A prediction model for reaction time growth due to psychological fatigue responses in civil aviation flight dispatchers

doi:10.16265/j.cnki.issn1003-3033.2026.07.0446

Abstract

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)

CLC Number:

X911安全心理学

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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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