民航飞行签派员心理疲劳反应时增长预测模型

doi:10.16265/j.cnki.issn1003-3033.2026.07.0446

摘要/Abstract

摘要：

为准确测量与预测心理疲劳对民航飞行签派员反应时的影响,首先,选取97名航空公司飞行签派员作为被试,筛选出可有效反映心理疲劳情况的指标,设计并开发签派员心理疲劳反应时测试系统,按任务开展4批次测试;然后,结合飞行签派员警觉性三过程模型(TPMA),基于测试数据构建签派员心理疲劳反应时增长预测模型;最后,通过实测结果验证预测模型的有效性,并采用脑电(EEG)信号数据辅助验证。结果表明:反应时增长模型预测得到的持续注意力平均绝对占比误差为2%、细微注意力为3%、视觉疲劳为3%;嗜睡状态下的预测反应时与EEG信号数据平均相关系数为84%,当工作时间≥7 h时,平均反应时和错误个数分别增加14%和35%;航班量≥80架次时分别增加15%和30%;卡罗林斯卡嗜睡量表评分≥7时分别增加12%和39%。

关键词: 飞行签派员, 心理疲劳, 反应时, 警觉性三过程模型(TPMA), 疲劳增长预测, 贝叶斯网络(BN)

Abstract:

To accurately measure and predict the effect of mental fatigue on reaction time in civil aviation flight dispatchers, 97 airline flight dispatchers were selected as subjects. Indicators capable of effectively reflecting mental fatigue were screened, a psychological-fatigue reaction time testing system was designed and developed, and four batches of task-based tests were conducted. Then, based on TPMA for flight dispatchers, a reaction time growth prediction model for dispatcher mental fatigue was established using the test data. Finally, the effectiveness of the model was validated with measured results and further supported by electroencephalography (EEG) signal data. The results show that the mean absolute percentage errors of the predicted reaction time growth are 2% for sustained attention, 3% for subtle attention, and 3% for visual fatigue. In the drowsy state, the average correlation coefficient between predicted reaction time and EEG signal data is 84%. When working time is ≥ 7 h, the average reaction time and number of errors increase by 14% and 35%, respectively; when flight volume is ≥ 80 flights, they increase by 15% and 30%, respectively; and when the Karolinska Sleepiness Scale (KSS) score is ≥ 7, they increase by 12% and 39%, respectively.

Key words: flight dispatcher, mental fatigue, reaction time, three-process model of alertness(TPMA), fatigue growth prediction, Bayesian network(BN)

中图分类号:

X911安全心理学

王岩韬, 胡雨寒, 时统宇. 民航飞行签派员心理疲劳反应时增长预测模型[J]. 中国安全科学学报, 2026, 36(7): 241-250.

Wang Yantao, Hu Yuhan, Shi Tongyu. A prediction model for reaction time growth due to psychological fatigue responses in civil aviation flight dispatchers[J]. China Safety Science Journal, 2026, 36(7): 241-250.

