Simulating Flight Crew Workload Settings to Mitigate Fatigue Risk in Flight Operations
Aerospace
Abstract and Figures
In flight operations, the workload settings refer to the shift work, duty time, flight time, number of sectors, rest periods, time of day, duty patterns, number of time-zone transitions, number of consecutive duty days, and changes in the schedule. Workload factors, together with the biological mechanisms (the circadian rhythm, homeostatic sleep pressure, sleep inertia), can lead to the appearance of fatigue. Fatigue affects numerous tasks, such as performing inaccurate flight procedures, missing radio calls, missing or being too slow to pick up system warnings, forgetting or performing routine tasks inaccurately, and others. The focus of this paper is to determine which flight crew workload settings elements impact the appearance of fatigue. The process of collecting data regarding flight crew workload settings and fatigue is conducted on a sample of four airline pilots using an electronic CRD system of standardized chronometric cognitive tests and subjective self-assessment scales. Causal modeling tools of the IBM SPSS Statistics were used to detect correlations among flight crew workload settings, indicators of the subjective perception of fatigue, and measured fatigue indicators. In the final step, a set of simulations was created using simulation tools of the IBM SPSS Statistics to show how modifications of flight crew workload settings, such as modified duty time, number of days off, and others, can impact the level of fatigue. The obtained results can help improve the future planning of flight crew workload set-up and mitigate or prevent the appearance of fatigue in flight operations.
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This paper examines the role of flight dispatchers in analyzing the workload balancing of scheduled domestic flights at the airport in Pasay City. Various factors were analyzed to understand if there was a significant effect on how flight dispatchers balance their workload. Using the qualitative and quantitative research design, results were able to be identified. Out of 24 respondents, one factor shows a significant difference in Professional Adaptation in terms of the sex of the flight dispatchers. Using thematic analysis and phenomenology, Experience, and Maternity were the focal points of the informants when gender-related issues impacted the performance of flight dispatchers. The implications of this study are to identify and address issues on how these factors can affect the job performance and workload balance of a Flight Dispatcher.
Commercial aviation pilots are an occupational group that work in particular conditions, with frequent schedule changes, shift work, unfavorable environmental conditions, etc. These circumstances can lead to fatigue, work overload (WO), and daytime sleepiness, factors that can affect their health and safety. This study aimed to assess the prevalence and the association between these parameters in a sample of Spanish commercial airline pilots. The Raw TLX, Fatigue Severity Scale, and the Epworth Sleepiness Scale questionnaires were administered in a sample of 283 participants. The relationships of the total scores between all the questionnaires were studied by the chi-square test and the risk scores (odds ratio) were calculated. Different models using multiple linear regression were carried out to evaluate the effects of WO, fatigue, and daytime sleepiness, among the total scores, age, and flight hours. Additionally, the internal consistency of each questionnaire was estimated. A total of 28.2% presented WO above the 75th percentile, with mental and temporal demand the dimensions with the greatest weight. A total of 18% of pilots presented fatigue, 15.8% moderate sleepiness, and 3.9% severe sleepiness. We observed an association among WO, fatigue, and daytime sleepiness, important factors related to pilot health and aviation safety.
Mental fatigue (MF) can impair pilots’ performance and reactions to unforeseen events and is therefore an important concept within aviation. The physiological measurement of MF, especially with EEG and, in recent years, fNIRS, has gained much attention. However, a systematic investigation and comparison of the measurements is seldomly done. We induced MF via time on task during a 90-min simulated flight task and collected concurrent EEG-fNIRS, performance and self-report data from 31 participants. While their subjective MF increased linearly, the participants were able to keep their performance stable over the course of the experiment. EEG data showed an early increase and levelling in parietal alpha power and a slower, but steady increase in frontal theta power. No consistent trend could be observed in the fNIRS data. Thus, more research on fNIRS is needed to understand its possibilities and limits for MF assessment, and a combination with EEG is advisable to compare and validate results. Until then, EEG remains the better choice for continuous MF assessment in cockpit applications because of its high sensitivity to a transition from alert to fatigued, even before performance is impaired.
A predictive safety management methodology implies steps and tools of predictive safety management in aviation, i.e., use of predictive (forecasting) and causal modeling methods to identify potential and possible hazards in the future, as well as their causal factors which can help define timely and efficient mitigation measures to prevent or restrain emerging hazards turning into adverse events. The focus of this paper is to show how predictive analysis of an organization’s safety performance can be conducted, on the sample airport. A case study regarding implementation of predictive analysis of an organization’s safety performance, was performed at Split Airport. The predictive analysis of an airport’s safety performance was conducted through the analysis of Split Airport safety database, causal modeling of Split Airport organizational and safety performance indicators, outlier root cause analysis of Split Airport safety performance indicators, predictive analysis of safety performance (forecasting of Split Airport organizational and safety performance indicators), and scenario cases that simulate future behavior of Split Airport safety performance indicators. Based on detected future hazards, and their causal factors, the appropriate mitigation measures are proposed for the purpose of improving and maintaining an acceptable level of safety at the airport.
