Research on fatigue risk of air traffic controllers based on BP neural network
In order to more effectively identify and mitigate fatigue risks among controllers,this paper sought to break through the ambiguity inherent in previous fatigue risk identification and assessment methods.By incorporating the unique characteristics of control work,a quantifiable methodology was devised to provide a more accurate,objective,and reliable assessment of controller fatigue risk management.An evaluation index system was constructed in the past to measure the level of fatigue risk among controllers,categorizing it into distinct levels.Subsequently,the BP neural network model,deemed suitable for assessing controller fatigue risk levels,was selected and employed to conduct a quantitative assessment of fatigue risk levels among controllers in the past.To analyze data,a questionnaire was designed specifically to assess controller fatigue risk levels and administered within the scope of the XX tower platform.Following the analysis and summarization of the questionnaire responses,the BP neural network model was trained and validated using the data,yielding optimal parameters and results.The accuracy of the model was verified by comparing its output with the controllers′subjective,self-assessed fatigue levels from the past.This comparison demonstrated the reliability of the model in accurately assessing controller fatigue risk.
air transportationcontroller fatiguemachine learningsafetyriskrisk management
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