Analysis of factors that influence hazardous material transportation accidents based on Bayesian networks: A case study in China

Zhao, Laijun; Xulei, Wang; Qian, Ying

doi:10.1016/j.ssci.2011.12.003

Cited by 143 publications

(71 citation statements)

References 15 publications

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“…As a result, the relationship with the maximum of combined mass is accepted to stand for the relationship between two factors. 15 The partial results of these processes are listed in Table 2.…”

Section: Develop the Bayesian Network Structurementioning

confidence: 99%

“…To solve this problem, a concept called mutual information is applied to estimate whether two factors are dependent. 15 By definition, the mutual information I (F i ; F j ) between two factors F i and F j can be expressed as formula (3). And if I (F i ; F j ) is greater than a certain limitation, a causal relationship exists between F i and F j .…”

Section: Develop the Bayesian Network Structurementioning

confidence: 99%

“…13,14 To avoid this problem, Bayesian network as a promising approach has been applied. For example, Zhao et al 15 developed a Bayesian network to explore the interactions among the factors affecting hazardous material transportation accidents and demonstrated how such a model can be used for accident analysis. Simoncic 16 captured the interrelations between various factors contributing to two-car accidents using Bayesian networks.…”

Section: Introductionmentioning

confidence: 99%

“…Overcoming these limitations, 15,18 this article builds a Bayesian network structure based on expert knowledge using the Dempster-Shafer (D-S) evidence theory and modifies the structure according to a test for conditional independence. Then, the posterior probabilities of various factors are computed to find their influence on serious multi-fatality crashes using the expectation-maximization (EM) algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Analysis of factors affecting serious multi-fatality crashes in China based on Bayesian network structure

Zhu

Cai

Lü

et al. 2017

Advances in Mechanical Engineering

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This study aims to identify and analyze risk factors affecting serious multi-fatality crashes using Bayesian networks. First, a Bayesian network structure was constructed based on expert experience and the Dempster-Shafer evidence theory. Second, the structure was amended to satisfy the conditional-independence test. Finally, 484 serious multi-fatality crashes for the period 2000-2012 in China were inputted into the Bayesian network to calculate the posterior probability of each factor. Results showed that the most influential factor was driver behavior, followed by vehicle condition, road condition, and external environment. And compared to the other behaviors, speeding and mistaken adjustment had greater influence on serious crashes. The findings in this study provide useful and valuable information for engineers to take corrective and preventative measures to reduce the probability for serious multi-fatality crashes.

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Section: Develop the Bayesian Network Structurementioning

confidence: 99%

Section: Develop the Bayesian Network Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of factors affecting serious multi-fatality crashes in China based on Bayesian network structure

Zhu

Cai

Lü

et al. 2017

Advances in Mechanical Engineering

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show abstract

“…In some studies on dangerous goods specifically, Bayesian networks have been implemented to prioritize the factors that influence dangerous goods (according to Hazmat hazardous materials) transportation accidents (Zhao et al, 2012). The Bayesian network structure was built based on expert knowledge using DempsterShafer evidence theory, and the structure was modified based on a test for conditional independence.…”

Section: Literature Reviewmentioning

confidence: 99%

Managing Dangerous Goods Risks on Roads during Transportation under Normal Conditions

Janno¹,

Koppel²

2017

DAAAM International Scientific Book

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Prioritization of Logistics Risks with Plithogenic PIPRECIA Method

Ulutaş

Topal

Karabašević³

et al. 2021

Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation

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Rapidly changing markets, actors, new legal regulations, information and data intensity have increased uncertainty, and as a result, businesses that want to continue operating in the market need to pay more attention to risk criteria. Risk can be explained as unplanned event which affects a business's overall performance. Logistics practices that develop and change continuously show a great variety such as weather and road accidents to faults in operations. Logistics risks have important roles in supply chains efficiency as the risks in logistics may adversely affect all parts of the supply chains and lead to decreases in business performances. Multi-criteria decision making methods are commonly used in risk prioritisation. In this study, a newly developed method called Plithogenic PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) Method is used to prioritise logistics risks. For identifying weights, data were collected from three experts in the logistics field. Six logistics risks were considered and according to the results of Plithogenic PIPRECIA Transportation-related risk is determined as the most significant risk.

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Analysis of factors that influence hazardous material transportation accidents based on Bayesian networks: A case study in China

Cited by 143 publications

References 15 publications

Analysis of factors affecting serious multi-fatality crashes in China based on Bayesian network structure

Analysis of factors affecting serious multi-fatality crashes in China based on Bayesian network structure

Managing Dangerous Goods Risks on Roads during Transportation under Normal Conditions

Prioritization of Logistics Risks with Plithogenic PIPRECIA Method

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