que o grupo controle em figura-fundo e semelhante em fechamento. Foi observado que ocorreu uma probabilidade de ser CTA para o estresse em 5,59 vezes e para identificação de sentenças em escuta monótica à orelha direita de 1,24. Conclusão Fechamento e autorrelato de dificuldades de saúde, comunicação e atenção na maioria das questões foram similares em ambos os grupos. Figura-fundo foi significativamente melhor em CTA. Autorreferência de estresse e desempenho à orelha direita no SSI foram preditores significantes do indivíduo ser do CTA. ABSTRACT Purpose To investigate the auditory skills of closure and figure-ground and factors associated with health, communication, and attention in air traffic controllers, and compare these variables with those of other civil and military servants. Methods Study participants were sixty adults with normal audiometric thresholds divided into two groups matched for age and gender: study group (SG), comprising 30 air traffic controllers and control group (CG), composed of 30 other military and civil servants. All participants were asked a number of questions regarding their health, communication, and attention, and underwent the Speech-in-Noise Test (SIN) to assess their closure skills and the Synthetic Sentence Identification Test - Ipsilateral Competitive Message (SSI-ICM) in monotic listening to evaluate their figure-ground abilities. Data were compared using nonparametric statistical tests and logistic regression analysis. Results More individuals in the SG reported fatigue and/or burnout and work-related stress and showed better performance than that of individuals in the CG for the figure-ground ability. Both groups performed similarly and satisfactorily in the other hearing tests. The odds ratio for participants belonging in the SG was 5.59 and 1.24 times regarding work-related stress and SSI-ICM (right ear), respectively. Conclusion Results for the variables auditory closure, self-reported health, attention, and communication were similar in both groups. The SG presented significantly better performance in auditory figure-ground compared with that of the CG. Self-reported stress and right-ear SSI-ICM were significant predictors of individuals belonging to the SG. Document type: Article