An explorative context-aware machine learning approach to reducing human fatigue risk of traffic control operators

Fan Li, Chun Hsien Chen, Pai Zheng, Shanshan Feng, Gangyan Xu, Li Pheng Khoo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)


Traffic control operators are usually confronted with a high potential of human fatigue. Existing strategies to manage human fatigue in transportation are primarily by undertaking prescriptive “hours-of-work” regulations. However, these regulations lack certain flexibility and fail to consider dynamic fatigue-inducing factors in the context. To fill this gap, this study makes an explorative first step towards an improved approach for managing human fatigue. First, a fatigue causal network that can adequately represent the context factors and their dynamic interactions of human fatigue is proposed. Moreover, to overcome its problem of high dimension sparse matrix, a novel method based on the artificial immune system and extreme gradient boosting algorithm is introduced. A case study of vessel traffic management showed that the model could predict the fatigue level with high accuracy of 89%. Furthermore, to lower the risk of fatigue occurrence, a novel scheduling algorithm is also provided to adaptively arrange work for operators considering individual differences and work types. The study results showed that 27% of operators could be rearranged to reduce the possibility of human fatigue. Nevertheless, considering that more than half of operator were still fatigue in the case study, human fatigue is still a critical problem. It is hoped this research, as an explorative study, can offer insightful references to traffic management authorities in their safety management process with better operation experience.

Original languageEnglish
Article number104655
Number of pages11
JournalSafety Science
Publication statusPublished - May 2020


  • Adaptive work arrangement
  • Context-awareness
  • Human fatigue prediction
  • Machine learning
  • Traffic control operators

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Safety Research
  • Public Health, Environmental and Occupational Health


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