TY  - JOUR
PY  - 2020//
TI  - An explorative context-aware machine learning approach to reducing human fatigue risk of traffic control operators
JO  - Safety science
A1  - Li, Fan
A1  - Chen, Chun-Hsien
A1  - Zheng, Pai
A1  - Feng, Shanshan
A1  - Xu, Gangyan
A1  - Khoo, Li Pheng
SP  - e104655
EP  - e104655
VL  - 125
IS  - 
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0925-7535
UR  - http://dx.doi.org/10.1016/j.ssci.2020.104655
ID  - ref1
ER  -