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营运长途客车驾驶员疲劳累积规律及模型研究
引用本文:裴玉龙,刘颖慧,翟博韬.营运长途客车驾驶员疲劳累积规律及模型研究[J].交通信息与安全,2017,35(1):26-34.
作者姓名:裴玉龙  刘颖慧  翟博韬
作者单位:东北林业大学交通学院 哈尔滨 150040
摘    要:研究营运长途客车驾驶员的疲劳累积规律是保障公共交通安全的需要.针对黑龙江省海伦市营运长途客车驾驶员,设计疲劳累积随车实验,采集驾驶员状态视频及驾驶员感知判断能力、自我主观疲劳评价等指标,利用视频分析处理软件将驾驶员状态视频转化为PERCLOS-P80值,以此作为疲劳程度的衡量指标.分别从原始疲劳和驾驶疲劳2个方面,对营运长途客车驾驶员疲劳累积规律进行分析,得出原始疲劳与前1d的实际睡眠时间呈负相关、与驾驶时间差呈正相关;驾驶疲劳与原始疲劳及连续驾驶时间呈正相关、与累计休息时间呈负相关的结论.建立了疲劳累积与驾驶员工作、休息时间的关系模型,运用回归技术对模型进行标定,其拟合优度达到0.929.在此模型的基础上,对不同原始疲劳值的驾驶员提出了连续驾驶时间的风险临界值(F=0.3时),给出了营运长途客车驾驶员工作休息建议,为其安全驾驶提供指导. 

关 键 词:交通安全    营运长途客车驾驶员    疲劳累积模型    疲劳累积随车实验    原始疲劳

A Study on Rules of Fatigue Accumulation and a Model of Operating Long-distance Coach Drivers
Abstract:Analyses of accumulated fatigue level for long-distance coach drivers are considered as one effective way for traffic safety.Field experiments are conduct among drivers from Helen City,Heilongjiang.The driving video and measurements on perception j udgment ability,self-evaluation of fatigue level and other indicators are collected.The PER-CLOS-P80 is extracted from videos using analysis and processing software,which then used as the benchmark of fatigue level.Rules of fatigue accumulation for long-distance coach drivers are analyzed from two aspects:original fatigue and driving fatigue.The results show that original fatigue is of negatively correlation with sleeping hours in the day before driving and is of positive correlation with driving duration.Meanwhile,the driving fatigue is of positively correlation with original fatigue and continuous driving duration and is of negative correlation with cumulative resting time.A regression model of fatigue accumulation based on driving and resting time of drivers is established.Regression methods are used to calibrate the model and the calibrated model is with goodness of fit of 0.929.The fatigue accumulation model proposed in this study demonstrated the threshold of continuous driving time (when F=0.3)with different original fatigue values. Suggestions and recommendation could also be provided to commercial transportation companies or drivers based on the regression model for optimizing their operation schedule to reduce the risk of fatigue driving. 
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