Fatigue analysis of cargo pilot based on Bayesian network

RuiShan Sun

doi:10.1117/12.2619745

22 December 2021 Fatigue analysis of cargo pilot based on Bayesian network

RuiShan Sun

Proceedings Volume 12058, Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021); 1205851 (2021) https://doi.org/10.1117/12.2619745
Event: 5th International Conference on Traffic Engineering and Transportation System (ICTETS 2021), 2021, Chongqing, China

Abstract

Most cargo pilots are on duty at night, and their circadian rhythms are disturbed by the shift work, which leads to pilot fatigue and affects the safety of aviation. To make a study on the influencing factors of cargo pilot fatigue, a fatigue analysis method of cargo pilots based on Bayesian network is proposed. Construct the fatigue a priori Bayesian network for cargo pilots, take the pilots of a cargo airline as samples for investigation, collect the data to obtain the prior probability of root node, use Noisy-Or model, prior knowledge and expert evaluation to obtain the conditional probabilities of intermediate nodes and leaf nodes, use Netica software to diagnose the Bayesian network model, resulting in the key factors affecting the cargo pilot fatigue, which can provide a reference for the fatigue management of cargo pilots. The Bayesian network diagnosis results indicate that temporary work overtime, night duty and aircraft noise make cargo pilots easier to feel tired. Married pilots and pilots who do not exercise are more likely to feel tired. Sleep quality, drinking and smoking habits, health condition and temporary work overtime are the key factors affecting the fatigue of cargo pilots.

Citation Download Citation

RuiShan Sun "Fatigue analysis of cargo pilot based on Bayesian network", Proc. SPIE 12058, Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021), 1205851 (22 December 2021); https://doi.org/10.1117/12.2619745

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