Risk Models for Hazardous Material Transportation Subject to Weight Variation Considerations

Hao Hu, Jiaoman Du, Xiang Li, Changjing Shang, Qiang Shen

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)
165 Downloads (Pure)


Reasonable risk models in hazardous material transportation are of practical significance, for safeguarding the lives and properties, protecting the natural environment, and facilitating sustainable development. The existing risk models can be classified into summation risk and maximum risk models, which result in overreliance on overall or local risk. To overcome these problems, in this article, we present two novel risk models considering different aggregation methods on local risks. The first model is supported by an ordered weighted averaging (OWA) operator, which assigns the weights according to the position of the segment risk in the process of risk aggregation, and the second model is supported by state variable weight (SVW) vector, which adjusts the weights on segments according to the change of segment risk values. Generally speaking, an OWA risk model is used under the situation with complete weighting information, whereas an SVW risk model could be used under the situation with incomplete weighting information. Based on the analysis for variable weight mechanism, we show that both models could effectively balance the overall risk with the local risks assisted by weights variety. Numerical experiments are provided to illustrate the validity of the proposed risk models.
Original languageEnglish
Article number9099609
Pages (from-to)2271-2282
Number of pages12
JournalIEEE Transactions on Fuzzy Systems
Issue number8
Early online date25 May 2020
Publication statusPublished - 04 Aug 2021


  • Hazardous material (hazmat) transportation
  • Ordered weighted averaging (OWA) operator
  • Risk model
  • State variable weight (SVW) vector


Dive into the research topics of 'Risk Models for Hazardous Material Transportation Subject to Weight Variation Considerations'. Together they form a unique fingerprint.

Cite this