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Human activity under high pressure: A case study on fluctuation scaling of air traffic controller’s communication behaviors

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  • Wang, Yanjun
  • Zhang, Qiqian
  • Zhu, Chenping
  • Hu, Minghua
  • Duong, Vu

Abstract

Recent human dynamics research has unmasked astonishing statistical characteristics such as scaling behaviors in human daily activities. However, less is known about the general mechanism that governs the task-specific activities. In particular, whether scaling law exists in human activities under high pressure remains an open question. In air traffic management system, safety is the most important factor to be concerned by air traffic controllers who always work under high pressure, which provides a unique platform to study human activity. Here we extend fluctuation scaling method to study air traffic controller’s communication activity by investigating two empirical communication datasets. Taken the number of controlled flights as the size-like parameter, we show that the relationships between the average communication activity and its standard deviation in both datasets can be well described by Taylor’s power law, with scaling exponent α≈0.77±0.01 for the real operational data and α≈0.54±0.01 for the real-time training data. The difference between the exponents suggests that human dynamics under pressure is more likely dominated by the exogenous force. Our findings may lead to further understanding of human behavior.

Suggested Citation

  • Wang, Yanjun & Zhang, Qiqian & Zhu, Chenping & Hu, Minghua & Duong, Vu, 2016. "Human activity under high pressure: A case study on fluctuation scaling of air traffic controller’s communication behaviors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 151-157.
  • Handle: RePEc:eee:phsmap:v:441:y:2016:i:c:p:151-157
    DOI: 10.1016/j.physa.2015.08.040
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    Cited by:

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    3. Zeng, Hong-Li & Zhu, Chen-Ping & Wang, Shu-Xuan & Guo, Yan-Dong & Gu, Zhi-Ming & Hu, Chin-Kun, 2020. "Scaling behaviors and self-organized criticality of two-dimensional small-world neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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