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参考文献 39

[1]	Van Cutsem J, Marcora S, De Pauw K, et al. The effects of mental fatigue on physical performance: a systematic review[J]. Sports Medicine, 2017, 47(8): 1569-1588. doi: 10.1007/s40279-016-0672-0 pmid: 28044281
[2]	Appelle S, Oswald L E. Simple reaction time as a function of alertness and prior mental activity[J]. Perceptual and Motor Skills, 1974, 38: 1263-1268. pmid: 4421761
[3]	Carlson J S, Jensen C M, Widaman K F. Reaction time, intelligence, and attention[J]. Intelligence, 1983, 7(4): 329-344. doi: 10.1016/0160-2896(83)90008-9
[4]	National Aeronautics and Space Administration,Aviation Safety Reporting System. ASRS database report set: fuel management issues[R/OL], 2024. https://asrs.arc.nasa.gov/docs/rpsts/fuel.pdf.
[5]	National Aeronautics and Space Administration,Aviation Safety Reporting System. ASRS database report set: commuter and corporate flight crew fatigue reports[R/OL]. [2025-03-15]. https://asrs.arc.nasa.gov/docs/rpsts/com_fatigue.pdf.
[6]	Dinges D F, Powell J W. Microcomputer analyses of performance on a portable, simple visual RT task during sustained operations[J]. Behavior Research Methods, Instruments, & Computers, 1985, 17(6): 652-655. doi: 10.3758/BF03200977
[7]	Folkard S, Åkerstedt T, Macdonald I, et al. Beyond the three-process model of alertness: estimating phase, time on shift, and successive night effects[J]. Journal of Biological Rhythms, 1999, 14(6): 577-587. pmid: 10643755
[8]	Shahraki S, Abu Bakar N B. Evaluating bio-mathematical models of fatigue in workplace[J]. Journal of Information Systems: New Paradigms, 2011, 1(1): 24-31.
[9]	Hursh S R, Redmond D P, Johnson M L, et al. Fatigue models for applied research in warfighting[J]. Aviation, Space, and Environmental Medicine, 2004, 75: 44-53. pmid: 15018265
[10]	Dawson D, Noy Y I, Härmä M, et al. Modelling fatigue and the use of fatigue models in work settings[J]. Accident Analysis and Prevention, 2011, 43(2):549-564. doi: 10.1016/j.aap.2009.12.030
[11]	Wilson M K, Strickland L, Ballard T. FIPS: an R package for biomathematical modelling of human fatigue related impairment[J]. Journal of Open Source Software, 2020, 5(51): DOI:10.21105/joss.02340.
[12]	McCauley M E, McCauley P, Riedy S M, et al. Fatigue risk management based on self-reported fatigue: expanding a biomathematical model of fatigue-related performance deficits to also predict subjective sleepiness[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2021, 79: 94-106. doi: 10.1016/j.trf.2021.04.006 pmid: 33994837
[13]	Song Anping, Niu Chaoqun, Ding Xuehai, et al. Mental fatigue prediction model based on multimodal fusion[J]. IEEE Access, 2019, 7: 177 056-177 062.
[14]	Powell D M C, Spencer M B, Petrie K J. Automated collection of fatigue ratings at the top of descent: a practical commercial airline tool[J]. Aviation, Space, and Environmental Medicine, 2011, 82(11):1037-1041. pmid: 22097638
[15]	王岩韬, 王则政, 时统宇. 飞行签派员警觉性预测三过程模型[J]. 中国安全科学学报, 2023, 33(10):104-110. doi: 10.16265/j.cnki.issn1003-3033.2023.10.1471
	Wang Yantao, Wang Zezheng, Shi Tongyu. Three-process model for alertness prediction of flight dispatchers[J]. China Safety Science Journal, 2023, 33(10): 104-110. doi: 10.16265/j.cnki.issn1003-3033.2023.10.1471
[16]	Dai Jing, Luo Min, Hu Wendong, et al. Developing a fatigue questionnaire for Chinese civil aviation pilots[J]. International Journal of Occupational Safety and Ergonomics, 2020, 26(1): 37-45. doi: 10.1080/10803548.2018.1456796 pmid: 29570043
[17]	Tian Sen, Wang Yijun, Dong Guoya, et al. Mental fatigue estimation using EEG in a vigilance task and resting states[C]. 2018 40^th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018: 1980-1983.
[18]	孙瑞山, 陈毅, 孙立斌. 基于CFF的管制员疲劳评估方法[J]. 中国安全科学学报, 2022, 32(4):192-197. doi: 10.16265/j.cnki.issn1003-3033.2022.04.028
	Sun Ruishan, Chen Yi, Sun Libin. Fatigue assessment method of controllers based on CFF[J]. China Safety Science Journal, 2022, 32(4): 192-197. doi: 10.16265/j.cnki.issn1003-3033.2022.04.028
[19]	Levo A. Predicting pilot fatigue in commercial air transportation[D]. Espoo: Aalto University, 2016.
[20]	Zhao Qingyang, Wang Lihui, Li Liang, et al. Effectiveness evaluation of biomathematical model of fatigue based on Chinese civil aviation crew data[C]. 2023 International Conference on Applied Physics and Computing (ICAPC). IEEE, 2023: 130-136.
[21]	Migliaccio G M, Di F G, Russo L, et al. Effects of mental fatigue on reaction time in sportsmen[J]. International Journal of Environmental Research and Public Health, 2022, 19(21):DOI:10.3390/ijerph192114360.
[22]	Langner R, Steinborn M B, Chatterjee A, et al. Mental fatigue and temporal preparation in simple reaction-time performance[J]. Acta Psychologica, 2010, 133(1): 64-72. doi: 10.1016/j.actpsy.2009.10.001 pmid: 19878913
[23]	Smilek D, Carriere J S A, Cheyne J A. Failures of sustained attention in life, lab, and brain: ecological validity of the SART[J]. Neuropsychologia, 2010, 48(9): 2564-2570. doi: 10.1016/j.neuropsychologia.2010.05.002 pmid: 20452366
[24]	Ye Z, Abe Y, Kusano Y, et al. The influence of visual display terminal use on the physical and mental conditions of administrative staff in Japan[J]. Journal of Physiological Anthropology, 2007, 26(2): 69-73. pmid: 17435346
[25]	Åkerstedt T, Discacciati A, Miley-Åkerstedt A, et al. Aging and the change in fatigue and sleep: a longitudinal study across 8 years in three age groups[J]. Frontiers in Psychology, 2018, 9: DOI:10.3389/fpsyg.2018.00234.
[26]	Wylie G R, Pra Sisto A J, Genova H M, et al. Fatigue across the lifespan in men and women: state vs. trait[J]. Frontiers in Human Neuroscience, 2022, 16:DOI:10.3389/fnhum.2022.790006.
[27]	ICAO. Manual for the oversight of fatigue management approaches[R], 2016.
[28]	Guan Suzhen, Xiaerfuding Xiadiya, Ning Li, et al. Effect of job strain on job burnout, mental fatigue and chronic diseases among civil servants in the Xinjiang Uygur autonomous region of China[J]. International Journal of Environmental Research and Public Health, 2017, 14(8):DOI:10.3390/ijerph14080872.
[29]	Wright K P, Hull J T, Czeisler C A. Relationship between alertness, performance, and body temperature in humans[J]. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 2002, 283(6): R1 370-R1 377.
[30]	Åkerstedt T, Anund A, Axelsson J, et al. Subjective sleepiness is a sensitive indicator of insufficient sleep and impaired waking function[J]. Journal of Sleep Research, 2014, 23(3): 240-252. doi: 10.1111/jsr.12158 pmid: 24750198
[31]	Pastier N, Jansen E, Boolani A. Sleep quality in relation to trait energy and fatigue: an exploratory study of healthy young adults[J]. Sleep Science, 2022, 15: 375-379. doi: 10.5935/1984-0063.20210002 pmid: 35371410
[32]	Wang Yanjun, Wang Liwei, Lin Siyuan, et al. Effect of working experience on air traffic controller eye movement[J]. Engineering, 2021, 7(4): 488-494.
[33]	Zhang Jianping, Chen Zhenling, Liu Weidong, et al. A field study of work type influence on air traffic controllers' fatigue based on data-driven PERCLOS detection[J]. International Journal of Environmental Research and Public Health, 2021, 18(22): DOI: 10.3390/ijerph182211937.
[34]	BorbÉly A A, Daan S, Wirz-Justice A, et al. The two-process model of sleep regulation: a reappraisal[J]. Journal of Sleep Research, 2016, 25(2): 131-143. doi: 10.1111/jsr.12371 pmid: 26762182
[35]	Ji Qiang, Lan Peilin, Looney C. A probabilistic framework for modeling and real-time monitoring human fatigue[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2006, 36(5): 862-875. doi: 10.1109/TSMCA.2005.855922
[36]	Käthner I, Wriessnegger S C, Müller-Putz G R, et al. Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain-computer interface[J]. Biological Psychology, 2014, 102: 118-129. doi: 10.1016/j.biopsycho.2014.07.014 pmid: 25088378
[37]	Wang Xiaolu, Gao Xiang, Xu Minpeng, et al. Research on mental fatigue detecting method based on sleep deprivation models[J]. Journal of Biomedical Engineering, 2015, 32(3): 497-502. pmid: 26485967
[38]	Qi Peng, Hu Hongying, Zhu Li, et al. EEG functional connectivity predicts individual behavioural impairment during mental fatigue[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(9): 2080-2089. doi: 10.1109/TNSRE.2020.3007324 pmid: 32746312
[39]	Cajochen C, Brunner D P, Kräuchi K, et al. Power density in theta/alpha frequencies of the waking EEG progressively increases during sustained wakefulness[J]. Sleep, 1995, 18(10): 890-894. pmid: 8746397

