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应用商用车辆的人类因素分析和分类系统,事故调查和关键故障路线分析
Xuesong Wang1, Yanru Zhou2, Ashleigh Filtness3
1The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China; School of Transportation Engineering, Tongji University, Shanghai 201804, China; Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai 201804, China.
Accident; analysis and prevention
|April 24, 2025
概括
本研究介绍了用于商用车辆 (CMV) 事故的人类因素分析和分类系统 (HFACS). 它揭示了政府监管不足和公司监管不佳是严重交通事故的关键因果因素.
科学领域:
- 道路交通安全 道路交通安全
- 运输科学 运输科学
- 人类因素工程 人类因素工程
背景情况:
- 有多人死亡的商用车辆 (CMV) 碰撞是一个重大的安全问题.
- 了解这些严重事故的复杂因果因素对于开发有效干预至关重要.
- 现有的分析方法可能无法完全捕捉导致CMV事故的系统性和人为因素.
研究的目的:
- 开发和验证一个针对商用车辆 (CMV) 事故量身定制的人类因素分析和分类系统 (HFACS).
- 将贝叶斯网络 (BN) 与HFACS集成,以量化分析CMV交通事故中的因果关系.
- 为了确定关键的故障途径和导致严重的CMV崩的因素.
主要方法:
- 基于来自中国的100份多人死亡事故调查报告 (2001-2021) 开发HFACS-CMV框架.
- 使用几率比率和贝叶斯网络 (BN) 进行定量分析,以检查因果因素之间的关系.
- 在HFACS-CMV模型中,故障被分类为四个级别,12个类别和53个子子类别.
主要成果:
- 在安全系统实施,应急响应和超速行驶中确定了频率最高的故障.
- 揭示了公司监管不良和政府监管之间的重要关联.
- 突出了一个关键的失败路线:政府监督不足导致公司监督或运营不良,导致不安全的操作条件和行为.
结论:
- 开发的HFACS-CMV方法有效地识别了严重的CMV碰撞中的因果因素和故障路径.
- 政府监督和公司监督不足是导致事故的关键系统性故障.
- 带有BN模型的HFACS-CMV提供了一个强大的框架,用于提高道路安全,并为有针对性的对策提供信息.
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