安全风险和火车司机的看法:测量台湾铁路隐藏困难的测量
Yung-Hsiang Cheng1, Jen-Chun Chen1
1Department of Transportation and Communication Management Science, National Cheng Kung University, Tainan City, Taiwan.
概括
这项研究使用了SHELL模型和Rasch方法来评估火车司机的风险感知. 结果有助于识别高风险驾驶员进行有针对性的安全培训,减少铁路事故.
科学领域:
- 职业安全与健康问题 职业安全与健康问题
- 运输科学 运输科学
- 心理测量 心理测量 心理测量
背景情况:
- 评估火车司机的风险感知对于铁路安全至关重要.
- 现有的方法可能无法完全捕捉危险场景的复杂性.
- 贝模型为分析人机环境相互作用提供了一个框架.
研究的目的:
- 评估SHELL模型和多维拉什方法的适用性,以评估火车司机的风险感知.
- 识别驾驶员难以感知的特定风险.
- 描述与表现出更高风险行为的火车司机相关的特征.
主要方法:
- 利用了台湾铁路系统的数据.
- 应用了SHELL (软件,硬件,环境,现场软件) 模型.
- 采用心理测量多维拉什方法进行分析.
主要成果:
- 确定了特定的危险场景和相关风险,这些风险对于火车司机来说是具有挑战性的.
- 列车司机的特征特征倾向于更高的风险感知.
- 在这种情况下,证明了SHELL模型和Rasch方法的有效性.
结论:
- 某些火车司机对冒险的倾向更高.
- 安全监督人员可以利用这些发现来识别有风险的个人进行有针对性的培训.
- 该方法适用于其他铁路系统的风险感知评估,有助于减少事故.
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