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

In this study, we applied Bayesian networks to prioritize the factors that influence hazardous material (Hazmat) transportation accidents. The Bayesian network structure was built based on expert knowledge using Dempster–Shafer evidence theory, and the structure was modified based on a test for conditional independence. We collected and analyzed 94 cases of Chinese Hazmat transportation accidents to compute the posterior probability of each factor using the expectation–maximization learning algorithm. We found that the three most influential factors in Hazmat transportation accidents were human factors, the transport vehicle and facilities, and packing and loading of the Hazmat. These findings provide an empirically supported theoretical basis for Hazmat transportation corporations to take corrective and preventative measures to reduce the risk of accidents.

Bayesian networks structure after conditional independence test.Figure optionsDownload as PowerPoint slideHighlights
► Bayesian network is applied to determine the contribution of 11 factors.
► Bayesian network structure is developed based on domain-expert knowledge.
► The expert-based Bayesian network structure is modified using CI tests.
► We compute the posterior probabilities for factors using EM learning algorithm.