Abstract
Bayesian network (BN) is an effective tool for causal inferences of accidents. However, it is often criticized for the difficulty in obtaining accurate/sufficient data needed to get precise probability numbers, and expert knowledge is necessary on this occasion. Such numbers provided by experts lead to great cognitive uncertainty in the model. Credal Network (CN), regarded as an extension of BN, uses imprecise probabilities to well express the cognitive uncertainty, but how to give the available interval probability is a key issue in its application. In this paper, a CN model based on Imprecise Dirichlet Model (IDM) is proposed to solve this problem, and a case study of hazardous material road transportation accidents (HMRTAs) is conducted. Firstly, based on the accident causation model, the CN topology is established by analyzing 30 investigation reports of HMRTAs in China. Secondly, the conditional probability table is calculated and then the CN model of HMRTAs is obtained by transforming the point probability into interval probability using IDM. Finally, causes and consequences of HMTRAs are analyzed. The results show that the method in this study can well represent the uncertainty propagation in the accident causal model and provide a new way for accident risk analysis.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