编号	文献	指标	类型	方法	优点	缺点
1	[1,5]	警觉性	客观	PVT	行业适用便于测试	—
2	[23]	持续注意力		反应时测试
3	[23]	细微注意力
4	[24]	视觉疲劳
5	[29]	体温		体温计
6	[25]	年龄		问卷	易获取, 体现差异	—
7	[26]	性别		问卷	易获取, 体现差异	—
︙	︙	︙	︙	︙	︙	︙
24	[30]	主观嗜睡程度	主观	KSS量表	易获取行业适用,体现个人差异	偏差大
25	[31]	睡眠情况		斯坦福嗜睡量表,匹兹堡睡眠质量指数,视觉模拟量表
26	[32]	工作经验		问卷
27	[33]	工作席位		问卷
28	[27]	工作时长		视觉模拟量表
29	[28]	工作压力		视觉模拟量表,疲劳、情绪和知觉量表,情绪状态量表
30	[27]	航班量		视觉模拟量表

增长率/(ms/h)	u	b	v
高增长	47.19	2 819.26	176.01
低增长	33.26	1 634.38	176.01

结果	PVT	u	b	v
AE/ms	6.42	47.40	1 076.28	61.21
ME/ms	5.91	53.43	1 064.28	43.47
RMSE/ms	6.53	47.15	1 077.47	62.15
MAE/ms	5.69	50.52	1 063.42	64.50
MAPE/%	2.21	2.24	2.62	3.47
PA/%	98.75	98.93	98.44	97.81

被试	类型	预测反应时的p值和相关系数
指标		PVT	u	v	b
1	θ	0.01/0.81	0.02/0.79	0.02/0.80	0.02/0.80
1	α	0.01/0.82	0.02/0.80	0.02/0.80	0.02/0.81
2	θ	0.01/0.86	0.01/0.83	0.03/0.76	0.02/0.80
2	α	0.01/0.82	0.01/0.87	0.02/0.78	0.01/0.82
︙	︙	︙
8	θ	0.02/0.78	0.01/0.84	0.03/0.76	0.01/0.83
8	α	0.02/0.80	0.01/0.85	0.02/0.78	0.02/0.81