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http://hdl.handle.net/10397/92492
Title: | An explorative context-aware machine learning approach to reducing human fatigue risk of traffic control operators | Authors: | Li, F Chen, CH Zheng, P Feng, S Xu, G Khoo, LP |
Issue Date: | May-2020 | Source: | Safety science, May 2020, v. 125, 104655 | Abstract: | 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. | Keywords: | Adaptive work arrangement Context-awareness Human fatigue prediction Machine learning Traffic control operators |
Publisher: | Elsevier | Journal: | Safety science | ISSN: | 0925-7535 | DOI: | 10.1016/j.ssci.2020.104655 | Rights: | © 2020 Elsevier Ltd. All rights reserved. © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. The following publication Li, F., Chen, C.-H., Zheng, P., Feng, S., Xu, G., & Khoo, L. P. (2020). An explorative context-aware machine learning approach to reducing human fatigue risk of traffic control operators. Safety Science, 125, 104655 is available at https://dx.doi.org/10.1016/j.ssci.2020.104655. |
Appears in Collections: | Journal/Magazine Article |
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Zheng_Explorative_Context-Aware_Machine.pdf | Pre-Published version | 2.2 MB | Adobe PDF | View/Open |
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