Dynamic risk analysis of flammable liquid road tanker based on fuzzy Bayesian network

Abstract

Abstract Due to environmental variability of combustible liquid road transportation, it is difficult to analyze risk accurately. This article proposes a method for real‐time analysis of the risk of flammable liquid road transportation using a fuzzy Bayesian network. This method combines the binary logistic regression model with the bow‐tie model to analyze the influencing factors and accident evolution. Then, a Bayesian network framework is established based on the fault tree model. In the case of limited historical accident statistics, the fuzzy set theory and Leaky Noisy‐OR theory are used to determine the prior probability and conditional probability of Bayesian network nodes, and ALOHA software is used to simulate the dangerous area of the accident to define further the safe evacuation range and emergency rescue material demand. Taking the road transportation of a tanker in Beijing as an example, the accident probability is updated in real time by monitoring the data of transportation nodes, and the change rule of risk level with internal and external conditions at different times is analyzed. The research shows that this method can dynamically describe the road transportation accidents of hazardous chemicals with the GPS data of vehicles.

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