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基于高阶和多层网络识别危险货物运输事故的关键因素

Identifying Key Factors of Hazardous Materials Transportation Accidents Based on Higher-Order and Multilayer Networks.

作者信息

Ren Cuiping, Chen Bianbian, Xie Fengjie

机构信息

School of Modern Post, Xi'an University of Posts and Telecommunications, Xi'an 710061, China.

出版信息

Entropy (Basel). 2023 Jul 10;25(7):1036. doi: 10.3390/e25071036.

DOI:10.3390/e25071036
PMID:37509983
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10378565/
Abstract

This paper focuses on the application of higher-order and multilayer networks in identifying critical causes and relationships contributing to hazardous materials transportation accidents. There were 792 accidents of hazardous materials transportation that occurred on the road from 2017 to 2021 which have been investigated. By considering time sequence and dependency of causes, the hazardous materials transportation accidents causation network (HMTACN) was described using the higher-order model. To investigate the structure of HMTACN such as the importance of causes and links, HMTACN was divided into three layers using the weighted k-core decomposition: the core layer, the bridge layer and the peripheral layer. Then causes and links were analyzed in detail. It was found that the core layer was tightly connected and supported most of the causal flows of HMTACN. The results showed that causes should be given hierarchical attention. This study provides an innovative method to analyze complicated accidents, which can be used in identifying major causes and links. And this paper brings new ideas about safety network study and extends the applications of complex network theory.

摘要

本文聚焦于高阶网络和多层网络在识别导致危险货物运输事故的关键原因及关系方面的应用。对2017年至2021年期间发生在道路上的792起已调查的危险货物运输事故进行了研究。通过考虑原因的时间顺序和依赖性，使用高阶模型描述了危险货物运输事故因果网络（HMTACN）。为了研究HMTACN的结构，如原因和链接的重要性，利用加权k-核分解将HMTACN分为三层：核心层、桥梁层和外围层。然后对原因和链接进行了详细分析。研究发现，核心层紧密相连，支撑着HMTACN的大部分因果流。结果表明，应对原因进行分层关注。本研究提供了一种分析复杂事故的创新方法，可用于识别主要原因和链接。本文为安全网络研究带来了新的思路，扩展了复杂网络理论的应用。

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本文引用的文献

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2
The Situation of Hazardous Materials Accidents during Road Transportation in China from 2013 to 2019.2013 年至 2019 年中国道路运输危险货物事故情况。
Int J Environ Res Public Health. 2022 Aug 5;19(15):9632. doi: 10.3390/ijerph19159632.
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Identifying critical higher-order interactions in complex networks.
识别复杂网络中的关键高阶交互。
Sci Rep. 2021 Oct 28;11(1):21288. doi: 10.1038/s41598-021-00017-y.
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Causality-Network-Based Critical Hazard Identification for Railway Accident Prevention: Complex Network-Based Model Development and Comparison.基于因果网络的铁路事故预防关键危险识别：基于复杂网络的模型开发与比较
Entropy (Basel). 2021 Jul 6;23(7):864. doi: 10.3390/e23070864.
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