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挖掘铁路危险货物运输事故原因:基于物流-DT-TFP的方法

Huiyan Fa¹, Bin Shuai², Zhenlong Yang¹

¹School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; Institute of System Science and Engineering, Southwest Jiaotong University, Chengdu Sichuan, 611756 China.

Accident; analysis and prevention

|December 7, 2023

概括

本研究介绍了一种数据挖掘方法,用于分析铁路危险货物运输 (RDGT) 事故. 该方法识别事故原因及其与严重程度的关系,改善安全管理.

科学领域:

铁路工程铁路工程是指铁路工程.
运输安全运输安全
数据挖掘数据挖掘

背景情况:

铁路危险货物运输 (RDGT) 的有效安全管理依赖于了解事故原因.
目前用于分析RDGT事故报告的方法可能缺乏提取信息的速度和准确性.

研究的目的:

提出和验证一种新的数据挖掘方法,即Logistics-DT-TFP,用于分析RDGT事故的原因.
量化评估事故原因及其严重程度之间的关系.
为提高RDGT的安全性制定可行的管理策略.

主要方法:

对运输过程的分析,以提取事故原因并对事故严重程度进行分类.
顺序多重分类的应用逻辑回归用于相关性分析.
使用决策树 (DT) 功能选与事故合规则的C5.0算法.
采用FP-Growth算法,增强了TOP-K,以根据升级度挖掘频繁的项目集和关联规则.
计算连接链内因原因的升力度.

主要成果:

后勤-DT-TFP方法有效地识别和分析RDGT事故的原因.
确定了各种原因和事故严重程度之间的定量关系.
协会规则和事故合规则被挖掘出来,优化了效率并减少了计算影响.
该研究确定了导致RDGT事故的关键因素.

结论:

拟议的物流-DT-TFP方法提供了一个准确而有效的方法,用于从RDGT事故报告中挖掘信息.
这些发现支持开发针对铁路危险货物的有针对性的安全管理策略.
这项研究有助于提高铁路危险货物运输的整体安全性.

关键词:

协会规则分析协会规则分析合规则提取方法合规则提取方法数据挖掘是一种数据挖掘.后勤-DT-TFP 后勤-DT-TFP 后勤采矿导致事故的原因是采矿.铁路危险货物运输铁路危险货物运输

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