基于高阶和多层网络的危险物质运输事故的关键因素的识别
Cuiping Ren1, Bianbian Chen1, Fengjie Xie1
1School of Modern Post, Xi'an University of Posts and Telecommunications, Xi'an 710061, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
这项研究使用复杂网络理论来分析792起危险物质运输事故. 调查结果揭示了核心原因和关联,这些关联对于预防未来的道路事故至关重要.
科学领域:
- 复杂网络理论 复杂网络理论
- 运输安全分析分析
- 事故发生原因建模.
背景情况:
- 危险物质运输事故带来了重大风险.
- 传统的事故分析往往忽视了复杂的因果关系和依赖关系.
- 识别这些事故中的关键因素对于有效的风险减轻至关重要.
研究的目的:
- 应用高阶和多层网络模型,以确定危险物质运输事故的关键原因和关系.
- 在网络框架内分析因果因素的结构和重要性.
- 为分析复杂事故因果关系提供一种创新方法.
主要方法:
- 使用更高阶模型构建危险物质运输事故原因网络 (HMTACN).
- 应用加权的k核心分解来分层HMTACN成核心,桥梁和外围层.
- 详细分析每个层内的原因和联系,以了解网络结构和影响.
主要成果:
- HMTACN有效地模拟了事故原因的时间序列和依赖性.
- 在加权的k核心分解中,在事故网络中确定了不同的层 (核心,桥梁,外围).
- 核心层被发现是密集的相互连接,支持大多数因果流.
结论:
- 事故原因需要分层关注,核心原因至关重要.
- 开发的网络分析方法为识别复杂事故的主要原因和联系提供了一种创新的方法.
- 这项研究将复杂网络理论的应用扩展到安全网络研究和事故分析.
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