Evidence map›Paper›PMID 36293920›Full record

ArticleInternational journal of environmental research and public health2022

Understanding Hazardous Materials Transportation Accidents Based on Higher-Order Network Theory.

Cuiping Ren, Bianbian Chen, Fengjie Xie, Xuan Zhao, Jiaqian Zhang, Xueyan Zhou

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Cuiping RenSchool of Modern Posts, Xi'an University of Posts and Telecommunications, Xi'an 710061, China.ORCID 0000-0003-4260-9490
Bianbian ChenSchool of Modern Posts, Xi'an University of Posts and Telecommunications, Xi'an 710061, China.
Fengjie XieSchool of Modern Posts, Xi'an University of Posts and Telecommunications, Xi'an 710061, China.
Xuan ZhaoKey Laboratory of Transportation Industry of Automotive Transportation Safety Enhancement Technology, Chang'an University, Xi'an 710064, China.
Jiaqian ZhangSchool of Modern Posts, Xi'an University of Posts and Telecommunications, Xi'an 710061, China.
Xueyan ZhouSchool of Modern Posts, Xi'an University of Posts and Telecommunications, Xi'an 710061, China.

Funding

Chinese National Funding of Social Sciences 20BGL282National Natural Science Foundation of China 52102418Natural Science Foundation of Shaanxi Province of China 2022JQ-394Opening Fund of Key Laboratory of Transportation Industry of Automotive Transportation Safety Enhancement Technology (Chang'an University) 300102222507Special Research Project of Shaanxi Department of Education 20JK0363
6 · The paper itself

Abstract

In hazardous materials transportation systems, accident causation analysis is important to transportation safety. Complex network theory can be effectively used to understand the causal factors of and their relationships within accidents. In this paper, a higher-order network method is proposed to establish a hazardous materials transportation accident causation network (HMTACN), which considers the sequences and dependences of causal factors. The HMTACN is composed of 125 first- and 118 higher-order nodes that represent causes, and 545 directed edges that denote complex relationships among causes. By analyzing topological properties, the results show that the HMTACN has the characteristics of small-world networks and displays the properties of scale-free networks. Additionally, critical causal factors and key relationships of the HMTACN are discovered. Moreover, unsafe tank or valve states are important causal factors; and leakage, roll-over, collision, and fire are most likely to trigger chain reactions. Important higher-order nodes are discovered, which can represent key relationships in the HMTACN. For example, unsafe distance and improper operation usually lead to collision and roll-over. These results of higher-order nodes cannot be found by the traditional Markov network model. This study provides a practical way to extract and construct an accident causation network from numerous accident investigation reports. It also provides insights into safety management of hazardous materials transportation.

Indexed as

AccidentsHazardous SubstancesCausalitySafety ManagementTransportationHazardous Substancesaccident causation analysishazardous materials transportation accidenthigher-order networktopological properties

Identifiers

PMID36293920
PMCPMC9603339

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.