Due to the continuous growth of air traffic and the development of aviation systems, the current safety management methodologies should be improved and upgraded. Safety management systems help aviation organizations to manage, maintain and increase safety efficiently. The focus of the research is on the development of the predictive safety management methodology to upgrade current reactive and proactive safety management methodologies and to improve the overall safety level in aviation organizations. Predictive methods are used in various aviation sectors (air navigation services, airport operations, airline operations) for planning purposes but not in the segment of safety management. Available examples of predictive methods were tested and analyzed. Time series decomposition methods were selected as most suited for implementation in aviation safety management. The paper explicitly emphasizes correlations between safety management methodologies in the sample aviation organization. The paper also shows how causal links among organizational and safety performance indicators can be detected, by developing causal models of mutual influences using causal modeling methods, on the sample organization. This research defined steps and tools of the conceptual model of predictive safety management methodology, which enables an organization to identify and mitigate future adverse events.
Pilot fatigue and alertness are critical for civil aviation safety. Intercontinental pilots are more prone to fatigue and sleepiness due to jet lag, prolonged workdays, and disrupted rhythms. The Civil Aviation Administration of China excused enlarged flight crews from mandatory layovers and reimposed flight duration restrictions during COVID-19. This study investigates the sleep quality and attentional performance of pilots on intercontinental flights. The fifteen pilots who performed intercontinental flights in different crew compositions wore a body movement recorder, which has been proven to accurately estimate sleep duration and sleep efficiency. The crew’s attentional performance and self-report were monitored at specified flight phases. In conclusion, the larger crews slept longer and more efficiently on board, particularly pilots in charge of takeoff and landing responsibilities. Crews on four-pilot layover flights were more alert before the takeoff of the inbound flights than exempt flights, but there was no significant difference towards the end of the mission. The new long-haul flight organization did not result in fatigue or decreased attention in the pilots. This study expands on the research by validating a novel intercontinental flight operation model under the COVID-19 scenario and highlighting critical spots for future fatigue management in various crew compositions.
An increase in evidence-based studies into the deleterious effects of fatigue on flight operations has been reported by key aviation groups globally. The collegiate aviation flight training environment has not been researched at the same level when compared to military and airline operations. College aged students are unique in the sense that they are tasked with classwork, studying, participation in student organizations, social activities, and often have part time jobs within and outside of the academic environment. These conditions may cause errors, incidents, accidents, poor academic performance, and undesirable health metrics. The purpose of this study was to understand fatigue as a multi-factorial dimension and to assess potential relationships among these factors using hypothesized measurement models. The research team distributed the Collegiate Aviation Fatigue Inventory II (CAFI-II) to eight small, medium, and large collegiate aviation programs in the United States. The CAFI-II primarily focuses on fatigue awareness, causes and symptoms of fatigue, and lifestyle choices. Four hundred and twenty-two (n = 422) valid responses were obtained. Results suggested a direct predictive relationship between fatigue in collegiate flight training and the perceptions of respondents of conditions that are known to cause fatigue. Findings also suggested that respondents who had a favorable perception of fatigue risk and management programs had a better understanding of the causes of fatigue.
Fatigue can lead to sluggish responses, misjudgments, flight illusions and other problems for pilots, which could easily bring about serious flight accidents. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) device was used to record the changes of hemoglobin concentration of pilots during flight missions. The data was pre-processed, and 1080 valid samples were determined. Then, mean value, variance, standard deviation, kurtosis, skewness, coefficient of variation, peak value, and range of oxyhemoglobin (HbO2) in each channel were extracted. These indexes were regarded as the input of a stacked denoising autoencoder (SDAE) and were used to train the identification model of pilots’ fatigue state. The identification model of pilots’ fatigue status was established. The identification accuracy of the SDAE model was 91.32%, which was 23.26% and 15.97% higher than that of linear discriminant analysis (LDA) models and support vector machines (SVM) models, respectively. Results show that the SDAE model established in our study has high identification accuracy, which can accurately identify different fatigue states of pilots. Identification of pilots’ fatigue status based on fNIRS has important practical significance for reducing flight accidents caused by pilot fatigue.
Safety performance indicators (SPIs) are parameters that show where has organization been; where is it now; and where is it headed, in relation to its safety performance. Organizational indicators (OIs) represent parameters or data sets that show operational performance of an organization. For the purpose of this paper, an actual operational and safety data sets of an aviation organization, are used. Focus of this paper is to find and define correlations between safety performance indicators and organizational indicators, in order to detect cause-effect relations between them. Cause-effect relations can help find organization's deficiencies or emerging threats and handle them timely and properly, by means of safety management. Cause-effect relations can determine precisely which indicator should be increased or decreased in order to improve safety performance of an organization.