Human Fatigue Predictions in Complex Aviation Crew Operational Impact Conditions

In this last decade, several regulatory frameworks across the world in all modes of transportation had brought fatigue and its risk management in operations to the forefront. Of all transportation modes air travel has been the safest means of transportation. Still as part of continuous improvement efforts, regulators are insisting the operators to adopt strong fatigue science and its foundational principles to reinforce safety risk assessment and management. Fatigue risk management is a data driven system that finds a realistic balance between safety and productivity in an organization. This work discusses the effects of mathematical modeling of fatigue and its quantification in the context of fatigue risk management for complex global logistics operations. A new concept called Duty DNA is designed within the system that helps to predict and forecast sleep, duty deformations and fatigue. The need for a robust structure of elements to house the components to measure and manage fatigue risk in operations is also debated. By operating on the principles of fatigue management, new science-based predictive, proactive and reactive approaches were designed for an industry leading fatigue risk management program

Accurately predicting sleep is very critical to predicting fatigue and alertness. Mathematical models are being developed to track the biological processes quantitatively and predicting temporal profile of fatigue given a person’s sleep history, planned work schedule including night and day exposure. As these models are being continuously worked to improve, a new limited deep learning machine learning based approach is attempted to predict fatigue for a duty in isolation without knowing much of work schedule history. The model within also predicts the duty disruptions and predicted fatigue at the end state of duty.