Effect of Fatigue and Nervousness of Tower Controller on the Control Efficiency

  • Xingjian ZhangEmail author
  • Peng Bai
  • Xinglong Wang
  • Yifei Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10906)


Fatigue and nervousness are two of the most common bad states of air traffic controllers at work. To analyze the effects of fatigue and nervousness of tower controllers on the control efficiency, a tower control simulation experiment was designed to collect 22 participants’ control performance data and the data of 19 participants were collected successfully. Four states, sober (SO), fatigue (FA), nervous (NE) and fatigue & nervous (FN), were designed. Seven indices of control efficiency were defined and analyzed, including three objective indices: task duration (TD), mean called frequency (MCF) by pilot per-flight and mean speech service time (MSST) for each flight and four subjective evaluation indices: instruction moment score (IMS), situation awareness score (SAS), transient mistake times (TMT) and result mistake times (RMT). The analysis results showed that all the indices were significantly different among the four states. Both fatigue and nervousness can impair control performance and reduce control efficiency. Under the influence of fatigue or nervousness, controllers’ initiative and situation awareness will decrease and be more likely to make transient mistakes. At same time, controllers in fatigue state need more time and more communication speech per flight to manage the operation. Controllers in nervous state will make mistake more easily than sober and fatigue states. It can be inferred that fatigue mainly makes controllers’ work speed slower and nervousness leads to more control mistakes. These findings are expected to improve the optimization of control efficiency and work management of air traffic.


Tower controller Control efficiency Fatigue Nervousness 



This study was supported by the National Natural Science Foundation of China project: Research on the Recognition Method of Bad working state of Controller Based on Individual Speech Characteristics, No. U1533117, the National Key Research and Development Plan of China project: Tracing, Recognition and Warning of High Risk Flight Trajectory, No. 2016YFB0502405, and the Science Research Starting Foundation of Civil Aviation University of China (No. 2014QD02X).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xingjian Zhang
    • 1
    Email author
  • Peng Bai
    • 1
  • Xinglong Wang
    • 1
  • Yifei Zhao
    • 1
  1. 1.Civil Aviation University of ChinaTianjinPeople’s Republic of China

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