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用于运输行业疲劳风险评估的昼夜警觉模拟器:应用于降低卡车事故的频率和严重程度。

Circadian alertness simulator for fatigue risk assessment in transportation: application to reduce frequency and severity of truck accidents.

作者信息

Moore-Ede Martin, Heitmann Anneke, Guttkuhn Rainer, Trutschel Udo, Aguirre Acacia, Croke Dean

机构信息

Circadian Technologies, Inc., Lexington, MA 02421, USA.

出版信息

Aviat Space Environ Med. 2004 Mar;75(3 Suppl):A107-18.

Abstract

The Circadian Alertness Simulator (CAS) was developed as a practical tool for assessing the risk of diminished alertness at work. Applications of CAS include assessment of operational fatigue risk, work schedule optimization, and fatigue-related accident investigation. Based on the documented work schedules of employees, sleep and alertness patterns are estimated and a cumulative fatigue score is calculated. The risk assessment algorithms are based on physiological sleep/wake principles including homeostatic and circadian processes. The free parameters of the algorithms were optimized using over 10,000 d of sleep and alertness data sets collected from transportation workers performing their regular jobs. The validity and applicability of the CAS fatigue score was then tested using work/rest and accident data from three trucking operations. Heavy truck drivers involved in DOT-recordable or high-cost accidents were found to have significantly higher CAS fatigue risk scores than accident-free drivers. Implementing a risk-informed, performance-based safety program in a 500 power-unit trucking fleet, where dispatchers and managers were held accountable for minimizing driver CAS fatigue risk scores, significantly reduced the frequency and severity of truck accidents. Further examination of CAS risk assessment validity using scenarios provided in a fatigue modeling workshop indicated that the CAS Model also performed well in estimating alertness with a real-world transportation scenario of railroad locomotive engineer work/rest patterns.

摘要

昼夜警觉性模拟器(CAS)是作为一种评估工作中警觉性降低风险的实用工具而开发的。CAS的应用包括评估操作疲劳风险、优化工作时间表以及调查与疲劳相关的事故。根据员工记录在案的工作时间表,估算睡眠和警觉性模式,并计算累积疲劳分数。风险评估算法基于包括稳态和昼夜节律过程在内的生理睡眠/觉醒原理。利用从从事日常工作的运输工人收集的超过10000天的睡眠和警觉性数据集,对算法的自由参数进行了优化。然后,使用来自三个货运业务的工作/休息和事故数据,测试了CAS疲劳分数的有效性和适用性。发现涉及美国运输部可记录事故或高成本事故的重型卡车司机的CAS疲劳风险分数显著高于无事故司机。在一个拥有500个动力单元的货运车队中实施基于风险信息、基于绩效的安全计划,调度员和经理对将司机的CAS疲劳风险分数降至最低负责,这显著降低了卡车事故的频率和严重程度。使用疲劳建模研讨会提供的场景对CAS风险评估有效性进行的进一步检验表明,在估算铁路机车工程师工作/休息模式的实际运输场景中的警觉性方面,CAS模型也表现良好。

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