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首页> 外文期刊>Reliability Engineering & System Safety >Probabilistic classification of hazardous materials release events in train incidents and cargo tank truck crashes
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Probabilistic classification of hazardous materials release events in train incidents and cargo tank truck crashes

机译：危险材料的概率分类在火车事件和货物坦克卡车崩溃中释放事件

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摘要

As two of the main modes of hazmat surface transportation, quantifying conditional probability of release of hazmat from trains in rail incidents and Cargo Tank Trucks (CTTs) in highway crashes is an important component of risk assessment. The objective of this study was identifying computational tools with reliable performance for quantifying probability of hazmat release in train incidents and CTT crashes, based on the type of the decision-making problem. Events of release of hazmat were probabilistically classified by statistical and machine learning methods (Mixed Logistic Regression, Naive Bayes, Random Forests, and Support Vector Machines) using relevant explanatory variables. The datasets were Federal Railroad Administration rail equipment incident data, and combined Nebraska and Kansas police-reported traffic crash data. Given the rarity of these events, the classification performance of different methods was compared based on precision, recall and area under ROC curves (AUC). Random Forests had the best performance in classifying hazmat release for trains and railcars, based on different criteria. For Gil s, Support Vector Machines and Random Forests had the highest precision, while logistic regression and naive Bayes performed better based on recall and AUC. The research provides recommendations regarding usage of the methods depending on the purpose of analysis.
机译：作为Hazmat表面运输的两种主要模式，量化从轨道事件和货物坦克卡车（CTTS）在路径崩溃中释放Hazmat的条件概率是风险评估的重要组成部分。本研究的目的是根据决策问题的类型，识别具有可靠性性能的计算工具，以便在火车事件中量化哈姆马特释放的概率和CTT崩溃。利用相关的解释变量，通过统计和机器学习方法（混合逻辑回归，天真冰水，随机森林和支持向量机）进行概率分类Hazmat的事件。数据集是联邦铁路管理轨道设备事件数据，以及内布拉斯加州和堪萨斯警察报告的交通崩溃数据。鉴于这些事件的稀有性，基于ROC曲线（AUC）下的精度，召回和面积进行了比较了不同方法的分类性能。随机森林根据不同标准对列车和铁路车的释放进行了最佳表现。对于GIL S，支持向量机和随机森林具有最高的精度，而Logistic回归和幼稚贝叶斯基于召回和AUC进行更好。该研究根据分析目的提供关于使用方法的建议。

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